Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Network Reconstruction Based on Binary Compressed Sensing via ADMM

At present, network model is a general framework for the representation of complex system, and its structure is the fundamental and prerequisite for control and other applications of networked system. Due to the advent of Big Data era, the network structure scale is expanding sharply. Obviously, the traditional centralized reconstruction methods require high-performance computing resources and can hardly be suitable in practice. Therefore, it is a challenge to reconstruct large-scale networks with limited resources. To resolve the problem, a distributed local reconstruction method is proposed for unweighted networks. Specifically, the local reconstruction problems of nodes are distributed to multiple computing units. ADMM is introduced for compressed sensing framework to decompose the complex reconstruction problem into multiple subproblems, so it can reduce the high requirement of computing resources. Through parallel computing, network reconstruction subproblems are solved simultaneously. In addition, to further guarantee the reconstruction accuracy, a binary constraint is introduced based on the characteristics obtained by analyzing the network structure. Finally, extensive experiments are conducted to demonstrate the superiority of the proposed method. Compared with some state-of-the-art methods, the proposed method can reconstruct networks of different scales and types with limited computing resources, and it is accurate and robust against noise.
Electric field fluctuations in turbulent plasmas

It is well known that the properties of stable quiescent plasma may be calculated by a method known as the ‘superposition of dressed particles’. In this paper an analogous superposition principle is constructed for turbulent plasmas. This shows that the turbulent fluctuations may suppress the growth of the instabilities that give rise to them. The fluctuation spectrum, diffusion coefficient and dielectric constant of turbulent plasma are calculated for the guiding centre model. The relationship of this theory to that of Dupree and Weinstock is discussed.
Positive-definite correction of covariance matrix estimators via linear shrinkage

In this work, we study the positive definiteness (PDness) problem in covariance matrix estimation. For high dimensional data, many regularized estimator are proposed under structural assumptions on the true covariance matrix including sparsity. They are shown to be asymptotically consistent or rate-optimal in estimating the true covariance matrix and its structure. However, many of them do not take into account the PDness of the estimator and produce a non-PD estimate. To achieve the PDness, researchers consider additional regularizations (or constraints) on eigenvalues, which make both the asymptotic analysis and computation much harder. In this paper, we propose a simple modification of the regularized covariance matrix estimator to make it PD. To do it, we revisit the idea of linear shrinkage and propose to take a convex combination between the first-stage estimator (the regularized covariance matrix without PDness) and a given form of diagonal matrix. The proposed modification, which we denote as LSPD (linear shrinkage for positive definiteness) estimator, is shown to preserve the asymptotic properties of the first-stage estimator, if the shrinkage parameters are carefully selected. It has a closed form expression and its computation is optimization-free, unlike existing PD sparse estimators. In addition, the LSPD is generic in the sense that it can be applied to any non-PD matrix including the precision matrix. The LSPD estimator is numerically compared with other sparse PD estimators to understand its finite sample properties as well as its computational gain. It is also applied to two multivariate procedures relying on the covariance matrix estimator --- the linear minimax classification problem and the well-known mean-variance portfolio optimization problem --- and is shown to substantially improve the performance of both procedures.
Image reconstruction for optical tomography using photon density waves

The forward model in this work is based on the frequency- dependent diffusion approximation. The diffusion approximation is solved using the Finite Element Method with the Robin Boundary condition. The model is 2D, with a circular domain simulating the cross section of a limb. The meshes are generated with FIDAP, a computational fluid dynamics package. The diffusion matrix is solved using Cholesky decomposition, and results on the boundary for a modulated source include AC and DC data for a given set of optical parameters.
REDUCING BIAS OF MLE IN A DYNAMIC PANEL MODEL

This paper investigates a simple dynamic linear panel regression model with both fixed effects and time effects. Using “large n and large T” asymptotics, we approximate the distribution of the fixed effect estimator of the autoregressive parameter in the dynamic linear panel model and derive its asymptotic bias. We find that the same higher order bias correction approach proposed by Hahn and Kuersteiner (2002, Econometrica 70, 1639–1659) can be applied to the dynamic linear panel model even when time specific effects are present.We thank Peter Phillips and three anonymous referees for helpful comments. The first author gratefully acknowledges financial support from NSF grant SES-0313651. The second author appreciates the Faculty Development Awards of USC for research support.
Retaining circular features on deforming subdivision surface

