bluuebunny/crossref_metadata_2025 huggingface dataset:
Inductive effect as a universal concept to design efficient catalysts for CO2electrochemical reduction: electronegativity difference makes a difference
Authors: Chen Huihuang , Fu Weng , Geng Zhigang , Zeng Jie , Yang Bo
Published: January 2021

A schematic illustration of various electrocatalysts associated with the inductive effect and construction methods of the inductive effect, including heteroatom substitution, vacancy introduction and synergy construction.
A cation selective separator induced cathode protective layer and regulated zinc deposition for zinc ion batteries
Authors: Wu Buke , Wu Yize , Lu Zhendong , Zhang Jianshuo , Han Ning , Wang Yameng , Li Xiao-min , Lin Meng , Zeng Lin
Published: January 2021

The present work investigates the application of a Nafion separator in aqueous zinc ion batteries.
Magnetic triggers in biomedical applications – prospects for contact free cell sensing and guidance
Authors: Almeida Ana F. , Vinhas Adriana , Gonçalves Ana I. , Miranda Margarida S. , Rodrigues Márcia T. , Gomes Manuela E.
Published: January 2021

This review overviews the unique cues of magnetic nanoparticles as sophisticated and resourceful nanotools for advanced diagnosis and therapy-oriented technologies.
Ultrawide-bandgap semiconductor AlN crystals: growth and applications
Authors: Yu Ruixian , Liu Guangxia , Wang Guodong , Chen Chengmin , Xu Mingsheng , Zhou Hong , Wang Tailin , Yu Jiaoxian , Zhao Gang , Zhang Lei
Published: January 2021

This review systematically summarizes the latest research advances of AlN crystals grown by the PVT method and their applications.
The design strategies and applications for organic multi-branched two-photon absorption chromophores with novel cores and branches: a recent review
Authors: Xu Liang , Lin Wenjie , Huang Binbin , Zhang Junzheng , Long Xueting , Zhang Wenying , Zhang Qichun
Published: January 2021

This review provides a timely summary of recent advances in the construction of OMBCs with novel cores and branches. Their structure–property relationships and applications are also summarized.
A Kalman‐filter‐based fusion method for accurate urban localisation
Authors: Alfakih Marwan , Keche Mokhtar , Benoudnine Hadjira
Published: January 2021

The 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

In 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

Facial 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

The 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.
Synthesising the repeating FRB population using frbpoppy
Authors: Gardenier D. W. , Connor L. , van Leeuwen J. , Oostrum L. C. , Petroff E.
Published: January 2021

The observed fast radio burst (FRB) population can be divided into one-off and repeating FRB sources. Either this division is a true dichotomy of the underlying sources, or selection effects and low activity prohibit us from observing repeat pulses from all constituents making up the FRB source population. We attempted to break this degeneracy through FRB population synthesis. With that aim in mind, we extendedfrbpoppy(which previously only handled one-off FRBs) to also simulate repeaters. We next modelled the Canadian Hydrogen Intensity Mapping Experiment FRB survey (CHIME/FRB). Using this implementation, we investigated the impact of luminosity functions on the observed dispersion measure (DM) and distance distributions of both repeating and one-off FRBs. We show that for a single, intrinsically repeating source population with a steep luminosity function, selection effects should shape the DM distributions of one-off and repeating FRB sources differently. This difference is not yet observed. We next show how the repeater fraction over time can help in determining the repetition rate of an intrinsic source population. We simulated this fraction for CHIME/FRB, and we show that a source population comprised solely of repeating FRBs can describe CHIME/FRB observations with the use of a flat luminosity function. From the outcome of these two methods, we thus conclude that all FRBs originate from a single and mostly uniform population of varying repeaters. Within this population, the luminosity function cannot be steep, and there must be minor differences in physical or behaviour parameters that correlate with the repetition rate.
Impacts of phosphorus loads on the water quality and the proliferation of harmful cyanobacteria in Foum-Gleita Reservoir (Mauritania)
Authors: Sadegh Ahmed Sidi , Sidoumou Zeinebou , Dia Mamadou , Pinchetti Juan Luis Gomez , Bouaïcha Noureddine
Published: January 2021

