Seyed Behbood Issa-Zadeh

Ship collisions in the North Sea remain a significant maritime safety concern because the region combines merchant traffic, fishing activity, offshore operations, and traffic separation schemes. This study examines five purposively selected North Sea ship collisions occurring between 2010 and 2023 to identify recurring human, organisational, and navigational factors and develop practical lessons for reducing collision risk. A qualitative comparative case-study approach was employed using official Marine Accident Investigation Branch and Dutch Safety Board reports. Documentary analysis and thematic coding covered 22 factors across five cases. The most consistently supported weaknesses concerned lookout and monitoring, situation awareness, risk of collision assessment, and electronic navigational information. Communication, bridge redundancy, and organisational controls also showed deficiencies, although their form and evidential support differed. Evidence concerning fatigue and some organisational explanations was qualified. The cross-case interpretation identified erosion of overlapping safety barriers before impact, while recognising functioning safeguards and limitations in the report evidence. The findings support recommendations for observable watchkeeping standards, effective technology use, bridge-team support, fatigue-aware operations, and organisational verification. These conclusions concern the selected cases and do not establish the frequency or causal strength of the factors across all North Sea collisions.

Livestock carriers contribute to greenhouse gas emissions through the continued use of conventional fossil fuels. This study presents a theoretical, steady-state case study of replacing the Wärtsilä W7X35 diesel engine of the livestock carrier Motor Vessel MV Ganado Express with a Wärtsilä 46F methanol-fueled engine (7500 kW, operated at 81.2% load to deliver the required 6090 kW), using published specific fuel consumption values and emission factors. Fuel consumption and exhaust emissions were estimated with a bottom-up method over a 24 h sailing period, and the annual well-to-wake (WtW) greenhouse gas fuel intensity (GFI) and compliance costs were assessed for 2028–2035 against the two-tier targets of the IMO Net-Zero Framework approved at MEPC 83. Because methanol is a carbon-bearing fuel, tank-to-wake carbon dioxide emissions are nearly unchanged, declining by only 5.4%, whereas nitrogen oxide emissions fall by 73.2% and sulfur oxide emissions are eliminated. The decarbonization benefit arises instead on a well-to-wake basis: with renewable e-methanol, annual lifecycle emissions fall from 29,897 to 2291 tCO_2eq. Diesel operation exceeds both targets from 2028, at a compliance cost rising from USD 0.91 to 3.85 million per year, while e-methanol remains in direct compliance throughout the period. E-methanol nevertheless remains the more expensive option, the annual cost gap narrowing from USD 15.1 to 12.2 million by 2035.

This work focuses on studying the magnetohydrodynamic flow and heat transfer mechanism of a Casson–Maxwell nanofluid due to a stretching surface through a porous medium, using a physics-informed neural network (PINNs) approach as the main tool for the solution of the physical problem. The mathematical model describes the phenomena of viscosity variation with temperature, viscous dissipation, thermal slip, Brownian motion, thermophoresis, and drag force due to a porous medium, which give a realistic physical scenario of the coupled transport phenomena of momentum, heat, and nanoparticles. First, the nonlinear partial differential equations are converted into a dimensionless boundary layer model using similarity transformations. Then, the yielded system is solved via the PINNs approach, which integrates physical law within the optimization procedure. The proposed technique does not require a significant number of labeled datasets and provides accurate and stable predictions of the strongly nonlinear flow. A comprehensive parametric analysis was performed to explore the impact of the dimensionless controlling factors on the velocity, temperature, and nanoparticle concentration distributions. It is found that the interaction of magnetic field effects, porous media resistivity, thermal and concentration slip, viscosity variation, and viscous heating significantly modifies the transport features for the studied model of the Casson–Maxwell nanofluid, which can be used effectively to control the rate of heat and mass transfer. This study proves the efficiency of the PINN technique in solving this type of model, and it also provides useful insights for designing thermal systems, energy conversion devices, and electrically conducting viscoelastic nanofluid transport problems. The close concordance between the present findings and established data from the literature validates the precision and dependability of the developed PINN-based framework.

