5,143 research outputs found
A Survey on Forensics and Compliance Auditing for Critical Infrastructure Protection
The broadening dependency and reliance that modern societies have on essential services
provided by Critical Infrastructures is increasing the relevance of their trustworthiness. However, Critical
Infrastructures are attractive targets for cyberattacks, due to the potential for considerable impact, not just
at the economic level but also in terms of physical damage and even loss of human life. Complementing
traditional security mechanisms, forensics and compliance audit processes play an important role in ensuring
Critical Infrastructure trustworthiness. Compliance auditing contributes to checking if security measures are
in place and compliant with standards and internal policies. Forensics assist the investigation of past security
incidents. Since these two areas significantly overlap, in terms of data sources, tools and techniques, they can
be merged into unified Forensics and Compliance Auditing (FCA) frameworks. In this paper, we survey the
latest developments, methodologies, challenges, and solutions addressing forensics and compliance auditing
in the scope of Critical Infrastructure Protection. This survey focuses on relevant contributions, capable of
tackling the requirements imposed by massively distributed and complex Industrial Automation and Control
Systems, in terms of handling large volumes of heterogeneous data (that can be noisy, ambiguous, and
redundant) for analytic purposes, with adequate performance and reliability. The achieved results produced
a taxonomy in the field of FCA whose key categories denote the relevant topics in the literature. Also, the
collected knowledge resulted in the establishment of a reference FCA architecture, proposed as a generic
template for a converged platform. These results are intended to guide future research on forensics and
compliance auditing for Critical Infrastructure Protection.info:eu-repo/semantics/publishedVersio
Digital Innovations for Occupational Safety: Empowering Workers in Hazardous Environments
Background:
The quest to increase safety awareness, make job sites safer, and promote decent work for all has led to the utilization of digital technologies in hazardous occupations. This study investigated the use of digital innovations for safety and health management in hazardous industries. The key challenges and recommendations associated with such use were also explored.
Method:
Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a total of 48 studies were reviewed to provide a framework for future pathways for the effective implementation of these innovations.
Findings:
The results revealed four main categories of digital safety systems: wearable-based systems, augmented/virtual reality-based systems, artificial intelligence-based systems, and navigation-based systems. A wide range of technological, behavioral, and organizational challenges were identified in relation to the key themes.
Conclusion:
Outcomes from this review can inform policymakers and industrial decision-makers about the application of digital innovations for best safety practices in various hazardous work conditions
Sustainable Collaboration: Federated Learning for Environmentally Conscious Forest Fire Classification in Green Internet of Things (IoT)
Forests are an invaluable natural resource, playing a crucial role in the regulation of both local and global climate patterns. Additionally, they offer a plethora of benefits such as medicinal plants, food, and non-timber forest products. However, with the growing global population, the demand for forest resources has escalated, leading to a decline in their abundance. The reduction in forest density has detrimental impacts on global temperatures and raises the likelihood of forest fires. To address these challenges, this paper introduces a Federated Learning framework empowered by the Internet of Things (IoT). The proposed framework integrates with an Intelligent system, leveraging mounted cameras strategically positioned in highly vulnerable areas susceptible to forest fires. This integration enables the timely detection and monitoring of forest fire occurrences and plays its part in avoiding major catastrophes. The proposed framework incorporates the Federated Stochastic Gradient Descent (FedSGD) technique to aggregate the global model in the cloud. The dataset employed in this study comprises two classes: fire and non-fire images. This dataset is distributed among five nodes, allowing each node to independently train the model on their respective devices. Following the local training, the learned parameters are shared with the cloud for aggregation, ensuring a collective and comprehensive global model. The effectiveness of the proposed framework is assessed by comparing its performance metrics with the recent work. The proposed algorithm achieved an accuracy of 99.27 % and stands out by leveraging the concept of collaborative learning. This approach distributes the workload among nodes, relieving the server from excessive burden. Each node is empowered to obtain the best possible model for classification, even if it possesses limited data. This collaborative learning paradigm enhances the overall efficiency and effectiveness of the classification process, ensuring optimal results in scenarios where data availability may be constrained
Southern Adventist University Undergraduate Catalog 2023-2024
Southern Adventist University\u27s undergraduate catalog for the academic year 2023-2024.https://knowledge.e.southern.edu/undergrad_catalog/1123/thumbnail.jp
Hybrid energy system integration and management for solar energy: a review
The conventional grid is increasingly integrating renewable energy sources like solar energy to lower carbon emissions and other greenhouse gases. While energy management systems support grid integration by balancing power supply with demand, they are usually either predictive or real-time and therefore unable to utilise the full array of supply and demand responses, limiting grid integration of renewable energy sources. This limitation is overcome by an integrated energy management system. This review examines various concepts related to the integrated energy management system such as the power system configurations it operates in, and the types of supply and demand side responses. These concepts and approaches are particularly relevant for power systems that rely heavily on solar energy and have constraints on energy supply and costs. Building on from there, a comprehensive overview of current research and progress regarding the development of integrated energy management system frameworks, that have both predictive and real-time energy management capabilities, is provided. The potential benefits of an energy management system that integrates solar power forecasting, demand-side management, and supply-side management are explored. Furthermore, design considerations are proposed for creating solar energy forecasting models. The findings from this review have the potential to inform ongoing studies on the design and implementation of integrated energy management system, and their effect on power systems
The Pragmatic Development of a Carbon Management Framework for UK SMEs
The UK's commitment to net-zero emissions by 2050 is challenged by critics citing current government strategies as inadequate, marked by a lack of concrete action and aspirational guidelines. Notably, businesses, including small and medium-sized enterprises (SMEs) which constitute about half of all business emissions, are pivotal to this goal. Yet, existing policies and standards often neglect the significant role of SMEs, who face barriers such as limited knowledge and resources in implementing carbon management practices.
