Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging ASR N-Best in Deep Entity Retrieval

Entity Retrieval (ER) in spoken dialog systems is a task that retrieves entities in a catalog for the entity mentions in user utterances. ER systems are susceptible to upstream errors, with Automatic Speech Recognition (ASR) errors being particularly troublesome. In this work, we propose a robust deep learning based ER system by leveraging ASR N-best hypotheses. Speciﬁcally, we evaluate different neural architectures to infuse ASR N-best through an attention mechanism. On 750 hours of audio data taken from live trafﬁc, our best model achieves 11.07% relative error reduction while maintaining the same performance on rejecting out-of-domain ER requests.
Thermal management of an unloaded hybrid dryer by generalized predictive control

Abstract The temperature control inside a hybrid dryer has always remained the main concern in drying applications. At present, the majority of the drying community researchers use classical controllers to command the thermal environment inside the drying chamber. Such a control strategy leads to diminish the dryer efficiency and alters the quality of the dried products. In this regard, the objective of this work is to develop an advanced temperature control system for the thermal management of a hybrid solar-electrical dryer. In addition, the proposed system must be convenient, efficient, and affordable in developing countries. A Generalized Predictive Controller (GPC) is designed and validated for temperature control covering a wide range of 40 °C–75 °C. Mathematical and identified models are developed to represent the thermal behavior of the dryer. The identified linear model is used to design the GPC controller. This controller has proven great feasibility and effectiveness by maintaining the chamber temperature with a settling time that remained under 22 min, with a static error of 0.5 °C and no overshoot or dips. Moreover, the GPC algorithm is implemented with an Arduino board which represents an easy, and low-cost solution for temperature control in drying applications.
Influence of Working Memory Capacity on the Frequency of Self-Repairs

This article explored the influence of working memory capacity on the frequency of self-repairs. The narrative task and listening span task were used. Twenty post-graduate students participated in this study. Overall, the results of this study illustrated that the working memory is a factor of self-repairs. Speakers who have higher working memory capacity produce lesser self-repairs. This finding provides teachers with a new insight into second language teaching; that is, teachers can improve the amount of lexical knowledge when teaching students who have lower working memory in order to help them produce more accurate language during the process of L2 speech production.
Conformal invariance of the 1-point statistics of the zero-isolines of 2 d scalar fields in inverse turbulent cascades

For three decades there have been speculations about the existence of conformal invariance in two-dimensional turbulence and possible implications thereof. These speculations have been confirmed by numerical experiments. However, there is a scarcity of relevant analytical studies on this topic. In our work we analyze the underlying equation for the one-point probability density function of a scalar in two-dimensional turbulence. We derive conditions under which the probability measure is conformally invariant and show that with this transformation certain statistics of non-homogeneous fields can be derived based on solutions of the homogeneous one.
Examining the Influence of Social Media Education Through Mobile- Phone on the Change in Physical Activity Behavior of Pregnant Women: An Intervention Study

Background: Nowadays, a new model of health approach through mobile-phones is widely used to deal with improving physical activity and its beneficial effect on pregnancy. This study was aimed to determine the influence of social networking through mobile phones on changing the physical activity behavior in pregnant women.Methods: This randomized interventional study was conducted with parallel groups on 90 pregnant women referring to Urmia Health centers in 2018-2019. The participants from various social and economic backgrounds were randomly included. The participants were randomly assigned to a control and a treatment group. Demographic and Pregnancy Physical Activity Questionnaire (PPAQ) questionnaires were filled. In addition to routine care, the treatment group received educational intervention through social network within 16 training sessions related to physical activities and nutrition in 8 weeks.Results: The mean physical activity level and weight in both control and treatment groups differed significantly after intervention (P<0.001). At the beginning of the study, control and treatment groups were not different in terms of daily physical activity level (P=0.17), meanwhile, there was a statistically significant difference at the end of intervention in the level of daily physical activity (P<0.001). Comparison of both groups, based on different categories of activity level at the end of the treatment, showed a non-significant difference in sedentary activities (P=0.89), but the intervention led to a significant difference based on the other level of activities; light (P<0.001), medium (P<0.001), and intense (P=0.05). Conclusion: Educational intervention based on social networks through mobile phones had an effective role in behavior change in physical activity during pregnancy.Trial registration code: IRCT20151004024340N15, Registration date: 2020/06/05
A Combined Model Based on EOBL-CSSA-LSSVM for Power Load Forecasting

