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Authorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.

A decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.

The comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.

The efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

This paper aims to assess the current and future dynamics of land cover transitions and analyze the vegetation conditions in Abuja city since its establishment as the capital of Nigeria in 1991.

A random forest classifier embedded in the Google Earth Engine platform was used to classify Landsat imagery for the years 1990, 2001, 2014 and 2020. A post-classification comparison was used to detect the dynamics of land cover transitions. A hybrid simulation model that comprised cellular automata and Markovian was used to model the probable scenario of land cover changes for 2050. The trend of Normalized Difference Vegetation Index was examined using Mann–Kendall and Theil Sen’s from 2014 to 2022. Nighttime band data from the National Oceanic and Atmospheric Administration were obtained to analyze the trend of urbanization from 2014 to 2022.

The findings show that built-up areas increased by 40%, while vegetation, bare land and agricultural land decreased by 27%, 7% and 8%, respectively. Vegetation had the highest declining rate at 3.15% per annum. Built-up areas are expected to increase by 17.1% between 2020 and 2050 in contrast with other land cover. The proportion of areas with moderate vegetation improvement is estimated to be 15.10%, while the proportion of areas with no significant change was 38.10%. The overall proportion of degraded areas stands at 46.8% due to urbanization.

The findings provide a comprehensive insight into the dynamics of land cover transitions and vegetation variability induced by rapid urbanization in Abuja city, Nigeria. In addition, the findings provide valuable insights for policymakers and urban planners to develop a sustainable land use policy that promotes inclusivity, safety and resilience.

This work conducts a comprehensive analysis of how to incorporate resilience and sustainability into capacity expansion strategies for business-to-business (B2B) chemical supply chains. This study aims to guide both researchers and managers on ensuring profitability in B2B chemical supply chains while minimizing environmental impacts, complying with regulations and mitigating disruptions and risks.

A systematic literature review is conducted to analyze the interplay between sustainability and resilience in chemical B2B supply chains, specify the quantitative and qualitative methods used to tackle this challenge and identify the drivers and barriers concerning capacity expansion. In addition, a comprehensive conceptual framework is suggested to outline a compelling research agenda.

The findings emphasize the increasing importance of modeling and resolving decision-making challenges related to sustainable and resilient supply chains, particularly in capital-intensive chemical industries. Yet, there is no standardized strategy for addressing these challenges. The predominant solution methods are heuristic and metaheuristic, and the selection of performance metrics tends to be empirical and tailored to specific cases. The main barriers to achieving sustainability and resilience arise from resource limitations within the supply chain. Conversely, the key drivers of performance focus on enhancing efficiency, competitiveness, cost effectiveness and risk management.

This work offers practitioners a conceptual framework that synthesizes the knowledge and tackles the challenges of designing sustainable and resilient supply chains as well as managing their operations in the context of B2B chemical supply chains. Results provide a practical guide for navigating the complex interplay of sustainability, resilience and chemical supply chain expansion.

The key concepts and dimensions associated with capacity expansion planning for a resilient and sustainable chemical supply chain are identified through structured and comprehensive analyses of existing literature. A conceptual framework is proposed for delineating the intersections among sustainability, resilience and chemical supply chain expansions. This mapping endeavor aims to facilitate a future characterized by the deployment of a nexus of resilience and sustainability in chemical supply chains. To this end, a promising future research agenda is accordingly outlined.

Buried pipelines under various soil embankment heights are cost-effective alternatives to transporting liquid products. This paper aims to assist pipeline architects and professionals in selecting the most cost-effective buried reinforced concrete pipelines under deep embankment soil with minor structural reinforcement while meeting shear stress requirements, safety and reliability constraints.

It is unfeasible to experimentally assess pipeline efficiency with high soil fill depth. Thus, to fill this gap, this research uses a dependable finite element analysis (FEA) to conduct a parametric study and carry out such an issue. This research considered reinforced concrete pipes with diameters of 25, 50, 75, 100, 125 and 150 cm at depths of 5, 10, 15 and 20 m.

According to this research, the proposed best pipeline diameter-to-thickness (D/T) proportions for soil embankment heights 5, 10, 15 and 20 m are 8.75, 4.8, 3.5 and 3.1, correspondingly. The cost-effective reinforced concrete (RC) pipeline thickness dramatically rises if the soil embankment reaches 20 m, indicating that the soil embankment depth highly influences it. Most of the analyzed reinforced concrete pipelines had a maximum deflection value of less than 1 cm, telling that the FEA accurately identified the pipeline width, needed flexural steel reinforcement, and concrete crack width while avoiding significant distortion.

