bluuebunny/crossref_metadata_2025 huggingface dataset:
New Mass Estimates for Massive Binary Systems: A Probabilistic Approach Using Polarimetric Radiative Transfer
Authors: Fullard Andrew G. , O’Brien John T. , Kerzendorf Wolfgang E. , Shrestha Manisha , Hoffman Jennifer L. , Ignace Richard , van der Smagt Patrick
Published: May 2022

Understanding the evolution of massive binary stars requires accurate estimates of their masses. This understanding is critically important because massive star evolution can potentially lead to gravitational-wave sources such as binary black holes or neutron stars. For Wolf–Rayet (WR) stars with optically thick stellar winds, their masses can only be determined with accurate inclination angle estimates from binary systems which have spectroscopic

M
sin
i

measurements. Orbitally phased polarization signals can encode the inclination angle of binary systems, where the WR winds act as scattering regions. We investigated four Wolf–Rayet + O star binary systems, WR 42, WR 79, WR 127, and WR 153, with publicly available phased polarization data to estimate their masses. To avoid the biases present in analytic models of polarization while retaining computational expediency, we used a Monte Carlo radiative-transfer model accurately emulated by a neural network. We used the emulated model to investigate the posterior distribution of the parameters of our four systems. Our mass estimates calculated from the estimated inclination angles put strong constraints on existing mass estimates for three of the systems, and disagree with the existing mass estimates for WR 153. We recommend a concerted effort to obtain polarization observations that can be used to estimate the masses of WR binary systems and increase our understanding of their evolutionary paths.
The Double Detonation of a Double-degenerate System, from Type Ia Supernova Explosion to its Supernova Remnant
Authors: Ferrand Gilles , Tanikawa Ataru , Warren Donald C. , Nagataki Shigehiro , Safi-Harb Samar , Decourchelle Anne
Published: May 2022

Type Ia supernovae (SNe) are believed to be caused by the thermonuclear explosion of a white dwarf (WD), but the nature of the progenitor system(s) is still unclear. Recent theoretical and observational developments have led to renewed interest in double-degenerate models, in particular the “helium-ignited violent merger” or “dynamically driven double-degenerate double-detonation” (D6). In this paper we take the output of an existing D6 SN model and carry it into the supernova remnant (SNR) phase up to 4000 yr after the explosion, past the time when all the ejecta have been shocked. Assuming a uniform ambient medium, we reveal specific signatures of the explosion mechanism and spatial variations intrinsic to the ejecta. The first detonation produces an ejecta tail visible at early times, while the second detonation leaves a central density peak in the ejecta that is visible at late times. The SNR shell is off-center at all times, because of an initial velocity shift due to binary motion. The companion WD produces a large conical shadow in the ejecta, visible in projection as a dark patch surrounded by a bright ring. This is a clear and long-lasting feature that is localized, and its impact on the observed morphology is dependent on the viewing angle of the SNR. These results offer a new way to diagnose the explosion mechanism and progenitor system using observations of a Type Ia SNR.
Centrally Concentrated H i Distribution Enhances Star Formation in Galaxies
Authors: Yu 余 Niankun 捻坤 , Ho Luis C. , Wang Jing
Published: May 2022

We use a sample of 13,511 nearby galaxies from the ALFALFA and Sloan Digital Sky Survey spectroscopic surveys to study the relation between the spatial distribution of H i 21 cm emission and star formation rate (SFR). We introduce a new nonparametric quantity K, measured from the curve of growth of the line, to describe the shape of the integrated H i profile. The value of K increases from double-horned to single-peaked profiles, depending on projection effects and the spatial and velocity distribution of the gas. Using carefully chosen samples to control for the competing factors that influence the integrated line profile, we argue that useful inferences can be made on the spatial distribution of the gas. We find that galaxies with a high value of K tend to have more centrally concentrated H i distribution within the optical disk of the galaxy at fixed conditions, and that larger values of K are associated with higher levels of total and central SFR. The results suggest that the global concentration of H i plays an important role in facilitating the conversion of neutral atomic hydrogen to molecular hydrogen gas, which, in turn, affects the star formation activity throughout the optical disk. Our sample is biased against quiescent galaxies, and thus the conclusions may not hold for galaxies with low SFR or low H i content.
The Nonuniformity of Poleward Flux Transport on the Solar Surface: A Statistical Method Applied to Solar Cycles 21–24
Authors: Wang Zi-Fan , Jiang Jie , Wang Jing-Xiu
Published: May 2022

