Replication packages are crucial for enabling transparency, validation, and reuse in software engineering (SE) research. While artifact sharing is now a standard practice and even expected at premier SE venues such as ICSE, the practical usability of these replication packages remain underexplored. In particular, there is a marked lack of studies that comprehensively examine the executability and reproducibility of replication packages in SE research. In this paper, we aim to fill this gap by evaluating 100 replication packages published in ICSE proceedings over the past decade (2015 - 2024). We assess the (1) executability of the replication packages, (2) efforts and modifications required to execute them, (3) challenges that prevent executability, and (4) reproducibility of the original findings for those that are executable. We spent approximately 650 person-hours in total to execute the artifacts and reproduce the study findings. Our analysis shows that only 40 of the 100 evaluated artifacts were fully executable. Among these, 32.5% ran without any modification. However, even executable artifacts required varying levels of effort: 17.5% required low effort, while 82.5% required moderate to high effort to execute successfully. We identified five common types of modifications and 13 challenges that lead to execution failure, encompassing environmental, documentation, and structural issues. Among the executable artifacts, only 35% (14 out of 40) reproduced the original results. These findings highlight a notable gap between artifact availability, executability, and reproducibility. Our study proposes three actionable guidelines to improve the preparation, documentation, and review of research artifacts, thereby strengthening the rigor and sustainability of open science practices in SE research.
The key to the realization of air-water trans-medium flight lies in the profile design of trans-medium flight and the satisfaction of different requirements of aerodynamic efficiency for air cruise and airfoil for underwater glide. In this paper, a trans-medium flight profile scheme based on the fusion design of traditional fixed-wing vehicles and underwater gliders was proposed with a small trans-medium vehicle as the platform. Several typical working conditions were determined, and alternative airfoils based on NACA00 and NACA44 series were selected according to the working conditions. The compressible flow model of Fluent was used to carry out numerical analysis on the alternative airfoil set. The aerodynamic and hydrodynamic characteristics of the alternative airfoils in air and water, such as lift-to-drag ratios, lift line slope, lift and drag coefficients, and torque coefficients were calculated by numerical simulation, which were then used as the optimal objective function and constraint conditions of the airfoil of the trans-medium fixed-wing vehicle. The relationship between the preferred airfoil under the underwater navigation profile and the corresponding flight/underwater motion parameters was emphatically analyzed, especially the influence of the change in airfoil camber on the underwater endurance time and range, so as to provide the airfoil optimization decision for the scheme design of the trans-medium vehicle, and the established analysis process can provide a reference for the parameter optimization of the airfoil.
Plankton are small drifting organisms found throughout the world's oceans and can be indicators of ocean health. One component of this plankton community is the zooplankton, which includes gelatinous animals and crustaceans (e.g. shrimp), as well as the early life stages (i.e., eggs and larvae) of many commercially important fishes. Being able to monitor zooplankton abundances accurately and understand how populations change in relation to ocean conditions is invaluable to marine science research, with important implications for future marine seafood productivity. While new imaging technologies generate massive amounts of video data of zooplankton, analyzing them using general-purpose computer vision tools turns out to be highly challenging due to the high similarity in appearance between the zooplankton and its background (e.g., marine snow). In this work, we present the ZooplanktonBench, a benchmark dataset containing images and videos of zooplankton associated with rich geospatial metadata (e.g., geographic coordinates, depth, etc.) in various water ecosystems. ZooplanktonBench defines a collection of tasks to detect, classify, and track zooplankton in challenging settings, including highly cluttered environments, living vs non-living classification, objects with similar shapes, and relatively small objects. Our dataset presents unique challenges and opportunities for state-of-the-art computer vision systems to evolve and improve visual understanding in dynamic environments characterized by significant variation and the need for geo-awareness. The code and settings described in this paper can be found on our website: https://lfk118.github.io/ZooplanktonBench_Webpage.
