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Human emotions recognition, analysis and transformation by bioenergy field in smart grid using image processing
Get PDFThe passage of electric signals throughout the human body produces an electromagnetic
field, known as the human-biofield, carries information about a person's psychological
health. The human biofield can be rehabilitated by using healing techniques like sound
therapy, and many others in smart grid. However, psychiatrists, and psychologists often
face difficulties in clarifying the mental state of a patient in a quantifiable form. Therefore,
the objective of this research work was to transform human emotions using sound healing
therapy and produce visible results as a novel. The present research is based on the
amalgamation of image processing and machine learning techniques, including a real-time
aura-visualization-interpretation and an emotion-detection classifier. The experimental
results highlight the effectiveness of healing emotions through the aforementioned
techniques. The accuracy of the proposed method, specifically the module combining both
emotion and aura, was determined to be ~88%. Additionally, the participants’ feedbacks
were recorded and analyzed based on prediction and overall satisfaction. The participants
were strongly satisfied with the prediction level (~81%) and future recommendation level
(~84%). The results indicate the positive impact of sound therapy on emotions and the
biofield. In future, experimentation using different therapies, and integrating more
advanced techniques are anticipated to open a new gateways in healthcare
Victim–Survivor–Warrior–Healer: An autoethnographic account of a male childhood sexual violence survivor’s activist journey
Get PDFIt has been argued that stories inform our perceptions of reality and social change
is driven by stories (Sarbin, 1986; Bochner, 2012; Frank, 2011/2013). Sexual violence is a
complex cultural challenge for societies (Rape Crisis, 2020). Individual survivor identity is
formed in that complexity and personal posttraumatic growth (PTG) can be forged in such
challenges (Tedeschi & Calhoun, 2004). Activism is one way the survivor can help forge
social change both for themselves and the ‘community of interest’ they belong to (Raskovic,
2020; Herman, 1992). This article uses autoethnography to explore one male survivor’s
story of childhood sexual violence and his 22-year journey of activism. It adopts a novel
approach weaving metaphors taken from episodes of the long-running British television
series Doctor Who. It attempts to link social action to PTG in its reflections on meaning and
redemption beyond shame via activism and lived experience witnessing (Bruner, 2002).
The power of lived experience can powerfully bring the ‘unspeakable’ to society’s conscious
awareness (Herman, 1992; Balfour, 2013). By sharing the raw reality of victim blaming
when challenging the status quo. The reality of political and professional agents’ resistance
to change is evidenced. It uses psychological and other theories, aiming to weave them
through the story and illuminate one activist’s journey. Its limitation is its just one story,
However, within that lies an authentic strength. It does not claim to be objective. Instead,
it knits both the subjective and objective together to allow you to experience something as
old as humans, a real story told in a new form (Gottschall, 2012
Time-series data modelling using advanced machine learning and AutoML – experimental work
Get PDFA prominent area of data analytics is "time-series modeling" where it is possible to forecast future values for the same variable using previous data. Numerous usage examples, including the economy, the weather, stock prices, and the development of a corporation, demonstrate its significance. Experiments with time series forecasting utilizing machine learning (ML), deep learning (DL), and AutoML are conducted in this paper. Its primary contribution consists of addressing the forecasting problem by experimenting with additional ML and DL models and AutoML frameworks and expanding the AutoML experimental knowledge. In addition, it contributes by breaking down barriers found in past experimental studies in this field by using more sophisticated methods.
