Joško Šoda

The use of satellite imagery to monitor the Sustainable Development Goals is increasingly common, driven by advances in remote sensing and deep learning. Introducing remote sensing into academic graduate programs at maritime institutions not only follows the current state of the research field but also raises awareness of the opportunities this technique offers for sustainable development and ecological research. For instance, remote sensing enables monitoring of ships discharging oil. A multidisciplinary approach is necessary for the highest-level research results, so to apply scientific methods effectively, an expert in the legal aspects of artificial intelligence also participated in this research. In this paper, we introduce remote sensing into seafarers’ education at the graduate level, which is now incorporated as a new course. The results indicate a high level of student engagement and demonstrate that integrating remote sensing technologies into maritime education could enhance students’ technical competencies and environmental awareness. The findings of this study highlight the potential of satellite remote sensing as an educational tool for supporting sustainability-oriented maritime education and provide practical insights for integrating emerging technologies into higher education curricula, providing practical insights for integrating emerging technologies into higher education curricula while establishing a transferable blueprint for bridging advanced data sciences with compliance-driven vocational training.

Aging is associated with structural and functional changes of the vocal folds that may result in presbyphonia, often perceived as a weak or shaky voice. However, the quantitative characterization of underlying age-related vocal tremor across the adult lifespan remains limited. This cross-sectional study investigated the characteristics of vocal tremor across the adult lifespan using automated acoustic analysis. A total of 291 native speakers aged 18–94 years were recruited and underwent perceptual voice evaluation and acoustic analysis during sustained phonation of the vowel /a/. Vocal tremor was quantified using digital signal processing, focusing on the prominence of fundamental frequency tremor (PF0T) and the prominence of amplitude tremor (PAT). A moderate-to-strong positive correlation between age and PF0T was observed in both males and females, indicating increasing instability of fundamental frequency with advancing age. In contrast, PAT did not show a significant age-related increase after correction for multiple comparisons. Perceptual ratings of tremor demonstrated only weak correlations with age but were moderately associated with acoustic measures of tremor. Normative models revealed that physiological tremor in healthy aging remains well below pathological thresholds reported in neurological disorders. These findings indicate that age-related vocal tremor is characterized predominantly by increasing instability of fundamental frequency rather than amplitude modulation, localizing the dominant age effect to laryngeal control of vocal fold tension rather than to respiratory drive. Automated acoustic analysis provides a sensitive and objective method for detecting subtle age-related vocal changes and may support future biomarker development for distinguishing physiological from pathological vocal tremor. Graphical abstract

Background/Objectives: Visual evoked potential (VEP) P100 maximum latency is known to predict fatigue and cognition in multiple sclerosis (MS). However, the relationship between VEP and neurological (impairment)/neuropsychological status is not well understood in MS. Therefore, the aim of the present prospective cross-sectional study was to investigate associations among VEP, neurological impairment, and neuropsychological status in MS. Methods: The study included 25 subjects with primary progressive MS (PPMS), with a median EDSS score of 4.5, and 30 control subjects. Neurological status of PPMS subjects was evaluated with the Expanded Disability Status Scale (EDSS), while neuropsychological assessment included the Symbol Digit Modalities Test (SDMT), Nine-Hole Peg Test (9HPT), Fatigue Severity Scale (FSS), Depression, Anxiety and Stress Scale (DASS-21), and Multiple Sclerosis Impact Scale (MSIS-29). Statistical analysis included group comparisons, Spearman correlations, multivariable linear regression analyses, bidirectional stepwise selection, and a parsimonious multivariable model. Results: PPMS subjects had significantly prolonged P100 maximum latencies (p < 0.01) compared to control subjects, as well as significantly poorer performance on the 9HPT (p < 0.01) and higher fatigue scores (FSS; p < 0.01). In multivariable analyses within the PPMS group, disease duration was independently associated with the EDSS (p = 0.009), whereas neuropsychological measures were intercorrelated (ρ > 0.45) and associated with the EDSS at intermediate stages of model selection. P100 maximum latency showed a positive but non-significant association after adjustment for sex and disease duration in the parsimonious model, which explained approximately 49% of the variance in the EDSS. Conclusions: The findings provide insight into the complex interplay among neurophysiological (VEP), neuropsychological and neurological status in MS.

Aids to Navigation (AtoNs) are essential elements of maritime safety infrastructure, yet their planning and spatial deployment still lack a harmonized, evidence-based, and operationally applicable methodological framework. This paper presents a structured analysis of scientific literature, International Organization for Marine Aids to Navigation (IALA) guidelines, and national legal frameworks from sixteen maritime countries in order to examine how AtoN planning is currently approached and to identify key methodological limitations. The findings show that existing approaches remain fragmented: scientific studies provide partial analytical tools, IALA documentation offers a strong normative basis but limited site-specific planning procedures, and national legal frameworks define responsibilities without establishing transparent and reproducible methods for AtoN placement. Building on these findings, the paper develops an integrated analytical foundation structured around spatial, operational, and technological domains, and demonstrates its indicative application through a case study of the 2024 grounding of the passenger catamaran Princ Zadra near Bračići Reef in the Adriatic Sea. The case study illustrates how hazard marking status, proximity to effective AtoNs, and coastal complexity can be combined to support structured and risk-informed decision-making. Although the proposed framework remains conceptual and requires further quantitative development and validation, it provides a consistent basis for future decision-support tools and more transparent AtoN planning across complex maritime environments.

