Miracle-dz/UniFAR-FTUs huggingface dataset:
An Example of Easy Synthesis of Active Filter and Oscillator using Signal Flowgraph Modification and Controllable Current Conveyors

An Example of Easy Synthesis of Active Filter and Oscillator using Signal Flowgraph Modification and Controllable Current Conveyors The paper deals with synthesis of active frequency filters and oscillators for applications in the video band. The synthesis is based on the modified signal flow graph design approach in the current mode. The commercially available high frequency current conveyors and second order structures are used. Basic structure is employing loss-less and lossy current mode integrators in two distributed feedback loops. Some of the used active elements allow to control current gain which is very useful in applications. High frequency multi-functional filter and electronically adjustable quadrature oscillator configurations are discussed. Most of the used passive elements (mainly capacitors) are grounded which is suitable for IC implementation. Simulation and experimental results demonstrate function and features very well.
Application of line loadability concepts to operating studies

The author describes a methodology for determining the loading limits of extra-high-voltage transmission lines, taking into account the system environment under which these lines operate. This methodology is based on the line loadability concepts developed for planning applications and extends their use to operating studies. For this purpose, specific line and system parameters are used to construct curves of thermal duty, bus voltage, and steady-state stability as a function of the power carried by the line. These curves, referred to as the operating loadability characteristics, not only make it possible to quickly identify the principal operating limitations to line loadability, but also provide a convenient mechanism for quantifying the trade-offs which exist between a given choice of system operating criteria and line loadability. >
The effects of the contact angle on sloshing in containers

We determine the effects of a constant contact angle on (i) the natural slosh frequencies in rectangular and cylindrical containers, and (ii) the nonlinear sloshing in a two–dimensional rectangular container. In analysing the former we use perturbation and other methods to get some explicit results which are then checked and extended using a new modal method somewhat similar to that of Perko. The modal method is then used to calculate the nonlinear slosh response in (ii). It is seen that the response depends significantly on the contact angle.
Distinct sensory‐ and goal‐related signals underlie the gap effect in the superior colliculus

The removal of a fixation point (FP) prior to the appearance of a saccade target (gap effect) influences pre‐motor circuits and reduces saccadic reaction time (SRT). Saccade preparation signals underlying the gap effect have been observed within the intermediate layers of the superior colliculus (SCi). Neurons in the caudal SCi, coding a target location, increase their activity during the gap, while neurons in the rostral SCi, with tonic activity related to visual fixation, decrease activity. However, the gap effect confounds two factors: (1) a goal‐driven temporal warning component (upcoming saccade target appearance) and (2) a stimulus‐driven sensory component (FP disappearance). These factors combine to reduce SRT and elicit pre‐target responses in the SCi. To dissociate warning and sensory effects, we altered the luminance of the FP during the gap period (renamed warning period) such that it could increase, decrease, or stay the same. Faster SRTs resulted with larger decrements in FP luminance. Different categories of SCi warning period activity were evaluated: (1) always increasing or decreasing or (2) sensory‐linked responses to changes in FP luminance. In the caudal SCi (at the location coding the target), all activity correlated negatively with SRT (i.e., saccade facilitation), and two categories of activity were observed (always increasing or opposing FP luminance changes). In the rostral SCi, four categories of activity were observed: activity that increased or followed the change in FP luminance correlated positively with SRT (i.e., saccade inhibition), while activity that decreased or opposed FP luminance changes correlated negatively with SRT. Such SCi activity reflected both goal‐driven saccade preparation signals and FP sensory properties.
Current transformer circuits for power electronics applications

This thesis investigates the operation of the current transfonner (CT) when sensing retum-to-zero current pulses in power electronic circuitry. The CT's output signal is nonnally rectified when sensing current pulses and the effects of the different rectification techniques on peak current and average current droop are evaluated. Initially, the various current sensing techniques and their application in power electronics circuits are reviewed. The CT and both diode and synchronous rectification are then reviewed in more detail. Operation of the CT with diode rectification (DR) and natural resetting is investigated. Three operating modes are identified. These are the discontinuous magnetizing current, continuous magnetizing current and discontinuous secondary current modes. The error (droop) in the average output signal obtained is found to be predominantly defined by CT core losses. Coefficients are given for correcting the error due to droop, provided that the discontinuous secondary current mode is avoided. Diode rectification with the dual CT arrangement is also investigated. Operation of the CT with synchronous rectification (SR) and natural resetting is then investigated. The SR topologies possible using a discrete MOSFET are categorized. During experimentation the arrangement used to drive the MOSFET's gate is found to be important if distortion is to be minimized. It also is found that the average current droop is dependent on the oscillatory behaviour of the resetting circuit and has an effectively random component. The magnitude of this component is defined by the voltage drop exhibited by the SR MOSFET's intrinsic anti-parallel diode. SR is then implemented using a commercially available analogue switch. The problems detailed with the use of a discrete MOSFET are largely alleviated. Another benefit is that the increased restriction on maximum duty factor imposed by introducing a discrete MOSFET is also eased. However, whichever SR technique is implemented, an operational amplifier is used and the transient response of this circuit element is important. A method of minimizing droop by indirect sensing of the CT's peak core flux excursion is then presented. A corresponding correcting voltage is applied in series with the CT's output terminals during a current pulse. The magnitude of this voltage is based on the magnitude of the resetting voltage sensed during previous switching cycles. A circuit is implemented and simulated. Experimental results are presented. A switched-mode circuit operating at a frequency higher than that of the main power circuit is then used to apply the correcting voltage with the objective of reducing the power drawn. Again, the circuit is implemented and simulated and experimental results are presented.
Effect of Hydraulic Retention Time on Operating Parameters in Membrane Bioreactor System for Wastewater Treatment

