Miracle-dz/UniFAR-FTUs huggingface dataset:
Scattering of Water Waves by Vertical Cylinders

The scattering of small amplitude water waves by an array of vertical cylinders is studied theoretically and experimentally. In the theoretical study, a method of matched asymptotic expansions is first developed to find the reflection and the transmission coefficients without considering real fluid effects. Energy loss due to the flow separation near the cylinders is modeled by adopting a linearized form of the quadratic resistance law. It is shown that this is equivalent to introduce a complex blockage coefficient. The energy loss coefficients for square cylinders and circular cylinders are determined by comparing theoretical results with experimental data.
Scholarship Repository @ Florida Tech Scholarship Repository @ Florida Tech

fMRI was employed to investigate the relationship between pre-stimulus neural activity and associative encoding of words and pictures in humans. While undergoing scanning, subjects studied randomly interleaved word or picture pairs. A pre-stimulus cue preceded the presentation of each study pair and signaled whether it would comprise words or pictures. Memory for the study pairs was later tested with an associative recognition test, which comprised word or picture pairs presented either in the same (intact) or a different (rearranged) pairingasatstudy,alongwithpairsofnewitems.ThecriticalfMRIcontrastwasbetweenstudyactivityassociated with pairs later correctly judged intact and pairs incorrectly judged as rearranged. A key question was whether material-selectivepre-stimulusencodingeffectscouldbeidenti ﬁ edwhichoverlappedregionsselectivelyactivat-ed by the respective study material. Picture-selective pre-stimulus effects were identi ﬁ ed in bilateral fusiform and the intraparietal sulcus (IPS), whereas word-selective effects could not be identi ﬁ ed. Material-invariant pre-stimulus subsequent memory effects were also identi ﬁ ed in several neocortical regions as well as in the hippocampus.Whereasthelocioftheneocorticaleffectssuggestthattheyre ﬂ ectthebene ﬁ ttoencodingthatac-cruesfromengagementofcognitivecontrolprocesses,theirmagnitudewasnegativelycorrelatedacrosssubjects with associativerecognition performance and positivelyrelatedto false alarm rate.Conversely, thehippocampal effects also predicted unique variance in associative memory and were negatively related to hit rate. It is sug-gestedthat the neocorticalpre-stimulus effects may re ﬂ ect encodingprocesses thatincrease familiarity of single items, whereas the hippocampal pre-stimulus effects are proposed to re ﬂ ect either the encoding of task-irrelevant features or the retrieval of task-relevant information associated with the pre-stimulus cues. Overall, theresultsprovideevidencethatpre-stimulusprocessesmaybedeleterious,ratherthanbene ﬁ cial,toassociative encoding.
Optimization of an inductively coupled plasma–optical emission spectrometry method for the rapid determination of high‐precision Mg/Ca and Sr/Ca in foraminiferal calcite

The ability to measure Mg/Ca and Sr/Ca in foraminiferal calcite has increased in importance following their identification as palaeoceanographic tracers with significant potential to constrain past changes in ocean chemistry and temperature. However, in order to tackle palaeoceanographic problems, it is essential to develop methods for the determination of these ratios that have both rapid sample throughput and high analytical precision. Here, we demonstrate the importance of simultaneous measurement for high‐precision Mg/Ca and Sr/Ca analysis. Our method is readily applicable to automated analysis over a wide range of Ca concentrations and is not subject to significant matrix effects. Using a “dual view” inductively coupled plasma–optical emission spectrometer (ICP‐OES), supported by a dedicated autosampler, we have investigated three nebulizers and two spray chambers and determined optimum analytical parameters. We show that there are consistent relationships between signal‐to‐noise ratio and gas flow rates and that the working sample dilution range has an important effect on linear response. Further, for this instrument it is important to manually constrain the timing of measurements to obtain truly simultaneous readings. Using the method developed, precisions of better than 0.21% are obtained for Mg/Ca and Sr/Ca in solutions containing between 1 and 4 ppm Ca with no significant matrix effect, using a Glass Expansion “Conikal” nebulizer coupled with a “Tracey” spray chamber, in autosampler mode.
Impact of variable reservoir releases on management of downstream water temperatures

