Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving ambient noise cross-correlations in the noisy ocean bottom environment of the Juan de Fuca plate

S U M M A R Y Ambient noise tomography exploits seismic ground motions that propagate coherently over long interstation distances. Such ground motions provide information about the medium of propagation that is recoverable from interstation cross-correlations. Local noise sources, which are particularly strong in ocean bottom environments, corrupt ambient noise cross-correlations and compromise the effectiveness of ambient noise tomography. Based on 62 ocean bottom seismometers (OBSs) located on Juan de Fuca (JdF) plate from the Cascadia Initiative experiment and 40 continental stations near the coast of the western United States obtained in 2011 and 2012, we attempt to reduce the effects of local noise on vertical component seismic records across the plate and onto US continent. The goal is to provide better interstation cross-correlations for use in ambient noise tomography and the study of ambient noise directionality. As shown in previous studies, tilt and compliance noise are major sources of noise that contaminate the vertical channels of the OBSs and such noise can be greatly reduced by exploiting information on the horizontal components and the differential pressure gauge records, respectively. We find that ambient noise cross-correlations involving OBSs are of significantly higher signal-to-noise ratio at periods greater than 10 s after reducing these types of noise, particularly in shallow water environments where tilt and compliance noise are especially strong. The reduction of tilt and compliance noise promises to improve the accuracy and spatial extent of ambient noise tomography, allowing measurements based on coherently propagating ambient noise to be made at stations in the shallower parts of the JdF plate and at longer periods than in previous studies. In addition such local noise reduction produces better estimates of the azimuthal content of ambient noise.
Quantum Chemical, Molecular Docking, and ADMET Predictions of Ketorolac and its Modified Analogues

Ketorolac (K) is a non-steroidal agent with potent analgesic and moderate anti-inflammation activity. Adverse health effect and resistant of drugs indicate the importance of the discovery of new potential candidate. Recently, it has been seen the trait of modifying drugs using halogens and alkyl groups play an important role in improving drug performance. All the structures are optimized by employing density functional theory with B3LYP/6-31g (d,p) level theory. Thermal, molecular orbital, electrostatic potential properties have been calculated to investigate their physicochemical properties. From molecular docking, nonbonding interactions, and ADMET calculation it is found that most of the designed structures have better biochemical activity. Finally, this study can be helpful to design a new analgesic agent.
BlockTrack-L: A Lightweight Blockchain-based Provenance Message Tracking in IoT

Data tracking is of great significance and a central part in digital forensics. In today's complex network design, Internet of Things (IoT) devices communicate with each other and require strong security mechanisms. In maintaining an audit trail of IoT devices or provenance of IoT device data, it is important to know the origins of requests to ensure certain level of trust in IoT data. Blockchain can provide traceability of records generated from IoT devices in a sensitive environment. In this paper, we present an application layer data provenance model that works on execute-order architecture for cloud based IoT networks. It supports high throughput of transactions on the blockchain network with lightweight security overhead by using outsourced encryption on edge nodes. All communications among the IoT devices are connected to a blockchain network and stored on permissioned blockchain peers. The proposed system is evaluated to have less cryptographic load by offloading the IoT nodes with Edge nodes.
Dislocation Density and Internal Stress in an Al-5.7 at%Mg Alloy

Pour etudier l'origine de la contrainte interne dans un alliage durci par solution solide deforme a haute temperature, on a determine simultanement la contrainte interne et la densite de dislocations par un essai de variation de contrainte pendant le fluage entre 573 et 673 K sous des contraintes de 4,5 a 40 MPa
A hybrid RBFNN–BBMO methodology for robust energy management in grid‐Connected microgrid

This dissertation shows energy scheduling in grid‐connected micro grid (MG) using an amazing hybrid method for optimal energy management. The hybrid method is the mixture of Radial Basis Neural Function Network (RBFNN) and Bumble Bees Mating Optimization (BBMO) and is therefore referred to as RBFNN–BBMO technique. The MG system consists of photovoltaic, wind turbine, battery storage and micro turbine systems. Here, batteries are used to maintain a constant and stable output power. The necessary load demand of the grid‐connected MG scheme is continually monitored using the RBFNN method. BBMO optimizes the perfect combination of MG taking into account the expected load demand. In addition to decrease the impact of renewable energy prediction mistakes, a two strategy for MG energy management is adopted. The first approach is to program various renewable energy sources (RESs) during the operation of MG in order to minimize the electricity costs. The second approach is power flow balancing and forecasting errors, decreasing from the scheduled energy reference based on the summarized rule. Access of RESs, power supply and charging modules are the constraints. Two existing methods such as the Genetic Algorithm and the Cuttlefish Algorithm compare the supremacy of the proposed method.
Intended College Enrollment and Educational Inequality: Do Students Lack Information?

