Miracle-dz/UniFAR-FTUs huggingface dataset:
Dead-Time Correction Applied for Extended Flux-Based Sensorless Control of Assisted PMSMs in Electric Vehicles

Sensorless control technology of PMSMs is of great importance for safety and reliability in electric vehicles. Among all existing methods, only the extended flux-based method has great performance over all speed range. However, the accuracy and reliability of the extended flux rotor position observer are greatly affected by the dead-time effect. In this paper, the extended flux-based observer is adopted to develop a sensorless control system. The influence of dead-time effect on the observer is analyzed and a dead-time correction method is specially designed to guarantee the reliability of the whole control system. A comparison of estimation precision among the extended flux-based method, the electromotive force (EMF)-based method and the high frequency signal injection method is given by simulations. The performance of the proposed sensorless control system is verified by experiments. The experimental results show that the proposed extended flux-based sensorless control system with dead-time correction has satisfactory performance over full speed range in both loaded and non-loaded situations. The estimation error of rotor speed is within 4% in all working conditions. The dead-time correction method improves the reliability of the control system effectively.
A DC to 220-GHz High-Isolation SPST Switch in 22-nm FDSOI CMOS

A wideband single-pole single-throw (SPST) switch covering the dc to 220-GHz frequency range is presented in this letter. A four-element distributed topology is used to extend operation to the upper millimeter-wave band. With a combination of front-gate and back-gate biasing, unique to fully depleted silicon-on-insulator (FDSOI) MOSFETs, and enabled by the thick metal/dielectric backend of the technology, the switch achieves an insertion loss of 3.1 dB and an isolation of 37 dB at 220 GHz, as well as a peak isolation of 58 dB at 200 GHz, without deembedding the pads. A $G$ -band variable gain low-noise amplifier featuring the SPST switch shows >50-dB gain control range, with a maximum peak gain of 9.5 dB at 190 GHz and a 3-dB bandwidth from 180 to 203 GHz.
Bending of the concentration discharge relationship can inform about
in-stream nitrate removal

Abstract. Nitrate (NO3−) excess in rivers harms aquatic ecosystems and can induce detrimental algae growths in coastal areas. Riverine NO3− uptake is a crucial element of the catchment scale nitrogen balance and can be measured at small spatiotemporal scales while at the scale of entire river networks, uptake measurements are rarely available. Concurrent, low frequency NO3− concentration and stream flow (Q) observations at a basin outlet, however, are commonly monitored and can be analyzed in terms of concentration discharge (C-Q) relationships. Previous studies suggest that more positive log(C)-log(Q) slopes under low flow conditions (than under high flows) are linked to biological NO3− uptake, creating a bent rather than linear log(C)-log(Q) relationship. Here we explore if network scale NO3− uptake creates bent log(C)-log(Q) relationships and when in turn uptake can be quantified from observed low frequency C-Q data. To this end we apply a parsimonious mass balance based river network uptake model in 13 mesoscale German catchments (21–1450 km2) and explore the linkages between log(C)-log(Q) bending and different model-parameter combinations. The modelling results show that uptake and transport in the river network can create bent log(C)-log(Q) relationships at the basin outlet from log-log linear C-Q relationships describing the NO3− land to stream transfer. We find that the bending is mainly shaped by geomorphological parameters that control the channel reactive surface area rather than by the biological uptake velocity itself. Further we show that in this exploratory modelling environment, bending is positively correlated to percentage NO3− load removed in the network (Lr.perc) but that network wide flow velocities should be taken into account when interpreting log(C)-log(Q) bending. Classification trees, finally, can successfully predict classes of low (~ 4 %), intermediate (~ 32 %) and high (~ 68 %) Lr.perc using information on water velocity and log(C)-log(Q) bending. These results can help to identify stream networks that efficiently attenuate NO3− loads based on low frequency NO3− and Q observations and generally show the importance of the channel geomorphology on the emerging log(C)-log(Q) bending at network scales.
Unsupervised automated retinal vessel segmentation based on Radon line detector and morphological reconstruction