Excess phosphorus and nitrogen inputs into freshwater ecosystems is one of the main causes of expansion of harmful cyanobacterial blooms worldwide. This work was conducted to study the impacts of phosphorus leaching from the exploitation of phosphate mines present in the watershed of Foum-Gleita Reservoir (Mauritania) on the water quality and its major contribution to the proliferation of harmful cyanobacteria throughout the year. The physicochemical parameters, the occurrence and abundance of phytoplankton (including cyanobacteria), and the microcystins concentration were monitored monthly from September 2017 to August 2018. The relationships between limnological and biological variables were explored by using Pearson's correlation and forward stepwise multiple linear regression (MLR) analysis. Microcystins were detected by high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Our results showed that this reservoir can be classified as hypereutrophic throughout the year and thatMicrocystisaeruginosa(M.aeruginosa) andDolichospermum flos-aquae(D.flos-aquae) were dominant species during the rainy season (July-September), and only the congener microcystin-LR (MC-LR) was detected with a peak at 3.55 µg L−1. Pearson's correlation and MLR analysis showed that water temperature pH, phosphorus, nitrogen, and iron concentrations were the most important variables accounting forM.aeruginosaandD.flos-aquaeabundance and MC-LR concentration. Our study provides new insight into the effects of moderate nitrogen concentrations on phytoplankton community composition with dominance of the cyanobacteria phylum in phosphorus-rich freshwater ecosystems.
The influence of the shaft angle value on the normal stress and displacement repartition by bevel gear pairs − a TCA investigation
Authors: Bodzás Sándor
Published: January 2021

Five types of bevel gear pairs having straight teeth were designed to analyze the effect of the modification of the shaft angle. This angle is the parameter − subject of TCA. After the geometric design process and the creation and assembly of the CAD models, Tooth Contact Analysis (TCA) was performed in order to get the mechanical parameters focusing of the tooth roots of the connecting teeth. We repeated this process five times since the load torques were also changed. The received mechanical parameters on the teeth connection of the pinion and the driven gear were analyzed with taking in consideration all four-tooth root surfaces that limit the contacting teeth pair. Finally, we referred to the useful selection of the appropriate gear pair for the engineering application.
Self-determination Theory as Mediator in the Nexus of Gamification and Customer Purchasing Behaviour
Authors: Gajanova Lubica , Radišić Mladen
Published: January 2021

Gamification in marketing can be understood as the use of game mechanisms to motivate customers and encourage them to behave in a specific way and interact in some activities or communities in order to increase their satisfaction, loyalty and commitment, thereby contributing to brand building. There are many foreign works confirming the benefits of gamification. However, there is an absence of any known studies explicitly conducted into gamification within a marketing context in the Slovak Republic. This paper therefore attempts to address this kind of research gap and answer the question of whether gamification is a more effective form of promotion for improving the purchasing behaviour of customers in Slovakia. The results of the research confirm that gamification as part of sales promotion has a positive effect on the purchasing behaviour, search intentions and attitudes of Slovak customers. The results also indicate the positive mediation role of motivation based theory related to gamification and customer purchasing behaviour.
Analytic Hierarchy Process in Multiple–Criteria Decision–Making: A Model Example
Authors: Siekelova Anna , Podhorska Ivana , Imppola Jorma J.
Published: January 2021

Managers have to make decisions several times a day. The decision-making process can be defined as an essential activity realized by managers every day. Decisions can be implemented intuitively, or by using relevant decision-making methods. This depends on the nature of the decision, as well as the intensity of its possible future effects. The theory of decision-making can be defined as a relatively young discipline. It can be stated that decision-making is no longer an intuitive process. Most decision-making situations are of a multiple criteria character. In this contribution, the authors focus on multiple-criteria decision-making, to which several methods can be applied. In the practical part, the authors use Saaty's method, also known as the Analytic Hierarchy Process. Saaty is considered to be the most important researcher dealing with the issue of multiple-criteria decision-making. The set multiple-criteria decision-making problem was to choose one business partner out of eight under consideration. The decision-making criteria included selected financial indicators and non-financial criteria. The aim of the contribution is to use the Analytic Hierarchy Process to assess potential business partners and to select an optimal candidate.
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

