Enzymes targeting distinct hydrolysis blind-spots of thermal and biological pre-treatments significantly uplift biogas production

Thermal hydrolysis process (THP) and biological hydrolysis (BH) are key pre-treatment technologies for anaerobic digestion (AD), termed advanced anaerobic digesters (AADs). They target the rate-limiting hydrolysis step in AD. This study evaluates full-scale pre-treatments for macromolecule bias and the implementation of hydrolysis enzymes to enhance biogas yield. Findings show THP significantly improves protein and carbohydrate solubilisation by 30% and 25%, respectively, but fully hydrolyses only carbohydrates. In contrast, BH targets fibres and proteins, achieving 35% and 23% solubilisation, and only partially hydrolyses carbohydrates. Biomethane potential (BMP) tests indicate that protease enzymes raise biomethane yield by 20-30% for AAD with THP pre-treatment. In comparison, α-amylase increases it by over 30% for AAD with BH pre-treatment. This study tailors enzyme selection and dosage to specifically address the unique "hydrolysis blind spot" of each pre-treatment, providing a strategic framework to enhance AD technologies by an improved understanding of macromolecule selectivity and their transformation pathways.Bioresource Technolog

Multi-agent deep reinforcement learning-based key generation for graph layer security

All research work was conducted whilst all authors were at Cranfield University.Recently, the emergence of Internet of Things (IoT) devices has posed a challenge for securing information and avoiding attacks. Most of the cryptography solutions are based on physical layer security (PLS), whose idea is to fully exploit the properties of wireless channel state information (CSI) for generating symmetric keys between two communication nodes. However, accurate channel estimation is vulnerable for attackers and relies on powerful signal processing capability, which is not suitable for low-power IoT devices. In this paper, we expect to apply graph layer security (GLS) to exploit the common features of physical dynamics detected by IoT sensors placed in networked systems to generate keys for data encryption and decryption, which we believe is a new frontier to security for both industry and academic research. We propose a distributed key generation algorithm based on multi-agent deep reinforcement learning (MADRL) approach, which enables communication nodes to cooperatively generate symmetric keys based on their locally detected physical dynamics (e.g., water/gas/oil/electrical pressure/flow/voltage) with low computational complexity and without information exchange. In order to demonstrate the feasibility, we conduct and evaluate our key generation algorithm in both a simulated and real water distribution network. The experimental results show that the proposed algorithm has considerable performance in terms of randomness, bit agreement rate (BAR), and so on.This work has been supported by the PETRAS National Centre of Excellence for IoT Systems Cybersecurity, which has been funded by the UK EPSRC under grant number EP/S035362/1.ACM Transactions on Privacy and Securit

International interlaboratory study to normalize liquid chromatography-based mycotoxin retention times through implementation of a retention index system

Monitoring for mycotoxins in food or feed matrices is necessary to ensure the safety and security of global food systems. Due to a lack of standardized methods and individual laboratory priorities, most institutions have developed their own methods for mycotoxin determinations. Given the diversity of mycotoxin chemical structures and physicochemical properties, searching databases, and comparing data between institutions is complicated. We previously introduced incorporating a retention index (RI) system into liquid chromatography mass spectrometry (LC-MS) based mycotoxin determinations. To validate this concept, we designed an interlaboratory study where each participating laboratory was sent N-alkylpyridinium-3-sulfonates (NAPS) RI standards, and 36 mycotoxin standards for analysis using their pre-optimized LC-MS methods. Data from 44 analytical methods were submitted from 24 laboratories representing various manufacturer platforms, LC columns, and mobile phase compositions. Mycotoxin retention times (tR) were converted to RI values based on their elution relative to the NAPS standards. Trichothecenes (deoxynivalenol, 3-acetyldeoxynivalenol, 15-acetyldeoxynivalenol) showed tR consistency (± 20–50 RI units, 1–5 % median RI) regardless of mobile phase or type of chromatography column in this study. For the remaining mycotoxins tested, the RI values were strongly impacted by the mobile phase composition and column chemistry. The ability to predict tR was evaluated based on the median RI mycotoxin values and the NAPS tR. These values were corrected using Tanimoto coefficients to investigate whether structurally similar compounds could be used as anchors to further improve accuracy. This study demonstrated the power of employing an RI system for mycotoxin determinations, further enhancing the confidence of identifications.Genome Canada, FWF Austrian Science Fund, Agriculture and Agri-Food Canada, Ministry of Education, Universities and Research, National Research Council Canada, MitacsThis research was supported by the NRC (Biotoxin Metrology, Nova Scotia), the ALIFAR project (Italian Ministry of University, Dipartimenti di Eccellenza 2023–2027), Genome Canada Technology Development Grant and MITACS scholarship, with resources provided by the VetCore Facility (Mass Spectrometry) of the University of Veterinary Medicine Vienna.Moreover, this research was supported by the Austrian Science Fund (FWF, P33188), the Mass Spectrometry Centre of the Faculty of Chemistry and the Exposome Austria Research Infrastructure at the University of Vienna.Journal of Chromatography

