Miracle-dz/UniFAR-FTUs huggingface dataset:
Profile of Problem Based Learning to Improve Students' Critical Thinking Skills

Sections Info ABSTRACT Article history: Submitted: October 31, 2021 Final Revised: November 17, 2021 Accepted: November 28, 2021 Published: November 30, 2021 This article was created to identify the effect of the Problem Based Learning model in improving students' critical thinking. The data collection method is in the form of a literature study. This article explains that problem-based learning is a learning model that focuses on students' solving problems in physics concepts. Critical thinking is the student's ability to analyze and evaluate the information received to conclude appropriately. The results of the literature study showed an increase in students' critical thinking skills in the learning process with the Problem Based Learning (PBL) learning model. The application of Problem Based Learning has an average increase of about 50% after the learning pattern is applied. The existence of the PBL learning model is effectively used to improve students' critical thinking, which incidentally in solving physics concepts requires the ability to analyze and evaluate to conclude.
Transcriptomic Analysis of the Erythritol High-Yielding Mutant Strain Yarrowia lipolytica Y44

Colin Ratledge Center for Microbial Lipids, School of Agriculture Engineering and Food Science, Shandong University of Technology, Zibo, China Baolingbao Biology Co., Ltd., Dezhou, China State Key Laboratory of Microbiology, Shandong University, Qingdao, Shandong, China Chongqing Academy of Science and Technology, Chongqing, China China National Research Institute of Food and Fermentation Industry Co., Ltd. Beijing, China
EVAPOTRANSPIRATION ESTIMATES FOR GREENHOUSE LETTUCE USING AN INTERMITTENT NUTRIENT FILM TECHNIQUE

Lettuce production using the nutrient film technique (NFT) in Brazil has increased considerably since 1990. However, the procedure used to supply the nutrient solution is based on interval timers, which does not account for the dynamic environment. In general, nutrient solution is recirculated for 10 to 15 min followed by a resting period of 15 to 20 min, which is used for saving electricity and to provide oxygen to the roots. The objective of this work was to evaluate the Penman-Monteith model for estimating crop evapotranspiration (ETc) for three cultivars of lettuce (Grand Rapids, Great Lakes, and Regina). Measurements and evapotranspiration estimates were carried out for 30 min intervals. A closed-loop hydroponic system was specially designed for this research so that measurements of ETc could be conducted for short time periods. The hydroponic system was installed in a greenhouse without environmental controls, as is typical for hydroponic lettuce production in Brazil. Measurements of incident solar radiation, relative humidity, air velocity, air temperature, and nutrient solution temperature were stored every minute. The Penman-Monteith model provided excellent estimates of evapotranspiration for leaf area indices (LAI) greater than 0.5. However, the method was not adequate to estimate ETc in the first two weeks after transplanting (LAI < 0.5). During this period, the model sometimes over- or underestimated ETc, depending on the cultivar. Additionally, the model overestimated ETc early in the morning and late in the afternoon because a constant value of the surface diffusive resistance (rs) was used. The results of this research can be used to improve the electric energy usage efficiency for hydroponic lettuce production and allow the implementation of automatic systems for adjusting the concentration of nutrients in the irrigation water based on estimates of evapotranspiration.
Influence of Network Capacity and Information and Knowledge Sharing on Open Innovation

Objective: This article analyzes the influence of network capacity and information and knowledge sharing on open innovation in startups related to large companies. Method: A survey was carried out with managers of startups ranked on the 100 Open Startups platform. The survey population comprises 324 startups and the sample contains 144 valid responses. For data analysis, structural equation modeling by partial least squares was applied. Results: The results revealed a direct and significant influence between information sharing and open innovation, as well as between knowledge sharing and open innovation. Although the direct influence of network capacity in open innovation was not found, mediation of information and knowledge sharing was observed in this relationship, which indicates that network capacity can be an important antecedent and indirectly impact the performance of open innovation. It is concluded that the investigated variables are important drivers of open innovation performance, in order to reduce risks and bring benefits to startups in interorganizational relationships. Contributions: The results contribute to the literature by revealing the mediating effect of information and knowledge sharing on the relationship between network capacity and open innovation performance, an advance in the understanding of shared resources. They also have practical and social implications, as they provide information about interorganizational relationships. It is expected that the findings help managers in conducting the relationship, with the purpose of reducing asymmetries between those involved and directing towards a beneficial relationship for both parties.
A new approach to dynamic analysis of AC networks incorporating detailed modeling of DC systems. II. Application to interaction of DC and weak AC systems

