Miracle-dz/UniFAR-FTUs huggingface dataset:
Nullification of Differential Ionospheric Delay for Long-Baseline Real-Time Kinematic Applications

One of the major challenges in resolving ambiguities for longer baselines is the presence of unmodelled ionospheric delays. In this paper, we describe a new ionospheric approach that does not rely on the convergence of an ionospheric parameter, and that instantaneously nullifies the effect of the differential ionospheric delay in an ambiguity search process. The performance of the ionosphere-nullification approach was demonstrated using the data recorded at a 1 Hz data rate at a pair of base stations on either side of the Bay of Fundy in eastern Canada and on the ferry boat, at the terminals of an approximately 74 km ferry route, on 21 May 2004. The UNB RTK software was enhanced to perform this study. For both static and kinematic tests over the longer 74 km baseline, a few millimeter mean differences were observed in each Cartesian component, and the comparison 1σ noise level was at the few centimeter level. For further enhancements of the UNB RTK software in near future, two issues are discussed in this paper, including a filtering process on the ionosphere observables to reduce the effects of multipath and an estimation of the residual zenith tropospheric delay to remove its residual effects.
Enhancing Nonlinear Interferometers for Imaging with Undetected Photons: Seeding and High-Gain

Infrared (IR) imaging and spectroscopy is invaluable to many disciplines for its ability to probe molecular responses, from material analysis to diagnostic medicine. However, these applications are often limited by inefficient, noisy detectors. Non-degenerate nonlinear interferometers (NLIs) offer an alternative route through a technique known as imaging with undetected photons [1]. For an NLI producing visible-IR photon pairs, a change in the IR due to an object can be observed as a change to the interference of the visible photons. The IR does not need to be detected, bypassing the need for IR detectors completely.
Thermoelectric Materials and Applications: A Review

Solid-state energy conversion has been established as one of the most promising solutions to address the issues related to conventional energy generation. Thermoelectric materials allow direct energy conversion without moving parts and being deprived of greenhouse gases emission, employing lightweight and quiet devices. Current applications, main thermoelectric material classes, and manufacturing methods are the topics of this work; the discussion revolves around the crucial need for highly performing materials in the mid-temperature range, and around the development of more scalable fabrication technologies. The different manufacturing methods for thermoelectric bulk materials and films are also discussed. Small-scale technologies are generating increasing interest in research; the high potential of aerosol jet printing is highlighted, stressing the many advantages of this technology. A promising approach to scale the production of miniaturized thermoelectric devices that combines high energy ball milling and aerosol jet printing is proposed in the conclusion.
Using a socio -cultural framework to evaluate farmland preservation policy success in Maryland

Title of Document: USING A SOCIO-CULTURAL FRAMEWORK TO EVALUATE FARMLAND PRESERVATION POLICY SUCCESS IN MARYLAND Richard A. Russo, Ph.D., 2009 Directed By: Dr. Martha E. Geores, Associate Professor Department of Geography The intent of Maryland’s farmland preservation policy has remained constant over the past three decades -to preserve productive farmland and woodland for the continued production of food and fiber for all of Maryland’s citizens . Therefore, thirty years after this statutory goal was made, how effective hav Maryland’s farmland preservation programs been in reaching this goal? This study addres ses the absence of cultural and social analysis in the evaluation of farmland preservat ion program success in Maryland’s metropolitan counties. In utilizing a socio-cultural framework of analysis, this study shows that farmland preservation policies (in their drafting, implementation, and evaluati on) re a cultural process, the outcomes of which create and sustain a particular soc ial space and cultural landscape. Theories on the social production of space and landscape are relevant to the task of farmland preservation and agricultural economic developm ent in metropolitan areas. The failure of farmland preservation policy in Maryland h s, in part, been the failure to take culture seriously. Quantitative indicators show that Maryland’s state farmland preservation program has achieved moderate success in securing a productive agricultural land base over its first three decades, but ha s not been successful in preserving farming as a viable “way of life,” has not stopped t he erosion in the value of agricultural sales, and has not reversed the marketplace alienation between producers and consumers in the state. USING A SOCIO-CULTURAL FRAMEWORK TO EVALUATE FARMLAND PRESERVATION POLICY SUCCESS IN MARYLAND
Procollagen type 1 N-terminal propeptide is associated with adverse outcome in acute chest pain of suspected coronary origin

