bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices
Authors: Lassalle Guillaume , Fabre Sophie , Credoz Anthony , Hédacq Rémy , Dubucq Dominique , Elger Arnaud 
Published: January 2021

AbstractMonitoring plant metal uptake is essential for assessing the ecological risks of contaminated sites. While traditional techniques used to achieve this are destructive, Visible Near-Infrared (VNIR) reflectance spectroscopy represents a good alternative to monitor pollution remotely. Based on previous work, this study proposes a methodology for mapping the content of several metals in leaves (Cr, Cu, Ni and Zn) under realistic field conditions and from airborne imaging. For this purpose, the reflectance of Rubus fruticosus L., a pioneer species of industrial brownfields, was linked to leaf metal contents using optimized normalized vegetation indices. High correlations were found between the vegetation indices exploiting pigment-related wavelengths and leaf metal contents (r ≤ − 0.76 for Cr, Cu and Ni, and r ≥ 0.87 for Zn). This allowed predicting the metal contents with good accuracy in the field and on the image, especially Cu and Zn (r ≥ 0.84 and RPD ≥ 2.06). The same indices were applied over the entire study site to map the metal contents at very high spatial resolution. This study demonstrates the potential of remote sensing for assessing metal uptake by plants, opening perspectives of application in risk assessment and phytoextraction monitoring in the context of trace metal pollution.
Connections of climate change and variability to large and extreme forest fires in southeast Australia
Authors: Abram Nerilie J. , Henley Benjamin J. , Sen Gupta Alex , Lippmann Tanya J. R. , Clarke Hamish , Dowdy Andrew J. , Sharples Jason J. , Nolan Rachael H. , Zhang Tianran , Wooster Martin J. , Wurtzel Jennifer B. , Meissner Katrin J. , Pitman Andrew J. , Ukkola Anna M. , Murphy Brett P. , Tapper Nigel J. , Boer Matthias M. 
Published: January 2021

AbstractThe 2019/20 Black Summer bushfire disaster in southeast Australia was unprecedented: the extensive area of forest burnt, the radiative power of the fires, and the extraordinary number of fires that developed into extreme pyroconvective events were all unmatched in the historical record. Australia’s hottest and driest year on record, 2019, was characterised by exceptionally dry fuel loads that primed the landscape to burn when exposed to dangerous fire weather and ignition. The combination of climate variability and long-term climate trends generated the climate extremes experienced in 2019, and the compounding effects of two or more modes of climate variability in their fire-promoting phases (as occurred in 2019) has historically increased the chances of large forest fires occurring in southeast Australia. Palaeoclimate evidence also demonstrates that fire-promoting phases of tropical Pacific and Indian ocean variability are now unusually frequent compared with natural variability in pre-industrial times. Indicators of forest fire danger in southeast Australia have already emerged outside of the range of historical experience, suggesting that projections made more than a decade ago that increases in climate-driven fire risk would be detectable by 2020, have indeed eventuated. The multiple climate change contributors to fire risk in southeast Australia, as well as the observed non-linear escalation of fire extent and intensity, raise the likelihood that fire events may continue to rapidly intensify in the future. Improving local and national adaptation measures while also pursuing ambitious global climate change mitigation efforts would provide the best strategy for limiting further increases in fire risk in southeast Australia.
A Kalman‐filter‐based fusion method for accurate urban localisation
Authors: Alfakih Marwan , Keche Mokhtar , Benoudnine Hadjira 
Published: January 2021

AbstractThe outage and degradation of the global navigation satellite system (GNSS) signals caused by the multipath phenomena reduce the location accuracy of these systems in urban environment. Hence, integrating an additional localisation technology with the GNSS, so that each technology complements the weakness of the other one, is an efficient solution to improve this accuracy. The widespread availability of the Wi‐Fi technology makes it the most appropriate additional technology. In this work, a fusion algorithm based on a Kalman filter is used to integrate the GPS localisation with Wi‐Fi fingerprinting localisation in urban environment. The fusion algorithm uses the positions delivered by these two systems to achieve an accurate estimation of the mobile position. The experimental results show that the performance of the proposed fusion method is more accurate than those of the individual methods and other fusion methods from the literature.
Real‐time long‐term tracking with reliability assessment and object recovery
Authors: Liu Jun , Ye Ping , Zhang Xingchen , Xiao Gang 
Published: January 2021