The wavelet transform (WT) is an integral transform primarily used for processing and analyzing nonstationary signals due to its multiresolution property. Multiresolution analysis is one method that finds applications in many fields because of the characteristics of the transform. Over the years, WT has become standard and is integrated into many coding protocols and applications without special mention. Decades of research in the field of wavelets have revealed several stages of development. In the initial stage, the focus was on wavelet families, with scientists deriving new families for emerging applications. The second stage addressed implementation issues, emphasizing more efficient implementation techniques. The next stage involved artificial neural networks (ANNs) that perform WT. This paper reviews the development of WT with examples from maritime applications. We also provide an overview of cutting-edge trends in wavelets and propose the aforementioned stages as a new taxonomy of WT development.

The maritime industry is undergoing a digital transformation, in which predictive maintenance and intelligent diagnostics play a crucial role in enhancing operational safety and efficiency. This paper investigates the application of infrared thermography (IRT) for fault detection and condition monitoring of ship machinery, with particular emphasis on its integration within condition-based and predictive maintenance frameworks. A systematic review was conducted in accordance with the PRISMA 2020 methodology, analyzing 210 publications retrieved from the Web of Science (WoS), Scopus, and Google Scholar databases to identify prevailing technological trends and research gaps. The results indicate that IRT enables early detection of critical faults such as overheating, insulation degradation, and poor electrical connections, thereby reducing unplanned downtime and improving system reliability. When integrated with artificial intelligence (AI), deep learning (DL), and convolutional neural networks (CNNs), diagnostic accuracy can be automated through enhanced data interpretation. Despite its proven effectiveness, standardized protocols and real-world validation of IRT–AI systems remain limited in the maritime sector. IRT is therefore recognized as a key enabler of safer, smarter, and more sustainable ship maintenance within the broader maritime digitalization framework.

Background/Objectives: The relationship between multiple sclerosis (MS) disability and co-occurring non-motor symptomatology is not well understood. This study examined the association between disability status and non-motor symptoms—sleep quality, depression, anxiety, and fatigue—in people with multiple sclerosis (MS), as well as the contribution of sleep quality to the prediction of fatigue, depression, and anxiety in MS. Methods: A cross-sectional study included 469 MS and 369 control subjects. Disability status of MS subjects was assessed with the Expanded Disability Status Scale (EDSS), while fatigue, depression, anxiety, and sleep quality were evaluated with the Fatigue Severity Scale (FSS), the Hospital Anxiety and Depression Scale (HADS), and the Pittsburgh Sleep Quality Index (PSQI), respectively. Statistical analyses encompassed group comparisons, Pearson correlations, and hierarchical regression models adjusted for age, sex, and EDSS. Results: The results show that MS subjects exhibited higher FSS, HADS-D, and PSQI scores than controls, with intercorrelations and only weak associations with EDSS severity (r = 0.15–0.29). Moreover, PSQI global and HADS-D scores increased with higher EDSS severity, while FSS scores peaked in the moderate EDSS range (4.5–6.5). Global PSQI score independently predicted FSS, HADS-D, and HADS-A. Daytime dysfunction, sleep disturbances, and sleep medication use significantly predicted FSS, HADS-D, and HADS-A scores. Conclusions: Study findings highlight the role of sleep quality in exacerbating depression, anxiety, and fatigue in MS.

Recently, augmented reality and machine learning have become integral parts of many developed systems. In the maritime domain, it is particularly interesting to develop a concept that combines augmented reality with the visualization of collision risks, using machine learning for motion prediction as its foundation. Hence, this research aims to propose a system that visualizes the risk in an augmented reality application. The paper presents a distance estimation method that mainly uses a single stationary camera placed at the harbor entrance. The machine learning component involves training the YOLO algorithm on the Split Port Ship Classification Dataset. This distance estimation is an input for the speed estimation algorithm. Speed is a key parameter for the prediction of collision risk. Preliminary experiments were conducted to provide proof of concept for further research, and the description of a case study is included in this paper.

The remote monitoring of maritime signaling facilities is one of the marine navigation safety rules essential for ensuring global maritime traffic. Some maritime signaling facilities have not yet implemented remote monitoring systems. This challenge is posed by factors such as insufficient signal range, limited availability of electrical energy, or various economic reasons. Therefore, this paper reviews the current and relevant scientific literature on 10 communication technologies for maritime signaling facilities in the last two decades using PRISMA guidelines. PRISMA 2020 represents guidelines for conducting systematic review papers using mixed methods, including their applicability to various reviews. In addition, this paper analyzes the selection of the best-suited communication technology for communication between maritime signaling facilities. The results show that, initially, 214 papers met the specified criteria, and after applying the filtering, it was narrowed to 29 relevant papers for the research topic. Surprisingly, almost half of them were found in databases other than WOS, SCOPUS, and GOOGLE SCHOLAR. Also, LoRa WAN is the most energy-efficient and cost-effective option, with a consumption rate 2.14 times lower than AIS and NB-IoT. To summarize, it has been found that LoRa WAN represents the optimal communication technology for transmitting data from maritime signaling facilities across long distances.

Maritime transportation significantly contributes to air pollution, especially in coastal cities. Air pollution represents the greatest health risk related to the environment in the European Union. Therefore, the European Commission published the European Green Deal, which introduces the rule of zero-emission requirements for ships at berths with the mandatory use of power supply from shore or alternative technologies without emissions. The electrification of ferries has proven to be a key approach in reducing the negative impact on the environment; hence, it is necessary to provide adequate infrastructure for charging electric ferries. To determine the energy needs of the shore connection, a daily energy profile of the ferry fleet was created. Due to the sailing schedule, daily energy needs may be non-periodic. By optimizing the charging process, a reduction in peak charging power can be achieved. The charging process was optimized using particle swarm optimization. To improve the function goal, the parameters of the model were analyzed and optimized. It was found that the correct selection of population size and inertia weight factor can significantly enhance the optimization effect. The proposed model can be applied to other ports of interest, considering the specifics of the exploitation of the fleet of ships.