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Positioning systems are integral to Unmanned Underwater Vehicle (UUV) operation, enabling precise navigation and control in complex underwater environments. This paper comprehensively reviews the key technologies employed for UUV positioning, including acoustic systems, inertial navigation, Doppler velocity logs, and GPS when near the surface. These systems are essential for seabed mapping, marine infrastructure inspection, and search and rescue operations. The review highlights recent technological advancements and examines the integration of these systems to enhance accuracy and operational efficiency. It also addresses ongoing challenges, such as communication constraints, environmental variability, and discrepancies between theoretical models and field applications. Future trends in positioning system development are discussed, with a focus on improving reliability and performance in diverse underwater conditions to support the expanding capabilities of UUVs across scientific, commercial, and rescue missions. [ABSTRACT FROM AUTHOR]

Natural language understanding is a foundational task in medical dialogue systems. However, there are still two key problems to be solved: (1) Multiple meanings of a word lead to ambiguity of intent; (2) character errors make slot entity extraction difficult. To solve the above problems, this paper proposes a character-word information interaction framework (CWIIF) for natural language understanding in the Chinese medical dialogue domain. The CWIIF framework contains an intent information adapter to solve the problem of intent ambiguity caused by multiple meanings of words in the intent detection task and a slot label extractor to solve the problem of difficulty in yellowslot entity extraction due to character errors in the slot filling task. The proposed framework is validated on two publicly available datasets, the Intelligent Medical Consultation System (IMCS-21) and Chinese Artificial Intelligence Speakers (CAIS). Experimental results from both datasets demonstrate that the proposed framework outperforms other baseline methods in handling Chinese medical dialogues. Notably, on the IMCS-21 dataset, precision improved by 2.42%, recall by 3.01%, and the F1 score by 2.4%. [ABSTRACT FROM AUTHOR]

This paper addresses the assessment of woody biomass resources in Russia and the Baikal region. The analysis of the literature demonstrates that the Baikal region has considerable amounts of waste from the logging, timber processing, and pulp and paper industries (up to 220 PJ). A review of utilization technologies for woody biomass demonstrates that the existing technologies based on biomass gasification are promising for energy purposes. The gasification of biomass for small-capacity power plants has some advantages compared to its combustion. This paper considers an autonomous power system that consists of photovoltaic converters, wind turbines, storage batteries, a biomass gasification power plant, and a diesel power plant. A mathematical model used to optimize the system's structure finds the minimum of the total discounted costs for the creation and operation of the system with some constraints met. Based on mathematical modeling, the cost-effectiveness of such a power supply system is assessed for different climatic zones of the Baikal region and the coastal area of Lake Baikal. The findings indicate that the optimal solution is the integration of various renewable energy sources in hybrid power systems. The proportion of energy sources of different types in the installed capacities is found. The study demonstrates that the optimal structure of the power system can provide significant savings (the total discounted costs are reduced by almost 2.5 times compared to the option using a diesel power plant alone). [ABSTRACT FROM AUTHOR]

Featured Application: The SERS-based paper nanosensors reported in here may be used for ultrasensitive label-free detection of biomolecules in disease monitoring, as well as in any other field of application in need of ultrasensitive analytical tools such as environment, food safety or water control, among others. Surface-enhanced Raman scattering (SERS) spectroscopy stands out due to its sensitivity, selectivity, and multiplex ability. The development of ready-to-use, simple, and low-cost SERS substrates is one of the main challenges of the field. In this paper, the intrinsic reproducibility of microfluidics technology was used for the fabrication of self-assembled nanoparticle structures over a paper film. The paper SERS substrates were fabricated by assembling anisotropic particles, gold nanostars (GNSs), and nanorods (NRs) onto paper to offer an extra enhancement to reach ultra-sensitive detection limits. A polydimethylsiloxane PDMS-paper hybrid device was used to control the drying kinetics of the nanoparticles over the paper substrate. This method allowed a high reproducibility and homogeneity of the fabrication of SERS substrates that reach limits of detection down to the picomolar range. This simple and low-cost fabrication of a paper-based sensing device was tested for the discrimination of different cell lineages. [ABSTRACT FROM AUTHOR]

Featured Application: An improvement in adhesion (gluing) in the printing production process. In this study, the effect of air plasma on the surface properties of printed and coated cardboard was investigated. The material was activated by low-pressure cold plasma for 1, 10, 20, and 30 s. Wettability changes on the surface were examined by contact angle measurements using the sessile droplet technique. The differences in the surface free energy were calculated using the Lifshitz–van der Waals/acid–base and Contact Angle Hysteresis approaches. Optical profilometry was used for the surface roughness evaluation and an X-ray photoelectron spectroscopy analysis was performed to find changes in surface chemistry. Adhesive strength tests were carried out to estimate the adhesion changes after the material's modification. It was found that the water and formamide contact angles increased after the plasma treatment while the diiodomethane contact angle did not change. As a result of the modification, the surface free energy also increased significantly and the surface roughness increased. The pull-off tests confirmed the improvement in the material's surface properties. Moreover, it was demonstrated that the optimal effect can be obtained after just 10 s of the plasma process. [ABSTRACT FROM AUTHOR]

Featured Application: Generation of a Dynamic Innovation Information System (DIIS) for optimized planning and decision-making thanks to the dynamic evaluation of innovations over their life cycle, applying a methodology for Digital Ecosystems in the Fourth Industrial Revolution and an innovation management model based on the Viable System Model. Innovations are essential for global development and market dynamics. Innovation management is central to organizations for gaining adaptability and dynamic capabilities to ensure their sustainability over time. Right decisions are essential for the implementation of innovations. However, on many occasions, especially in the product development process, decisions are taken based on static analysis, qualitative criteria, questionnaires, and/or quantitative evaluations that are outdated. Moreover, many innovation developments do not consider the existing databases in their information systems of similar innovation projects, especially in the early phases of new innovations when evaluations are mainly driven by area, group, or person. Furthermore, inventions are introduced in different regions, plants, and socio-economic situations, providing different results. In this context, considering that innovations shape our current and future world, including all products and services, as well as how humans, organizations, and machines interact, the significance of the paper is clear. Therefore, it is necessary to develop an innovation management model based on the Viable System Model to cope with any potential future environment based on internal organizational capabilities. For this purpose, the paper designs a Digital Ecosystem for the Fourth Industrial Revolution (DE4.0) based on the Plan-Do-Check-Act methodology applicable to any information system consisting of a digital twin, a simulation model, databases from existing information systems, and quality management techniques. This DE4.0 provides a huge advantage for the applicability and scalability of innovations as it allows one to plan, monitor, assess, and improve. Moreover, based on the conceptual model, a generic project evaluation scheme is developed, providing a platform for innovation project management and control during the whole innovation life cycle. As a result, the research provides a scientific and practical contribution for an integrated management of innovations based on the best information and set of techniques available. Based on this framework, a supply-chain case study is developed. The results show how, depending on the intended goals, the past experiences, the evolution of the innovation, and the innovation scope, indicators can be influenced towards reaching the initial goals and reducing the innovation risks. Finally, a discussion about the potential use and role of the DE4.0 for innovation projects and the related learning process is performed. [ABSTRACT FROM AUTHOR]

