Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of a Variable Energy Penetrometer and Geo-Endoscopic Imaging in the Performance Assessment of Working Platforms Constructed with Large Size Unconventional Aggregates

Transportation agencies commonly use large size aggregates, often referred to as rock cap or aggregate subgrade, e.g., by Illinois Department of Transportation (IDOT), for stabilizing weak subgrades at wet of optimum moisture states. Adequate characterization of these large rocks is not possible in the laboratory with the use of standard tests. Accordingly, a cone penetration based strength index is the best field assessment tool since shear strength profile is closely linked to unbound aggregate or aggregate subgrade layer performance. To this end, an innovative variable energy dynamic cone penetration (DCP) device, popularly known as PANDA in France, was utilized in a recent Illinois Center for Transportation (ICT) research study involving the performance assessment of large size aggregates over soft subgrades. Twelve full scale working platform sections were constructed with six different types of virgin and recycled large size aggregate materials. Accelerated pavement testing (APT) was carried out on these sections to monitor the rutting progression with number of passes of a certain wheel load assembly. To evaluate layer properties and adequately relate them to rutting performance, PANDA tests were conducted along with traditional DCP soundings on the loading applied pavement test section centerlines. A geo-endoscopic probe was also used in the holes opened by the PANDA tests to identify layer interfaces and visually document subsurface moisture conditions. The PANDA and geo-endoscopy testing has proven very beneficial in the performance assessment of the large size aggregate subgrade materials under simulated traffic loading. This paper presents current detailed technical knowledge on the PANDA and geo-endoscopy test equipment and highlights field results associated with the recent ICT project soundings conducted in the pavement working platform test sections.
APPLICATION OF BEZIER CURVE ON THE 3-D BLADE PROFILING OF BOTTOMHOLE INTENSIFIER CENTRIFUGAL PUMP

Bezier curve has be widely applied in the geometry design and the machining. In the field of low specific speed centrifugal pump design, the application of Bezier curve fitting on the blade modeled lines has shown distinct advantages over other methods which not only eliminates the approximation of cylindrical modeled lines by one or several arcs, but also improves the design accuracy. The method of applying Bezier curve to the 3-D profiling of cylindrical blades was presented in this paper, and as an example, the blade design of a multi-level centrifugal pump used in the bottomhole intensifier drilling was also introduced.
A Four-Indicator Model for Autonomy Level Evaluation of Unmanned Platforms

A new four-indicator model was presented for evaluating the autonomy level of unmanned platforms.The man-platform interaction capability,situational awareness,environmental adaptability and decision-making capability of the unmanned platform were determined as four indicators applied to evaluate the autonomy level.Then,the indicators were given corresponding scores according to their effect on autonomy levels.Fuzzy comprehensive evaluation was used to quantify the four indicators.Finally,the average of the four indicators was taken as the value of the autonomy level.The model was applied to judge the autonomy level of Global Hawk UAV,Red FlagHQ3 UGV and Spartan Scout USV.The results show that the four indicator model is comprehensive,visualized,and simple in quantification.
UvA-DARE (Digital Academic Repository) Teacher self-efficacy and its effects on classroom processes, student academic adjustment, and teacher well-being A synthesis of 40 years of research

This study integrates 40 years of teacher self-efficacy (TSE) research to explore the consequences of TSE for the quality of classroom processes, students’ academic adjustment, and teachers’ psychological well-being. Via a criteria-based review approach, 165 eligible articles were included for analysis. Results suggest that TSE shows positive links with students’ academic adjustment, patterns of teacher behavior and practices related to classroom quality, and factors underlying teachers’ psychological well-being, including personal accomplishment, job satisfaction, and commitment. Negative associations were found between TSE and burnout factors. Last, a small number of studies indicated indirect effects between TSE and academic adjustment, through instructional support, and between TSE and psychological well-being, through classroom organization. Possible explanations for the findings and gaps in the measurement and analysis of TSE in the educational literature are discussed.
Physical Therapy Education Program Faculty Challenges, Concerns, and Priorities During the COVID-19 Pandemic: Looking Back and Moving Forward