AbstractIn recent years, many visual tracking algorithms based on discriminative correlation filters have been proposed and proved to be successful in short‐term tracking. However, most algorithms do not handle long‐term tracking well due to factors such as occlusion and deformation. The aim of this paper is to propose a long‐term tracking method with reliability assessment and object recovery. First, the relationship between the overlap rate and the response value is extensively studied, and then from the perspective of the time axis, the tracking process is evaluated by the fluctuation trend of the continuous response value. In the object recovery mechanism, we propose to alternately use local search and global search to improve the efficiency of detection. To this end, a sliding window is designed for cyclic shifting in the local region to achieve dense sampling within the region of interest, and EdgeBox is used in the global search to achieve target detection. Further, flexible switching between local search and global search is achieved by the difference in displacement of the object. Extensive experimental results on several benchmark datasets demonstrate that the proposed long‐term tracker can achieve state‐of‐the‐art accuracy with real‐time speed of about 27 frames per second.
A hybrid feature descriptor with Jaya optimised least squares SVM for facial expression recognition
Authors: Kar Nikunja Bihari , Nayak Deepak Ranjan , Babu Korra Sathya , Zhang Yu‐Dong 
Published: January 2021

AbstractFacial expression recognition has been a long‐standing problem in the field of computer vision. This paper proposes a new simple scheme for effective recognition of facial expressions based on a hybrid feature descriptor and an improved classifier. Inspired by the success of stationary wavelet transform in many computer vision tasks, stationary wavelet transform is first employed on the pre‐processed face image. The pyramid of histograms of orientation gradient features is then computed from the low‐frequency stationary wavelet transform coefficients to capture more prominent details from facial images. The key idea of this hybrid feature descriptor is to exploit both spatial and frequency domain features which at the same time are robust against illumination and noise. The relevant features are subsequently determined using linear discriminant analysis. A new least squares support vector machine parameter tuning strategy is proposed using a contemporary optimisation technique called Jaya optimisation for classification of facial expressions. Experimental evaluations are performed on Japanese female facial expression and the Extended Cohn–Kanade (CK+) datasets, and the results based on 5‐fold stratified cross‐validation test confirm the superiority of the proposed method over state‐of‐the‐art approaches.
Intelligent IoT systems for traffic management: A practical application
Authors: Guillen‐Perez Antonio , Cano Maria‐Dolores 
Published: January 2021

AbstractThe incorporation of Artificial Intelligence algorithms in Intelligent Transportation Systems gives rise to new opportunities for a more sustainable urban mobility. However, one of the main challenges is to decide when and where these techniques should be applied; several options appear, such as cloud computing, fog computing, edge computing, or even edge devices. In this paper, an Internet of Things‐based solution for smart traffic management is presented. Using the lightweight Random Early Detection for Vehicles Dynamic mechanism as a basis, we optimize using evolutionary algorithms. Random Early Detection for Vehicles Dynamic can be applied in signaled intersections to proactively detect incipient congestion and set the best cycle and phases of traffic lights. Then, the authors demonstrate that once Random Early Detection for Vehicles Dynamic has been appropriately optimised offline, it can be later used in unknown traffic scenarios without the burden of applying Artificial Intelligence in constrained Internet of Things devices. The performance of this mechanism is widely tested with the SUMO simulation tool. Results show that this improved version, called iREDVD, notably reduces the vehicles’ waiting time, average trip time, fuel consumption, and emission of particles and gaseous pollutants compared with other proposals.
Physical and Mechanical Properties of Resins Blends Containing a Monomethacrylate with Low-polymerization Shrinkage
Authors: Martins Aurealice Rosa Maria , Machado-Santos Luciana , Grassia Regis Cleo Fernandes , Vitti Rafael Pino , Sinhoreti Mário Alexandre Coelho , Brandt William Cunha 
Published: January 2021