The still prevalent use of paper conformity lists in the automotive industry has a serious negative impact on the performance of quality control inspectors. We propose instead a hybrid quality inspection system, where we combine automated detection with human feedback, to increase worker performance by reducing mental and physical fatigue, and the adaptability and responsiveness of the assembly line to change. The system integrates the hierarchical automatic detection of the non-conforming vehicle parts and information visualization on a wearable device to present the results to the factory worker and obtain human confirmation. Besides designing a novel 3D vehicle generator to create a digital representation of the non conformity list and to collect automatically annotated training data, we apply and aggregate in a novel way state-of-the-art domain adaptation and pseudo labeling methods to our real application scenario, in order to bridge the gap between the labeled data generated by the vehicle generator and the real unlabeled data collected on the factory floor. This methodology allows us to obtain, without any manual annotation of the real dataset, an example-based F1 score of 0.565 in an unconstrained scenario and 0.601 in a fixed camera setup (improvements of 11 and 14.6 percentage points, respectively, over a baseline trained with purely simulated data). Feedback obtained from factory workers highlighted the usefulness of the proposed solution, and showed that a truly hybrid assembly line, where machine and human work in symbiosis, increases both efficiency and accuracy in automotive quality control. [ABSTRACT FROM AUTHOR]

This Special Issue on "Selected Papers from IEEE ICASI 2018" includes excellent papers presented at the IEEE ICASI 2018 regarding the "applied system innovation" topic. Mechanical engineering and design innovations are both academic and practical engineering fields, which involve systematic technological materialization through scientific principles and engineering designs. Technological innovations in mechanical engineering include IT-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanosciences and nanotechnology. The aim is to encourage the attendees at the IEEE ICASI 2018 to publish their experimental and theoretical research relating to applied system innovation. [ABSTRACT FROM AUTHOR]

Paper sludge ash (PSA) typically has a low Si abundance and significant Ca content because of the presence of calcite fillers, which interfere with the zeolitic conversion of PSA. Ca-masking with ethylenediaminetetraacetic acid (EDTA) was used to reduce Ca interference during zeolite synthesis so that a zeolitic product with a high cation exchange capacity (CEC) could be synthesized. Hydroxysodalite, zeolite-P, hydroxycancrinite, tobermorite, and zeolite-A can be synthesized from PSA by an alkali reaction with EDTA. With the addition of EDTA, calcium ions in the solution were trapped by chelation, and the number of zeolitic crystals with low Si/Al (Si/Al = 1), zeolite-A, increased owing to the promotion of the synthesis reaction. A product with a high CEC that has a high zeolite-A content was obtained. The chelating agent can inhibit Ca interference for zeolite synthesis by Ca-masking, and a product with a high zeolite-A content can be obtained from PSA using EDTA. [ABSTRACT FROM AUTHOR]

Hyper-heuristics have arisen as methods that increase the generality of existing solvers. They have proven helpful for dealing with complex problems, particularly those related to combinatorial optimization. Their recent growth in popularity has increased the daily amount of text in the related literature. This information is primarily unstructured, mainly text that traditional computer data systems cannot process. Traditional systematic literature review studies exhibit multiple limitations, including high time consumption, lack of replicability, and subjectivity of the results. For this reason, text mining has become essential for researchers in recent years. Therefore, efficient text mining techniques are needed to extract meaningful information, patterns, and relationships. This study adopts a literature review of 963 journal and conference papers on hyper-heuristic-related works. We first describe the essential text mining techniques, including text preprocessing, word clouds, clustering, and frequent association rule learning in hyper-heuristic publications. With that information, we implement visualization tools to understand the most frequent relations and topics in the hyper-heuristic domain. The main findings highlight the most dominant topics in the literature. We use text mining analysis to find widespread manifestations, representing the significance of the different areas of hyper-heuristics. Furthermore, we apply clustering to provide seven categories showing the associations between the topics related to hyper-heuristic literature. The vast amount of data available that we find opens up a new opportunity for researchers to analyze the status of hyper-heuristics and help create strategic plans regarding the scope of hyper-heuristics. Lastly, we remark that future work will address the limitations of collecting information from multiple data sources and analyze book chapters related to hyper-heuristics. [ABSTRACT FROM AUTHOR]

The second paper, authored by M. Kara, A. Laouid, M. AlShaikh, M. Hammoudeh, A. Bounceur, R. Euler, A. Amamra, and B. Laouid proposed a multi-round Proof of Work (PoW) consensus algorithm for preserving energy consumption and resisting attacks [[9]]. Special Features on AMC The most commonly seen AMC algorithms include genetic algorithm (GA), genetic programming (GP), evolutionary strategy (ES), evolutionary programming (EP), memetic algorithm (MA), particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), differential evolution (DE), firefly algorithm (FA), simulated annealing (SA), tabu search (TS), scatter search (SS), variable neighborhood search (VNS), and GRASP, to name a few. The first paper, authored by J. Chou, T. Pham, and C. Ho, developed a metaheuristic-optimized machine-learning algorithm for multi-level classification in civil and construction engineering [[1]]. The adaptive shrinking grid search chaos wolf optimization algorithm was proposed to optimize the parameters of the neural network to enhance the image recognition accuracy. [Extracted from the article]

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI). [ABSTRACT FROM AUTHOR]