A coupled unsteady flow and heat transport model is used to determine the impacts of fluctuating reservoir releases on downstream water temperatures. Maintenance of stream temperatures is one of the most common reasons cited for imposition of minimum flow requirements in regulated (reservoir controlled) rivers. Minimum flow constraints for temperature control are typically developed using worst‐case scenarios (i.e., maximum air temperature, clear sky, etc.) of atmospheric conditions. We show that short‐ term modifications to reservoir releases based on local meteorological conditions can reduce the volume of water released, while still meeting temperature objectives. A case study of the Green River below Flaming Gorge Dam shows that for certain sets of temperature objectives and atmospheric conditions, a diurnally varying release may be the only way to meet multiple temperature objectives at different downstream locations. In the examples discussed, savings of nearly 20% in total release volume could be realized by using variable releases.
Pb isotopes in anorthositic breccias 67075 and 62237 - A search for primitive lunar lead

The lunar Pb isotopic evolution is explored based on an investigation of the U-Th-Pb systematics of the primitive Apollo 16 anorthositic breccias 76075 and 62237. The isotope data for acid-leached residues from 67075 lie within or on a triangle formed by plagioclase, opaques, and a pyroxen-olivine mixture, and the isotope data for 62237 lie within or on a triangle formed by plagioclase, olivine, and pyroxene. The results suggest that the moon had high first-stage values for the Pb evolution equations from the onset.
On the design of a model predictive controller for a DC-DC buck converter

This paper discusses a systematic approach to employ model predictive control (MPC) on a DC-DC buck converter. The design procedure requires the closed-loop bandwidth as input specification and produces the controller parameters. The procedure is derived based on a qualitative and quantitative analysis of the converter under a constrained MPC controller. The results are verified in an experimental setup, where a converter switching at 200 kHz is controlled with the proposed controller.
Drone Control through Deep Learning based on Pose Recognition

This research introduces an innovative approach to personal video capture using Unmanned Aerial Vehicles (UAVs) with pose recognition. The primary aim is to automate the video capture process for high-quality, self-captured videos. A deep learning algorithm was employed to develop a pose recognition model for real-time detection of an individual’s pose. This model was integrated into the UAV’s control system, enabling autonomous navigation. The trained model showed significant pose prediction accuracy (80.35%) within the training location with minor background changes. However, performance diminished under different perspectives (50.02%), locations (62.39%), and low-light conditions (29.44%). A prediction filter was implemented to enhance performance, enabling drone movement only after consecutive identical pose prediction. The findings demonstrate the effectiveness of pose recognition in capturing high-quality video footage autonomously. The integration of this technology in UAVs presents several advantages for self-capturing video, including unique perspectives and distinct angles without the need for an operator, leading to superior footage quality.1
Frequency optimisation for DC/DC converters in DC-connected offshore wind turbines

In all-DC wind farms, DC/DC converters connect the low voltage wind turbine output to the high voltage DC array. One potential benefit of using DC/DC converters is that they require a relatively small transformer due to their higher frequency operation. However, there is no consensus as to what this frequency should be. This paper aims to determine the optimal operating frequency for DC/DC converters in 15 MW wind turbines connected to an 80 kV DC array. A multi-objective optimisation is performed taking into account the DC/DC converter volume, weight and losses. Frequencies ranging from 500 Hz to 5 kHz were tested for unidirectional, bidirectional, single-phase and three-phase converters. The optimal frequencies for unidirectional and bidirectional converters were found to be approximately 2 kHz and 1 kHz, respectively.
Extended Temperature Modeling of InGaAs/InP SPADs