Abstract
Objectives The aim of this study was to evaluate the Knoop hardness (KH), cross-link density (CLD), water sorption (WS), water solubility (WSB), and volumetric shrinkage (VS) of experimental resins blends containing a monomethacrylate with low-polymerization shrinkage.
Materials and Methods A blend of bisphenol glycidyl methacrylate (BisGMA) as base monomer was formulated with (Bis-GMA)/triethyleneglycol dimethacrylate (TEGDMA), Bis-GMA/isobornyl methacrylate (IBOMA), or Bis-GMA/TEGDMA/IBOMA in different concentrations (40, 50, or 60 wt%). The camphorquinone (CQ)/2-(dimethylamino) ethyl methacrylate (DMAEMA) was used as the photoinitiator system. The KH and CLD were measured at the top surface using an indenter. For WS and WSB, the volume of the samples was calculated in mm3. The samples were transferred to desiccators until a constant mass was obtained (m1) and were subsequently immersed in distilled water until no alteration in mass was detected (m2). The samples were reconditioned to constant mass in desiccators (m3). WS and WSB were determined using the equations m2 − m3/V and m1 − m3/V, respectively. VS results were calculated with the density parameters before and after curing.
Statistical Analysis Data were submitted to ANOVA and Tukey’s test (α = 0.05).
Results The resins containing IBOMA showed lower VS results. TEGDMA 40% and TEGDMA/IBOMA 20/20 wt% showed higher KH values. The IBOMA groups showed lower CLD, while TEGDMA groups had higher values of CLD. The BisGMA/TEGDMA resin presented the highest values of WS, and for WSB, all groups showed no significant differences among themselves.
Conclusion The monomethacrylate with low-polymerization shrinkage IBOMA used alone or in combination with TEGDMA may decrease VS, WS, and CLD values.
Continuous Positive Airway Pressure-Mandibular Advancement Device Combination Therapy for Moderate-to-Severe Obstructive Sleep Apnea: A Preliminary Study
Authors: Premaraj Thyagaseely Sheela , Stadiem Jacob , Premaraj Shyamaly Arya , Davies Charles R. , Dennis Matthew , Harrington John J. 
Published: January 2021

Abstract
Objectives The purpose of this pilot study was to determine whether compliance to auto-adjusting positive airway pressure (APAP) improves with the addition of a mandibular advancement device (MAD). Secondary outcome measures included were APAP pressure, subjective daytime sleepiness, apnea–hypopnea index (AHI), and mask leaks.
Setting and Sample Population Participants included were diagnosed with moderate-to-severe obstructive sleep apnea (OSA) and became noncompliant to prescribed APAP. Thirteen participants with a mean age of 61.6 years were recruited for this study.
Materials and Methods All participants were given a MAD to use with their APAP. Parameters measured included APAP pressure, AHI, mask leak reported via ResMed AirViewTM software, and self-reported daytime sleepiness (Epworth Sleepiness Scale [ESS]). A paired two-sample for mean t-test was performed to determine significance.
Results The mean difference of pre- and postintervention APAP compliance was 23.1%, which was statistically significant (p = 0.015). The mean APAP air pressures were unchanged. The difference between pre- and postintervention mean ESS scores was 1.4 and was statistically significant (p = 0.027). The mean difference between pre- and postintervention AHI values and mask leak showed no significant difference.
Conclusion This study showed that combination of APAP-MAD therapy, for patients with moderate-to-severe OSA who were noncompliant to APAP use, significantly increased compliance with APAP therapy, and significantly decreased the daytime sleepiness of participants.
A Study on the Effect of Alcoholism on the Family Members of Alcoholic Patients
Authors: Chinnusamy Manokaran , Eugin Priscilla Rubavathy , Janakiraman Sathiyanarayanan 
Published: January 2021

