J. Sasikala, G. Shylaja, Naidu V. Kesavulu, B. Venkatesh and S.M. Mallikarjunaiah
A finite element computational methodology on a curved boundary using an efficient subparametric point transformation is presented. The proposed collocation method uses one-side…
Abstract
Purpose
A finite element computational methodology on a curved boundary using an efficient subparametric point transformation is presented. The proposed collocation method uses one-side curved and two-side straight triangular elements to derive exact subparametric shape functions.
Design/methodology/approach
Our proposed method builds upon the domain discretization into linear, quadratic and cubic-order elements using subparametric spaces and such a discretization greatly reduces the computational complexity. A unique subparametric transformation for each triangle is derived from the unique parabolic arcs via a one-of-a-kind relationship between the nodal points.
Findings
The novel transformation derived in this paper is shown to increase the accuracy of the finite element approximation of the boundary value problem (BVP). Our overall strategy is shown to perform well for the BVP considered in this work. The accuracy of the finite element approximate solution increases with higher-order parabolic arcs.
Originality/value
The proposed collocation method uses one-side curved and two-side straight triangular elements to derive exact subparametric shape functions.
Details
Keywords
The purpose of this paper is to investigate the indoor environmental quality benefits of plants in offices by undertaking trials using live plants.
Abstract
Purpose
The purpose of this paper is to investigate the indoor environmental quality benefits of plants in offices by undertaking trials using live plants.
Design/methodology/approach
Using two offices in the same building, one with plants and one as a control, daily tests were undertaken for relative humidity, carbon dioxide, carbon monoxide and volatile organic compounds (VOCs). Results were analysed to identify any differences between the office with plants and the one without.
Findings
Relative humidity increased following the introduction of plants and more significantly following additional hydroculture plants being installed, taking it to within the recommended range. Carbon dioxide was slightly higher in the planted office for the majority of the trial, although there was an overall reduction in both offices. Carbon monoxide levels reduced with the introduction of plants and again with the additional plants. VOC levels were consistently lower in the non‐planted office.
Research limitations/implications
It would be useful to extend this research in a greater range of buildings and with more flexible VOC‐monitoring equipment.
Practical implications
This paper suggests that plants may provide an effective method of regulating the indoor environmental conditions within buildings. This can potentially lead to performance gains for the organisation and a reduction in instances of ill health among the workforce.
Originality/value
The majority of previous studies have relied on laboratory work and experimental chambers. This research aims to apply previous findings to a real working environment to determine whether the air‐purifying abilities of plants have practical relevance in the workplace.
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Andrew Smith, Matthew Tucker and Michael Pitt
The purpose of this paper is to investigate office users' perceptions of their working environment in relation to the addition of plants.
Abstract
Purpose
The purpose of this paper is to investigate office users' perceptions of their working environment in relation to the addition of plants.
Design/methodology/approach
Office users' perceptions were examined using a survey, administered to an experimental group and a control group before and after the installation of plants. The results were analysed to determine any statistically significant differences between the two groups and between the pre‐ and post‐test surveys for the experimental group. Absence data were analysed to establish any changes in absence rates.
Findings
Significant differences were found between the experimental and control groups for the work environment contributing to pressure, health concerns, morale and preference for plants. There were also perceived improvements in productivity, pressure, privacy and comfort although these were non‐significant. Sickness absence reduced substantially in the area with plants and increased slightly in the control area.
Research limitations/implications
It would be useful to extend this research over a longer time frame and in a greater range of buildings to validate the results.
Practical implications
By providing well‐designed workplaces, including living plants, organizations can potentially improve employee perceptions, leading to performance gains and reduced absence. This paper suggests that significant savings can be achieved in comparison to the cost of plants.
Originality/value
The role of indoor nature has received relatively little attention compared to the number of studies on outdoor nature. Additionally, this paper applies the research to a real working environment as opposed to experimental designs, which have formed the majority of previous studies.
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Dacosta Essel, Zhihong Jin, Joseph Oliver Bowers and Rafiatu Abdul-Salam
The objective to achieve economic growth and sustainable development (SD) within the maritime industry has ever since been the ultimate goal of the International Maritime…
Abstract
Purpose
The objective to achieve economic growth and sustainable development (SD) within the maritime industry has ever since been the ultimate goal of the International Maritime Organization and its stakeholders. Coupled with this effect, the United Nations organization has also mandated all its bodies to adopt sustainable working policies and practices towards the achievement of SD in its 2030 Agenda. From the standpoint of an emerging economy, this study aims to examine green maritime practices adopted by maritime authorities towards the achievement of SD in the maritime industry of Ghana. The proposed conceptual model of this study supports the natural resource-based view theory advocated by Hart (1995).