The poleward migration of the active regions’ magnetic flux on the solar surface plays an important role in the development of the large-scale field development, especially the polar-field reversal, which is a key process in the Babcock–Leighton-type solar dynamos. The poleward flux transport is nonuniform, centered around poleward surges as suggested by previous observations. The strong, long-lasting surges are related to activity complexes, and often result in violent polar-field reversal. However, the nonuniformity of poleward flux transport has not been evaluated quantitatively. We propose a statistical method to analyze the poleward flux transport during solar cycles 21–24 by considering the frequency distributions of the magnetic field at latitudes of poleward surges occurring during solar cycles. The nonuniformity is quantified as the kurtosis statistics representing the tailedness of the distributions. We test the method on results of surface flux transport simulations, and apply it to WSO, National Solar Observatory, MWO, and HMI data. We confirm that the poleward surges are of significance during solar cycles 21–24 in general. The kurtosis within a solar cycle is affected by different latitudes of the magnetic field and different data sources. The southern hemisphere of cycle 24 exhibits the largest kurtosis, agreeing with the super-surge concept from previous work. The significant nonuniformity of poleward flux transport originates from the nonrandomness of active regions, which favors the activity complexes as the origin of poleward surges.
Recovering the CMB Signal with Machine Learning
Authors: Wang Guo-Jian , Shi Hong-Liang , Yan Ye-Peng , Xia Jun-Qing , Zhao Yan-Yun , Li Si-Yu , Li Jun-Feng
Published: May 2022

The cosmic microwave background (CMB), carrying the inhomogeneous information of the very early universe, is of great significance for understanding the origin and evolution of our universe. However, observational CMB maps contain serious foreground contaminations from several sources, such as Galactic synchrotron and thermal dust emissions. Here, we build a deep convolutional neural network (CNN) to recover the tiny CMB signal from various huge foreground contaminations. Focusing on CMB temperature fluctuations, we find that the CNN model can successfully recover the CMB temperature maps with high accuracy, and that the deviation of the recovered power spectrum C

ℓ
is smaller than the cosmic variance at ℓ > 10. We then apply this method to the current Planck observations, and find that the recovered CMB is quite consistent with that disclosed by the Planck Collaboration, which indicates that the CNN method can provide a promising approach to the component separation of CMB observations. Furthermore, we test the CNN method with simulated CMB polarization maps based on the CMB-S4 experiment. The result shows that both the EE and BB power spectra can be recovered with high accuracy. Therefore, this method will be helpful for the detection of primordial gravitational waves in current and future CMB experiments. The CNN is designed to analyze two-dimensional images, thus this method is not only able to process full-sky maps, but also partial-sky maps. Therefore, it can also be used for other similar experiments, such as radio surveys like the Square Kilometer Array.
Alien plants tend to occur in species-poor communities
Authors: Cubino Josep Padullés , Těšitel Jakub , Fibich Pavel , Lepš Jan , Chytrý Milan
Published: May 2022

Invasive alien species can have severe negative impacts on natural ecosystems. These impacts may be particularly pronounced within ecological communities, where alien species can cause local extinctions. However, it is unclear whether individual alien plant species consistently occur in species-poor or species-rich communities across broad geographical scales and whether this pattern differs amongst habitat types. Using ~17,000 vegetation plots sampled across the Czech Republic, we calculated the median, range and skewness of the distribution in community species richness associated with 73 naturalised alien plant species. We compared the observed values with those obtained under a null expectation to test whether alien species occurred at random with respect to species richness in forest and grassland communities. We found that the relationship between the occurrence of alien species and the diversity of local plant communities was species-dependent and varied across habitats. Overall, however, alien species occurred in species-poor communities more often than expected by chance. These patterns were more pronounced in grasslands, where alien species also occurred in communities with a lower range of species richness than under random expectation. Our study represents one of the most comprehensive quantitative analyses relating alien plant invasion to resident community diversity at a broad geographical scale. This research also demonstrates that multi-species studies are needed to understand the processes of community assembly and to assess the impact of alien plant invasions on native diversity.
Clarifying the type locality of Liotyphlops wilderi (Garman, 1883) (Serpentes, Anomalepididae), with comments on other reptiles from São Cyriaco, Minas Gerais
Authors: Costa Henrique C.
Published: May 2022

The snake species Typhlops wilderi (today Liotyphlops wilderi) was described in 1883 based on specimens from São Cyriaco, in Minas Gerais, Brazil. The name of this type locality has been cited in different ways in the literature, making its geographic location confusing. Solving this question is an important issue for future taxonomy and systematic studies. After searching for information on the collector of the type series of L. wilderi (John Casper Branner) using the Google Scholar database, I found that São Cyriaco was a gold mining company located in the current municipality of Alvorada de Minas. Besides elucidating the type locality of L. wilderi, I searched for reptile specimens collected by Branner, deposited in collections registered at the VertNet Portal and SpeciesLink, and personally examined the extant material from Minas Gerais.
Économie verte : marchandiser la planète pour la sauver ?
Authors: ETC Group  , Heinrich Böll Foundation  , Carlier Marielle
Published: May 2022

L’avènement d’une nouvelle génération de technologies « vertes » serait la solution aux crises multiples qui menacent l’humanité et la planète. Les nouveaux gisements de profit que la bioéconomie laisse entrevoir reconfigurent le jeu d’alliances entre grands groupes industriels – les biomassters 3 . Le passé récent nous enseigne cependant que cette vision monopolistique et techniciste du changement est lourde de risques et de déconvenues.
Spiritual Care Competence of Nurses
Authors: Özakar Akça Selen , Gülnar Emel , Özveren Hüsna
Published: May 2022

Background

Health care has become more patient centered, and spiritual care has become more critical during the past several decades because researchers have identified associations between met spiritual needs and positive health outcomes. This study investigated nurses' spiritual care competence.

