Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine-Supported Bridge Inspection Image Documentation Using Artificial Intelligence

The purpose of a routine bridge inspection is to assess the physical and functional condition of a bridge according to a regularly scheduled interval. The Federal Highway Administration (FHWA) requires these inspections to be conducted at least every 2 years. Inspectors use simple tools and visual inspection techniques to determine the conditions of both the elements of the bridge structure and the bridge overall. While in the field, the data is collected in the form of images and notes; after the field work is complete, inspectors need to generate a report based on these data to document their findings. The report generation process includes several tasks: (1) evaluating the condition rating of each bridge element according to FHWA Recording and Coding Guide for Structure Inventory and Appraisal of the Nation’s Bridges; and (2) updating and organizing the bridge inspection images for the report. Both of tasks are time-consuming. This study focuses on assisting with the latter task by developing an artificial intelligence (AI)-based method to rapidly organize bridge inspection images and generate a report. In this paper, an image organization schema based on the FHWA Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation’s Bridges and the Manual for Bridge Element Inspection is described, and several convolutional neural network-based classifiers are trained with real inspection images collected in the field. Additionally, exchangeable image file (EXIF) information is automatically extracted to organize inspection images according to their time stamp. Finally, the Automated Bridge Image Reporting Tool (ABIRT) is described as a browser-based system built on the trained classifiers. Inspectors can directly upload images to this tool and rapidly obtain organized images and associated inspection report with the support of a computer which has an internet connection. The authors provide recommendations to inspectors for gathering future images to make the best use of this tool.
Army embeddedness, political opportunities and threats, and the dynamics of contention: Understanding the varying role of the armed forces in the Egyptian, Syrian, and Libyan 2011 revolts

In many Middle East and North Africa (MENA) countries, the army has traditionally been a central pillar of the authoritarian regimes, responsible for the security and integrity of the state and a symbol of national sovereignty and social unity. Nevertheless, the 2011 Arab revolts witnessed stark differences in the response of the armies. This article argues that a relational reading of the Structure of Political Opportunities and Threats, particularly when its dimension of the state’s capacity and propensity for repression is informed by a MENA-salient regime feature—army embeddedness—offers a compelling solution to the puzzle. An analysis of the Egyptian, Syrian, and Libyan episodes of contention, based on a comparative method that combines mechanism-based process tracing and typological theorizing, demonstrates the theoretical payoffs of this sensitized dimension. Cross-case similarities underscore the value of thinking about the army as a full-fledge agent embedded within a web of relations with social and political forces. Specifically, findings reveal how army embeddedness shapes the respective operation and effect of the mechanisms “political opportunities” and “political threats,” and highlight the importance of differentiating between the state’s capacity and the state’s propensity for repression. Within-case variations highlight the historically specific development of such embeddedness and how it plays out distinctively in each case, forming different scenarios of high and low capacity and propensity for repression.
A Simulation-Experiment Method to Characterize the Heat Transfer in Ex-Vivo Porcine Hepatic Tissue with a Realistic Microwave Ablation System

A simulation-experiment method is proposed to characterize the performance of a realistic microwave ablation (MWA) device. Ex-vivo porcine livers are used in the study. The catheter is modeled without understanding its electrical structure or the actual ablation power; so, it is adaptable to other ablators. The optimized 3-D finite difference time domain (FDTD) simulation can efficiently reflect the lesion profile in heterogeneous tissues and is clinically beneficial. This method can serve as a guideline to evaluate the device performance and the treatment plan. The influence of the temperature dispersive dielectric and the thermal properties has also been discussed.
Convolution Functional Linear Regression Model