Background Extracellular matrix (ECM) is an integral player in the pathophysiology of a variety of cardiac diseases. Cardiac ECM is composed mainly of collagen, of which type 1 is the most abundant with procollagen type 1 N-terminal Propeptide (P1NP) as a formation marker. P1NP is associated with mortality in the general population, however, its role in myocardial infarction (MI) is still uncertain, and P1NP has not been investigated in acute chest pain. The objective of the current study was to assess the role of P1NP in undifferentiated acute chest pain of suspected coronary origin. Methods and results 813 patients from the Risk in Acute Coronary Syndromes study were included. This was a single-center study investigating biomarkers in consecutively enrolled patients with acute chest pain of suspected coronary origin, with a follow-up for up to 7 years. Outcome measures were a composite endpoint of all-cause death, new MI or stroke, as well as its individual components at 1, 2, and 7 years, and cardiac death at 1 and 2 years. In multivariable Cox regression analysis, quartiles of P1NP were significantly associated with the composite endpoint at 1 year of follow-up with a hazard ratio for Q4 of 1.82 (95% CI, 1.12–2.98). There was no other significant association with outcomes at any time points. Conclusion P1NP was found to be an independent biomarker significantly associated with adverse clinical outcome at one year in patients admitted to hospital for acute chest pain of suspected coronary origin. This is the first report in the literature on the prognostic value of P1NP in this clinical setting. Clinicaltrials.ygov Identifier NCT00521976.
Smart Temperature Sensor Design and High-Density Water Temperature Monitoring in Estuarine and Coastal Areas

Acquiring in situ water temperature data is an indispensable and important component for analyzing thermal dynamics in estuarine and coastal areas. However, the long-term and high-density monitoring of water temperature is costly and technically challenging. In this paper, we present the design, calibration, and application of the smart temperature sensor TS-V1, a low-power yet low-cost temperature sensor for monitoring the spatial–temporal variations of surface water temperatures and air temperatures in estuarine and coastal areas. The temperature output of the TS-V1 sensor was calibrated against the Fluke-1551A sensor developed in the United States and the CTD-Diver sensor developed in the Netherlands. The results show that the accuracy of the TS-V1 sensor is 0.08 °C, while sensitivity tests suggest that the TS-V1 sensor (comprising a titanium alloy shell with a thermal conductivity of 7.6 W/(m °C)) is approximately 0.31~0.54 s/°C slower than the CTD-Diver sensor (zirconia shell with thermal conductivity of 3 W/(m °C)) in measuring water temperatures but 6.92~10.12 s/°C faster than the CTD-Diver sensor in measuring air temperatures. In addition, the price of the proposed TS-V1 sensor is only approximately 1 and 0.3 times as much as the established commercial sensors, respectively. The TS-V1 sensor was used to collect surface water temperature and air temperature in the western part of the Pearl River Estuary from July 2022 to September 2022. These data wells captured water and air temperature changes, frequency distributions, and temperature characteristics. Our sensor is, thus, particularly useful for the study of thermal dynamics in estuarine and coastal areas.
Water availability and quality from the stratified drift in Anguilla Brook basin, Stonington and North Stonington, Connecticut

