Miracle-dz/UniFAR-FTUs huggingface dataset:
An extended stochastic harmonic analysis algorithm: Application for dissolved oxygen

An extension of classical harmonic analysis is used to determine the Fourier coefficients with a constraint of forcing the function through a specified value for developing an extended stochastic harmonic analysis (ESHA). The methodology was applied to the diurnal variations of dissolved oxygen (DO) in streams within a semiempirical modeling scheme to demonstrate the formulation of an ESHA algorithm. Since DO values are often measured at different times during the day, the ESHA algorithm was used to convert data measured at any clock time to those at a standard or reference time. The model was evaluated for five different streams in Minnesota that capture the impact of different ecoregions and different sizes of watersheds. Data were normalized to increase the general applicability of the fitted parameters. The ESHA was successful in representing observed diurnal variations in DO. The root‐mean‐square error (RMSE) for predicting hourly DO and standard DO ranged respectively from 0.43 to 0.77 mg/L and 0.37 to 0.90 mg/L among the five streams. Estimated model parameters were robust in terms of both spatial and temporal variations. Parameter uncertainties and associated model sensitivity were demonstrated analytically. The developed algorithm can be used for similar applications in numerous science and engineering disciplines. In particular, it is a potentially useful tool for total maximum daily load (TMDL) assessment of aquatic ecosystem health across a range of spatial and temporal scales.
Cosserat modeling for deformation configuration of shape memory alloy unimorph actuators

Shape memory alloys (SMAs) can contract their length via a crystalline phase transition that is dependent upon their temperature and stress state. SMAs have been used as linear micro-actuators due to their high strength to weight ratio and compact structure. However, the relatively low linear contraction ( ~ 4%–5% in length) limits their use. To remedy this, the SMA can be offset from a passive structure, which acts to magnify the deformation. The resulting amount of deformation depends upon the material properties and geometry of both the SMA and the passive structure. In this work, geometrically exact beam theory (also known as Cosserat theory) is coupled with SMA constitutive relations to model the maximum deformation configuration of these actuators. Four of these actuators of various lengths were fabricated and tested to verify the model. For the four actuators tested, the mean squared error between the experimental results and the Cosserat model were between 0.0702 mm (0.1% error) for the shortest actuator (66 mm in length) and 3.59 mm (2.7% error) for the longest actuator (135 mm in length). These results show that the closed form solution derived for this Cosserat beam model can accurately model the deformation of these active structures.
Barrier of Hypertensive Drugs among Patients in Tanzania: Case Study of Southern Highlands, Tanzania

Experience with patients shows that prescribed drugs for treating hypertension disease fail to work effectively, causing high mortality rates in African Ethnic types. This situation has resulted in the query as to why the situation exists. The study was conducted to investigate factors affecting the failure of hypertension drugs among patients suffering from the disease. Particularly, the study aimed at examining cultural, individual and financial factors affecting drug failure among Tanzania patients. The sample consisted of 300 respondents from Afya Medicare Specialized Clinic and Mbeya Referral hospital in Tanzania. Questionnaires were distributed and interviews were conducted with patients and their caretakers, doctors and nurses in both health centres. The study revealed that the drugs fail to provide relief to the patients because of the negative attitude towards the consumption of the drugs in the belief that the diseases affected only rich people and developed countries. From the findings, it was concluded that education should be provided to the patients as well as their caretakers on the fact that the disease can affect anyone and emphasize preventive measures.
Performance of LTMLE in the Presence of Missing Data in Control-Matched Longitudinal Studies

Abstract Conventional controlled trials employ randomization and blinding to ensure the balance of baseline covariates between study arms. Treatment effect is formally tested via a pre-specified hypothesis reflecting trial’s primary objective defined by the primary efficacy endpoint, such as, is experimental treatment effective in slowing cognitive decline at a pre-specified landmark time in a neurologic therapeutic development? To address the same clinical question, but, as a safety endpoint in an observational study post radiopharmaceutical imaging drug approval due to concerns of radiation retention in the brain after accumulated real-world use over time, the targeted minimum loss-based estimation (TMLE) method has been suggested. Overall, for a longitudinal control-matched cohort study, assessing treatment effect (efficacy or safety) in the presence of missingness can be very challenging, which depends also on trial duration and missingness pattern of outcome data between study arms. TMLE is a two-step procedure to estimate a target parameter and has been shown to be doubly robust. The objectives of our research are a few folds. First, we investigate the performance of the TMLE method using longitudinal structure (LTMLE) in a prospective control-matched longitudinal cohort study. Second, we evaluate the performance of LTMLE and a few methods that are often proposed in regulatory submissions for longitudinal data analysis or in the presence of missing data with various missing data mechanisms. Third, we assess the impacts of various missing data mechanisms to treatment effect estimation via extensive simulation studies. Finally, we discuss the results of the simulation studies and their implications to conducting a feasible real-world control-matched longitudinal cohort study.
Assessing neurocognitive hypotheses in a likelihood-based model of the free-recall task ∗

