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Privacy and security concerns shaping smart city adoption: Evidence from Qatar
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The research data are available at https://figshare.com/articles/journal_contribution/Untitled_It_b_Data_Privacy_and_Security_Concerns_and_Readiness_to_Accept_Smart_Cities_Empirical_Evidence_from_Qatar_b_em/25764780 (accessed on January 22, 2025). Consent for publication: Informed consent for publication of anonymized
participant data was obtained from all participants.Information security remains a significant concern for the adoption of smart cities (SCs) worldwide, particularly in relation to the development and implementation of digital ecosystems. SCs entail the interconnectedness of networks and systems that collect and process huge volumes of diverse data. This study analyzes the impact of data privacy and data security issues on the citizens’ willingness to adopt smart city environments. A critical review of the existing literature was conducted regarding the relationship between data privacy and security concerns and the adoption of the smart city ecosystem. The data collected from two sample groups, experts and citizens, were analyzed using statistical techniques, including independent samples t-tests and correlation analysis. The findings indicate that citizens and experts had significantly different perceptions of the characteristics of SCs. Still, both groups exhibited a strong positive correlation between key adoption variables and citizens’ readiness to accept SCs. Based on the findings, several recommendations are proposed to increase citizens’ acceptance of SCs.None
Frequency hopping wireless power transfer system for charging electric vehicles
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWireless Power Transfer (WPT) is a means of transferring electric power through a magnetic or electric field via an airgap. This technology has resulted to the recent independence of plug in systems to charge equipment. The propagation of electric vehicles (EV) necessitates advancements in WPT technologies to enhance security, efficiency and reduced EV dwell time for charging. It is expected that wireless EV chargers will be installed in hospitals, railway and other non-domestic car parks which has equipment that emits electromagnetic waves thus, the potential to interfere with the wireless charging sessions. Conversely, the operation of the wireless EV chargers, if not shielded, has similar potential in its electromagnetic field strength to negatively impact the operation of near field equipment. Consequently, this research has been undertaken to develop an engineering solution to address these issues.
Sparse research has been undertaken in the field of immunization of WPT power signals. Most studies focused on the vulnerability of WPT as it operates at 85kHz which apparently can be a single point of failure as interference, spoofing, power theft, and jamming can severely impact its charging session. The few solutions propounded by the researchers have not been generally developed and implemented this may be due to the lack of urgency of this technology.
In this research the Frequency Hopping (FH) technique was incorporated in the WPT power and control circuitry as a solution to the risks associated with a single operating frequency. This novel design intervention founded the FH Wireless Power Transfer (FHWPT). FHWPT was developed as a system to introduce resilience and redundant operating frequencies in the WPT. FH is an existing technology used in communications to send and receive messages securely. This scheme was adopted to enable the WPT to operate in environments where conditions e.g. radio frequency interferences, power theft and jamming are hostile towards the predominant operating frequency.
This thesis demonstrates the feasibility of enabling FHWPT with key circuit modifications to existing WPT systems. It highlights the potential of FH to enhance security and operational resilience in EV charging and covers system design, implementation, and prototype development
Influence of 3D printing process parameters and design on mechanical properties of tissue scaffolds
Get PDFThis project investigated the influence of printing process parameters on the printability and mechanical properties of bone tissue scaffolds fabricated using fused deposition modelling (FDM). Given that tissue scaffolds require specific structural and mechanical characteristics for their intended applications, the manufacturing process was examined to evaluate the effects of distinct printing parameters on the final scaffold properties. Specimens were fabricated with varying parameter values, assessed for structural integrity, and subjected to uniaxial compression testing to determine their mechanical behaviour. The results revealed that printing parameters significantly influenced both the structural quality and mechanical performance of the scaffolds, notably affecting defect formation, compressive strength, and the Young’s modulus
Research on local visual global localization method based on out-of-view reference of spatial point association
