DSDCLNet: Dual-Stream Encoder and Dual-Level Contrastive Learning Network for supervised multivariate time series classification

In recent years, deep learning approaches have shown remarkable advancements in multivariate time series classification (MTSC) tasks. However, the existing approaches primarily focus on capturing the long-term correlations of time series or identifying local key sequence fragments, inevitably neglecting the synergistic properties between global and local components. Additionally, most representation learning methods for MTSC rely on self-supervised learning, which limits their ability to fully exploit label information. Hence, this paper proposes a novel approach termed Dual-Stream Encoder and Dual-Level Contrastive Learning Network (DSDCLNet), which integrates a dual-stream encoder (DSE) and dual-level contrastive learning (DCL). First, to extract multiscale local-global features from multivariate time series data, we employ a DSE architecture comprising an attention-gated recurrent unit (AGRU) and a dual-layer multiscale convolutional neural network (DMSCNN). Specifically, DMSCNN consists of a series of multi-scale convolutional layers and a max pooling layer. Second, to maximize the utilization of label information, a new loss function is designed, which combines classification loss, instance-level contrastive loss, and temporal-level contrastive loss. Finally, experiments are conducted on the UEA datasets and the results demonstrate that DSDCLNet achieves the highest average accuracy of 0.77, outperforming traditional approaches, deep learning approaches, and self-supervised approaches on 30, 23, and 27 datasets, respectively

Advancing the digital frontier in agri-food supply chains

EditorialIn recent years, the agri-food industry has witnessed a transformative wave propelled by digital technologies, revolutionizing the way we perceive and manage the entire supply chain. This special issue delves into the intricate landscape of digital solutions and their profound impact on enhancing transparency, security, and efficiency within agri-food supply chains

Chapter 8: research agenda

This book illustrates the applications of mobile robot systems in warehouse operations with an integrated decision framework for their selection and application. Mobile robot systems are an automation solution in warehouses that make order fulfillment agile, flexible and scalable to cope with the increasing volume and complexity of customer orders. Compared with manual operations, they combine higher productivity and throughput with lower operating costs. As the practical use of mobile robot systems is increasing, decision-makers are confronted with a plethora of decisions. Still, research is lagging in providing the needed academic insights and managerial guidance. The lack of a structured decision framework tailored for mobile robot system applications in warehouses increases the probability of problems when choosing automation systems. This book demonstrates the characteristics of mobile robot systems which reinforce warehouse managers in identifying, evaluating and choosing candidate systems through multiple criteria. Furthermore, the managerial decision framework covering decisions at strategic, tactical and operational levels in detail helps decision-makers to implement a mobile robot solution step-by-step. This book puts special emphasis on change management and operational control of mobile robots using path planning and task allocation algorithms. The book also introduces focus areas that require particular attention to aid the efficiency and practical application of these systems, such as facility layout planning, robot fleet sizing, and human-robot interaction. It will be essential reading for academics and students working on digital warehousing and logistics, as well as practitioners in warehouses looking to make informed decisions

Supersonic flow field reconstruction using CNNs

The accurate prediction of a projectile’s aerodynamic coefficients is crucial in high-precision external ballistic calculations. The aerodynamic forces and moments exerted on a projectile in flight influence key performance parameters such as range, accuracy, time of flight and stability. A large body of work has therefore been dedicated to understanding the flow dynamics around projectile bodies and obtaining the critical force and moment coefficients. This has been traditionally achieved in aeroballistic range experiments, wind tunnel set-ups and through the use of numerical models. Nevertheless, a widespread still exists between different techniques, revealing the fluid physics is not yet fully understood. A better understanding of the aerodynamics at play is accessible through a combination of the three techniques. However, reliable wind tunnel results will require matching a series of similarity parameters imposed by the firing conditions, which will inevitably relate to the physical scale of the models used. The size of small calibre projectiles may prove challenging for measurement in wind tunnel set-ups, however upscaling the models inappropriately will result in unrepresentative flow fields due to wall interactions and blockage effects. On the other hand, sting supports for wind tunnel models disturb a smaller portion of the flow with increasing projectile scale, particularly in terms of wake perturbation - a key contributor to aerodynamic coefficients. Clearly, scale effects have important consequences, however they have not been explicitly treated in the supersonic projectile literature. This study aims to explore the effects and limits of projectile scaling in supersonic wind tunnels, through a series of experimental techniques (Schlieren visualization, pressure measurements, force balance measurements...) and numerical modelling. Additionally, we aim to develop the Background-Oriented-Schlieren technique a step further through the use of machine learning models to reconstruct complete flow fields from optical data.Royal Higher Institute for Defense, BelgiumDefence and Security Doctoral Symposia 2024 (DSDS24