Supplemental Digital Content is Available in the Text. Introduction. Faculty in U.S. physical therapy education programs made a rapid, reactive curricular modifications in response to the COVID-19 pandemic. The purpose of this study was to analyze the challenges and concerns of faculty in New York (NY) and New Jersey (NJ) during the pandemic, and their priorities for being adaptable, well-resourced programs moving forward. Review of Literature. The COVID-19 pandemic was a major program disruption for many physical therapy education programs. Historically, health profession programs making curricular transitions in response to unplanned disruptions realize benefits, but also challenges, limitations, and resource needs. Subjects. One hundred thirty-seven faculty across the 47 physical therapy education programs in NY and NJ participated in this study. Methods. A team of NY and NJ physical therapy education program directors and directors of clinical education generated an internet-based survey for analyzing the challenges and concerns NY and NJ faculty experienced during the COVID-19 pandemic in online teaching, personal challenges, academic issues, and campus activities. A mixed-methods approach was used to analyze the data from closed-end and open-ended questions. Statistical analysis was carried out on faculty demographics and ratings of challenges and concerns, and inductive thematic analysis of faculty responses to the open-ended questions. Results. Major faculty challenges were lack of contact, increased workload, and learning online technology. Major concerns were decreased quantity of hands-on laboratories, difficulty assessing student hands-on skills, delays in clinical education experiences, and student and faculty safety during on-campus activities. Six themes were identified: online education challenges, student and faculty safety, faculty personal needs, quality of student outcomes, communication, and administrative/institutional support. Discussion and Conclusion. An appreciation of faculty challenges and concerns during the COVID-19 pandemic and priorities for moving forward are important for programs making curricular transformations hybrid education to be better prepared for program disruptions.
THE EFFECT OF LOGISTICAL MANAGEMENT ON THE STRATEGIC AGILITY IN AQABA SPECIAL ECONOMIC ZONE AUTHORITY

This study aimed to analyze the impact of logistical management on the strategic agility in the authority of Aqaba Special Economic Zone. To achieve the objectives of this study, a questionnaire was developed for the purpose of data collection and distributed to (150) employees. They were chosen in a simple random sample method. The study reached a set of results; the most important of which are: The most important implications: The results of this study indicate that the features that distinguish international business are also related to the marketing agility, speed and critical accuracy of increasing levels of export propensity and expansion in the export market resulting from the response of the opportunity. Entrepreneurial entrepreneurs were found to amplify them by the accuracy of internal adaptive capabilities with lightness Movement Practicality This study contributes by exploring further the influence of different sources of agility. It focuses on the internationalization of entrepreneurial projects and demonstrates the link between entrepreneurs exposed to export market expansion, international marketing and agility. teams management and to emerging innovations and approach. The studied strategy, leadership theory, and strategic agility capabilities at the enterprise level, which is a combination of agility and its with the theory of Strategic to produce and creation, the results are that strategic concepts can be adopted for Job to be by and the need for an approach that shows the and competition through the agility teams that seek innovations that create value for new important practical
Logic-Guided Message Generation from Raw Real-Time Sensor Data

Natural language generation in real-time settings with raw sensor data is a challenging task. We find that formulating the task as an end-to-end problem leads to two major challenges in content selection – the sensor data is both redundant and diverse across environments, thereby making it hard for the encoders to select and reason on the data. We here present a new corpus for a specific domain that instantiates these properties. It includes handover utterances that an assistant for a semi-autonomous drone uses to communicate with humans during the drone flight. The corpus consists of sensor data records and utterances in 8 different environments. As a structured intermediary representation between data records and text, we explore the use of description logic (DL). We also propose a neural generation model that can alert the human pilot of the system state and environment in preparation of the handover of control.
Simple Low Cost Open Source UHF RFID Reader