The cost-effective thickness for the analyzed structured concrete pipes was calculated by considering the lowest required value of steel reinforcement. An algorithm was developed based on the parametric scientific findings to predict the ideal pipeline D/T ratio. A construction case study was also shown to assist architects and professionals in determining the best reinforced concrete pipeline geometry for a specific soil embankment height.

This paper aims to propose a hybrid force/position controller based on the adaptive variable impedance.

First, the working space is divided into a force control subspace and a position subspace, the force control subspace adopts the position impedance control strategy. At the same time, the contact force model between the robot and the surface is analyzed in this space. Second, based on the traditional position impedance, the model reference adaptive control is introduced to provide an accurate reference position for the impedance controller. Then, the BP neural network is used to adjust the impedance parameters online.

The experimental results show that compared with the traditional PI control method, the proposed method has a higher flexibility, the dynamic response accommodation time is reduced by 7.688 s and the steady-state error is reduced by 30.531%. The overshoot of the contact force between the end of robot and the workpiece is reduced by 34.325% comparing with the fixed impedance control method.

The proposed control method compares with a hybrid force/position based on PI control method and a position fixed impedance control method by simulation and experiment.

The adaptive variable impedance control method improves accuracy of force tracking and solves the problem of the large surfaces with robot grinding often over-polished at the protrusion and under-polished at the concave.

This study aims to provide a comprehensive review of electric tugboat deployment in maritime transportation, including an in-depth assessment of its advantages and disadvantages. Along with the identification of advantages and disadvantages of electric tugboat deployment, the present research also aims to provide managerial insights into the economic viability of different tugboat alternatives that can guide future investments in the following years.

A detailed literature review was conducted, aiming to gain broad insights into tugboat operations and focusing on different aspects, including tugboat accidents and safety issues, scheduling and berthing of tugboats, life cycle assessment of diesel tugboats and their alternatives, operations of electric and hybrid tugboats, environmental impacts and others. Moreover, a set of interviews was conducted with the leading experts in the electric tugboat industry, including DAMEN Shipyards and the Port of Auckland. Econometric analyses were performed as well to evaluate the financial viability and economic performance of electric tugboats and their alternatives (i.e. conventional tugboats and hybrid tugboats).

The advantages of electric tugboats encompass decreased emissions, reduced operating expenses, improved energy efficiency, lower noise levels and potential for digital transformation through automation and data analytics. However, high initial costs, infrastructure limitations, training requirements and restricted range need to be addressed. The electric tugboat alternative seems to be the best option for scenarios with low interest rate values as increasing interest values negatively impact the salvage value of electric tugboats. It is expected that for long-term planning, the electric and hybrid tugboat alternatives will become preferential since they have lower annual costs than conventional diesel tugboats.

The outcomes of this research provide managerial insights into the practical deployment of electric tugboats and point to future research needs, including battery improvements, cost reduction, infrastructure development, legislative and regulatory changes and alternative energy sources. The advancement of battery technology has the potential to significantly impact the cost dynamics associated with electric tugboats. It is essential to do further research to monitor the advancements in battery technology and analyze their corresponding financial ramifications. It is essential to closely monitor the industry’s shift toward electric tugboats as their prices become more affordable.

The maritime industry is rapidly transforming and facing pressing challenges related to sustainability and digitization. Electric tugboats represent a promising and innovative solution that could address some of these challenges through zero-emission operations, enhanced energy efficiency and integration of digital technologies. Considering the potential of electric tugboats, the present study provides a comprehensive review of the advantages and disadvantages of electric tugboats in maritime transportation, extensive evaluation of the relevant literature, interviews with industry experts and supporting econometric analyses. The outcomes of this research will benefit governmental agencies, policymakers and other relevant maritime transportation stakeholders.

Due to the complexity and diversity of megaprojects, the architectural programming process often involves multiple stakeholders, making decision-making difficult and susceptible to subjective factors. This study aims to propose an architectural programming methodology system (APMS) for megaprojects based on group decision-making model to enhance the accuracy and transparency of decision-making, and to facilitate participation and integration among stakeholders. This method allows multiple interest groups to participate in decision-making, gathers various perspectives and opinions, thereby improving the quality and efficiency of architectural programming and promoting the smooth implementation of projects.

This study first clarifies the decision-making subjects, decision objects, and decision methods of APMS based on group decision-making theory and value-based architectural programming methods. Furthermore, the entropy weight method and fuzzy TOPSIS method are employed as calculation methods to comprehensively evaluate decision alternatives and derive optimal decision conclusions. The workflow of APMS consists of four stages: preparation, information, decision, and evaluation, ensuring the scientific and systematic of the decision-making process.