This thesis explores the development of a novel carbon management framework specifically designed for medium-sized organisations in the UK to address these problems. The research adopts a practical approach through collaboration with an industry partner, facilitating a case study for real-world application.
Adopting a mixed-methods research design grounded in pragmatism, the study commenced with a qualitative study in the form of a focus group. This exploratory phase, critical for understanding SME challenges, yielded rich data revealing key management themes in strategy, energy, and data. The framework design was supported by a materiality assessment and input from key stakeholders on three major iterations. The final framework comprises three phases: establishing a baseline carbon footprint, creating a carbon reduction plan, and strategically implementing this plan. The validation process, conducted at Knowsley Safari, successfully tested the initial two phases but faced constraints in fully assessing the third phase due to time limitations.
While the research achieved its primary aim of developing a novel carbon management framework for SMEs, it encountered limitations, notably in time and the generalisability of findings due to reliance on a single case study. Future research could test the framework across diverse SME settings to establish its broader applicability and effectiveness in aiding the UK's net-zero emission goals
Natural and Technological Hazards in Urban Areas
Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events
Studio e progettazione di un impianto di trattamento di fogli in poliuretano destinati alla filtrazione del sangue
Lo studio e la progettazione di un impianto produttivo su scala industriale coinvolgono due figure principali: il cliente, o ente commissionante, che definisce gli obiettivi di processo, i requisiti di natura tecnica e i vincoli di progetto; l’ente progettista, privato o azienda, che, con la propria esperienza e competenze, traduce le direttive del cliente in una serie di scelte impiantistiche, realizzando un impianto industriale che soddisfi le richieste dell’ente commissionante.
L’elaborato è volto allo studio e alla progettazione di un impianto di lavorazione di un prodotto biomedicale, percorrendo alcune delle principali fasi inerenti all’industrializzazione di un processo produttivo. Il metodo di progettazione utilizzato verte sull’illustrare le attività di carattere analitico, decisionale e progettuale che vengono condotte dall’ente commissionante e dall’ente progettista.
Il prodotto biomedicale di interesse è una schiuma poliuretanica, in forma di fogli di dimensioni definite, utilizzata in applicazioni di trasporto e filtraggio del sangue. Il trattamento da eseguire ha lo scopo di rimuovere un residuo di lavorazione generatosi durante la reazione di polimerizzazione del poliuretano, ed evitare la formazione di schiuma durante il passaggio del sangue attraverso il prodotto.
Il metodo di progettazione seguito si sviluppa in diverse fasi: lo svolgimento di uno studio di processo dedito al riconoscere le scelte progettuali da intraprendere; l’utilizzo di un impianto pilota per validare le tecniche di lavorazione selezionate e individuare i parametri di processo influenti sul risultato finale; la scrittura di un’analisi di rischio preliminare sull’impianto pilota, per individuare le azioni preventive da implementare per rendere l’impianto futuro intrinsecamente sicuro; la stesura del documento di User Requirements Specification; la descrizione di un’ipotesi di impianto industriale funzionante, con annessa una stima preliminare del costo capitale complessivo
Unveiling the frontiers of deep learning: innovations shaping diverse domains
Deep learning (DL) enables the development of computer models that are
capable of learning, visualizing, optimizing, refining, and predicting data. In
recent years, DL has been applied in a range of fields, including audio-visual
data processing, agriculture, transportation prediction, natural language,
biomedicine, disaster management, bioinformatics, drug design, genomics, face
recognition, and ecology. To explore the current state of deep learning, it is
necessary to investigate the latest developments and applications of deep
learning in these disciplines. However, the literature is lacking in exploring
the applications of deep learning in all potential sectors. This paper thus
extensively investigates the potential applications of deep learning across all
major fields of study as well as the associated benefits and challenges. As
evidenced in the literature, DL exhibits accuracy in prediction and analysis,
makes it a powerful computational tool, and has the ability to articulate
itself and optimize, making it effective in processing data with no prior
training. Given its independence from training data, deep learning necessitates
massive amounts of data for effective analysis and processing, much like data
volume. To handle the challenge of compiling huge amounts of medical,
scientific, healthcare, and environmental data for use in deep learning, gated
architectures like LSTMs and GRUs can be utilized. For multimodal learning,
shared neurons in the neural network for all activities and specialized neurons
for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
Analysis of the Adherence of mHealth Applications to HIPAA Technical Safeguards
The proliferation of mobile health technology, or mHealth apps, has made it essential to protect individual health details. People now have easy access to digital platforms that allow them to save, share, and access their medical data and treatment information as well as easily monitor and manage health-related issues. It is crucial to make sure that protected health information (PHI) is effectively and securely transmitted, received, created, and maintained in accordance with the rules outlined by the Health Insurance Portability and Accountability Act (HIPAA), as the use of mHealth apps increases. Unfortunately, many mobile app developers, particularly those of mHealth apps, do not completely understand the HIPAA security and privacy requirements. This offers a unique opportunity for research to create an analytical framework that can help programmers maintain safe and HIPAA-compliant source code while also educating users about the security and privacy of private health information. The plan is to develop a framework which will serve as the foundation for developing an integrated development environment (IDE) plugin for mHealth app developers and a web-based interface for mHealth app consumers. This will help developers identify and address HIPAA compliance issues during the development process and provide consumers with a tool to evaluate the privacy and security of mHealth apps before downloading and using them. The goal is to encourage the development of secure and compliant mHealth apps that safeguard personal health information
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