Nonlinear soil–pile–structure interaction (SPSI) phenomena are known to play a vital role in the response of bottom-fixed marine structures. For such structures, these phenomena are commonly considered by the imposition of p-y, τ-z, and q-z springs, representing the lateral and axial shaft and axial base soil resistances, respectively. The importance of each resistance mechanism depends on the type of foundation system, with only very limited studies investigating their roles in the response of piled marine structures, such as jetties. Within this context, this study presents numerical three-dimensional pushover analysis results for two marine jetties, a smaller model with four piles and a larger model supported by twenty-four piles. SPSI effects are considered through p-y, τ-z, and q-z springs, the behaviours of which are determined by following commonly employed procedures. The structures’ responses are investigated under the influence of various assumptions regarding the behaviours of springs, as well as steel plasticity. The current investigation underscores the substantial influence of the axial soil–pile interaction on the response of the jetty, particularly in terms of its failure mode. Moreover, it demonstrates the importance of incorporating p-y springs, even though the choice between their linear or nonlinear constitutive behaviour is found to be less critical. Finally, the study concludes that the behaviours of the springs significantly affect the system’s ductility and the degree of steel yielding in the piles, while also highlighting the unconservative influence of neglecting SPSI phenomena.
With the widespread application of array signal processing, the estimation of direction of arrival(DOA) as the core problem of array signal processing has made significant progress. This paper first summarizes the traditional algorithms based on beamforming for narrowband target direction estimation relying on uniform linear arrays and emerging algorithms in the past decade. Then, it analyzes the reasons for the limited resolution of traditional beamforming-based methods and discusses higher-resolution methods such as adaptive beamforming direction spectrum, subspace methods, and compressed sensing. Furthermore, for the needs of practical applications, the paper summarizes the progress of broadband target DOA estimation methods, sparse array-based DOA estimation methods, and two-dimensional DOA estimation methods. Finally, the new advances of artificial intelligence-based methods in DOA estimation are introduced. The research in this paper can be applied to modern radar/sonar detection, radio communication, and navigation, showing high application value.
Chih-Wen Cheng, Yu-An Tzeng, Ming-Hsiung Chang
et al.
This study presents an optimized system for ship route planning. Computational fluid dynamics simulations were used to modify Kwon’s semi-empirical speed loss estimation method, enabling efficient route planning under variable sea conditions. The study focused on improving the prediction of speed loss in irregular waves for container ships and further applying this to ship-optimized voyage planning. Dynamic programming was used for optimized voyage planning by modifying the ship course in response to meteorological data; this approach could balance both energy efficiency and safety. The modified speed loss predictions aligned closely with the simulation results, enhancing the reliability of weather routing decisions. Case studies for trans-Pacific and trans-Atlantic voyages demonstrated that the proposed system could significantly reduce the voyage time. These findings highlight the potential of real-time updates in voyage planning. The proposed system is a valuable tool for captains and fleet managers. The applicability of this system can be further broadened by validating it on different ship types.
The paper discusses the role of social marketing in preventing health-related harmful habits such as tobacco consumption and smoking. These habits are the causes of deadly diseases such as lung cancer, tuberculosis, and other chronic infections which are detrimental to life of the people. Children fall prey to the wrong habits in the wrong company and become tobacco addicts. So many cases of teen drug addicts are reported in a large number. They have a lack of conscience at a tender age and negligence of their counselling and awareness increases the number of smokers, drunkards, and drug addicts. Once they are afflicted with the diseases they must run for medicines and treatment. Therefore, it should be prevented before suffering as the saying goes, “Prevention is better than cure “. They are unaware that they are prevented not only by clinical treatment and medicines but also by social awareness and education. Social mobilization of the people through awareness programs, education, camps, campaigns, etc. is known as social marketing. The significance of social marketing is its effects on the prevention of physically detrimental habits in the youth which contributed a lot to the reduction of cases of diseases. The role of government programs, educational and medical institutions, social workers, and NGOs is worth applauding in India which undertake and complete projects, organize awareness camps, and educate parents and youths to save themselves from the consumption of harmful substances. It has also produced good output in India that the cases of smoking and drug addiction have reduced to support the country’s development as India is advancing towards becoming the third largest economy and a developed country by 2030 and 2047 respectively.
Benjamin Decardi-Nelson, Abdulelah S. Alshehri, Akshay Ajagekar
et al.
This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.
