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Balancing family, education and work: a qualitative study of mature women studying in higher education
Full text linkThis article explores the transformative experiences of ten mature, female, widening participation students, and their families, as they merged higher education (HE) study into the home to manage diverse demands. Set in a university in Northwest England, this smallscale, qualitative approach, influenced by social constructionism and feminism, incorporated semi-structured interviews (10) and a focus group. It explores experiences of work-life balance (WLB) and wellbeing as HE study was added to daily life and merged into the home sphere. The findings reveal the benefits of home-based study and work-based learning for mature learners with varied responsibilities. The women developed academically and practically, and transformed as they experienced university life to gain an increased sense of belonging, which normalised university attendance in households, a change here referred to as the 'learning family'
Collaboration in times of crisis: leading UK schools in the early stages of a pandemic
No full textThis paper examines the political and relational dimensions of leading and managing schools in the early stages of pandemic-induced school closure in the four nations of the United Kingdom. It draws on in-depth interviews with 12 headteachers from primary, secondary and special schools. Headteachers used adaptive leadership strategies, including bridging, brokering and buffering, to recalibrate provision at pace. School closures demanded enhanced levels of coordination and communication around what mattered most. However, despite exercising creative agency, headteachers spoke of “clipped wings”, with some feeling “vulnerable” or “alone” in attempting to mitigate often unknown risks amid constantly shifting guidance
The nexus between e-marketing, e-service quality, e-satisfaction and e-loyalty: a cross-sectional study within the context of online SMEs in Ghana
Full text linkThe spread of the Internet, the proliferation of mobile devices, and the onset of the COVID-19
pandemic have given impetus to online shopping in Ghana and the subregion. This situation
has also created opportunities for SMEs to take advantage of online marketing technologies.
However, there is a dearth of studies on the link between e-marketing and e-loyalty in terms
of online shopping, thereby creating a policy gap on the prospects for business success for
online SMEs in Ghana. Therefore, the purpose of the study was to examine the relationship
between the main independent variable, e-marketing and the main dependent variable, e-loyalty, as well as the mediating roles of e-service quality and e-satisfaction in the link between
e-marketing and e-loyalty. The study adopted a positivist stance with a quantitative method.
The study was cross-sectional in nature with the adoption of a descriptive correlational design.
A Structural Equation Modelling approach was employed to examine the nature of the
associations between the independent, mediating and dependent variables. A sensitivity
analysis was also conducted to control for the potential confounding effects of the
demographic factors. A sample size of 1,293 residents in Accra, Ghana, who had previously
shopped online, responded to structured questionnaire in an online survey via Google Docs.
The IBM SPSS Amos 24 software was used to analyse the data collected. Positive
associations were found between the key constructs in the study: e-marketing, e-service
quality, e-satisfaction and e-Loyalty. The findings from the study gave further backing to the
diffusion innovation theory, resource-based view theory, and technology acceptance model.
In addition, e-service quality and e-satisfaction individually and jointly mediated the
relationship between e-marketing and e-loyalty. However, these mediations were partial,
instead of an originally anticipated full mediation. In terms of value and contribution, this is the
first study in a developing economy context to undertake a holistic examination of the key
marketing performance variables within an online shopping context. The study uniquely tested
the mediation roles of both e-service quality and e-satisfaction in the link between e-marketing
and e-loyalty. The findings of the study are novel in the e-marketing literature as they
unearthed the key antecedents of e-loyalty for online SMEs in a developing economy context.
The study suggested areas for further related studies and also highlighted the limitations
PFDI: a precise fruit disease identifcation model based on context data fusion with faster-CNN in edge computing environment
Full text linkFruits significantly impact everyday living, i.e., Citrus fruits. Numerous fruits have a solid
nutritious value and are packed with multivitamins and trace components. Citrus fruits
are delicate and susceptible to many diseases and infections. Many researchers have
suggested deep and machine learning-based fruit disease detection and classification
models. This research presents a precise fruit disease identification model based on
context data fusion with Faster-CNN in an edge computing environment. The goal is
to develop an accurate, efficient, and trustable fruit disease detection model, a critical
component of autonomous food production in a robotic edge platform. This research
examines and explores four different diseases of Citrus fruits using CNN deep learning
models to be adopted as edge computing solutions. Identification of citrus diseases
such as cankers black spot, greening, scab, melanosis, and healthy citrus fruits are
implemented using the proposed sequential model without pruning, with pruning
having different sparsity levels followed by post quantization. Through the transfer
learning method, this model is optimized for the assignment of fruit disease detection employing visuals from two patterns: Near-infrared (NIFR) and RGB. Early and late
data fusion techniques for integrating multi-model (NIFR and RGB) facts are evaluated.