Digital transformation and profit growth: a configurational analysis of regional dynamics

This study adopts Configuration Theory to explore how diverse combinations of regional factors contribute to profitability, emphasizing the principle of equifinality, which posits that multiple, equally effective configurations can lead to similar outcomes. This study examines the interplay of multiple factors—enterprise informatization, digital infrastructure, e-commerce, technological investment, innovation, hardware, and software—across four key themes: Digital Readiness and Technological Integration, Market and Economic Enablers, Innovation Capacity and Activity, and Foundational Artifacts and Resources. Using data from 31 provinces in China from 2015 to 2022, this study employs fuzzy-set Qualitative Comparative Analysis (fsQCA) to uncover pathways to regional profit growth. The study identifies five distinct configurations contributing to profit growth across China's provinces. In most configurations, e-commerce and technological investment emerge as central drivers. However, in less developed regions, profit growth relies more on improvements in digital infrastructure and hardware, with innovation and enterprise informatization playing a less significant role. The findings also reveal that profit growth requires addressing the weakest elements in the ecosystem—whether digital infrastructure, technological capabilities, or other factors. Strategies tailored to regional conditions must prioritize improving these weaker components to achieve sustained growth, as ignoring them can limit overall success.IEEE Transactions on Engineering Managemen

Chip away everything that doesn't look like an elephant

This paper addresses the question of how conceptual models are created in a simulation modelling activity. Assuming an entity-based approach to simulation, some techniques for discovering good entity classes are considered, including personation. Also considered are the notations by which a conceptual model can be represented, and the modes of thought required for good conceptual modelling. Specifically excluded from consideration is the idea of applying a cut-and-dried method. The shortcomings of computers for conceptual modelling are remarked upon.12th Simulation Workshop (SW25

EXPRESS: Complexity as a domain between order and chaos: implications for organizational scholarship

Organizations are grappling with increasingly complex challenges, including those stemming from technological disruptions, geopolitical uncertainty, and climate change. Despite the increasing acknowledgement of the complexity inherent in many organizational problems, complexity theory has had limited impact on mainstream management scholarship. Synthesizing contemporary complexity literature, we conceptualize complexity as a systemic property of a certain – and, we argue, rather broad – domain of organizational problems, and complexity theory as a theory of change in such organizational contexts. This view of complexity implies that complexity theory has important implications to organizational scholarship at large, indicates limitations of using conventional scientific methods, and suggests that the credibility and replication crises in many branches of organizational research may not be treatable simply by better statistical designs. Instead, methodological choices in complex organizational domains should take account of the properties of non-linearity and emergence, and organizational scholars should embrace complexity theory not only as an explanatory framework but also to inform research design.Strategic Organizatio

Development of soiling process characterisation methods for solar mirrors, for analysing mirror cleaning processes

Sansom, Christopher L. - Associate Supervisor - University of Derby Schiller, Tara - Associate Supervisor - University of WarwickConcentrated Solar Power has the potential to provide power for the developing global economies towards a sustainable future. This solar radiation-based technology, reflects the radiation received by a solar mirror onto a receiver device which absorbs heat. Maintenance is required to keep the solar mirrors clean, and remove airborne particulate matter that settles on the mirror, which has an impact on the solar collector efficiency. Constant research to optimize cleaning methods and cleaning–strategies is paramount. An artificial soiling test rig and soiling methods were developed, which are capable of simulating repeatable soiling events and to specific soiling load. These features are necessary to simulate cleaning cycles with a period of several days. The developed test rig has a capability to provide a minimum soiling load of 0.25g/m² and has a constant error of 16%. Repeatable soiling tests were carried out up to 10 times. Extensive soiling experiments with two soiling materials (silt material and ground taken material from Almeria, Spain) and numerical simulation have revealed the exponential nature of the soiling process. An empirical model was formulated, which calculates specular reflectance, and includes material intrinsic parameters and soiling load data. This model highlighted the fact that compared to a linear model, between 7-20% lower soiling load is predicted, which potentially has a positive influence on cleaning cycles and therefore the costs attributed to them. A simulation series of a 10day cleaning cycles, which includes repeatable soiling and condensation events, used the artificial soiling test rig and a cooling plate located in a dry chamber. The adhesion effect (particle caking and capillary aging) were analysed by a centrifuge and the coverage ratio of the mirror samples before and after the experiments were calculated. It was noted that the repeatable soiling test (soiling and condensation) had a visible difference compared to the one-off soiling and condensation test series. The experimental modelling work will help to improve the considerable maintenance effort involved in mirror cleaning in solar field operations.Engineering and Physical Sciences Research Council (EPSRC)EngD in Sustainable Materials and Manufacturin

