Search Results (206)

Given the projection of the impact of climate change and the uncertainty caused by geopolitical volatility, minimising emissions has become an urgent priority for the shipping industry. In this context, the aim of the present study is the calculation and estimation of emissions generated by ship operations within a maritime transportation network, as well as the identification of the optimal route that minimises both emissions and travel time. Emission estimation is carried out using methodologies and assumptions from the Fourth IMO GHG Study. The decision-making, along with the optimisation process, is performed through backward dynamic programming, following a multi-objective optimisation framework. Specifically, the analysis is carried out on both a theoretical and a realistic network. In both cases, various scenarios are examined, including different approaches to vessel speed, some of which incorporate probabilistic speed distributions, as well as scenarios involving uncertainty regarding port availability. Additionally, the resilience of the network is examined, focusing on the additional burden in terms of emissions and travel time when a port is unexpectedly unavailable and a route adjustment is required. The calculations and optimisation are carried out using Excel and the @Risk software by Palisade, with the latter enabling the incorporation of probability distributions and the execution of Monte Carlo simulations. Full article

With the increasing demand to scale the chip industry, attention is turning to the vital role that phosphanes and silanes play in semiconductor manufacturing processes such as chemical vapor deposition, plasma etching, and impurity doping. High-performance semiconductors often require a supply of ultra-pure gaseous phosphine (≥99.999%) to ensure the formation of defect-free thin-film structures with high integrity and strong functionality. In recent years, research on high-purity PH₃ synthesis methods has mainly focused on two pathways: the acidic route with fewer side reactions, high by-product economics, and higher exergy of high-purity PH₃, and the alkaline alternative with greater potential for practical application through lower reaction temperatures and a simpler reaction process. This paper presents the first comparative study and analysis on the preparation of ultra-high-purity PH₃ and its process energy consumption. Using Aspen and its related software, the energy consumption and cost issues are discussed, and the process heat exchange network is established and optimised. By combining Aspen Plus V14 with MATLAB 2023, an artificial neural network (ANN) prediction model is established, and the parameters of the distillation section equipment are optimised through the NSGA-II model to solve problems such as low product yield and large equipment exergy loss. After optimisation, it can be found that in terms of energy consumption and cost indicators, the acidic process has greater advantages in large-scale production of high-purity PH₃. The total energy consumption of the acidic process is 1.6 × 10⁸ kJ/h, which is only one-third that of the alkaline process, while the cost of the heat exchange equipment is approximately three-quarters that of the alkaline process. Through dual-objective optimisation, the exergy loss of the acidic distillation part can be reduced by 1714.1 kW, and the economic cost can be reduced by USD 3673. Therefore, from the perspective of energy usage and equipment manufacturing, the comprehensive analysis of the acidic process has more advantages than that of the alkaline process. Full article

The paper proposes an improved A* Algorithm based on historical AIS data for the multi-objective optimisation of ship weather routes, explicitly focusing on optimising voyage distance, economic costs, and emission costs within Sulphur Emission Control Areas. The method utilises trajectory interpolation, Ordering Points to Identify the Clustering Structure, and the Douglas–Peucker algorithm to preprocess AIS data, thereby enhancing the flexibility and accuracy of multi-objective path planning. The method incorporates different cost weights and the time dimension to optimise different routes dynamically. The technique also optimises the route in real time by treating ship power as a decision variable, adjusting the power according to different task requirements. The proposed method is compared with other commonly used path planning algorithms within a specific maritime area. The results show that it offers better adaptability in terms of multi-objective costs and timeliness. Full article

Online peer support platforms are vital, scalable resources for mental health, yet their effectiveness is frequently undermined by inefficient user matching, severe participation inequality, and subsequent “super-helper” burnout. This study introduces TrailMap, a novel peer-matching algorithm inspired by the decentralised foraging strategies of ant colonies. By treating user interactions as paths that gain or lose “pheromone” based on helpfulness ratings, the system enables the community to collectively and adaptively identify its most effective helpers. A two-phase validation study was conducted. First, an agent-based simulation demonstrated that TrailMap reduced the mean time to a helpful response by over 70% and improved workload equity compared to random routing. Second, a four-week randomised controlled pilot study with human participants confirmed these gains, showing a 76% reduction in median wait time and significantly higher perceived helpfulness ratings. The findings suggest that by balancing the workload, TrailMap enhances not only the efficiency but also the socio-technical sustainability of online support communities. TrailMap provides a practical, nature-inspired method for building more resilient and equitable online support communities, enhancing access to effective mental health support. Full article