Many studies have revealed the benefits of augmented reality (AR) to enhance productivity and fulfill a quality function in industry. Yet, the potential adverse effects of AR, such as physical stress, have not been extensively studied. This study investigates the effect of task difficulty (simple versus complex maintenance tasks) on the overall performance and physical stress of individuals while interacting with multimedia instructions (i.e., AR via HoloLens versus. paper-based guidance). Twenty-eight healthy male participants with a mean age of 32.12 years (standard deviation of 2.45 years) were randomly assigned to two groups: AR-based group and paper-based group. The metrics of total maintenance time, NASA-TLX scores, and heart rate variability (HRV) were used to evaluate overall performance, perceived workload, and physical stress, respectively. The findings suggest that AR-based instruction for a complex task significantly reduced both total maintenance time (by 14.94%) and physical stress in comparison with paper-based instruction. Paper-based instruction was found to destabilize heart rhythm, increase heart rate, and cause a shift in autonomic balance due to heightened sympathetic nerve activation. This study concludes that AR-based instruction is more efficient and less stressful than paper-based instruction, indicating that the AR system may offer independent benefits in reducing physical workloads. [ABSTRACT FROM AUTHOR]

This paper presents a bibliometric analysis within the research domain dedicated to the utilization of agent-based modeling (ABM) in the field of transportation. By employing specific keywords related to both agent-based modeling and transportation, we have identified and extracted 1016 scholarly papers from the ISI Web of Science database, spanning the period from 2002 to 2023. Through the application of bibliometric methods, we have systematically examined key contributors, affiliations of academic institutions, influential publications, and renowned journals within this domain. Our analysis reveals a consistent and robust growth in scholarly interest pertaining to agent-based modeling in the field of transportation throughout the considered period. Notably, within approximately four decades of ABM's application in transportation, a distinct upward trajectory began in 2008, culminating in the year 2021. The entire considered period witnessed a remarkable surge in paper production, characterized by an annual growth rate of 21.67%. Furthermore, employing an n-gram analysis, we have delineated and discussed the principal areas within transportation that have progressively benefited from the advancements in agent-based modeling. Prominently, the domains of air transport and road transport have exhibited substantial development over time, while the implications of climate change have emerged as a persistent concern throughout the entire study period. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) plays a pivotal role in modern life by enabling computers to comprehend, analyze, and respond to human language meaningfully, thereby offering exciting new opportunities. As social media platforms experience a surge in global usage, the imperative to capture and better understand the messages disseminated within these networks becomes increasingly crucial. Moreover, the occurrence of adverse events, such as the emergence of a pandemic or conflicts in various parts of the world, heightens social media users' inclinations towards these platforms. In this context, this paper aims to explore the scientific literature dedicated to the utilization of NLP in social media research, with the goal of highlighting trends, keywords, and collaborative networks within the authorship that contribute to the proliferation of papers in this field. To achieve this objective, we extracted and analyzed 1852 papers from the ISI Web of Science database. An initial observation reveals a remarkable annual growth rate of 62.18%, underscoring the heightened interest of the academic community in this domain. This paper includes an n-gram analysis and a review of the most cited papers in the extracted database, offering a comprehensive bibliometric analysis. The insights gained from these efforts provide essential perspectives and contribute to identifying pertinent issues in social media analysis addressed through the application of NLP. [ABSTRACT FROM AUTHOR]

This Special Issue covers the latest research on fatigue, performance, and damage assessments of concrete. Concrete fatigue is known as a phenomenon characterized by the repetition of loads. The fatigue-based performance of concrete may be affected by a combination of diverse loads, mechanical strength properties, and environmental changes. These factors eventually lead to poor concrete performance. In this Special Issue, nine papers were accepted, divided into three subgroups: two papers on fatigue, two papers on performance, and five papers on damage assessment. Unlike articles published in past Special Issues, our Special Issue contains papers that address concrete performance in concrete pavement applications. In particular, six of the articles contained in this issue concentrate on pavement performance and damage assessments, especially in terms of the Pavement Condition Index. Together with a brief introduction, the keywords, research significance and potential impacts of each article are summarized in this Editorial. [ABSTRACT FROM AUTHOR]

Automatic speech recognition (ASR) is one of the ways used to transform acoustic speech signals into text. Over the last few decades, an enormous amount of research work has been done in the research area of speech recognition (SR). However, most studies have focused on building ASR systems based on adult speech. The recognition of children's speech was neglected for some time, which means that the field of children's SR research is wide open. Children's SR is a challenging task due to the large variations in children's articulatory, acoustic, physical, and linguistic characteristics compared to adult speech. Thus, the field became a very attractive area of research and it is important to understand where the main center of attention is, and what are the most widely used methods for extracting acoustic features, various acoustic models, speech datasets, the SR toolkits used during the recognition process, and so on. ASR systems or interfaces are extensively used and integrated into various real-life applications, such as search engines, the healthcare industry, biometric analysis, car systems, the military, aids for people with disabilities, and mobile devices. A systematic literature review (SLR) is presented in this work by extracting the relevant information from 76 research papers published from 2009 to 2020 in the field of ASR for children. The objective of this review is to throw light on the trends of research in children's speech recognition and analyze the potential of trending techniques to recognize children's speech. [ABSTRACT FROM AUTHOR]

The growing prevalence of disposable toilet seat covers in public restrooms stems from concerns about personal hygiene, given the direct contact between the seat and various users' skin. To enable these disposable cover sheets to be flushed down the toilet instead of being discarded in the trash, they must possess specific properties. These include rapid water absorption for quick disintegration, strength to endure user movement or moisture on the toilet seat surface, and a comfortable texture. To address these challenges, the study investigated the disintegration characteristics of flushable cover sheets prepared under different refining conditions. Alkyl ketene dimer (AKD) was also employed to enhance water resistance, while an organic antibacterial agent was used to impart antimicrobial properties. The findings revealed that adding 0.2% AKD and 1% organic antibacterial agent to pulp stock with a freeness of about 650 mL CSF was suitable for manufacturing disposable cover sheets with disintegration characteristics like toilet tissue paper. [ABSTRACT FROM AUTHOR]

This article investigates the impact of the COVID-19 pandemic on logistics and supply chain processes through a two-phase analysis. First, a literature review maps the existing studies, published from 2021 to 2023 (101 papers), offering a view of the multiple challenges faced by supply chains during the pandemic emergency. The literature analysis makes use of descriptive statistics, thematic classifications and cross-analyses to provide a detailed overview of the issues raised by the COVID-19 pandemic and of the related implications. Second, a case study targeting a logistics operator was conducted, to derive practical insights into the real-world implications of pandemic disruptions. The study highlights the importance of proactive risk management strategies and the role of Industry 4.0 technologies to improve supply chain resilience and sustainability. Hence, this research contributes to a deeper understanding of the impact of the COVID-19 pandemic on logistics and supply chain processes and offers valuable guidance for companies, especially those working in the transport and logistics field. [ABSTRACT FROM AUTHOR]