High-precision machining behavior of the single crystal scintillator, bismuth germanate (Bi4Ge5O12)

This study focuses on understanding the machinability of a single-crystal scintillator, Bismuth Germanate (BGO), a material widely used in Time-of-Flight Positron Emission Tomography (ToF-PET). The micromachining process of such a hard, brittle material presents several challenges, particularly in maintaining surface integrity without inducing fractures or microcracks. In this work, we employed the Johnson-Holmquist 2 (JH-2) material model to simulate the micro-milling process of BGO. Experimental data from quasi-static uniaxial compression and split tests were used to estimate the key parameters for the JH-2 model. The simulation results closely aligned with experimental outcomes, confirming the reliability of the model in capturing the mechanical behavior of BGO under stress. Simulations were conducted with different machining parameters, successfully replicating the conditions observed in practical machining tests. Our findings demonstrate the impact of feed rate and depth of cut on the machinability of BGO, validating the use of the JH-2 model of this material. Looking ahead, this robust computational framework offers the potential to further optimize the machining process, ultimately enabling the production of high-performance heterostructures for scintillator applications in TOF-PET.Engineering and Physical Sciences Research Council (EPSRC)This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/S013652/1 for Cranfield University. The authors would like to thank Dr D. Johnson and Mrs C. Kimpton for SEM measurements.Materials Today Communication

Investigation of aircraft auxiliary power unit acoustic signatures for condition monitoring

Ali, Fakhre - Associate SupervisorThe auxiliary power unit (APU) of an aircraft is a key system responsible for providing electrical and pneumatic power during ground operations and in-flight emergencies. APU failures can result in delay or cancellation of a flight and fault diagnostic practices are in place to identify the cause of failure. The existing strategies generally require human intervention to identify the fault by traversing through a troubleshooting manual and examining the data acquired from multiple intrusive sensors. The complete process is cumbersome and prone to misjudgement; fault identification in its entirety may not be possible due to limited sensor coverage. Incorporating additional sensors may not be feasible due to accessibility issues, space constraints and certification requirements. However, incorporating microphones, which have previously been used for noise source characterization and verification of noise abatement solutions, is a promising non- intrusive approach. This PhD focuses on ascertaining the potential of microphones for fault detection / identification and condition monitoring of an aircraft APU. The research has been based on the far-field and near-field acoustic data acquired from Cranfield University’s Boeing 737-400 aircraft and the aim has been to determine the degradation / faults that can be detected using microphones. While addressing this aim, a far-field noise model has been developed for sensitivity analysis, near-field data has been analysed, classification / regression models have been proposed and an acoustics-based scheme for ignition system monitoring has been conceived. The results suggest that the far-field acoustic data is not suitable for condition monitoring, and the near-field microphones are unable to monitor tonal frequencies for monitoring the gearbox and bearing. However, there is a huge potential in using microphones for monitoring the lubrication system, pneumatic system components and ignition system for faulty / degraded conditions. The proposed methodologies have online capability and require only a limited set of microphones inside the APU compartment.PhD in Transport System

Critical success factors for ICT integration in agri-food sector: pathways for decarbonization and sustainability

A decarbonized agri-food sector may provide consumers with nutritious, secure, and reasonably priced food with a lower carbon impact. Decarbonizing the agri-food sector is intricate and necessitates a holistic strategy. Technological advancements, like Information and Communication Technologies (ICT), might be the solution. This study analyses the critical success factors (CSFs) for ICT integration in the agri-food sector in the Western and North Western States of India based on empirical data collected and analyzed. The study proposes a framework that determines and ranks the significant factors for ICT integration in the agri-food sector to achieve the decarbonization goals by utilizing the fuzzy evidential reasoning approach (FERA) and the evidential reasoning approach (EFA). The factors are examined based on the Technological, Organization, and Environmental (TOE) criteria. The results show that the most significant factors contributing to the effective implementation of ICT in the agri-food sector are continuous innovation and R&D, supportive policies and regulations, and cost-effectiveness. The results will assist managers and decision-makers in creating effective policies and making knowledgeable choices that will support sustainable growth in the agri-food industry by lowering carbon emissions through effective ICT integration.Cleaner Engineering and Technolog