This thesis develops a new set of tools in Functional Data Analysis. A general historical functional linear model called “convolution functional linear model” is developed to handle a stationary response to the recent history of a covariate process. We argue that the dependent variable yi t is continuously related to values of covariate ξiP w where w ∈ [t − δp , t] explained by βp(w). βp(w) is the coefficient function of the convolution of the independent variable and give a weight to the values of ξiP w for the estimation of yi t as w → t. The coefficient of the convolution βp(w) as well as δp are unknown and estimated by least squares and cross-validation methods. In addition, diagnostic methods are also developed with the purpose of estimating the variability of βp(w), once δp has been estimated, and the effects that each curve sample has over model estimation or solution. In particular, the methodology of the non-overlapping block bootstrapping (NBB) was expanded to provide a solution for the distortion caused by dividing the sample data into blocks of length "l" and resample them with replacement. This method handles the discontinuity caused at the interior points where the blocks ordinarily should be connected in the block bootstrapped sample. In other words, the block bootstrapped sample becomes less rough and maintains the smoothness of the original data. In addition, the block bootstrapped resembles the autocovariance structure of the original data allowing estimating accurate confidence interval for βp(w). The statistical methods of Cook‟s and Mahalanobis distance also were modified with the objective of accounting for the time structure and serial correlation of the data. This was specifically done by restricting the estimation of the covariance using the autocovariance estimates. These two methods allow identifying curve or functional data that are causing a significant effect in the solution and estimates of the model. Finally, the model was used for the estimation of the continuous trajectory of particulate matter given driving behavior variables. Estimates of the parameters and diagnostics for this particular case are provided.
DEFICIT FUNCTION BUS SCHEDULING WITH DEADHEADING TRIP INSERTIONS FOR FLEET SIZE REDUCTION

This paper begins with a brief review of the operational aspects of bus scheduling at Egged (The Israel National Bus Carrier), and their experience with an optimal bus scheduling algorithm. Due to the limitations of this algorithm, Egged Management has decided it is still not justified to replace the manually and currently used planning procedures (comprised of 60 schedulers using Gantt charts). Consequently, an approximate procedure incorporating a man/computer interface was requested which would allow the inclusion of practical considerations that experienced schedulers may wish to introduce in the schedule. Due to its visual nature, the deficit function was selected as a natural tool to form the basis of such a man-machine interactive scheduling procedure. A deficit function defines the net number of departures required at each terminal up to and including time t . According to the well-known fleet size formula, the minimum number of vehicles required to service a multiterminal fixed schedule of trips is equal to the sum of the maximal deficits at all terminals. As deficit function theory stops at a fixed schedule of trips, it was necessary to develop procedures to (1) primarily determine when and where to insert deadheading trips, and (2) secondarily include such considerations as garage limitations, inclusion of refueling trips, and driver limitations. The final algorithm is capable of generating schedules automatically in a purely computer mode. In an interactive mode it allows the user to select one of several computer suggested improvements or to interject his own suggestions and immediately see the effects on the final schedule through observation of the deficit functions on a CRT or computer generated output.
Annexin A3: a newly identified player in store‑operated calcium entry

Store‑operated calcium entry (SOCE) is important for refilling endoplasmic reticulum (ER) Ca2+ stores and Ca2+ signaling. Extracellular Ca2+ is conducted by highly Ca2+‑selective Orai channels that are activated by stromal interaction molecule proteins (STIMs), which are sensitive ER Ca2+ sensors. We found an approximately five‑fold increase in annexin A3a (anxa3a) expression levels in stim2b knockout zebrafish. The present study investigated whether annexin A3 protein is involved in SOCE. We used Ca2+ imaging and electrophysiological recordings to determine the effect of annexin A2, A3, and A6 overexpression on SOCE and Orai-dependent Ca2+ currents (ICRAC) in cultured cells. Annexin A3 increased SOCE amplitude and potentiated Ca2+ ICRAC currents. These results suggest that annexin A3 is a positive modulator of SOCE.
A Synopsis of AIRS Global‐Mean Clear‐Sky Radiance Trends From 2003 to 2020