Despite increasing access to university education, students from disadvantaged or non-academic family backgrounds are still underrepresented at universities. In this regard, the economic literature mainly studies the effect of financial constraints on post-secondary educational decisions. Our knowledge on potential effects of other constraints regarding university education is more limited. We investigate the causal relationship between information and educational expectations using data from a German randomized controlled trial in which students in high schools were treated with information on the benefits as well as on different funding possibilities for university education. We find that the provision of information increases intended college enrollment for students from a non-academic family background, while it leads students from academic backgrounds to lower their enrollment intentions. Our results suggest that educational inequality can be reduced by providing students with relevant information, while simultaneously improving post-secondary education matches.
Comparative assessment of proteins between cell lines and tissues representing carcinomas of the breast using 2-dimensional gel electrophoresis

Venugopal R. Bovilla1,2, Chaitra Rai3, Mahadevaswamy G. Kuruburu1,2, SubbaRao V. Madhunapantula*1,2,4 1Center of Excellence in Molecular Biology and Regenerative Medicine (DST-FIST Supported Center), JSS Medical College, JSS Academy of Higher Education & Research, Mysore – 570015, Karnataka, India 2Department of Biochemistry (DST-FIST Supported Department), JSS Medical College, JSS Academy of Higher Education & Research, Mysore – 570015, Karnataka, India 3Molecular Nutrition Department, CSIR-CFTRI, Mysuru, Karnataka, India 4Special Interest Group in Cancer Biology and Cancer Stem Cells (SIG-CBCSC), JSS Academy of Higher Education & Research, Mysore – 570015, Karnataka, India
Automated Planning of System Rollback in Declarative IT System Update

The automation of system management has been expanding, and there has been interest lately in an automated workflow generation that automatically generates the workflows of system updates. However, because these automation technologies operate under the assumption that systems work in accordance with their underlying system model, they are not good at handling unexpected behaviors of target systems.In this paper, we propose a way to incorporate unexpected handling into our declarative system update mechanism by automatically generating a "recovery workflow" to roll back a target system in the event of abnormal system stops. We evaluate our tool through a practical three-tier architecture system operating a simple Web service, and found that our method can complete generation of a recovery workflow in one second, and roll back the system from all system states.
Natural Language Processing for Mobile App Privacy Compliance

Many Internet services collect a flurry of data from their users. Privacy policies are intended to describe the services’ privacy practices. However, due to their length and complexity, reading privacy policies is a challenge for end users, government regulators, and companies. Natural language processing holds the promise of helping address this challenge. Specifically, we focus on comparing the practices described in privacy policies to the practices performed by smartphone apps covered by those policies. Government regulators are interested in comparing apps to their privacy policies in order to detect non-compliance with laws, and companies are interested for the same reason. We frame the identification of privacy practice statements in privacy policies as a classification problem, which we address with a three-tiered approach: a privacy practice statement is classified based on a data type (e.g., location), party (i.e., first or third party), and modality (i.e., whether a practice is explicitly described as being performed or not performed). Privacy policies omit discussion of many practices. With negative F1 scores ranging from 78% to 100%, the performance results of this three-tiered classification methodology suggests an improvement over the state-of-the-art. Our NLP analysis of privacy policies is an integral part of our Mobile App Privacy System (MAPS), which we used to analyze 1,035,853 free apps on the Google Play Store. Potential compliance issues appeared to be widespread, and those involving third parties were particularly common.
Nanofibrillar networks enable universal assembly of superstructured particle constructs

Topological variety of robust networks from nanofibrils enables the formation of strong and tough particle constructs ubiquitously. Superstructured colloidal materials exploit the synergies between components to develop new or enhanced functions. Cohesion is a primary requirement for scaling up these assemblies into bulk materials, and it has only been fulfilled in case-specific bases. Here, we demonstrate that the topology of nanonetworks formed from cellulose nanofibrils (CNFs) enables robust superstructuring with virtually any particle. An intermixed network of fibrils with particles increases the toughness of the assemblies by up to three orders of magnitude compared, for instance, to sintering. Supramolecular cohesion is transferred from the fibrils to the constructs following a power law, with a constant decay factor for particle sizes from 230 nm to 40 μm. Our findings are applicable to other nanofiber dimensions via a rationalization of the morphological aspects of both particles and nanofibers. CNF-based cohesion will move developments of functional colloids from laboratory-scale toward their implementation in large-scale nanomanufacturing of bulk materials.
Chronic Illnesses and Student Academic Performance