ABSTRACTIn this paper, a method is proposed for retaining size and shape of circular feature on a deforming subdivision surface. An effective alternating local/global optimization method with projections is used to retain features. The multi-resolution property and the ability to generate smooth surface from arbitrary topology of subdivision surface allows user to model freeform objects. The multi-resolution editing of subdivision surface is especially useful for obtaining a smooth mesh by editing a low-complexity mesh. Constrained deformation is applied to the low complexity mesh. This speed up the computation involved, and allows the constrained deformation of complex mesh to be performed in a real-time manner. The method proposed illustrates that constrained deformation can be applied to multi-resolution model constructed with subdivision surface such that essential functional features can be retained in a shape editing process.
Automatic Identification System (AIS) data case study : vessel traffic through the Yaquina Bay breakwater at Newport, Oregon

The navigation staff at US Army Corps of Engineers (USACE) Portland District (NWP) asked for information on vessel transits through the two existing openings in the breakwater on the north side of Yaquina Bay in Newport, Oregon. Currently, no authorized federal channel passes through the breakwater openings; however, the design for a possible federal channel is under consideration. NWP staff were interested in historical vessel transits, with a special focus on isolating transits for the largest (i.e., longest) vessels, identified as vessels 80 feet or longer, currently utilizing the area inside the breakwater. The Automatic Identification System Analysis Package (AISAP) software created by USACE-ERDC (2018) was used to analyze vessel traffic.
On an estimation problem in multiple inspections

It is shown that a multiple 100% inspection plan, in which type I error is assumed to be negligible, can be described by a multinomial model. Based on this model, the number of nonconforming items in the original batch can be estimated using well-known results and this estimate can then be used to estimate the percentage of nonconforming items remaining in the inspected batch
Experimental Research on the Air Explosion Shock Wave Parameters of TNT,PBX and Hexel

The air explosion parameters of TNT,PBX and Hexel with equal quality,different density and different charge types were tested by an air explosion test system.The shock wave overpressure and impulse at the same testing point of three explosives were compared.The relationships between shock wave overpressure and impulse with proportional distance were studied.The results show that the shock wave overpressure and impulse of TNT explosive is lower than those of PBX and Hexel.In the range of 1.8-4.9m·kg-1/3,the shock wave overpressure and impulse of Hexel explosive is higher than those of PBX.When proportional distance is 6.1mm·kg-1/3,Hexel has the shock wave overpressure and impulse level approaching those of PBX.
THERMAL STRESS PROBLEM FOR MIXED HEAT CONDUCTION BOUNDARY AROUND AN ARBITRARILY SHAPED HOLE WITH CRACK UNDER UNIFORM HEAT FLUX

This article deals with a thermal stress problem for thermal conduction around an arbitrarily shaped hole with a crack under uniform heat flux. Two cases for the hole edge and the crack faces are assumed: adiabatic and isothermal conditions or vice versa (isothermal and adiabatic). A closed-form solution is obtained using conformal mapping, dislocation functions, and the complex variable method. Results of temperature, heat flux, stress, and stress intensity factor are illustrated.
Spatio-Temporal Evolution and Propagation of Meteoro-Hydrological Drought in Yalong River Basin

Based on the meteorological and hydrological data of the Yalong River Basin from 1960 to 2019, meteorological and hydrological droughts were analyzed using the standardized precipitation evapotranspiration index (SPEI) and standardized runoff index (SRI); then, the spatio-temporal evolution and propagation characteristics of the droughts were studied on multiple time scales. The results showed that, firstly, on the annual scale, the frequencies of meteorological and hydrological droughts in the basin were 28.3% and 34.0%, respectively, in the past 60 years. From upstream to downstream, the longer the alternating period of dry and wet periods, the more significant the frequency of droughts. Secondly, on a seasonal scale, the frequency of meteorological droughts is high in autumn, and the frequency of hydrological drought is high in autumn and winter. The frequency of drought in different seasons decreases from the upper reaches to the lower reaches of the basin. Thirdly, on a monthly scale, the severe and exceptional meteorological drought frequencies are high from March to May, and the severe and exceptional hydrological drought frequencies are high in January, March, October, and December. The frequency of hydrological droughts is much higher than that of meteorological droughts, especially with respect to severe and exceptional drought. Meteorological and hydrological droughts spread in the same period without lag, but they tend to expand. The propagation time of drought is short in summer and autumn, but long in spring and winter. The conclusions can serve as a decision-making basis for the water diversion planning of the west route of China’s South-to-North Water Diversion Project and the cascade hydropower operation of the basin.
Government Policy in Community Waste Management: A Case Study of Waste Management in Subang Regency

