Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimates of a ‘Pandemic Taylor rule’ loss function

ABSTRACT This article applies a central bank-style loss function to policymaker decision-making during the Covid-19 pandemic. An empirical Taylor rule-style model is used to estimate preference weights associated with lost economic activity and Covid-19 cases and deaths for the four largest US states. Results demonstrate that there are preference differences across states with Republican-led states placing greater weight on economic loss than do those with Democrat governors. Moreover, all states in the sample responded to changes in Covid over the sample period.
Benefit of Introgression Depends on Level of Genetic Trait Variation in Cereal Breeding Programmes

We investigated the benefit from introgression of external lines into a cereal breeding programme and strategies that accelerated introgression of the favourable alleles while minimising linkage drag using stochastic computer simulation. We simulated genomic selection for disease resistance and grain yield in two environments with a high level of genotype-by-environment interaction (G × E) for the latter trait, using genomic data of a historical barley breeding programme as the base generation. Two populations (existing and external) were created from this base population with different allele frequencies for few (N = 10) major and many (N ~ 990) minor simulated disease quantitative trait loci (QTL). The major disease QTL only existed in the external population and lines from the external population were introgressed into the existing population which had minor disease QTL with low, medium and high allele frequencies. The study revealed that the benefit of introgression depended on the level of genetic variation for the target trait in the existing cereal breeding programme. Introgression of external resources into the existing population was beneficial only when the existing population lacked variation in disease resistance or when minor disease QTL were already at medium or high frequency. When minor disease QTL were at low frequencies, no extra genetic gain was achieved from introgression. More benefit in the disease trait was obtained from the introgression if the major disease QTL had larger effect sizes, more selection emphasis was applied on disease resistance, or more external lines were introgressed. While our strategies to increase introgression of major disease QTL were generally successful, most were not able to completely avoid negative impacts on selection for grain yield with the only exception being when major introgression QTL effects were very large. Breeding programmes are advised to carefully consider the level of genetic variation in a trait available in their breeding programme before deciding to introgress germplasms.
TLS-Detectable Plane Changes for Deformation Monitoring

TLS is nowadays often used for deformation monitoring. As it is not able to scan identical points in different time epochs, mathematical models of objects derived from point clouds have to be used. The most common geometric form to describe built objects is a plane, which can be described by four parameters. In this study, we aimed to find out how small changes in the parameters of the plane can be detected by TLS. We aimed to eliminate all possible factors that influence the scanning. Then, we shifted and tilted a finite physical representation of a plane in a controlled way. After each controlled change, the board was scanned several times and the parameters of the plane were calculated. We used two different types of scanning devices and compared their performance. The changes in the plane parameters were compared with the actual change values and statistically tested. The results show that TLS detects shifts in the millimetre range and tilts of 150″ (for a 1 m plane). A robotic total station can achieve twice the precision of TLS despite lower density and slower performance. For deformation monitoring, we strongly recommend repeating each scan several times (i) to check for gross errors and (ii) to obtain a realistic precision estimate.
Field Comparison of Infiltration in Conventional and Alternate Furrow Irrigation under Various Initial and Boundary Conditions

A field study was conducted to evaluate the effect of two‐dimensional infiltration on management of conventional furrow irrigation (CFI) and alternate furrow irrigation (AFI). For this purpose, 48 infiltration tests were carried out in CFI and AFI under two different irrigation intervals (4 and 9 days) with two different water heads (5 and 10 cm). The results indicated that under higher water head and lower initial water content, cumulative infiltration is increased by up to 102 and 62% in CFI and by up to 106 and 54% in AFI, respectively. Moreover, by increasing the water head, the lateral infiltration in CFI and AFI increased by 5 and 4.5 times, respectively. For a lower initial water content, lateral infiltration can increase by up to 96 and 53% in CFI and AFI, respectively. In all cases, cumulative and lateral infiltrations in AFI were greater than in CFI. Furthermore, the relative lateral infiltration in AFI was more than CFI. In both CFI and AFI, the relative lateral infiltration increased for increased water head and extended irrigation intervals. Application of AFI with higher water heads and extended irrigation intervals can lead to increased lateral infiltration and decreased deep percolation, which will ultimately result in improved irrigation efficiency. Copyright © 2017 John Wiley & Sons, Ltd.
Development and validation of RP-HPLC method for the analysis of Cobicistat and related impurities in bulk and pharmaceutical dosage forms