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

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

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

While 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.
Paint release control of brush
Authors: Wang Ronggeng , Zhao Feng , Li Peiliu , Wang Lei , Tang Shousheng , Liu Jing
Published: January 2021

Paint release rate is an important indicator for evaluating the property of the paint product, which is predetermined by the brush and paint materials. In the painting process, a brush is always used to coat the liquid materials on the solid base surface. The brush fiber with different wettability, flexibility, and geometrical size affects the appearance, stability, and solidification velocity of paint. In this study, a kind of polyethylene terephthalate/polybutylene terephthalate-based brush is successfully fabricated, whose flexibility is adjusted by the content of fiberglass. The flexibility and appearance dimensions are proved to be the critical factors for enhancing the paint release rate through theory analysis and examples. With the designed brush, liquid coating materials with different surface tension are coated on the paper surface, whose effects are precisely controlled.
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.
Microstructure, tensile and bending behaviour of the as-cast AM50 alloy modified with different antimony and copper additions
Authors: Bagherzadeh hoseini Seyed Mohamadreza , Malekan Mehdi , Emamy Massoud , Lotfpour Mehrab
Published: January 2021

The tensile and bending behaviours of the as-cast AM50 alloy were studied after adding different amounts of Sb/Cu ( x = 0, 0.5, 1, 3 and 5 wt-%). The optimum levels of the Ultimate Tensile Strength (UTS) and tensile elongation (El%) values were found by adding 0.5 wt-% Sb and 0.5 wt-% Cu. The Yield Strength (YS) and hardness values improved with further additions of these elements. The tensile and hardness properties were investigated to be associated with microstructural features. With Sb additions, the new Mg3Sb2 intermetallics were formed. Moreover, by adding different Cu additions, three Cu-containing intermetallics were scrutinised by using the X-ray diffraction (XRD), thermodynamic calculations and also scanning electron microscopy (SEM). The formation of the new intermetallics could change the morphology of the β-Mg17Al12 phase and suppress its creation. Moreover, the grain refinement effects were different for each alloying element. Therefore, the grain refinement and the presence of new intermetallics had prominent impacts on the tensile and bending properties of the AM50 alloy. While grain refinement had a more positive effect on the UTS values, the YS and bending properties were chiefly controlled by morphology and size of intermetallics.
The influence of post-annealing and ultrasonic vibration on the formability of multilayered Al5052/MgAZ31B composite
Authors: Rahmatabadi D. , Tayyebi M. , Najafizadeh N. , Hashemi R. , Rajabi M.
Published: January 2021

In this article, the influences of ultrasonic vibrations and post-annealing on the formability of multilayered Al5052/MgAZ31B composite were evaluated experimentally. Accordingly, a multilayered Al/Mg composite was produced and then annealed to explore the role of post-annealing on the microstructure and mechanical properties. Additionally, the effect of ultrasonic vibrations on the formability of the annealed Al5052/MgAZ31B composite was examined using the biaxial stretch-forming test. It is concluded that the cold rolling process can significantly enhance tensile strength and microhardness due to increased dislocations’ density and applied strain. Moreover, the formability analysis revealed the potential role of post-annealing and high-frequency vibrations in improving the forming limits. Ultrasonic vibrations can also reduce the amount of brittle intermetallic compounds, facilitating dislocation's movement.
Mechanical response of a metastable austenitic stainless steel under different deformation modes
Authors: Zergani A. , Mirzadeh H. , Mahmudi R.
Published: January 2021