Waste management must start from the initial waste disposal site, both at household, institutional and temporary disposal sites. The involvement of shareholders is very much needed in waste operational activities, because they have their respective duties which include starting from the stages of taking, processing, then final disposal, and utilizing waste. Likewise, the government certainly has other ways of controlling waste by involving the participation of the community. Because the role of the government is very crucial, it can be in the form of direct assignments to the community through the relevant agencies, or in the process of enforcing the rules that have been applied. The research used is a qualitative-interpretative descriptive method with a phenomenological approach as an exploration in the application of the e-performance system to government employees in Subang district. This study focuses on the Subang Regency government's policy in managing community waste. The purpose of this study is to measure the extent to which the Subang Regency Government implements policies related to waste management. 5 of 2018 has not run optimally, the availability of facilities and infrastructure such as government-owned waste banks so that the management of waste services is still low, then socialization has not been maximized so that people lack awareness in disposing of waste.
The Eye as a Diagnostic Tool for Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder impacting cognition, function, and behavior in the elderly population. While there are currently no disease-modifying agents capable of curing AD, early diagnosis and management in the preclinical stage can significantly improve patient morbidity and life expectancy. Currently, the diagnosis of Alzheimer’s disease is a clinical one, often supplemented by invasive and expensive biomarker testing. Over the last decade, significant advancements have been made in our understanding of AD and the role of ocular tissue as a potential biomarker. Ocular biomarkers hold the potential to provide noninvasive and easily accessible diagnostic and monitoring capabilities. This review summarizes current research for detecting biomarkers of Alzheimer’s disease in ocular tissue.
Observer-based event-triggered adaptive neural control for time-delay nonlinear systems with input saturation and external disturbances

In this article, the issue of adaptive event-triggered tracking control is investigated for time-delay nonlinear systems with input saturation and external disturbances. In the whole process of control design, the radial basis function neural networks are utilized to approximate uncertain nonlinearities. To estimate the unknown states, a neural network-based observer is constructed. Pade approximation method is adopted to eliminate the effect of input delay. A smooth non-affine function is introduced to replace input saturation, and an auxiliary variable is employed to obtain the actual control input. An event-triggered strategy is designed to reduce the utilization of communication and computation resources. Moreover, the command filtering technique is applied to handle the issue of “explosion of complexity” in the conventional backstepping method. The designed controller can assure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Therefore, the proposed event-triggered neural control scheme can not only save network resources, but also improve the robustness of the system by dealing with input constraints and external disturbances. Finally, two simulation examples are given to verify the availability and feasibility of the designed controller.
Adsorption-induced magnetic properties and metallic behavior of graphene

Magnetic properties and electronic structures of graphene with Cl, S, and P adsorption have been investigated using ab initio calculations. The adsorption of Cl leads to Fermi level shifting to valence band, which results in metallic graphene. A band gap of 0.6 eV emerges in a S-absorbed graphene, leading to the semiconducting graphene. The unpaired electrons in the absorbed P atom are polarized and thus exhibit a magnetic moment of 0. 86μB, while no magnetic moment has been observed after Cl and S adsorption. This demonstrates that the magnetic properties and conductive behavior of graphene can be modified via atom adsorption. Specially, P-absorbed graphene may be useful for spintronic applications, such as tunneling magnetoresistance.
Role of Functional Company Characteristics on Food Franchisee Behavior

ABSTRACT Purpose Due to increasing competition, creating and enhancing franchisee patronage behaviors are critical elements in the food franchise industry. Franchise brand-related characteristics are known to influence franchisees’ evaluations. Thus, this paper examines the antecedents of franchisees’ perceived value, which leads to positive behaviors in the food franchise sector. Design/methodology/approach A research model is proposed to describe relationships among different types of functional company characteristics (i.e. quality, innovation, and price) on perceived value and behavioral intentions, and impact of operational involvement in the context of franchisees. A quantitative approach was leveraged to collect 266 usable online questionnaires, together with SmartPLS to analyze the measurement model and proposed hypotheses. Findings Results show that perceived quality on the part of franchisees is the most significant determinant of perceived value, followed by perceived innovation and price. Also, the impact of perceived value on franchisees’ intentions – including continuous and word-of-mouth intentions – is supported. Last, the role of operational involvement (i.e. duration and number of food franchise operations) in perceived value is examined. Originality/value To add to the limited research in the behaviors of franchisees, this study provides useful insights to achieve sustainable growth in the franchise brand context.
Status and Future Scope of Soft Nanoparticles-Based Hydrogel in Wound Healing

