Monitoring and counting maritime traffic is important for efficient port operations and comprehensive maritime research. However, conventional systems such as the Automatic Identification System (AIS) and Vessel Traffic Services (VTS) often do not provide comprehensive data, especially for the diverse maritime traffic in Mediterranean ports. The paper proposes a real-time vessel counting system using land-based cameras is proposed for maritime traffic monitoring in ports, such as the Port of Split, Croatia. The system consists of a YOLOv4 Convolutional Neural Network (NN), trained and validated on the new SPSCD dataset, that classifies the vessels into 12 categories. Further, the Kalman tracker with Hungarian Assignment (HA) algorithm is used as a multi-target tracker. A stability assessment is proposed to complement the tracking algorithm to reduce false positives by unwanted objects (non-vessels). The evaluation results show that the system has an average counting accuracy of 97.76% and an average processing speed of 31.78 frames per second, highlighting its speed, robustness, and effectiveness. In addition, the proposed system captured 386% more maritime traffic data than conventional AIS systems, highlighting its immense potential for supporting comprehensive maritime research.

To keep pace with global energy efficiency trends and, in particular, emission reduction targets in the maritime sector, both onshore and maritime power distribution systems need to be adapted to the relevant new technologies and concepts. As an important link in the distribution chain, medium-voltage switchgear (MV) is expected to be stable and reliable while operating as efficiently as possible. Failures of MV equipment, while rare because the equipment must be safe to handle and use, have far-reaching consequences. The consequences of such failures, whether to the shore or marine power system, present risks to the entire power plant, so an accurate assessment of equipment condition is required to identify potential failures early. The solution is an emerging concept of digital switchgear, where the implementation of sensor technology and communication protocols enables effective condition monitoring, and the creation of a database that, when combined with machine learning algorithms, enables predictive maintenance and/or fault detection. This paper presents, step by step, the previous challenges, the current research (divided into predictive maintenance, condition monitoring, and fault detection categories), and the future directions in this field. The use of artificial intelligence is discussed to eliminate the disadvantage of manually interpreting operational data, and recommendations for future work are formulated, such as the need to standardize test procedures and data sets to train and compare different algorithms before they are used in practice.

Energy efficiency generally implies the efficient use of energy in all sectors of final consumption—industry, services, agriculture, households and transport. Shipping accounts for nearly 3% of global greenhouse gas emissions, making it the sixth largest CO₂ producer in the world. This is a result of inefficient ship design, lack of planning and optimal use of resources. As the transport sector expands, so does the pressure for a greener and cleaner maritime industry. Reducing fuel consumption is a major driver of the need for energy efficiency on ships. In this paper, due to the importance of maritime transport, we observed the impact of reducing the dimensions of the main switchboard as a contribution to energy efficiency. This contribution is not of great importance as is the case with the optimization of the navigation route, etc., but it certainly affects the weight and, thus, the fuel consumption, which contributes to energy efficiency in the designed system. The aim of this paper is to optimize the design of the main switchboard by using 2D simulations of possible bus topologies, in order to develop six different busbar models and find one that best meets the requirements. The simulation results indicate the optimal location and dimensions of the busbars in the main switchboard in accordance with the switchgear parameters. Apart from the change in layout and dimensions of the busbars, the replacement of conventional instrument transformers with new current/voltage sensors contributes to a significant reduction in the weight and size of the switchboard, which ultimately benefits energy efficiency.

Raman spectroscopy is a powerful analytical technique based on the inelastic scattering of photons. Conventional macro-Raman spectrometers are suitable for mass analysis but often lack the spatial resolution required to accurately examine microscopic regions of interest. For this reason, the development of micro-Raman spectrometers has been driven forward. However, even with micro-Raman spectrometers, high resolution is required to gain better insight into materials that provide low-intensity Raman signals. Here, we show the development of a micro-Raman spectrometer with implemented zoom lens technology. We found that by replacing a second collimating mirror in the monochromator with a zoom lens, the spectral resolution could be continuously adjusted at different zoom factors, i.e., high resolution was achieved at a higher zoom factor and lower spectral resolution was achieved at a lower zoom factor. A quantitative analysis of a micro-Raman spectrometer was performed and the spectral resolution was analysed by FWHM using the Gaussian fit. Validation was also performed by comparing the results obtained with those of a high-grade laboratory Raman spectrometer. A quantitative analysis was also performed using the ANOVA method and by assessing the signal-to-noise ratio between the two systems.