Retinal blood vessel segmentation and analysis is critical for the computer-aided diagnosis of different diseases such as diabetic retinopathy. This study presents an automated unsupervised method for segmenting the retinal vasculature based on hybrid methods. The algorithm initially applies a preprocessing step using morphological operators to enhance the vessel tree structure against a non-uniform image background. The main processing applies the Radon transform to overlapping windows, followed by vessel validation, vessel reﬁnement and vessel reconstruction to achieve the ﬁnal segmentation. The method was tested on three publicly available datasets and a local database comprising a total of 188 images. Segmentation performance was evaluated using three measures: accuracy, receiver operating characteristic (ROC) analysis, and the structural similarity index. ROC analysis resulted in area under curve values of 97.39%, 97.01%, and 97.12%, for the DRIVE, STARE, and CHASE-DB1, respectively. Also, the results of accuracy were 0.9688, 0.9646, and 0.9475 for the same datasets. Finally, the average values of structural similarity index were computed for all four datasets, with average values of 0.9650 (DRIVE), 0.9641 (STARE), and 0.9625 (CHASE-DB1). These results compare with the best published results to date, exceeding their performance for several of the datasets; similar performance is found using accuracy.
A Study on the Performance of the ARIMAX-ANN Hybrid Forecasting Model over the other Time Series Forecasting Models ARIMAX and ANN in Forecasting the Rice Yield

Rice (Oryza sativa L.) is the most important cereal crop of the world both in respect to area and production. It is the important staple food for more than 50% of the world population and provides 60-70 per cent body caloric intake to the consumers. Asia is the largest producer and consumer of rice in the entire world. The total Rice production in the world is 501.96 million metric tonnes as estimated by the United states Department of Agriculture in november 2020 (USDA). India ranks second in rice production in the world with the production of 118.0 million metric tonnes where as China ranks first with 149.0 million metric tonnes (Statistica, the statistical portal, 2020). India is a developing country with limited input requirements, soilenriching properties and suitability for growing in areas, rice occupies a unique place in our agriculture system. Rice finds a International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 10 Number 01 (2021) Journal homepage: http://www.ijcmas. com
Influence of the Changes in Land-Use and Land Cover on Temperature over Northern and North-Eastern India

This study explores the influence of land-use and land cover (LULC) changes on the temperature over North India (NI) and North-Eastern India (NEI) during 1981–2006 by subtracting the reanalysis temperature from the observed temperature (observation minus reanalysis (OMR) method). The normalized difference vegetation index (NDVI) data of the AVHRR satellite for the period 1981–2006 were analyzed to understand the type of LULC changes during this period and their linkage with the temperature change over the two regions. The results from OMR indicated that the LULC change over NI during 1981–2006 resulted a warming of 0.03 °C, and that of NEI during this period resulted a cooling of 1.5 °C. The results from LULC changes during the said period indicated an increase of dry land/snow cover and agriculture/fallow land by ~0.1% of total area and a decrease of shrubs/small vegetation and dense forest over NI by about 0.1–0.2%. Over NEI, the areas under agricultural/fallow land, open forest and dense forest showed an increase by about 0.8–2.4% during this period, and the areas under dry/snow cover and shrubs/small vegetation indicated a decrease by ~0.7–3.6%. The comparison between the OMR analysis and LULC changes indicated that the warming over NI during 1981–2006 is due to the expansion of the dry land and the decline of dense forest. On the other hand, the cooling over NEI during the period is attributed to the decline of non-vegetated/small vegetated lands and the expansion of agricultural land/forest covers in that period. This study has an overall implication towards the modeling studies for the impact assessment of LULC changes in the present as well as future climate.
Ethical issues in focus by the autonomous vehicles industry