The prime aim of the current work is to develop and validate a novel, specific, sensitive, precise, rapid and faster isocratic elution, RP HPLC method for estimation of Cobicistat and related impurities in bulk and pharmaceutical dosage forms. Chromatographic separation was achieved on Inertsil ODS – 3V column (250 mm X 4.6 mm, 5µ) using an isoratic mode with mobile phase composed of potassium dihydrogen phosphate buffer (PH 2.5) and acetonitrile in the ratio of 30:70 v/v. The flow rate was 1.0ml/min, temperature is maintained at ambient and detection was made at 240 nm. The run time was 15 min. The developed method was validated according to the ICH guide lines and different analytical parameters such as linearity, precision, accuracy, specificity, limit of detection, limit of quantitation were determined. The linearity of calibration curve for each analyte in concentration range of 400 µg/ml -1200µg/ml. is good. There exists good correlation between peak area and analyte concentration. Relative standard deviation values for cobicistat is 0.099 and impurity is 0.6636. LOD for drug and impurity is 0.02 % and 0.20 % respectively. LOQ for drug and impurity is 0.06 % and 0.60% respectively. Hence the proposed method is highly sensitive, precise, accurate, robust and fast. The short retention time allows the analysis of large number of samples in short period of time and it is cost effective, so it can be successfully applied for routine analysis of active pharmaceutical ingredient and related impurities in bulk and pharmaceutical dosage forms.
VEHICLE NAVIGATION USING ADVANCED OPEN SOURCE COMPUTER VISION

The current operational transport and vehicle systems consist of vehicles running on fossil fuels or battery powered systems. The navigation requires control by a human driver who is responsible for a safe and comfortable journey from one place to another. However, with human intervention there are several drawbacks that may lead to a poor performance by the system. Negligence in driving leading to fatal accidents, environmental damage, infrastructure damage and destruction, health problems due to constrained sitting postures, long duration of operation and several others, have motivated researchers to look for solutions that will automate the driving process. Considering all these shortcomings of current systems, the new research consists of the use of self driving cars for transport and navigation. The complexity of this problem was seen when the initial systems were built using machine learning techniques that tried to understand and model the dynamic nature of the environment. As the research progressed, we realized that the system must be trained to respond to a number of unpredictable situations such as rain, snow, lightning, oil spills, potholes, passerby pedestrians and animals, approaching vehicles and many more. We need to consider all these aspects before a fully functional real-time system can be used. We consider the problem of autonomous vehicle by focusing on three major aspects of any self driving car which form the foundation of the entire system. Firstly, we need to be able to detect the lane lines so that our vehicle can orient itself correctly and continue to follow a safe path while being aware of the dynamic environment. Further, it needs to know its departure from the center of the lane in the scenario that it needs to move in order to avoid potholes or other road obstacles.
The Microbiological Drivers of Temporally Dynamic Dimethylsulfoniopropionate Cycling Processes in Australian Coastal Shelf Waters

