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In most seaports, pilotage is compulsory for incoming and outgoing vessels to use pilotage services. Maritime pilots play a critical role in providing local knowledge and assistance to ensure the safety of vessels and seafarers. Typically, pilots are classified into multiple classes on the basis of their experiences and qualifications. Moreover, pilotage duration is usually stochastic due to various factors, including harsh weather conditions, visibility, and traffic changes. In this study, we address a multi-class pilot dispatching problem with stochastic pilotage duration. We formulate this problem as a stochastic programming model that considers the expected penalty cost for late arrival at each task's location. To solve this problem, we develop a branch-and-price algorithm that integrates column generation within the branch and bound framework. Additionally, we develop a novel labelling algorithm and several effective acceleration techniques to enhance the efficiency of the proposed algorithm. Extensive numerical experiments demonstrate the effectiveness of the proposed algorithm and show that it can handle realistic-sized instances while outperforming a metaheuristic search method. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the daily berth planning problem for Xiamen Hai-Tian Container Terminal (XHCT) at the Port of Xiamen, China, and propose the development and implementation of a berth planning system. The aim of the berth planning problem is to optimize daily berth plans by considering various decisions, including berth allocation, quay crane assignment, fairway traffic control, and berthing safety requirements. Among these decisions, the berthing safety requirement is a novel but practical problem in berth allocation that concerns the resource allocation related to berthing safety and interrelation with other decisions. A mathematical programming-driven methodological framework is designed with a 0-1 integer linear programming model for problem formulation and a highly efficient decomposition heuristic algorithm for solving the problem. This framework establishes the core for the berth planning system. The adoption of the berth planning system contributes to the increase of container throughput and berth capacity by transforming the planning process of XHCT. Moreover, the mathematical programming-driven daily berth planning informs further intelligent operations development in the Port of Xiamen and other container ports. History: This paper was refereed. Funding: This research was supported by the National Natural Science Foundation of China [Grants 72394360, 72394362, 72025103, 71831008, 72361137001, 72071173, and 72371221]. [ABSTRACT FROM AUTHOR]

Pavement crack detection has always been an important problem in pavement maintenance. With rapid development in computer vision technology, crack detection using deep learning has become one of the major tasks in automatic pavement condition detection and assessment. This paper aims to develop a method of crack grid detection based on convolutional neural network. First, an image denoising operation is conducted to improve image quality. Next, the processed images are divided into grids of different scales (10 x 10, 20 x 20, 30 x 30), and each grid is fed into a convolutional neural network for detection. The pieces of the grids with cracks are marked and then returned to the original images. Finally, on the basis of the detection results, threshold segmentation is performed only on the marked grids. Information about the crack parameters is obtained via pixel scanning and calculation, which realizes complete crack detection. The experimental results show that 30 x 30 grids perform the best with the accuracy value of 97.33%. The advantage of automatic crack grid detection is that it can avoid fracture phenomenon in crack identification and ensure the integrity of cracks. Thresholding smaller grids allows better combinations of the differences between objects and backgrounds, which makes segmentation results more accurate. Key words: convolutional neural network, pavement crack detection, maximum entropy threshold segmentation, crack type judgment, parameter calculation. La detection de fissures dans la chaussee a toujours ete un probleme important dans l'entretien de la chaussee. Avec le developpement rapide de la technologie de vision par ordinateur, la detection des fissures par l'application de l'apprentissage en profondeur est devenue l'une des taches principales de la detection et de l'evaluation automatiques de l'etat des chaussees. Cet article vise a developper une methode de detection de fissures par grille, et ce, basee sur un reseau neuronal convolutif. Tout d'abord, une operation de debruitage d'images est effectuee pour ameliorer la qualite d'image. Ensuite, les images traitees sont divisees en grilles differentes (10 x 10, 20 x 20, 30 x 30), et chaque grille est alimentee dans un reseau neuronal convolutif pour la detection. Les morceaux des grilles avec des fissures sont marques puis retournes aux images originales. Enfin, sur la base des resultats de detection, la segmentation des seuils n'est effectuee que sur les grilles marquees. L'information sur les parametres des fissures est obtenue par le balayage et le calcul des pixels, qui realisent la detection complete de fissures. Les resultats experimentaux montrent que les grilles 30 x 30 sont les plus performantes avec une precision de 97,33%. L'avantage de la detection automatique des fissures par grille est qu'elle peut eviter le phenomene de rupture lors de l'identification des fissures et assurer l'integrite des fissures. Des grilles plus petites permettent une meilleure combinaison des differences entre les objets et les arriere-plans, ce qui rend les resultats de segmentation plus precis. [Traduit par la Redaction] Mots-cles: reseau neuronal convolutif, detection de fissures dans la chaussee, segmentation maximale du seuil d'entropie, jugement du type de fissure, calcul de parametres., Introduction Pavement maintenance has always been a very important problem, which is inseparable from the detection of pavement distress. After protracted use, any pavement, whether made of asphalt or cement, [...]

