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High-efficient single-phase, non-isolated, multi-input microinverter with common ground for photovoltaic systems
No full textSingle-phase non-isolated microinverters used in photovoltaic (PV) systems commonly encounter two persistent challenges: High-frequency leakage current and fluctuating power delivery. This paper presents a novel single-phase, non-isolated multi-input microinverter topology with a common-ground structure that effectively eliminates ground leakage current without requiring additional active components. The proposed microinverter architecture integrates a dual-boost configuration and uses only four active switches. This is especially advantageous in terms of the component count, which is beneficial to enhance reliability, reduce cost, and simplify the overall system design. With one, two, or four PV inputs, it can operate without interruption under unbalanced voltage or partial shading and even if some inputs drop to zero. A tailored modulation scheme minimizes conduction losses while maintaining a stable direct-current (DC)-link voltage, and a decoupling capacitor efficiently absorbs the single-phase pulsating power, thus overcoming one major limitation in existing microinverter designs. By validating with a 1-kW GaN-based prototype, both the simulated and experimental results demonstrate its high efficiency, robustness, and practical suitability for cost-effective PV applications, with a peak efficiency value of 94.8%.This research was supported by Libyan Cultural Affair/London, Libya under Grant No. 13840.Journal of Electronic Science and Technolog
Improving food supply chain resilience: a case study of chicken tikka masala
Get PDFData comprises three methodological tools used in the study. These comprise:
1. Interview instrument : list of key questions and prompt questions used in semi-structured interviews
2. Survey instrument : list of survey questions in online survey completed by participants using Qualtrics
3. Plausible future scenarios: a set of four narrative scenarios produced for the online workshop incorporating qualitative data gathered through survey and interview responses describing possible future disruptions that could impact the supply chain.
File format: .pdf/a
Study data cannot be shared for ethical reasons.Food supply chain resilience can improve food security in the face of environmental disruptions such as climate change and disease outbreaks. There is a need to understand the how resilience is operationalised to clarify how businesses and governmental regulators can maintain and enhance resilience.
This study seeks to understand how resilience is perceived and operationalised by food chain actors. Resilience strategies in a specific supply chain are investigated, focusing on chicken tikka masala manufactured by a small and medium enterprise (SME). A theoretical framework, based on robustness, recovery, and reorientation, is presented and applied to analyse resilience strategies.
The research employs an embedded case study approach comprising surveys, interviews, and a workshop with supply chain actors across three tiers. Thematic analysis reveals that actors prioritise robustness and recovery strategies. Reorientation strategies, such as long-term adaptability and early warning systems, receive less focus due to perceived investment and capacity constraints, while visibility and collaboration are curtailed in SMEs with limited influence with larger actors.
Key barriers include fragmented information flows, limited government policy alignment, and challenges of digital technology adoption. Recommendations include the need for policy consultation frameworks that improve policymakers’ understanding of food supply chains and actors’ decision-making processes.Biotechnology and Biological Sciences Research Council (BBSRC
An integrated agent-based modelling and artificial intelligence framework for enhancing the experience of minority ethnic communities in digital energy services
No full textDigitalisation plays a pivotal role in enhancing energy efficiency; however, it also highlights significant governance challenges and exacerbates various forms of energy injustice. This study explores how technological injustice exacerbates energy poverty, particularly via disparities in digital service access. The focus is on understanding and addressing challenges faced by minority ethnic (ME) communities, who often encounter heightened barriers to essential online energy services. While previous research has noted barriers ME communities face in energy markets, this study broadens this literature to analyse these issues for access to digital energy services. The study integrates modelling, simulation, and AI to address these inequalities. The framework comprises three core modules: AI, Environment Configuration, and Agent-Based Modelling (ABM) and Simulation. Its primary aim is to identify effective strategies, policy changes, and adjustments that enhance online service experiences while addressing the unique challenges faced by these communities. The AI Module uses ensemble-based ML pipelines to develop region-specific models. It addresses issues such as high dimensionality and overfitting by incorporating methods like Principal Component Analysis, Recursive Feature Elimination, and hyperparameter optimization. The Environment Configuration Module supports tailored simulations by adapting datasets and regional characteristics, ensuring the accuracy and relevance of the simulations to the target communities. The ABM and Simulation Module facilitates in-depth analysis of policy impacts and service provider attributes. This framework offers valuable insights into improving online service delivery, promoting fairness, and addressing disparities in digital experiences. This work advances energy justice research by quantifying how socio-technical barriers disproportionately affect ME communities.This work was supported by the Engineering and Physical Sciences Research Council, part of the UK Research and Innovation (UKRI), under the grant number EP/W032082/1.Energy and A
