Miracle-dz/UniFAR-FTUs huggingface dataset:
DIPLOMA THESIS SDR-based RFID Reader with Sub-Symbol-Synchronization

Radio-frequency identification (RFID) is a wireless technology for identification of objects based on attached tags. RFID readers used for identification of tags are already commercially available. A recent research field is the localization of passive RFID tags. During the course of this thesis an electronic product code ultra high frequency RFID reader based on a National Instruments USRP software defined radio (SDR) platform was designed and evaluated. This work provides a versatile platform for developing real time localization systems in upcoming research projects. Therefore, special requirements for a localization algorithm based on a superimposed Direct Sequence-Spread Spectrum signal were considered. Since some of the requirements couldn’t be fulfilled with the driver framework provided by the manufacturer of the SDR platform a custom fieldprogrammable gate array implementation was necessary. The implementation is based on a soft core processor system with attached user generated custom logic peripherals. An important element of the design is the decoder, which not only decodes the backscattered data, but also provides real time sub-symbol synchronization for the planned localization algorithm. Different decoder strategies have been analyzed and compared within the thesis. Considerable attention was drawn on a hardware efficient design, such that future developments are possible and have enough free FPGA resources left. Finally, tests with commercially available tags were made to verify the overall reader design.
The Impact of Behavior of Tax Inspection Report Application User on Tax Performance Approach Technology Acceptance Model (TAM) on Tax Office Madya Makassar

Objective - This study aims to determine the impact of tax inspectors behavior observed of dimensional perception of convenience and expediency perception Tax Application Inspection Report (ALPP) on the performance of tax inspection on the Tax Office Madya Methodology/Technique - Methology usingMultiple linear regression analysis. The research data was obtained from questionnaires given to related parties in the KPP Madya from the examination and the public. Samples were obtained by the method of random sampling. Findings - Based on the total sample of 50 respondents obtained partial results of the study that each variable utilization and ease of use of ALPP significant effect on the performance of tax inspection with TAM approach. Simultaneously two independent variables (utilization and ease of use) has a significant effect on the performance of tax inspection. Novelty - The important role technology that affect the performance at the individual level are described in the Technology Acceptance Model (TAM). The core of this model is to make information technology a positive impact on the individual performance of these technologies should be exploited and the technology must be in accordance with the type of work performed. Type of Paper - Empirical Keywords: Utilization, Ease of useand Performanceof tax audits.
QED and X-Ray Pulsars

Abstract Techniques in QED (quantum electrodynamics) have been developed previously (see for example Melrose and Parle 1983) allowing one to treat electron-photon and photon-photon interactions exactly in the magnetized vacuum and allowing one to include the effects of a medium. These techniques are extended to include particle-particle interactions. Exact cross-sections for electron-electron collisions are derived and compared with known expressions. Such calculations have application in studies of the formation and transfer of radiation in the atmospheres surrounding neutron stars.
IGIgram: An improved Gini index-based envelope analysis for rolling bearing fault diagnosis

The transient impulse features caused by rolling bearing faults are often present in the resonance frequency band which is closely related to the dynamic characteristics of the machine structure. Informative frequency band identification is a crucial prerequisite for envelope analysis and thereby accurate fault diagnosis of rolling bearings. In this paper, based on the ratio of quasi-arithmetic means and Gini index, improved Gini indices (IGIs) are proposed to quantify the transient impulse features of a signal, and their effectiveness and advantages in sparse quantification are confirmed by simulation analysis and comparisons with traditional sparsity measures. Furthermore, an IGI-based envelope analysis method named IGIgram is developed for fault diagnosis of rolling bearings. In the new method, an IGI-based indicator is constructed to evaluate the impulsiveness and cyclostationarity of the narrow-band filtered signal simultaneously, and then a frequency band with abundant fault information is adaptively determined for extracting bearing fault features. The performance of the IGIgram method is verified on the simulation signal and railway bearing experimental signals and compared with typical sparsity measures-based envelope analysis methods and log-cycligram. The results demonstrate that the proposed IGIs are efficient in quantifying bearing fault-induced transient features and the IGIgram method with appropriate power exponent can effectively achieve the diagnostics of different axle-box bearing faults.
The Artificial Intelligence and Neural Network in Teaching