Inaccurate electricity load forecasting can lead to the power sector gaining asymmetric information in the supply and demand relationship. This asymmetric information can lead to incorrect production or generation plans for the power sector. In order to improve the accuracy of load forecasting, a combined power load forecasting model based on machine learning algorithms, swarm intelligence optimization algorithms, and data pre-processing is proposed. Firstly, the original signal is pre-processed by the VMD–singular spectrum analysis data pre-processing method. Secondly, the noise-reduced signals are predicted using the Elman prediction model optimized by the sparrow search algorithm, the ELM prediction model optimized by the chaotic adaptive whale algorithm (CAWOA-ELM), and the LSSVM prediction model optimized by the chaotic sparrow search algorithm based on elite opposition-based learning (EOBL-CSSA-LSSVM) for electricity load data, respectively. Finally, the weighting coefficients of the three prediction models are calculated using the simulated annealing algorithm and weighted to obtain the prediction results. Comparative simulation experiments show that the VMD–singular spectrum analysis method and two improved intelligent optimization algorithms proposed in this paper can effectively improve the prediction accuracy. Additionally, the combined forecasting model proposed in this paper has extremely high forecasting accuracy, which can help the power sector to develop a reasonable production plan and power generation plans.
Electrified Postsunrise Ionospheric Perturbations at Millstone Hill

We provide evidence that midlatitude postsunrise traveling ionospheric disturbances (TIDs) are comprised of electrified waves with an eastward propagation component. The post‐sunrise gravity wave (GW) wind‐induced dynamo action effectively generated periodic meridional polarization electric fields (PEFs), facilitating TID zonal propagation in a similar fashion as GW‐driven neutral perturbations. A combination of near‐simultaneous eastward and upward observations using the Millstone Hill incoherent scatter radar along with 2‐dimensional total electron content maps allowed resolution of TID vertical and horizontal propagation as well as zonal ion drifts Veast (meridional PEFs). In multiple observations, Veast oscillated in the early morning during periods when TIDs exhibited downward phase progression, 30–60 min period, ∼ 140 m/s eastward speed, and 70 km vertical wavelength. Inside these TIDs, multiple flow vortexes occurred in a vertical‐zonal plane spanning the ionospheric topside and bottomside. Subsequently, PEFs weakened after a few hours as TID horizontal wavefronts rotated clockwise.
Individual and contextual effects on science identity among American ninth-grade students (HSLS:09): hierarchical linear modeling

ABSTRACT Background Research has shown that students in the United States have been developing negative perceptions of science as a field of study, which is associated with low science identity. The science identity can be influenced by a variety of personal demographic predictors, non-cognitive science-related variables (e.g. science self-efficacy and science interest), and contextual predictors (e.g. school locale and geographical location). Purpose The current study explored the variability in the effect of student-level factors (i.e. gender, SES, ethnicity, science self-efficacy, science utility, and science interest) and school-level factors (i.e. school type, locale, and geographical location) on ninth-grade students’ science identity. Sample Data on 16,216 ninth-grade students in the United States were obtained from a large-scale national database, the High School Longitudinal Study of 2009. Design and Methods Three models were tested using Hierarchical Linear Modeling. Results The findings showed that student-level factors accounted for a significant amount (36%) of the variability in students’ science identity and a much smaller amount (< 5%) of the variability in identity was due to the variation between schools. Students’ science identity was bolstered by a higher level of science self-efficacy, utility, interest, and socioeconomic status. Females, African Americans, and Hispanics had lower levels of science identity. Science identity was lower among students in rural schools and town schools compared with students in city schools and higher among students in private schools compared with students in public schools. Students in the Northeast and South had higher science identity compared to students in the West. Conclusion Establishing valuable contributions in the STEM field is strengthened when students hold constructive science identities. The current study found that various individual and contextual variables formulate students’ science identities. These influences are salient in rural and town public schools with poor SES status.
Toward Driver Intention Prediction for Intelligent Vehicles: A Deep Learning Approach

High-level scene understanding and situational awareness are fundamental for autonomous vehicles before being widely used on public roads in a thoroughly efficient and safe manner. These tasks involve not only perceiving the current surrounding states but also predicting the future behavior of nearby human-driven vehicles. A large amount of vehicular sensing data can be collected using sensors and networking systems in vehicles. Moreover, with the advent of Dedicated Short Range Communication, vehicles can further transfer intention information to surrounding vehicles. However, real-time intention inference of nearby drivers is still challenging. Our primary focus is to present a novel deep learning-based approach to predict drivers driving behavior at unsignalized T-junctions. We use temporal convolutional networks to analyze sequences of trajectory data for a vehicle approaching the T-junction and classify the maneuver seconds before actual maneuver occurrence. The cross-validation results demonstrate the efficiency of the proposed methodology.
A new practical purification method for type D Clostridium perfringens Epsilon toxin by Size-exclusion chromatography (SEC) and Ultrafiltration (UF)