For pt.I see ibid., vol.3, no.4, p.2005-11 (1988). A digital simulation concept which combines the detailed capability of the Electromagnetic Transients Program with the large system dynamic capability of a transient stability program is applied to the study of the interaction of a DC system with weak AC systems. The interface locations between the two simulations are extended to strategic points within the AC network. Greater predictability and authenticity of the response to disturbances have been achieved than was previously possible. >
Learning governing equations of interacting particle systems using Gaussian process regression

Interacting particle or agent systems that display a rich variety of collection motions are ubiquitous in science and engineering. The fundamental and challenging goals are to infer individual interaction rules that yield collective behaviors and establish the governing equations. In this paper, we study the data-driven discovery of second-order interacting particle systems with distance-based interaction laws, which are known to have the capability to reproduce a rich variety of collective patterns. We propose a learning approach that models the latent interaction function as a Gaussian process, which can simultaneously fulﬁll two inference goals: one is the nonparametric inference of interaction function with the pointwise uncertainty quantiﬁcation, and the other one is the inference of unknown parameters in the non-collective forces of the system. We test the learning approach on Dorsogma model and numerical results demonstrate the effectiveness.
Perspective on SOD 1 mediated toxicity in Amyotrophic Lateral Sclerosis *

A lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive degeneration of spinal motor neurons. Although mutations in dozens of proteins have been associated with ALS, the enzyme, superoxide dismutase 1 (SOD1) was the first protein identified with the development of ALS and accounts for ~20% of familial cases. In experimental animals and patient samples, mutant SOD1 is found in cytoplasmic deposits implicating SOD1 aggregates as the toxic entities. Here we discuss the various biochemical and structure-based hypotheses proposed for mutant SOD1-associated ALS. Although much remains to be discovered about the molecular mechanism of SOD1 mediated toxicity, these hypotheses offer new avenues for therapeutic development.
Deep reinforcement learning based algorithmic trading with ensemble models

In the financial sector, any type of trading is considered, algorithmic trading where the determination of the order parameters (timing, price, quantity) as well as the order management is performed by a computer algorithm, with or without limited human intervention. The research resulting in the dissertation aims to find ways to develop and refine deep reinforcement learning models to execute trading that maximize revenue and minimize investment risk. Developments using deep reinforcement learning in the literature have already demonstrated the viability of machine trading algorithms in portfolio management. The aim of the present research is to create a more robust reinforcement learning model that can simulate trading with varying risk appetite in line with market dynamics while maximizing portfolio value and minimizing risk and other negative consequences. The dissertation begins with a theoretical overview of state-of-the-art methods of reinforcement learning, paying special attention to the practical advantages and disadvantages of each algorithm and model. This is followed by the introduction of an ensemble trading model that provides the baseline for new developments, along with a description of the simulated stock market environment, factors and data used to model market dynamics. The main focus of the research is on a new ensemble technique based on the distribution of
Improving Perceptual Quality of Adversarial Images Using Perceptual Distance Minimization and Normalized Variance Weighting

Neural networks are known to be vulnerable to adversarial examples, which are obtained by adding intentionally crafted perturbations to original images. However, these perturbations degrade their perceptual quality and make them more difﬁcult to perceive by humans. In this paper, we propose two separate attack agnostic methods to increase the perceptual quality, measured in terms of perceptual distance metric LPIPS, while preserving the target fooling rate. The ﬁrst method intensiﬁes the perturbations in the high variance areas in the images. This method could be used in both white-box and black-box settings for any type of adversarial examples with only the computational cost of calculating the pixel based image variance. The second method aims to minimize the perturbations of already generated adversarial examples independent of the attack type. In this method, the distance between benign and adversarial examples are reduced until adversarial examples reach the decision boundaries of the true class. We show that these methods could also be used in conjunction to improve the perceptual quality of adversarial examples and demonstrate the quantitative improvements on CIFAR-10 and NIPS2017 Adversarial Learning Challenge datasets.
Practical W-CDMA receiver and transmitter system design and simulation

As terminal system designers begin to get to grips with the air-interface technology of W-CDMA and the other IMT-2000 technologies, there is a demand for a system-level simulation environment that is truly cross-disciplinary. The 3G and software radio concepts require an unprecedented degree of amalgamation between RF and DSP technology-areas that traditionally have been well separated. This point is illustrated by way of examples of both receiver and transmitter system design using Entegra's "3G Design Suite" for SystemView by Elanix. First, an analysis of the UMTS terminal receiver noise budget and hence expected implementation margin is presented. Second, an example of a UMTS basestation transmitter simulation showing how RF amplifier requirements are strongly dependant upon the statistics of multiple-user W-CDMA waveforms.
PLANT MEDIATED SYNTHESIS OF SILVER NANOPARTICLES USING EXTRACTS FROM Tilia cordata, Matricaria chamomilla, Calendula officinalis AND Lavandula angustifolia FLOWERS