ABSTRACT The onset of autonomous driving has provided fertile ground for discussions about ethics in recent years. These discussions are heavily documented in the scientific literature and have mainly revolved around extreme traffic situations depicted as moral dilemmas, i.e. situations in which the autonomous vehicle (AV) is required to make a difficult moral choice. Quite surprisingly, little is known about the ethical issues in focus by the AV industry. General claims have been made about the struggles of companies regarding the ethical issues of AVs but these lack proper substantiation. As private companies are highly influential on the development and acceptance of AV technologies, a meaningful debate about the ethics of AVs should take into account the ethical issues prioritised by industry. In order to assess the awareness and engagement of industry on the ethics of AVs, we inspected the narratives in the official business and technical reports of companies with an AV testing permit in California. The findings of our literature and industry review suggest that: (i) given the plethora of ethical issues addressed in the reports, autonomous driving companies seem to be aware of and engaged in the ethics of autonomous driving technology; (ii) scientific literature and industry reports prioritise safety and cybersecurity; (iii) scientific and industry communities agree that AVs will not eliminate the risk of accidents; (iv) scientific literature on AV technology ethics is dominated by discussions about the trolley problem; (v) moral dilemmas resembling trolley cases are not addressed in industry reports but there are nuanced allusions that unravel underlying concerns about these extreme traffic situations; (vi) autonomous driving companies have different approaches with respect to the authority of remote operators; and (vii) companies seem invested in a lowest liability risk design strategy relying on rules and regulations, expedite investigations, and crash/collision avoidance algorithms.
Remaining Useful Life Prediction of IIoT-Enabled Complex Industrial Systems With Hybrid Fusion of Multiple Information Sources

Industrial Internet of Things has significantly boosted predictive maintenance for complex industrial systems, where the accurate prediction of remaining useful life (RUL) with high-level confidence is challenging. By aggregating multiple informative sources of system degradation, information fusion can be applied to improve the prediction accuracy and reduce the uncertainty. It can be performed on the data-level, feature-level, and decision-level. To fully exploit the available degradation information, this article proposes a hybrid fusion method on both the data level and decision level to predict the RUL. On the data level, genetic programming (GP) is adopted to integrate physical sensor sources into a composite health indicator (HI), resulting in an explicit nonlinear data-level fusion model. Subsequently, the predictions of the RUL based on each physical sensor and the developed composite HI are synthesized in the framework of belief functions theory, as the decision-level fusion method. Moreover, the decision-level method is flexible for incorporating other statistical data-driven methods with explicit estimations of the RUL. The proposed method is verified via a case study on NASA’s C-MAPSS data set. Compared to the single-level fusion methods, the results confirm the superiority of the proposed method for higher accuracy and certainty of predicting the RUL.
Tourism and Air Quality: Factors Influencing the Role of Air Quality in Visitors Travel Planning

ABSTRACT Air quality (AQ) is gaining considerable visibility within the tourism sector. However, despite several studies analysing the impact of tourism activities on the AQ of destinations, little is known about the inverse relation. This paper analyses the role of AQ on travel planning and the factors influencing it, specifically the influence of environmentally friendly daily behaviours and concerns with AQ impacts on visitor health. To accomplish this, a questionnaire survey (N = 834) was carried out to visitors. Two Principal Component Analysis (PCA) and two multivariate regression models were used to identify the factors influencing the role of AQ in travel planning. Results reveal that despite AQ not having a substantial role in travel planning of tourism activities, the environmentally friendly daily behaviours of visitors have a positive influence on the role of AQ in travel planning. The paper ends with some theoretical and practical implications.
Winter N2O accumulation and emission in sub-boreal grassland soil depend on clover proportion and soil pH