Designing products that feel simple and intuitive while remaining efficient to manufacture and meaningful across cultures remains a key challenge in contemporary product development. Although minimalist design has achieved commercial success, most methodologies fail to systematically connect aesthetic intentions with engineering, usability, and production realities. This study proposes a four-phase iterative framework—Framing, Translation, Materialisation, and Experience that integrates human-centred design, simplicity heuristics—including ‘SHE’ (Shrink, Hide, Embody) tactics, quantitative aesthetic measurement, Design for X methods, and cross-cultural considerations to operationalise minimalist principles such as formal reduction, seriality, and industrial materiality. An empirical case study applied the framework to three competing automotive interior concepts through image-based evaluation by 113 respondents. Results provided preliminary evidence that a balanced hybrid approach consistently outperformed both traditional button-heavy and extreme single-screen minimalist designs across measures of usability, sense of order, trust, and user recommendation. Findings confirm that effective minimalism does not merely remove elements but strategically redistributes complexity into interface logic, production systems, and material quality. The framework offers designers a structured yet flexible path to create manufacturable, user-validated, and culturally sensitive minimalist products.

This research presents a physics-informed deep learning framework for investigating the magnetohydrodynamic flow of a dissipative non-Newtonian micropolar nanofluid induced by a stretching sheet, incorporating Stefan blowing, internal heat generation, and the zero nanoparticle mass flux condition. The physical model consists of the interplay between the microrotation dynamics, resistance of porosity on the microrotation, Brownian diffusion, and thermophoretic transport phenomenon. The numerical solutions for the nonlinear yielded equations that result from the above interaction are obtained by employing a PINN that considers the laws of physics and boundary conditions. With this technique, the flow behavior, temperature, concentration, and microrotation fields can be predicted accurately without requiring huge datasets. This shows the ability of PINNs to numerically treat highly-coupled nonlinear transport equations in a very efficient manner compared to other traditional methods. The important discoveries from this study include that the porous and magnetic factors increased the skin friction coefficient, but the magnetic effect and viscous dissipation decreased the rate of heat transfer, and the thermophoresis effect decreased the rate of mass transfer while the Brownian effect increased it. The precision of the PINN algorithm is confirmed by comparison of the results with the earlier findings, which proves very high accuracy and hence the robustness of the current computing framework. Results of this research are useful for the development of some thermal management systems, energy converters, cooling methods, chemical reaction processes, fuel cell technology, porous media reactors, and ocean engineering involving the transport of complicated non-Newtonian nanofluids.

Maritime pilotage is increasingly shaped by decarbonisation, digitalisation, and wider sustainability pressures, yet the integration of sustainability thinking into pilotage training remains insufficiently understood. This study addresses that gap by examining sustainability thinking not simply as an issue of awareness, but as a problem of training integration in a safety-critical professional context. Using an exploratory, theory-informed quantitative design, the study reinterprets primary survey data from 39 active maritime pilots through a deductive analytical framework combining sustainability pillars, integration domains, sustainability thinking sub-competencies, composite analytical conditions, and structured interpretive synthesis. The findings show that sustainability-oriented thinking is already present in pilotage practice, but that its integration into training remains uneven. It appears strongest where it is embedded in operational judgement and socially established professional norms, and weaker where it depends on pedagogical reinforcement, institutional consistency, and digital credibility. The main challenge is therefore not the absence of sustainability awareness, but the inconsistent translation of that awareness into trainable, repeatable, and professionally reinforced competence. By deriving barriers, enablers, drivers, and risks, the study offers a more applied framework for understanding sustainability thinking as a training and professional development issue in maritime pilotage.