The organic sulfur compounds dimethylsulfoniopropionate (DMSP) and dimethyl sulfoxide (DMSO) play major roles in the marine microbial food web and have substantial climatic importance as sources and sinks of dimethyl sulfide (DMS). Seasonal shifts in the abundance and diversity of the phytoplankton and bacteria that cycle DMSP are likely to impact marine DMS (O) (P) concentrations, but the dynamic nature of these microbial interactions is still poorly resolved. Here, we examined the relationships between microbial community dynamics with DMS (O) (P) concentrations during a 2-year oceanographic time series conducted on the east Australian coast. Heterogenous temporal patterns were apparent in chlorophyll a (chl a) and DMSP concentrations, but the relationship between these parameters varied over time, suggesting the phytoplankton and bacterial community composition were affecting the net DMSP concentrations through differential DMSP production and degradation. Significant increases in DMSP were regularly measured in spring blooms dominated by predicted high DMSP-producing lineages of phytoplankton (Heterocapsa, Prorocentrum, Alexandrium, and Micromonas), while spring blooms that were dominated by predicted low DMSP-producing phytoplankton (Thalassiosira) demonstrated negligible increases in DMSP concentrations. During elevated DMSP concentrations, a significant increase in the relative abundance of the key copiotrophic bacterial lineage Rhodobacterales was accompanied by a three-fold increase in the gene, encoding the first step of DMSP demethylation (dmdA). Significant temporal shifts in DMS concentrations were measured and were significantly correlated with both fractions (0.2–2 μm and >2 μm) of microbial DMSP lyase activity. Seasonal increases of the bacterial DMSP biosynthesis gene (dsyB) and the bacterial DMS oxidation gene (tmm) occurred during the spring-summer and coincided with peaks in DMSP and DMSO concentration, respectively. These findings, along with significant positive relationships between dsyB gene abundance and DMSP, and tmm gene abundance with DMSO, reinforce the significant role planktonic bacteria play in producing DMSP and DMSO in ocean surface waters. Our results highlight the highly dynamic nature and myriad of microbial interactions that govern sulfur cycling in coastal shelf waters and further underpin the importance of microbial ecology in mediating important marine biogeochemical processes.
Osteoconductive Silk Fibroin Binders for Bone Repair in Alveolar Cleft Palate: Fabrication, Structure, Properties, and In Vitro Testing

Osteoconductive silk fibroin (SF) binders were fabricated for the bone repair of an alveolar cleft defect. Binders were prefigureared by mixing different ratios of a mixture of random coils and SF aggregation with SF fibrils: 100:0 (SFB100), 75:25 (SFB75), 50:50 (SFB50), 25:75 (SFB25), and 0:100 (SFB0). The gelation, molecular organization, structures, topography, and morphology of the binders were characterized and observed. Their physical, mechanical, and biological properties were tested. The SF binders showed gelation via self-assembly of SF aggregation and fibrillation. SFB75, SFB50, and SFB25 had molecular formation via the amide groups and showed more structural stability than SFB100. The morphology of SFB0 demonstrated the largest pore size. SFB0 showed a lowest hydrophilicity. SFB100 showed the highest SF release. SFB25 had the highest maximum load. SFB50 exhibited the lowest elongation at break. Binders with SF fibrils showed more cell viability and higher cell proliferation, ALP activity, calcium deposition, and protein synthesis than without SF fibrils. Finally, the results were deduced: SFB25 demonstrated suitable performance that is promising for the bone repair of an alveolar cleft defect.
Ultra‐Broadband Wide‐Angle Polarization‐Independent Enhanced Absorber with Ultraviolet to Far‐Infrared Absorption Performance

Ultra‐thin broadband metamaterial absorbers exhibit high absorptivity and broad bandwidths. Herein, an ultra‐broadband wide‐angle polarization‐independent enhanced absorber with a metal–insulator–metal (MIM) structure is proposed and simulated. The enhanced absorber exhibits ultra‐wideband characteristics from ultraviolet to far‐infrared range (0.35–10 μm), where it achieves an average absorption > 96%. It is insensitive to variations in the polarization state, exhibits a wide‐angle absorption because of a high degree of geometric symmetry, and shows a satisfactory absorption performance for a combination of different materials. Thus, the distinct structural characteristics of the device play a pivotal role in absorption. These advantages improve the flexibility of material selection in the design and manufacturing process of ultra‐broadband enhanced absorbers, thereby broadening their application potential in solar energy harvesting, infrared detectors, and thermal imaging.
Effects of Group-Play Moderate to Vigorous Intensity Physical Activity Intervention on Executive Function and Motor Skills in 4- to 5-Year-Old Preschoolers: A Pilot Cluster Randomized Controlled Trial