Atmospheric Infrared Sounder (AIRS) aboard the National Aeronautics and Space Administration (NASA) Aqua satellite has been operating since September 2002. Its information content, superb instrument performance, and dense sampling pattern make the AIRS radiances an invaluable data set for climate studies. The trends of global‐mean, nadir‐view, clear‐sky AIRS radiances from 2003 to 2020 are studied here, together with the counterparts of synthetic radiances based on two reanalyzes, European Centre for Medium‐Range Weather Forecasts Reanalysis V5 (ECMWF ERA5) and NASA Goddard Earth Observing System V5.4. 1 (GEOS‐5.4.1; a reanalysis product without assimilation of hyperspectral radiances such as AIRS). The AIRS observation shows statistically significant negative trends in most of its CO2 channels, positive but non‐significant trends in the channels over the window regions, and statistically significant positive trends in some of its H2O channels. The best agreements between observed and simulated radiance trends are seen over the CO2 tropospheric channels, while the observed and simulated trends over the CO2 stratospheric channels are opposite. ERA5 results largely agree with the AIRS observation over the H2O channels. The comparison in the H2O channels helps reveal a data continuity issue in the GEOS‐5.4. 1. Contributions from individual variables to the radiance trends are also assessed by performing separate simulations. This study provides the first synopsis of the global‐mean trend of AIRS radiances over all its thermal‐IR channels.
Pharyngeal Inflammation on Positron Emission Tomography/Magnetic Resonance Imaging Before and After Obstructive Sleep Apnea Treatment.

RATIONALE There is upper airway inflammation in OSA patients which reduces with CPAP therapy. OBJECTIVES Validate use of positron emission tomography/magnetic resonance imaging (PET/MRI) to quantify metabolic activity within the pharyngeal mucosa of patients with OSA against nasal lavage proteomics, and assess the impact of continuous positive airway pressure (CPAP) therapy. METHODS Adults with OSA underwent [18F]-Fluoro-2-deoxy-D-glucose (FDG)-PET/MRI of the neck before and three months after initiating CPAP. Nasal lavage samples were collected. Inflammatory protein expression from samples was analyzed using the Olink platform. Upper airway imaging segmentation was performed. Target-to-background ratio (TBRmax) were calculated from target pharyngeal maximum standard uptake values (SUV) and personalized background mean SUV. Most-diseased segment-TBRmax were identified per participant at locations with highest PET avidity. Correlation analysis was performed between baseline TBRmax and nasal lavage proteomics. TBRmax was compared pre- and post-CPAP using linear mixed-effect models. RESULTS Among thirty-eight participants, baseline mean age was 46.3 years (SD 12.5), 21% were female, mean BMI was 30.9 kg/m2 (SD 4.6), and mean pRDI was 31 events/hour (SD 16.4). There was a significant positive correlation between pharyngeal mucosa most-diseased segment-TBRmax and nasal lavage proteomic inflammation (r=0.41 [p<0.001, FDR=0.002]). Primary analysis revealed a reduction in the most-diseased segment-TBRmax after median 2.91 months of CPAP therapy (-0.86 [SE±0.30 p=0.007]). Stratified analysis by smoking status revealed a significantly decreased most-diseased segment-TBRmax post-CPAP therapy among never smokers but not among ever smokers (-1.01 [SE±0.39, p=0.015] versus -0.64 [SE±0.49, p=0.201]). CONCLUSIONS CPAP therapy reduces metabolic activity measured by PET/MRI within the upper airway of adults with OSA. Further, PET/MRI measures of upper airway metabolic activity correlate with a non-invasive marker of inflammation, i.e., nasal lavage inflammatory protein expression.
Beyond the species name: an analysis of publication trends and biases in taxonomic descriptions of rainfrogs (Amphibia, Strabomantidae, Pristimantis )

