Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Evaluation of Pre‐Earthquake TEC Enhancements

Here we test the precursory enhancement in ionospheric total electron content (TEC) which has been reported by Heki (2011, https://doi.org/10.1029/2011GL047908) and numerous Global Navigation Satellite System (GNSS) TEC observational studies before the 2011 Mw9.0 Tohoku‐Oki and many great earthquakes. We verify the frequency of this TEC enhancement via analysis of a 2‐month vertical TEC (VTEC) time series that includes the Tohoku‐Oki earthquake using the procedure, based on Akaike's information criterion, and threshold of Heki and Enomoto (2015, https://doi.org/10.1002/2015JA021353). The averaged occurrence rate of the TEC enhancement is much larger than that reported by Heki and Enomoto (2015, https://doi.org/10.1002/2015JA021353) when all of the visible GPS satellites at a given station are taken into account. The frequency assessment by Heki and Enomoto (2015, https://doi.org/10.1002/2015JA021353) using only one satellite underestimates the frequency of the TEC enhancement. In fact, the TEC enhancement is sufficiently frequent to explain all the reported enhancements prior to the great earthquakes as a product of chance. We also analyze the spatial distribution of the preseismic TEC enhancement and coseismic TEC depletion for the Tohoku‐Oki earthquake with the data after reducing intertrace biases. We observe significant postseismic depletion that lasted at least 2 h after the earthquake and extended at least 500 km around the center of the large‐slip area. This means that evaluation of the enhancements using reference curves which was adopted by Heki (2011, https://doi.org/10.1029/2011GL047908) and even by the recent papers (e.g., He & Heki 2016, https://doi.org/10.1002/2016GL069863; 2017, https://doi.org/10.1002/2017JA024012; 2018, https://doi.org/10.1029/2017JA024871) is in danger of mistaking a large and long‐lasting postseismic TEC depletion for a preseismic enhancement.
A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals

Bruxism is a sleep disorder in which the patient clinches and gnashes their teeth. Bruxism detection using traditional methods is time-consuming, cumbersome, and expensive. Therefore, an automatic tool to detect this disorder will alleviate the doctor workload and give valuable help to patients. In this paper, we targeted this goal and designed an automatic method to detect bruxism from the physiological signals using a novel hybrid classifier. We began with data collection. Then, we performed the analysis of the physiological signals and the estimation of the power spectral density. After that, we designed the novel hybrid classifier to enable the detection of bruxism based on these data. The classification of the subjects into “healthy” or “bruxism” from the electroencephalogram channel (C4-A1) obtained a maximum specificity of 92% and an accuracy of 94%. Besides, the classification of the sleep stages such as the wake (w) stage and rapid eye movement (REM) stage from the electrocardiogram channel (ECG1-ECG2) obtained a maximum specificity of 86% and an accuracy of 95%. The combined bruxism classification and the sleep stages classification from the electroencephalogram channel (C4-P4) obtained a maximum specificity of 90% and an accuracy of 97%. The results show that more accurate bruxism detection is achieved by exploiting the electroencephalogram signal (C4-P4). The present work can be applied for home monitoring systems for bruxism detection.
Modeling neural connectivity of caenorhabditis elegans

Little research has been done regarding the use of modeling techniques to estimate the connectivity of a nervous system by analyzing data recorded from its neurons. In this thesis, three main methods were implemented to solve this task: Bayesian inference, artificial neural networks, and ODE reverse engineering. The goal is to apply these methods to data recorded from C. elegans neurons and estimate the connections between these. The top two performing methods on simulated data were feed-forward neural networks and ODE reverse engineering. The worst performing methods were recurrent neural networks and Bayesian inference. Out of the methods tried, feed-forward neural network was the most robust to changes in parameters of the simulation network and noise. Furthermore, this technique is easily generalizable since it did not rely on any particular feature of the simulation network to achieve its good performance. Nevertheless, all methods are easily reproducible for any further research.
Revealing mechanisms in a transdisciplinary Community Reforestation Research Programme

Background: The Community Reforestation Research Programme is a joint initiative between eThekwini Municipality and the University of KwaZulu-Natal in South Africa. Addressing complex sustainable development issues requires collaboration across disciplines and between researchers, practitioners and policy-makers. Understanding how this occurs has the potential to inform and improve transitions to sustainability. Objectives: A summative evaluation of one project within the broader programme was commissioned to develop in-depth understandings into the underlying systemic influences or mechanisms that supported or hindered transdisciplinary research and practices. Method: A realist evaluation method informed the analysis of questionnaires, documents, interviews, focus group discussions and participation in programme reporting and planning processes. This approach supported the development of a contextual profile within which underlying mechanisms were identified. These mechanisms were considered in relation to the outcomes of the project. Results: The main mechanisms identified in the evaluation process related to orientations to research, orientations to education, orientations to value creation, orientations to environmental management and orientations to organisational leadership. These orientations either enabled or hindered particular outcomes in the research on, and environmental management of, community reforestation in the context of climate change. The mechanisms also had significant implications for understanding ‘what worked well for whom in what circumstances and how’. Conclusion: Transdisciplinary research and practice across institutional boundaries are enabled or hindered by underlying mechanisms. By identifying and understanding these mechanisms, insights were developed that have the potential to enhance transdisciplinary sustainability initiatives at the local level.
Assessment of Forest Biomass Estimation from Dry and Wet SAR Acquisitions Collected during the 2019 UAVSAR AM-PM Campaign in Southeastern United States