Wounds are alterations in skin integrity resulting from any type of trauma. The healing process is complex, involving inflammation and reactive oxygen species formation. Therapeutic approaches for the wound healing process are diverse, associating dressings and topical pharmacological agents with antiseptics, anti-inflammatory, and antibacterial actions. Effective treatment must maintain occlusion and moisture in the wound site, suitable capacity for the absorption of exudates, gas exchange, and the release of bioactives, thus stimulating healing. However, conventional treatments have some limitations regarding the technological properties of formulations, such as sensory characteristics, ease of application, residence time, and low active penetration in the skin. Particularly, the available treatments may have low efficacy, unsatisfactory hemostatic performance, prolonged duration, and adverse effects. In this sense, there is significant growth in research focusing on improving the treatment of wounds. Thus, soft nanoparticles-based hydrogels emerge as promising alternatives to accelerate the healing process due to their improved rheological characteristics, increased occlusion and bioadhesiveness, greater skin permeation, controlled drug release, and a more pleasant sensory aspect in comparison to conventional forms. Soft nanoparticles are based on organic material from a natural or synthetic source and include liposomes, micelles, nanoemulsions, and polymeric nanoparticles. This scoping review describes and discusses the main advantages of soft nanoparticle-based hydrogels in the wound healing process. Herein, a state-of-the-art is presented by addressing general aspects of the healing process, current status and limitations of non-encapsulated drug-based hydrogels, and hydrogels formed by different polymers containing soft nanostructures for wound healing. Collectively, the presence of soft nanoparticles improved the performance of natural and synthetic bioactive compounds in hydrogels employed for wound healing, demonstrating the scientific advances obtained so far.
Boosting Immunity and Management against Wheat Fusarium Diseases by a Sustainable, Circular Nanostructured Delivery Platform

Fusarium head blight (FHB) and Fusarium crown rot (FCR) are managed by the application of imidazole fungicides, which will be strictly limited by 2030, as stated by the European Green Deal. Here, a novel and eco-sustainable nanostructured particle formulation (NPF) is presented by following the principles of the circular economy. Cellulose nanocrystals (CNC) and resistant starch were obtained from the bran of a high amylose (HA) bread wheat and employed as carrier and excipient, while chitosan and gallic acid were functionalized as antifungal and elicitor active principles. The NPF inhibited conidia germination and mycelium growth, and mechanically interacted with conidia. The NPF optimally reduced FHB and FCR symptoms in susceptible bread wheat genotypes while being biocompatible on plants. The expression level of 21 genes involved in the induction of innate immunity was investigated in Sumai3 (FHB resistant) Cadenza (susceptible) and Cadenza SBEIIa (a mutant characterized by high-amylose starch content) and most of them were up-regulated in Cadenza SBEIIa spikes treated with the NPF, indicating that this genotype may possess an interesting genomic background particularly responsive to elicitor-like molecules. Quantification of fungal biomass revealed that the NPF controlled FHB spread, while Cadenza SBEIIa was resistant to FCR fungal spread. The present research work highlights that the NPF is a powerful weapon for FHB sustainable management, while the genome of Cadenza SBEIIa should be investigated deeply as particularly responsive to elicitor-like molecules and resistant to FCR fungal spread.
Using machine learning to evaluate 1.2 million studies on small-scale farming and post-production food systems in low- and middle-income countries

Recent developments have emphasized the need for agrifood systems to move beyond a production-oriented approach to recognize agriculture as part of a broader agrifood system that prioritizes livelihoods, social equity, diets, and climate and environmental outcomes. At the same time, the knowledge base for agriculture is growing exponentially. Using artificial intelligence and machine learning approaches, we reviewed more than 1.2 million publications from the past 20 years to assess the current landscape of agricultural research taking place in low- and middle-income countries. The result is a clearer picture of what research has been conducted on small-scale farming and post-production systems from 2000 to the present, and where persistent evidence gaps exist. We found that the greatest focus of the literature is on economic outcomes, such as productivity, yield, and incomes. There is also some emphasis on identifying and measuring environmental outcomes. However, noticeable data gaps exist for agricultural research focused on nutrition and diet, and gender and inclusivity.
Altered Distribution of Unesterified Cholesterol among Lipoprotein Subfractions of Patients with Diabetes Mellitus Type 2

Biomarkers are important tools to improve the early detection of patients at high risk for developing diabetes as well as the stratification of diabetic patients towards risks of complications. In addition to clinical variables, we analyzed 155 metabolic parameters in plasma samples of 51 healthy volunteers and 66 patients with diabetes using nuclear magnetic resonance (NMR) spectrometry. Upon elastic net analysis with lasso regression, we confirmed the independent associations of diabetes with branched-chain amino acids and lactate (both positive) as well as linoleic acid in plasma and HDL diameter (both inverse). In addition, we found the presence of diabetes independently associated with lower concentrations of free cholesterol in plasma but higher concentrations of free cholesterol in small HDL. Compared to plasmas of non-diabetic controls, plasmas of diabetic subjects contained lower absolute and relative concentrations of free cholesterol in all LDL and HDL subclasses except small HDL but higher absolute and relative concentrations of free cholesterol in all VLDL subclasses (except very small VLDL). These disbalances may reflect disturbances in the transfer of free cholesterol from VLDL to HDL during lipolysis and in the transfer of cell-derived cholesterol from small HDL via larger HDL to LDL.