Method

A cross-sectional, descriptive research design was used. The study sample consisted of 201 nurses of a training and research hospital. Data were collected using a sociodemographic characteristics questionnaire and the Spiritual Care Competence Scale (SCCS).

Results

The participants had a mean age of 27.62 ± 5.28 years. They had a mean SCCS score of 3.88 ± 0.50. Three factors affected participants' spiritual care competence. First, head nurses had a higher mean SCCS score than nurses (4.16 ± 0.26 vs. 3.87 ± 0.50). Second, participants who met patients' spiritual care needs had a higher mean SCCS score than those who could not (4.02 ± 0.54 vs. 3.09 ± 1.12). Third, participants who could diagnose patients for spiritual care had a higher mean SCCS score than those who could not (
p
< .05).

Conclusion

These nurses had above-average spiritual care competence. Universities and health care institutions should provide nurses with training to help them develop spiritual care competence.

[
J Contin Educ Nurs
. 2022;53(5):225–231.]
The Intransparency of Political Legitimacy
Authors: Brinkmann Matthias
Published: May 2022

Some moral value is transparent just in case an agent with average mental capacities can feasibly come to know whether some entity does, or does not, possess that value. In this paper, I consider whether legitimacy–that is, the property of exercises of political power to be (at least) permissible–is transparent. Implicit in much theorising about legitimacy is the idea that it is. I will offer two counter-arguments. First, injustice can defeat legitimacy, and injustice can be intransparent. Second, legitimacy can play a critical function in our practical thought, which sometimes requires intransparency.
Extending the Construct of Centrality of Visual Product Aesthetics
Authors: Dumitrescu Andrei
Published: May 2022

The concept of Centrality of Visual Product Aesthetics (CVPA) and the associated construct were introduced to measure the level of significance that product design has for certain consumers. The aim was that, after identifying consumers with high CVPA, designers and manufacturers can successfully address to them products with a remarkable design. The initial construct proposed by other researchers was composed by three factors (Value, Acumen and Response). The author of this paper aimed to amplify the construct and tested a new construct containing four additional new factors: Brand Loyalty, Product Involvement, Personality Congruence and Price indifference. After applying Exploratory Factor Analysis, it was found that practically the Value and Brand Loyalty items were part of the same factor. The same situation was encountered for Response and Personality Congruence. After removing the less significant items, a new construct resulted, validated by Confirmatory Factor Analysis. The new construct has been used successfully to test the hypothesis that people with high CVPA attach special importance to products with a high aesthetic content (in the case of loudspeakers).
Strategic Design and reality levels: scenarios as prisms for creating possible futures
Authors: Guglieri Alessandra Dias
Published: May 2022

Strategic Design, when seen as a metaprojectual methodological approach, continuously expands its meaning effects, creating projects that intervene in systems for sociocultural transformation. It carries an open vision of design, permeated by the reflexive dominion (BENTZ, 2014), which welcomes the arbitrary character and the multiplicity of interactions, uncertainties, indeterminations and random phenomena (MORIN, 2015).  Such intervention allows the creation of scenarios as a process guided by the imagination of other possible realities and contemplate the systems in the perspective of their openness. In this sense, the epistemological vision that integrates open systems, as well as the perspective of Strategic Design, joins the Transdisciplinarity method to discuss the elaboration of scenarios from the different reality levels. Guided by an abductive reasoning this method considers the existence of an invariable set of systems under the action of several General Laws, which constitutes more than one reality, without necessarily having a discontinuity separating them (NICOLESCU, 1999). Called the Principle of the Included Third, this conception permeated by the interdependence of simultaneous realities, leads to the elaboration of prismatic scenarios by their spectral character,  and brings light to different but complementary perspectives, as well as the colors reflected in optical dispersive prisms.
Scholarly Communication Practice and Strategies in Institutions of Higher Learning in Africa
Authors: Adakawa Murtala Ismail
Published: May 2022

This chapter explores scholarly communication practices and strategies in institutions of higher learning in Sub-Saharan Africa to increase visibility and reputation. For long, it has been acknowledged that foundations of institutions of higher learning rest upon creating and disseminating knowledge, which serves as an engine for scientific progression leading to a knowledge society. This is true despite scholarly communications receiving limited recognition from senior leadership at most institutions of learning. Visibility of institutions via scholarly communications is of high significance considering the inputs made by scholars and the applicability of study findings for perfecting routinized works or correcting faulty systems in departments, ministries, and agencies in nations. This is evident in developed countries who furnish their scholarly communication offices with librarians to enhance the production and communication of knowledge. Strategies and practices of scholarly communications among institutions of higher learning are discussed.
Article Processing Charges and Their Impact in Open Access Publishing
Authors: Jayaraj Mary Metilda , Pinto Anil Joseph , Pachiyappan Sathish
Published: May 2022