Forest above-ground biomass (AGB) estimation from SAR backscatter is affected by varying imaging and environmental conditions. This paper quantifies and compares the performance of forest biomass estimation from L-band SAR backscatter measured selectively under dry and wet conditions during the 2019 AM-PM NASA airborne campaign. Seven Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images acquired between June and October 2019 over a temperate deciduous forest in Southeastern United States with varying moisture and precipitation conditions are examined in conjunction with LIDAR and field measurements. Biomass is estimated by fitting a 3-parameter modified Water Cloud Model (WCM) to radiometric terrain corrected SAR backscatter. Our experiment is designed to quantify the biomass estimation errors when biomass models are calibrated and validated on varying acquisition conditions (dry or wet). Multi-temporal estimation strategies are also evaluated and compared with single-acquisition estimation approaches. As an outcome, the experiment shows that the WCM model calibrated and validated on single acquisitions adapts to different soil moisture conditions with RMSD up to 18.7 Mg/ha. The AGB estimation performance, however, decreases with RMSD upwards of 30 Mg/ha when the model is cross-validated on moisture and precipitation conditions different than the calibration conditions. Results confirm that calibrating the model over the multi-temporal data using averaged backscatter or weighted combinations of individual AGB estimates, improves the biomass estimation accuracy up to about 20% at L-band. This study helps design biomass cal/val procedures and biomass estimation algorithms for dense time-series to be collected by low-frequency radar missions such as NASA-ISRO SAR (NISAR) and BIOMASS.
Automation risk and support for welfare policies: how does the threat of unemployment affect demanding active labour market policy support?

Abstract How does unemployment risk affect workers’ support for demanding active labour market policies (ALMPs)? There may be a substantial number of workers who experience unemployment risk from labour market disruptions. Yet, we know less about its impact on demanding ALMP support than the impact of unemployment status. Here, I explore the impact of unemployment risk through automation. Automation-threatened workers’ support for demanding ALMPs may be influenced by two opposing considerations that are linked to their potential reliance on welfare. First, they may worry about barriers to welfare access. Second, they may worry about welfare competition, especially under austerity. Their support for demanding ALMPs would hence depend on which consideration they find to be most salient. Based on the European Social Survey (2016) data on West European countries, I find that automation-threatened workers significantly support such policies. This may indicate that they find welfare competition concerns more salient than welfare access ones.
Extending Ozone‐Precursor Relationships in China From Peak Concentration to Peak Time

High ozone concentrations have become the major summertime air quality problem in China. Extensive in situ observations are deployed for developing strategies to effectively control the emissions of ozone precursors, that is, nitrogen oxides (NOX = NO + NO2) and volatile organic compounds (VOCs). The modeling analysis of in situ observations often makes uses of the dependence of ozone peak concentration on NOX and VOC emissions, because ozone observations are among the most widely available air quality measurements. To extract more information from regulatory ozone observations, we extend the ozone‐precursor relationship to ozone peak time in this study. We find that the sensitivities of ozone peak time and concentration are complementary for regions with large anthropogenic emissions such as China. The ozone peak time is sensitive to both VOC and NOX emissions, and the sensitivity is nearly linear in the transition regime of ozone production compared to the changing ozone peak concentration sensitivity in this regime, making the diagnostics of ozone peak time particularly valuable. The extended ozone‐precursor relationships can be readily applied to understand the effects on ozone by emission changes of NOX and VOC and to assess potential biases of NOX and VOC emission inventories. These observation constraints based on regulatory ozone observations can complement the other measurement and modeling analysis methods nicely. Furthermore, we suggest that the ozone peak time sensitivity we discussed here to be used as a model evaluation measure before the empirical kinetic modeling approach (EKMA) diagram is applied to understand the effectiveness of emission control on ozone concentrations.
Cullin3-RING ubiquitin ligases are intimately linked to the unfolded protein response of the endoplasmic reticulum