In this paper, we present a simple low-cost SDR RFID UHF reader capable of reading a tag in real time. This reader is designed around a simple asynchronous OOK modulator in transmission and an envelope detector in reception. All tasks specific to the RFID protocol including clock recovery, data recovery and frame detection are handled in software by a Arduino Uno micro-controller. This reader is able to generate any RFID command supported by the protocol and to decode any message backscattered by the tag in real time. The details of hardware and software associated with this reader are released in open source for the community.
for Automatic Speech Recognition

The large size of Weighted Finite-State Transducers (WFSTs) used in Automatic Speech Recognition (ASR), such as the language model or integrated networks, is an important problem for many ASR applications. To address this problem, we present a general purpose compression technique for WFSTs that is specially designed for the ﬁnite-state machines most commonly used in ASR. Experiments run on two large tasks show the method to be very effective, typicality reducing memory and disk requirements to less than 35%. By combining it with “on-the-ﬂy” composition, the memory requirements are further reduced to below 14%. These reductions show no negative impact on recognition speed.
The Efficacy of Expiratory Muscle Training during Inspiratory Load in Healthy Adult Males: A Randomized Controlled Trial

This study aimed to evaluate the effects of expiratory muscle training (EMT) on respiratory muscle strength and respiratory distress during inspiratory load. Thirty-one healthy adult males were randomly divided into an EMT group who underwent EMT (n = 15) and a control group who did not undergo EMT (n = 16). The EMT group underwent EMT with a 50% load of maximum expiratory mouth pressure (PEmax) for 15 min, twice a day, every day, for 4 weeks. The parameter of respiratory muscle fatigue was a decrease in maximum inspiratory mouth pressure (PImax) and PEmax during 20 min of inspiratory load; thus, PImax and PEmax during inspiratory load were measured. Respiratory distress during inspiratory load was assessed using the Borg scale. These assessments were performed on the same subjects in each group before and after the 4 week study. In the EMT group, the PEmax values after the study were significantly higher than those before the study (p < 0.01). Furthermore, before the study, the PImax and PEmax values for the EMT group during inspiratory load were significantly lower than those before inspiratory load (p < 0.01). However, after the study, there was no difference in these values between during and before inspiratory load. In the EMT group, the Borg scale value during inspiratory load from 6 to 20 min was significantly lower after the study than before the study (p < 0.05). EMT increases expiratory muscle strength, thereby attenuating decreased respiratory muscle strength (PImax and PEmax) and respiratory distress during inspiratory load in healthy subjects.
Health-care Workers’ Willingness to Pay for COVID-19 Vaccines in Eastern Ethiopia: Using Contingent Valuation Method

Background Health-care workers (HCWs) are among the highest risk groups for COVID-19 infection. The vaccine is found to be vital for HCWs, their household contacts, and their patients to protect against COVID-19 infection and maintain the safety of health systems. The actual willingness to pay for COVID-19 vaccination and associated factors remain uncertain among health-care workers in Ethiopia. Therefore, studying health-care workers’ willingness to pay (WTP) for COVID-19 vaccination helps to have an insight on valuation of the vaccine. Methods Institution-based cross-sectional study was conducted among 403 randomly selected health-care workers working in health facilities in eastern Ethiopia from February 3 to March 20, 2021. Pretested structured questionnaire was used to collect data. Binary logistic regression analysis was fitted to test the associations between outcome and explanatory variables. A p-value of <0.05 with 95% confidence interval was used to declare statistical significance. Results The magnitude of willingness to pay for a COVID-19 vaccine was 42.8%. The median amounts of money respondents willing to pay was 400 ETB (US$ 10.04). Sex (male, AOR = 2.33; 95% CI: 1.39, 3.93), monthly income (>7000 ETB, AOR = 1.22; 95% CI: 1.11, 2.51), affordability (AOR = 1.99; 95% CI: 1.18, 3.35), fear of side effects (AOR = 3.75; 95% CI: 2.13, 6.60), support vaccinations (AOR = 2.97; 95% CI: 1.65, 5.35), the likelihood of getting COVID-19 infection (AOR = 2.11; 95% CI: 1.26, 3.52) were independent determinants of WTP for a COVID-19 vaccine. Conclusion Health-care workers’ willingness to pay for COVID-19 vaccination was found to be low. Detailed health education and training about COVID-19 vaccines are required regarding their side effects, and efficacy to make an informed decision to enhance the willingness to pay for the vaccine. Moreover, the government should consider providing COVID vaccines free of charge for low-income groups and at an affordable price for those who could pay.
Wave-Mode Configurable Ultrasonic Non-Destructive Evaluation System using Optoacoustic Prism