The valley of Anguilla Brook is underlain by saturated stratified-drift deposits that, where thick and transmissive, have the potential to yield large quantities of ground water. These deposits are collectively termed the Anguilla Brook aquifer. Long-term yields of four subareas within this aquifer are estimated to range from less than 0.3 to 1.0 million gallons per day. The total yield of all four subareas is estimated to be 2.6 million gallons per day. These yield estimates are based on using the 90-percent duration flow of Anguilla Brook as an index of the water potentially available and on maximum sustainable pumping rates calculated by a mathematical model that used the Theis nonequilibrium equation and image well theory. Development of one or more subareas assumes that most ground water would be derived from induced recharge. This would reduce the flow of Anguilla Brook, and the effect will be most significant during periods when streamflow is low. Limited sampling and analysis indicate that the quality of both surface and ground water in the Anguilla Brook basin is excellent. The concentrations of all constituents analyzed, with the exception of dissolved manganese and iron, were below the drinking-water limits established by the State of Connecticut, or recommended by the U.S. Environmental Protection Agency.
Compréhension des processus magmatiques et localisation des sources sismo-volcaniques avec des antennes sismiques multicomposantes

In this thesis, we study the seismo-volcanic source localization using data recorded by new sensor arrays composed of three-component (3C) seismometers deployed on Ubinas stratovolcano (Peru). We develop a new framework (MUSIC-3C) of source localization method based on the well-known MUSIC algorithm. To investigate the performance of the MUSIC-3C method, we use synthetic datasets designed from eight broadband isotropic seismic sources located beneath the crater floor at different depths. The fundamental scheme of the MUSIC-3C method exploits the fact of the cross-spectral matrix of 3C array data, corresponding to the first seismic signal arrivals, provides of useful vector components (slowness, back-azimuth and incidence angle) from the seismic source. Application of the MUSIC-3C method on synthetic datasets shows the recovery of source positions. Real data used in this study was collected during seismic measurements with two seismic antennas deployed at Ubinas volcano in 2009, whose experiment conduced by volcanic teams of IRD-France (l'Institute de Recherche pour le Develeppment), Geophysics group University College Dublin Ireland and Geophysical Institute of Peru (IGP). We apply the MUSIC-3C algorithm to investigate wave fields associated with the magmatic activity of Ubinas volcano. These analysis evidence a complex mechanism of vulcanian eruptions in which their seismic sources are found at two separated sources located at depths of 300 m and 1100 m beneath the crater floor. This implies the reproduction of similar mechanisms into the conduit. Based on the eruptive mechanisms proposed for other volcanoes of the same type, we interpret the position of this sources as the limits of the conduit portion that was involved in the fragmentation process.
Iterative Pilot-Based Reference Frame Estimation for Improved Data Rate in Two-Dimensional Display Field Communications

Recently, display-to-camera (D2C) communication, including display field communication (DFC), has gained attention due to advancements in display technology and the widespread availability of cameras in handheld devices. In this study, we proposed an iterative pilot-based reference-frame estimation scheme to increase the data rate of a 2D-DFC system. To estimate the reference frame, pilot symbols are inserted between the data symbols of the transmitted image frames. Using pilot symbols, we can compensate for the distortion in the received frame and estimate the data pixels of the reference frames. After the first iteration, we use some of the data symbols as virtual pilot symbols for the next iteration. This process is repeated using both the original and virtual pilots; furthermore, by conducting several iterations, all the data pixels of the reference frame are estimated to reconstruct the reference frame. Simulation results show that the proposed scheme significantly boosts the achievable data rate of the 2D-DFC communication system by almost twofold, while maintaining the unobtrusiveness of the display.
On Coverage Probability and Cost Efficiency Maximization of Virtual Network Operators for Delay tolerant Cellular IoT Applications

Wireless Virtualization Network (WiVi) is gaining popularity fast among cellular network operators as it enables sharing of their existing infrastructures through virtual network operators (VNOs) to serve emerging IoT applications. A VNO rents the base stations and radio spectrum from the cellular operators, also referred as infrastructure providers (IPs), to serve its IoT subscribers, also referred as device operators (DOs). In this paper, we consider a VNO that serves two DOs simultaneously and hires the necessary base stations from two independent IPs. The objectives are: (i) total coverage probability maximization and (ii) cost-efficiency maximization under a budget constraint of the VNO. To this end, we present a constrained optimization framework and obtain global optimal solutions in terms of the base station densities to be hired from the IPs. Necessary numerical results are shown to highlight the variation of the optimal solution under key system parameters.
Concurrent and Longitudinal Associations Between Parent Math Support in Early Childhood and Math Skills: A Meta-Analytic Study