Alloying is regarded as one of the most promising strategies for boosting performance of catalysts for hydrogen evolution reaction (HER) due to the adjustable electronic structure and intermediate adsorption. However, there is no theory (including d‐band center theory) can accurately guide the preparation and design of alloy catalysts, and thus resulting all the reported alloy catalysts are obtained by time‐consuming and laborious experimental exploration. Herein, we proposed a mean d‐band center (εas) as a new accurate descriptor for alloy activity prediction. Theoretical simulation and experiment results revealed that this descriptor exhibits a strong scaling relation with H adsorption energy. Besides, the obtained Cu–Ag alloy displays an optimal overpotential of 223 mV at 10 mA/cm2 in 0.5 mol/L H2SO4, which is more than 300 mV lower than those of pristine Cu (530 mV) and Ag (569 mV) powder. Our work provides a new idea toward designing highly efficient HER catalysts and broadens the applicability of d‐band theory to activity prediction of alloys. [ABSTRACT FROM AUTHOR]

Highly active, durable, and cost‐effective electrodes for hydrogen evolution reaction (HER) at ultrahigh current densities (≥1 A cm−2) are extremely demanded for industrial high‐rate hydrogen production, but challenging. Here, a robust strongly coupled Ag(S)@NiO/nickel foam (NF) electrode is reported. Taking advantage of millisecond laser direct writing in liquid nitrogen technique, lattice‐matched and coherent interfaces are formed between Ag nanoparticles with stacking faults (denoted by Ag(S)) and NiO nanosheets, leading to strong interfacial electronic coupling, not only promoting H2O adsorption and dissociation on Ni2+ but also enhancing H* adsorption on intrinsically inactive but most electrically conductive Ag. Strong chemical bonding is established at NiO/NF interface, guaranteeing rapid electron transfer and excellent mechanical durability under high‐rate hydrogen evolution. The physicochemically stable electrode achieves record‐low alkaline HER overpotential of 167 and 180 mV at 1 and 1.5 A cm−2, respectively, along with negligible activity decay after 120 h test at ≈1.5 A cm−2, surpassing reported non‐platinum group metal electrocatalysts. [ABSTRACT FROM AUTHOR]

With the development of green logistics, logistics companies gradually are paying attention to the application of hybrid electric vehicles (HEVs). HEVs have the advantages of low energy consumption and pollution, while their disadvantage mainly lies in their limited continuous driving range. Therefore, it is necessary to optimize the use of fuel during the distribution process. We study the mode selection system in HEVs based on the background of green logistics and the above characteristics of HEVs. The mode selection system can adjust the driving mode of the HEV according to different road conditions to obtain the optimal use of fuel. In this paper, we propose a new study of a hybrid electric vehicle routing problem with mode selection. This problem is formulated as a mixed integer linear programming model. An improved particle swarm optimization algorithm (IPSO) is developed to solve this problem. Extensive numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method. The experimental results show that our proposed algorithm not only obtains the optimal solution for some small-scale problem instances and some medium-scale problems but also solves some large-scale situations (one hundred customers, eleven vehicles, eleven charging stations, eleven gas stations and four modes) within an hour. [ABSTRACT FROM AUTHOR]

Large‐scale application of alkaline water electrolysis for high‐rate hydrogen production is severely hindered by high electricity cost, mainly due to difficulties to acquire cost‐effective catalytic electrodes with both extremely low overpotential and long‐term durability at ultrahigh current densities (≥1 A cm−2). Here it is demonstrated that by adopting a synthetic method of laser direct writing in liquid nitrogen via a commercial laser welding machine, a remarkably efficient and durable electrode with large area and low platinum content is obtained, where PtNi nanocatalysts with dislocation network are firmly welded on a nickel foam (NF). The dense dislocation network not only improves intrinsic activity of a majority of surface‐active sites induced by coupled compressive‐tensile strains synergistically promoting both Volmer and Tafel steps of alkaline hydrogen evolution reaction (HER), but also well stabilizes surface dislocations for HER at ultrahigh current densities. Such a robust electrode achieves record‐low overpotentials of 5 and 63 mV at 10 and 1000 mA cm−2 in alkaline medium, respectively, exhibiting negligible activity decay after 300 h chronoamperometric test at 1 A cm−2. It displays a high Pt mass activity 16 times higher than 20 wt% Pt/C loaded on NF, surpassing most of the recently reported efficient Pt‐based catalysts. [ABSTRACT FROM AUTHOR]

This paper proposes a blockchain-based framework to improve the efficiency of ship traffic in port. In the framework, ship agents, terminals, tug company, pilot station, and government share information and the information is stored in a blockchain. Based on the shared information, we discuss three categories of data-driven models that can improve the operations management of the above five parties. The first category is decisions made by a single party. The second category involves decisions of at least two ship agents. The third category relates to multi-party decision-making under uncertainty. This study hopes to stimulate maritime practitioners to embrace blockchain technology and data-driven approaches to enhance the competitiveness of the industry. [ABSTRACT FROM AUTHOR]

In this study, we aim to minimize the total waiting time between successive treatments for inpatients in rehabilitation hospitals (departments) during a working day. Firstly, the daily treatment scheduling problem is formulated as a mixed-integer linear programming model, taking into consideration real-life requirements, and is solved by Gurobi, a commercial solver. Then, an improved cuckoo search algorithm is developed to obtain good quality solutions quickly for large-sized problems. Our methods are demonstrated with data collected from a medium-sized rehabilitation hospital in China. The numerical results indicate that the improved cuckoo search algorithm outperforms the real schedules applied in the targeted hospital with regard to the total waiting time of inpatients. Gurobi can construct schedules without waits for all the tested dataset though its efficiency is quite low. Three sets of numerical experiments are executed to compare the improved cuckoo search algorithm with Gurobi in terms of solution quality, effectiveness and capability to solve large instances. [ABSTRACT FROM AUTHOR]