Abstract
Introduction Alcohol use disorder is commonly known as “alcoholism” but is also known as a family disorder, not because it runs in families but because it tends to impact the family members more than just the individual. Alcoholism is one of the leading causes of marital problems, divorce, domestic violence, child abuse and neglect, and strained relationships between the partners and also between the parent and child. The per capita alcohol consumption has increased from 2.2 L (2005) to 2.7 L (2016) and is expected to increase by another 2.2 L by 2025. According to the World Health Organization, 55% of domestic violence is related to alcohol abuse by the partner. This study aims to identify the impact of alcoholism on the individual’s health as well as the family members depending on the level of alcohol dependence.
Materials and Methods A cross-sectional study was conducted among patients admitted in the ward with a history of alcohol intake. AUDIT-C (Alcohol Use Disorders Identification Test–C) questionnaire was used to detect dependence and also a structured questionnaire was used to gather information regarding the financial and psychological well-being of the family from the family member accompanying the person. Other parameters of the patient were also considered such as liver function test, abdomen ultrasonography, and upper gastrointestinal endoscopy, wherever available.
Results A total of 100 patients were included in the study; 59 patients were admitted due to various reasons secondary to alcohol intake. A score of >4 was suggestive of alcohol dependence: 73% (73) of the participants had a score of >4; 65% of the participants’ family members gave a positive history of impaired interpersonal relationships; 45% gave a positive history of battering; and 69% had mental health issues such as depression, lack of confidence, inferiority complex, and decreased sleep. Also, 51% (51) gave a positive history of financial contribution by the participant toward the family expenses.
Conclusion The study shows that the problems of alcohol abuse have been associated with increased suffering to the family members, which contributes to a high level of interpersonal conflict, domestic violence, financial difficulties, and psychological disturbances, along with increasing the risk of developing comorbidities associated with alcohol abuse by the individual, which further adds to burden of the family.
Predicting urban innovation from the US Workforce Mobility Network
Authors: Bonaventura Moreno , Aiello Luca Maria , Quercia Daniele , Latora Vito 
Published: January 2021

AbstractWhile great emphasis has been placed on the role of social interactions as a driver of innovation growth, very few empirical studies have explicitly investigated the impact of social network structures on the innovation performance of cities. Past research has mostly explored scaling laws of socio-economic outputs of cities as determined by, for example, the single predictor of population. Here, by drawing on a publicly available dataset of the startup ecosystem, we build the first Workforce Mobility Network among metropolitan areas in the US. We found that node centrality computed on this network accounts for most of the variability observed in cities’ innovation performance and significantly outperforms other predictors such as population size or density, suggesting that policies and initiatives aiming at sustaining innovation processes might benefit from fostering professional networks alongside other economic or systemic incentives. As opposed to previous approaches powered by census data, our model can be updated in real-time upon open databases, opening up new opportunities both for researchers in a variety of disciplines to study urban economies in new ways, and for practitioners to design tools for monitoring such economies in real-time.
Lateral variations of the surface electric potential and elastic stiffness of ultrathin Hf0.5Zr0.5O2 films on silicon
Authors: Bolotov Leonid , Uchida Noriyuki , Migita Shinji 
Published: January 2021

Lateral variations of the surface electric potential and the elastic properties of ultrathin HfxZr1−xO2 films have strong impact on the performance of lead-free ferroelectric devices. Here, we compared lateral uniformity of electric and elastic properties of polycrystalline thin Hf0.55Zr0.45O2 films (10 nm–50 nm) prepared by sputtering on Si. Crystallization of 10-nm-thick films at 600° C and 700 °C resulted in predominantly crystal grains with the orthorhombic and tetragonal phases. Scanning probe microscopy methods including the Kelvin potential microscopy and the force modulation microscopy were employed to investigate the material properties at the nanoscale. Lateral variation of the elastic stiffness and the surface electric potential corresponded to the lateral dimension of grains in pristine films. Point sub-100-nm capacitors formed with a Pt-coated cantilever as a moving electrode showed the ferroelectric behavior. The position-dependent polarization switching and a relative stability of the crystal phases at 1 MV/cm–2 MV/cm were observed in the annealed films. The absence of the monoclinic phase, minimal surface roughness, uniformity of the electric potential, and high elastic modulus made 10-nm Hf0.55Zr0.45O2 films annealed at 600 °C–700 °C in nitrogen as the appealing material for applications in scaled ferroelectric devices.
Mössbauer studies on magnetism in FeSe
Authors: Choi Hyunkyung , Seo Jae Yeon , Uhm Young Rang , Sun Gwang Min , Kim Chul Sung 
Published: January 2021

