The impact of emerging cloud security threats : a focus on advanced persistent threats

The rapid advancement in cloud computing technology is continually evolving, with threat actors refining their tactics, exploiting new vulnerabilities, and expanding their influence. This dynamic environment exposes cloud infrastructure to emerging cyber-attacks, including Advanced Persistent Threats (APT), impacting both customers and service providers. Understanding the gap in APT detection literature is crucial for researchers. The research aims to comprehensively understand APTs' influence on cloud security, analyse existing approaches, emulate adversary plans, simulate attacks using Mitre Caldera, employ Snort for detection, and utilise the Nessus vulnerability scanning tool. The study addresses critical questions about APTs' exploitation of cloud environments, strengths and weaknesses of mitigation methods, impacts of successful APT attacks, vulnerabilities in cloud infrastructures, and techniques for detecting APTs. The findings underscore the intricate interplay between APT activities and cloud environments, emphasising the need for robust detection and mitigation strategies. The combination of APT simulation, vulnerability assessment, and detection mechanism analysis yields invaluable insights into the evolving threat landscape within cloud ecosystems. As organisations increasingly embrace cloud technologies, the lessons from this study contribute substantially to the ongoing discourse on fortifying cloud security against persistent and evolving cyber threats

Amino clay – Copper phytate hybrid coating A sustainable approach to flame retardancy in textiles

Fire accidents are a significant cause of fatalities and property damage worldwide, with flammable textiles, especially cotton, posing a notable risk due to their widespread use and high susceptibility to ignition. To address this issue, we developed an innovative, environmentally friendly fire-retardant coating, inspired by traditional Indian practices of applying mud (clay) coatings in homes for enhanced safety. Our dual-layer coating consists of a primary layer of amino clay (AC) providing initial protection, and a secondary layer of copper phytate, which can be easily applied using a simple dip-coating technique. The coated cotton achieves a remarkable limiting oxygen index (LOI) of 61.3 %, signifying substantial fire resistance. Furthermore, in vertical flammability tests, the treated cotton immediately self-extinguishes upon ignition with no afterglow, demonstrating its high efficacy in fire prevention. This study highlights the potential of our coating to contribute to sustainable and economical fire safety solutions, aligning with global needs for enhanced fire protection in textile applications.[Display omitted]•For the first time, an amino clay (AC)-based fire-retardant coating is reported.•A combination of amino clay and copper phytate is used through the dip-coating method to create a fire-retardant coating for textiles.•The coated cotton achieves a remarkable limiting oxygen index (LOI) of 61.3 %, signifying substantial fire resistance

Effect of pandemic and lockdown on the performance and operations of farmers’ markets in Southwest, Nigeria

The study critically examined the effect of the pandemic and lockdown on the performance and operations of farmers' markets in Southwest, Nigeria. Primary data were used and the information was sourced using questionnaires. A multistage sampling technique was used to randomly select farmers for the study. Descriptive statistics, budgetary technique and two-stage least squares regression were used for the analysis. The results showed that age, revenue, perceived COVID-19 effect, household size, experience, market space acquisition, and frequent visits to farmers markets were the significant factors that influenced the performance of the farmers in the study area. Challenges faced by the farmers after lockdown on farmers markets were increased price, high cost of farm input, reduced quantity of farm products and high transportation cost. Therefore, there should be more government intervention/assistance programs as a way of assisting the farmers to boost food production and alleviate poverty in the area

Effects of intervention implemented in MOOC discussion forums contributing factors and participation analysis

Discussion Forums are important collaborative spaces to enhancelearning experiences, however, the research shows that they are usedsparingly. Hence, there is a need to design innovative strategies topromote participation in the Discussion Forums. A Moderator AugmentedExtended Discussion Forum was created based on the Learner-CentricMOOC Model. The aim of this study is to examine the effect of thisintervention on participation and course completion, as well as identifythe factors influencing forum participation through the extended UTUATmodel. Data was collected from MOOC course logs and a survey. PartialLeast Square-Structural Equation Modelling and Necessary ConditionAnalysis were used to determine the intentions to use the DiscussionForum. The results indicate that usage of the forum was instrumental inincreasing participation and motivation to complete the course.Subsequently, the results from the extended Unified Theory ofAcceptance and Use of Technology model indicate that PerformanceExpectancy, Self-Efficacy, and Discussion Forum uniqueness were themajor factors influencing the Behavioural Intention and UsageBehaviour. The study concludes that the Augmented Discussion Forumfacilitated peer learning, improved participation, and enhanced learnerperformance, thus resulting in better course completion rates. Thesefindings provide course designers, course instructors, MOOC developers,peer researchers, and academicians with insights into the optimizationof forums and adoption of learner-centric strategies while designingDiscussion Forums and related activities