The deformation behaviour of AISI 304L stainless steel was examined under uniaxial tension and shear deformation modes in the temperature range of 0–500°C. A linear relationship between the tensile and shear yield stresses was observed over the whole temperature range, which was in a good agreement with the predictions of the von-Mises criterion. The relationship between the ultimate shear and tensile strength values, however, showed three distinct temperature-dependent regimes. In the temperature ranges corresponding to the transformation-induced plasticity effect and the dynamic strain aging phenomenon, a deviation from the expected behaviour was observed, which was related to the dependency of these phenomena on the loading mode.
Key-Value Combination for Scholarship Disbursal Analysis through Map-Reduce
Authors: Pandey Rajiv , Sahai Archana , Nagpal Namrata
Published: January 2021

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

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

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.
RETRACTED: Estimating the Imaging in Medical Science Using Image Processing Techniques
Authors: Mathur Sandeep , Shantanu Aditya , Rana Ajay
Published: January 2021

Automatic image search is proving prominent in the medical imaging domain because it includes commands such as division, extraction of vital elements of the agent, coordination, layout, monitoring, the examination of movement, estimation of defacement, and three-dimensional recreation. The character of the input images takes on a polar job within the scope of any image review task. The higher image quality produces simplified and less advanced procedures for image processing. Consequently, inexpensive processes such as clamping, geometric adjustment, edge enhancement, differentiation, or brightness review will be required for the image processing. Despite the difficulties, automatic techniques for processing and searching for images are applicable for processing and searching for images is applicable for a good range of uses. In this paper, there are techniques we have appraised to process and examine objects in the images. In the following sections, these techniques will be introduced in applications from drugs and biomechanics to development and material sciences.
IOT Model - UV based system for Sanitization of package surfaces
Authors: Yadav Anil Kumar , Rajpoot Dharmveer Singh , Prasad Shukla Shiv Shankar
Published: January 2021

Global issue corona pandemic and lock down has stopped the human life or disturbed. But this situation will not continue for longer period. Normality will take place and life smoothen, but people will fear at workplace due to unknown factors of the corona pandemic, they will be in a dwell mind situation whether to touch the things or not. In this research to mitigate or avoid such situation to think develop IOT devices along with a programmable-Ultra-Violet (UV) based system for sanitization of package surfaces. In this paper, we proposed an emphasized artificial intelligence based IOT - UV system for Sanitization of package surfaces, also developed novel algorithm for the IOT-UV based devices to enhance the efficiency of the disinfection devices in the context of low energy consumption. In order to evaluate performance of IOT-UV will be more efficient than Programmable-UV light in the context of low energy consumption.
Diabetes disease prediction using significant attribute selection and classification approach
Authors: Tiwari P , Singh V
Published: January 2021

Data Mining performs a major role in healthcare services because disease recognition and investigation contains a vast amount of data. These conditions generate several data managing problems, and to operate efficiently. The healthcare datasets are undefined and influential and it is extremely monotonous to manage and to operate. To get better of the exceeding problems, numerous analyses present various ML algorithms for different disease examination and prediction. The undertaking of disease identification and prediction is an element of classification and forecasting. In this paper, diabetes is estimated by major characteristics and the relation of contradictory characteristics is also categorized. Significant features selection was done via the recursive feature elimination with random forest. The estimation of our system specifies a powerful alliance of diabetes with (BMI) and with glucose level was drawing out using the Apriori approach. XGBoost has examined for the estimation of diabetes. The XGBoost gives better accuracy of 78.91% compared to the ANN approach and might help support medicinal professionals through treatment decisions.
Utilization of Iron ore fines using flocculating reagents
Authors: Gajbhiye Pratima , Kumari Ajita
Published: January 2021

The wastewater treatment is being given more importance nowadays, especially those which are not from the biological processes. If the polymers used for flocculation purpose are so that they do not cause harm to the water bodies and are environment friendly then they are being given more importance. We propose here a very simple flocculent which is prepared by blending of Polyvinyl alcohol (PVA) and Polyacrylic acid (PAA) which is safe for the water bodies and effective too. The proposed polymer has been successfully tried upon the iron ores flocculation tests at 0, 15, 20 and 50ppm. The obtained results were analysed for its iron content; they indicated enhancement of iron ore grade from 52.6% to 70.37% at 50ppm flocculent dose. The percentage recovery was found to be more for 15% pulp density at 20 % flocculent dosage.
Identification of Underground Faults using Internet of Things (IoT)
Authors: Rengaraj R , Venkatakrishnan G R , Adithya Pillai R , Abinandhan R , Dev Ganesh S , Aravind K
Published: January 2021