The accuracy obtained from the proposed model for the canker disease is 97%, scab
95%, melanosis 99%, Greening 97%, Black spot 97% and healthy 97%. In this paper, the
results of the proposed model are compared and evaluated with the sparsity levels
of 50–80%, 60–90%, 70–90%, and 80–90% pruning and also obtained the results of
post-quantization on each level. The results show that the model size with 60–90%
pruning can be counteracted to the 47.64 of the baseline model without significant
loss of accuracy. Moreover, post-quantization can reduce the 60–90% pruning from
28.16 to 8.72. In addition to enhanced precision, the above initiative is much faster to
implement for new fruit diseases because it needs bounding box annotation instead of
pixel-level annotation
Post-COVID-19: can digital solutions lead to a more equitable global healthcare workforce?
Full text linkAn unintended consequence of the COVID-19 pandemic has been the exponential growth of telemedicine, with automation of healthcare becoming more common. Face-to-face meetings and training events have been replaced relatively seamlessly with online versions, taking clinical or academic expertise to distant parts of the world and making them more accessible and affordable. The wide reach of digital platforms offering remote healthcare offers the opportunity of democratising access to high-quality healthcare, However, certain challenges remain: (a) clinical guidance developed in one geographical area may need adaptation for use in others; (b) regulatory mechanisms from one jurisdiction need to offer patient safety across other jurisdictions; (c) barriers created by disparity in technology infrastructure and the variation in pay for services across different economies, leading to brain drain and an inequitable workforce. The World Health Organization's Global Code of Practice on the International Recruitment of Health Personnel could offer the preliminary framework on which solutions to these challenges could be built
Data-driven analysis of privacy policies using LexRank and KL summarizer for environmental sustainability
Full text linkNatural language processing (NLP) is a field in machine learning that analyses and manipulate huge amounts of data and generates human language. There are a variety of applications of NLP such as sentiment analysis, text summarization, spam filtering, language translation, etc. Since privacy documents are important and legal, they play a vital part in any agreement. These documents are very long, but the important points still have to be read thoroughly. Customers might not have the necessary time or the knowledge to understand all the complexities of a privacy policy document. In this context, this paper proposes an optimal model to summarize the privacy policy in the best possible way. The methodology of text summarization is the process where the summaries from the original huge text are extracted without losing any vital information. Using the proposed idea of a common word reduction process combined with natural language processing algorithms, this paper extracts the sentences in the privacy policy document that hold high weightage and displays them to the customer, and it can save the customer’s time from reading through the entire policy while also providing the customers with only the important lines that they need to know before signing the document. The proposed method uses two different extractive text summarization algorithms, namely LexRank and Kullback Leibler (KL) Summarizer, to summarize the obtained text. According to the results, the summarized sentences obtained via the common word reduction process and text summarization algorithms were more significant than the raw privacy policy text. The introduction of this novel methodology helps to find certain important common words used in a particular sector to a greater depth, thus allowing more in-depth study of a privacy policy. Using the common word reduction process, the sentences were reduced by 14.63%, and by applying extractive NLP algorithms, significant sentences were obtained. The results after applying NLP algorithms showed a 191.52% increase in the repetition of common words in each sentence using the KL summarizer algorithm, while the LexRank algorithm showed a 361.01% increase in the repetition of common words. This implies that common words play a large role in determining a sector’s privacy policies, making our proposed method a real-world solution for environmental sustainability
Internet of Vehicles-based application using deep learning approach
Full text linkEdge Computing is an optimistic technology that can extend the necessary support for vehicular
applications. In this paper, an effective edge-computing framework is developed to improvise
task scheduling. A task partition and scheduling algorithm are developed to decide the workload allocation and schedule the execution order of tasks offloaded. Then, according to the
characteristics of task scheduling, design the corresponding state-action space and reward
function; and finally, taking into consideration the complexity of task scheduling and computing
resource allocation, the pointer network is trained by multi-agent fuzzy deep reinforcement
learning; this allows the pointer network to account for the dynamic nature of During the process
of network fusion, it is used to find a solution for the issue of weight distribution for each agent.
The simulation showcases that the proposed method is superior. Furthermore, it has significant
advantages in terms of convergence speed and optimal performance. It has a high degree of
flexibility in the ever-changing and intricate electromagnetic environment. The capabilities of
the Internet of Vehicles' job offloading system have been significantly increased because of this
improvement.
It is widely believed that the Internet of Vehicles (IoV), which incorporates cutting-edge
technologies such as connectivity, big data, and artificial intelligence, will play a significant role
in the development of the next-generation intelligent transportation system. In recent years, the
Internet of Vehicles has given rise to a significant number of novel computer jobs, such as
augmented reality and autonomous driving, to name a few. The completion of these computer
jobs must adhere to stringent real-time constraints, and it takes a significant amount of
computing resources to bring these tasks to a successful conclusion. Since the volume, weight,
and other limitations that restrict vehicles prevent them from being outfitted with powerful
computing devices, the computing resources of the onboard devices that are now in use are often
not enough to fulfill the processing requirements of these jobs. Install edge servers in the
immediate area of the vehicle. Edge computing, in contrast to cloud computing, can provide
consumers with computer services that are located relatively near them. Instead of being sent to
the cloud, the computing duties that are created by the vehicle are immediately offloaded to the
edge server. This reduces the amount of time it takes for computing activities to be transmitted.