The association of compassion and positive psychology among people who offend: a scoping review of the literature

PurposeGlobal recidivism rates remain high, with re-offending being a common issue. Traditional offender rehabilitation programmes often rely on the risk-need-responsivity model and cognitive behavioural therapy. However, the emergence of positive psychology and strengths-based approaches, such as the good lives model and desistance-based approaches, have started to challenge this dominant model, offering alternatives for reducing re-offending. Despite growing interest, the literature on positive psychology and compassion-based approaches in offender rehabilitation is still limited. This paper aims to explore the existing literature and assess its impact on offender populations.Design/methodology/approachA scoping review was conducted, which initially identified 925 articles. After removing duplicates and applying eligibility criteria, 46 articles were included in the final analysis.FindingsFour key themes emerged: the effects of compassion and positive psychology on emotions and traits, the role of relationships and identity, the connection between compassion and desistance, and the protective role of strengths in personal growth and reducing offending. The review underscores the potential benefits of integrating compassion and positive psychology into offender rehabilitation. However, the existing research is sparse, and further studies, such as randomised controlled trials or longitudinal research, are necessary to establish the long-term effects of these interventions on recidivism. Incorporating these approaches could signal a paradigm shift towards a more holistic, human-centred approach to offender rehabilitation.Originality/valueTo the best of the authors’ knowledge, this is the first review to explore both compassion and positive psychology-based interventions among people who offend

Automated Infrastructure Sustainability Assessment: A Deep Learning Approach For Real-Time CO2 Image Analysis

This study investigates the potential of using deep learning for real-time image analysis in assessing sustainable infrastructure and urban development. Convolutional Neural Networks (CNNs) are implemented to evaluate live-captured building images, enabling automated classification and data extraction for decision-making. The proposed approach overcomes the limitations of existing methods by facilitating real-time analysis and large-scale data processing. A dataset exceeding 12,000 images rigorously evaluates the CNN model's performance. This research establishes a framework for leveraging deep learning for real-time assessment of sustainable infrastructure, paving the way for improved data-driven urban planning and development decision-making. The study confirms that the Inception Net V3-based feature extraction technique accurately classifies images based on CO2 emission levels. This classification task is best performed using the Neural Network model. Advanced feature extraction techniques are essential for improved environmental image classification

System automation and organisation for intelligent electricity networks

The transition from conventional energy generation to clean energy generation based onrenewable energies is leading to a rapidly growing share of decentralised energy sources in theelectricity supply. As a result, fundamental changes in the electricity supply structure are takingplace, creating new challenges for the decentralised operation of future electricity grids. TheClustering Power Systems Approach (CPSA) provides a solution in terms of the organisationand subdivision of the electricity grid by allocating cluster areas for its structured automationand control. This research focuses on providing a suitable software system for decentralisedautomation and control systems based on the CPSA to meet the rapid changes and futurechallenges in electrical power networks.Using this approach, a developed software architecture design for automation and controlsystems, the so-called Smart Grid Cluster Controller (SGCC), was developed and is presentedin this doctoral thesis. A suitable method for digitally describing the structure of powernetworks and the data organisation of clustered power system status was researched, developedand validated under real grid operating conditions. The topology of decentralised power gridsis mapped by graph-based fundamental structures and enhanced by a novel Neighbour ClusterOverlapping Method (NCOM). In addition, a time-series database was used for decentralisedprocess data mapping, whereby a direct reference to the topology description was realised.Decentralised neighbouring grid cluster areas can be coordinated concerning the necessaryprocess data exchange.The results of the validated software architecture design, the graph-based cluster topologydescription using NCOM, and the organisation for decentralised process data exchange show asignificant contribution to conventional industrial automation systems for the application ofdecentralised automation and control. The results developed based on the research discussed inthis thesis provide the possibility of an organised and structured operation of increasinglydecentralised power networks

An investigation of the impact of Islamic microfinance on women's entrepreneurship development in rural areas of Bangladesh a case study of indigenous women in Chittagong