The aim of the present study is to examine the effect of group-play intervention on executive function (EF) in preschoolers. This group-play intervention was integrated as moderate to vigorous physical activity and cognitively loaded exercise to promote EF in preschoolers. An 8-week group-play MVPA intervention program, consisting of a series of outdoor physical and cognitively loaded games, was designed to improve preschoolers’ EF. This intervention program was implemented in group-play form, and conducted by teachers who received standardized training before the intervention. Two classes of second grade preschoolers (N = 62) were randomly allocated to experimental (n = 30, Mage = 4.16, SD = 0.29) and control (n = 32, Mage = 4.7, SD = 0.43) groups. The intervention group received the intervention three times a week, while the control group exercised as usual in preschool. Before, in the middle of, and after the intervention, 10-m running, standing broad jump, throwing, body flexion, balance beam, and skip jump were assessed as tests of motor skills. In addition, three components of EF were measured separately before, in the middle of, and after the intervention: inhibitory control was assessed by using the silly sound Stroop task, working memory was tested using the empty house task, and shifting was assessed using the dimensional change card sorting task. Although both groups showed an increasing trend in terms of motor skills and EF during the intervention, the increasing amounts of the intervention group were significantly higher than the control group. The findings of the present study suggested that group-play intervention has positive effects on aspects of EF in addition to motor skills in preschoolers.
Machine detector interface for the $e^+e^-$ future circular collider

The international Future Circular Collider (FCC) study aims at a design of $pp$, $e^+e^-$, $ep$ colliders to be built in a new 100 km tunnel in the Geneva region. The $e^+e^-$ collider (FCC-ee) has a centre of mass energy range between 90 (Z-pole) and 375 GeV (tt_bar). To reach such unprecedented energies and luminosities, the design of the interaction region is crucial. The crab-waist collision scheme has been chosen for the design and it will be compatible with all beam energies. In this paper we will describe the machine detector interface layout including the solenoid compensation scheme. We will describe how this layout fulfills all the requirements set by the parameters table and by the physical constraints. We will summarize the studies of the impact of the synchrotron radiation, the analysis of trapped modes and of the backgrounds induced by single beam and luminosity effects giving an estimate of the losses in the interaction region and in the detector.
Application and Evaluation of Sports Event Management Method Based on Recurrent Neural Network

The methods of sports event management are the life cycle management method, which can be divided into four stages: start-up planning, plan preparation, real-time control, and followup evaluation. The administrative measures management law includes administrative orders, instructions, regulations, and systems of administrative organizations at all levels. The system management law has four characteristics: mandatory, authoritative, stable, and preventive. The current management mode of sports events is mainly that the government is deeply involved in the operation of the entire event, and relevant personnel are selected from various government departments to form an organizing committee, and the resources related to the event are controlled and regulated by the government. However, in our country, there is currently no unified sports event management standard. Therefore, this paper proposes the application and evaluation of sports event management method based on recurrent neural network (RNN). The comments of sports events are extracted from the network, classified with an RNN, and finally, an improvement plan is obtained through evaluation. The main work of this paper is as follows: (1) the development status of sports event management at home and abroad and the application of RNN are introduced, and RNN is used in the evaluation and classification of sports event management. (2) We propose a sentiment classification model GCNN-GRU that fuses local feature extraction. Aiming at the defect that the basic model is easy to lose key phrase information, a CNN with a gating mechanism is used to extract and filter local features. The classification experiment results show that the proposed GCNN-GRU has the best classification effect on the Chinese sentiment dataset.
TOMATO FRUIT QUALITY BETWEEN CLUSTERS IS DIFFERENTIALLY AFFECTED BY NITROGEN AND POTASSIUM SUPPLY