This study aims to explore the application of artificial intelligence (AI) and network technology in teaching. By studying the AI-based smart classroom teaching mode and the advantages and disadvantages of network teaching using network technology and taking the mathematics classroom as an example, this study makes an intelligent analysis of the questioning link of classroom teachers in the teaching process. For the questions raised by teachers, the network classification models of convolutional neural network (CNN) and long short-term memory (LSTM) are used to classify the questions according to the content and types of questions and carry out experimental verification. The results show that the overall performance of the CNN model is better than that of the LSTM model in the classification results of the teacher's question content dimension. CNN has higher accuracy, and the classification accuracy of essential knowledge points reaches 86.3%. LSTM is only 79.2%, and CNN improves by 8.96%. In the classification results of teacher question types, CNN has higher accuracy. The classification accuracy of the prompt question is the highest, reaching 87.82%. LSTM is only 83.2%, and CNN improves by 4.95%. CNN performs better in teacher question classification results.
Evaluation of Infant Injury Prevention Education Provided during Antenatal Classes after Two Years: A Pilot Prospective Cohort Study

This study examined the long-term effects of an infant injury prevention program implemented during an antenatal class of 131 mothers. Questionnaires were completed 2 years postpartum to assess the incidence of injury (medically attended or home-care), mothers’ perception of injury prevention, implementation of safety practices, and active attitudes toward injury prevention. Responses were obtained from 68 (51.9%) mothers (intervention group, 40; control group, 28), including 24 who reported medically attended injuries and 55 who reported home-care injuries. The incidence of medically attended injuries did not differ between groups. The incidence of home-care injuries was also not significantly different, but was lower in the intervention group (72.5% vs. 92.9%, p = 0.050). Significantly fewer children in the intervention group experienced “injury due to being caught between objects” (12.5% vs. 39.3%, p = 0.014). Mothers in the intervention group were significantly more aware of injury prevention than those in the control group (p = 0.033). The risk of home-care injuries was inversely related to mothers’ injury-prevention perception (odds ratio [OR]: 0.55, p = 0.035). This study suggests that group education during pregnancy regarding injury prevention increases mothers’ perception of injury prevention. These findings support implementing injury prevention education during antenatal classes.
Application of Neuroengineering Based on EEG Features in the Industrial Design of Comfort

The smart wheelchair is a service robot that can be used as a means of transportation for the elderly and the disabled. The patients were given an intelligent wheelchair designed by electroencephalogram (EEG), which was used for more than 8 hours and tested continuously for 1 month. By ridit analysis, the difference between the two groups was statistically significant (U = 3.72, P < 0.01). The scores of visual analogue scale (VAS) and joint ground visuality (JGV) in the observation group were significantly better than those in the control group. The modules of physiological function (PF), physical pain (PP), overall health (OH), vitality (VT), social function (SF), emotional function (EF), and mental health (MH) in the SF-36 scores of the two groups were significantly improved (P < 0.05), and the improvement of each module in the observation group was significantly better than that in the control group (P < 0.05). The levels of serum IL-6, IL-10, and superoxide dismutase (SOD) in the two groups were significantly improved (P < 0.05), and the improvement of serum IL-6, IL-10, and SOD in the observation group was significantly better than that in the control group (P < 0.05). It is suggested that neural engineering based on EEG characteristics can be well applied in comfort industrial design.
Modified Q-Learning Method for Automatic Voltage Regulation in Wide-Area Multigeneration Systems