With the rapid growth in urban construction in Malaysia, road breakage has challenged traditional manual inspection methods. In order to quickly and accurately detect the extent of road breakage, it is crucial to apply automated road crack detection techniques. Researchers have long studied image-based road crack detection techniques, especially the deep learning methods that have emerged in recent years, leading to breakthrough developments in the field. However, many issues remain in road crack detection methods using deep learning techniques. The field lacks state-of-the-art systematic reviews that can scientifically and effectively analyze existing works, document research trends, summarize outstanding research results, and identify remaining shortcomings. To conduct a systematic review of the relevant literature, a bibliometric analysis and a critical analysis of the papers published in the field were performed. VOSviewer and CiteSpace text mining tools were used to analyze and visualize the bibliometric analysis of some parameters derived from the articles. The history and current status of research in the field by authors from all over the world are elucidated and future trends are analyzed. [ABSTRACT FROM AUTHOR]

The aim of this study was to validate a workflow that allows structural identification of plant volatiles that induce a behavioral response in insects. Due to the complexity of plant volatile emissions and the low levels at which these bioactive components tend to occur, gas chromatography-electroantennography (GC-EAD) was applied as the prime differentiator technique, i.e., to indicate particular peaks of interest in the chromatogram. In a next step, the analysis was repeated under identical conditions using GC-Orbitrap mass spectrometry (MS). Combining electron impact (EI) ionization and chemical ionization (CI) with the superior spectral resolution and mass accuracy of the technique enabled straightforward identification of these unknowns, with high confidence in a minute amount of time. Moreover, because of the intrinsic sensitivity of the technique, components that occur at trace amounts but may induce disproportional large behavioral responses are evenly well-identified. We were able to positively identify β-caryophyllene as a bioactive compound in female carob moths. Behavioral attraction was negatively correlated with the amount of β-caryophyllene in host fruits. In an oviposition experiment on filter paper, β-caryophyllene was stimulated in the range of 40–100 ng, while concentrations above 200 ng inhibited oviposition. [ABSTRACT FROM AUTHOR]

Featured Application: This work gives indications for cleaning and preservation of ice cores, which will be drilled in Antarctica during the EU project Beyond EPICA Oldest Ice and provides general guidelines for ice drilling activities and preservation of ice cores. To reconstruct climate history of the past 1.5 Million years, the project: Beyond EPICA Oldest Ice (BEOI) will drill about 2700 m of ice core in East Antarctica (2021–2025). As drilling fluid, an aliphatic ester fluid, EstisolTM 140, will be used. Newly drilled ice cores will be retrieved from the drill soaked in fluid, and this fluid should be removed from the cores. Most of it will be vacuum-cleaned off in a Fluid Extraction Device and wiped off with paper towels. Based on our experiences in Greenland deep ice coring, most of the residual fluid can be removed by storing the cores openly on shelves in a ventilated room. After a week of "drying", the cores have a dry feel, handling them do not give "wet" gloves and they can easily be marked with lead pencils. This paper presents a theoretical investigation and some simple testing on the "drying" process. The rates of sublimation of ice and evaporation of fluid have been calculated at different temperatures. The calculations show that sublimation of the ice core should not occur, and that evaporation of fluid should be almost negligible. Our test results support these calculations, but also revealed significant fluid run-off and dripping, resulting in the removal of most of the fluid in a couple of days, independent of temperature and ventilation conditions. Finally, we discuss crucial factors that ensure optimal long-term ice core preservation in storage, such as temperature stability, defrosting cycles of freezers and open core storage versus storage of cores in insulated crates. [ABSTRACT FROM AUTHOR]

The effect of catalysts in the form of a transformer copper winding with Kraft paper insulation on the lifetime of transformer oil was studied via ultraviolet-visible (UV-Vis) spectroscopy and Fourier transform infrared (FT-IR) spectroscopy. The tested naphthenic mineral oil was exposed to accelerated thermo-oxidative aging with limited access to air at temperatures of 110, 120, and 130 °C for times ranging from 24 to 2424 h in the presence and absence of catalysts. The UV-Vis technique focused on assessing the change in the color of the oil and quantified a 35%–45% reduction in oil lifetime, depending on the aging temperature, due to the influence of catalysts. The FT-IR analysis focused on the decrease in the spectral band at 3650 cm−1, which corresponds to the low-temperature antioxidant present in the oil, and quantified the 35% reduction in the oil lifetime independent of the aging temperature. This high rate of reduction in the transformer oil lifetime points to the dominant catalytic role of the Kraft paper and copper during the aging process of the transformer insulation system. [ABSTRACT FROM AUTHOR]

Featured Application: The dual adaptive filter proposed in this paper can identify sudden change in structural systems under dynamic excitations. The proposed filter method including the tuning process can be applied to a variety of engineering areas in which near-real-time tracking of system parameter is needed. This paper proposes a dual adaptive Kalman filter to identify parameters of a dynamic system that may experience sudden damage by a dynamic excitation such as earthquake ground motion. While various filter techniques have been utilized to estimate system's states, parameters, input (force), or their combinations, the filter proposed in this paper focuses on tracking parameters that may change suddenly using sparse measurements. First, an advanced state-space model of parameter estimation employing a regularization technique is developed to overcome the lack of information in sparse measurements. To avoid inaccurate or biased estimation by conventional filters that use covariance matrices representing time-invariant artificial noises, this paper proposes a dual adaptive filtering, whose slave filter corrects the covariance of the artificial measurement noises in the master filter at every time-step. Since it is generally impossible to tune the proposed dual filter due to sensitivity with respect to parameters selected to describe artificial noises, particle swarm optimization (PSO) is adopted to facilitate optimal performance. Numerical investigations confirm the validity of the proposed method through comparison with other filters and emphasize the need for a thorough tuning process. [ABSTRACT FROM AUTHOR]