Improving the isotropic magnetic properties of FeSi electrical steels has traditionally focused on enhancing their crystallographic texture and microstructural morphology. Strengthening the cube texture within a ferritic matrix of optimal grain size is known to reduce core losses and increase magnetic induction. However, conventional cold rolling followed by annealing remains insufficient to optimise the magnetic performance of thin FeSi strips fully. This study explores an alternative approach based on grain boundary migration driven by temperature gradients combined with deformation gradients, either across the sheet thickness or between neighbouring grains, in thin, weakly deformed non-oriented (NO) electrical steel sheets. The concept relies on deformation-induced grain growth supported by rapid heat transport to promote the preferential formation of coarse grains with favourable orientations. Experimental material consisted of vacuum-degassed FeSi steel with low silicon content. Controlled deformation was introduced by temper rolling at room temperature with 2–40% thickness reductions, followed by rapid recrystallisation annealing at 950 °C. Microstructure, texture, and residual strain distributions were analysed using inverse pole figure (IPF) maps, kernel average misorientation (KAM) maps, and orientation distribution function (ODF) sections derived from electron backscattered diffraction (EBSD) data. This combined thermomechanical treatment produced coarse-grained microstructures with an enhanced cube texture component, reducing coercivity from 162 A/m to 65 A/m. These results demonstrate that temper rolling combined with dynamic annealing can surpass the limitations of conventional processing routes for NO FeSi steels. Full article

Shipping in complex marine environments requires a balance between navigational safety, minimising travel time and optimising logistics management, which is particularly challenging in areas with geometric obstructions and Offshore Wind Farms (OWFs). This study focuses on the maritime route networks in the Croatian ports of Pula and Rijeka, including the main access routes to OWFs and zones characterised by multiple navigational challenges. The aim of the research is to develop an empirically based and practically applicable framework for the optimisation of sea routes that combines analytical precision with operational efficiency. The parallel application of Dijkstra and A* algorithms enables a comparative analysis between deterministic and heuristic approaches in terms of reducing navigation risk, optimising route costs and ensuring fast logistical access to OWFs. The applied methods include the analysis of real and simulated route networks, the evaluation of statistical route parameters and the visualisation of the results for the evaluation of logistical and operational efficiency. Adaptive heuristic modifications of the A* algorithm, combined with the parallel implementation of Dijkstra’s algorithm, enable dynamic route planning that takes into account real-world conditions, including variations in wind speed and direction. The results obtained provide a comprehensive framework for safe, efficient and logistically optimised navigation in complex marine environments, with direct applications in the maintenance, inspection and operational management of OWFs. Full article

This study presents an optimisation algorithm to solve a collaborative vehicle routing problem with time windows. The algorithm was developed and tested on a real-world case study to investigate the potential for a shared-fleet operation involving public organisations, specifically, the Isle of Wight Council (IWC) and the National Health Service (NHS). The aim was to evaluate whether collaborative use of public-sector vehicles could reduce total fleet size, operational costs, and vehicle-kilometres travelled, while maintaining existing service levels. The study develops a two-stage optimisation algorithm that incorporates real-world constraints such as vehicle capacity, time windows, and pre-assigned mandatory stops. The first stage maximises the number of assignable collaborative tasks across fleets, while the second stage minimises the total travel cost conditional on this maximum assignment. Using historical data and a novel optimisation algorithm, vehicle movements were modelled to evaluate benefits in terms of cost savings, reduced CO₂ emissions and vehicle usage. The case study results generated by the algorithm suggested that considerable improvements could be made by integrating patient diagnostic collection rounds into the existing IWC minibus routes: (a 10.6% reduction in CO₂ emissions (644 kg/month) and vehicle kilometres (2300 km/month), a 20.2% reduction in working hours (219 h/month), and a 17.8% saving in cost (GBP (£) 3596/month) leading to IWC gaining a potential additional revenue of GBP (£) 54,829 annually while reducing costs by 22.4% for the NHS. The findings highlighted the potential benefits of shared fleet collaborations between public sector organisations, offering a model for similar collaborations in other public sector contexts. Full article