Design/methodology/approach
The dataset of this study was gathered using semi-structured questionnaires. A total of 635 valid responses were received as feedback which were tested and analyzed using partial least square structural equation modelling. The rationale for the adoption of this analytical tool is its resilient ability to handle a relatively small quantity of datasets. It is also suitable for empirical studies involving model development and at the early stage of theory development.
Findings
The findings of the study are as follows; firstly, quality maritime education and training directly and significantly influence green maritime transport (GMT), clean ocean and maritime resource conservation (COMRC), green port operations and services (GPOS), SD and waste management and treatment systems (WMTS). Secondly, GMT, COMRC, GPOS and WMTS have a direct significant influence on SD. Lastly, GMT, COMRC, GPOS and WMTS partially mediate the relationship between quality maritime education and training and SD.
Practical implications
This study proposes a conceptual model that attempts to explain to maritime authorities and stakeholders that although the adoption of green maritime practices significantly influences SD, yet, it may be insufficient without quality maritime education and training provided to maritime professionals. Hence, emphasizing that all maritime personnel receive quality maritime education and training to enhance the long-term achievement of SD in the maritime industry. It also attempts to prove and suggest to maritime authorities how they can collectively integrate both onshore and offshore green maritime practices to achieve SD.
Originality/value
The originality of this study shows in testing a conceptual model that affirms that, achieving SD in the maritime industry is dependent on quality maritime education and training received by maritime personnel, hence, demonstrating the significant role of maritime training institutions towards the maritime industry and the achievement of SD.
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Gulpreet Kaur Chadha, Seema Rawat and Praveen Kumar
In this chapter, the problem of facial palsy has been addressed. Facial palsy is a term used for disruption of facial muscles and could result in temporary or permanent damage of…
Abstract
In this chapter, the problem of facial palsy has been addressed. Facial palsy is a term used for disruption of facial muscles and could result in temporary or permanent damage of the facial nerve. Patients suffering from facial palsy have issues in doing normal day-to-day activities like eating, drinking, talking, and face psychosocial distress because of their physical appearance. To diagnose and treat facial palsy, the first step is to determine the level of facial paralysis that has affected the patient. This is the most important and challenging step. The research done here proposes how quantitative technology can be used to automate the process of diagnosing the degree of facial paralysis in a fast and efficient way.
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Subbaraju Pericherla and E. Ilavarasan
Nowadays people are connected by social media like Facebook, Instagram, Twitter, YouTube and much more. Bullies take advantage of these social networks to share their comments…
Abstract
Purpose
Nowadays people are connected by social media like Facebook, Instagram, Twitter, YouTube and much more. Bullies take advantage of these social networks to share their comments. Cyberbullying is one typical kind of harassment by making aggressive comments, abuses to hurt the netizens. Social media is one of the areas where bullying happens extensively. Hence, it is necessary to develop an efficient and autonomous cyberbullying detection technique.
Design/methodology/approach
In this paper, the authors proposed a transformer network-based word embeddings approach for cyberbullying detection. RoBERTa is used to generate word embeddings and Light Gradient Boosting Machine is used as a classifier.
Findings
The proposed approach outperforms machine learning algorithms such as logistic regression, support vector machine and deep learning models such as word-level convolutional neural networks (word CNN) and character convolutional neural networks with short cuts (char CNNS) in terms of precision, recall, F1-score.
Originality/value
One of the limitations of traditional word embeddings methods is context-independent. In this work, only text data are utilized to identify cyberbullying. This work can be extended to predict cyberbullying activities in multimedia environment like image, audio and video.
Details
Keywords
Smarty P. Mukundan, Ananthi Rajayya and K. A. Zakkariya
Shubham Bharti, Arun Kumar Yadav, Mohit Kumar and Divakar Yadav
With the rise of social media platforms, an increasing number of cases of cyberbullying has reemerged. Every day, large number of people, especially teenagers, become the victim…
Abstract
Purpose
With the rise of social media platforms, an increasing number of cases of cyberbullying has reemerged. Every day, large number of people, especially teenagers, become the victim of cyber abuse. A cyberbullied person can have a long-lasting impact on his mind. Due to it, the victim may develop social anxiety, engage in self-harm, go into depression or in the extreme cases, it may lead to suicide. This paper aims to evaluate various techniques to automatically detect cyberbullying from tweets by using machine learning and deep learning approaches.
