Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of T-scan Occlusal Analysis and Adjustment Versus Articulating Paper on Stresses Transmitted to Single Mandibular Implant Supported Prosthesis

AIM: The study aimed to compare two different methods for occlusal analysis and adjustment of implant-supported prostheses (computerized T-scan and articulating papers) regarding the stresses transmitted to the implants. MATERIALS AND METHODS: Seven patients with completely edentulous mandible opposing dentate maxilla have been selected and four implants have been inserted. Two identical mandibular zirconia implant supported prostheses have been constructed for each patient. Occlusal analysis and adjustments have been done using T-scan for one prosthesis and using articulating papers for the other prosthesis. Two identical acrylic casts for the mandibular arch (with four implant analogs on each cast) have been constructed for each patient. The prostheses on their acrylic casts were applied to cyclic loading using chewing simulator of 250,000 cycle (1 year loading). Strain gauges have been installed mesial and distal to each implant (28 implants of T-scan group and 28 implants of articulating paper group) and strain gauge analysis was conducted using the universal testing machine upon the application of bilateral and unilateral load after cyclic loading. Data were collected, tabulated, and statistically analyzed. RESULTS: Higher micro strain values were found around implants among articulating paper group than T-scan group and that difference was statistically insignificant under bilateral loading and statistically significant under unilateral loading. CONCLUSION: T-scan allows better occlusal stresses distribution as it brings additional information regarding force intensity and occlusal timing resulting in less stresses transmitted around implants.
Why Software Projects need Heroes (Lessons Learned from 1100+ Projects)

A "hero" project is one where 80% or more of the contributions are made by the 20% of the developers. In the literature, such projects are deprecated since they might cause bottlenecks in development and communication. However, there is little empirical evidence on this matter. Further, recent studies show that such hero projects are very prevalent. Accordingly, this paper explores the effect of having heroes in project, from a code quality perspective. We identify the heroes developer communities in 1100+ open source GitHub projects. Based on the analysis, we find that (a) hero projects are majorly all projects; and (b) the commits from "hero developers" (who contribute most to the code) result in far fewer bugs than other developers. That is, contrary to the literature, heroes are standard and very useful part of modern open source projects.
Quantitative remote sensing of ammonium minerals, Cedar Mountains, Esmeralda County, Nevada

Mineral-bound ammonium (NH4+) was discovered by the U.S. Geological Survey in the southern Cedar Mountains of Esmeralda County, Nevada in 1989. At 10 km in length, this site is 100 times larger than any previously known occurrence in volcanic rocks. The ammonium occurs in two hydrothermally altered, crystal-rich rhyolitic tuff units of Oligocene age, and is both structurally and stratigraphically controlled. This research uses Advanced Visible/Infrared Imaging Spectrometer (AVIRIS) data to quantitatively map the mineral-bound ammonium (buddingtonite) concentration in the altered volcanic rocks. Naturally occurring mineral-bound ammonium is fairly rare; however, it has been found to occur in gold-bearing hydrothermal deposits. Because of this association, it is thought that ammonium may be a useful too in exploration for gold and other metal deposits. Mineral-bound ammonium is produced when an ammonium ion (NH4+) replaces the alkali cation site (usually K+) in the crystal structure of silicate minerals such as feldspars, micas and clays. Buddingtonite is an ammonium feldspar. The ammonium originates in buried organic plant matter and is transported to the host rock by hydrothermal fluids. Ammonium alteration does not produce visible changes in the rock, and it is barely detectable with standard x-ray diffraction methods. It is clearly identified, however, by absorption features in short wave-infrared (SWIR) wavelengths (2.0 - 2.5 micrometers). The ammonium absorption features are believed to be caused by N-H vibrational modes and are analogous to hydroxyl (O-H) vibrational modes, only shifted slightly in wavelength. Buddingtonite absorption features in the near- and SWIR lie at 1.56, 2.02 and 2.12 micrometers. The feature at 2.12 micrometer is the strongest of the three and is the only one used in this study. The southern Cedar Mountains are sparsely vegetated and are an ideal site for a remote sensing study.
Driver Acceptance of Truck Platooning: State-of-the-Art and Insights from a Field Trial on Rural Roads

Truck platooning denotes virtually linking two or more trucks by use of communication and sensor technologies. With increasing automation, platoon driver roles and tasks are likely to change. Some drivers may be encouraged by prospects of teamwork and more flexible work schedules. Others may be concerned about safety and monotony, and fear loss of independence. Acceptance from drivers will depend on the perceived benefits and constraints of truck platooning in the context of their work. However, it is difficult to assess impacts of new technology without first-hand experience. The current study investigated acceptance of platooning in a field trial on rural roads. Three professional drivers operated a three-truck platoon along a 380 km route in Northern Norway, subject to large variations in road conditions. The trucks had automated longitudinal control. Although increasing usefulness and satisfaction were reported during the trial, participants appeared undecided or slightly negative towards truck platooning in interviews and in post hoc ratings. The participants stated that platooning may be advantageous on highways while requiring substantial effort from drivers to work on rural roads.
Indigenous Forest Use as an Agent of Change in Plant and Animal Communities of the Temperate Sikkim-East Nepal Himalaya