This paper provides an overview of several secure boot architectures with a focus on key rotation. It expands on a practitioner note that the authors submitted to the 2023 IEEE Secure Development Conference. Key rotation is important due to the frequency of lost signing keys and the difficulty of managing secret keys for the long lifetimes of Industrial Internet of Things (IIOT) devices. Key rotation is not simple for IIOT due to limited resources during a secure boot process and the constraints of the firmware utilities that come from the chip vendors. This paper reviews and compares five common architectures for a secure boot that are seen across the IIOT community. For each architecture, it provides some key strengths and weaknesses associated with that architecture. The paper then provides a detailed comparison and analysis of the architectures to convince the IIOT community to move towards a strong use of certificates (instead of the traditional use of raw public keys). The intent of this paper is to provide a practitioner's perspective on these challenges and the tradeoffs in hopes of inviting comments from chip vendors and the broader community. [ABSTRACT FROM AUTHOR]

In the process of shield tunneling, the cutter will inevitably be worn and damaged and will need to be replaced frequently. The low efficiency and high safety risk of traditional manual cutter change have prompted robotic cutter change to become the mainstream trend of current research. However, it is difficult to realize mechanical automation disassembly due to the existence of many fragmented parts and complex disassembly steps in the traditional cutter system. Therefore, this paper proposes an eccentric integrated cutter system, which greatly simplifies the disassembly process while retaining the excellent fastening structure of the traditional cutter system. The evaluation system of the cutter system was established through the analytic hierarchy process, and it was verified that the eccentric integrated cutter system has obvious superiority in realizing automated disassembly and assembly and, at the same time, that it has good structural strength. Finally, vibration experiments were carried out based on shield construction conditions. The experimental results show that after 14 h of continuous vibration, the residual preload ratio of the integrated cutter sample stabilized at more than 90%, which indicates good anti-loosening performance. [ABSTRACT FROM AUTHOR]

This document provides a summary of a special issue titled "Advances in Particle Acceleration: Novel Techniques, Instruments, and Applications." The special issue covers various topics related to particle acceleration. It discusses recent advancements in accelerator technologies, including new materials, fabrication techniques, and novel acceleration methods. The document specifically focuses on the advancements in radiotherapy methods for cancer treatment, as well as the development of radioisotope therapy. It also explores the pursuit of compact light sources and advancements in accelerator technologies for enhanced miniaturization, power capacity, reliability, sustainability, and cost-efficiency. The special issue includes several papers that cover topics such as beam dynamics software, RF cavity optimization, niobium cavity coating, efficient accelerator lattice design, plasma accelerator concept, and improvements to electrostatic dust accelerators. Overall, the special issue provides valuable insights into the latest advancements in particle acceleration. [Extracted from the article]

Healthcare is one of the industries that seeks to deliver medical services to patients on time. One of the issues it currently grapples with is real-time patient data exchange between various healthcare organizations. This challenge was solved by both centralized and decentralized cloud computing architecture solutions. In this paper, we review the current state of these two cloud computing architectures in the health sector with regard to the effect on the efficiency of Health Information Exchange (HIE) systems. Our study seeks to determine the relevance of these cloud computing approaches in assisting healthcare facilities in the decision-making process to adopt HIE systems. This paper considers the system performance, patient data privacy, and cost and identifies research directions in each of the architectures. This study shows that there are some benefits in both cloud architectures, but there are also some drawbacks. The prominent characteristic of centralized cloud computing is that all data and information are stored together at one location, known as a single data center. This offers many services, such as integration, effectiveness, simplicity, and rapid information access. However, it entails providing data privacy and confidentiality aspects because it will face the hazard of a single point of failure. On the other hand, decentralized cloud computing is built to safeguard data privacy and security whereby data are distributed to several nodes as a way of forming mini-data centers. This increases the system's ability to cope with a node failure. Thus, continuity and less latency are achieved. Nevertheless, it poses integration issues because managing data from several sites could be a problem, and the costs of operating several data centers are higher and complex. This paper also pays attention to the differences in aspects like efficiency, capacity, and cost. This paper assists healthcare organizations in determining the most suitable cloud architecture strategy for deploying secure and effective HIE systems. [ABSTRACT FROM AUTHOR]

This paper primarily investigates enhanced object detection techniques for indoor service mobile robots. Robot operating systems (ROS) supply rich sensor data, which boost the models' ability to generalize. However, the model's performance might be hindered by constraints in the processing power, memory capacity, and communication capabilities of robotic devices. To address these issues, this paper proposes an improved you only look once version 5 (YOLOv5)-Lite object detection algorithm based on efficient multi-scale attention and bounding box regression combined with ROS. The algorithm incorporates efficient multi-scale attention (EMA) into the traditional YOLOv5-Lite model and replaces the C3 module with a lightweight C3Ghost module to reduce computation and model size during the convolution process. To enhance bounding box localization accuracy, modified precision-defined intersection over union (MPDIoU) is employed to optimize the model, resulting in the ROS–YOLOv5–FleetEMA model. The results indicated that relative to the conventional YOLOv5-Lite model, the ROS–YOLOv5–FleetEMA model enhanced the mean average precision (mAP) by 2.7% post-training, reduced giga floating-point operations per second (GFLOPS) by 13.2%, and decreased the params by 15.1%. In light of these experimental findings, the model was incorporated into ROS, leading to the development of a ROS-based object detection platform that offers rapid and precise object detection capabilities. [ABSTRACT FROM AUTHOR]

This document discusses the recent advancements in decision support systems (DSSs) for disease detection and diagnosis. The combination of biomedical studies and information technology has led to the development of accessible and accurate solutions that can improve patient survival rates. The document highlights several research papers that cover a wide range of topics, including multiple sclerosis detection, neurodegenerative disease detection, breast lesion classification, COVID-19 mortality prediction, melanoma diagnosis, and prediction of second primary skin cancer. The adoption of efficient DSSs can aid clinical assessment, reduce misdiagnosis, and facilitate evidence-based decision-making. However, challenges such as validation, training, and user interface design need to be addressed for widespread application of DSSs in clinical practice. The document concludes by emphasizing the importance of future studies and developments in overcoming limitations and expanding the use of DSSs in different contexts. [Extracted from the article]