The aim of present study is to study effect of sludge retention time (SRT) on operating parameters such as Mixed Liquor Suspended Solids (MLSS) and Mixed Liquor Volatile suspended solids (MLVSS), Food to Micro-organisms ration (F/M ratio) and Organic loading rate(OLR) for hospital wastewater and residential quarter wastewater. The experiments have been performed on laboratory-scale hollow fiber submerged membrane bioreactor (MBR) for two different seasons. The two seasons considered was dry season i.e. summer season and another is wet season i.e. rainy season. For hospital wastewater , the MLSS is varied from 9.2 to 14.3 g/l. MLVSS is varied from 6.70 to 10.18 g/l. F/M is varied from 0.08 to 0.21 /day and OLR is varied from 1.07 to 2.14 kg/m 3 . day. For residential quarter wastewater , the MLSS is varied from 9 to 13.8 g/l. MLVSS is varied from 6.47 to 9.82 g/l. F/M is varied from 0.08 to 0.19 /day and OLR is varied from 0.94 to 1.87
Assessment of Patient Satisfaction with Generic Medication in Emerging Economy Using the Treatment Satisfaction Questionnaire for Medication

The purpose of this study was to explore the factors influencing patient satisfaction with generic medication in India using the treatment satisfaction questionnaire for medication (TSQM) scale. This study proposed a set of hypotheses that were tested using structural equation modeling (SEM). This study was based on primary data collected from 542 sample respondents using the judgmental sampling method. The findings of this study revealed an affirmative relationship of effectiveness and convenience with patient satisfaction with generic medication. The study's findings demonstrated that the TSQM is a psychometrically sound tool with high reliability and construct validity. Path analysis revealed that the effectiveness and convenience of generic medication contributed significantly to patient satisfaction, with standardized coefficients of 0.254 and 0.237, respectively.
Short-Term Impact of COVID-19 on Indian Stock Market

The onset of the COVID-19 pandemic and lockdown announcements by governments have created uncertainty in business operations globally. For the first time, a health shock has impacted the stock markets forcefully. India, one of the major emerging markets, has witnessed a massive fall of around 40% in its major stock indices’ value. Therefore, we examined the short-term impact of the pandemic on the Indian stock market’s major index (NIFTY50) and its constituent sectors. For our analysis, we used three different models (constant return model, market model, and market-adjusted model) of event study methodology. Our results are heterogeneous and largely depend on the sectors. All the sectors were impacted temporarily, yet the financial sector faced the worst. Sectors like pharma, consumer goods, and IT had positive or limited impacts. We discuss the potential explanations for the same. These results may be useful for investors in safeguarding equity portfolios from unforeseen shocks and making better investment decisions to avoid large, unexpected losses.
Causes of Death and End-of-Life Care in Patients With Intracranial High-Grade Gliomas