This study numerically investigates the hydrothermal behaviour of a Jeffrey nanofluid with relevance to maritime thermal systems. The coupled nonlinear governing equations for momentum, heat, and mass transport are solved using a shooting technique that accounts for magnetohydrodynamic effects, Darcy porous-media resistance, viscous dissipation, and spatially varying internal heat generation. Variable thermophysical properties, including temperature-dependent viscosity and density, are also considered. The results reveal that porous resistance, fluid elasticity, and thermophysical variations significantly influence velocity, temperature, and concentration fields. The combined effects of porous drag and variable properties markedly alter the characteristics of heat and mass transfer. These findings provide insights into thermal and mass-transport performance, including skin friction, heat transfer, and concentration distributions, which are critical metrics for porous heat exchangers and nanofluid-based maritime coatings. Here, maritime relevance is represented via a generalised porous nanofluid model rather than a specific material. Among the key findings, increasing the slip velocity factor can reduce the surface skin-friction coefficient by approximately 48.7%, while the heat-transfer rate increases by nearly 27.1%, accompanied by a decrease of about 18.9% in the Sherwood number. Conversely, raising the density factor enhances the skin friction coefficient by roughly 103.8% and also augments the heat and mass transfer rates by about 61.3% and 106.1%, respectively. Likewise, at zero relaxation–retardation ratio, the flow reduces to the Newtonian case. Increasing this factor reduces the local Nusselt number by about 1.45%, indicating a slight weakening of heat transfer due to elastic effects. Furthermore, the reliability of the current numerical framework is established through a dual-validation approach, including an analytical assessment of limiting cases and a rigorous comparison with established data from the literature.

This paper examines the biomass energy issue, particularly its potential and challenges in urban sustainable development. With the increase in demand for energy worldwide and the gradual depletion of traditional fossil fuel sources, biomass, and other new and sustainable sources are becoming increasingly critical. A qualitative approach is taken to develop the framework, considering literature reviews, which use PRISMA-SCR and VOS viewer to filter literature and content related to the main issue of the study and then categorize opportunities and challenges of biomass energy in urban planning. The results highlight the potential of biomass in meeting energy demands on a global scale but simultaneously stress the need for expert management to leverage this potential. The advantages of biomass energy include but are not limited to enhancing economic development, environmental improvement, and boosting energy security by decreasing reliance on fossil fuels. Land allocation disputes and negative energy balance issues complicate the situation, but recent technologies have addressed such challenges. The policy and planning implications show that the potential of biomass energy can only be fully exploited with government policy support, infrastructure investment, and international cooperation. Above all, this research demonstrates that, despite the limitations, biomass energy provides significant benefits for city sustainability by diversifying the energy supply, intending to achieve goals compatible with global sustainable development. Areas for future research are provided, focusing on more quantitative and empirical research to solve problems and verify theories. In fact, cities need to use biomass energy from ecological and equitable perspectives.

This research develops a dual physics-based machine learning system to forecast fuel consumption and CO₂ emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship.

This literature review critically examines the development and optimization of sustainable energy and exergy analysis software specifically designed for offshore wind farms, emphasizing the transformative role of machine learning (ML) in overcoming operational challenges. Offshore wind energy represents a cornerstone in the global transition to low-carbon economies due to its scalability and superior energy yields; however, its complex operational environment, characterized by harsh marine conditions and logistical constraints, necessitates innovative analytical tools. Traditional deterministic methods often fail to capture the dynamic interactions within wind farms, thereby underscoring the need for ML-integrated approaches that enhance precision in energy forecasting, fault detection, and exergy analysis. This PRISMA-ScR review synthesizes recent advancements in ML techniques, including Random Forest, Long Short-Term Memory networks, and hybrid models, demonstrating significant improvements in predictive accuracy and operational efficiency. In addition, it critically identifies current gaps in existing software tools, such as inadequate real-time data processing and limited user interface design, which hinder the practical implementation of ML solutions. By integrating theoretical insights with empirical evidence, this study proposes a unified framework that leverages ML algorithms to optimize turbine performance, reduce maintenance costs, and minimize environmental impacts. Emerging trends, such as incorporating digital twins and Internet of Things (IoT) technologies, further enhance the potential for real-time system monitoring and adaptive control. Overall, this review provides a comprehensive roadmap for the next generation of software tools to revolutionize offshore wind farm management, thereby aligning technological innovation with global renewable energy targets and sustainable development goals.