Innovations in healthcare have improved survival in children with illnesses previously considered fatal. Chronic illnesses in school-age children are increasing significantly, and these children are at higher risk for school problems than their healthy peers. To review the state of research on the impact of chronic illnesses on student academic performance, we reviewed original studies that investigated the relationship of child development, cognitive abilities, and academic achievement with common chronic illnesses. Research reveals evidence that chronic illnesses have both direct (educational impacts) and indirect (social and psychological impacts) impacts on education. Specifically, epilepsy, asthma, diabetes mellitus, cancer, congenital heart diseases, and Human Immunodeficiency Virus infection affect student achievement and some specific areas of cognitive ability. Success at school for the chronically ill student requires the coordinated efforts of educators, parents, students, and medical professionals. This review also proposes ways to assess and assist children with chronic illnesses in different educational settings. Hospital schooling, homebound instruction, and school-based interventions are examined. The critical aspects of each educational service are discussed with recommendations for educators and healthcare practitioners.
Spin-1 resonances in a non-linear left-right dynamical Higgs context

All the possible CP–conserving non–linear operators up to the p–order in the Lagrangian expansion are clearly and completely listed for the case of an emerging new physics field content in the nature, more specifically, from spin–1 resonances sourced by the straightforward extension of the SM local gauge symmetry to the larger local group SU(2)L ⊗ SU(2)R ⊗ U(1)B−L, within a non– linear electroweak chiral context coupled to a light dynamical Higgs. The physical effects induced by integrating out the right handed fields from the physical spectrum are briefly analysed. The relevant set of effective operators have been identified at low energies.
Divergent Pathways of Gentrification

Gentrification has inspired considerable debate, but direct examination of its uneven evolution across time and space is rare. We address this gap by developing a conceptual framework on the social pathways of gentrification and introducing a method of systematic social observation using Google Street View to detect visible cues of neighborhood change. We argue that a durable racial hierarchy governs residential selection and, in turn, gentrifying neighborhoods. Integrating census data, police records, prior street-level observations, community surveys, proximity to amenities, and city budget data on capital investments, we find that the pace of gentrification in Chicago from 2007 to 2009 was negatively associated with the concentration of blacks and Latinos in neighborhoods that either showed signs of gentrification or were adjacent and still disinvested in 1995. Racial composition has a threshold effect, however, attenuating gentrification when the share of blacks in a neighborhood is greater than 40 percent. Consistent with theories of neighborhood stigma, we also find that collective perceptions of disorder, which are higher in poor minority neighborhoods, deter gentrification, while observed disorder does not. These results help explain the reproduction of neighborhood racial inequality amid urban transformation.
An introductory study to white dwarfs

White dwarfs are compact objects that stand against gravitational collapse by their internal pressure of degenerate matter. In this work we aimed to perform an introductory study on these stars, using two equations of state (EOS): (I) an ideal Fermi gas and (II) the one by Baym Pethick and Sutherland (BPS). In addition, we analyzed these two equations of state in two scenarios, the Newtonian and the one from General Relativity, which allowed us to analyze the effects of curved space-time. We also studied a rotating white dwarf, finding a metric for this case. With these two cases, with rotation (J = cte) and static (J = 0) well determined, we introduced the concepts of instability by the turning point criterion and dynamic instability criterion. Finally, with the RNS program, we performed the numerical resolution with BPS equation of state in the rotating case in order to analyze the behavior of the turning point criterion in these two cases.
Systematic literature review on designing trust-based security for WSNs

The study assessed the design and development of trust-based security for wireless sensor networks. A qualitative approach was opted by employing systematic review analysis, which was assessed using secondary data collection. The design and development of trust-based security were mainly targeted, and 140 publications were randomly reviewed, from which 24 studies were finalized after excluding irrelevant data and studies that only contained abstracts. The results were analyzed focusing on the designs, applications and protocols and trust factors. Different studies have been evaluated, which have suggested that proper design and development of trust-based security should be the foremost aim to be applied in user environments. The study suggested that designing the trust management models based on the taxonomy of routing applications and relevant algorithms could be a part of future investigations.
An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images