Inclusion of legume species into grass leys reduces nitrogen (N) fertilizer need but increases the risk of freeze-thaw induced N2O emissions. We investigated how liming and presence of clover affect N2O accumulation under snowpack and its emission during freeze-thaw cycles in autumn and spring under sub-boreal conditions. A field experiment was performed in southern Norway in limed and control plots containing grasses only (fertilized with 270 kg N ha−1 yr−1), a grass-red clover mixture (fertilized with 140 kg N ha−1 yr−1) and unfertilized pure red clover. Soil air samples were collected at 8, 24, and 40 cm depths and analyzed for gas concentrations including N2O, and N2O fluxes measured by a fast-chamber robot. Red clover produced more N2O than the grass-only plots during freeze-thaw cycles in autumn and spring and accumulated more N2O under snow cover (emissions were not measured during this period). Contrary to expectations, limed red clover plots emitted more N2O than control plots during freeze-thaw cycles. Liming reduced subnivean N2O accumulation in grass-only but not in grass-clover or pure clover plots. After spring fertilization, grass-only plots had larger N2O emissions than red clover plots. Our data suggest that winter-sensitive, N-rich clover biomass fuels decomposition and nitrification, thereby increasing NO3 - and depleting O2, resulting in increased N2O emissions from denitrification. Although liming of pure clover leys exacerbated the risk of high N2O emissions during freeze-thaw, this effect was not observed in grass-clover mixtures. Interestingly, grass-clover mixtures also emitted less N2O than expected from their proportions and the emissions recorded in pure grass and clover stands. This warrants further studies into off-season functional diversity effects on N cycling and N2O loss in temperate and boreal forage production.
Estimating individual fish school biomass using digital omnidirectional sonars, applied to mackerel and herring

Economic profitability and responsible fisheries are objectives of fishermen and fisheries managers. In purse seine fisheries, an accurate biomass estimate of the targeted school is crucial to accomplish this. For this study, omnidirectional fisheries sonar was used to estimate individual school biomass of Norwegian spring spawning herring (Clupea harengus) and Atlantic mackerel (Scomber scombrus). A sonar sampling design based on professional skipper’s experience provided detailed information on school dimensions and acoustic backscattering. Using calibrated digital sonar data, school volume and fish densities were obtained, and school biomass computed. A positive linear relation was found between the estimated sonar school biomass and purse seine catches for both species (r2 = 0.92; residual standard error, RSE = 4.7 t). Large variability in volume backscattering coefficient and uncertainty in side-aspect target strength (TS) are the main sources of discrepancy between the estimates and the catch. Using a 4 dB (39%) weaker mean TS for mean side-aspect TS than the normal mean dorsal aspect TS was needed for optimizing the 1:1 relationship between sonar biomass estimate and catch. Accurate estimation of single school biomass can reduce the catch of unexpectedly large schools, leading to improvements in economic efficiency and reduced release of dead or dying fish.
Extraction And Determination Of Antioxidant Activities Of Phenolic Compounds In Defatted Perilla Seed Meal