The rainfrogs of the genus Pristimantis are one of the most diverse groups of vertebrates, with outstanding reproductive modes and strategies driving their success in colonizing new habitats. The rate of Pristimantis species discovered annually has increased continuously during the last 50 years, establishing the remarkable diversity found in this genus. In this paper the specifics of publications describing new species in the group are examined, including authorship, author gender, year, language, journal, scientific collections, and other details. Detailed information on the descriptions of 591 species of Pristimantis published to date (June 2022) were analyzed and extracted. John D. Lynch and William E. Duellman are the most prolific authors
Enforced Drainage Terrain Models Using Minimum Norm Networks and Smoothing Splines

Some techniques to overcome the problem of enforced drainage in mathematical terrain modeling are presented. Data available about the terrain consist of a set of scattered benchmarks and an idealized, piecewise linear hydrographic net. First, a cubic spline minimum norm network, MNN, on a triangulation of the benchmark data is created, in order to create a first impression of the terrain. The cubics from the MNN supply temporary profiles along the edges of the triangulation; these are modified on those edges which intersect the hydrographic net in order to simulate erosion of the terrain. This is done by weighted smoothing spline techniques. A network of monotonic cubic arcs is created on the edges of the hydrographic net. To arrive at the final surface model, a blending method is applied that requires the specification of elevations and gradient vectors along all edges. Although the elevations along the edges that lie in the hydrographic net are monotonic by construction, the gradient there should also be parallel to the direction of the hydrographic net. This is achieved approximately in an L2-sense. The result is a differentiable surface which interpolates the stream channels as well as the modified spline MNN.
Detection of Bottle Marine Debris Using Unmanned Aerial Vehicles and Machine Learning Techniques

Bottle marine debris (BMD) remains one of the most pressing global issues. This study proposes a detection method for BMD using unmanned aerial vehicles (UAV) and machine learning techniques to enhance the efficiency of marine debris studies. The UAVs were operated at three designed sites and at one testing site at twelve fly heights corresponding to 0.12 to 1.54 cm/pixel resolutions. The You Only Look Once version 2 (YOLO v2) object detection algorithm was trained to identify BMD. We added data augmentation and image processing of background removal to optimize BMD detection. The augmentation helped the mean intersection over the union in the training process reach 0.81. Background removal reduced processing time and noise, resulting in greater precision at the testing site. According to the results at all study sites, we found that approximately 0.5 cm/pixel resolution should be a considerable selection for aerial surveys on BMD. At 0.5 cm/pixel, the mean precision, recall rate, and F1-score are 0.94, 0.97, and 0.95, respectively, at the designed sites, and 0.61, 0.86, and 0.72, respectively, at the testing site. Our work contributes to beach debris surveys and optimizes detection, especially with the augmentation step in training data and background removal procedures.
DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation

Dual-energy computed tomography (DECT) is an advanced CT computed tomography scanning technique enabling material characterization not possible with conventional CT scans. It allows the reconstruction of energy decay curves at each 3D image voxel, representing varied image attenuation at different effective scanning energy levels. In this paper, we develop novel unsupervised learning techniques based on mixture models and functional data analysis models to the clustering of DECT images. We design functional mixture models that integrate spatial image context in mixture weights, with mixture component densities being constructed upon the DECT energy decay curves as functional observations. We develop dedicated expectation–maximization algorithms for the maximum likelihood estimation of the model parameters. To our knowledge, this is the first article to develop statistical functional data analysis and model-based clustering techniques to take advantage of the full spectral information provided by DECT. We evaluate the application of DECT to head and neck squamous cell carcinoma. Current image-based evaluation of these tumors in clinical practice is largely qualitative, based on a visual assessment of tumor anatomic extent and basic one- or two-dimensional tumor size measurements. We evaluate our methods on 91 head and neck cancer DECT scans and compare our unsupervised clustering results to tumor contours traced manually by radiologists, as well as to several baseline algorithms. Given the inter-rater variability even among experts at delineating head and neck tumors, and given the potential importance of tissue reactions surrounding the tumor itself, our proposed methodology has the potential to add value in downstream machine learning applications for clinical outcome prediction based on DECT data in head and neck cancer.
Review—Micro-Fuel Cell Principal Biosensors for Monitoring Transdermal Volatile Organic Compounds in Humans