Author(s): Bland, James Dean | Advisor(s): Walter, Hartmut S; Gillespie, Thomas W | Abstract: With this study, I advance our knowledge of the effects of indigenous forest resource use on plant and animal communities in the Temperate Sikkim-East Nepal Himalaya. In Chapter 1, I describe the region’s rich biodiversity, when and by whom it was discovered, how seriously it is threatened, and how institutions of rural development and nature conservation are responding. I also describe the theoretical and methodological approaches I take, and the geographic, biotic, and cultural scope of the work, which was conducted at Chitre Village in northeastern Nepal. In Chapter 2, I assess household collection and consumption of woody plant resources, including fuelwood, timber, tree fodder, bamboo, pollarded stems, and leaf litter, and I investigate how these harvests impact resource supplies and natural habitats around the village. In Chapter 3, I analyze plant species assemblages along a gradient of anthropogenic disturbance extending out from the village. I hypothesize the non-random distributions of woody plant species are more influenced by anthropogenic disturbance than by environmental heterogeneity. In Chapter 4, I study habitat associations of animal species and species assemblages in order to establish baseline quantitative knowledge of species-habitat associations for the Temperate Sikkim-East Nepal Himalaya, and to better understand how forest use practices impact the composition and structure of the region’s wildlife habitats. In Chapter 5, I investigate changes in woody plant and small animal communities that result from the harvest of woody plant resources, using distance from the village center as a surrogate for period and degree of anthropogenic disturbance. I estimate the intensity of resource harvests across three progressively disturbed habitat zones, then assess the effects of resource harvest on wildlife habitats and the abundance, diversity, and composition of plant and small animal communities. In Chapter 6, I use data from Chitre Village to create a graphic model of generalized changes in plant and animal communities at successive stages of rural development, at both landscape and patch scales. The model’s four stages depict landscape-scale changes in vegetation and village development as an area transitions from seasonal pasture to commercial outpost. Six patch-scale models depict habitat conditions associated with the changing landscapes. Corresponding changes in animal communities are presented in narrative, and referenced to supporting evidence from the preceding chapters. Results of this study can be instrumental in identifying needs, priorities, and methodological approaches for future research, but much more baseline ecological knowledge is needed. Additional research is also needed to determine how best to encourage local forest users to trade off traditional forest use liberties for environmental conservation, and how best to train local people to implement science-based environmental monitoring.
Cirrus and Polar Stratospheric Cloud Studies Using Claes Data

We've concluded a 3 year (Period of Performance- January 21, 1998 to February 28, 2001) study of cirrus and polar stratospheric clouds using CLAES (Cryogenic Limb Array Etalon Spectrometer) data. We have described the progress of this study in monthly reports, UARS (Upper Atmosphere Research Satellite) science team meetings, American Geophysical Society Meetings, refereed publications and collaborative publications. Work undertaken includes the establishment of CLAES cloud detection criteria, the refinement of CLAES temperature retrieval techniques, compare the findings of CLAES with those of other instruments, and present findings to the larger community. This report describes the progress made in these areas.
ScholarWorks@UNO ScholarWorks@UNO

Facial emotion recognition is a widely studied area with applications in diverse domains such as human-computer interaction, affective computing, and social robotics. The objective of this thesis is to study facial emotion recognition models, based on deep learning and neural networks, and to also obtain an improved model. In order to improve the classification accuracy of facial emotion recognition, this thesis proposes a two-stage model. The first stage consists of a set of binary classifiers. The output of each binary classifier represents the probability that an image belongs to a particular class. The second stage incorporates a second neural network trained on the original probabilities generated by the binary classifiers, as well on transformed probabilities. The performance of proposed model has been compared to other techniques. The thesis begins with a thorough review of available datasets and technologies used for data collection, while highlighting the challenges associated with these datasets. A detailed analysis of various facial emotion detection models, including the baseline model and its different architectures, is presented. The thesis also explores the pre-processing of datasets for using with binary classifiers and investigates the effects of developing an ensemble of binary classifiers.
A Modeling and Verification Study of Summer Precipitation Systems Using NASA Surface Initialization Datasets

