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This article explores techniques for the detection and classification of fish as an integral part of underwater environmental monitoring systems. Employing an innovative approach, the study focuses on developing real-time methods for high-precision fish detection and classification. The implementation of cutting-edge technologies, such as YOLO (You Only Look Once) V5, forms the basis for an efficient and responsive system. The study also evaluates various approaches in the context of deep learning to compare the performance and accuracy of fish detection and classification. The results of this research are expected to contribute to the development of more advanced and effective aquatic monitoring systems for understanding underwater ecosystems and conservation efforts.

Photovoltaic (PV) temperature is an important parameter that can influence PV performance. The increase in PV temperature can cause a drop in PV output voltage, which indicates that the PV does not operate optimally. In this paper, a design of an integrated cooling system based on fuzzy logic to control the PV temperature is presented. This, in effect, will potentially ensure that the PV performance is at its maximum condition. In this work, the temperature of the PV is kept constant by attaching a cooling system that carries water inside it. The constant temperature of the cooling system is carried out through a radiator and thermoelectric with electronic control. The experiments are carried out using two 320-watt PV systems; one with the cooling mechanism and the latter without the cooling system as a control. Several important data such as temperature, voltage, current, and radiation measurement in each PV were collected for three days from 10:00 to 17:00 with an interval of 10 minutes for each measurement. The results of the integrated cooling system measurement showed an increase in PV power generation and efficiency of 1.32% compared to the control PV unit. From the results of the study, it can be concluded that the fuzzy logic-controlled cooling system contributed to the increase in PV performance.

This article begins with a dynamical analysis of the Permanent Magnet Synchronous Generator (PMSG) in a wind turbine system with quadratic nonlinearities. The dynamical behaviors of the PMSG are analyzed and examined using Poincare map, bifurcation model, and Lyapunov spectrum. Finally, an adaptive type-2 fuzzy controller is designed for different flow configurations of the PMSG. An analysis of the performance for the proposed approach is evaluated for effectiveness by simulating the PMSG. In addition, the proposed controller uses advantages of adaptive type-2 fuzzy controller in handling dynamic uncertainties to approximate unknown non-linear actions.

Corrosion is an undesirable phenomenon resulting in material deterioration and degradation through electrochemical or chemical reactions with the surrounding environment. Additionally, corrosion presents considerable threats in both the short and long term because of its ability to create failures, leakages, and damage to materials, equipment, and environment. Despite swift technological developments, it remains difficult to determine the degrees of corrosion due to the different textures and the edgeless boundary of corrosion surfaces. Hence, there is a need to investigate the robust corrosion detection algorithms that are suitable for all degrees of corrosion. Recently, many computer vision and image processing algorithms have been developed for corrosion prediction, assessment, and detection, such as filtering, texture, color, pixelation, image enhancement, wavelet transformation, segmentation, classification, and clustering approaches. As a result, this paper reviews and discusses the state-of-the-art computer vision and image processing methods that have been developed for corrosion detection in various applications, industries, and academic research. The challenges for corrosion detection using computer vision and image processing algorithms are also explored. Finally, recommendations for future research are also detailed.

Iris segmentation is a process to isolate the accurate iris region from the eye image for iris recognition. Iris segmentation on non-ideal and noisy iris images is accurate with active contour. Nevertheless, it is currently unclear on how active contour responds to blurry iris images or motion blur, which presents a significant obstacle in iris segmentation. Investigation on blurry iris images, especially on the initial contour position, is rarely published and must be clarified. Moreover, evolution or convergence speed remains a significant challenge for active contour as it segments the precise iris boundary. Therefore, this study carried out experiments to achieve an efficient iris segmentation algorithm in terms of accuracy and fast execution, according to the aforementioned concerns. In addition, initial contour was explored to clarify its position. In order to accomplish these goals, the Wiener filter and morphological closing were used for preprocessing and reflection removal. Next, the adaptive initial contour (AIC), δ, and stopping function were integrated to create the adaptive Chan-Vese active contour (ACVAC) algorithm. Finally, the partly -normalization method for normalization and feature extraction was designed by selecting the most prominent iris features. The findings revealed that the algorithm outperformed the other active contour-based approaches in computational time and segmentation accuracy. It proved that in blurry iris images, the accurate initial contour position could be established. This algorithm is significant to solve inaccurate segmentation on blurry iris images.

Safe, accurate, and predictable autonomous systems in marine vehicles are paramount. An understanding of an intelligent system fitted inside a ship is critical to ensure an autonomous ship is safe to be operated. Although the use of artificial intelligence in the design of the road-based vehicle has arrived at the self-driving level, there exists a significant gap within the research of autonomous ship to operate in restricted water (riverine and ports). Hence, this article shall discuss the relevant works of literature to set a preliminary guiding principle for the design of an autonomous ship. We present a simple illustrative framework as a starting point for ship designers to begin working in a simulated environment, which can be used as a foundation before the physical autonomous-ships are constructed and tested in a real-world situation. The framework consists of a virtual 3D environment and a surface vehicle with distance sensors, controlled by a neuroevolution-based autonomous piloting system. In this work, two scenarios will be presented: navigation in restricted waters, and obstacle avoidance capability of an autonomous ship. Results show that the resulting autonomous surface vehicle (ASV) is also capable of performing obstacle avoidance in the test track, albeit not being trained to do so in the training track. The work demonstrated in this paper is useful to the ship designers and can be extended for scenario-based planning for autonomous ship design.