This study conducted field research and empirical analysis on a practical megaproject of a comprehensive transport hub to verify the effectiveness of APMS. The results show that, in terms of both short-distance and long-distance transportation modes, the decision-making results of APMS are largely consistent with the preliminary programming outcomes of the project. However, regarding transfer modes, the APMS decision-making results revealed certain discrepancies between the project's current status and the preliminary programming.

APMS addresses the shortcomings in decision accuracy and stakeholder participation and integration in the current field of architectural programming. It not only enhances stakeholder participation and interaction but also considers various opinions and interests comprehensively. Additionally, APMS has significant potential in optimizing project performance, accelerating project processes, and reducing resource waste.

This study aims to promote the anti-disturbance and tracking accuracy performance of the servo systems, in which a modified active disturbance rejection control (MADRC) scheme is proposed.

An adaptive radial basis function (ARBF) neural network is utilized to estimate and compensate dominant friction torque disturbance, and a parallel high-gain extended state observer (PHESO) is employed to further compensate residual and other uncertain disturbances. This parallel compensation structure reduces the burden of single ESO and improves the response speed of permanent magnet synchronous motor (PMSM) to hybrid disturbances. Moreover, the sliding mode control (SMC) rate is introduced to design an adaptive update law of ARBF.

Simulation and experimental results show that as compared to conventional ADRC and SMC algorithms, the position tracking error is only 2.3% and the average estimation error of the total disturbances is only 1.4% in the proposed MADRC algorithm.

The disturbance parallel estimation structure proposed in MADRC algorithm is proved to significantly improve the performance of anti-disturbance and tracking accuracy.

Recent advancements in technology have led to the exploration of solar-based thermal radiation and nanotechnology in the field of fluid dynamics. Solar energy is captured through sunlight absorption, acting as the primary source of heat. Various solar technologies, such as solar water heating and photovoltaic cells, rely on solar energy for heat generation. This study focuses on investigating heat transfer mechanisms by utilizing a hybrid nanofluid within a parabolic trough solar collector (PTSC) to advance research in solar ship technology. The model incorporates multiple effects that are detailed in the formulation.

The mathematical model is transformed using suitable similarity transformations into a system of higher-order nonlinear differential equations. The model was solved by implementing a numerical procedure based on the Wavelets and Chebyshev wavelet method for simulating the outcome.

The velocity profile is reduced by Deborah's number and velocity slip parameter. The Ag-EG nanoparticles mixture demonstrates less smooth fluid flow compared to the significantly smoother fluid flow of the Ag-Fe3O4/EG hybrid nanofluids (HNFs). Additionally, the Ag-Ethylene Glycol nanofluids (NFs) exhibit higher radiative performance compared to the Ag-Fe3O4/Ethylene Glycol hybrid nanofluids (HNFs).

Additionally, the Oldroyd-B hybrid nanofluid demonstrates improved thermal conductivity compared to traditional fluids, making it suitable for use in cooling systems and energy applications in the maritime industry.

The originality of the study lies in the exploration of the thermal transport enhancement in sun-powered energy ships through the incorporation of silver-magnetite hybrid nanoparticles within the heat transfer fluid circulating in parabolic trough solar collectors. This particular aspect has not been thoroughly researched previously. The findings have been validated and provide a highly positive comparison with the research papers.

When solving the cogging torque of complex electromagnetic structures, such as consequent pole hybrid excitation synchronous (CPHES) machine, traditional methods have a huge computational complexity. The notable feature of CPHES machine is the symmetric range of field-strengthening and field-weakening, but this type of machine is destined to be equipped with a complex electromagnetic structure. The purpose of this paper is to propose a hybrid analysis method to quickly and accurately solve the cogging torque of complex 3D electromagnetic structure, which is applicable to CPHES machine with different magnetic pole shapings.

In this paper, a hybrid method for calculating the cogging torque of CPHES machine is proposed, which considers three commonly used pole shapings. Firstly, through magnetic field analysis, the complex 3D finite element analysis (FEA) is simplified to 2D field computing. Secondly, the discretization method is used to obtain the distribution of permeance and permeance differential along the circumference of the air-gap, taking into account the effect of slots. Finally, the cogging torque of the whole motor is obtained by using the idea of modular calculation and the symmetry of the rotor structure.

This method is applicable to different pole shapings. The experimental results show that the proposed method is consistent with 3D FEA and experimental measured results, and the average calculation time is reduced from 8 h to 4 min.

This paper proposes a new concept for calculating cogging torque, which is a hybrid calculation of dimension reduction and discretization modules. Based on magnetic field analysis, the 3D problem is simplified into a 2D issue, reducing computational complexity. Based on the symmetry of the machine structure, a modeling method for discretized analytical models is proposed to calculate the cogging torque of the machine.