Islamic microfinance presents a promising solution for fostering women's entrepreneurship and alleviating poverty in emerging economies. This study examines the impact of Islamic microfinance (IMF) on tribal women in Bangladesh, exploring how it aids them in developing entrepreneurial initiatives. Furthermore, it seeks to investigate the effects of Islamic microfinance on tribal women's financial empowerment, the well-being of their families, poverty reduction, and access to economic resource schemes.This study utilises qualitative data collection methods to assess the impact of Islamic microfinance in achieving its objectives. Additionally, this investigation conducts semi-structured interviews with successful tribal women entrepreneurs and those aspiring to entrepreneurship. The interviews encompass two focus groups: effective tribal women entrepreneurs with at least one year of experience, and individuals who have recently participated in or are interested in entrepreneurial programmes and training to engage in entrepreneurial schemes through the Islamic microfinance sector in the future. The qualitative data is analysed through thematic analysis and coded using NVivo software.Furthermore, this thesis evaluates the impact of Islamic microfinance on tribal women's entrepreneurship development in rustic regions of Bangladesh, where there is a shortage of understanding regarding Islamic microfinance facilities. It determines how Islamic microfinance approaches can improve tribal women's entrepreneurship, increasing their contribution to household income and family welfare. In addition, it measures the extent to which Islamic microfinance approaches significantly contribute to expanding the entrepreneurial activities of tribal women. Based on empirical outcomes, the study has highlighted that Islamic microfinance programs contribute considerably to tribal women’s entrepreneurship development and poverty elimination. The research findings also underscore that, through the effective support of Islamic microfinance, tribal women entrepreneurs have contributed to women's empowerment, gender equity, household well-being, and economic enfranchisement in developing countries like Bangladesh.However, the empirical outcomes also have emphasised that tribal women entrepreneurs can succeed better if IMFIs offer them entrepreneurial schemes tailored solely for tribal women, easier access to finance, and formal entrepreneurship training. Finally, these empirical outcomes have contributed to the broader Islamic microfinance literature by reviewing Bangladesh's moderately less investigated emerging economy. This thesis also theoretically and methodologically explores Islamic microfinance's impact and influence on rural women's entrepreneurship development, specifically among tribal women in Chittagong, Bangladesh

AI Agents: A Comprehensive Review of Evolution, Architectures, Applications, and Future Directions

Technology is rapidly evolving, with the use and adoption of AI agents on the rise. This paper presents a critical review of the evolution, architectures, applications, and future directions of AI agents. This review brings to fore the fact that currently, Ai agents had become highly sophisticated, with an ability to execute tasks that are complex across several fields of such as business, health and medicine and cybersecurity. However, despite these benefits, there still exist concerns such as limited reasoning, inefficient context management, and idea generalisation. Other challenges of AI agents include algorithmic bias and privacy violations. To address the identified challenges, this paper suggests that for future works on AI agents should prioritise coming up with architectures with advanced reasoning, efficient and good memory usage, AI-human collaboration, introduction of standardised frameworks, and applications of AI in scientific discovery. The contribution of this work to the existing body of academic discussion is a comprehensive assessment of AI agents, showing the capabilities, challenges, and the areas for future development. Although AI agents promise great advantages, the utilisation of these goods that it promises depends on clear understanding to the challenges, limitations and the inherent ethical and security risks and providing clear and timely solutions through continued interdisciplinary research and engagements

Collective Violence, Strengths, and Perceived Posttraumatic Growth: A Scoping Review

Collective violence—such as armed conflict, state-sponsored violence, and terrorism—represents a profound form of trauma, which can harm individuals, communities, and societies. Existing research has largely examined risk factors and negative psychosocial outcomes from collective violence, neglecting the potential for survivors to draw upon a range of strengths that may allow them to perceive benefits from their experiences, known as posttraumatic growth (PTG). This scoping review uses the resilience portfolio model to highlight a potential portfolio of meaning-making, regulatory, and interpersonal-ecological strength-based resources and assets that are conducive to perceived PTG (PPTG) and possible better functioning following collective violence. The present review identified 52 papers from CINAHL, MEDLINE, PsycArticles, and PsychInfo, spanning from January 1995 to May 2023, which specifically focused on strengths and PTG in populations who reside (or had resided) in over 20 countries. This review highlights individual- and group-level meaning making, regulatory, and interpersonal strengths used by survivors in both individualistic and collectivist societies, providing a more comprehensive understanding of resilience and PPTG after collective violence. Some strengths, such as religious coping, positive reappraisal, and social support, demonstrated mixed relations with PPTG. The research also identified previously uncategorized ecological/systemic supports for PPTG such as political climate, access to education, and sanitation infrastructure, which require more research. The findings call for culturally sensitive approaches that recognize and promote individual and community efforts to enhance well-being among populations disproportionately affected by collective violence