Additive manufacturing (AM), or 3D printing, is increasingly recognised as a disruptive production technology with the capacity to reduce greenhouse gas (GHG) emissions across manufacturing and transportation sectors. By enabling material efficiency, lightweighting, part consolidation, and decentralised, on-demand production, AM offers pathways to lower embodied energy, minimise waste, and shorten supply chains. This review critically evaluates AM’s role in decarbonisation, with a focus on clean transportation applications, including electric vehicles, fuel cells, and hydrogen storage systems. Case studies quantify energy savings, operational efficiency gains, and life-cycle GHG reductions compared to conventional manufacturing routes. The analysis also addresses technical and economic limitations—such as material availability, scalability, certification, and cost competitiveness—and explores synergies with circular economy principles, digital design optimisation, and artificial intelligence. Policy recommendations and industry–academia collaboration models are proposed to accelerate AM adoption, integrate renewable energy sources, and strengthen recycling infrastructure. By synthesising technical, economic, and policy perspectives, the study positions AM as a critical enabler of net-zero manufacturing and a catalyst for sustainable industrial transformation. Full article

Rapid access to defibrillators, blood products, and time-critical medicines can improve survival, yet urban congestion and fragmented infrastructure delay deliveries. We present and evaluate an end-to-end framework for beyond-visual-line-of-sight (BVLOS) UAV logistics in Essex (UK), integrating (I) strategic depot placement, (II) a hybrid obstacle-aware route planner, and (III) a time-window-aware (TWA) Mixed-Integer Linear Programming (MILP) scheduler coupled to a battery/temperature feasibility model. Four global planners—Ant Colony Optimisation (ACO), Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Rapidly Exploring Random Tree* (RRT*)—are paired with lightweight local refiners, Simulated Annealing (SA) and Adaptive Large-Neighbourhood Search (ALNS). Benchmarks over 12 destinations used real Civil Aviation Authority no-fly zones and energy constraints. RRT*-based hybrids delivered the shortest mean paths: RRT* + SA and RRT* + ALNS tied for the best average length, while RRT* + SA also achieved the co-lowest runtime at $v=60\mathrm{km}{\mathrm{h}}^{-1}$. The TWA-MILP reached proven optimality in 0.11 s, showing that a minimum of seven UAVs are required to satisfy all 20–30 min delivery windows in a single wave; a rolling demand of one request every 15 min can be sustained with three UAVs if each sortie (including service/recharge) completes within 45 min. To validate against a state-of-the-art operations-research baseline, we also implemented a Vehicle Routing Problem with Time Windows (VRPTW) in Google OR-Tools, confirming that our hybrid planners generate competitive or shorter NFZ-aware routes in complex corridors. Digital-twin validation in AirborneSIM confirmed CAP 722-compliant, flyable trajectories under wind and sensor noise. By hybridising a fast, probabilistically complete sampler (RRT*) with a sub-second refiner (SA/ALNS) and embedding energy-aware scheduling, the framework offers an actionable blueprint for emergency medical UAV networks. Full article

Waste Electrical and Electronic Equipment (WEEE) management is a critical global challenge. This study proposes a model for the WEEE Transportation Problem using advanced evolutionary algorithms such as the Genetic Algorithm (GA), the Offspring-Selected Genetic Algorithm (OSGA), the Evolution Strategy (ES), and the Offspring-Selected Evolution Strategy (OSES). These algorithms, which are part of the field of Artificial Intelligence (AI), are applied to optimise transportation routes, minimising time and costs, and promoting sustainability by reducing the carbon footprint. Test instances and solutions are presented to demonstrate the feasibility of the model and the effectiveness of the proposed algorithms. Rather than providing technical detail, the focus is placed on the novelty of applying these algorithms to the WEEE Transportation Problem in Mexico, particularly for minimising operational cost. While reductions in carbon emissions are discussed as a natural consequence of cost optimisation, a formal dual-objective formulation is beyond the present scope and is identified as a direction for future work. Full article

This study investigates the influence of key heat treatment parameters on the microstructure and mechanical properties of the powder metallurgy tool steel Vanadis 60. A fractional factorial design of experiments was applied to evaluate the effects of austenitising temperature, quenching medium, tempering temperature, and number of tempering cycles on hardness, flexural strength, and microstructure, using detailed phase characterisation by X-ray diffraction. The results reveal two distinct processing routes tailored to different performance objectives. Maximum hardness was achieved by combining austenitisation at 1180 °C, rapid oil quenching, and tempering at 560 °C. These conditions enhance the solubility of carbon and other alloying elements, promote secondary hardening, and reduce retained austenite. Conversely, higher toughness and ductility were obtained by austenitising at 1020 °C, air cooling, and tempering at 560 °C. These parameters favour the formation of a bainitic microstructure, together with lower martensite tetragonality and minimal retained austenite. A statistically significant interaction was identified between the austenitising temperature and the number of tempering cycles; three temperings were sufficient to compensate for the lower hardness associated with reduced austenitising temperatures. The results provide a robust guidance for optimising thermal processing in highly alloyed tool steels, enabling the precise tailoring of microstructure and properties in accordance with specific mechanical service requirements. Full article