Background and Objectives To understand patterns of care and circumstances surrounding end of life in patients with intracranial gliomas. Methods We retrospectively analyzed end-of-life circumstances in patients with intracranial high-grade gliomas at Columbia University Irving Medical Center who died from January 2014 to February 2019, including cause of death, location of death, and implementation of comfort measures and resuscitative efforts. Results There were 152 patients (95 men, 57 women; median age at death 61.5 years, range 24–87 years) who died from January 2014 to February 2019 with adequate data surrounding end-of-life circumstances. Clinical tumor progression (n = 117, 77.0%) was the most common cause of death, with all patients transitioned to comfort measures. Other causes included, but were not limited to, infection (19, 12.5%); intratumoral hemorrhage (5, 3.3%); seizures (8, 5.3%); cerebral edema (4, 2.6%); pulmonary embolism (4, 2.6%); autonomic failure (2, 1.3%); and hemorrhagic shock (2, 1.3%). Multiple mortal events were identified in 10 (8.5%). Seventy-three patients (48.0%) died at home with hospice. Other locations were inpatient hospice (40, 26.3%); acute care hospital (34, 22.4%), including 27 (17.8%) with and 7 (4.6%) without comfort measures; skilled nursing facility (4, 3.3%), including 3 (2.0%) with and 1 (0.7%) without comfort measures; or religious facility (1, 0.7%) with comfort measures. Acute cardiac or pulmonary resuscitation was performed in 20 patients (13.2%). Discussion Clinical tumor progression was the most common (77.0%) cause of death, followed by infection (12.5%). Hospice or comfort measures were ultimately implemented in 94.7% of patients, although resuscitation was performed in 13.2%. Improved understanding of circumstances surrounding death, frequency of use of hospice services, and frequency of resuscitative efforts in patients with gliomas may allow physicians to more accurately discuss end-of-life expectations with patients and caregivers, facilitating informed care planning.
Sentiment classification in social media data by combining triplet belief functions

Sentiment analysis is an emerging technique that caters for semantic orientation and opinion mining. It is increasingly used to analyze online reviews and posts for identifying people's opinions and attitudes to products and events in order to improve business performance of companies and aid to make better organizing strategies of events. This paper presents an innovative approach to combining the outputs of sentiment classifiers under the framework of belief functions. It consists of the formulation of sentiment classifier outputs in the triplet evidence structure and the development of general formulas for combining triplet functions derived from sentiment classification results via three evidential combination rules along with comparative analyses. The empirical studies have been conducted on examining the effectiveness of our method for sentiment classification individually and in combination, and the results demonstrate that the best combined classifiers by our method outperforms the best individual classifiers over five review datasets.
Continuous-time forward equalization for the decision-feedback-equalizer-based read channel

A novel adaptive continuous-time forward equalizer (CTFE) for use with a decision-feedback equalizer (DFE) in the magnetic-recording read channel is presented. The forward equalizer is composed of one adaptive zero and can be constructed by adding a weighted version of the read-signal derivative to the read signal itself. The new equalizer is a viable alternative to traditional finite-impulse-response (FIR) forward equalizers in both performance and complexity.
Climate risk-informed decision analysis (CRIDA): ‘top-down’ vs ‘bottom-up’ decision making for planning water resources infrastructure

Climate risk-informed decision analysis (CRIDA) is a guidebook that lays out an evaluation framework and decision procedures to deal with climate uncertainties that are consistent with traditional agency water resources planning frameworks. CRIDA guidelines complement existing institutional guidance on recognizing circumstances when more complex risk-based climate analysis may be needed, above those required by standard planning procedures. The procedures are based on the concept of ‘decision-scaling’ judgments to qualitatively assess levels of future risk and analytical uncertainty stemming from climate change-related uncertainties, and as a guide for choosing specific analytical approaches and appropriate levels of analysis. CRIDA addresses how much detail is appropriate for a given problem setting, depending on infrastructure type and function, whether it is new design or rehabilitation of existing infrastructure, modular design or long-life infrastructure. CRIDA was structured to resolve the contentious issue of deciding under what circumstances a ‘top-down’ climate scenario-driven analysis ought to be conducted versus a more traditional ‘bottom-up’ vulnerability assessment, based on conventional agency project feasibility procedures. The procedures for such vulnerability assessments and planning procedures are well-represented in classical approaches, such as those included in the 1983 U.S. Water Resources Council's ‘Principles and Guidelines’. These commonly used procedures promote normative evaluation protocols and decision rules that generate alternative solutions which minimize risk-cost outcomes.
Enhanced Processability and Thermal Fatigue Reliability With Low Melting Point SnBi Solder Alloy LMPA-Q

SnBi based solder alloys become an interesting alternative for standard SnAgCu as they can be used to solder components at much lower temperature. The typically 50°C lower solder reflow temperature is less damaging for PCB and components, and also prevents hot tear and head-in-pillow failures for large fine pitch BGA components. A reasonable concern for these low-melting temperature solders is the thermal cycling reliability performance, in particular for harsh conditions such as automotive products. In this work, thermal cycling testing and failure analysis have been performed on 9 × 9 mm size QFN components and large chip components (2010 and 2512) which are typically sensitive to thermal fatigue. The results are benchmarked to standard SAC alloy. Also the process advantages from the low temperature solder alloys are depicted in this paper. Finally, the effect of Pb contamination on this SnBi based solder is investigated.
Development of Data-Processing Framework for Transit Performance Analysis