Quantitative microbial risk assessment of bioaerosol emissions from squat and bidet toilets during flushing

Bioaerosol emissions during toilet flushing are an often‐overlooked source of potential health risks in shared public facilities. This study systematically investigated the emission characteristics of Staphylococcus aureus and Escherichia coli bioaerosols in washrooms with squat and bidet toilets under varying flushing conditions and ventilation scenarios. Using Monte Carlo simulation–based quantitative microbial risk assessment and sensitivity analysis, the study estimated the disease burden and identified key factors influencing risk. The results showed that squat toilets generated 1.7–2.6 times higher concentrations of S. aureus bioaerosols and 1.2–1.4 times higher concentrations of E. coli bioaerosols compared to bidet toilets. After the first flush, bioaerosol concentrations were 1.3–1.8 times (S. aureus) and 1.2–1.4 times (E. coli) lower than those observed after the second flush. The second flush released a higher proportion of fine bioaerosol particles (<4.7 µm), increasing inhalation risks. The disease health risk burden was consistently one order of magnitude lower after the first flush than the second one. Ventilation with a turned‐on exhaust fan further reduced the risk by one order of magnitude. Sensitivity analysis identified exposure concentration as the most influential parameter, contributing up to 50% of the overall risk. This study highlights the importance of optimizing toilet design and ventilation systems to mitigate bioaerosol emissions and associated health risks. It provides actionable insights for improving public washroom hygiene and minimizing bioaerosol exposure.F.C., Z.A.N., and C.Y. gratefully acknowledge the support of the Environmental Microbiology and Human Health Programme (Grant Reference NE/M010961/1) and the SPF Clean Air Programme (Grant NE/V002171/1) in facilitating this collaborative study.Risk Analysi

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

Past, present, and future of battlefield forensics - Presentation

The full report on the conference, is available at: https://ccdcoe.org/library/publications/report-on-the-conference-on-the-law-applicable-to-the-use-of-biometrics-by-armed-forces-tallinn-7th-8th-of-may-2024/Conference in the Law Applicable to the use of Biometrics by Armed Forces 202

Dataset DrivAer hp-F: Wake Total Pressure Measurements in Yaw Conditions

Dataset for the wake total pressure measurements conducted on the 35% scale DrivAer hp-F model at various yaw angles in the 8x6 Wind Tunnel at Cranfield University. The measurements are performed on the DrivAer hp-F rear wing configuration with an angle of attack of 15°. The dataset includes the total pressure coefficient results from measurements on the P1, P2, and P3 wake planes, which are located 400 mm, 700 mm, and 1000 mm downstream of the vehicle model respectively. Additionally, the horizontal and vertical measurements positions (in mm) are provided for each wake plane. A horizontal sweep on the P3 wake plane has been conducted three times for repeatability. In reference to the publication: Steven Rijns, Tom-Robin Teschner, Kim Blackburn, Anderson Ramos Proenca, James Brighton; Experimental and numerical investigation of the aerodynamic characteristics of high-performance vehicle configurations under yaw conditions. Physics of Fluids 1 April 2024; 36 (4): 045112. https://doi.org/10.1063/5.0196979 CAD files for the DrivAer hp-F rear wing configuration are available at: Rijns, Steven; Teschner, Tom-Robin; Blackburn, Kim; Ramos Proenca, Anderson; Brighton, James (2024). DrivAer hp-F: Spoiler & Rear Wing Configurations Geometry Pack. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.25715202 Note: The updated dataset retains all original data while adding calibrated data to provide (new) users with an additional reference option

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

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