One of the biggest problems the maritime industry is currently experiencing is corrosion, resulting in short and long-term damages. Early prediction and proper corrosion monitoring can reduce economic losses. Traditional approaches used in corrosion prediction and detection are time-consuming and challenging to execute in inaccessible areas. Due to these reasons, artificial intelligence-based algorithms have become the most popular tools for researchers. This study discusses state-of-the-art artificial intelligence (AI) methods for marine-related corrosion prediction and detection: (1) predictive maintenance approaches and (2) computer vision and image processing approaches. Furthermore, a brief description of AI is described. The outcomes of this review will bring forward new knowledge about AI and the development of prediction models which can avoid unexpected failures during corrosion detection and maintenance. Moreover, it will expand the understanding of computer vision and image processing approaches for accurately detecting corrosion in images and videos.

Pemphigus vulgaris is an autoimmune skin disorder with development of blisters in the skin and mucosa, and it can be a life-threatening disease if not treated. Corticosteroids have been a cornerstone for treating PV, but because of side effects the treatment is combined with other conventional immune modulating drugs and rituximab. The Danish treatment protocol for pemphigus vulgaris is similar to the other Scandinavian countries, and therefore this study is of importance for clinicians in the Scandinavian countries as well as other European countries. We retrospectively identified all patients with Pemphigus vulgaris in our tertiary center over a 7-year period in order to register patient characteristics, treatment, adverse events, comorbidities and the effect of prednisolone dose on remission. In this study 19 patients met the inclusion criteria and remission was seen after a mean of 19.9 weeks, and relapse was seen in 50% after the mean time of 15 weeks. Time to relapse in our study is relatively short compared to studies in which rituximab is used as a first-line drug in treating pemphigus vulgaris.

Corrosion is an undesirable phenomenon resulting in material deterioration and degradation through electrochemical or chemical reactions with the surrounding environment. Additionally, corrosion presents considerable threats in both the short and long term because of its ability to create failures, leakages, and damage to materials, equipment, and environment. Despite swift technological developments, it remains difficult to determine the degrees of corrosion due to the different textures and the edgeless boundary of corrosion surfaces. Hence, there is a need to investigate the robust corrosion detection algorithms that are suitable for all degrees of corrosion. Recently, many computer vision and image processing algorithms have been developed for corrosion prediction, assessment, and detection, such as filtering, texture, color, pixelation, image enhancement, wavelet transformation, segmentation, classification, and clustering approaches. As a result, this paper reviews and discusses the state-of-the-art computer vision and image processing methods that have been developed for corrosion detection in various applications, industries, and academic research. The challenges for corrosion detection using computer vision and image processing algorithms are also explored. Finally, recommendations for future research are also detailed. [ABSTRACT FROM AUTHOR]

The Sailing ships in Aceh are usually conventional; this shipbuilder usually requires 3 people. Sailing ships in Aceh are usually made conventionally. Shipbuilders need 3 workers. Construction time for ships weighing 1 to 3 gross tons (GT) takes 1 - 2 months. Strategies and improvements in shipbuilding methods have been well-studied in the shipbuilding literature. The location and process of making the ship are in the Banda Sakti sub-district, Lhokseumawe City. It has multiple workstations, such as measuring and cutting stations and assembly stations. Operators must work 8 hours daily in various postures, including standing, bending, and lifting heavy objects. On the basis of the preamble of the Nordic Body Map (NBM), intense exercise damages the musculoskeletal system by comparing the Rapid Upper Limb Assessment and Rapid Entire Body Assessment for workers' assessment of their working posture. In the process of making fishing boats. Results of REBA Labor Calculation: Worker 1 gets a score of 9, Worker 2 gets a score of 11, and Worker 3 gets a score of 5. In RULA, Workers 1 and 2 get the same score of 6, and Worker 3 gets the same score of one score out of 4. REBA received a final score of 2 at low-risk level 1, and REBA 2 workers received a final score of 2 at level 1, low-risk, and performance level 1. Working conditions at lifting stations must be improved to prevent musculoskeletal system diseases in the timber lifting station employees. [ABSTRACT FROM AUTHOR]

This article begins with a dynamical analysis of the Permanent Magnet Synchronous Generator (PMSG) in a wind turbine system with quadratic nonlinearities. The dynamical behaviors of the PMSG are analyzed and examined using Poincare map, bifurcation model, and Lyapunov spectrum. Finally, an adaptive type-2 fuzzy controller is designed for different flow configurations of the PMSG. An analysis of the performance for the proposed approach is evaluated for effectiveness by simulating the PMSG. In addition, the proposed controller uses advantages of adaptive type-2 fuzzy controller in handling dynamic uncertainties to approximate unknown non-linear actions. [ABSTRACT FROM AUTHOR]