Design/methodology/approach
The authors applied machine learning algorithms approach and after analyzing the experimental results, the authors postulated that deep learning algorithms perform better for the task. Word-embedding techniques were used for word representation for our model training. Pre-trained embedding GloVe was used to generate word embedding. Different versions of GloVe were used and their performance was compared. Bi-directional long short-term memory (BLSTM) was used for classification.
Findings
The dataset contains 35,787 labeled tweets. The GloVe840 word embedding technique along with BLSTM provided the best results on the dataset with an accuracy, precision and F1 measure of 92.60%, 96.60% and 94.20%, respectively.
Research limitations/implications
If a word is not present in pre-trained embedding (GloVe), it may be given a random vector representation that may not correspond to the actual meaning of the word. It means that if a word is out of vocabulary (OOV) then it may not be represented suitably which can affect the detection of cyberbullying tweets. The problem may be rectified through the use of character level embedding of words.
Practical implications
The findings of the work may inspire entrepreneurs to leverage the proposed approach to build deployable systems to detect cyberbullying in different contexts such as workplace, school, etc and may also draw the attention of lawmakers and policymakers to create systemic tools to tackle the ills of cyberbullying.
Social implications
Cyberbullying, if effectively detected may save the victims from various psychological problems which, in turn, may lead society to a healthier and more productive life.
Originality/value
The proposed method produced results that outperform the state-of-the-art approaches in detecting cyberbullying from tweets. It uses a large dataset, created by intelligently merging two publicly available datasets. Further, a comprehensive evaluation of the proposed methodology has been presented.
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Marcus Felson and Daniel Reinhard
A growing literature emphasizes violence occurring in public places. Yet, police seldom report such violence separately from violent incidents occurring elsewhere. This paper aims…
Abstract
Purpose
A growing literature emphasizes violence occurring in public places. Yet, police seldom report such violence separately from violent incidents occurring elsewhere. This paper aims to distinguish assaults that occur in public vs private, outdoors vs indoors and in homes vs the night-time economy.
Design/methodology/approach
The authors reorganize police data to classify 1,062 assault locations for Boulder, Colorado, USA, 2020–2021, providing basic descriptive statistics that are seldom calculated or published.
Findings
In this city, almost two-thirds of police-recorded assaults occur away from home, often within night-time economy zones. Almost half of police-recorded assaults occur outdoors.
Research limitations/implications
Public assaults are probably under-reported and under-recorded in police data. The share of assaults occurring in public is likely to vary greatly among cities, along with reporting practices.
Practical implications
Public assaults can create special problems for police and social services. Poor management of public space can contribute to such violence. Alcohol policy and enforcement in public places is especially relevant to public assaults. Poor urban design might explain some of the problem.
Social implications
Public assaults are seen by many people and may do extra harm to children and even adults.
Originality/value
Police reports and academic work based on them seldom distinguish public from private assaults and seldom enumerate outdoor assaults in comparison to those indoors. In addition, statistics estimating violence in the night-time economy might not compare risks to other settings.
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Keywords
Walaa M. El-Sayed, Hazem M. El-Bakry and Salah M. El-Sayed
Wireless sensor networks (WSNs) are periodically collecting data through randomly dispersed sensors (motes), which typically consume high energy in radio communication that mainly…
Abstract
Wireless sensor networks (WSNs) are periodically collecting data through randomly dispersed sensors (motes), which typically consume high energy in radio communication that mainly leans on data transmission within the network. Furthermore, dissemination mode in WSN usually produces noisy values, incorrect measurements or missing information that affect the behaviour of WSN. In this article, a Distributed Data Predictive Model (DDPM) was proposed to extend the network lifetime by decreasing the consumption in the energy of sensor nodes. It was built upon a distributive clustering model for predicting dissemination-faults in WSN. The proposed model was developed using Recursive least squares (RLS) adaptive filter integrated with a Finite Impulse Response (FIR) filter, for removing unwanted reflections and noise accompanying of the transferred signals among the sensors, aiming to minimize the size of transferred data for providing energy efficient. The experimental results demonstrated that DDPM reduced the rate of data transmission to ∼20%. Also, it decreased the energy consumption to 95% throughout the dataset sample and upgraded the performance of the sensory network by about 19.5%. Thus, it prolonged the lifetime of the network.