The world is accelerating globalization and urbanization; thus, planning plays an irreplaceable role in the macro development, especially in sustainable development. Planning research is important in turning theories of planning into urban practices. This research based on the theory that planning research is crucial, as it represents that planning-related activities improve processes in urbanization. However, the current understanding of the importance of planning research is limited. There is no research on the interaction between planning research and macro national development to support the importance of planning or its research. This study uses 750,000 articles on urban planning fields from WoS from 1950 to the present. Firstly, the study concludes the investment pattern changes of the global planning research. Over the past 70 years, the total number and diversity of countries engaged in planning research have grown rapidly, and developing countries have gradually integrated into the mainstream research community and become the main contributors. Secondly, the investment intensity and characteristics of planning research are consistent with the speed and characteristics of urbanization, which proves that to some extent, the demand of urbanization development drives the investment of planning research. Then, according to the different characteristics of planning research investment in different countries, this paper summarizes the main investment characteristics of major countries, analyzes the development rule behind the investment characteristics, and predicts the interaction with the international political and economic pattern. By analyzing the relationship between planning research intensity and urbanization, it is found that the investment intensity of planning research has a time rule with urbanization and has different interactions in different stages of urbanization development. It was found that the intensity of planning research was strongly correlated with HDI, and the amount of research was crucial. [ABSTRACT FROM AUTHOR]

Context: Software product lines (SPLs) have reached a considerable level of adoption in the software industry. The most commonly used models for managing the variability of SPLs are feature models (FMs). The analysis of FMs is an error-prone, tedious task, and it is not feasible to accomplish this task manually with large-scale FMs. In recent years, much effort has been devoted to developing reasoning algorithms for FMs. Aim: To synthesize the evidence on the use of reasoning algorithms for feature modeling. Method: We conducted a systematic mapping study, including six research questions. This study included 66 papers published from 2010 to 2020. Results: We found that most algorithms were used in the domain stage (70%). The most commonly used technologies were transformations (18%). As for the origins of the proposals, they were mainly rooted in academia (76%). The FODA model continued to be the most frequently used representation for feature modeling (70%). A large majority of the papers presented some empirical validation process (90%). Conclusion: We were able to respond to the RQs. The FODA model is consolidated as a reference within SPLs to manage variability. Responses to RQ2 and RQ6 require further review. [ABSTRACT FROM AUTHOR]

Tendon-related disorders are a common condition in both sports medicine and orthopedic clinical practice. Ultrasonography, power doppler, and magnetic resonance imaging (MRI) are the most widespread technologies, but the use of ultrasound elastography—including strain elastography and shear wave elastography—has been increasing in the last years. The aim of this paper is to evaluate the use of ultrasound elastography in tendon-related disorders. Research in PubMed, Scopus, and Web of Science databases is performed, and 364 papers are exported. After the study selection process, 38 papers are included in this systematic review. The risk of bias of each paper is evaluated using the RoBANS tool. Blinding, confounding variables, and measurement of exposure are the most affected items. From the included papers, tendinopathy is the most analyzed pathology, followed by tenosynovitis and rotator cuff disease. The Achilles tendon, patellar tendon, and common extensor tendon of the hand are the most analyzed tendons. Ultrasound elastography results in being the method providing good sensitivity and specificity (up to 100% and 100%, respectively, in tendinosis of the long head of the biceps tendon, in transverse plan examination) and accuracy (up to 97.8% in Achilles tendinopathy); furthermore, this technique is able to ensure real-time feedback on tissue elasticity and appears more sensitive than B-mode alone. [ABSTRACT FROM AUTHOR]

Rockfall movement is a significant hazard for infrastructure located in mountainous regions, and Paper [[9]] presents a numerical investigation on the relationship between damping and coefficient of restitution (COR) in rockfall movement. Geotechnical engineering is a complex field that deals with various hazards that can impact soil, rock, and other geologic materials. [Extracted from the article]

The application of Life Cycle Assessment in the construction sector can be a very useful tool to reduce the environmental impact generated by the sector. In order to quantify the improvement in environmental terms with the use of artificial lightweight aggregates (LWA) manufactured with waste, in this work, we conducted a comparative evaluation of the life cycle of LWAs for a total of five different scenarios: LWAs obtained in a traditional way, i.e., using exclusively clay in their manufacture (Spanish blond clay, Portuguese red clay and Portuguese blond clay), and LWAs manufactured with four different wastes, with a partial substitution of 2.5% for each of the clays per waste (almond and hazelnut shells, sludge from the purification of paper money, cork dust and coffee grounds). The functional unit was set as the production of 1 kilo of lightweight aggregates and the CML 2000 methodology and the SimaPro software were used. The results obtained in this research allow us to conclude that the addition of organic wastes showed a slightly higher environmental performance than the conventional system, the ALAs manufactured with almond and hazelnut shells being the most environmentally friendly option, with reductions of more than 30% in some cases, followed by the LWAs manufactured with coffee grounds. On the other hand, the addition of paper sewage sludge and cork dust represents minimal environmental optimization. [ABSTRACT FROM AUTHOR]

The ultrasound technique is a well-known non-destructive and efficient testing method for on-line corrosion monitoring. Wall thickness loss rate is the major parameter that defines the corrosion process in this approach. This paper presents a smart corrosion monitoring system for offshore wind turbines based on the ultrasound pulse-echo technique. The solution is first developed as an ultrasound testbed with the aim of upgrading it into a low-cost and low-power miniaturized system to be deployed inside offshore wind turbines. This paper discusses different important stages of the presented monitoring system as design methodology, the precision of the measurements, and system performance verification. The obtained results during the testing of a variety of samples show meaningful information about the thickness loss due to corrosion. Furthermore, the developed system allows us to measure the Time-of-Flight (ToF) with high precision on steel samples of different thicknesses and on coated steel samples using the offshore standard coating NORSOK 7A. [ABSTRACT FROM AUTHOR]

In line with the requirements of the circular economy, the European Union's waste management legislative changes also concern the treatment of sewage sludge. Although sewage sludge production cannot be prevented, its quantities may be reduced by the synergetic effect of energy recovery via choosing a proper technology. Sewage sludge is difficult to apply as fuel alone, because of its high moisture and ash content. However, its energy use will be increased by adding suitable waste materials (different types of plastics, waste tires and paper rejects). Most recently, the thermal utilization of sewage sludge via incineration or pyrolysis has grown in importance. This article describes the fuel parameters of particular waste materials and of their blends with sewage sludge in connection with laboratory-scale thermal decomposition in an inert atmosphere, for their potential use in a semi-pilot plant pyrolysis unit. For pyrolytic application, the results of thermogravimetric analysis are needed in order to know the maximal temperature of thermal decomposition in an inert atmosphere, maximal mass losses, and weight loss rates. The samples of different thermoplastics mixed with sewage sludge, and low-density polyethylene blends with sewage sludge, had the lowest residual masses (70–74%) and the highest weight loss rates (11–19%/min). On the other hand, the blend of polyester rejects from tire processing, paper rejects and sewage sludge had the second highest residual mass (60%) and the lowest weight loss rate (3%/min). [ABSTRACT FROM AUTHOR]