We present a simulation method to estimate the dark count rate (DCR), photon detection probability (PDP), and dark current of InGaAs/InP single-photon avalanche diodes (SPADs) from 225K to 300K. All simulations are performed completely in TCAD and match well with the measurement results of our novel selective area growth (SAG) based InGaAs/InP SPAD. An optimized simulation environment has the potential of estimating the device performance without costly fabrication iterations. Hence, it will accelerate the development of high-performance InGaAs/InP SPADs.
ROI-YOLOv8-Based Far-Distance Face-Recognition

This paper presents a model for far-distance face recognition using ROI-YOLOv8. We achieve this by training YOLOv8 for 3 target faces on our custom datasets: (i) The first dataset is the original dataset with training and validation images of 2412 and 229, respectively. (ii) The second dataset augments the more pixelated images from the 1st one with training and validation images of 4824 and 458, respectively. (iii) The third dataset considers various exposure, noise, and blur from the 2nd one with training and validation images of 14,272 and 459, respectively. To enhance the far-distance recognition, a two-stage recognition is considered. At first, a pre-trained YOLOv8 model for human detection is achieved. A Region-of-Interest (ROI) including detected humans is segmented as the size of 640 × 640 pixels for the input of another YOLOv8, i.e., ROI-YOLOv8-FR. A computer with Intel i5-12400F, 16GB RAM, and NVIDIA RTX 3080Ti with 12GB VRAM is used as computing platform. The trained times for these 3 datasets are 50, 95, and 219 minutes, respectively. Their mAP50s are 99.5% and mAP50-95s are slightly different as 88.112%, 87.962%, and 88.103% respectively. More important, the 3rd trained model can successfully recognize a face at 30 and 35m with the confidences of 65.1% and 75.6%.
Cancer Detection in Lungs Using Image Processing and Liquid Biopsy Analysis

Lung cancer is the most fatal type of cancer, which results from abnormal cell growth in the lung tissue. It is a leading cause of cancer-related deaths globally, and early detection is crucial for successful treatment and improved patient outcomes. Various methods are available for detecting lung cancer, such as imaging techniques like chest X-rays, computed tomography (CT) scans, and magnetic resonance imaging (MRI), as well as non-invasive diagnostic methods like liquid biopsy. Liquid biopsy involves identifying biomarkers in blood or other bodily fluids, such as circulating tumor cells (CTCs) or cell-free DNA (cfDNA), which can indicate the presence of lung cancer. The proposed system uses CT scans to obtain lung tissue images and applies image processing algorithms to identify and classify potentially cancerous regions. In addition, the system uses liquid biopsy to detect CTCs or cfDNA in the patient's blood. The system employs a multi-stage process, including pre-processing, segmentation, feature extraction, and classification, to enhance the accuracy of lung cancer detection. Evaluation of the system's performance using a dataset of CT images and liquid biopsy results from patients shows that the combination of image processing and liquid biopsy improves the accuracy of lung cancer detection compared to using image processing alone. These findings are significant and have implications for the development of advanced systems for automated lung cancer detection that incorporate image processing and liquid biopsy.
Critical Short Circuit Ratio of an EV Charging System

The critical short circuit ratio (CSCR), as an important metric for grid stability evaluation, is not clearly defined in the literature. Aimed at clarifying the misunderstandings, the paper compares the different CSCR definitions. Moreover, CSCR reduction-oriented design is studied for electric vehicle chargers. Simulations verify the analysis.
Improving Steinmetz Parameters for Iron Loss Characterization with a Magnetic Measurement Kit

The non-linear features of iron losses for high-frequency components have posed great challenges for accurately estimating iron loss. Engineers commonly rely on practical models such as Steinmetz Equation (SE) families to predict iron losses. The constant parameters of the SE models for a certain magnetic material are obtained through curve fitting of its datasheet. However, it is crucial to acknowledge that the iron loss density varies across different shapes and sizes of cores from the same material, which consequently affects the SE parameters. In this paper, a four-wire measurement kit is presented. Modified SE parameters for different sizes of a specific magnetic material are derived based on experimental data. The predictions using these modified parameters demonstrate higher accuracy compared to results obtained using constant Steinmetz parameters.
Alveolar ridge preservation using free gingival grafts. A review article