In the free-recall task, a participant studies a list of words, usually presented one at a time. This is followed by a recall period in which the participant reports as many items as they can from the study list, in whatever order they come to mind. As such, behavioral responses in this task are characterized as recall sequences: The series of responses made by the participant during the recall period. Theorists have developed a variety of cognitive models designed to account for the behavioral dynamics observed in different versions of the free-recall task. These models often describe a hypothetical set of cognitive operations that give rise to the observed behavioral data. This chapter provides a tutorial overview of a particular application of free-recall modeling, in which we assess neural signals in terms of their potential correspondence with the cognitive processes embodied in a model of free recall. Specifically, we examine a set of analyses described by Kragel et al. (2015), in which blood-oxygen level dependent (BOLD) signals recorded with fMRI were related to mechanisms from a retrieved-context model, using a direct-input approach, in which neural signals are used to directly control aspects of a behavioral model (Turner et al., 2017; Purcell et al., 2010). The reader is invited to download companion Python code that implements many of the simulations described in this chapter, hosted at github.com/vucml/ncms toolbox. The README file at that URL will direct the reader to the Python script containing the tutorial (KragEtal15 tutorial.py) as well as directions for installing the cymr toolbox the tutorial uses This work was supported by a grant from the National Science Foundation to SMP (#1756417). Cite as: Polyn, S. M. (2022) Assessing neurocognitive hypotheses in a likelihood-based model of the free-recall task. In Forstmann, B. U. and Turner, B. (Ed.). Introduction to Model-Based Cognitive Neuroscience (2 edition). Springer, New York.
Measuring HLT Research Equality of European Languages

This work explores quantitative indicators that could potentially measure the equality and inequality research levels among the languages of the European Union in the field of human language technologies (HLT research equality). Our ultimate goal is to investigate European language equality in HLT research considering the number of papers published on several HLT research venues that mention each language with respect to their estimated number of speakers. This way, inequalities affecting HLT research in Europe will depend on other factors such as history, political status, GDP, level of social or technological development, etc. We have identified several groups of EU languages in the proposed measurement of HLT research equality, each group comprising languages with large differences in the number of speakers. We have discovered a relative equality among surprisingly different languages in terms of number of speakers and also relevant inequalities within the most spoken languages. All data and code will be released upon acceptance.
Computational Imaging using Ultrasonically-Sculpted Virtual Lenses

Ultrasonically-sculpted waveguides provide interesting opportunities for in situ optical imaging in transparent and scattering media. The interference of ultrasonic waves can be designed to form a spatially-varying refractive index pattern in the target medium, acting as a virtual lens to guide light and relay images. The images formed by such lenses are subject to a large amount of spatially-varying blur, which significantly reduces their contrast. To alleviate this issue, the images can be computationally deblurred post experiment to restore the image. First, we demonstrate a brute force deconvolution technique to deblur the relayed images. While effective, this method proves to be computationally intensive due to the spatially-varying blur kernel. The reconfigurability of ultrasonically sculpted waveguides can be leveraged to address this issue by measuring line integrals of the image at multiple angles to form the Radon image of the target, which can be deblurred very efficiently with a simple linear model. We validate this method using simulated and experimental results. The tantalizing notion of using the reconfigurable ultrasonically-sculpted waveguides as part of the imaging systems, demonstrated in this paper, opens new opportunities for hybrid physical-computational optical systems.
THE EFFECTS OF THE COVID-19 PANDEMIC ON THE EXCHANGE RATES OF THE FRAGILE SEVEN COUNTRIES

This study aims to analyze the exchange rate fluctuations in seven fragile emerging market economies (EMEs), with similar macroeconomic structure characteristics such as a high short-term external debt, low official international reserves, and dependency on foreign capital inflows, underlined by the IMF at the onset of the COVID-19 crisis. After the COVID-19 outbreak, the first sudden impact of uncertainties appeared on the portfolio outflows from EMEs. The spillover effect of the excess liquidity through unconventional expansionary monetary policy and helicopter money created by the expansionary monetary policy by the FED did not lead to any repeating portfolio flows from advanced economies to EMEs, contrary to the Global Financial Crisis of 2008-2009. Thus, the risk-off sentiment of investors led to capital reversals, which in turn, brought about the sudden depreciation of many emerging market currencies. However, differences in the size and time of depreciation rates raise a question about the relative importance of push and pull factors on the fluctuations in these currencies. From this point of view, the research found out that push factors like the Volatility Index (VIX) affect national currency immediately and adversely. In contrast, the effects of the pull factors on exchange rates are asymmetrical. In this setting, good economic performance helps to mitigate depreciation while the bad indicators accelerate this depreciation in the currency.
Improved Multiband Rectangular Microstrip Patch Antenna for 5G Application

