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

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

High performance rechargeable aluminium ion batteries enabled by strategy of covalent organic frame material

Emerging rechargeable aluminium-ion batteries (RAIBs) are a sustainable option for the next generation of low-cost, high-safety and large-scale energy storage technologies. While the unsatisfying availability of traditional inorganic materials has limited the development of RAIBs, the advance of organic materials is expected to be a breakthrough towards high-performance cathode. However, the existing extensive research often focuses on the selection of appropriate organic monomers or stay in the tentative stage of preliminary polymerization. It is difficult to break through the inherent characteristics of the instability of small organic ones and the easy aggregation and accumulation of macromolecular polymers, which is no doubt ignoring the huge potential of organic compounds for structural design at the molecular level. In this connection, our study demonstrates a material design strategy that introduces active functional groups to small molecular monomers and polymerizes them into REDOX active covalent organic framework (COF) with multiple N-containing groups. Theoretical simulations and ex-situ analysis revealed the key function of C-N and C=N as active sites for reversible storage of AlCl2 + ions. In addition, the macro-ring frame brings enhanced structural stability and environmental tolerance for COF in complex electrolyte, resulting in significantly improved electrochemical performance. At 1 A g−1, it exhibits a high specific capacity of 161.2 mAh g−1 and an excellent cycle life of approximately 100 % coulombic efficiency after more than 3,000 cycles. This work fully demonstrates the operability of the design strategy to synthesize COF from small molecular organics by introducing reactive functional groups and its great potential in the role of cathode materials in RAIBs. The success meanwhile provides an inspiration for the development of COF-based organic battery system in large-scale energy storage.This work was supported by Opening Project of Guangxi Key Laboratory of Calcium Carbonate Resources Comprehensive Utilization (Grant No. HZXYKFKT202206), Guangxi Natural Science Foundation (Grant No. 2024GXNSFFA010003), Project entrusted by enterprise (Grant No. HX20210521 and Grant No. HX20230264) and Hezhou University Research Project (Grant No. 2023ZDPY01).Chemical Engineering Journa

Robustness and resilience of different solid-liquid separation technologies for tertiary phosphorus removal to low levels by coagulation

In this study, three tertiary solid separation technologies were assessed on their robustness and resilience against an effluent phosphorus target of <0.3 mg P/L at steady state and dynamic conditions. The ballasted flocculation system was found to be very robust at delivering the low P target. Alternatively, cloth filtration provided a more sustainable option for less strict consents of sub 0.5 mg P/L. The effluent from the membrane system was more variable but it was shown to meet the low consents even with increased phosphorus and solids content in the feed. A molar ratio of 1.37 Fe: P was shown to be sufficient to meet the P target at short contact times as with the ballasted flocculation process. It was highlighted that optimisation of up-stream flocculation can be a considerable factor for consistent performance. Overall, the study determined key attributes of the different technologies tested providing valuable insights for technology selection at full scale.Funding for this study was gratefully received by Severn Trent Water.Science of The Total Environmen

High foot traffic power harvesting technologies and challenges: a review and possible sustainable solutions for Al-Haram Mosque

The growing global demand for sustainable energy solutions has led to increased interest in kinetic energy harvesting as a viable alternative to traditional power sources. High-foot-traffic environments, such as public spaces and religious sites, generate significant mechanical energy that often remains untapped. This study explores energy-harvesting technologies applicable to public areas with heavy foot traffic, focusing on Al-Haram Mosque in Saudi Arabia—one of the most densely populated religious sites in the world. The research investigates the potential of piezoelectric, triboelectric, and hybrid systems to convert pedestrian foot traffic into electrical energy, addressing challenges such as efficiency, durability, scalability, and integration with existing infrastructure. Piezoelectric materials, including PVDF and BaTiO3, effectively convert mechanical stress from footsteps into electricity, while triboelectric nanogenerators (TENGs) utilize contact electrification for lightweight, flexible energy capture. In addition, this study examines material innovations such as 3D-printed biomimetic structures, MXene-based composites (MXene is a two-dimensional material made from transition metal carbides, nitrides, and carbonitrides), and hybrid nanogenerators to improve the longevity and scalability of energy-harvesting systems in high-density footfall environments. Proposed applications for Al-Haram Mosque include energy-harvesting mats embedded with piezoelectric and triboelectric elements to power IoT devices, LED lighting, and environmental sensors. While challenges remain in material degradation, scalability, and cost, emerging hybrid systems and advanced composites present a promising pathway toward sustainable, self-powered infrastructure in large-scale, high-foot-traffic settings. These findings offer a transformative approach to energy sustainability, reducing reliance on traditional energy sources and contributing to Saudi Arabia’s Vision 2030 for renewable energy adoption.Applied Science

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

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

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

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