Knowledge transfer is the key to the sustained growth of human civilization. This chapter on open access and article processing charges aims at addressing the various issues associated with open access publishing as a business model with a distinct opportunity. The chapter throws light on article processing charges (APC), the relevance of impact factor, citations, and pricing. The complexities the researchers confront, and the rise of predatory journals and their concerns are discussed in detail. The ethical dimensions of publishing and the role and relevance of the Committee on Publication Ethics (COPE) are also explored. This chapter dwells on some of the remedial measures to improve the awareness and practices among the diverse stakeholders.
A Convolutional Neural Network With Feature Selection-Based Network Intrusion Detection
Authors: Chaibi Nassima , Atmani Baghdad , Mokaddem Mostéfa
Published: May 2022

This paper attempts to provide a demonstration the importance of the feature selection (FS) in the data mining filed for the optimization. The author’s aim to develop a Convolutional Neural Network (CNN) based Network Intrusion Detection System (NIDS). The CNN was trained using the NSL-KDD dataset. The approach is divided into two methodologies: the first one, is to apply the CNN to the NSL-KDD dataset without FS, in the second methodology: the Information Gain (IG), Grain Ratio (GR) and Correlation Attribute (CA) were applied as FS methods then the CNN is use to classify the intrusion. The performance is proven by comparing our results with other previous works. Our experimentation results show that CNN with FS has a good accuracy 99,72%, true positive rate: 99,29%, false positive rate: 0,18%. Thus, the CNN with FS has outperform the other methods. But the methods use in the FS phase don’t guarantee the use of the best subset or the optimal subset. As future orientation is to develop another method for FS which guarantee the selection of the best and the optimal relevant feature.
Cost of Debt and Corporate Opacity
Authors: Vrouva Sofia , Tzovas Christos , Chalevas Constantinos G. , Sykianakis Nicos
Published: May 2022

This paper expands upon the study of Bethani et. al (2019) regarding the association between firms’ cost of debt and their ownership structure. It is investigated whether corporate information opacity is associated with the level of cost of debt for family- owned firms. We examined a sample of companies listed to the Athens Stock Market at the period of 2017-2019. In line with the findings of Bethani et al. (2019) for the period 2009-2016 we found no significant association between corporate opacity and family ownership. Τhe cost of debt of the sample firms is negatively associated with their liquidity. In addition, we found that sample firms with certain stock price behavior and trading volume tend to finance their activities through the stock market than rather than using bank loans.
Patterns of Touch Screen Technology Use in Religious Tourism and Pilgrimage
Authors: Gazzaz Osman Bakur , Mohamed Hamza Saad
Published: May 2022

The purpose of this study is to examine the effective use of interactive touchscreen technology by pilgrims and the Umrah performers during the Hajj and Umrah rituals. The study relied on a media survey methodology. A questionnaire was used as a tool to collect data. A total of 400 pilgrims and Umrah performers participated in the study. The study drew from the uses and gratifications approach. Results indicated that the bulk of pilgrims and the Umrah performers are active and heavy users on using the interactive touchscreen technology. Moreover, the findings confirmed that there is a strong correlation between the intensity of usage of touchscreen technology by pilgrims as well as Umrah performers and their demographic variables. More importantly, the findings revealed that touchscreen usage increased the spread of awareness, guidance, and information among pilgrims and Umrah performers about the Hajj and Umrah rituals.
An Examination of Customer Relationship Management and Business Sustainability in Small and Medium Enterprises
Authors: Hanaysha Jalal Rajeh , Al-Shaikh Mohammed Emad , Kumar Pranav
Published: May 2022

The aim of this paper was to investigate the impact of customer relationship management (CRM) dimensions on business sustainability in small and medium enterprises (SMEs). A quantitative research approach was employed for data collection and fulfilling the objective of this study. In particular, the data was gathered through a survey instrument from 183 employees of SMEs in Saudi Arabia. The obtained responses were analyzed using the partial least square approach (PLS-SEM). The outcomes showed that customer orientation and CRM organization have significant positive effects on business sustainability. The results also verified that CRM technology and knowledge management play important roles in affecting business sustainability. These findings confirm the importance of customer relationship management and its dimensions in enabling firms to sustain their businesses and thrive in today's changing business environment.
Determinants of Hotel Brand Image
Authors: Latif Wasib
Published: May 2022