In this work, a multiband microstrip patch antenna for 5G mobile applications was designed and simulation was conducted using HFSS (High Frequency Simulation structure) software. The frequency range of 3.5 GHz through 11.65 GHz was considered and the antenna dimensions were obtained from a well-established design equations. During simulation, the designed antenna was considered for an antenna without an inset feed and an inset fed antenna using layers of RT/Duroid 5880 substrate with a patch size of 33 mm × 28.8 mm and height of 1 mm. The antenna performance was analyzed in terms of S11 parameters, return loss, Voltage standing wave ratio (VSWR), reflected power, and forwarding power. The result showed that the antenna radiates at a frequency of 3.5 GHz, 5.93 GHz, 7.49 GHz, 10.07 GHz and 11.65 GHz with an S11 value of -19.33, -17.40, -25.84, -32.94, -26.25 dB and VSWR of 1.88, 2.35, 1.68, 1.78 and 1.03. The designed antenna can be used for 5G applications that require the frequencies obtained in this research work.
Memory-Optimized Multi-Version Concurrency Control for Disk-Based Database Systems

Pure in-memory database systems offer outstanding performance but degrade heavily if the working set does not fit into DRAM, which is problematic in view of declining main memory growth rates. In contrast, recently proposed memory-optimized disk-based systems such as Umbra leverage large in-memory buffers for query processing but rely on fast solid-state disks for persistent storage. They offer near in-memory performance while the working set is cached, and scale gracefully to arbitrarily large data sets far beyond main memory capacity. Past research has shown that this architecture is indeed feasible for read-heavy analytical workloads. We continue this line of work in the following paper, and present a novel multi-version concurrency control approach that enables a memory-optimized disk-based system to achieve excellent performance on transactional workloads as well. Our approach exploits that the vast majority of versioning information can be maintained entirely in-memory without ever being persisted to stable storage, which minimizes the overhead of concurrency control. Large write transactions for which this is not possible are extremely rare, and handled transparently by a lightweight fallback mechanism. Our experiments show that the proposed approach achieves transaction throughput up to an order of magnitude higher than competing disk-based systems, confirming its viability in a real-world setting.
A D EEP L EARNING A PPROACH FOR D EFECT D ETECTION AND S EGMENTATION IN X-R AY C OMPUTED T OMOGRAPHY S LICES OF A DDITIVELY M ANUFACTURED C OMPONENTS

Additive manufacturing is an emerging and crucial technology that can overcome the limitations of traditional manufacturing techniques to accurately manufacture highly complex parts. X-ray Computed Tomography (XCT) is a widely used method for non-destructive testing of AM parts. However, detection and segmentation of defects in XCT images of AM have many challenges due to contrast, size, and appearance of defects. This study developed deep learning techniques for detecting and segmenting defects in XCT images of AM. Due to a large number of required defect annotations, this paper applied image processing techniques to automate the defect labeling process. A single-stage object detection algorithm (YOLOv5) was applied to the problem of defect detection in image data. Three different variants of YOLOv5 were implemented and their performances were compared. U-Net was applied for defect segmentation in XCT slices. Finally, this research demonstrates that deep learning techniques can improve the automatic defect detection and segmentation in XCT data of AM.
Practical considerations on using private sampling for synthetic data

Artificial intelligence and data access are already mainstream. One of the main challenges when designing an artificial intelligence or disclosing content from a database is preserving the privacy of individuals who participate in the process. Differential privacy for synthetic data generation has received much attention due to the ability of preserving privacy while freely using the synthetic data. Private sampling is the first noise-free method to construct differentially private synthetic data with rigorous bounds for privacy and accuracy. However, this synthetic data generation method comes with constraints which seem unrealistic and not applicable for real-world datasets. In this paper, we provide an implementation of the private sampling algorithm and discuss the realism of its constraints in practical cases.
Detection of Pathogenic E. Coli by Electrochemical Biosensors Based on Aptamers Designed by Bioinformatic Tools