6G will revolutionize the software world allowing faster cellular communications and a massive number of connected devices. 6G will enable a shift towards a continuous edge-to-cloud architecture. Current cloud solutions, where all the data is transferred and computed in the cloud, are not sustainable in such a large network of devices. Current technologies, including development methods, software architectures, and orchestration and offloading systems, still need to be prepared to cope with such requirements. In this paper, we conduct a Systematic Mapping Study to investigate the current research status of 6G Software Engineering. Results show that 18 research papers have been proposed in software process, software architecture, orchestration and offloading methods. Of these, software architecture and software-defined networks are respectively areas and topics that have received the most attention in 6G Software Engineering. In addition, the main types of results of these papers are methods, architectures, platforms, frameworks and algorithms. For the five tools/frameworks proposed, they are new and not currently studied by other researchers. The authors of these findings are mainly from China, India and Saudi Arabia. The results will enable researchers and practitioners to further research and extend for 6G Software Engineering.
There are ever-increasing efforts on utilising non-conventional energy resources so as to reduce the global carbon footprint and reduce pollution caused by extensive use of energy from conventional sources. At the same time, the energy extraction from the abundant marine renewable energy resources available offshore need more development due to difference in the levelized cost of energy (LCOE) compared to energy extracted onshore, though the difference in the cost of energy is getting reduced faster than expected. Meanwhile hundreds of offshore platforms already existing in oil and gas industry will undergo decommissioning at the end of its life; which will require the platform to be removed or reused. In this context, the reuse possibility of such decommissioned offshore platforms to be used as support structures for marine renewable energy applications is examined. This paper presents a review on the research efforts on this topic and a discussion on the various aspects involved in the possible reuse of decommissioned offshore fixed platforms for marine renewable energy applications. Journal of Naval Architecture and Marine Engineering Vol 19(2), December, 2022 p. 71-82
Maritime transportation is one of the major contributors to the development of the global economy. To ensure its safety and reduce the occurrence of a maritime accident, intelligent maritime monitoring and ship behavior identification have been drawing much attention from industry and academia, among which, the accurate prediction of ship trajectory is one of the key questions. This paper proposed a trajectory prediction model integrating the Convolutional LSTM (ConvLSTM) and Sequence to Sequence (Seq2Seq) models to facilitate simultaneous extraction of temporal and spatial features of ship trajectories, thereby enhancing the accuracy of prediction. Firstly, the trajectories are preprocessed using kinematic-based anomaly removal and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to improve the data quality for the training process of trajectory prediction. Secondly, the ConvLSTM-based Seq2seq model is designed to extract temporal and spatial features of the ship trajectory and improve the performance of long-time prediction. Finally, by using real AIS data, the proposed model is compared with the Seq2Seq and Bidirectional LSTM based on attention mechanism (Bi-Attention-LSTM) models to verify its effectiveness. The experimental results demonstrate that the proposed model achieves excellent performance in predicting turning trajectories, good predictive accuracy on straight line motions, and greater improvement in prediction accuracy compared to the other two benchmark models. Overall, the proposed model represents a promising contribution to improving ship trajectory prediction accuracy and may enhance the safety and quality of ship navigation in complex and volatile marine environments.
During the process of container ship transportation, the berthing time cost of the ship in port is extremely important. Container allocation and quay crane (QC) operation greatly affect the berthing time. Currently, few scholars have combined import/export container allocation and QC operation, making it urgent to study ship stowage and QC collaboratively. In this paper, a mixed-integer programming model is established for the ship multi-port master bay plan problem (MP-MBPP), based on the operation of twin 40-foot QCs. The aim of this model is to minimize container rehandling and the time required for twin 40-foot QCs operation movement. A variety of new stowing strategies have been designed, and the improved coded particle swarm optimization algorithm (PSO) is used to optimize the position of double-bays, reducing the number and distance of QC movements and minimizing ship berthing time. By comparing the impact of different stowage rules on ship berthing time through examples, verification shows that the proposed stowage model and solving algorithm can obtain optimized solutions. Under the same initial conditions, the double-bay stowage based on the twin 40-foot QCs can improve operation efficiency by at least 20.3%, compared to the single-bay with ordinary QC, verifying the effectiveness of the proposed method.