In this work, a simple, rapid and efficient green method for silver nanoparticles synthesis was developed by using extracts from four medicinal plants: flowers of Tilia cordata, Matricaria chamomilla, Calendula officinalis and Lavandula angustifolia, acting as reducing and capping agents. Silver nitrate was used as a precursor. The effect of the sunlight exposure and/or the sonication treatment on the synthesis process was investigated. The ultraviolet–visible spectroscopy (UV-Vis) studies showed surface Plasmon resonance peaks between 432 nm and 448 nm, characteristic of spherical silver nanoparticles. The morphology of the nanoparticles produced was investigated by transmission electron microscopy (TEM) and X-ray diffraction (XRD). The proposed method could be easily implemented for any kind of scientific or industrial application due to its cost effective nature.
Response of metal oxide arrester in gas-insulated substation and methods to improve its dynamic characteristics

It is well known that the metal oxide arrester (MOA) has the inability to operate against very fast transient overvoltage (VFTO). The aim of this paper is to study the dynamic performance of MOA in gas-insulated substation (GIS). A 230 kV GIS is taken from a local utility and the system is modelled for very fast transients. The dynamic performance of arrester is analysed for current surges of different front times, varying from microsecond to nanosecond using electromagnetic transient program. The dynamic characteristics can be obtained for front time, more than 1 µs. However, there is a deviation in it when front time is less than 1 µs, showing failure of arrester conduction because of delay in its initial response, especially under VFTO. The dynamic characteristic is improved by making modifications in the system as well as in the arrester. A dynamic hysteresis curve is constructed to confirm the delay between the residual voltage and current surge. By decreasing the delay, it is possible to improve the dynamic characteristics of the arrester so that the effective utilisation of MOA can be enhanced in VFTO applications.
Automated anomaly detection processor

Robust exploitation of tracking and surveillance data will provide an early warning and cueing capability for military and civilian Law Enforcement Agency operations. This will improve dynamic tasking of limited resources and hence operational efficiency. The challenge is to rapidly identify threat activity within a huge background of noncombatant traffic. We discuss development of an Automated Anomaly Detection Processor (AADP) that exploits multi-INT, multi-sensor tracking and surveillance data to rapidly identify and characterize events and/or objects of military interest, without requiring operators to specify threat behaviors or templates. The AADP has successfully detected an anomaly in traffic patterns in Los Angeles, analyzed ship track data collected during a Fleet Battle Experiment to detect simulated mine laying behavior amongst maritime noncombatants, and is currently under development for surface vessel tracking within the Coast Guard's Vessel Traffic Service to support port security, ship inspection, and harbor traffic control missions, and to monitor medical surveillance databases for early alert of a bioterrorist attack. The AADP can also be integrated into combat simulations to enhance model fidelity of multi-sensor fusion effects in military operations.
Thermal Design and Thermal Analysis of Control Cabinet for Pattern Sewing Machine

Thermal design and thermal analysis of control cabinet for pattern sewing machine was researched to make components’ temperature meets the requirement. Basic flow sheet of research process was introduced. Then, preliminary thermal design and thermal analysis was performed, and the computed result indicated that temperature of some components was out of limit. Next, by optimizing the location of fan and components in cabinet, the temperature of servo driver 1 to 3 dropped 11.1 oC, 10.8 oC and 11.3 oC respectively. The data shows that cabinet’s heat dissipation is improved, and the thermal design of the cabinet is reasonable. Finally, Thermal test of the prototype was performed, which proves the thermal design is effective, and the numerical simulation result is correct.
A New Support Structure in Waverider Force Measurement

In this paper a new support structure was given to solve the problem met in the force measurement with engine jet. The force measurements of waverider were undertaken in the hypersonic wind tunnel. The test condition was at Ma=6, angle of attack α=-6°-6°, at which we researched the effects on the vehicle aerodynamics of inlet cowl opening and closing, support system, engine jet, and pressure ratios. To decrease the effects of strut on the jet flow-field, we took sharp belly strut to support the model in the wind tunnel. The belly strut could support the waverider model, force measurement balance, and it could make the inlet flow set up, and provide the high pressure jet. The test results showed that the belly sharp strut had little effects on the flowfield and could inject the inlet flow, and could provide very high quality jet.
Identifying Homogeneous Periods in Bus Route Origin-Destination Passenger Flow Patterns from Automatic Passenger Counter Data