One of the most challenging weather forecast problems in the southeastern U.S. is daily summertime pulse-type convection. During the summer, atmospheric flow and forcing are generally weak in this region; thus, convection typically initiates in response to local forcing along sea/lake breezes, and other discontinuities often related to horizontal gradients in surface heating rates. Numerical simulations of pulse convection usually have low skill, even in local predictions at high resolution, due to the inherent chaotic nature of these precipitation systems. Forecast errors can arise from assumptions within parameterization schemes, model resolution limitations, and uncertainties in both the initial state of the atmosphere and land surface variables such as soil moisture and temperature. For this study, it is hypothesized that high-resolution, consistent representations of surface properties such as soil moisture, soil temperature, and sea surface temperature (SST) are necessary to better simulate the interactions between the surface and atmosphere, and ultimately improve predictions of summertime pulse convection. This paper describes a sensitivity experiment using the Weather Research and Forecasting (WRF) model. Interpolated land and ocean surface fields from a large-scale model are replaced with high-resolution datasets provided by unique NASA assets in an experimental simulation: the Land Information System (LIS) and Moderate Resolution Imaging Spectroradiometer (MODIS) SSTs. The LIS is run in an offline mode for several years at the same grid resolution as the WRF model to provide compatible land surface initial conditions in an equilibrium state. The MODIS SSTs provide detailed analyses of SSTs over the oceans and large lakes compared to current operational products. The WRF model runs initialized with the LIS+MODIS datasets result in a reduction in the overprediction of rainfall areas; however, the skill is almost equally as low in both experiments using traditional verification methodologies. Output from object-based verification within NCAR s Meteorological Evaluation Tools reveals that the WRF runs initialized with LIS+MODIS data consistently generated precipitation objects that better matched observed precipitation objects, especially at higher precipitation intensities. The LIS+MODIS runs produced on average a 4% increase in matched precipitation areas and a simultaneous 4% decrease in unmatched areas during three months of daily simulations.
Deep learning methods for underground deformation time-series prediction

: Prediction is a vague concept that is why we need to conceptualize it specifically for underground deformation time-series data. For this impending issue, this paper employs an advanced deep learning model Bi-LSTM-AM to address it. The results show its applicability for practical engineering. The proposed model is compared with other basic deep learning models including long short-term memory (LSTM), Bi-LSTM, gated recurrent units (GRU), and temporal convolutional networks (TCN). These models cover the most common three forms of deep learning for time-series prediction: recurrent neural networks (RNN) and convolutional neural networks (CNN). This research is supposed to benefit the underground deformation time-series prediction.
Negative bias for sad imagery in depression: An ERP study

Individuals with depression experience more negative imagery and less vivid positive imagery, and the late positive potential (LPP) is considered as a viable biomarker for negative attentional and memory biases in depression; however, the LPP response to emotional imagery in depressed individuals remains unclear. This study aims to investigate neural response to emotional imagery in depressed individuals. ERPs were recorded from 40 depressed participants and 44 healthy controls during the encoding-imagery task. Depressed participants scored signiﬁcantly lower in the valence rating of sad and neutral imagery compared to healthy participants. Importantly, the LPP amplitudes to sad imagery in depressed participants were signiﬁcantly larger than healthy controls, particularly in the middle (800-1,400 ms) and late time windows(1,400-2,000 ms). Furthermore, depressed individuals exhibited signiﬁcantly higher LPP amplitudes for sad imagery compared to happy imagery, whereas healthy participants showed the opposite pattern. The present study provides evidence that depressed individuals display abnormal electrophysiological reactivity to sad imagery, which oﬀers a new perspective for understanding the mechanisms underlying
Fragility analysis of slopes exposed to seismic hazard employing surrogate modeling techniques

Natural disasters may lead to failure of critical assets part of railway systems. Slope failure often results in major economic consequences. Due to the economic importance that railways systems have, it is pertinent to ensure good performance and long‐term. The objective of the present research is to propose tools that may allow obtaining the failure probability of soil slopes under a seismic event by employing surrogate models to ease the computational cost of considering the uncertainties associated with geotechnical and geometrical properties of a case study located in Portugal. The presented methodology and derived fragility curves, using peak ground acceleration (PGA) as the intensity measure (IM), can be used to assess slope performance under a seismic event for different safety levels, thus providing useful information for prioritizing assets and taking preventive actions to maintain the desired performance of the railway system.
The application of modern methods for bridge diagnostics and load testing

The paper deals with the modern methods of testing and diagnostics of existing, mainly steel, bridges. The focus is on the DIC method (digital image correlation) and its application for the static measurements and dynamic measurements of the bridge deflection at high speeds and on the detection of the behavior of bridge details, such as pins and fatigue cracks. The sources of potential related error values are identified and possible strategies for the minimization of errors corresponding to the DIC field measurements are developed and described in the paper. The proposed DIC methods were successfully applied on the deflection measurement on long distances and on the measurement of the strain on details in‐situ. Relevant applications were done in the Czech Republic as a part of the bridge load testing and the results are discussed in the paper.
Text Recognition for 2D Bridge Plans Using OCR‐Algorithms