Knowledge of transduction mechanisms in biosensing applications paves the way for ultrasensitive and dynamic detection in living systems. Real-world biosensing applications where ultra-sensitivity and dynamic detection are paramount include monitoring the anesthetic agent concentration during surgery; the slightest variation in concentration can potentially result in a life-threatening overdose or, on the other end of the spectrum, the patient’s awareness during the procedure. We review the benefits and functions of the transcutaneous biosensor device compared with other current technology and discuss the sensor’s capability to accurately measure volatile anesthetic gas concentration in blood using fuel cell technology. We review fundamental concepts of fuel-cell technology for wearable bio-sensing applications. The fuel cell sensor can also continuously monitor other volatile organic compounds making it versatile with numerous potential applications.
Mixed linear and semidefinite programming for combinatorial and quadratic optimization

We use the semidefinite relaxation to approximate combinatorial and quadratic optimization problems subject to linear, quadratic, as well as boolean constraints. We present a dual potential reduction algorithm and show how to exploit the sparse structure of various problems. Coupled with randomized and heuristic methods, we report computational results for approximating graph-partition and quadratic problems with dimensions 800 to 10,000. This finding, to the best of our knowledge, is the first computational evidence of the effectiveness of these approximation algorithms for solving large-scale problems.
Ovarian Reserve Disorders, Can We Prevent Them? A Review

The ovarian reserve is finite and begins declining from its peak at mid-gestation until only residual follicles remain as women approach menopause. Reduced ovarian reserve, or its extreme form, premature ovarian insufficiency, stems from multiple factors, including developmental, genetic, environmental exposures, autoimmune disease, or medical/surgical treatment. In many cases, the cause remains unknown and resulting infertility is not ultimately addressed by assisted reproductive technologies. Deciphering the mechanisms that underlie disorders of ovarian reserve could improve the outcomes for patients struggling with infertility, but these disorders are diverse and can be categorized in multiple ways. In this review, we will explore the topic from a perspective that emphasizes the prevention or mitigation of ovarian damage. The most desirable mode of fertoprotection is primary prevention (intervening before ablative influence occurs), as identifying toxic influences and deciphering the mechanisms by which they exert their effect can reduce or eliminate exposure and damage. Secondary prevention in the form of screening is not recommended broadly. Nevertheless, in some instances where a known genetic background exists in discrete families, screening is advised. As part of prenatal care, screening panels include some genetic diseases that can lead to infertility or subfertility. In these patients, early diagnosis could enable fertility preservation or changes in family-building plans. Finally, Tertiary Prevention (managing disease post-diagnosis) is critical. Reduced ovarian reserve has a major influence on physiology beyond fertility, including delayed/absent puberty or premature menopause. In these instances, proper diagnosis and medical therapy can reduce adverse effects. Here, we elaborate on these modes of prevention as well as proposed mechanisms that underlie ovarian reserve disorders.
Experimental Platform for Sensor Fusion Positioning Methods Evaluation

—Position and context awareness is becoming greater in importance as one of the key enablers of the Industry 4.0 concept, with impact on analysis and optimizing production processes, their direct control, increasing and ensuring the safety of personnel and technology, as well as enabling traceability of products. There are many approaches to achieve a real-time location system (RTLS) capable of providing positioning data, yet each approach usually has its limits given by the physical principles of technology which it is primarily based on; mostly a wireless signal transmission where the performance boundaries lay in non-line-of-sight (nLoS) scenarios. Current literature already presents various approaches capable of dealing with these shortcomings and presents various combinations of sensors based on different phenomena, the drawbacks of which are mutually compensated, known as sensor fusion. This work presents practical design of a compact platform, which utilizes Ultra-WideBand (UWB) as one of the predominant wireless technologies for wireless positioning with with IEEE 802.15. 4z compliant radio integrated circuits in cooperation with a 9-axis absolute orientation sensor, expandable with add-on boards.