Proactive cybersecurity in industry 4.0: a survey of cybersecurity threat prediction approaches in manufacturing systems
Get PDFThis review paper provides a literature review of predictive methods and cybersecurity frameworks essential to safeguard Industry 4.0 manufacturing systems against cyber threats. The review focuses on two key areas: the prediction method and the data used for this prediction. These areas are critical for anticipating cyber threats and implementing effective countermeasures. They underscore the need to combine predictive analytics, proactive threat management, and comprehensive frameworks to safeguard against evolving cyber threats. This review assesses the current state and capability of predictive cybersecurity methods within the manufacturing sector, focusing specifically on their effectiveness in predicting threats. The review also identifies gaps in the current research and suggests directions for future studies to further enhance cybersecurity measures in these vital sectors. The study discusses the main features of each method and highlights promising avenues for future research and applications. This literature review is based on a review of relevant publications from 2010 to February 2025. The analysis reveals significant gaps, particularly in the proactive identification and handling of proactively identifying and handling emerging threats. The review concludes with an analysis of the practical implications of implementing a predictive cybersecurity method and outlines future research directions, underscoring the necessity of adaptive, intelligent cybersecurity solutions to defend the manufacturing industry against cybercrime.International Journal of Information Securit
Near optimal reinforcement learning control of linear time-delay systems
Get PDFThe control of time-delay linear systems is a common challenge in real-world autonomous system applications. Time delays can negatively affect the stability and performance of controllers, necessitating the exploration of alternative approaches. In this context, this paper proposes the implementation of a reinforcement learning (RL)-based policy iteration (PI) algorithm by transforming a time-delay system into an augmented state approximate linear system. This transformation is achieved by segmenting the delay into discrete delays, which allows for the application of RL algorithms to solve the optimal control problem. Through simulation studies in scenarios such as chemical plants and regenerative chatter systems, the effectiveness of the proposed methodology is demonstrated and associated challenges are identified. This approach offers a solution to address the complexity of controller design for time-delay systems, facilitating system management through approximations.15th IFAC Workshop on Adaptive and Learning Control Systems ALCOS 2025IFAC-PapersOnLin
Do new CEOs really care about innovation?
Get PDFThis study asks whether new CEOs care about innovation post-turnover. Using a large sample of Chinese listed firms between 2008 and 2019, our identification strategy relies on the exogenous variation in CEO turnovers. Our difference-in-difference estimates indicate that new CEOs improve R&D efficiency and generate more and higher quality patents. We further show that this positive effect is more pronounced when CEOs have longer career horizons and overseas experience. Overall, our findings indicate that CEO turnover represents an effective mechanism for fostering innovation and new CEOs indeed care about innovation. Our results could benefit several groups of stakeholders with respect to CEO selection process
Metal‐mediated nitrogen doping of carbon supports boosts hydrogen production from ammonia
No full textAmmonia is an attractive hydrogen carrier, yet its practical use is limited by the need for efficient catalytic decomposition. We demonstrate that in‐situ N‐doping of Ru nanoparticles and graphitized carbon nanofiber supports during reaction produces a sharp increase in hydrogen production during the first 40 h, followed by stable activity. Spectroscopic and microscopic analyses, together with density functional theory simulations, reveal that Ru nitridation is rapid and support‐independent, resulting in a mechanistic shift from the traditional Langmuir–Hinshelwood to a Mars–van Krevelen pathway, further confirmed by isotopic labelling experiments. In contrast, the progressive nitridation of the carbon support, observed via X‐ray photoelectron spectroscopy, modulates the electronic environment of Ru and functions as a dynamic nitrogen reservoir that enables reversible N atoms exchange with the Ru particles, facilitating N desorption from the Ru surface and thereby governing the catalytic activity enhancement. These new findings provide new mechanistic insight into ammonia decomposition and establish progressive nitrogen doping of carbon supports as a strategy for designing efficient metal‐based catalysts for hydrogen production.The authors would like to acknowledge the financial support from the Engineering and Physical Sciences Research Council(EPSRC) programme grant “Metal Atoms on Surfaces &Interfaces (MASI) for Sustainable Future” (EP/V000055/1)and EPSRC Centre for Doctoral Training in Sustainable Hydrogen (EP/S023909/1)Angewandte Chemie International Editio