Fruit quality is significantly affected by the nutrient status of plants; nonetheless, it has not yet been deeply explored how nitrogen (N) and potassium (K) supply affects tomato fruit quality between clusters during the vegetative and reproductive stages of the crop. The aim of this study was to evaluate the effects of N and K on fruit quality parameters of three clusters of hydroponically grown tomato (Solanum lycopersicum L.). Nitrogen (10, 12, 14 and 16 molc N m-3) was evaluated at the vegetative stage, and potassium (5, 7, 9, 11 and 13 molc K m-3) at the reproductive stage. The evaluated N and K concentrations showed significant main effects on the quality attributes total soluble solids, citric acid percentage, reducing sugars, color and lycopene concentration; yield was affected only by K, while the effects of N × K interaction were not significant. Increasing N concentrations from 10 to 16 molc m-3 enhanced hue angle, brightness, reducing sugars and citric acid percentage, but decreased total soluble solids and lycopene. As K increased from 5 to 13 molc m-3 in the nutrient solution, total soluble solids, citric acid, reducing sugars, lycopene, yield and fruit brightness increased, but hue decreased. Total soluble solids, reducing sugars and lycopene gradually increased from the first to the fifth cluster, while yield, color attributes of hue and brightness tended to decrease as the crop cycle progressed. Titratable acidity percentage showed no clear trends between clusters. It is concluded that N and K differentially affect tomato quality between clusters.
Perpetual Sensing: Experiences with Energy-Harvesting Sensor Systems

Industry forecasts project the number of connected devices will outpace the global population by orders of magnitude in the next decade or two. These projections are application driven: smart cities, implantable health monitors, responsive buildings, autonomous robots, driverless cars, and instrumented infrastructure are all expected to be drivers for the growth of networked devices. Achieving this immense scale—potentially trillions of smart and connected sensors and computers, popularly called the “Internet of Things”—raises a host of challenges including operating system design, networking protocols, and orchestration methodologies. However, another critical issue may be the most fundamental: If embedded computers outnumber people by a factor of a thousand, how are we going to keep all of these devices powered? In this dissertation, we show that energy-harvesting operation, by which devices scavenge energy from their surroundings to power themselves after they are deployed, is a viable answer to this question. In particular, we examine a range of energy-harvesting sensor node designs for a specific application: smart buildings. In this application setting, the devices must be small and sleek to be unobtrusively and widely deployed, yet shrinking the devices also reduces their energy budgets as energy storage often dominates their volume. Additionally, energy-harvesting introduces new challenges for these devices due to the intermittent access to power that stems from relying on unpredictable ambient energy sources. To address these challenges, we present several techniques for realizing effective sensors despite the size and energy constraints. First is Monjolo, an energy metering system that exploits rather than attempts to mask the variability in energy-harvesting by using the
Self-injection Locked Time Domain Mode-locked Optoelectronic Oscillator

A high-quality frequency comb generator based on self-injection locked time domain mode-locked optoelectronic oscillator (SIL-TDML-OEO) is demonstrated. The scheme applies a self-injection locked TDML-OEO structure, in which a long optical fiber delay loop without oscillation is used to inject-locked the TDML-OEO. The spur suppression ratio (SSR) of the generated frequency comb was obviously enhanced by ~21 dB under SIL. The phase noise of the proposed SIL-TDML-OEO is -125.86 dBc/Hz@10 kHz which is improved by ~7.91 dB compared with that of free-running TDML-OEO. In addition, the tunability of central frequency of the frequency comb generator is also discussed.
Wear Analysis of Wind Turbine Bearings

Bearings are among the key mechanical components for conversion from wind to electrical energy in wind turbines, but their reliability are affected by the external and internal environment. Wear is among the most frequent cause of failure of wind turbine tribological components such as bearings. In this paper, a fault tree analysis (FTA) is conducted for bearing failures due to wear. Influence of material bearing and its place of installation have been investigated. Occurrence of different wear types and failure detection in bearings are studied.