The state-estimation and optimal control of multigeneration systems are challenging for wide-area systems having numerous distributed automatic voltage regulators (AVR). This paper proposes a modified Q-learning method and algorithm that aim to improve the convergence of the approach and enhance the dynamic response and stability of the terminal voltage of multiple generators in the experimental Western System Coordinating Council (WSCC) and large-scale IEEE 39-bus test systems. The large-scale experimental testbed consists of a six-area, 39-bus system having ten generators that are connected to ten AVRs. The implementation shows promising results in providing stable terminal voltage profiles and other system parameters across a wide range of AVR systems under different test scenarios including N-1 contingency and fault conditions. The approach could provide significant stability improvement for wide-area systems as compared to the implementation of conventional methods such as using standalone AVR and/or power system stabilizers (PSS) for the wide-area control of power systems.
A NMR-Based Metabolomic Approach to Investigate the Antitumor Effects of the Novel [Pt(η1-C2H4OMe)(DMSO)(phen)]+ (phen = 1,10-Phenanthroline) Compound on Neuroblastoma Cancer Cells

NMR-based metabolomics is a very effective tool to assess the tumor response to drugs by providing insights for their mode of action. Recently, a novel Pt(II) complex, [Pt(ƞ1-C2H4OMe)(DMSO)(phen)]+ (phen = 1,10-phenanthroline), Pt-EtOMeSOphen, was synthesized and studied for its antitumor activity against eight human cancer cell lines. Pt-EtOMeSOphen showed higher cytotoxic effects than cisplatin in most of the cancer cell lines and in particular against the neuroblastoma cell line (SH-SY5Y). In this study, the mechanism of action of Pt-EtOMeSOphen on SH-SY5Y cells was investigated using 1H NMR-based metabolomics and compared with cisplatin. The observed time response of SH-SY5Y cells under treatment revealed a faster action of Pt-EtOMeSOphen compared with cisplatin, with a response already observed after six hours of exposure, suggesting a cytosolic target. NMR-based metabolomics demonstrated a peculiar alteration of the glutathione metabolism pathway and the diacylglycerol expression.
The robustness of in racetrack inflation

A spectral index appears to be a generic prediction of racetrack inflation models. Reducing a general racetrack model to a single-field inflation model with a simple potential, we obtain an analytic expression for the spectral index, which explains this result. By considering the limits of validity of the derivation, possible ways to achieve higher values of the spectral index are described, although these require further fine-tuning of the potential.
Localizing weak microseismic events using transfer learning with a deep neural network

Retrieving accurate microseismic source locations induced by hydraulic‐fracturing operations is an important step to gain insights into the hydraulically stimulated reservoir volume. Recently, deep neural networks have been proposed that directly recover source locations from the seismic waveforms. The optimal performance of the proposed deep neural networks usually requires large training sets. The need for a large training set can be circumvented if a previously trained deep neural network can be used to start the training process with its weights instead of randomly initialized weights. These weights can then be fine‐tuned using a smaller training set, which is also known as transfer learning . In this work, we implement a transfer learning workflow to update the weights of a deep neural network that was initially trained on a large synthetic dataset to localize microseismic events. We present two methods of processing, namely one post‐monitoring mode and one continuous mode where the processing takes place during the monitoring period. We apply the methods to field data from a hydraulic fracturing site in Texas, USA. In the first scenario, a subset of the field data from the entire monitoring period is used to update the weights of the deep neural network, which is then applied to the remaining data resulting in mean and median distances of 227 and 182 m, respectively, compared to the results of a good localization method. In the second scenario, the deep neural network is updated daily with previously detected and located events and applied to the events detected the following day. Since the observed data used for training generally do not cover a wide range of source locations, we enrich the training set with synthetic data. The addition of synthetics for transfer learning ensures that the updated deep neural network provides accurate source locations for events with locations far from locations used during transfer learning. Transfer learning combining synthetic and real data performs significantly better (more consistent) locations than transfer learning without synthetics.
Radiation Response of a Semi-insulating GaAs Semiconductor Detector for Charged Particle at Variable Operating Temperature

