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Presented at the 8th International Physical Internet Conference (IPIC 2021) 14-16 June 2021, Virtual, The maritime branch of the EU container supply chain is configured as hub-and spoke networks where large containerships transport cargo to Deep-Sea Shipping (DSS) ports and a limited number of Short Sea Shipping (SSS) feeder lines and container trucks on RoPax vessels distribute cargo to smaller ports for further transhipment. However, economically, environmentally, and socially unsustainable land-based transportation still covers most of the cargo transhipment from Hub ports. MOSES aims to significantly improve the modal shift to SSS by creating sustainable feeder services to small ports that have limited or no infrastructure (Figure 1). This will be achieved by developing the following highly automated/autonomous technologies and integrating them in existing operational processes: i) a digital collaboration and matchmaking platform (MOSES platform), ii) an innovative, green feeder vessel with a robotic container handling system, iii) a manoeuvring and docking scheme where autonomous tugboats collaborate with an automated mooring system. MOSES Matchmaking platform proposes a solution where data feeds from various sources allow logistics stakeholders to optimise and adjust routing plans, bringing the PI one step closer to the targets set for 2030 and 2040.

Additional file 1: Table S1. Product profile and % w/w cellulose conversion into soluble oligosaccharides from pure cellulosic substrates. Table S2. Product profile and % w/w cellulose conversion into soluble oligosaccharides from lignocellulosic substrates. Table S3. Cellobiose yield for 24 and 48 h of hydrolysis of organosolv-pretreated spruce (S1) and birch (B1). The response factor (% w/w cellobiose) refers to the % w/w conversion of cellulosic biomass content into cellobiose. Table S4. Final equations in terms of actual components for spruce (S1) and birch (B1) experimental design. Table S5. Average water permeability for all the membranes. Table S6. Upper and lower limits for all variables used for the experimental design. Table S7. Enzyme combinations for the optimization tests generated with D-optimal experimental design (Design Expert® 7.0.0, Stat-Ease Inc.). Figure S1. Ternary plots displaying the predicted cellobiose concentration (mg/mL) from birch hydrolysis at 24 (A) and 48 h (B), as a function of three out of four enzymes. For each plot, the fourth enzyme (“Actual Component”) is set at the relative proportion of the point yielding the maximum amount of cellobiose, as predicted by the statistical model. Figure S2. Ternary plots displaying the predicted cellobiose concentration (mg/mL) from spruce hydrolysis at 24 (A) and 48 h (B), as a function of three out of four enzymes. For each plot, the fourth enzyme (“Actual Component”) is set at the relative proportion of the point yielding the maximum amount of cellobiose, as predicted by the statistical model.

European ports are currently facing the challenge of adapting to the current trends in global trade and efficiently handling the increasing volumes placed on them. The aim of this paper is to present an innovative framework supported by disruptive technologies for cargo ports to handle upcoming and future capacity, traffic, efficiency and environmental challenges.The innovations to be implemented within the proposed framework will contribute to the Port of the Future objectives regarding reduction of port’s total environmental footprint associated with intermodal connections; the improvement of operational efficiency, and increase of data sharing and information visibility; and the promotion on the innovation in the port-urban context. Among the solutions presented, the model-driven tool for Real-time Control of port operations, the advanced Truck Appointment System and the Cargo Flow Optimization tool, aim to pave the way into interconnected port systems with information at various steps of the transportation flow. Overall, the proposed framework aims to develop models and tools which can support ports to improve their efficiency and gradually participate in a Physical Internet network.