The use of waste from the regional production of waste wood, waste paper, and cardboard in the form of briquettes may be causing an increase in local emissions, both of major elements and trace elements. When burning paper and cardboard briquettes, more than 70% of Mn, Zn, As, and Pb is released into the air from the total content of trace elements in the fuel. The largest amounts of major and trace elements are released when burning paper briquettes (56 g/kg of fuel); half of these amounts are released from burning briquettes from waste wood and coal (23 g/kg of fuel). The pursuit of alternative uses for those cardboard components that are not suitable for recycling cannot be directed to the production of briquettes for residential combustion in the framework of the application of the principles of the circular economy. In particular, the high concentrations of undesirable elements in the emissions released in the gas phase into the atmosphere are an obstacle existing even when the parameters of the PM10 emissions of a boiler are met. This is related to the high ash content of the cardboard (13.5%). Waste paper or cardboard could be added to waste wood at a maximum of 10% to make the pollution produced comparable to the burning of coal briquettes. [ABSTRACT FROM AUTHOR]

Long Short-Term Memory (LSTM)-Based Dog Activity Detection Using Accelerometer and Gyroscope. The third paper, authored by Jeong et al. [[14]], introduced a fall detection system that combines a simple threshold method (STM) and LSTM, creating the STM-LSTM-based fall detection system. With rapid advancements in artificial intelligence (AI), the Internet of Things (IOTs), and communication networks, the future of information and communication engineering (ICE) promises remarkable opportunities and challenges. 10.3390/app12199801 18 Zhang M., Wang C., Zhu W., Shen Y. An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning. [Extracted from the article]

Cyber Physical Systems (CPSs) are systems that are developed by seamlessly integrating computational algorithms and physical components, and they are a result of the technological advancement in the embedded systems and distributed systems domains, as well as the availability of sophisticated networking technology. Many industrial CPSs are subject to timing predictability, security and functional safety requirements, due to which the developers of these systems are required to verify these requirements during the their development. This position paper starts by exploring the state of the art with respect to developing timing predictable and secure embedded systems. Thereafter, the paper extends the discussion to time-critical and secure CPSs and highlights the key issues that are faced when verifying the timing predictability requirements during the development of these systems. In this context, the paper takes the position to advocate paramount importance of security as a prerequisite for timing predictability, as well as both security and timing predictability as prerequisites for functional safety. Moreover, the paper identifies the gaps in the existing frameworks and techniques for the development of time- and safety-critical CPSs and describes our viewpoint on ensuring timing predictability and security in these systems. Finally, the paper emphasises the opportunities that artificial intelligence can provide in the development of these systems. [ABSTRACT FROM AUTHOR]

The research presented in this paper aims at creating a bilingual (sr-en), easily searchable, hypertext, born-digital, corpus-based terminological database of raw material terminology for dictionary production. The approach is based on linking dictionaries related to the raw material domain, both digitally born and printed, into a lexicon structure, aligning terminology from different dictionaries as much as possible. This paper presents the main features of this approach, data used for compilation of the terminological database, the procedure by which it has been generated and a mobile application for its use. Available (terminological) resources will be presented—paper dictionaries and digital resources related to the raw material domain, as well as general lexica morphological dictionaries. Resource preparation started with dictionary (retro)digitisation and corpora enlargement, followed by adding new Serbian terms to general lexica dictionaries, as well as adding bilingual terms. Dictionary development is relying on corpus analysis, details of which are also presented. Usage examples, collocations and concordances play an important role in raw material terminology, and have also been included in this research. Some important related issues discussed are collocation extraction methods, the use of domain labels, lexical and semantic relations, definitions and subentries. [ABSTRACT FROM AUTHOR]

Albeit in different ways, both machine learning and gamification have transfigured the user experience of information systems. Although both are hot research topics, so far, little attention has been paid to how these two technologies converge with each other. This relation is not obvious as while it is feasible to enhance gamification with machine learning, it is also feasible to support machine learning with gamification; moreover, there are applications in which machine learning and gamification are combined yet not directly connected. In this study, we aim to shed light on the use of both machine learning in gamification and gamification in machine learning, as well as the related topics of using gamification in machine learning education and machine learning in gamification research. By performing a systematic literature mapping, we not only identify prior works addressing these respective themes, but also analyze how their popularity evolved in time, investigate the areas of application reported by prior works, used machine learning techniques and software tools, as well as the character of research contribution and the character of evaluation results for works that presented them. [ABSTRACT FROM AUTHOR]

Collaborative localization is a technique that utilizes the exchange of information between multiple sensors or devices to improve localization accuracy and robustness. It has a wide range of applications in autonomous driving and unmanned aerial vehicles (UAVs). In the field of UAVs, collaborative localization can help UAVs perform autonomous navigation and mission execution in complex environments. However, when GNSS is not available, it becomes challenging to position the UAV swarm relative to each other. This is because the swarm loses its perception and constraint of the position relationship between each member. Consequently, the swarm faces the problem of the serious drift of relative accuracy for an extended period. Furthermore, when the environment is faced with complex obstruction challenges or a camera with low texture scenes, noise can make it more difficult to solve the relative position relationship between drones, and a single UAV may lose positioning capability. To solve these specific problems, this paper studies a swarm co-operative localization method in a GNSS-denied environment with loud noise interference. In this paper, we proposed a method that utilizes a distributed scheme based on an incremental smoothing and mapping (iSAM) algorithm for state estimation. It incorporates new anchor-free topological constraints to prevent positioning failures and significantly improve the system's robustness. Additionally, a new switching function is applied in front of each factor of the loss function, which adjusts the switches in real time in response to the input information to improve observably the accuracy of the system. A novel co-operative incremental smoothing and mapping (CI-SAM) method is proposed and the method does not require a complete relative position measurement, which reduces the need for vehicle measurement equipment configuration. The effectiveness of the method is verified by simulation. [ABSTRACT FROM AUTHOR]