We demonstrate a multi-mode ultrasonic non-destructive evaluation (NDE) system based on a hybrid transducer including an optoacoustic (OA) prism. In this system, the OA prism with a flexible curved transmitter is capable of not only generating acoustic signals with high OA conversion efficiency but also covering a wide angular range to initiate various acoustic wave modes into a specimen whose interface is in contact with the OA prism. The latter angular manipulation provides great flexibility for NDE, which is uniquely enabled by developing and employing the curved OA transmitter coated on the prism. Using finite-element-based simulation, we validate acoustic signal generation and propagation inside an aluminum specimen. Then, excitation, propagation, and acquisition of shear and Rayleigh wave modes are confirmed experimentally. As feasibility demonstration, our hybrid transducer system, consisting of the OA prism and a piezoelectric receiver, is utilized for NDE of an aluminum specimen which includes air void discontinuity of 5-mm diameter. This is realized by multi-mode acoustic excitation from the OA prism with incident angles of 33°, 42°, 47°, and 60°. This exhibits detection accuracy less than 2% as confirmed by comparing calculated and measured time-of-flight values. Such OA prism-based realization of all range of angular wave modes allows our system to be potentially useful for NDE over broad metallic materials as long as their sound speed is faster than that of the OA prism, without causing additional reverberation noise commonly observed in conventional piezoelectric counterparts.
Experimental parametric analysis of an energy harvester based on highly nonlinear solitary waves

We investigate experimentally five different designs of an energy harvester based on mechanical vibration and highly nonlinear solitary waves. The harvester consists of a metamaterial formed by granular chains, an oscillator that taps the metamaterial, a solid in contact with the metamaterial, and a piezoelectric element glued to the solid. The overall principle is that the oscillator taps the metamaterial and creates a train of solitary waves along each chain. At the interface between the chains and the solid, part of the acoustic energy refracts into the solid where it coalesces at a point and triggers the vibration of the solid. Here, a transducer converts the focalized stress wave and the waves generated by the reverberation with the edges into electric potential. In the study presented in this article, we evaluate the effect of certain harvester parameters on the amount of energy that can be extracted. We considered five different designs by changing the oscillator, the dimension of the array, the solid material, and the transducer boundary condition. For each design we computed the power density, and we found that the density obtained with the best design is four orders of magnitude higher than the worst design.
Genome-wide identification and expression analysis of tomato glycoside hydrolase family 1 β-glucosidase genes in response to abiotic stresses

Abstract In plants, β-glucosidases (BGLUs) are important glycoside hydrolases involved in several biological phenomena, including the response to stresses via the activation of phytohormones and the release of alpha-hydroxy nitriles to protect against stresses. Due to the importance of BGLUs in plant growth and stress response, genome-wide analyses have been conducted in Arabidopsis and rice, while not in tomato (Solanum lycopersicum). In this study, we identified 20 BGLU genes in the tomato that were unevenly distributed on nine chromosomes and divided into five subgroups. There were a variety of plant hormones and stress response cis-elements in the promoter region of the SlBGLU gene, indicating that the BGLU gene may be involved in several aspects of the tomato stress response. SlBGLU gene expression analysis showed that the BGLU gene of tomato was expressed in many tissues, especially in the roots and leaves. Transcriptome data and results of real-time quantitative reverse transcription-polymerase chain reaction showed that most SlBGLU genes could be induced by abscisic acid, chlorothalonil, NaCl and cold (4 °C), especially SlBGLU13 and SlBGLU19, which may be key BGLU genes in response to stress and hormonal stimulation in the tomato. In addition, we constructed a competing endogenous RNA (ceRNA) network, which confers a new direction for studies on the function of SlBGLU genes. These findings not only further clarify the potential function of the BGLU gene family in mediating abiotic stresses in the tomato, but also provide valuable information for the study of functional genomics of the tomato in the future.
Unraveling the Multiple Drivers of Greening-Browning and Leaf Area Variability in a Socioeconomically Sensitive Drought-Prone Region