A methodological data-processing framework is developed to process a massive amount of transit data, including vehicle location, passenger count, and electronic fare transactions. The developed data analysis methodology can allow a number of applications, such as transit route performance measurement, to support decision making for transit planning and operation. The data analysis methodology is demonstrated by the use of 1 month of archived transit data obtained from Metro Transit in the Twin Cities of Minnesota. A route-based transit performance analysis at time point level is discussed to evaluate route running time and schedule adherence. An application interface was developed to analyze bus adherence at time points and link travel time performance. The data-processing framework has the capability to support studies on transfer activities and transit rider's origin and destination inference. The developed data analysis methodology has demonstrated its capability to analyze transit performance and to support further research on other intelligent transit applications.
Navigation and target localization performance of the autonomous underwater vehicle REMUS

A frequent stipulation in the design of Autonomous Underwater Vehicles (AUVs) is the requirement that the vehicle be small and inexpensive. Such a constraint precludes the use of costly, highly accurate sensors. As a result, to achieve a highly accurate and robust navigation system, navigation data from any and all sources must be processed. This information may come from a number of sources such as an Acoustic Doppler Current Profiler, Long Baseline acoustic travel times to known beacons, Inertial Measuring Units, or Sonar. The objective of this research was to develop a Kalman filter-based navigation algorithm for the AUV REMUS that improves positioning accuracy, provides rejection of poor fixes, and decreases energy use due to excessive corrections in course. Research was conducted in the context of Naval Special Warfare and its current vision for use of the REMUS vehicle in shallow water mine hunting. Navigation performance is illustrated using REMUS data for a Phase I search of a shallow water environment. Results are presented from a navigation sensor data fusion algorithm being developed for this scenario. Results demonstrate outlier rejection and track smoothing, both of which are beneficial to improving sensor data and increasing the reliability of target reacquisition. Thesis Supervisor: John J. Leonard Title: Assistant Professor of Ocean Engineering
School Autonomy Policies Lead to Increases in Principal Autonomy and Job Satisfaction

We explore whether decentralization of decision-making influences school principals’ subjective experience of autonomy, job demands, burnout, and job satisfaction. Using six-years of longitudinal data, we used two Australian education reforms as a natural experiment of the effect of decentralization. Exploiting state-to-state variation in the policies, we used difference-in-differences models, finding that the decentralization policies had a small influence on increasing self-perceptions of autonomy without increasing job demands. We also found that the policies had a small positive effect on job satisfaction.
Comparative Risk: Dread and Unknown Characteristics of the COVID‐19 Pandemic Versus COVID‐19 Vaccines

This research characterizes risk perceptions of the COVID‐19 pandemic and the COVID‐19 vaccines based on the dread and unknown dimensions of the psychometric paradigm. We examine if mental risk comparisons of these two risk objects influence risk mitigation behaviors (vaccination intention; vaccine acceptance; preventive behaviors) and emotional responses among unvaccinated and vaccinated Americans. A survey (N = 1532) was conducted based on a nationally representative sample of U.S. adults in May 2021. Results reveal considerable impact of risk comparison, especially along the dread dimension, on the outcomes of interest. In essence, this research reveals critical insights regarding vaccine hesitancy and risk communication about vaccination.
High-Performance English–Chinese Machine Translation Based on GPU-Enabled Deep Neural Networks with Domain Corpus

The ability to automate machine translation has various applications in international commerce, medicine, travel, education, and text digitization. Due to the different grammar and lack of clear word boundaries in Chinese, it is challenging to conduct translation from word-based languages (e.g., English) to Chinese. This article has implemented a GPU-enabled deep learning machine translation system based on a domain-specific corpus. Our system takes an English text as input and uses an encoder-decoder model with an attention mechanism based on Google’s Transformer to translate the text to Chinese output. The model was trained using a simple self-designed entropy loss function and an Adam optimizer on English–Chinese bilingual text sentences from the News area of the UM-Corpus. The parallel training process of our model can be performed on common laptops, desktops, and servers with one or more GPUs. At training time, we not only track loss over training epochs but also measure the quality of our model’s translations with the BLEU score. We also provide an easy-to-use web interface for users so as to manage corpus, training projects, and trained models. The experimental results show that we can achieve a maximum BLEU score of 29.2. We can further improve this score by tuning other hyperparameters. The GPU-enabled model training runs over 15x faster than on a multi-core CPU, which facilitates us having a shorter turn-around time. As a case study, we compare the performance of our model to that of Baidu’s, which shows that our model can compete with the industry-level translation system. We argue that our deep-learning-based translation system is particularly suitable for teaching purposes and small/medium-sized enterprises.