Background: The growing demand for fresh food leads to extensive use of cold chain logistics (CCL) that significantly contributes to greenhouse gas (GHG) emissions due to its dependence on energy-intensive transport refrigeration units (TRUs). Understanding the need to balance food preservation with environmental sustainability, this paper explores practical strategies for reducing GHG emissions in CCL, focusing on fresh food products. Methods: The quantitative and qualitative analyses are applied to analyse data from Transport for London and Transport Scotland. Emission data were assessed to evaluate the impact of alternative TRU technologies and route optimisation practices. Results: The findings reveal that electric and cryogenic TRUs, along with improved route planning and operational practices, can significantly reduce the emissions of carbon dioxide, nitrogen oxides and particulate matter. These results highlight the potential strategy for industry-led emission reductions without compromising food quality. Conclusions: This paper recommends the coordination of government policy and industry to support technological adaptation and infrastructure upgrades and to research into real-time monitoring and renewable energy integration in CCL systems. Full article

This study investigates the hot deformation behaviour and flow stress prediction of metastable β-Ti-15Mo alloy, a promising material for biomedical applications requiring strength–modulus optimisation and thermomechanical tunability. Isothermal compression tests were performed within the temperature range of 923–1173 K and at strain rates of 0.17, 1.72, and 17.2 ${\mathrm{s}}^{-1}$ to assess the material’s response under industrially relevant hot working conditions. The alloy showed significant sensitivity to temperature and strain rate, with dynamic recovery (DRV) and dynamic recrystallisation (DRX) dominating the softening behaviour depending on the conditions. A strain-compensated Arrhenius-type constitutive model was developed and validated, resulting in an apparent activation energy of approximately 234 kJ/mol. Zener–Hollomon parameter analysis confirmed a transition in deformation mechanisms. Although microstructural and diffraction data suggest possible contributions from nanoscale phase transformations, including ω-phase dissolution at high temperatures, these aspects remain to be fully elucidated. The model offers reliable predictions of flow behaviour and supports optimisation of thermomechanical processing routes for biomedical β-Ti alloys. Full article

Aviation is widely recognised to have global-scale climate impacts through the formation of ozone (O₃) in the upper troposphere and lower stratosphere (UTLS), driven by emissions of nitrogen oxides (NO_X). Ozone is known to be one of the most potent greenhouse gases formed from the interaction of aircraft emission plumes with atmospheric species. This paper follows up on previous research, where a Photochemical Trajectory Model was shown to be a robust measure of ozone formation along flight trajectories post-flight. We use a combination of a global Lagrangian chemistry-transport model and a box model to quantify the impacts of aircraft NO_X on UTLS ozone over a five-day timescale. This work expands on the spatial and temporal range, as well as the chemical accuracy reported previously, with a greater range of NO_X chemistry relevant chemical species. Based on these models, route optimisation has been investigated, through the use of network theory and algorithms. This is to show the potential inclusion of an understanding of climate-sensitive regions of the atmosphere on route planning can have on aviation’s impact on Earth’s Thermal Radiation balance with existing resources and technology. Optimised flight trajectories indicated reductions in O₃ formation per unit NO_X are in the range 1–40% depending on the spatial aspect of the flight. Temporally, local winter times and equatorial regions are generally found to have the most significant O₃ formation per unit NO_X; moreover, hotspots were found over the Pacific and Indian Ocean. Full article

Intelligent transport systems are a major component of smart cities because their deployment should result in reduced journey times, less traffic congestion and a significant reduction in road deaths, which will greatly improve the quality of life of their citizens. New technologies such as vehicular networks allow more information be available in realtime, and this information can be used with new analytical models to obtain more accurate estimates of journey times. This would be extremely useful to drivers and will also enable transport authorities to optimise the transport network. This paper addresses these issues using a model-based approach to provide a new way of estimating the delay along specified routes. A journey is defined as the traversal of several road links and junctions from source to destination. The delay at the junctions is analysed using the zero-server Markov chain technique. This is then combined with the Jackson network to analyse the delay across multiple junctions. The delay at road links is analysed using an M/M/K/K model. The results were validated using two simulators: SUMO and VISSIM. A real scenario is also examined to determine the best route. The preliminary results of this model-based analysis look promising but more work is needed to make it useful for wide-scale deployment. Full article