The complex attribution of climatic, hydrologic, and anthropogenic drivers to vegetation and agricultural production and their consequential societal impacts are not well understood, especially in socioeconomically sensitive states like Maharashtra, India. Here, we analyzed trends and variability in the MODIS leaf area index (LAI) time series, along with spatiotemporal patterns in precipitation, groundwater storage, agriculture statistics, and irrigation infrastructure, to identify their influences on the vegetation response and discuss their implications for farmers. The state showed greening in all biomes except forests, with a net gain of 17.478 × 103 km2 of leaf area during 2003–2019, where more than 70% of the trend in LAI is represented in croplands. Maximum greening was observed in irrigated croplands, attributable to increased crop productivity, whereas inadequate irrigation facilities with erratic rainfall patterns and droughts were primarily responsible for cropland browning. We discerned the dynamics and variability of vegetation response by incorporating a spectrum of synergistic feedbacks from multiple confounding drivers and found that uneven distribution of water availability across the administrative divisions governed the quantitative distinction in leaf area change. Despite the observed greening trends, the state witnessed a high number of farmer suicides related to droughts and agriculture failures hampering their socioeconomic security; therefore, improved irrigation infrastructure and comprehensive policy interventions are crucial for abatement of farmer distress.
Big-Little Adaptive Neural Networks on Low-Power Near-Subthreshold Processors

This paper investigates the energy savings that near-subthreshold processors can obtain in edge AI applications and proposes strategies to improve them while maintaining the accuracy of the application. The selected processors deploy adaptive voltage scaling techniques in which the frequency and voltage levels of the processor core are determined at the run-time. In these systems, embedded RAM and flash memory size is typically limited to less than 1 megabyte to save power. This limited memory imposes restrictions on the complexity of the neural networks model that can be mapped to these devices and the required trade-offs between accuracy and battery life. To address these issues, we propose and evaluate alternative ‘big–little’ neural network strategies to improve battery life while maintaining prediction accuracy. The strategies are applied to a human activity recognition application selected as a demonstrator that shows that compared to the original network, the best configurations obtain an energy reduction measured at 80% while maintaining the original level of inference accuracy.
A Hybrid Framework for Lung Cancer Classification

Cancer is the second leading cause of death worldwide, and the death rate of lung cancer is much higher than other types of cancers. In recent years, numerous novel computer-aided diagnostic techniques with deep learning have been designed to detect lung cancer in early stages. However, deep learning models are easy to overfit, and the overfitting problem always causes lower performance. To solve this problem of lung cancer classification tasks, we proposed a hybrid framework called LCGANT. Specifically, our framework contains two main parts. The first part is a lung cancer deep convolutional GAN (LCGAN) to generate synthetic lung cancer images. The second part is a regularization enhanced transfer learning model called VGG-DF to classify lung cancer images into three classes. Our framework achieves a result of 99.84% ± 0.156% (accuracy), 99.84% ± 0.153% (precision), 99.84% ± 0.156% (sensitivity), and 99.84% ± 0.156% (F1-score). The result reaches the highest performance of the dataset for the lung cancer classification task. The proposed framework resolves the overfitting problem for lung cancer classification tasks, and it achieves better performance than other state-of-the-art methods.