ABSTRACT The aim of this study was to summarize evidence about the relations between parent math support in children aged 3–5 years (from several countries in America, Asia, and Europe) and concurrent and longitudinal math outcomes. The (bio)ecological model of human development guided our hypotheses. The design and reporting of this meta-analysis used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We screened 2,163 abstracts, from which 135 full-text studies were assessed for eligibility. Forty-five studies fulfilled our inclusion criteria and were retained (representing 244 effect sizes); 90 studies were discarded as they did not include preschool children or measures of both parent math support and children’s math skills. Meta-analysis using Correlated and Hierarchical Effect (CHE) models showed a significant positive association between parent math support and child math skills for both concurrent and longitudinal studies. On average, higher parent math support was associated with better math skills in children, albeit these being small effect sizes. We found non-significant or inconclusive moderator effects related to formal vs. informal parent math support, type of children’s math skills, participants’ characteristics (e.g., child age and child/parent gender), parent education, and study characteristics. There was a significant moderator effect of the specificity of parent math support, with global assessments showing higher correlations with math outcomes than specific assessments. The publication bias analysis showed small-study effects for longitudinal but not for concurrent studies. Conclusions are drawn regarding the importance of promoting parent math support and informing future intervention studies.
Retrospective quantification of clinical abdominal DCE-MRI using pharmacokinetics-informed deep learning: a proof-of-concept study

Introduction Dynamic contrast-enhanced (DCE) MRI has important clinical value for early detection, accurate staging, and therapeutic monitoring of cancers. However, conventional multi-phasic abdominal DCE-MRI has limited temporal resolution and provides qualitative or semi-quantitative assessments of tissue vascularity. In this study, the feasibility of retrospectively quantifying multi-phasic abdominal DCE-MRI by using pharmacokinetics-informed deep learning to improve temporal resolution was investigated. Method Forty-five subjects consisting of healthy controls, pancreatic ductal adenocarcinoma (PDAC), and chronic pancreatitis (CP) were imaged with a 2-s temporal-resolution quantitative DCE sequence, from which 30-s temporal-resolution multi-phasic DCE-MRI was synthesized based on clinical protocol. A pharmacokinetics-informed neural network was trained to improve the temporal resolution of the multi-phasic DCE before the quantification of pharmacokinetic parameters. Through ten-fold cross-validation, the agreement between pharmacokinetic parameters estimated from synthesized multi-phasic DCE after deep learning inference was assessed against reference parameters from the corresponding quantitative DCE-MRI images. The ability of the deep learning estimated parameters to differentiate abnormal from normal tissues was assessed as well. Results The pharmacokinetic parameters estimated after deep learning have a high level of agreement with the reference values. In the cross-validation, all three pharmacokinetic parameters (transfer constant Ktrans, fractional extravascular extracellular volume ve, and rate constant kep) achieved intraclass correlation coefficient and R2 between 0.84–0.94, and low coefficients of variation (10.1%, 12.3%, and 5.6%, respectively) relative to the reference values. Significant differences were found between healthy pancreas, PDAC tumor and non-tumor, and CP pancreas. Discussion Retrospective quantification (RoQ) of clinical multi-phasic DCE-MRI is possible by deep learning. This technique has the potential to derive quantitative pharmacokinetic parameters from clinical multi-phasic DCE data for a more objective and precise assessment of cancer.
Smart Crowdsensing in Disaster Management