As a result, the implementation of edge computing in IOVs is a potential solution to the problem
of inadequate processing power shown by vehicles and a means of satisfying the criteria of low
latency imposed by tasks.
The offloading of computational duties, in general, may effectively lower the amount of energy
that the vehicle requires to operate. Offloading chores is something that consumers are often
more likely to do in the interest of keeping the vehicle's battery alive for as long as possible. The
number of responsibilities that must be offloaded and carried out inside the Internet of Vehicles
will continue to grow because of this. When a significant number of tasks are offloaded and
performed, the server is unable to provide computer resources for all the tasks at the same time.
This means that tasks that are not allocated to computing resources must wait to be executed.
At present, it is not possible to disregard the waiting time if the computing jobs that are now
queued up to be done have delay requirements. Therefore, to effectively offer computing
services for a greater number of offloading jobs, it is important to establish an acceptable
scheduling strategy according to the execution time and delay needs of computing tasks.
This paper integrates software-defined networking (SDN) into the Internet of Vehicles,
constructs an SDN-assisted computing task offloading system for the Internet of Vehicles in an
edge computing environment, and presents a task of computing offloading for vehicles. This is
done since SDN can manage network resources more conveniently and effectively. Scheduling
model. After that, an improved pointer network is trained using deep reinforcement learning to
solve the offload scheduling problem of delay-constrained computing tasks in multi-edge
servers on the Internet of Vehicles. This is done in consideration of the complexity of task
scheduling and the allocation of computing resources
Secure web gateway on website in cloud
No full textDeveloping interest for transfer work from home associations needs to permit numbers of specialists to get to confidential organizations on the business nearby web. This leads to greater expenses to authorize the homeworker admittance to the association's assets secretly and safely through assigned gadgets and administration. The primary purpose of additionally supports the association execution with various administrations for telecommuters. SASE utilizes Zero Trust Engineering as its spine, without confiding in any gadget or client, yet validate and approve at each solicitation. The main purpose is to apply designated spot capabilities such as Secure Web Passage and Cloud Access Security Representative to additionally uphold the security of the association's resources that may not be trusted in the cloud. Toward this paper's end, we will comprehend how those strategies referenced for doing the work on secure systems for work with associations’ system associations and security. We conduct a systematic literature review of security challenges of cloud computing. In addition to security issues, the benefits of cloud computing security were also studied. Whenever a website is hacked by an attacker, businesses and organizations lose their valuable data. Thus, we choose the best platform to organize and secure the data online in a valuable way. The main point of attack in the website is the use of weak passwords and sharing authentication to other users
Innovative augmented and virtual reality applications for disease diagnosis based on integrated genetic algorithms
Full text linkIn this comprehensive paper the method of detecting various diseases within short period of time using Audio Reality/ Virtual Reality (AR/VR) techniques is proposed. For proper functioning of AR/VR models in medical applications three distinct algorithms such Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) is integrated where the entire operation is performed with respect to search space. Moreover, the detection process in AR/VR models depends on several factors where minimum error functions must be ensured. Hence in the integrated technique both Absolute Errors (AE) and Time Errors (TE) are measured and compared with existing methods. As the performance of detection is greatly improved with search space the fitness function of each algorithm is observed and it is considered as maximization objective in the proposed method. Furthermore, the complexity of AR/VR models in real time detection process is detected and it is realistic that high complex detections are converted to simple detections. In the comparative analysis of three algorithms ACO proves to be much better as errors are minimized with maximization of fitness function
Innovation and commercialisation: the role of the international dynamic marketing capability in Malaysian international entrepreneurial firms
Full text linkPurpose. This study investigated how international entrepreneurial firms (IEFs) successfully commercialise innovative products/services internationally. In doing so, the authors examined the role played by the international dynamic marketing capability (IDMC) in the relationship between explorative and exploitative innovation and commercialisation. In addition, the authors also evaluated how the breadth and depth of international networks facilitate IEFs in upholding the effects of the IDMC to influence commercialisation.
Design/methodology/approach. To test the research model, structural equation modelling is used based on time-lagged survey data drawn from 201 Malaysian IEFs. To validate the results, additional robustness tests and endogeneity analyses have been performed.
Findings. The findings show that the IDMC positively mediates the relationship between explorative and exploitative innovation and commercialisation. Furthermore, the finding exhibits that the effects of the IDMC on commercialisation are positively moderated by the breadth and depth of international networks.
Originality. Given the fragmented and general nature of the extant marketing research on the IDMC, the study contributes to the international marketing literature by providing rich and nuanced pertinent knowledge. This study advances dynamic capability theory in relation to IEFs by establishing the IDMC as a functional capability suited to enable them to successfully commercialise the products/services resulting from explorative and exploitative innovation