Get PDFThe global localisation of spatial points is a critical step in tasks such as object tracking, motion analysis and pose measurement. This paper addresses the critical issue of global localisation when spatial points are scattered and cannot be contained within the same field of view. It proposes a local visual global localisation method based on an out-of-view reference through spatial point association. By constructing a local measurement and localisation model using parallel binocular vision and a spatial coordinate transformation model that associates local regions with the global reference, the global localisation of spatial points inside and outside the field of view is achieved. Experimental results demonstrate that the localisation accuracy of spatial points is less than 0.1 mm in terms of distance measurement. This method is useful for cooperative multi-camera localization and multi-point measurement in large 3D spaces.The present study received financial support from the China Higher Education Society Project 23SZH0413, the National Higher Education Computer Basic Education Research Association Project 2024-AFCEC-460, and the Nankai University Educational Reform Project NKJG2025017
Hybrid and deep learning architectures for predictive maintenance: Evaluating LSTM, and attention-based LSTM-XGBoost on turbofan engine RUL
Get PDFAccurate prediction of a machines Remaining Useful Life (RUL) underpins modern, costeffective predictive-maintenance programmes. This paper proposes a two-stage hybrid pipeline that couples sequence learning with tree-based residual modelling. In stage 1, 50-cycle windows of NASA C-MAPSS sensor data (FD001 and FD004 subsets) are processed by a bi-layer Long Short-Term Memory (LSTM) network equipped with an attention mechanism; attention weights highlight degradation-relevant time steps and yield a compact, interpretable context vector. In stage 2, this vector is concatenated with four statistical descriptors (mean, standard deviation, minimum, maximum) of each window and passed to an extreme gradient-boosted decision-tree regressor (XGBoost) tuned via grid search. Identical preprocessing and earlystopping schedules are applied to a baseline LSTM for fair comparison. The attention-LSTM–XGBoost model lowers Mean Absolute Error (MAE) by 9.8 % on FD001 and 7.4 % on the more challenging FD004, and reduces Root Mean Squared Error (RMSE) by 8.1 % and 5.6 %, respectively, relative to the baseline. Gains on FD004 demonstrate robustness to multiple fault modes and six operating regimes. By combining temporal attention with gradient-boosted residual fitting, the proposed architecture delivers state-of-the-art accuracy while retaining feature-level interpretability, an asset for safety-critical maintenance planning
Multi-region Probabilistic Load Forecasting with Graph Bayesian Transformer Network
Get PDFAccurate probabilistic load forecasting is essential for efficient energy management and the safety operation of power system. Existing load forecasting methods suffer from two limitations: 1) Inadequate utilization of feature; 2) insufficient modelling capability for fine-grained dependencies. To end these problems, a multi-region probabilistic load forecasting method based on graph Bayesian Transformer network is proposed. Specifically, the proposed forecasting framework consists of graph neural network and hybrid Bayesian Transformer connected in cascaded configuration. The former one is used to develop multigraph spatial-temporal features, which can enhance the feature learning ability and share the graph structure information to realize the joint forecasting of multi-region. The latter one is used to capture multi-scale information, which can improve the adaptability of model to complex dynamic data and forecasting accuracy. For validation, a series of compared experiments and ablation analysis are conducted under New England dataset. The experimental results demonstrate that the proposed method has good performance in foresting accuracy, and adaptability. In particular, compared to other comparative methods, the Continuous Ranked Probability Score (CRPS) is reduced 32.7%.This work was supported in part by the National Natural Science Foundation of China under Grants (62206062), Yangtze River Delta Science and Technology Innovation Community Jointly Tackled Key Project (2023CSJGG1300), and Fundamental Research Funds for the Provincial University of Zhejiang under Grant GK229909299001-06
Cutting-edge advances in hydrogen applications for the medical and pharmaceutical industries
Get PDFThis article is part of a special issue entitled: ICEESEN-2024 (Akansu) published in International Journal of Hydrogen Energy.The adoption of clean hydrogen is expected to transform the global energy landscape, reducing greenhouse gas emissions, bridging gaps in renewable energy integration, and driving innovation across multiple sectors. In the medical and pharmaceutical industries, hydrogen offers unique opportunities for transformative progress. This review critically examines recent advances in three domains: hydrogen fuel cells as reliable, scalable, and sustainable energy solutions for hospitals; molecular hydrogen as a therapeutic and preventive medical gas, particularly for brain disorders; and hydrogenation technologies for the efficient and sustainable pharmaceutical production. Despite encouraging advancements, widespread adoption remains limited by economic constraints, regulatory gaps, and limited clinical evidence. Addressing these barriers through technological innovation, large-scale studies, and life-cycle sustainability assessments is essential to translate hydrogen's full potential into clinical and industrial practice. Responsible adoption of green hydrogen is poised to reshape the clinical approach to global health and enhance the quality of life for people worldwide.The reported work was supported by Air Products PLC under grant agreement: 216-206-P-F
A parallel distributed bargaining mechanism for joint electricity-carbon trading in multi-energy manufacturing plant networks