GaAs is a wide band gap semiconductor (1.42 eV) with potential use as a room temperature radiation detector. We have studied the radiation response of a prototype semiconductor detector by using a undoped semi-insulating (SI) GaAs wafer. The properties of the GaAs wafer are a resistivity of 10 ohm-cm, an orientation of (100) and a diameter of 50.8mm. The dimensions of the GaAs SI detectors were about 5x5 mm with a 350 um thickness. Prior to a metallization process, the surfaces of the GaAs wafer were cleaned by a solvent and rinsed with de-ionized (DI) water, and the removal of an oxidation layer by a diluted acid solution was performed. Metal/semiconductor contacts on the surface were fabricated by using a thermal evaporator in a high vacuum condition. The prototype GaAs detector had circular metal contacts of Ni/Au at each side and the diameter of a circular contact was 3 mm. The current-voltage characteristics of GaAs semiconductor detector were measured by using HP parameter analyzer with voltage sources. We measured the leakage currents at different temperature conditions. Also, the radiation response was evaluated by Am-241 source at variable operating temperature.
Stratosphere–troposphere coupling enhances subseasonal predictability of Northern Eurasian cold spells

Here we explore the stratospheric influence on the predictability of Eurasian cold‐spell events using the European Centre for Medium‐Range Weather Forecasts (ECMWF) ensemble hindcasts obtained from the Subseasonal‐to‐Seasonal (S2S) archive. To isolate the stratospheric influence, we subsampled two groups of hindcasts according to the strength of the stratospheric polar vortex preceding the cold spells at the surface. The predicted probability of cold spells, defined as the lowest 10th percentile of weekly mean temperature anomalies over northern Eurasia (10° W–130° E and 50° N–65° N), is systematically higher, by 0.05–0.2, at lags 7–24 days in the forecasts initialized during the weak stratospheric vortex compared to the strong stratospheric vortex group, extending the predictability of cold spells by 3–5 days. Our results suggest that, in the case of the weak polar vortex, stratosphere–troposphere coupling favors the negative Northern Annular Mode (NAM) regime and the cold‐air outbreaks in Eurasia. As a consequence, the long stratospheric predictability extends the predictability of the cold spells. On the other hand, when the polar vortex is strong, the stratospheric anomalies do not favor the observed negative NAM regime, which thus results from the internal tropospheric processes only. In this case the predictability of cold‐air spells is limited. Furthermore, we show that the extended predictability of cold spells arising from the stratosphere–troposphere coupling is captured by a simple statistical model, suggesting that governing large‐scale dynamics behave effectively linearly over some limited periods. Quantified contribution of the stratosphere–troposphere coupling to the enhanced skill of the extended‐range cold‐spell forecasts documented in our paper may prove useful in the development of forecasting tools.
Free-Space Metamaterial Superlenses Using Transmission-line Techniques

Free-Space Metamaterial Superlenses using Transmission-Line Techniques Ashwin K. Iyer Doctor of Philosophy Graduate Department of Electrical Engineering University of Toronto 2009 Free-space imaging with a resolution beyond that dictated by the classical diffraction limit may be achieved with a ‘Veselago-Pendry’ superlens made from a metamaterial possessing a number of specific properties, including a negative refractive index (NRI). Although a planar NRI transmission-line (NRI-TL) metamaterial based on the periodic lumped loading of a host TL network has successfully verified the phenomenon of superlensing in a 2D microstrip environment, a true Veselago-Pendry superlens capable of interacting with and manipulating fields in free space remained elusive, largely due to the difficulty of meeting its stringent design constraints and also to the problem of realizing a full 3D isotropic, polarization-independent structure. This work presents the first experimental verification of free-space Veselago-Pendry superlensing using a new class of volumetric metamaterials based on 2D NRI-TL layers that, although polarizationspecific, may be easily constructed using available lithographic techniques to interact with free-space sources. An equivalent-circuit model is developed to enable accurate design of the metamaterial’s dispersion and transmission characteristics, including those associated with Veselago-Pendry superlensing, and is validated using full-wave simulations. First, a volumetric NRI-TL metamaterial employing fully printed loading elements is fabricated to verify the salient properties of a free-space metamaterial-slab lens. This lens demonstrates diffraction-limited focusing at X-band and, thus, affirms theoretical results that suggest that electrically thick and lossy metamaterials are unable to perform
Clinical features, microbiology, and vancomycin regimens in sepsis patients: A retrospective study conducted at a single infectious diseases center