Physics-Informed machine learning models have recently emerged with some interesting and unique features that can be applied to reservoir engineering. In particular, physics-informed neural networks (PINN) leverage the fact that neural networks are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations. The transient diffusivity equation is a fundamental equation in reservoir engineering and the general solution to this equation forms the basis for Pressure Transient Analysis (PTA). The diffusivity equation is derived by combining three physical principles, the continuity equation, Darcy's equation, and the equation of state for a slightly compressible liquid. Obtaining general solutions to this equation is imperative to understand flow regimes in porous media. Analytical solutions of the transient diffusivity equation are usually hard to obtain due to the stiff nature of the equation caused by the steep gradients of the pressure near the well. In this work we apply physics-informed neural networks to the one and two dimensional diffusivity equation and demonstrate that decomposing the space domain into very few subdomains can overcome the stiffness problem of the equation. Additionally, we demonstrate that the inverse capabilities of PINNs can estimate missing physics such as permeability and distance from sealing boundary similar to buildup tests without shutting in the well.
The control centers of wind power plants are usually located in coastal tidal flat areas. A thick fill should be placed at the original ground level to ensure that the design elevation of the control centers is maintained above the water table. However, the filling would cause a long-term ground settlement and further lead to the development of the negative skin friction (NSF) of the pile foundations for the control centers. The CPTU tests were conducted to calibrate the soil properties, of which the rationalities were verified by comparisons of the pile-bearing capacities between the full-scale axial compressive tests and β-method. The numerical analysis method was then established to investigate the influence of additional ground pressures on the pile axial bearing behavior over time and the influence of NSF caused by consolidation on pile-bearing capacity. Finally, a simple procedure was further employed to investigate the evolution of the long-term pile-bearing behavior.
In crowded waters, the incidence of collision accidents of multiple vessels has increased significantly, most of which can be ascribed to human factors, particularly, human decision-making failures and inobservance with International Regulations for Preventing Collisions at Sea (COLREGS). On this basis, an automatic collision avoidance strategy for the encounter situations of multiple vessels is proposed herein. First of all, a COLREGS-complied evasive action decision-making mechanism is established, which can determine the evasive action from the give-way vessel and stand-on vessel separately. It is worth emphasizing that the situation of vessels against COLREGS is taken into consideration herein. Furthermore, a novel automatic collision avoidance controller of multiple vessels on account of model predictive control (MPC) is devised, which can carry out control action ahead of time and handle the problem of rudder saturation. Finally, the effectiveness of the proposed automatic collision avoidance strategy of multiple vessels is illustrated via extensive simulations.
Bonggil Hyun, Pung-Guk Jang, Kyoungsoon Shin
et al.
Copepods, the dominant member of zooplankton and major grazers of phytoplankton in the pelagic ecosystem, are at risk from exposure to antifouling biocides. To evaluate the developmental toxicity of antifouling biocides (Diuron, Irgarol 1051, Sea-nine 211) and wastewater (from high-pressure water blasting (WHPB) and its MeOH extract (WHPB-MeOH)) in the copepod <i>Paracalanus parvus</i> sl, we investigated the chemical concentration, egg-hatching rate, and nauplius mortality. WHPB samples were obtained through hull-cleaning activities involving WHPB in a dry dock. Among the biocides, Sea-nine 211 had the strongest effects on hatching rates and nauplius mortality, which was followed by Diuron and Irgarol 1051. In the WHPB and WHPB-MeOH samples, there was no significant difference between the experimental groups in terms of the egg-hatching rate; however, WHPB was found to be more toxic in terms of nauplius mortality, suggesting that metals in WHPB may also adversely affect nauplius survival in <i>P. parvus</i> sl. A comparison of the LC<sub>50</sub> results of Sea-nine 211 and WHPB revealed that WHPB had a negative effect on nauplius mortality even at a 100-fold lower concentration. Therefore, if chemical contaminants generated during in-water cleaning activity are discharged continuously into the ports without being properly collected through a post-treatment system, they are expected to negatively impact the population of copepods near the port. Although verification is needed through additional experiments, our results could be used for a baseline study concerning the toxicity of antifouling biocides on marine copepod species.