We developed a concept to lower risk at businesses, organizations, schools, colleges, hospitals, etc. in the era of growing technology. We developed a plan to deploy at these kinds of busy workspaces as a trio. “SMART CROWDSENSING IN DISASTER MANAGEMENT” is what we are now working on. Artificial intelligence, or AI, is the dominant field dictating global affairs. The cameras that assist in keeping track of who is now present within the organization, who has gone, and who has entered. This assists businesses in the event of accidents, fires, or other problems. Every organization has these fire assembly points set up so that they can keep track of the staff members, guests, and while also counting the number of persons still trapped within the building who need to be rescued. We can provide the system with precise numbers using artificial intelligence (AI), making it simpler for the cameras to identify people. These cameras will be able to determine the count based on the movement of the figures. Every organization has these fire assembly sites set up so they can monitor the employees, visitors while also keeping track of how many people are still within the building and in need of rescue. Artificial intelligence (AI) allows us to give the system accurate numbers, which makes it easier for the cameras to recognize humans. Based on the movement of the figures, these cameras will be able to count.
Modelling in-device inference and classification of binary digits using nonlinear dynamics of spin Hall oscillator

In this work, we employ micromagnetic modelling of a spin Hall oscillator for a direct inference and classification of binary digit inputs. The spectral characteristics of the oscillation is utilized for the classification. We observed a direct inference of binary digit inputs up to a sequence of four binary digits. Subsequently, handwritten digit image recognition is tested with the Modified National Institute of Standards and Testing (MNIST) handwritten digit database and acquired an accuracy of 88.6% with a linear classifier network.
Learning style preferences among clinical year physiotherapy students in Ghana

Background. Learning style is the manner by which one learns. Every student has a different and unique learning style. However, the educational implication of learning style preferences has been a source of controversy among researchers and educators.Objectives. To determine the learning style preferences of clinical year physiotherapy students.Methods. This cross-sectional study was conducted in 82 undergraduate physiotherapy students from two universities in Ghana. A data-capturing form was used to obtain participants’ sociodemographic information, while the Kolb learning style questionnaire was used to determine the learning style preferences of the students. The χ2 statistic was used to determine the association between gender and learning style preferences, as well as between level of study and learning style preferences. Results. The participants comprised 43 (52.4%) male students and 39 (47.6%) female students. Eight (9.8%) students preferred the accommodating learning style, 46 (56.0%) preferred the diverging learning style, 15 (18.3%) preferred the assimilating learning style and 13 (15.9%) preferred the converging learning style. There was a significant association between gender and preferred learning style (p=0.027), but no significant association between level of study and preferred learning style (p=0.179). Conclusion. This study revealed that the diverging learning style was the most preferred style, followed by the assimilating learning style. However, there was no association between gender and learning style preferences, as well as level of study and learning style preferences. Further research should be conducted to find the association between the learning environments and the learning style preferences.
Ultra-Low Repetition Rate Frequency Comb for Precision Spectroscopy

Optical frequency combs (OFCs) are a powerful tool for optical frequency metrology [1]. In the frequency domain, OFCs consist of spectral modes equally spaced by the repetition rate, which typically lies in the MHz-GHz range. Frequency conversion using non-linear processes can extend optical frequency metrology to exotic wavelengths. For example, our planned experiment for precision spectroscopy of the 1S-2S transition in He+ ions requires an OFC in the extreme ultraviolet (XUV) at 60.8 nm [2]. XUV combs are often generated by intracavity high-order harmonic generation, where high average power laser pulses are stored in an enhancement cavity and HHG takes place at the intracavity focus [3]. Alternatively, OFCs can be operated at a lower repetition rate to achieve similarly high pulse energy without using a high average power laser system and enhancement cavities. OFCs with a repetition rate of several tens of kHz may have sufficiently large mode spacing for precision spectroscopy of transitions with very narrow natural linewidths, e.g. the nuclear transition in 229mTh at ~150 nm (natural linewidth of $\sim 100\ \mu\text{Hz}$)[4] and the 1S-2S transition in He+ ions (natural linewidth of 84 Hz) [2]. Although high-intensity CEP-stabilised low-repetition rate laser systems based on the chirped pulse amplification (CPA) scheme have been shown to be suitable for studying ultrafast phenomena in the time domain[5], the existence of a low-noise comb structure in the frequency domain remains unproven.