CUL3-RING ubiquitin ligases (CRL3s) are involved in various cellular processes through different Bric a brac, Tramtrack and Broad-Complex (BTB)-domain proteins. KLHL12, a BTB-domain protein, appears to play an essential role in export of large cargo molecules like procollagen from the endoplasmic reticulum (ER). It has been suggested that CRL3KLHL12 mono-ubiquitinates SEC31 and mono-ubiquitinated SEC31 increases the dimension of a COPII coat to accommodate the large cargo molecules. As we examined this model, we found that functional CRL3KLHL12 was indeed critical for the assembly of large COPII structures. Interestingly, we noticed that CRL3KLHL12 influences collagen synthesis in human skin fibroblasts (HSFs). Our results also suggest that there is a CRL3KLHL12–independent collagen secretion route in HSFs. In addition, we found that CRL3KLHL12 strongly influences levels of sensors of the unfolded protein response (UPR). Different cell lines reacted differently to CUL3 depletion with respect to UPR regulation. This cell line-dependency appears to rely on a cell line-specific BTB-domain protein(s). Consistent with this idea, depletion of a muscle-specific BTB-domain protein KLHL41 recapitulated the effects of CUL3 depletion in C2C12 myotubes in UPR regulation. Based on these results we propose that CRL3KLHL12 and CRL3KLHL41 are regulators of the UPR.
Multidisciplinary Analysis of Ground Movements: An Underground Gas Storage Case Study

The paper presents a multi-physics investigation of the ground movements related to the cyclical and seasonal injection and withdrawal of natural gas in/from a depleted reservoir located in the Po Plain area, Italy. Interferometric Synthetic Aperture Radar (InSAr) data (from 2003) and Global Navigation Satellite System (GNSS) data (from 2008) provided a full and coherent panorama of almost two decades of ground movement in the monitored area (more extended than the field boundary). The analysis of the acquired millimetric-scale movements together with the detailed geological analysis, both at reservoir and at regional scale, represents the focal point for understanding the investigated phenomena. Based on this information, a fully integrated and multidisciplinary geological, fluid-flow and geomechanical numerical modeling approach was developed to reproduce the main geometrical and structural features of the involved formations together with the poromechanics processes induced by the storage operations. The main achievement of the adopted methodology is a deep knowledge of the system and the involved processes, which is mandatory for the safety of the urbanized areas and the effective management of the underground resources.
A dictionary-based method for detecting machine-generated domains

ABSTRACT Internet robots, also known as bots, have transformed the businesses and society with convenience. However, the dynamics of these interactions could be under adversarial circumstances with detrimental effects on network security. Bots that use domain-generation algorithms (DGAs) can generate many random domains dynamically so that static domain blacklists become ineffective in preventing malicious attacks by botnets. Various families of recent botnets have used DGA to establish communication with the bots. Researchers have introduced various detection methods with moderate success. Methods proposed so far either detect only DGAs that use non-variations forms or focus on the classification accuracy instead of time complexity, which would be critical in real-world production. The goal of this article is to explore how machine learning can help in detecting machine-generated domain names. To that end, we propose a dictionary-based n-gram method that can detect 39 DGA variations. We compared our method with existing research and found that our method can improve the performance of the existing classification algorithms. At last, our method can achieve competitive results as the LSTM model while requiring less time and complexity. Our research helps real-time production for DGA detection and provides insight in protecting DNS server and information security.
Mediating Role Of Brand Preference On The Relationships Between Brand Cues And Brand Loyalty In Electronic Products

: This study aims to investigate the mediating role of brand preference in explaining the relationships between brand cues such as brand name, country of origin and brand communication with brand loyalty in the context of electronic brands in the emerging markets. This paper strives to improve a model that extends the understanding of how brand cues identify consumers’ preference towards brand loyalty. Accordingly, the study conducted a survey through quota sampling technique on laptop users in Saudi Arabia. A total of 800 questionnaires were distributed. The study provides insights into the multi dimension of brand cues in developing brand preference which would affect consumer brand loyalty. Also, the results confirm the significant effect of brand preference as mediator on the relationship between brand cues and brand loyalty. Hence, the study model contributes a new perspective for building reputable brands by obtaining consumers preference towards brand to increase brand loyalty. In addition, this study distinguishes the mediating role of brand preference in different contexts and offers evidence for marketing success.
Theoreme de renouvellement pour un systeme d'equations de renouvellement

– We show that the classical renewal theorems of Feller hold in the case system of renewal equations, when the distributions involved are supported on the who line. We extend Feller’s methods and also use Perron–Frobenius theory and potential the  2003 Éditions scientifiques et médicales Elsevier SAS
Criteria-Based Approach to Select Relevant Environmental SDG Indicators for the Automobile Industry