One of the biggest problems the maritime industry is currently experiencing is corrosion, resulting in short and long-term damages. Early prediction and proper corrosion monitoring can reduce economic losses. Traditional approaches used in corrosion prediction and detection are time-consuming and challenging to execute in inaccessible areas. Due to these reasons, artificial intelligence-based algorithms have become the most popular tools for researchers. This study discusses state-of-the-art artificial intelligence (AI) methods for marine-related corrosion prediction and detection: (1) predictive maintenance approaches and (2) computer vision and image processing approaches. Furthermore, a brief description of AI is described. The outcomes of this review will bring forward new knowledge about AI and the development of prediction models which can avoid unexpected failures during corrosion detection and maintenance. Moreover, it will expand the understanding of computer vision and image processing approaches for accurately detecting corrosion in images and videos. [ABSTRACT FROM AUTHOR]

Unmanned autonomous surface vehicles (ASVs) carry out tasks beyond human limitations, such as offshore surveillance, offshore target detection, search & rescue (SAR), bathymetry and coastal monitoring. An ASV equipped with sensors and decision-making support may operate for long duration and distances without human intervention. Although ASVs can perform autonomous navigation safely and accurately in open waters, there is a gap in the study of ASVs for river transportation and berthing. This paper uses a neuroevolution algorithm to train ASV navigation in restricted waters. Two scenarios are investigated using preliminary training data; autonomous manoeuvring in an inland waterway and autonomous berthing with the assistance of path trajectory. This work employs a geofencing method to assist ASV navigation owing to the limited detection range of distance sensors. Although the ASVs are trained for restricted waters, the results show that the proposed algorithm can adapt to different waterway environments. It is demonstrated that the neuroevolutionary technique is robust and efficient for controlling ASVs in restricted waters. [ABSTRACT FROM AUTHOR]

Iris segmentation is a process to isolate the accurate iris region from the eye image for iris recognition. Iris segmentation on non-ideal and noisy iris images is accurate with active contour. Nevertheless, it is currently unclear on how active contour responds to blurry iris images or motion blur, which presents a significant obstacle in iris segmentation. Investigation on blurry iris images, especially on the initial contour position, is rarely published and must be clarified. Moreover, evolution or convergence speed remains a significant challenge for active contour as it segments the precise iris boundary. Therefore, this study carried out experiments to achieve an efficient iris segmentation algorithm in terms of accuracy and fast execution, according to the aforementioned concerns. In addition, initial contour was explored to clarify its position. In order to accomplish these goals, the Wiener filter and morphological closing were used for pre-processing and reflection removal. Next, the adaptive initial contour (AIC), δ, and stopping function were integrated to create the adaptive Chan-Vese active contour (ACVAC) algorithm. Finally, the partly-normalization method for normalization and feature extraction was designed by selecting the most prominent iris features. The findings revealed that the algorithm outperformed the other active contour-based approaches in computational time and segmentation accuracy. It proved that in blurry iris images, the accurate initial contour position could be established. This algorithm is significant to solve inaccurate segmentation on blurry iris images. [ABSTRACT FROM AUTHOR]

Ruta chalepensis L., an evergreen shrub in the citrus family, is well-known around the world for its essential oils and variety of bioactivities, indicating its potential medicinal applications. In this study, we investigated the effect of different culture conditions, including plant growth regulators, media types, pH of the medium, and carbon sources, on in vitro regeneration from nodal explants of R. chalepensis. Following 8 weeks of culture, the highest percentage of regeneration (96.3%) and maximum number of shoots (40.3 shoot/explant) with a length of 4.8 cm were obtained with Murashige and Skoog (MS) medium at pH 5.8, supplemented with 3.0% sucrose and 5.0 µM 6-Benzyladenine (BA) in combination with 1.0 µM 1-naphthaleneacetic acid (NAA). For rooting, individually harvested shootlets were transferred on ½ MS (half-strength) supplemented with IAA (indole-3-acetic acid), IBA (indole 3-butyric acid), or NAA, and the best response in terms of root induction (91.6%), number of roots (5.3), and root mean length (4.9 cm) was achieved with 0.5 µM IBA after 6 weeks. An average of 95.2 percent of healthy, in vitro regenerated plantlets survived after being transplanted into potting soil, indicating that they were effectively hardened. DNA assays (PCR-based markers) such as random amplification of polymorphic DNA (RAPD) and directed amplification of minisatellite-region (DAMD) were employed to assess in vitro cultivated R. chalepensis plantlets that produced a monomorphic banding pattern confirming the genetic stability. Additionally, no changes in the flow cytometric profile of ploidy between regenerated plantlets and donor plants were detected. Regeneration of this valuable medicinal plant in vitro will open up new avenues in pharmaceutical biotechnology by providing an unconventional steadfast system for mass multiplication and might be effectively used in genetic manipulation for enhanced bioactive constituents. [ABSTRACT FROM AUTHOR]