Academic researchers publish their work in various formats, such as papers, patents, and research reports, on different academic sites. When searching for a particular researcher's work, it can be challenging to pinpoint the right individual, especially when there are multiple researchers with the same name. In order to handle this issue, we propose a name disambiguation scheme for researchers with the same name based on heterogeneous academic sites. The proposed scheme collects and integrates research results from these varied academic sites, focusing on attributes crucial for disambiguation. It then employs clustering techniques to identify individuals who share the same name. Additionally, we implement the proposed rule-based algorithm name disambiguation method and the existing deep learning-based identification method. This approach allows for the selection of the most accurate disambiguation scheme, taking into account the metadata available in the academic sites, using a multi-classifier approach. We consider various researchers' achievements and metadata of articles registered in various academic search sites. The proposed scheme showed an exceptionally high F1-measure value of 0.99. In this paper, we propose a multi-classifier that executes the most appropriate disambiguation scheme depending on the inputted metadata. The proposed multi-classifier shows the high F1-measure value of 0.67. [ABSTRACT FROM AUTHOR]

Featured Application: Functional packaging, temperature monitoring, hidden information, security of printed products, brand protection. In the present research, properties and performance of special effect printing inks were observed with the aim of obtaining a printed product with dual functional properties. Thermochromic liquid crystal-based printing ink (TLC) and UV-visible (daylight invisible) fluorescent inks (UVF), pure and as hybrid ink systems, were printed using a screen-printing technique on two types of uncoated paper substrates. Characterization of the paper substrates was performed, as well as detailed analysis of printed layers. Thickness, surface roughness, surface free energy, and adhesion parameters of printed layers were analysed. Spectral reflectance of pure UVF and TLC printing inks, as well as the spectral reflectance of the proposed hybrid ink systems were measured. The thermochromic effect of the TLC ink and hybrid systems was analysed. Microscopy was used to display the visual colour play effect and the effect of the fluorescence. Results of the measurements showed high compatibility of used materials in the proposed hybrid ink systems. Since the effect of luminescence and the colour play effect in the hybrid systems were preserved, it can be concluded that TLC/UVF hybrid ink systems can find their application in the development of functional packaging and in all other applications with special requirements for temperature monitoring and hidden information for different products. [ABSTRACT FROM AUTHOR]

Real-time prediction of remaining useful life (RUL) is one of the most essential works in prognostics and health management (PHM) of the micro-switches. In this paper, a linear degradation model based on an inverse Kalman filter to imitate the stochastic deterioration process is proposed. First, Bayesian posterior estimation and expectation maximization (EM) algorithm are used to estimate the stochastic parameters. Second, an inverse Kalman filter is delivered to solve the errors in the initial parameters. In order to improve the accuracy of estimating nonlinear data, the strong tracking filtering (STF) method is used on the basis of Bayesian updating Third, the effectiveness of the proposed approach is validated on an experimental data relating to micro-switches for the rail vehicle. Additionally, it proposes another two methods for comparison to illustrate the effectiveness of the method with an inverse Kalman filter in this paper. In conclusion, a linear degradation model based on an inverse Kalman filter shall deal with errors in RUL estimation of the micro-switches excellently. [ABSTRACT FROM AUTHOR]

In this study, clove essential oil (CL), eugenol (EU), and cinnamaldehyde (CI) were immobilized in starch sodium octenyl succinate (SSO) using an emulsification method. The main characteristics, stability, and biological properties of the prepared emulsions were established. Particle size analyses using dynamic light scattering showed that the smaller droplets were characteristic of emulsions containing CI (205–218 nm) and EU (181–236 nm), while the largest droplets were determined for CL emulsions (293–348 nm). Moreover, the highest antioxidant activity was determined for CL (79–83%) and EU (80–88%) emulsions, while CI emulsions showed the greatest antibacterial activity. The obtained emulsions were applied to the paper sheets and the bioactive properties of coated paper were studied. Evaluation of antioxidant properties revealed that high antioxidant activity reaching 76–92% and 87–91% was characteristic of coatings containing CL and EU, respectively. Meanwhile, coatings containing CI showed quite low antioxidant activity (4–9%) but demonstrated the greatest antimicrobial effect on Gram-negative and Gram-positive bacteria as well as yeasts. Hence, CL and EU emulsions could be used as effective natural antioxidants, whereas CI emulsions could be applied as an antimicrobial agent on cellulose-based substrates for a wide range of human health protection applications. [ABSTRACT FROM AUTHOR]

In recent years, the number of applications of virtual reality (VR) for the Autism spectrum disorder (ASD) population has increased and has become one of the most suitable tools to address the psychological needs of these individuals. The present scoping review aims to provide a literature mapping of experimental studies that have used immersive and semi-immersive VR for assessments or interventions specifically addressing high-functioning autism. A total of 23 papers were included and analyzed following PRISMA guidelines. The identified studies concerned social skills (11 papers), eye gaze and joint attention (3 papers), motor learning (3 papers), job training (2 papers), and other aims or rationales (4 papers). The evidence shows that, despite the intellectual potential of high-functioning ASD individuals, little research has been conducted to provide interventions that offer concrete training to improve their adaptive functioning. In addition, the percentage of individuals below 18 years of age is representative of half of the included studies, so aiming future studies at the early stages of development might be an asset in preparing the next generation of young adults to cope with age-related challenges, as early assessments and interventions are more likely to produce major long-term effects. [ABSTRACT FROM AUTHOR]

The ship piping layout is one of the essential tasks in the detailed design stage of a ship. Traditional manual expert design has disadvantages such as low efficiency, reliance on experience, and subjective influence. Therefore, this paper systematically proposes an intelligent arrangement method for ships' single, parallel, and branch pipelines. Firstly, the traditional genetic algorithm is improved and combined with the A* algorithm to solve the intelligent arrangement problem of a single pipeline in ships. Then, the parallel pipeline and branch pipeline are split into multiple single pipelines by combining with the connection point strategy to solve the arrangement problem of parallel pipeline and branch pipeline. Finally, the optimized A*-genetic algorithm proposed in this paper is compared with the A* algorithm, particle swarm algorithm, and the labyrinth-genetic algorithm used in previous research through simulation experiments. The results show that the A*-genetic algorithm of this paper is optimal in six indexes, including length, number of elbows, energy value, fitness value, number of optimal solutions, and average number of convergence generations, in the arrangement of the single pipeline. In solving the parallel pipeline and branch pipeline arrangement problems, the all-around performance of this paper's algorithm is better than that of A*-genetic algorithm and maze–genetic algorithm, respectively. The A*-genetic algorithm of this paper considers the quality of pipeline arrangement and the solution's efficiency. It verifies the adaptability and superiority of the algorithm for the intelligent arrangement of various types of pipelines in ship pipelines. [ABSTRACT FROM AUTHOR]