The United Nations Sustainable Development Goals (SDGs) cannot be met without the private sector. In order to contribute to the fulfillment of the SDGs, companies have to identify their influence and select relevant SDGs. However, so far no research has been conducted on the influence of companies or industries at the most concrete level in the SDG framework—the 247 SDG indicators. In this paper, a criteria-based approach to select relevant environmental SDG indicators for the automobile industry is developed. The three criteria—environmental impact, direct impact, and automobile impact—are defined. By means of a qualitative analysis, 31 influenceable indicators are selected and substantiated by an empirical analysis of the automobile industry’s impact. These indicators belong to 12 SDGs and demonstrate the broad influence of the automobile industry. The outcome of this study is a structured procedure for selecting relevant environmental SDG indicators. This procedure can be applied by companies and can also be adapted to other economic sectors. Finally, it is possible to quantify the level of influence of the selected indicators and thus measure the contributions of companies or economic sectors to the fulfillment of the SDGs.
Core Placement Optimization for Multi-chip Many-core Neural Network Systems with Reinforcement Learning

Multi-chip many-core neural network systems are capable of providing high parallelism benefited from decentralized execution, and they can be scaled to very large systems with reasonable fabrication costs. As multi-chip many-core systems scale up, communication latency related effects will take a more important portion in the system performance. While previous work mainly focuses on the core placement within a single chip, there are two principal issues still unresolved: the communication-related problems caused by the non-uniform, hierarchical on/off-chip communication capability in multi-chip systems, and the scalability of these heuristic-based approaches in a factorially growing search space. To this end, we propose a reinforcement-learning-based method to automatically optimize core placement through deep deterministic policy gradient, taking into account information of the environment by performing a series of trials (i.e., placements) and using convolutional neural networks to extract spatial features of different placements. Experimental results indicate that compared with a naive sequential placement, the proposed method achieves 1.99× increase in throughput and 50.5% reduction in latency; compared with the simulated annealing, an effective technique to approximate the global optima in an extremely large search space, our method improves the throughput by 1.22× and reduces the latency by 18.6%. We further demonstrate that our proposed method is capable to find optimal placements taking advantages of different communication properties caused by different system configurations, and work in a topology-agnostic manner.
Kc and LAI Estimations Using Optical and SAR Remote Sensing Imagery for Vineyards Plots

Daily or weekly irrigation monitoring conducted per sub-field or management zone is an important factor in vine irrigation decision-making. The objective is to determine the crop coefficient (Kc) and the leaf area index (LAI). Since the 1990s, optic satellite imagery has been utilized for this purpose, yet cloud-cover, as well as the desire to increase the temporal resolution, raise the need to integrate more imagery sources. The Sentinel-1 (a C-band synthetic aperture radar—SAR) can solve both issues, but its accuracy for LAI and Kc mapping needs to be determined. The goals of this study were as follows: (1) to test different methods for integrating SAR and optic sensors for increasing temporal resolution and creating seamless time-series of LAI and Kc estimations; and (2) to evaluate the ability of Sentinel-1 to estimate LAI and Kc in comparison to Sentinel-2 and Landsat-8. LAI values were collected at two vineyards, over three (north plot) and four (south plot) growing seasons. These values were converted to Kc, and both parameters were tested against optic and SAR indices. The results present the two Sentinel-1 indices that achieved the best accuracy in estimating the crop parameters and the best method for fusing the optic and the SAR data. Utilizing these achievements, the accuracy of the Kc and LAI estimations from Sentinel-1 were slightly better than the Sentinel-2′s and the Landsat-8′s accuracy. The integration of all three sensors into one seamless time-series not only increases the temporal resolution but also improves the overall accuracy.
Molecular Simulations of Adsorption and Diffusion in Metal-Organic Frameworks (MOFs)

In order to test the feasibility of using Metal-Organic Frameworks (MOFs) to preconcentrate explosive molecules for detection, molecular simulations of RDX (hexahydro-1,3,5-trinitro-1,3,5-triazine) within IRMOF-1 were performed. Grand Canonical Monte Carlo (GCMC) simulations were used to generate adsorption isotherms for pure RDX, RDX in dry air, and RDX in wet air. In addition to the isotherms, the GCMC simulations provide adsorption energies and density distributions of the adsorbates within the MOF. Molecular Dynamics (MD) simulations calculate diffusivities and provide a detailed understanding of the change in conformation of the RDX molecule upon adsorption. The presence of dry air has little influence on the amount of RDX that adsorbs. The presence of wet air increases the amount of RDX that adsorbs due to favorable interactions between RDX and water. We found a Henry’s law constant of 21.2 mol/kg/bar for both pure RDX and RDX in dry air. The RDX adsorption sites are located (i) in big cages, (ii) near a vertex, and (iii) between benzene rings. The energy of adsorption of RDX at infinite dilution was found to be -9.2 kcal/mol. The distributions of bond lengths, bond angles and torsion angles in RDX are uniformly slightly broader in the gas phase than in the adsorbed phase, but not markedly so. The self-diffusivity of RDX in IRMOF-1 is a strong function of temperature, with an activation energy of 6.0 kcal/mol.