The datasets this empirical research utilized were secondary quantitative of the real prices of the currently most used cryptocurrencies. We found that AutoML for time-series is still in the development stage and necessitates more study to be a viable solution since it was unable to outperform
manually designed ML and DL models. The demonstrated approaches may be utilized as a baseline
for predicting time-series data
Flame retardants for epoxy resins: Application-related challenges and solutions
Get PDFOwing to their high versatility from chemical and processing perspectives and hence their
capability of being tailored for required properties, epoxy resins are used in a wide range of
applications ranging from general use to high performing materials. Most of the applications
though also require conformation to certain specified fire safety regulations. The flammability
(and other properties) of cured epoxy resins depend on the type of resin, curing agent and curing
process used, which have been highlighted in this article. The focus of the review though is on the
type of flame retardants required to achieve certain levels of flame retardancy. There are
numerous research articles and reviews dealing with flame retardancy of epoxy resins in the
open literature and it is beyond the scope of this review to cover them all, hence only selected
representative papers are discussed here, while references to previous reviews are provided that
cover additional work. Different flame retardants and their chemically modified/synthesized
variants developed by various researchers have been critically reviewed in terms of their flame
retardant efficiency relative to their commonly used/ commercially available counterparts. The
issues related to their suitability in terms of processability and performance in certain
applications have also been discussed
Secure smart wearable computing through Artificial Intelligence-enabled Internet of Things and Cyber-Physical Systems for health monitoring
Get PDFThe functionality of the Internet is continually changing from the Internet of Computers (IoC) to the
“Internet of Things (IoT)”. Most connected systems, called Cyber-Physical Systems (CPS), are formed from the
integration of numerous features such as humans and the physical environment, smart objects, and embedded
devices and infrastructure. There are a few critical problems, such as security risks and ethical issues that
could affect the IoT and CPS. When every piece of data and device is connected and obtainable on the
network, hackers can obtain it and utilise it for different scams. In medical healthcare IoT-CPS, everyday
medical and physical data of a patient may be gathered through wearable sensors. This paper proposes an AI-enabled IoT-CPS which doctors can utilise to discover diseases in patients based on AI. AI was created to find
a few disorders such as Diabetes, Heart disease and Gait disturbances. Each disease has various symptoms
among patients or elderly. Dataset is retrieved from the Kaggle repository to execute AI-enabled IoT-CPS
technology. For the classification, AI-enabled IoT-CPS Algorithm is used to discover diseases. The
experimental results demonstrate that compared with existing algorithms, the proposed AI-enabled IoT-CPS
algorithm detects patient diseases and fall events in elderly more efficiently in terms of Accuracy, Precision,
Recall and F-measure
Covid-19 fake news sentiment analysis
Get PDF’Fake news’ refers to the misinformation presented about issues or events, such as COVID-19.
Meanwhile, social media giants claimed to take COVID-19 related misinformation seriously,
however, they have been ineffectual. This research uses the Information fusion to obtain real
news data from News Broadcasting, Health, and Government websites, while Fake News data
are collected from social media sites. 39 features were created from multimedia texts and used to
detect fake news regarding COVID-19 using state-of-the-art deep learning models. Our model’s
fake news feature extraction improved accuracy from 59.20% to 86.12%. Overall high precision
is 85% using the Recurrent Neural Network (RNN) model; our best recall and F1-Measure for
fake news were 83% using the Gated Recurrent Units (GRU) model. Similarly, precision, recall,
and F1-Measure for real news are 88%, 90%, and 88% using the GRU, RNN, and Long short-term memory (LSTM) model, respectively. Our model outperformed standard machine learning algorithms
Settling into life at University
No full textThis chapter aims to:
• Demystify the university environment by exploring more about how universities work and what they
can offer to students.
• Alleviate concerns which students may have about their transition to university and provide advice as
to how best to prepare for academic life, both prior to starting their course and throughout the duration
of their studies.
• Set expectations for students to enhance their approach to and responsibility for studying and to grasp
opportunities that university life affords them.