Iron selenide (FeSe) was used to investigate magnetic properties by using Mössbauer spectroscopy. The crystalline structure of the sample was found to be tetragonal and hexagonal with a 3c structure. The temperature-dependent magnetic susceptibility curve under 100 Oe confirmed the spin rotation temperature TS = 150 K. Based on the applied field dependent magnetization measurements up to 15 kOe at 295 K, the saturation magnetization and coercivity were found to be 8.03 emu/g and 357.40 Oe, respectively. The spin rotation process of the sample from the dependence temperature ZFC-FC curves occurs at approximately TS. The Mössbauer spectra below the Néel temperature (TN) were fitted with a doublet for the tetragonal phase and three sextets (A, B, and C sites) for the hexagonal phase. The spectrum was fitted to a single line at TN = 500 K. We also observed abrupt changes in Hhf and ΔEQ at the spin rotation temperature. The Fe charge states in the tetragonal and hexagonal phases are found to be ferric and highly covalent ferrous ion (or high-spin ferric), respectively.
Key-Value Combination for Scholarship Disbursal Analysis through Map-Reduce
Authors: Pandey Rajiv , Sahai Archana , Nagpal Namrata 
Published: January 2021

Abstract
Big Data includes both structured and non-structured contents. Big data is being generated by devices and increasing in volumes. The social and digital landscape produces this colossal data, although devices, handheld, and IoT devices add to the size. Data set with respect to a Central University scholarship domain in a country like India can make the dataset still larger and complex. The complex dataset in the coming years is sure to certify as Big data in terms of velocity, volume, variety, veracity, and value, thus various Bigdata analytical tools are needed for analytics of this huge dataset. The purpose of research on such a dataset is to retrieve meaningful information that facilitates decision-makers for quick and flawless decision making and redirecting scholarships to the deserving candidates across the country. This paper explores the storage of big data and map reducing through a key-value analysis for scholarship disbursal decision making thus catalyzing Digital India vision.
Implementation of Demand Forecasting – A Comparative Approach
Authors: Gupta Punit , Ladia Harshit , Kakkar Kabir , Rai Kriti , Agrawal Yogesh , Mamgain Rishika , Gaur Navaditya 
Published: January 2021

Abstract
Forecasting is a crucial factor in development of store head businesses either commercial or economical that require frontend sales to outgrow. Demand forecasting hence deals with providing our stores a significant amount of backup supply prediction which hence can deal with the supply to meet the ups and downs of our demand. Hence, a predictive model has been developed to maintain the supply of goods and featuring the predictions of upcoming demand for the next few selected time period. The model involves being highly trained in the field of Machine Learning using various predefined models such as Linear Regression, GBT Model and even using the time series analysis for the defined period and using the outcome from the factor of inputs provided by the user to finally predict the upcoming demand and provide (increase or decrease) the given supply in the specific field, thereby saving the businesses from any uncertain downhill and hence helping analytics to provide more reasonable theories for increasing the profit margin in the upcoming foreseeable future and even get a track record and deep insights of the previous demand using a business intelligence software, like PowerBI.
Contactless Attendance Management System using Artificial Intelligence
Authors: Rajamanogaran M , Subha S , Baghavathi Priya S , Sivasamy Jeevitha 
Published: January 2021

Abstract
Attendance monitoring is one of the vital administrative processes in all educational institutions and organizations. A well-structured system will enable the institutions to have an increasing growth. It helps the students and teachers in all ways to have a good progress in the attendance, thereby reducing the teachers’ time and effort. In physical classrooms, traditional method of calling the students names and marking their presence/absence is the routine process followed regularly. Attendance monitoring system is proposed using artificial intelligence. Firstly, a database containing the facial images of the students in a particular class is constructed. Knowledge gained using Convolutional Neural Network (CNN) is reused in a perfect manner using transfer learning. This system is designed to improve the students’ engagement time inside the classroom, to communicate to the parents frequently, to avoid proxy attendance and to generate detailed reports for future reference.