This paper aims at the detection of the fault occurring in an underground cable system, pinpoint the location of the fault using Extreme Learning Machine and convey the parameters of the fault occurred using Internet of Things (IoT). The underground cables have seen a steep increase in usage due to its many inherent advantages compared to the overhead cables. However, various methods to pinpoint the presence of fault in the underground cables of large lengths has proved futile and the communication of the fault information has been proved to be expensive. Our proposed project uses Extreme Learning Machine to detect the type of fault and to pinpoint the location of the fault in the system. Extreme Learning Machine Classification and Regression Algorithms are utilized to predict the type of fault in the system and the distance of the fault from the sending end. The regression algorithm is compared to other ML algorithms relevant to the subject matter at hand. The fault location conveyed by the use of GSM is limited by the fact that only text messages in the form of Short Messaging Services can be sent to the user concerned increasing the cost incurred by the user. Our project uses the internet to communicate through a big arsenal of methods, like e-mail and other social media services possible, thus widening the scope of communication while decreasing the size of the product.
Internet of Things Information Analysis using Fusion based Learning with Deep Neural Network
Authors: Goyal S B , Bedi Pradeep , Yadav Dileep Kumar , Vakil Nargis A
Published: January 2021

The development of the Internet of Things (IoT) is driven by recent technical advances and a growing melding of fields including sensing and actuating technology, integrated networks, radio and data processing. The vast number of IoT sensors produce high volume data for a wide variety of applications, such as intelligent house, intelligent health, intelligent engineering, smart transport, intelligent grid and intelligent agriculture. In order to promote improved decision-making, efficiency and precision, reviewing these data is a vital mechanism that renders IoT a successful market concept and paradigm- enhancing quality of living. While it is promising to improve the quality of our lives to obtain covered knowledge and inferences from IoT data, this is a complex task which traditional paradigms cannot carry out. Deep learning can play a key role in developing smarter IoT, as it has demonstrated promising effects in a number of areas such as image recognition, knowledge gathering, voice recognition, natural language processing, indoor location, physiological and psychological condition identification, etc. In this context it becomes imperative to explore the capacity of Deep Learning for IoT data processing.
Realization of 8 x 4 Barrel shifter with 4-bit binary to Gray converter using FinFET for Low Power Digital Applications
Authors: Ashok Babu P. , Siva Nagaraju V. , Mariserla Ramya , Vallabhuni Rajeev Ratna
Published: January 2021

Barrel shifter architecture is well known for bit manipulation in a single clock cycle. Due to its various operations and advantages, it is most likely used in the Arithmetic logic unit of every processor. Gray code is also known as reflective code which is widely used in digital communication for the purpose of error correction and error detection. In this project, an 8 x 4 barrel shifter is designed which is further connected to 4-bit binary to gray code converter. The barrel shifter is cascaded with binary to gray code converter so that this combination can be useful for the application of encryption of binary data in digital communications. It is designed in cadence virtuoso tool using FinFET technology at 18 nm node. The simulation results proves that the power consumed by the proposed design with FinFET technology is 11.92% less when compared with the conventional design with MOS transistors. Hence, this design can be used in application of low power digital communications. The functionality testing and verification is done using cadence virtuoso tool.
Fault-Tolerant Metric Dimension of Cube of Paths
Authors: Saha Laxman
Published: January 2021

For a simple connected graph G = (V (G), E(G)), a set R ⊆ V (G) is said to be a resolving set of G if every pair of vertices of G are resolved by some vertices in R i.e., every pair of vertices of G are identified uniquely by some vertex elements in F. A resolving set of G containing the minimum number of vertices is the metric basis and the minimum cardinality of the metric basis is called the metric dimension of G. A resolving set F for the graph G is said to be fault tolerant if for each u
∈
F, F \ {u} is also a resolving set for G and the minimum cardinality of the fault-tolerant resolving set said to be the fault-tolerant metric dimension. In this article, we determine the exact value of fault-tolerant metric dimension of