This study aims to find out the factors affecting the Brand Image of Hotels applying a Unified Model of Customer-Based Brand Equity. Additionally, it tests the moderating effect of tourism management in the relationship between the determining factors and brand image of hotels. Responses were gathered from the customers of some selected Bangladeshi hotels in four and five-star category using a multi-stage sampling procedure. The Partial Least Square Structural Equation Modeling (PLS-SEM) was used to test the hypotheses of this study. Corporate social responsibility was found to be the most significant, followed by brand superiority, brand association, brand awareness and brand resonance. It was found that the relationship between brand superiority and brand image was moderated by tourism management; the relationship between brand resonance and brand image was also found to be moderated by tourism management. The findings of this study bear implications both in theoretical and practical aspects.
Distributed Business Process Discovery in Cloud Clusters
Authors: Meddah Ishak H. A , Guerroudji Fatiha , Remil Nour Elhouda
Published: May 2022

The processing of big data across different axes is becoming more and more difficult and the introduction of the Hadoop MapReduce framework seems to be a solution to this problem. With this framework, large amounts of data can be analyzed and processed. It does this by distributing computing tasks between a group of virtual servers operating in the cloud or a large group of devices. The mining process forms an important bridge between data mining and business process analysis. Its techniques make it possible to extract information from event reports. The extraction process generally consists of two phases: identification or discovery and innovation or education. Our first task is to extract small patterns from the log effects. These templates represent the implementation of the tracking from a business process report file. In this step we use the available technologies. Patterns are represented by finite state automation or regular expressions. And the final model is a combination of just two different styles.
Web Vulnerability Detection Analyzer Based on Python
Authors: Xu Dawei , Chen Tianxin , Tan Zhonghua , Wu Fudong , Gao Jiaqi , Yang Yunfan
Published: May 2022

In the information age, hackers will use Web vulnerabilities to infiltrate websites, resulting in many security incidents. To solve this problem, security-conscious enterprises or individuals will conduct penetration tests on websites to test and analyze the security of websites, but penetration tests often take a lot of time. Therefore, based on the traditional Web vulnerability scanner, the Web vulnerability detection analyzer designed in this article uses vulnerability detection technologies such as sub-domain scanning, application fingerprint recognition, and web crawling to penetrate the website. The vulnerability scanning process of the website using log records and HTML output helps users discover the vulnerability information of the website in a short time, patch the website in time. It can reduce the security risks caused by website vulnerabilities.
Security Enhancement Through Compiler-Assisted Software Diversity With Deep Reinforcement Learning
Authors: Wang Junchao , Wei Jin , Pang Jianmin , Zhang Fan , Li Shunbin
Published: May 2022

Traditional software defenses take corresponding actions after the attacks are discovered. The defenders in this situation are comparatively passive because the attackers may try many different ways to find vulnerability and bugs but the software remains static. This leads to the imbalance between offense and defense. Software diversity alleviates the current threats by implementing a heterogeneous software system. The N-Variant eXecution (NVX) systems, effective and applicable runtime diversifying methods, apply multiple variants to imporove software security. Higher diversity can lead to less vulnerabilities that attacks can exploit. However, runtime diversifying methods such as address randomization and reverse stack can only provide limited diversity to the system. Thus, we enhance the diversity of variants with a compiler-assisted approach. We use a Deep Reinforcement Learning-based algorithm to generate variants, ensuring the high diversity of the system. For different numbers of variants, we show the results of the Deep Q Network algorithm under different parameter settings.
Design and Implementation of a Reliable Reconfigurable Imaging System
Authors: Khalifat Jalal , Ebrahim Ali
Published: May 2022

Because of SRAM sensitivity to radiation, SRAM-based FPGA systems deployed in harsh environments require error mitigation methods to reduce their overall downtime. This paper presents a fault-tolerant reconfigurable imaging system that relies on the DPR feature for correcting errors in the configuration memory and loading camera system IPs. The system reliability is evaluated by injecting faults in the FPGA configuration memory at runtime using the Xilinx SEM IP. The faults are injected internally using the Internal Configuration Access Port (ICAP), which is shared between the fault injection core and system parts. The results showed that 95% of the errors can by corrected automatically. This paper also proposes a fast-Built-in-Self-Test (BIST) mitigation technique to reduce the overall downtime in case of errors. This technique can reduce the recovery time by 80%. Moreover, Triple Modular Redundancy (TMR) is used to increase the overall reliability without significantly increasing the resource overhead.
An Improved Unified AES Implementation Using FPGA
Authors: Prakash Brahm , Gupta Vrinda
Published: May 2022

Encryption is an essential process in electronic data transmission because it securely protects the data from unauthorized access. In this digital era, information and its security are of great concern with technology advancements. As we have entered into 5G technology that targets end-to-end security and speed to communicate with intelligent devices. These devices and systems need an AES module having both the operation as encryption and decryption in a single module to communicate in duplex mode to access the information in a real-time environment. This article has architecture of a unified module with modified round operation and has been implemented on Virtex-7 FPGA platform. Mix column adds vertical alteration in the algorithm and this design has managed the utilization of Mix column block to make an optimized AES algorithm. The unified AES has achieved a maximum frequency of 290.3MHz and resource utilization of 9416 slice LUTs design, including some modification in traditional AES, resulting in less resource utilization and high throughput.
Fuzzy Utility Matrix-Based Intelligent Decision-Making Model and Its Application to Diet Recommendation System for Metabolic Disorder Patients
Authors: Mondal Rajkrishna , Srivastava Pankaj
Published: May 2022