Despite increasing antibiotic resistance, vancomycin remains the first choice to treat severe infections due to drug-resistant gram-positive bacteria. This study aimed to summarise the clinical, microbiological, and vancomycin treatment outcomes in bacteremia patients. From July to December 2019, a retrospective cohort analysis was conducted on patients with bacteremia treated with vancomycin at a tertiary hospital in Hanoi, Vietnam. Patients without vancomycin concentrations were excluded from the study. Patients had a median age of 54 years (IQR, 41.8 - 63.3), with a male/female ratio of 1.86. Renal complication was markedly different; Clcr < 60, 60 - 130, and > 130 mL/min was 29%, 60%, and 11%, respectively. The median Charlson score was 3.0 (2-4.3), the qSOFA score was 1 (1-2), and the NEWS score was 76%, with a median of 6 (5-8). Twenty-one cases had positive blood culture where85.7% were gram-positive. 93% of patients with sepsis were treated with vancomycin as the first antimicrobial, of which 72% used vancomycin in combination with other antibiotics. Renal complications occurred in 12% of all patients, with grades R, I, and F accounting for 4%, 5%, and 3%, respectively. The median duration of treatment was 12 days (IQR, 7-17), with a success rate of 82 % and a failure rate of 18%, respectively. Conclusions: In most patients with bacteremia, microbiological tests reveal no detectable bacteria. When sepsis is suspected, a vancomycin regimen should be initiated.
Integrated Bioinformatics Identifies FREM1 as a Diagnostic Gene Signature for Heart Failure

Objective This study is aimed at integrating bioinformatics and machine learning to determine novel diagnostic gene signals in the progression of heart failure disease. Methods The heart failure microarray datasets and RNA-seq datasets have been downloaded from the public database. Differentially expressed genes (DE genes) are screened out, and then, we analyze their biological functions and pathways. Integrating three machine learning methods, the least absolute shrinkage and selection operator (LASSO) algorithm, random forest (RF) algorithm, and support vector machine recursive feature elimination (SVM-RFE) are used to determine candidate diagnostic gene signals. Then, external independent RNA-seq datasets evaluate the diagnostic value of gene signals. Finally, the convolution tool CIBERSORT estimated the composition pattern of immune cell subtypes in heart failure and carried out a correlation analysis combined with gene signals. Results Under the set threshold, we obtained 47 DE genes with the most significant differences. Enrichment analysis shows that most of them are related to hypertrophy, matrix structural constituent, protein binding, inflammatory immune pathway, cardiovascular disease, and inflammatory disease. Three machine learning methods assisted in determining the potential characteristic signals Fras1-related extracellular matrix 1 (FREM1) and meiosis-specific nuclear structural 1 (MNS1). Validation of external datasets confirms that FREM1 is a diagnostic gene signal for heart failure. Immune cell subtypes of tissue specimens found T cell CD8, mast cell resting, T cell CD4 memory resting, T cell regulation (Tregs), monocytes, macrophages M2, T cell CD4 naive, macrophages M0, and neutrophils are associated with HF. Conclusion The gene signal FREM1 may be a potential molecular target in the development of HF and is related to the difference in immune infiltration of HF tissue.