The 2D bridge plans play an essential role in renovations or new replacements of transport infrastructures. As an indispensable component of plans, text serves as dimension labels, descriptions of materials, etc. Optical Character Recognition (OCR) can convert text in image into machine‐encoded format, and has high potential and invaluable practical worth for analyzing, managing, and extracting textual information from plans. First, the selection of OCR‐algorithms and the sampling of textual fragments from 2D bridge plans, including historic plans with handwritten text and digital plans with printed text, was carried out. Subsequently, the performances of different open‐source OCR‐algorithms on different textual fragments from 2D bridge plans are compared. In this process, the accuracy at character level and word level are chosen as two main factors to evaluate their performances. Finally, the most potential OCR‐algorithms for the text recognition on 2D bridge plans are proposed.
Sustainability assessment of bridge structures in the operation phase based on a digital twin

There are about 65,000 bridge structures in the infrastructure network of the Federal Republic of Germany. Due to the long operating phase of bridges, they have an influence on sustainability. To evaluate the sustainability of bridges in the operational phase, digital building models can serve as a consistent and structured database to increase the efficiency and transparency.
Does vehicle‐bridge interaction resemble the effect of tuned‐mass dampers on bridges during earthquakes?

In many seismic studies, the coupled vehicle‐bridge system is treated as a tuned‐mass damper (TMD)‐bridge system, and unsurprisingly, the vehicle usually reduces the seismic bridge response. The current study challenges this common belief. To this end, it investigates the influence of running vehicles on the seismic performance of bridges. Specifically, it compares the effect of vehicle‐bridge interaction (VBI) to that of a TMD on bridges subjected to earthquakes. The discussion explores the underlying physical mechanisms of VBI and TMD, and highlights the superficial similarities and the substantial differences between them. In this context, the study presents pertinent numerical simulations for a set of natural earthquake records.
Hyperspectral imaging systems for corrosion detection from remotely operated vehicles

The detection of corrosion, especially in early stages, is a key factor for cost reduction in the maintenance of steel infrastructure. However, manual inspection is time consuming and takes considerable effort of people and equipment. Remotely operated vehicles with application‐specific sensors may overcome this problem. Novel sensing approaches like hyperspectral imaging (HSI) systems in combination with machine learning algorithms open new pathways for the rapid inspection of large surface areas in complex environments. In contrast to conventional RGB imaging, HSI contains both spatial and spectral reflectivity information over the complete visual spectral range and near infrared. This offers improved material characterization and classification possibilities using the chemical properties contained in the local reflection spectrum.
MT‐InSAR monitoring for tunnel induced settlements in urban area

Satellite Interferometric Synthetic Aperture Radar (InSAR) is an accredited technology used in many fields in the last decades, but represents a relatively new technology for civil infrastructures monitoring. It allows to measure displacement along the line of sight (LOS) of the sensor with a millimetric precision. This study was conducted to show the potential of SAR sensors, mounted on satellite platforms, in detecting displacements induced by the excavations of tunnels in urban areas. Advances in remote sensing, such as the development of Multi‐Temporal InSAR algorithms, have overcome the limit of InSAR due to spatial‐temporal decorrelation of the phase measurement, thus improving the precision of the outcomes. Here the COSMO‐SkyMed satellite constellation acquired data over a small area in the city of Naples, affected by the excavation of twin tunnels. Data were processed with the SARPROZ software, while tunnel induced settlement were predicted with empirical methods. To allow comparison, displacements along LOS were projected in the vertical plane. Results indicate a good agreement between the monitored data and the predicted profile of settlement, further confirming the potential of MT‐InSAR in detecting displacements induced by tunneling excavation in urbanized area.
Optical sensors for the durability assessment of cement‐based infrastructure

The corrosion‐related damages on concrete infrastructure account globally for several billion US dollars annually. Up to 38 % of these costs could be saved by the application of optimized materials and/or more efficient monitoring technologies. In this contribution a novel sensor technology is presented, based on luminescent pH sensitive dyes, to quantitatively determine the pH distribution in cement‐based construction materials. Different sensor platforms were explored resulting in high‐resolution imaging techniques, as well as in miniaturized sensor probes for field application and in situ monitoring. To this point, pH sensors were successfully applied for cementitious materials to (i) quantitatively characterize the carbonation state in the lab, (ii) gain further understanding on phase assemblages and internal pH evolution related to carbonation, (iii) reveal that the actual pH at the inflection point of phenolphthalein may strongly vary depending on physicochemical material properties, (iv) monitor the pH evolution during the early hydration of different cementitious materials, and (v) measure carbonation depths using miniaturized sensor probes adopted for field applications.