KURNIA NUR FARIDAH. Extraction and Determination of Antioxidant Activities of Phenolic Compounds in Defatted Perilla Seed Meal. Supervised by HARSI DEWANTARI KUSUMANINGRUM and SIRIRUNG WONGSAKUL. Perilla seed (Perilla frutescens) is an herb that usually used in cooking and traditional medicine. Defatted perilla seed meal (DPSM) is a by-product from extraction of perilla seed oil using screw press. This by-product might contain some bioactive compounds such as polyphenol that can be used as antioxidant. This research was conducted to study the extraction condition of polyphenol from DPSM which extracted using ethanol 70 % and 50 %, acetone 70 % and 50 %, nhexane, and mixture of hexane:ethanol (50:50 v/v). This study also determines the antioxidant activities of DPSM extracts. The proximate analysis showed that raw perilla seed contained 18.84 % crude protein and 28.63 % crude fat, while DPSM contained 27.11 % crude protein and 4.49 % crude fat. DPSM was extracted using solvents with different polarity at various extraction times. The total phenolic compounds of DPSM extracts were measured by Folin-Ciocalteu assays as well as antioxidant activities using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP). Longer extraction time yielded higher TPC and better antioxidant activity. Total phenolic content of DPSM extract which be extracted using acetone 70 % for 90 minutes (1031.99 ± 92.45 μmol GAE/100 ml) resulted in the highest yield. It was not significantly different (p<0.05) from extracting using ethanol 70 % for 90 minutes. While extraction using nhexane for 30 minutes (58.92 ± 7.60) indicated the lowest yield but it was not significantly different from extracting using nhexane for 60 and 90 minutes as well as hexane:ethanol for 30 minutes at p<0.05. Extraction using acetone 70 % for 90 minutes exhibited the high FRAP (1269.44 ± 134.45 μmol AAE/100 ml) and DPPH values (1029.52 ± 7.4 μM Trolox). While nhexane extraction for 30 minutes showed the lowest FRAP (216.20 ± 33.13 μmol AAE/100 ml) and DPPH values (18.48 ± 1.65 μM Trolox). Another polar components and phenolic compounds that have high activity in DPSM extracts can influence the antioxidant capacities. Based on the results, it is likely that hexane or mixture of hexane:ethanol have low yield of phenolic compounds but the antioxidant activities were not always low. When the extraction processes using polar or nonpolar solvent, it is suggested to use another method other than FRAP and DPPH which more suitable to know the antioxidant capacity in DPSM extracts. To optimize the solubility of polar or non-polar compounds of DPSM, the continuous solvents can be used. This research is expected to explore more to know the application of this result into food products.
The Relationship between the Presence of Depression and Attachment Styles of Mothers with Autistic Children

Abstract The objective of this study was to determine the relationship between the presence of depression and various socio-demographic characteristics and attachment styles of mothers with autistic children. The research was descriptive and correlational in design. One hundred fifty-six mothers were included in the study. A sociodemographic characteristics information form, the Experiences in Close Relationship Inventory (ECRI), and the Beck Depression Inventory were used as the data collection tools. The mean depression score of the participating mothers was 10.86 ± 7.92. The study findings showed that the majority of mothers developed preoccupied-style attachment to their children.
Decision Rules in Cost-Utility Analysis of Health Technologies

This review identified 4 distinct decision rules used in the cost-utility analysis of health technologies: threshold incremental cost-effectiveness ratio (ICER), league table, decision making (DM) plane, and linear programming. The threshold ICER is currently the most widely-used approach. However, it comes with certain disadvantages: unrealistic assumptions (perfect divisibility and constant return to scale), arbitrarily-set threshold, and ignorance of opportunity cost. League table involves comparison of several health technologies simultaneously. The issue related to comparability between evaluation of each health technology limits its application. DM plane ensures improvement in health and sufficient resources mostly by disinvestment of currently funded programs. Its major disadvantages are difficulties in identification and disinvestment of such programs. Linear programming is, in theory, the best approach. Ideally, it requires sufficient data from all currently-funded or potential health technologies in conducting the analysis. Hence, the approach is somewhat impractical. Ongoing development in the field of data science and increasing availability of big data might enable its application in the near future. Given the mentioned shortcomings, pragmatic applications of league table and DM plane use them to evaluate competitive treatment programs for single health conditions. Using linear programming in prioritizing health programs was proven possible at the district level.
Interviewers’ perceptions of interviewing Aboriginal Australian children

ABSTRACT Children from Indigenous cultures experience higher rates of abuse than non-Indigenous children, and their cases face extra challenges progressing through the legal system. When abuse is reported, an investigative interview is conducted with the child. The current study aimed to examine interviewers’ perceptions of interviewing Aboriginal Australian children. Sixteen experienced interviewers participated in semi-structured interviews about their experiences interviewing Aboriginal children. Thematic analysis revealed three perceived challenges: engaging the children, communication differences, and obtaining evidential details. Creating strong rapport with the child, simplifying language, and adapting questioning approaches were reported as key strategies to help overcome these challenges, but the interviewers voiced a need for more support and targeted resources to assist them in their roles. This study highlights the need for highly specialist interviewers versed in Aboriginal culture, and the need for future research into the appropriateness of contemporary protocols for children from minority cultural groups.