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

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

Techno-economic study for degraded gas turbine on pipeline application in the oil and gas industry.

Gas compression through pipelines is a capital intensive project. Therefore, it is imperative to investigate the viability of investing in such a project. Thus, the techno- economic and environmental risk assessment (TERA) tool to rapidly evaluate the entire natural gas pipeline project becomes vital. This research has investigated the impacts of gas turbine (GT) degradation in the application of TERA for a natural gas pipeline, taking into account the equipment selection, ambient conditions and periodic engine overhaul. Three scenarios (optimistic, medium and pessimistic) defining different levels of deterioration of the GT in comparison with the clean condition were examined in each season of the years (rainy, dry and hot season) based on the location of Trans-Saharan gas pipeline with 18 compression stations. The developed TERA model considered different modules such as the pipeline/gas compressor, performance, emission, a simplified lifing and economic module. The pipeline/gas compressor module evaluated the performance of the 4180km pipeline and gas compressor power across all compression stations in both isothermal and non- isothermal conditions. Aspen-Hysys/micro-soft excel and MATLAB were used to develop the model. The result showed that for every 1% increase in pipe exit pressure resulted in a 1.8% increase in the volume of the gas flow in the pipeline. Having evaluated the gas compressor (GC) power across the 18 compressor station, the investigation also revealed that for every 1% rise in the gas temperature resulted in a 3.4% rise in the power required by the gas compressor to move the gas. The GT performance was modelled using TURBOAMATCH at fixed power of the engine with respect to the different scenarios under investigation. The performance result was linked with the developed emission, lifing and economic model in MATLAB. The result revealed that for every 1% degradation (reduction in flow capacity and isentropic efficiency) at a constant power of engine operation, between an ambient temperature of 16.2ᴼC and 29ᴼC, CO₂ emission increases between 0.71% and 0.78% when compared with the clean condition. Also, at the same operating condition, the NOx emission increases between 1.66% and 1.8%. However, NOx emission at different compressor station varies from one station to another due to the influence of different ambient conditions, engine power settings and number of engines used. Lifing result showed that as the engine degrades, its creep life reduces at high TET to deliver the same power at a fixed number of engines Net present value (NPV) at different discount rates (DR) (0%, 5%, 10% and 15%) were used to evaluate the economic viability of the project, taking into account engine divestment and leasing for the redundant fleets after overhaul. The study further investigated how Rescheduling of GT Overhaul (ROH) from the baseline condition affects the economic viability of the pipeline project. The result showed that implementing the ROH reduces the number of GT used for the optimistic, medium and pessimistic scenarios by 8%, 2% and 4% respectively, for the same number of the compressor station and at the same operating conditions when compared with the baseline condition. The result also showed that running the engine on degraded mode increases the life cycle cost while the NPV reduces as the degradation increases. For instance, at 10% DR, the baseline NPV for the clean, optimistic, medium, and pessimistic scenarios were $21.5,$19.6, $18.4 and$17.1 billion, respectively showing that the NPV decreases with increase in degradation, unlike other studies that analysed the NPV on clean engine operation only. Remarkably, the NPV for engine divestment was 0.2% to 20.3% lower than the NPV for leasing depending on the different scenarios and DR, indicating that NPV leasing gives better benefits than that of engine divestment. Furthermore, the implementation of on-line compressor washing to investigate the impacts on the pipeline project and emission reduction using TURBOMATCH and MATLAB for the developed model revealed that the CO₂ emission and cost of CO₂ for the optimistic, medium and pessimistic scenarios had a reduction of 5.8%, 6.1% and 6.5% respectively when compared with the baseline condition. Also, at 5% DR, the NPV for the three scenarios after compressor washing increase by 6%, 5.2% and 4.8%, respectively when compared with the baseline case. The proposed methods and result in this research will offer a useful decision-making guide for all pipeline investors to invest in a natural gas pipeline business, taking into account different operating conditions and the impacts of engine degradation.PhD in Aerospac

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

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

Deep learning based secure transmissions for the UAV-RIS assisted networks: trajectory and phase shift optimization

This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS's phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV's trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.King Saud University; JCYJ20190806160218174This work was supported in part by the National Natural Science Foundation of China under Grants 62271399 and 62206221, in part by National Key Research and Development Program of China under Grant 2020YFB1807003, in part by Foundation of the Science, Technology, and Innovation Commission of Shenzhen Municipality under Grant JCYJ20190806160218174, in part by Zhejiang Provincial Natural Science Foundation of China under Grant LQ24F010003, in part by the Distinguished Scientist Fellowship Program (DSFP) at King Saud University, Riyadh, Saudi Arabia, and in part by the Bournemouth University Qualiy research funding: Flying ad-hoc networking and its applications.GLOBECOM 2024 - 2024 IEEE Global Communications Conferenc