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Analyzing Human Robotic Interaction in the Food Industry
Authors: Grobbelaar Wonda , Verma Amit , Shukla Vinod Kumar
Published: January 2021

The food chain from farmer to fork has grown into a very complex sector. Industrial robots are being integrated in every part of the food chain in order to increase production, create a higher quality product to exceed customer expectations. As the bar on quality standards raises higher year on year, food safety and hygiene becomes an even greater priority and customer’s demands value for money. Food manufacturing robots are used all aspects of the food chain. Although the food sector is slow to adapt robots, the Food sector is the fourth most automatable sector. Robots are used for farming, transport, food production, packaging and delivering. [1] Robots are especially convenient for packaging of food items, where speed, consistency or high levels of repetition are needed. Robots usually win over humans in terms of efficiency and accuracy. The latest robots are equipped with intelligent vision, can multi- task and understand verbal commands. More businesses see the value in automation especially robotics which plays a vital role in process improvement. A smaller minority is still hesitant, sees it as a threat to their business, and is reluctant to implement robots. They argue that it will reduce jobs and employees will lose their valuable skills. The purpose of this paper is to identify, analyze and understand robotics in one of the largest sectors, the food chain.
RETRACTED: Improved biometric iris recognition using watershed transform
Authors: Bharadwaj S , Deepika K , Upadhyay K
Published: January 2021

With the expanding accentuation on the security, individual ID and check dependent on biometrics have been gathering broad consideration over the previous years. Recognizable proof of person depends on remarkable conduct or physical component of the person. The present reality is making quick improvement in its journey to understand the fantasy about making an easy to use, client caring climate. On correlation with numerous other biometric highlights, iris because of its extraordinary natural properties are appropriate for distinguishing proof. That is shielded from the climate, interesting fit as a fiddle, stable after some time, and contains a high measure of segregating data. There is an impressive ascent in the examination of iris acknowledgment framework over a period. The vast majority of the scientist zeroed in on the advancement of new iris acknowledgment models and calculations for good pixels iris pictures. In this paper, iris acknowledgment framework utilizing watershed change and 4-level Haar wavelet. Also, the coordinating of the iris code with the one that is in put away information base is performed by hamming separation. Proposed model essentially diminishes FAR and FRR esteems when contrasted with past works. Trial results are showing critical upgrades in iris check measure.
A Deep Learning based Method for Image Splicing Detection
Authors: Meena Kunj Bihari , Tyagi Vipin
Published: January 2021

Image manipulation has become an easy task due to the availability of user-friendly multimedia tools. The images can be manipulated in several ways. Image splicing is one of such image manipulation methods in which two or more images are merged to obtain a single composite image. These manipulated images can be misused to cheat others. This paper proposes a deep learning-based method to detect image splicing in the images. First, the input image is preprocessed using a technique called ‘Noiseprint’ to get the noise residual by suppressing the image content. Second, the popular ResNet-50 network is used as a feature extractor. Finally, the obtained features are classified as spliced or authentic using the SVM classifier. The experiments performed on the CUISDE dataset show that the proposed method outperforms other existing methods. The proposed method achieves an average classification accuracy of 97.24%.
Analyzing Reliable Transport Layer Protocol Performance
Authors: Kushwah Deepika Singh , Kumar Mahesh , Verma Lal Pratap
Published: January 2021

Transmission Control Protocol (TCP) is a reliable protocol uses for transferring data end to end in the network. It is used by many internet applications. It was originally designed to handle congestion issue in the network. It has many variants like Tahoe, Reno, New Reno. This paper presents the performance of different variants of TCP to identify how they work with varying network characteristics. The simulation is done with the help of NS2. The results show how different variants give their performance on delay, packet drops, and throughput. This is immensely important analysis, to know which variant is better for a specific criterion. This paper covers many variants and their nature of handling congestion regarding to changes in the network.