Bus passenger origin-destination (O-D) flow matrices portray information on travel patterns that can be used for route planning, design, and operations functions. Because travel patterns are known to vary throughout the day, O-D flow matrices can be expected to vary throughout the day as well. A method identifies time-of-day periods of homogeneous normalized bus route O-D passenger flow matrices in which a normalized matrix depicts the probabilities that a random passenger in the homogeneous period will travel from various origin stops to various destination stops on the route. The method uses bus trip automatic passenger counter data to estimate trip-level O-D matrices, aggregates the trip-level O-D matrices into elemental matrices for relatively short time periods, and then considers these elemental matrices as inputs to a traditional clustering procedure that is modified to ensure that a cluster indicating a period of homogeneous normalized O-D flow spans a continuous time period during the day. The homogeneous O-D flow period method is applied to empirical automatic passenger counter data collected on a bus route for which temporal travel patterns are understood. The time periods identified correspond well to the a priori understanding of travel patterns. A parallel method that uses passenger volume, rather than estimated normalized O-D flow matrices, is applied to the same data. The periods identified by this volume-based approach are not responsive to the changes in the normalized O-D flow patterns determined by the homogeneous O-D flow period identification method.
RFID Tag Localization With a Sparse Tag Array

With the rapid growth of the Internet of Things (IoT), the radio-frequency identification (RFID) technology has been recognized as an effective and low-cost solution for many IoT applications. In this article, we study the problem of utilizing a sparse RFID tag array for backscatter indoor localization. We first theoretically and experimentally validate the feasibility of using sparse tag arrays for the direction of arrival (DOA) estimation. We then present the SparseTag system, which leverages a novel sparse tag array for high-precision backscatter indoor localization. The SparseTag system includes sparse array processing, difference co-array design, DOA estimation using a spatial smoothing-based method, and a localization method. A robust channel selection method based on the RFID tag array is adopted for mitigating the multipath effect. The SparseTag system is implemented with commodity RFID devices. Its superior performance is validated in two different environments with extensive experiments and comparison to baseline schemes.
Responses of paediatric emergency departments to the first wave of the COVID-19 pandemic in Europe: a cross-sectional survey study

Objective Understanding how paediatric emergency departments (PEDs) across Europe adapted their healthcare pathways in response to COVID-19 will help guide responses to ongoing waves of COVID-19 and potential future pandemics. This study aimed to evaluate service reconfiguration across European PEDs during the initial COVID-19 wave. Design This cross-sectional survey included 39 PEDs in 17 countries. The online questionnaire captured (1) study site characteristics, (2) departmental changes and (3) pathways for children with acute illness pre and during the first wave of COVID-19 pandemic (January–May 2020). Number of changes to health services, as a percentage of total possible changes encompassed by the survey, was compared with peak national SARS-CoV-2 incidence rates, and for both mixed and standalone paediatric centres. Results Overall, 97% (n=38) of centres remained open as usual during the pandemic. The capacity of 18 out of 28 (68%) short-stay units decreased; in contrast, 2 units (7%) increased their capacity. In 12 (31%) PEDs, they reported acting as receiving centres for diverted children during the pandemic. There was minimal change to the availability of paediatric consultant telephone advice services, consultant supervision of juniors or presence of responsible specialists within the PEDs. There was no relationship between percentage of possible change at each site and the peak national SARS-CoV-2 incidence rate. Mixed paediatric and adult hospitals made 8% of possible changes and standalone paediatric centres made 6% of possible changes (p=0.086). Conclusion Overall, there was limited change to the organisation or delivery of services across surveyed PEDs during the first wave of the COVID-19 pandemic.
Environmental factors influencing spotted hyena and lion population biomass across Africa

Abstract The spotted hyena (Crocuta crocuta Erxleben) and the lion (Panthera leo Linnaeus) are two of the most abundant and charismatic large mammalian carnivores in Africa and yet both are experiencing declining populations and significant pressures from environmental change. However, with few exceptions, most studies have focused on influences upon spotted hyena and lion populations within individual sites, rather than synthesizing data from multiple locations. This has impeded the identification of over‐arching trends behind the changing biomass of these large predators. Using partial least squares regression models, influences upon population biomass were therefore investigated, focusing upon prey biomass, temperature, precipitation, and vegetation cover. Additionally, as both species are in competition with one other for food, the influence of competition and evidence of environmental partitioning were assessed. Our results indicate that spotted hyena biomass is more strongly influenced by environmental conditions than lion, with larger hyena populations in areas with warmer winters, cooler summers, less drought, and more semi‐open vegetation cover. Competition was found to have a negligible influence upon spotted hyena and lion populations, and environmental partitioning is suggested, with spotted hyena population biomass greater in areas with more semi‐open vegetation cover. Moreover, spotted hyena is most heavily influenced by the availability of medium‐sized prey biomass, whereas lion is influenced more by large size prey biomass. Given the influences identified upon spotted hyena populations in particular, the results of this study could be used to highlight populations potentially at greatest risk of decline, such as in areas with warming summers and increasingly arid conditions.