In the present article an effort has been made to design and develop a diet recommendation system for Metabolic Disorders patients. The key feature of this system is to recommend a menu for dinner to maintain nutritional micros as per daily requirements.The proposed intelligent decision-making system is designed as per the following phases:Under the 1st Phase, we compute the requirement of calories as per the Patient's personal information (like sex, age, height, weight), physical activity, environmental situations, and food habits on a daily basis. Under the 2nd Phase, development of knowledge base as per Patient's foods habits information. 3rd Phase is based on designing the recommendation system for a dinner menu to maintain nutritional micros as per daily requirements. The results of the system have been validated by using the Degree of match algorithm and comments of Nutritional experts. This intelligent decision making system will help the ordinary people living in urban and rural areas, especially those not aware of the nutritional value concerned with their daily food items.
Promoting Social Relationships Using a Couch Cooperative Video Game
Authors: Garcia Manuel B. , Rull Vanessa Mae A. , Gunawardana Shirley Sean James D. , Bias Dylan Joshua M. , Chua Roseanne Catherine C. , Cruz Jairus Ezekiel C. , Raguro Ma.Corazon Fernando , Perez Maria Rona Lobo
Published: May 2022

Social relationships are a fundamental aspect of human existence. Unsurprisingly, policymakers are incessantly devising strategies that accentuate the benefits of social relationships and diminish the risks of social isolation. The natural manifestation of player-to-player interaction in a video game context poses a unique opportunity to study the effects of co-playing on social relationship formation. However, most studies recruited players with existing relationships (e.g., family and friendship), utilized random commercial video games, or experimented in an online environment. These research gaps warrant further investigation on the utility of video games for promoting social relationships among unacquainted players while in the same physical space. Thus, this study presents the development and evaluation of a couch cooperative video game grounded on sequential team-building mechanics. The findings of this study offer empirical evidence that would have significant practical implications for any organization seeking to increase teamwork and cooperation among its members.
Can Leadership Possess a Virtual Pair of Eyes?
Authors: Tahirkheli Sheryar
Published: May 2022

Studies intersecting organizational networks and cross-cultural leadership research are short supplied in literature. One explanation could be underwhelming motivation to integrate research areas that historically lack consent over operational definitions and contextual applications. This study brings a third variable, e-leadership, investigated as a moderating variable; in the process, the study lends a hand to start the process of arriving at an operational definition of e-leadership. The lack of integration studies encouraged the buildup of this study. What makes such a discussion important is the transition to the remote workspace during COVID-19 and expecting the traditional leadership research accountable for the gaps that were left in the rapid transition and governance of the virtual workspaces. The study is based on 18 international markets of an MNE that recently received ERP upgrades.
Online Herd Behavior in Virtual Communities
Authors: Chen Yi-Fen , Shen Meny-Wei , Lai Jing-Sian , Lai Chih-Wei
Published: May 2022

Previous studies on WOM have stressed the importance of peer consumer reviews in making purchase decision. When people follow the others purchase decision on the Internet, online herd behavior occurs. This paper presents three experiments that examine herd behavior in virtual communities: In experiment 1, a 2 (Volume: sales/recommendation) online experiment was conducted. Experiment 2 investigated herd effects using a 2 (Number of volume: relative/absolute)　×　2 (Number of scale: large/small) online experiment. Online experiment 3 examined herd effects using a 2 (Number of experience-sharing messages: more/fewer) × 2 (Number of recommendations: more/fewer) online experiment. These experiments involved 484 people in a Taiwanese virtual community. Results demonstrated that consumer recommendation volume causes significantly more frequent online herd behavior compared with sales volume. This study advances the literature on online herd behavior in virtual communities.
Managing New Product Development
Authors: Mandal Pratap Chandra
Published: May 2022

Companies should embrace innovation and introduce new products to succeed in the competitive business environment. The objective of the study is to underline the importance of innovation and new product development for company growth and to analyze the different approaches adopted by companies in innovation. Companies should develop a culture of innovation. The different approaches to innovation include customer-centered innovation, team-based innovation, and systematic innovation. Companies should realize the necessity of investing more in innovation and new product development during turbulent times and to target individuals at the bottom of the pyramid with innovation. The discussions will equip both academicians and practitioners to understand the different approaches in innovation better. Practicing managers may implement effective strategies and initiatives in their organizations to do better innovation and to achieve business excellence.
Software Architecture Recovery Using Integrated Dependencies Based on Structural, Semantic, and Directory Information
Authors: Puchala Shiva Prasad Reddy , Chhabra Jitender Kumar , Rathee Amit
Published: May 2022