As an essential ecological parameter, soil moisture is important for understanding the water exchange between the land surface and the atmosphere, especially in the Loess Plateau (China). Although Synthetic Aperture Radar (SAR) images can be used for soil moisture retrieval, it is still a challenge to mitigate the impacts of complex terrain over hilly areas. Therefore, the objective of this paper is to propose an improved approach for soil moisture estimation in gully fields based on the joint use of the Advanced Integral Equation Model (AIEM) and the Incidence Angle Correction Model (IACM) from Sentinel-1A observations. AIEM is utilized to build a simulation database of microwave backscattering coefficients from various radar parameters and surface parameters, which is the data basis for the retrieval modeling. IACM is proposed to correct the deviation between the local incidence angle at the scatterer and the radar viewing angle. The study area is located in the Loess Plateau of China, where the main land cover is mostly bare land and the terrain is complex. The Sentinel-1A SAR data in C-band with dual polarization acquired on October 19th, 2017 was adopted to extract the VV&VH polarimetric backscattering coefficients. The in situ measurements of soil moisture were collected on the same day of the SAR acquisition, for evaluating the accuracy of the SAR-derived soil moisture. The results showed that, firstly, the estimated soil moisture with volumetric content between 0% and 20% was in the majority. Subsequently, both the RMSE of estimation values (0.963%) and the standard deviation of absolute errors (0.957%) demonstrated a good accuracy of the improved approach. Moreover, the evaluation of IACM confirmed that the improved approach coupling IACM and AIEM was more efficient than employing AIEM solely. In conclusion, the proposed approach has a strong ability to estimate the soil moisture in the gully fields of the Loess Plateau from Sentinel-1A data.
Aalborg Universitet Inside-Outside Model Viewing A Low-cost Hybrid Approach to Visualization and Demonstration of 3 D Models

Visualization of large scale 3D models has become an important part of the development cycle in many fields like building design, machine design, construction and many more. Whether for design communication or demonstration, it is necessary for the viewers to fully understand the different components of the model, their proportions compared to each other and the overall design. A variety of augmented reality(AR) applications have been created for overall visualization of large scale models. For tours inside 3D renderings of models many immersive virtual reality (VR) applications exist. Both types of applications have their limitation, omitting either important details in the AR case or the full picture in the case of VR. This paper presents a low-cost way to demonstrate models using a hybrid virtual environment system (HVE), combining virtual reality and augmented reality visualization. The solution is built using a fully occlusive head mounted display(HMD), together with off-the-shelf web cameras and a game controller for interaction. A proof of concept is created using a commercial game engine, which is used in a subsequent case study. Based on this study, we demonstrate the validity of our proposed system.
Ascendancy of potentials over fields in electrodynamics

Multiple bases are presented for the conclusion that potentials are fundamental in electrodynamics, with electric and magnetic fields as quantities auxiliary to the scalar and vector potentials -- opposite to the conventional ordering. One foundation for the concept of basic potentials and auxiliary fields consists of examples where two sets of gauge-related fields are such that one is physical and the other is erroneous, with the information for the proper choice supplied by the potentials. A major consequence is that a change of gauge is not a unitary transformation in quantum mechanics; a principle heretofore unchallenged. The primacy of potentials over fields leads to the concept of a hierarchy of physical quantities, where potentials and energies are primary, while fields and forces are secondary. Secondary quantities provide less information than do primary quantities. Some criteria by which strong laser fields are judged are based on secondary quantities, making it possible to arrive at inappropriate conclusions. This is exemplified by several field-related misconceptions as diverse as the behavior of charged particles in very low frequency propagating fields, and the fundamental problem of pair production at very high intensities. In each case, an approach based on potentials gives appropriate results, free of ambiguities. The examples encompass classical and quantum phenomena, in relativistic and nonrelativistic conditions. This is a major extension of the quantum-only Aharonov-Bohm effect, both in supporting the primacy of potentials over fields, and also in showing how field-based conceptions can lead to errors in basic applications.
Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm

Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management. We used the random forest model and MODIS Global Fire Atlas dataset (2010~2016) to analyse the impacts of climate, topographic, vegetation and socioeconomic variables on forest fire occurrence in six geographical regions in China. The results show clear regional differences in the forest fire driving factors and their impacts in China. Climate variables are the forest fire driving factors in all regions of China, vegetation variable is the forest fire driving factor in all other regions except the Northwest region and topographic variables and socioeconomic variables are only the driving factors of forest fires in a few regions (Northwest and Southwest regions). The model predictive capability is good: the AUC values are between 0.830 and 0.975, and the prediction accuracy is between 70.0% and 91.4%. High fire hazard areas are concentrated in the Northeast region, Southwest region and East China region. This research will aid in providing a national-scale understanding of forest fire driving factors and fire hazard distribution in China and help policymakers to design fire management strategies to reduce potential fire hazards.