Petrified child mummies by Paolo Gorini (19th century CE, Lodi, Lombardy, Italy): anthropological, pathological, and conservation perspectives
No full textThis study presents an interdisciplinary analysis of six non-adult petrified specimens prepared by the Italian scientist Paolo Gorini (1813–1881) in Lodi, Lombardy, during the 19th century. Housed since 1981 in the Old Hospital, these individuals represent the entire known corpus of Gorini’s preserved children. The research combined macroscopic inspection, radiographic imaging, anthropological assessment, and entomological observations to document biological characteristics, embalming techniques, and conservation needs. Radiographic analysis enabled the estimation of ages at death, ranging from approximately 1.5–12 months, and provided detailed information on skeletal development, dental formation, and pathological conditions. Soft tissues were preserved to an exceptional degree, allowing for the identification of dermal, muscular, and visceral structures. Notable modifications, such as intraorbital inserts, revealed Gorini’s attention to appearance and presentation. The results demonstrate the effectiveness of Gorini’s petrification process, now better understood through recent discoveries of his embalming formulae. His technique achieved both anatomical preservation and long-term stability, even in fragile non-adult individuals. Beyond technical achievements, the specimens reflect broader 19th-century cultural attitudes toward childhood, mortality, and commemoration in a period of elevated death rates. By integrating biological, historical, and conservation perspectives, this study contributes both to the documentation of a unique anatomical collection and to the safeguarding of its future. It also situates Gorini’s work within the scientific and cultural milieu of his time, highlighting the intersection of experimental anatomy, public display, and the desire for permanence over death.This work was supported by Fondazione Cariplo and Fondazione Lodi ONLUS within the framework of research project no. 2023-3263.Frontiers in Medicin
From whales to waves: social media sentiment, volatility, and whales in cryptocurrency markets
No full textThis paper examines the relationship between cryptocurrency market dynamics and investor sentiment, employing advanced techniques like time-variant Granger causality and asymmetric time-varying parameter vector autoregression (TVP-VAR) frequency connectivity. We create unique sentiment analysis tools, including a custom cryptocurrency sentiment lexicon, to deeply analyze content in the cryptocurrency domain, particularly focusing on investor discussions and viewpoints. Our findings demonstrate a significant, evolving link between market sentiment and cryptocurrency movements. A key observation is that the volatility of shock transmission is tightly connected to major market events, often influenced by large-scale investors, or “whales”. Our study indicates that market sentiment consistently affects both short- and long-term cryptocurrency volatility, underlining the crucial influence of investor sentiment in driving the dynamics of the cryptocurrency market. This underscores the importance of understanding investor sentiment for predicting and navigating the cryptocurrency market.The British Accounting Revie
Use of methanol as a potential alternative fuel in a power generation gas turbine
No full textDecarbonisation and emissions reduction have become major priorities in industrial power generation. Achieving net-zero greenhouse gas emissions requires adopting alternative fuels such as ammonia, hydrogen, and alcohols, with methanol emerging as a promising candidate. This study investigates the feasibility of using methanol in the SGT5-2000E gas turbine at Killingholme Power Station by modelling the combustion performance of a Siemens Energy Dry Low NOX (DLN) Hybrid Burner, capable of liquid and gaseous fuel operation. A dual-phase strategy is proposed: initial liquid methanol firing to generate sufficient heat for a Waste Heat Recovery (WHR) system, followed by a transition to evaporated methanol. This approach could reduce fuel consumption by 5–6% and reduce NOX emissions. Chemical kinetics modelling of evaporated methanol combustion showed a potential 10% NOX reduction compared to methane, alongside challenges such as increased flashback risk and higher autoignition potential. A key challenge was the increased fuel injection pressure drop due to methanol’s higher mass flow. A RANS (Reynolds-Average Navier-Stokes) CFD (Computational Fluid Dynamics) model was developed, showing that non-uniform nozzle modifications most effectively improved mixing, lowered peak flame temperatures, reduced flashback risk, and significantly decreased NOX emissions. The results highlight the potential for retrofitting turbines for low-carbon bio- and e-methanol combustion, supporting greener energy solutions and longer turbine life. The methanol dual-phase concept shows strong promise for further development.The work presented here was funded by Cranfield University and Uniper Technologies Ltd and was undertaken to fulfil the research aspect of a Master of Science in Thermal Power and Propulsion.12th International Gas Turbine Conference (IGTC 2025)E3S Web of Conference