In this work, we present the in-silico design of a new aptamer (named Apt917) capable of interacting with E. coli and its evaluation as a recognition element in electrochemical biosensors. A search and bioinformatic analysis of various aptamers reported in the literature was carried out. Parameters such as low dissociation constant and short sequences were considered to design and model new hybrid sequences. The designed Apt917 has a minimum free energy and high percent to frequency, making it promising compared to the initial ones. Apt917 was synthesized and immobilized on gold nanoparticles-modified screen-printed electrodes. The evaluation of the obtained biosensor shows a promising detection of E. coli O157:H7 with limits of detection and quantification of 8 and 27 CFU/mL, respectively. AuNPs surface via Au-S bond formation. Briefly, 20 µL of a 20-100 nM aqueous solution of the aptamer were placed on SPEs and incubated overnight (16 h). The biosensors obtained were rinsed with Type-I water. The effect of the concentration of the aptamer in the solution to be immobilized toward E. coli detection was evaluated. Changes in the electrochemical response of SPEs during their modification were evaluated by electrochemical impedance spectroscopy (EIS) at a scan frequency between 50,000 to 1 Hz, the amplitude of 10 mV RMS and 0 V vs. OCP (DC) by using an electrolytic solution of Fe[(CN) 6 ] 3-/4- couple (5 mM) and KCl (0.1 M). Surface characteristics of AuNPs-modified SPEs were studied by scanning electron microscopy (SEM) using a Quanta Field Emission Gun (Model 650) microscope operated at 15.0 kV. The images were obtained in secondary electron mode.
Harmonics suppression of PMSM using repetitive control and application to improvement of sensorless control performance

Using the repetitive control, Authors have proposed the suppression method of harmonic currents in the vector control system for PMSM. In this paper, we propose a new simplified harmonics suppression system and a method improving the stability margin of the control system and convergence speed of harmonics. With stability analysis and experimental results, we confirm the proposed method. Moreover, this paper shows that the proposed control system can be used for improvement of position/velocity sensorless control performance with voltage commands and currents that are purified by the repetitive control. To evaluate our proposed system, the sensorless control system which is constructed on the basis of the extended EMF disturbance observer is taken as an example, and the improvement effectiveness of the proposed control system is confirmed by experimental results.
Enhancement of the Photoresponse in the Platinum Silicide Photodetector by a Graphene Layer

Background and Objectives: The use of two-dimensional materials in the photodetector fabrication has received much attention in recent years. Graphene is a two-dimensional material that has been extensively researched to make photodetectors. The responsivity of graphene photodetectors was limited by the low optical absorption in graphene ( ~ 2.3% for single layer graphene). Therefore, graphene along with other materials has been used to fabricate a photodetector with the desired properties. The graphene is used for the improvement of the silicide platinum photodetector. Methods: The platinum silicide photodetector with graphene has been experimentally fabricated and characterized, and all steps of the device fabrication and the characterization are completely provided in addition to required equations for device analysis is completely provided. A graphene layer is transferred on the platinum silicide layer, and the graphene layer creates the photoconductor gain in the platinum silicide photodetector. Results: In the proposed device, near-infrared light is detected in the platinum silicide, and by placing a layer of graphene on the platinum silicide, the optical current and responsivity increase compared to the platinum silicide photodetector without graphene. Experimental results show that the optical current, external quantum efficiency, and responsivity increase in the platinum silicide photodetector with graphene. The graphene not only functions as the charge transport channel, but also works as a photoconductor. Conclusion: The optical current and responsivity are increased by the platinum silicide photodetector with graphene. In our photodetector, the highest responsivity is 120 𝑚𝐴 𝑊 in the 1310 nm wavelength, and the optical current is 100 nA at the applied voltage of 8 V. Our photodetector has optical current, responsivity, and external quantum efficiency twice as much as platinum silicide photodetector. Experimental results show the good performance of graphene with platinum silicide photodetector.
Storage Cost Reduction in Blockchain and Prism via Secure Codes

In a blockchain network, full nodes aid in decentralization by validating incoming transactions and also by assisting new nodes who want to join the network. However, due to the high storage cost incurred by full nodes, the total number of full nodes in a system is falling. In order to curb this problem and maintain decentralization, we present Secure Raptor Encoder and Decoder (S-RED) built on Raptor Codes, which in addition to reducing storage costs, also has constant decoding time with respect to k (number of input blocks). We also present error-resilient double-peeling decoder that can recover original blocks with a high probability, even under the presence of adversaries. Blockchain popularity and application is not a new thing but blockchain is bounded by its physical capacity and is unable to accommodate the incoming traffic leading to high transaction confirmation latency. Hence a new data structure based on the direct acyclic graph is proposed popularly called blockdag which doesn’t limit blockchain physical capacity. Prism is a ledger based on blockdag technology, however, in Prism full nodes have to pay a high cost in terms of storage space due to the requirement of more blocks as compared to conventional blockchain. In order to reduce such high storage costs, we present Secure Luby Transform Raptor Encoder and Decoder (S-LTRED) built on LT and Raptor Codes. We present tri-peel decoder that can recover original blocks with a high probability, even under the presence of adversaries. S-LTRED shows to achieve a much lower bootstrap cost as compared to Prism and achieves this by reducing the storage consumption.