Additional file 1: Table S1. Product profile and % w/w cellulose conversion into soluble oligosaccharides from pure cellulosic substrates. Table S2. Product profile and % w/w cellulose conversion into soluble oligosaccharides from lignocellulosic substrates. Table S3. Cellobiose yield for 24 and 48 h of hydrolysis of organosolv-pretreated spruce (S1) and birch (B1). The response factor (% w/w cellobiose) refers to the % w/w conversion of cellulosic biomass content into cellobiose. Table S4. Final equations in terms of actual components for spruce (S1) and birch (B1) experimental design. Table S5. Average water permeability for all the membranes. Table S6. Upper and lower limits for all variables used for the experimental design. Table S7. Enzyme combinations for the optimization tests generated with D-optimal experimental design (Design Expert® 7.0.0, Stat-Ease Inc.). Figure S1. Ternary plots displaying the predicted cellobiose concentration (mg/mL) from birch hydrolysis at 24 (A) and 48 h (B), as a function of three out of four enzymes. For each plot, the fourth enzyme (“Actual Component”) is set at the relative proportion of the point yielding the maximum amount of cellobiose, as predicted by the statistical model. Figure S2. Ternary plots displaying the predicted cellobiose concentration (mg/mL) from spruce hydrolysis at 24 (A) and 48 h (B), as a function of three out of four enzymes. For each plot, the fourth enzyme (“Actual Component”) is set at the relative proportion of the point yielding the maximum amount of cellobiose, as predicted by the statistical model.

This is a public deliverable of the EU funded (H2020) project SafePASS. The deliverable number is D2.1. Executive Summary: State-of-the-art (SoTA) analysis is a preliminary step to specify the baseline with respect to demonstrate the novelty of the upcoming research results. Funded under the European Union’s Horizon2020 Framework Programme, the aim of SafePASS is to radically redefine the evacuation processes, evacuation systems/equipment and international regulations for passenger ships in all environments, hazards and weather conditions, independently of the demographic factor, by developing an integrated system that will collectively monitor, process and inform during emergencies both crew and passengers of the optimal evacuation routes, coupled with advanced, intuitive and easy to use LSA, resulting as such to a significant reduction of the total time required for ship evacuation and increased safety. The current document provides a review of the existing tools and products used for evacuation, as well as an identification of regulatory gaps and accident investigation. It is connected to Task 2.1: Ship evacuation current practices and regulation-gaps and needs within Work Package WP2: SafePASS Design, User and System requirements. The present document is considered as a guiding and referenced document for the whole project. The concluding remarks presented within this study have been discussed and evaluated during the SafePASS stakeholders’ workshop, conducted on 29th of January 2020, in Glasgow, Scotland. The identification of the evacuation processes in the context of the best practises, tools and gaps is crafted by conducting a State-of-the-Art analysis of the evacuation of the large passenger ships, using a five-step approach. The five discrete concurrent steps are referring to: i. the recording of the evacuation regulatory framework, ii. the documentation of the recent publications in the field of evacuation of the large passenger ships, iii. the identification of existing systems used during the evacuation, iv. the reporting of the recent evacuation incidents, and v. the review of previous project results and outcomes.

This is a public deliverable of the EU funded (H2020) project SafePASS. The deliverable number is D2.1. Executive Summary: State-of-the-art (SoTA) analysis is a preliminary step to specify the baseline with respect to demonstrate the novelty of the upcoming research results. Funded under the European Union’s Horizon2020 Framework Programme, the aim of SafePASS is to radically redefine the evacuation processes, evacuation systems/equipment and international regulations for passenger ships in all environments, hazards and weather conditions, independently of the demographic factor, by developing an integrated system that will collectively monitor, process and inform during emergencies both crew and passengers of the optimal evacuation routes, coupled with advanced, intuitive and easy to use LSA, resulting as such to a significant reduction of the total time required for ship evacuation and increased safety. The current document provides a review of the existing tools and products used for evacuation, as well as an identification of regulatory gaps and accident investigation. It is connected to Task 2.1: Ship evacuation current practices and regulation-gaps and needs within Work Package WP2: SafePASS Design, User and System requirements. The present document is considered as a guiding and referenced document for the whole project. The concluding remarks presented within this study have been discussed and evaluated during the SafePASS stakeholders’ workshop, conducted on 29th of January 2020, in Glasgow, Scotland. The identification of the evacuation processes in the context of the best practises, tools and gaps is crafted by conducting a State-of-the-Art analysis of the evacuation of the large passenger ships, using a five-step approach. The five discrete concurrent steps are referring to: i. the recording of the evacuation regulatory framework, ii. the documentation of the recent publications in the field of evacuation of the large passenger ships, iii. the identification of existing systems used during the evacuation, iv. the reporting of the recent evacuation incidents, and v. the review of previous project results and outcomes.