BACKGROUND: The high potential toxicity of epsilon toxin (Etx) produced by Clostridium perfringens (C. perfringens) type D, has made it the third most lethal clostridial toxin behind botulinum and tetanus, therefore, having a pure and concentrated Etx is very important. OBJECTIVES: The aim of this study was to purify Etx as pure as possible with an applicable, cost-effective, multistep purification protocol with the lowest and shortest time. METHODS: The purification of the Etx was carried out in multiple consecutive steps; ammonium sulfate precipitation, dialysis, size exclusion chromatography by G50, two concentration steps, and ultrafiltration. The Etx activity after different steps was evaluated by the minimum lethal dose (MLD) calculation, according to the standard operating procedure. Toxin quantification was determined using Lowry technique, and its presence and specificity was tracked to identify pure Etx by SDS- PAGE and western blotting. Finally, the purity of Etx was evaluated by capillary electrophoresis. RESULTS: The purified Etx formed a single band of about 32.9 kDa in SDS-PAGE and blotting. The pure Etx concentration was calculated to be 3.9 mg/ml and its MLD value was the dilution of 1/24000 after the ultrafiltration step. The presented purification processes to purify Etx resulted in ~ 87-fold concentration and 88.6% purity. CONCLUSIONS: Due to this high Etx purity, the processes used in this study can provide the technical knowledge of toxin production in a larger industrial scale that can be used in development of clostridial toxoid vaccines, as well as quality control and/or diagnostic tests.
Dewatering sludge by osmotic technique – A comparative experimental study

Abstract The present paper aims to introduce a novel and environment-friendly sludge dewatering method based on the osmotic technique, and to examine the efficiency of this method from macro- and micro-points of view. Intact sample, taken from the primary settler of a local wastewater treatment plant in Guangzhou (China), was firstly dewatered by mechanical methods (i.e. vacuum filtration and centrifugation), then by the osmotic technique. Macroscopic experimental results show that: the vacuum filtration and centrifugation can only dewater the sludge from 94.5 to 78.3% and to 78.8%, respectively, whereas, using the osmotic technique, water content of the sludge can be decreased to 44.2% in 12 h and to 35.9% in about two days. The micro-structure, obtained by scanning electron microscope (SEM), shows that: the sludge samples after vacuum filtration and centrifugation have a porous structure and water can be drained either through the cracks that have been developed under the vacuum pressure or by the micro-pores of several micrometers that have been created under the disturbance of the centrifugal force; on the other hand, in the case of the osmotic technique, the samples show a much denser structure, where both the macro- and micro-pores are significantly decreased or even closed at high concentration of the polyethylene glycol (PEG) solution. At last, the future application of the osmotic technique in practice is discussed from the viewpoints of deep dewatering and energy consumption.
Infrastructural Investments and Economic Growth: Evidence from Pakistan

Purpose: The goal of this study is to make an attempt to find out the relationships between infrastructural investments and economic growth. Design/Methodology/Approach: The study employs time series data over the years from 1972 to 2020. To observe the long-run and short-run impact of infrastructural investments on economic growth, an ARDL modeling approach to co- integration is used that is most suitable technique over some other techniques of integration after inspecting the stationary level of data via ADF test. Findings: The findings of the study indicate that Investments on Railways, Roads, Gas Projects, Telecommunication, Water Projects and Power Projects appear as efficient factors for enhancing economic growth of Pakistan in the long run. Implications/Originality/Value: It is suggested that government should increase the public and private investment for development of Railways, Roads, Telecommunication and Water projects in Pakistan.
Multimodal travel‐time maps with formally correct and schematic isochrones

The automatic generation of travel‐time maps is a prerequisite for many fields of application such as tourist assistance and spatial decision support systems, for example to analyze the accessibility of health and social facilities. The task is to determine outlines of zones that are reachable from a user’s location in a given amount of time. In this work we focus on travel‐time maps with a formally guaranteed SEPARATION PROPERTY in the sense that a zone exactly contains the part of the road network that is reachable within a pre‐defined time from a given starting point and start time. In contrast to other automated methods that create travel‐time maps, our approach generates schematized travel‐time maps that reduce the visual complexity by representing each zone by an octilinear polygon, that is, the edges of the polygons use only eight pre‐defined orientations. We aim for octilinear polygons with a small number of bends to further optimize the legibility of the map. The reachable parts of the road network are determined by the integration of timetable information for different modes of public transportation, for example buses, trains or ferries, and pedestrian walkways based on a multimodal time‐expanded network. Moreover, the travel‐time maps generated visualize multiple travel times using a map overlay of different time zones and taking natural barriers such as rivers into account. In experiments on real‐world data we compare our schematic visualizations to travel‐time maps created with other visualization techniques with respect to simple but robust quality measures such as the number of bends and the perimeter of the zones.
Aquatic‐Terrestrial Linkages Control Metabolism and Carbon Dynamics in a Mid‐Sized, Urban Stream Influenced by Snowmelt