INTRODUCTION: Tissue preservation after tooth removal is crucial for the subsequent implant and/or prosthodontic rehabilitation. A variety of ridge preservation procedures have been suggested. Socket sealing surgery with free gingival grafts was introduced to minimize soft tissue volumetric alterations and protect the clot or the bone grafting material in the socket. However, the method has some disadvantages, such as postoperative patient discomfort and a need for a second-stage surgery. AIM: This literature review aims to analyze, compare, and summarize the results on the application of free gingival grafts for ridge preservation and define the research gaps that necessitate further assessments. MATERIALS AND METHODS: The present review is based on the existing scientific electronic database, including 42 articles. It provides a comparative analysis of the reported results on socket sealing with free gingival grafts RESULTS: The database showed heterogeneity regarding graft survival and postoperative complications. Authors report that the method gives promising results and successfully preserves the hard and soft tissue volume. It is still disputable, however, whether it is superior to other ridge preservation procedures. CONCLUSION: Further investigation based on randomized clinical trials and precise clinical, histologic, and radiological evaluation is needed to clarify the role of free gingival grafts as a method for ridge preservation. Its application in the different areas of the ridge should be carefully evaluated.
Measurement of CP asymmetries in D-+/- -> eta 'pi(+/-) and D-S(+/-) -> eta 'pi(+/-) decays

A search for CP violation in D ± → η (cid:3) π ± and D ± s → η (cid:3) π ± decays is performed using proton–proton collision data, corresponding to an integrated luminosity of 3 fb − 1 , recorded by the LHCb experiment at centre-of-mass energies of 7 and 8 TeV. The measured CP -violating charge asymmetries are A CP ( D ± → η (cid:3) π ± ) = ( − 0 . 61 ± 0 . 72 ± 0 . 53 ± 0 . 12 ) % and A CP ( D ± s → η (cid:3) π ± ) = ( − 0 . 82 ± 0 . 36 ± 0 . 22 ± 0 . 27 ) %, where the ﬁrst uncertainties are statistical, the second systematic, and the third are the uncertainties on the A CP ( D ± → K 0 S π ± ) and A CP ( D ± s → φ π ± ) measurements used for calibration. The results represent the most precise measurements of these asymmetries to date. © 2017 The Author. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP 3 .
Implementation and Analysis of Soft Combination for High Reliability in Future Wireless LANs

Wireless local area network (WLAN) IEEE 802.11 communication devices employ channel coding and automatic repeat request (ARQ), commonly known as hybrid ARQ (HARQ) type I, to provide robust and reliable communications. For emerging use cases like industrial IoT and machine-to-machine (M2M) communications, the performance of HARQ type I is getting insufficient, especially in adverse channel conditions with low link budget. For this reason, this paper investigates enhanced HARQ operation for WLAN by soft combination of retransmissions. We first outline three different implementation variants of HARQ with soft combination, discuss their adaptation to WLAN physical layer and perform a rigorous analysis in terms of achievable reliability, throughput as well as latency. It turns out that for high reliability applications, HARQ type III (T3), in which retransmissions use codewords (CWs) with new parity information, performs best, whereas for high throughput application HARQ type II with incremental redundancy (IR), in which retransmissions consist of new parity information only, is more suitable.
Predicting Student Achievement Based on Fusion Models

This study aims to utilize machine learning techniques to predict student performance and enhance educational outcomes. Various factors influence student achievement, but many current prediction methods are unsatisfactory. Deep learning methods are less effective due to limited data, and a single model fails to capture the complex relationships that influence performance. To address this issue, the study proposes integrating both models by developing two fusion models, namely Multiple Machine Learning (MML), for predicting student performance. The first model utilizes a multilayer perceptron to automatically extract features, followed by a traditional machine learning model to predict student performance. The second model utilizes XGBoost (Extreme Gradient Boosting) and LightGBM (Light Gradient Boosting Machine) models for performance prediction. Finally, the performance of the proposed methods is validated through experimentation on a dataset. The performance prediction results of the fusion models are compared against those of a single model. The two fusion models offer improved accuracy in predicting student performance, as demonstrated through comparative analysis.
CAPSAICIN ATTENUATES BUPIVACAINE ANESTHESIA-INDUCED NEUROTOXICITY IN SH-SY5Y CELLS BY REGULATING APOPTOSIS