Architecture recovery techniques study dependencies in source code and reconstruct architecture. Most techniques either use structural or semantic dependencies and it is observed that the use of directory information helps in improving architecture recovery. The research carried out to date has focused on using the semantic information in a very limited manner, and directory information in a trivial manner without considering directory hierarchy. Further, all three (structural, semantic, and directory-structure) are reported to be very useful in architecture recovery but have not been used in a combined manner at all. So, this paper proposes a new scheme for architecture recovery using a weighted combination of all three dependencies. A new approach is designed to effectively mine semantic dependencies and extract directory dependencies. Finally, different dependency schemes are evaluated with four clustering algorithms on three open-source projects. The obtained results show that the proposed scheme performs better than the existing approaches in architecture recovery.
Efficient Cloudlet Allocation to Virtual Machine to Impact Cloud System Performance
Authors: Sahkhar Lizia , Balabantaray Bunil Kumar , Yadav Satyendra Singh
Published: May 2022

Performance is an essential characteristic of any cloud computing system. It can be enhance through parallel computing, scheduling and load balancing. This work evaluates the connection between the response time (RT) and virtual machine’s (VM) CPU utilization when cloudlets are allocated from the datacenter broker to VM. To accentuate the RT and VM’s CPU utilization, a set of 100 and 500 heterogeneous cloudlets are analyzed under hybridized provisioning, scheduling and allocation algorithm using CloudSim simulator. These includes space shared (SS) and time shared (TS) provisioning policy, shortest job first (SJF), first come first search (FCFS), round robin (RR) and a novel length-wise allocation (LwA) algorithm. The experimental analysis shows that the RT is the least when SJF is combined with RR allocation at 40.665 secs and VM’s CPU utilization is the least when SJF is combined with LwA policy at 12.48 in all combinations of SS and TS provisioning policy.
Fuzzy Multi-Objective Chance-Constrained Portfolio Optimization Under Uncertainty Considering Investment Return, Investment Risk, and Sustainability
Authors: Chiadamrong Navee , Suthamanondh Pisacha
Published: May 2022

Effective portfolio management can increase business value by maximizing investment return, reducing possible risk and, making the best use of limited resources. Achieving this goal is constrained by multiple conflicting objectives and uncertainty, which are inherent in all projects. The investment return, investment risk, and sustainability have been simultaneously evaluated in this study by fuzzy multi-objective chance-constrained portfolio optimization, aligned with an organization’s strategic direction. Through the fuzzy chance-constrained programming, the obtained portfolio under uncertainties can be optimized with a specified confidence level imposed by decision makers. With such an NP-hard problem, a Genetic Algorithm (GA) is embedded in the developed decision support tool for sustainable investment portfolio selection and optimization. The applicability of the proposed approach and the constructed tool are illustrated through an example.
Effect of Coconut Oil-Based Cutting Fluid on Cutting Performance During Turning With Minimal Fluid Application
Authors: Rengiah Robinson Gnanadurai
Published: May 2022

In the present work, an attempt was made to use vegetable oil as a cutting fluid during minimal fluid application to make the process more environmentally friendly. Coconut oil was selected as the vegetable oil in this study considering its availability, physical properties, and lubricating ability. The effect of newly formulated coconut oil-based emulsified cutting fluid on cutting performance during turning of hardened AISI 4340 steel with the minimal fluid application was investigated. It was observed that the coconut oil-based cutting fluid offered better cutting performance in terms of tool wear, surface roughness, cutting force, cutting temperature, and tool chip contact length when compared to mineral oil-based cutting fluid and raw coconut oil. It was also observed that the percentage of concentrate in the cutting fluid was to be maintained as 30% to achieve better cutting performance. The use of coconut oil-based cutting fluid holds promise as an environment friendly alternative for mineral oil-based cutting fluid.
A Framework for Additive Manufacturing Technology Selection
Authors: Muvunzi Rumbidzai , Mpofu Khumbulani , Khodja Malika , Daniyan Ilesanmi
Published: May 2022

Additive manufacturing is a popular emerging technology of producing parts directly from digital models. This technology has presented benefits such as freedom of design, the ability to customise, and shortened process chains. Presently, there are different Additive Manufacturing (AM) processes that are available in the market. Often, transport equipment manufacturing companies are faced with challenges while selecting the AM processes that are suited to their needs. Decision-makers need to consider all the underlining factors before a conclusion is reached. This paper proposes an approach that can be used by companies in the rail sector to select AM technologies that are suited to their applications. The approach involves identifying suitable parts, comparing applicable AM technologies and selecting the most suitable technology. The next stage involves re-designing the parts based on the selected technology. The approach is applied to benchmark parts from the industry. The study provides enlightenment on how AM can be applied in the rail industry.
Bi-Objective Optimizing for Data-Intensive ‎Scientific Workflow Scheduling in Cloud ‎Computing
Authors: Kouidri Siham , Kouidri Chaima
Published: May 2022