• Encourage reflection on the skills students currently possess and those which can be developed throughout their student life to enhance successful transition into further study or their chosen career
The Big Five Personality Traits as predictors of life satisfaction in Egyptian college students
Get PDFSeveral studies have indicated significant relations between the Big Five personality traits and life satisfaction. However, most of these studies have been carried out on Western samples. The present study aimed to explore the Big Five predictors of life satisfaction in an under-studied sample of Egyptian college students (N = 1,418). They responded to a self-rating scale of life satisfaction and the Arabic Big Five Personality Inventory. Both scales have acceptable to good reliabilities and validities. Men obtained significantly higher mean total scores than did women for extraversion, openness, and conscientiousness, whereas women obtained higher mean total scores than did their male counterparts on neuroticism and agreeableness. In both sexes, all the Pearson correlations between the Big Five and life satisfaction were significant and positive except for neuroticism (negative). The strongest correlation with life satisfaction scores was for neuroticism (negative). Principal components analysis extracted two components in both genders which were labelled: “Positive traits”, and “Well-Being versus neuroticism”. Big Five traits accounted for approximately 22% of the variance in life satisfaction scores among men, and 17% in women. Predictors of life satisfaction were low neuroticism, conscientiousness, extraversion, openness (men), low neuroticism and conscientiousness (women). It was concluded that personality traits are important for life satisfaction in the present sample of Egyptian college students. By and large, the relationships observed in Egyptian college students reflect the general pattern observed in other samples
SVM based generative adverserial networks for federated learning and edge computing attack model and outpoising
Get PDFMachine learning algorithms are prone to
attacks: An attackers can use the malicious
nodes to attack the training dataset to manipulate the process of learning and reduce
the efficiency of the algorithm working performance. Optimal poisoning attacks have
already been proposed to evaluate worst case scenarios, modelling attacks as a bilevel optimization problem. Solving these
problems is computationally demanding and
has limited applicability for some models
such as deep networks. In this paper we
introduce a novel generative model to craft
systematic poisoning attacks against machine
learning classifiers generating adversarial training examples, i.e. samples that look like genuine data points but that reduce the accuracy of the classifier in the process of training process. The proposed system have 3
components of Generative Adverserial networks (GAN) generator, discriminator, and
the target classifier. The proposed system
allows to detect the vulnerability easy and
it can be found as similar as realistic attacks
to detect the area where the underlying data
distribution have more possibility of poising attack which cause vulnerability to the
network. Our experimentation, proves the
claim our that the proposed model is effective on compromising the classifiers uses
the machine learning algorithms and also
deep learning networks
Energy harvesting based on triboelectric nanogenerators (TENGs) and applications
Get PDFThe rapid development of technologies and great progress of society has
placed serious demands on the supply of fossil fuels and ensued consequential
environmental pollution. Harvesting ambient energy from the environment is
regarded as an effective way to deal with this energy crisis. Recently,
triboelectric nanogenerators (TENG) based on contact electrification and
electrostatic induction have been demonstrated to be a novel and efficient
technique for efficiently harvesting ambient mechanical energy. TENGs have
the advantages of simple structure, low cost and easy fabrication, high energy
conversion efficiency at low frequencies (1-10 Hz), and are suitable for
applications of wearable and implantable electronics. The key to the
development of TENG is the high-performance output based on small size and
application as a self-powered sensor, which can be used to monitor
environmental changes such as temperature and humidity. This doctoral
research aims to explore new strategies to enhance the output of TENGs and
to develop TENG based self-powered sensors. The research work carried out
and the results obtained are summarized as follows.
Firstly, although significant work has been carried out to develop materials with
high surface charge density for high-performance TENG, little attention has
been paid to the roles of electrode materials that are responsible for charge
collection. This work reports on a facile synthesis of laser-induced graphene
(LIG) as high-efficiency electrodes for TENGs. Tribo-negative polyimide (PI)
and tribo-positive cellulosic paper were converted into PI-LIG and paper-LIG,
respectively, by a direct photothermal process using a conventional CO2 laser.