Objective: Bupivacaine administered for local anesthesia can cause critical neurotoxicity and neurological dysfunctions. Any substance that can reduce bupivacaine-mediated toxic effects will be of great interest during surgical procedures and in the pain management process. In this context, we evaluated capsaicin, an alkaloid of Capsicum annuum (cayenne pepper), which has been intensively researched for its neuroprotective effect due to its various biological effects such as cardioprotective, anti-inflammatory, analgesic, thermogenic, and benefits on the gastrointestinal tract. Methods: In this study, we researched the in vitro effect of capsaicin in SH-SY5Y cells with a model of bupivacaine-mediated neurotoxicity. Cell proliferation assay was handled by XTT, and apoptosis was determined by flow cytometry analysis. Results: We observed a notable increase in apoptosis induction with a significant decrease in the viability of cells exposed to bupivacaine at 1 mM. We found that bupivacaine-mediated cytotoxicity was reduced when increasing concentrations of capsaicin were applied to bupivacaine-treated cells. At the same time, capsaicin also reduced apoptosis in SH-SY5Y cells exposed to bupivacaine. Conclusion: According to our results, it is thought that the administration of capsaicin against bupivacaine-mediated neurotoxicity may be an alternative neuroprotective agent by suppressing the apoptosis response in neurons.
NVSS Scheme Based ATM Authentication Using Fingerprint Matching

: The growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Access codes for buildings, banks accounts and computer systems often use personal identification numbers (PIN's) for identification and security clearances. Conventional method of identification based on possession of ID cards or exclusive knowledge like a social security number or a password are not all together reliable. An embedded fingerprint biometric authentication scheme for automated teller machine (ATM) banking systems is proposed in this paper. In this scheme, a fingerprint biometric technique is fused with the ATM for person authentication to ameliorate the security level. Here the fingerprint is hidden using NVSS Scheme ie. , natural image based visual secret sharing scheme. In that secret images are transferred through various media to secure the secret and participant. The proposed scheme can share one digital secret image over n-1 arbitrary selected natural images and one noise-like share.
Peatland core domain sets: building consensus on what should be measured in research and monitoring

It is often difficult to compile and synthesise evidence across multiple studies to inform policy and practice because different outcomes have been measured in different ways or datasets and models have not been fully or consistently reported. In the case of peatlands, a critical terrestrial carbon store, this lack of consistency hampers the evidence-based decisions in policy and practice that are needed to support effective restoration and conservation. This study adapted methods pioneered in the medical community to reach consensus over peatland outcomes that could be consistently measured and reported to improve the synthesis of data and reduce research waste. Here we report on a methodological framework for identifying, evaluating and prioritising the outcomes that should be measured. We discuss the subsequent steps to standardise methods for measuring and reporting outcomes in peatland research and monitoring. The framework was used to identify and prioritise sets of key variables (known as core domain sets) for UK blanket and raised bogs, and for tropical peat swamps. Peatland experts took part in a structured elicitation and prioritisation process, comprising two workshops and questionnaires, that focused on climate (32 and 18 unique outcomes for UK and tropical peats, respectively), hydrology (26 UK and 16 tropical outcomes), biodiversity (8 UK and 22 tropical outcomes) and fire-related outcomes (13, for tropical peatlands only). Future research is needed to tackle the challenges of standardising methods for data collection, management, analysis, reporting and re-use, and to extend the approach to other types of peatland. The process reported here is a first step towards creating datasets that can be synthesised to inform evidence-based policy and practice, and contribute towards the conservation, restoration and sustainable management of this globally significant carbon store.