Cloud Computing is increasingly recognized as a new way to use on-demand, computing, ‎storage and network services in a transparent and efficient way. Cloud Computing environment ‎consists of large customers requesting for cloud resources. Nowadays, task scheduling problem ‎and data placement are the current research topic in cloud computing. In this work, a new ‎technique for workflow scheduling and data placement are proposed based on genetic ‎algorithm to fulfill a final bi-objective goal such as minimizing total workflow response time and ‎cost of their execution. the scheduling of scientific workflows is considered to be an NP-complete ‎problem, i.e. a problem not solvable within polynomial time with current resources. The ‎performance of this proposed algorithm has been evaluated using CloudSim toolkit, Simulation ‎results show the effectiveness of the proposed algorithm in comparison with well-known ‎algorithms such as genetic algorithm with Random data placement. ‎
A Survey on Mobile App Development Approaches With the Industry Perspective
Authors: Patidar Anil Kumar , Suman Ugrasen
Published: May 2022

Mobile applications (i.e., mobile apps) are dynamic from the user point of view, it is complex in some situations, and it is being developed in competitive and strict time frames. Therefore, the developers are required to pay attention to choose an appropriate software development process to cater these needs. Over the last decade, agile approaches have become very popular for software development in general, but there has only been limited research performed in the applicability of agile methods in the area of mobile app development. In this paper, a detailed review on mobile app development approaches with their best practices is prepared to explore the suitability of agile approaches. We have also conducted an online survey to know the current mobile app development trends in industries. It is observed that the agile approaches are the most suited approaches for mobile app development due to its flexibility in making changes, scalability, usability, etc. This crucial survey contributes towards a better comprehension of development trends in mobile app development industries.
Risks Associated With the Entrepreneurship Education During the Pandemic
Authors: Potluri Rajasekhara Mouly , Koppalakrishnan Premila , Jahadhiya Firdausi M.
Published: May 2022

The paper aims to investigate the perceptual displays of UAE business students on selecting entrepreneurship education as a career option along with challenges faced in the present pandemic situation. The study collected the opinions of 500 business program students from different universities in the UAE by administering questionnaires and personal interviews. Then, the compiled data were encapsulated and inscribed using Software R Studio and Microsoft Excel. The study emphasized that UAE business pupils are not concerned about selecting entrepreneurship as their career option during the pandemic due to the risks involved. Mobilizing capital, uncertainty, and risk of failure impede the UAE business students from becoming entrepreneurs during the pandemic. On the other hand, the empirical evidence indicates that entrepreneurship education is vital to motivating students' professional preferences. Therefore, higher education institutions must play a pivotal role in promoting entrepreneurship as another career option.
Prediction of Diabetic Retinopathy Using Health Records With Machine Learning Classifiers and Data Science
Authors: Sumathy B. , Chakrabarty Arindam , Gupta Sandeep , Hishan Sanil S. , Raj Bhavana , Gulati Kamal , Dhiman Gaurav
Published: May 2022

Diabetes is a rapidly spreading disease. When the pancreas produces insufficient insulin or the body cannot utilise it effectively. Diabetic Retinopathy (DR) and blindness are two major issues for diabetics. Diabetes patients increase the amount of data collected about DR. To extract important information and undiscovered knowledge from data, data mining techniques are required. DM is necessary in DR to improve society's health. Our study focuses on the early detection of Diabetic Retinopathy using patient information. DM approaches are used to extract information from these numeric records. The dataset was used to forecast DR using logistic regression, KNN, SVM, bagged tree, and boosted tree classifiers. Two cross-validations are used to find the best features and avoid overfitting. Our dataset includes 900 diabetes patients. The boosted tree produced the best classification accuracy (90.1%) with 10% hold-out validation. KNN also achieved 88.9% accuracy, which is impressive. As a result, our research suggests that bagged trees and KNN are good classifiers for DR.
An Approach to DNA Sequence Classification Through Machine Learning
Authors: Juneja Sapna , Dhankhar Annu , Juneja Abhinav , Bali Shivani
Published: May 2022

Machine learning (ML) has been instrumental in optimal decision making through relevant historical data, including the domain of bioinformatics. In bioinformatics classification of natural genes and the genes that are infected by disease called invalid gene is a very complex task. In order to find the applicability of a fresh protein through genomic research, DNA sequences need to be classified. The current work identifies classes of DNA sequence using machine learning algorithm. These classes are basically dependent on the sequence of nucleotides. With a fractional mutation in sequence, there is a corresponding change in the class. Each numeric instance representing a class is linked to a gene family including G protein coupled receptors, tyrosine kinase, synthase, etc. In this paper, the authors applied the classification algorithm on three types of datasets to identify which gene class they belong to. They converted sequences into substrings with a defined length. That ‘k value' defines the length of substring which is one of the ways to analyze the sequence.