The LIG-based TENGs showed higher electrical output characteristics with a
peak-to-peak voltage of up to ~625 Vp-p, a current density of ~20 mA.m-2 and a
transferred charge density of ~138 μC.m-2 with a maximum power output of ~2.25 W.m-2
, respectively, while the corresponding values for the conventional
Al-tape electrode-based paper-PI TENGs were 400 Vp-p, ~10 mA.m-2
, ~85
μC.m-2 and 0.9 W.m-2
, respectively. The mechanically robust LIG electrodes
show excellent stability with less than 5.0% variation in output over 12,000
contact cycles. Kelvin probe force microscopy (KPFM) measurements
confirmed that the average surface potentials of the LIG triboelectric surfaces
are smaller than those of the pristine ones, indicating the role of initial surface
chemistry in the formation of LIGs and the performance of TENG. The
performance enhancement for LIG-based TENGs is ascribed to the lowering of
the charge transfer barrier energy which results in a higher surface charge of
the dielectric layer, and the significantly (~ 6 orders) lower contact impedance
of LIG electrodes. Thus, via the removal of the additional interface between the
triboelectric surface and electrode, high-performance metal-free TENGs with
excellent prospects for enabling energy harvesting applications can be realised.
Secondly, a TENG based on the polarization effect of piezoelectric
nanomaterials working together with the piezoelectric and triboelectric effects
was proposed as a new strategy. The polarization effect of piezoelectric
material can provide a higher surface charge to the friction layer. For this, a
variety of ZnO materials with different nanostructures were prepared and
applied to TENGs to compare the effect of the effective contact area on the
output performance of TENGs. Compared with the pristine PDMS-based TENG,
the outputs of the five ZnO-PDMS TENG with different nanostructures have
been significantly improved. The TENG with disk-like nanostructure ZnO-PDMS
shows the highest peak output of ~780Vp-p, which is 136% higher than the
pristine PDMS-based TENG (~330Vp-p). Correspondingly, the short-circuit
current density and charge density has been increased by 205% and 114%,
respectively. The nanoflowers nanostructure showed the lowest peak output of ~470Vp-p, which was 42% higher than the pristine PDMS TENG.
Correspondingly, the short-circuit current density and charge density rose by
62% and 28%, from 52 mA.m-2
to 84 mA.m-2
, and from ~80 μC.m-2
to ~102
μC.m-2
, respectively. The enhancement produced by the disk-like ZnO
nanostructures arose from the increase in the surface contact area in the
vertical direction to the greatest extent. Furthermore, the surface charge
enhancement and distribution of ZnO with different nanostructures were
demonstrated by the piezoelectric microscopy (PFM).
Finally, utilizing wide absorption characteristics of a narrow bandgap (~1.8 eV)
semiconductor, we report on Bismuth Oxyiodide (BiOI) based photo-enhanced
TENG. The tribo-positive BiOI film deposited electrochemically on transparent
Fluorine doped Indium Tin Oxide (FTO) substrate provided a way to exploit
concurrently the photo-enhanced charge generation and triboelectric effects.
When utilized against tribo-negative PDMS films, under illumination, the
BiOI/PDMS TENGs’ outputs were significantly enhanced, wherein an increase
of 21% in peak to peak output voltage (from 59Vp-p to 73Vp-p, 38% in charge
density (from 40µC.m-2
to 55µC.m-2
), and 74% in overall power density (from
0.25 W.m-2
(in dark) and 0.44 W.m-2
(under illumination)), respectively, were
observed. Correspondingly, a dramatic enhancement (from ~25 mV to ~300 mV)
in the average surface potential, termed as surface photovoltage (SPV), for the
illuminated BiOI was observed by KPFM. For an isolated, grounded BiOI/FTO
electrode, this SPV increase is slow-decaying (~3.5 h) and is attributed to the
high dielectric constant, presence of deep-traps within BiOI, and slow charge-exchange with the ambient environment. The work thus not only provides an
approach for the enhancement of mechanical-to-electrical efficiency of TENGs
by light absorption, but also can be utilized for self-powered detection of
electromagnetic radiation and photodetectors.
All the high-performance TENGs produced have the potential to be used in the
realisation of self-powered systems and can be of great significance as a new
alternative energy harvesting source