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Data will be made available on request.Manufacturing plants (MPs) are energy-consuming and carbon-intensive industrial entities facing significant challenges in reducing energy costs and carbon emissions. Enabling local electricity and carbon trading among MPs offers a promising solution to address these challenges. However, existing research lacks effective trading mechanisms that support proactive local trading while ensuring fair profit distribution among MPs. To address this gap, this paper proposes a computation-efficient, privacy-preserving, and fair local trading mechanism for multi-energy MPs. Specifically, a novel parallel distributed bargaining mechanism is developed to facilitate local electricity and carbon trading among MPs. The trading process is formulated as a Nash bargaining problem, which is decomposed into two subproblems: a local electricity and carbon trading problem and a payment bargaining problem. To protect the privacy of MPs, we design an accelerated-adaptive alternating direction method of multipliers (AA-ADMM)-based distributed algorithm to solve the subproblems. Meanwhile, a diagonal quadratic approximation (DQA) method is introduced to enable parallel computation of the subproblems. The novelty of the proposed method lies in its ability to enable parallel and distributed solving of the formulated problems, while ensuring fast convergence and privacy protection without relying on any mediator or aggregator. Simulation results demonstrate the effectiveness of the proposed mechanism in conducting local electricity and carbon trading among MPs. Compared to the independent operation mode, the proposed framework reduces the total operation cost and carbon emissions of MPs by 57.75 % and 11.56 %, respectively. Moreover, compared to standard ADMM, the proposed method reduces the total solution time by 46.93 %.This work was supported in part by the National Natural Science Foundation of China under Grant 62303123, Grant 52207104, Grant 62320106008, and Grant 62206062; in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2024A1515012745
Generative Models for Time Series Anomaly Detection: A Survey
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Generative approaches have shown exceptional performance in TSAD. Various emerging generative methods have expanded in this field, signaling a shift from traditional to deep generative techniques. Although some studies have reviewed the use of generative models like GANs and Transformers in time series, a comprehensive synthesis of these methods for anomaly detection is still lacking. This paper reviews existing work on mainstream generative approaches for this purpose. We summarize datasets and analyze methods suited to different dataset characteristics, providing tailored recommendations for various application domains. The goal of this paper is to offer researchers a reliable review and valuable guidance for future work.Time series anomaly detection (TSAD) is a fundamental practice in information management, aimed at identifying unusual patterns in temporal datasets. This process is critical to maintaining the integrity and reliability of systems. Recently, generative models have significantly advanced the capabilities of artificial general intelligence, presenting novel methodologies to understand and interpret complex data structures. In this review, we examine the latest advancements in applying generative models to TSAD and highlight how these models present a paradigm shift in detecting and analyzing anomalies within sequential data. In particular, we first present the background information, including definitions of key concepts, a taxonomy of anomaly types, and the distinction between generative and discriminative models in time series data. Then, we investigate a range of generative models, offering mathematical summaries of the predominant techniques in TSAD. Furthermore, we provide a summary of the datasets and propose recommendations for appropriate generative methods tailored to various application domains. Finally, we address the significant challenges in current research and propose potential directions for future study.his research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors
Migration Predictions with a Pinch of Salt: Definitions and Reliability
No full textMigration prediction or forecasting is an emerging predictive IT method in migration governance. Advocates of migration prediction argue that forecasting migratory flows could enhance humanitarian preparedness and overall support the management of migration. However, while migration flow predictions could potentially be beneficial, they risk jeopardizing fundamental rights. Building on our previous work on the human rights challenges of migration prediction, this article zooms into the reliability of migration prediction. The article unearths persistent and deep-rooted muddling of legal definitions used in predictions and their inconsistent use, sometimes due to cross-disciplinary confusion and sometimes due to unresolved legal debates shaped by political undertones. It revisits terminological debates regarding the legal definition of ‘migrants’ and ‘refugees’ to argue in favour of an inclusive understanding of the term migrants as an umbrella term but is concerned that there is no coherence in the use of these terms by various stakeholders in predicting migration, including the main organisations producing datasets. The article concludes that contrary to the much-celebrated use of IT in predicting migration, the unreliability of such emerging data seriously undermines any ‘added value’ of such predictions to humanitarian preparedness and migration management.10.3030/882986 European Commission Horizon 2020 Grant agreement ID: 882986 [Modelling predicting and dealing with migration flows to avoid: ITFLOWS
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