Freshwater streams can exchange nutrients and carbon with the surrounding terrestrial environment through various mechanisms including physical erosion, flooding, leaf drop, and snowmelt. These aquatic‐terrestrial interactions are crucial in carbon mobilization, transformation, ecosystem productivity, and have important implications for the role of freshwater ecosystems in the global carbon budget. We utilized high‐frequency oxygen, temperature, and carbon dioxide (CO2) data to infer watershed connectivity in Boulder Creek, a mid‐sized (1160 km2) watershed located in Colorado, USA. Daily modeled gross primary production (GPP), ecosystem respiration (ER), net ecosystem production (NEP), and reaeration coefficients (K600) were paired with high‐frequency, in‐situ dissolved CO2 data to characterize changes in metabolic regime and carbon flux on a stream influenced by seasonal snowmelt. GPP and ER were correlated (ρ = −0.72, p ≪ 0.001) during the non‐snowmelt period and NEP was frequently negative. Mean FCO2 during the non‐snowmelt period was approximately 302 (±171) mmol C m−2 d−1 and was primarily supported by watershed CO2 inputs. During snowmelt, GPP and ER were not significantly correlated (ρ = −0.22, p = 0.05), and mean NEP was significantly more negative than during non‐snowmelt. Watershed connectivity was higher during snowmelt, as evidenced by significantly higher FCO2 (843 ± 338 mmol C m−2 d−1) and greater allochthonous CO2 inputs than during non‐snowmelt periods, emphasizing the effects of seasonal differences in aquatic‐terrestrial linkages in this stream. We suggest that our understanding of watershed carbon budgets is subject to temporal dynamics which control the degree of connectivity between terrestrial and aquatic ecosystems.
Reinterpreting the Stabilizer Formalism

Stabilizer codes, introduced in [2], [3], have been a prominent example of quantum codes constructed via classical codes. The paper [3], introduces the stabilizer formalism for obtaining additive quantum codes of length n from Hermitian self-orthogonal codes of length n over GF(4). In the present work, we reinterpret the stabilizer formalism by considering binary codes over the symbol-pair metric (see [9]). Specifically, the present work constructs additive quantum codes of length n from certain binary codes of length n considered over the symbol-pair metric. We also present the Modified CSS Construction which is used to obtain quantum codes with parameters.
Optimal Sizing of Energy Storage System for Operation of Wind Farms Considering Grid-Code Constraints

Transmission system operators impose several grid-code constraints on large-scale wind farms to ensure power system stability. These constraints may reduce the net profit of the wind farm operators due to their inability to sell all the power. The violation of these constraints also results in an imposition of penalties on the wind farm operators. Therefore, an operation strategy is developed in this study for optimizing the operation of wind farms using an energy storage system. This facilitates wind farms in fulfilling all the grid-code constraints imposed by the transmission system operators. Specifically, the limited power constraint and the reserve power constraint are considered in this study. In addition, an optimization algorithm is developed for optimal sizing of the energy storage system, which reduces the total operation and investment costs of wind farms. All parameters affecting the size of the energy storage systems are also analyzed in detail. This analysis allows the wind farm operators to find out the optimal size of the energy storage systems considering grid-code constraints and the local information of wind farms.
Data-Driven Forgetting and Discounting Factors for Vehicle Speed Forecasting in Ecological Adaptive Cruise Control

This paper investigates temporal correlations in human driving behavior using real-world driving for improving the accuracy of speed forecasting. These correlations can point to a measurement weighting function with two parameters: a forgetting factor for past speed measurements that the vehicle itself drove with, and a discounting factor for the speeds of vehicles ahead based on information from vehicle-to-vehicle (V2V) communication. The developed weighting approach is applied to a vehicle speed predictor using polynomial regression, a prediction method well-known in the literature. The performance of the developed approach is then assessed in both real-world and simulated traffic scenarios for accuracy and robustness. The new weighting method is applied to an ecological adaptive cruise control (eco-ACC), and its influence is analyzed on the prediction accuracy and the performance of the eco-ACC in an electric vehicle model. The results show that the new prediction method improves energy saving from the eco-driving by up to 4 % compared to a baseline least-square-based polynomial regression. This is a 10 % improvement over the constant speed/acceleration model, a conventional speed predictor.
Vulnerability interactive geographic viewer against COVID‐19 at the block level in Colombia: Analytical tool based on machine learning techniques

Abstract To mitigate the effects of the coronavirus disease 2019 (COVID‐19) pandemic, different countries have developed computational tools and dashboards that generate value for decision‐making in public health. We aimed to build an interactive geographic viewer for vulnerability to COVID‐19 at the block level in Colombia to identify the location of populations that, because of sociodemographic characteristics and health conditions, could have more complications from COVID‐19 infections. The vulnerability levels of the different blocks of 1,102 municipal capitals were calculated. Additionally, the institutions that provide health services and hotels were georeferenced, and changes in people’s mobility dynamics in large cities were identified.