Numerical analysis of crack path effects on the vibration behaviour of aluminium alloy beams and its identification via artificial neural networks

Understanding and predicting the behaviour of fatigue cracks are essential for ensuring safety, optimising maintenance strategies, and extending the lifespan of critical components in industries such as aerospace, automotive, civil engineering and energy. Traditional methods using vibration-based dynamic responses have provided effective tools for crack detection but often fail to predict crack propagation paths accurately. This study focuses on identifying crack propagation paths in an aluminium alloy 2024-T42 cantilever beam using dynamic response through numerical simulations and artificial neural networks (ANNs). A unified damping ratio of the specimens was measured using an ICP® accelerometer vibration sensor for the numerical simulation. Through systematic investigation of 46 crack paths of varying depths and orientations, it was observed that the crack propagation path significantly influenced the beam’s natural frequencies and resonance amplitudes. The results indicated a decreasing frequency trend and an increasing amplitude trend as the propagation angle changed from vertical to inclined. A similar trend was observed when the crack path changed from a predominantly vertical orientation to a more complex path with varying angles. Using ANNs, a model was developed to predict natural frequencies and amplitudes from the given crack paths, achieving a high accuracy with a mean absolute percentage error of 1.564%.Sensor

The interplay of agile capabilities in crisis response

Purpose Large-scale disruptions that lead to extreme environmental uncertainty, combined with perceived threats and time pressure, have prompted some organizations to rapidly form new networks. This research aims to focus on how actors in these newly formed networks leverage their agile capabilities in response to extreme disruptions. Design/methodology/approach Grounded in the agility literature, this study employs an abductive research approach and a multi-case design. Data were collected from 18 actors embedded in four newly formed networks located in the United Kingdom, Italy, Colombia and the USA. Findings Through six propositions and an empirically derived model of supply chain agility under extreme uncertainty, the findings reveal a dynamic interplay among agile capabilities. They also illustrate how the utilization of these capabilities shifts in environments characterized by severe unpredictability. Practical implications The research underscores the importance of allocating equal attention to both cognitive and physical dimensions of agility. Under conditions of extreme uncertainty, firms may need to adopt more entrepreneurial behaviors to enhance agility; however, this can increase risk exposure, which must be managed proactively. Originality/value This study contributes to the body of knowledge on supply chain agility by identifying the interrelationships between agility dimensions and demonstrating how extreme uncertainty influences their practical application.International Journal of Operations & Production Managemen

EEG signal processing techniques and applications—2nd Edition

Electroencephalography (EEG), as a well-established, non-invasive tool, has been successfully applied to a wide range of conditions due to its many evident advantages, such as economy, portability, easy operation, easy accessibility, and widespread availability in hospitals. EEG signals, with ultra-high time resolution, are vital in understanding brain functions. Traditionally, considerable attention in EEG signal processing and analysis has been paid to understanding brain activities from various perspectives, such as the detection and identification of abnormal frequencies in specific biological states, spatial–temporal and morphological characteristics of neurological disorder behaviours (e.g., paroxysmal or persistent discharges), the response of the brain nervous/neurological system to external stimuli, and the effects and responses to intermittent photic stimulation [1].Sensor

An analysis of factors that influence the spatial pattern of faecal matter flow in unsewered cities

The management of sanitation systems in unsewered cities in low and middle income countries is a critical issue, yet it is unclear where the risk hotspots are and where interventions should be focused. This study utilised a prototype model, developed by the authors, to map the spatial pattern of faecal flow in Rajshahi city, a secondary city in northwest Bangladesh with a population around a million. This city serves as a representative example of 60 such secondary cities in Bangladesh and hundreds more in the economically developing region in Asia, Africa and Latin America. The model relies on assumptions that carry significant uncertainties; hence, the study employed a sensitivity analysis with multiple plausible scenarios to characterise these uncertainties, aiming to identify ways to improve the model further. Five major influencing factors on the spatial pattern of faecal flow were identified: the emptying of septic tanks, the use of soak pits, and sludge removal from drains, variations in faecal matter production by building types, and the presence or absence of toilets. These factors were shown to collectively have a significant impact (almost 50 % changed) on the model outcome, depending upon the assumptions made. The study offers insights that will guide future data collection efforts by emphasising the need to understand these specific influencing factors and their spatial pattern. Consequently, this research has broader implications for urban sanitation management as well as associated public health research like wastewater surveillance, risk assessment, and disease dynamics in similar urban settings, offering insights into areas of uncertainty that need to be addressed in future modelling efforts.This work was supported by the UKRI Engineering and Physical Science Research Council (EPSRC) through a Ph.D. studentship received by the first author (M.S.S.) as part of the EPSRC Centre for Doctoral Training in Water and Waste Infrastructure and Services Engineered for Resilience (Water-WISER). EPSRC Grant No.: EP/S022066/1.Science of The Total Environmen