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This work presents a detailed review of the development of distributed acoustic sensors (DAS) and their newest scientific applications. It covers most areas of human activities, such as the engineering, material, and humanitarian sciences, geophysics, culture, biology, and applied mechanics. It also provides the theoretical basis for most well-known DAS techniques and unveils the features that characterize each particular group of applications. After providing a summary of research achievements, the paper develops an initial perspective of the future work and determines the most promising DAS technologies that should be improved.

Industrial microwave-heating systems are pivotal in various sectors, including food processing and materials manufacturing, where precise temperature control and safety are critical. Conventional systems often struggle with uneven heat distribution and high fire risks due to the intrinsic properties of microwave heating. In this work, a fiber-optic-sensor-assisted monitoring system is presented to tackle the pressing challenges associated with uneven heating and fire hazards in industrial microwave systems. The core innovation lies in the development of a sophisticated fiber-optic 2D temperature distribution sensor and a dedicated fire detector, both designed to significantly mitigate risks and optimize the heating process. Experimental results set the stage for future innovations that could transform the landscape of industrial heating technologies toward better process quality.

Instrumentation techniques, implementation and installation methods are major concerns in today’s distributed and quasi-distributed monitoring applications using fiber optic sensors. Although many successful traffic monitoring experiments have been reported using Fiber Bragg Gratings (FBGs), there has been no standardized solution proposed so far to have FBG seamlessly implemented in roads. In this work, we investigate a mobile platform including FBG sensors that can be positioned on roads for the purpose of vehicle speed measurements. The experimental results prove the efficiency of the proposed platform, providing a perspective toward weigh-in-motion systems.

Distributed acoustic sensors (DAS) are effective apparatus which are widely used in many application areas for recording signals of various events with very high spatial resolution along the optical fiber. To detect and recognize the recorded events properly, advanced signal processing algorithms with high computational demands are crucial. Convolutional neural networks are highly capable tools for extracting spatial information and very suitable for event recognition applications in DAS. Long-short term memory (LSTM) is an effective instrument for processing sequential data. In this study, we proposed a multi-input multi-output, two stage feature extraction methodology that combines the capabilities of these neural network architectures with transfer learning to classify vibrations applied to an optical fiber by a piezo transducer. First, we extracted the differential amplitude and phase information from the Phase-OTDR recordings and stored them in a temporal-spatial data matrix. Then, we used a state-of-the-art pre-trained CNN without dense layers as a feature extractor in the first stage. In the second stage, we used LSTMs to further analyze the features extracted by the CNN. Finally, we used a dense layer to classify the extracted features. To observe the effect of the utilized CNN architecture, we tested our model with five state-of-the art pre-trained models (VGG-16, ResNet-50, DenseNet-121, MobileNet and Inception-v3). The results show that using the VGG-16 architecture in our framework manages to obtain 100% classification accuracy in 50 trainings and got the best results on our Phase-OTDR dataset. Outcomes of this study indicate that the pre-trained CNNs combined with LSTM are very suitable for the analysis of differential amplitude and phase information, represented in a temporal spatial data matrix which is promising for event recognition operations in DAS applications.

Bending losses are very critical in many application hence should be monitored.

Background The aim of this study is to evaluate the anti-inflammatory and anti-fibrotic effects of halofuginone in caustic esophageal burn injury in rats. Materials and methods Corrosive esophageal injury (CEI) was produced in male Wistar albino rats by instilling NaOH solution (1 ml, 37.5%) into the distal esophagus. Rats were decapitated on the 3rd day (early group) or 28th day (late group), and treated daily with either saline or halofuginone (100 µg/kg/day; i.p.), continued on alternate days after the third day. Histopathological evaluation and measurement of nitric oxide (NO), peroxynitrite (ONOO-) and oxygen-derived radicals by chemiluminescence (CL) were made in the distal 2 cm of the esophagus. Non-irrigated proximal esophageal samples were assessed for the levels of nuclear factor (NF)-κB, caspase-3, glutathione (GSH), malondialdehyde (MDA) and myeloperoxidase (MPO) activity. Results GSH, MDA, NF-κB and caspase-3 levels, and MPO activity in the proximal esophagus were not different among groups. Increased number of TUNEL (+) cells in the irrigated esophagus of the early and late caustic injury groups was reduced by halofuginone treatment. High microscopic damage scores in both early and late CEI groups were decreased with halofuginone treatment. NO, ONOO- and CL levels, which were elevated in the saline-treated early CEI group, were reduced by halofuginone treatment, but reduced NO and ONOO- levels in the late period of saline-treated group were increased by halofuginone. Conclusion In addition to its anti-fibrotic effects, current findings demonstrate that halofuginone exerts antioxidant and anti-apoptotic actions and supports therapeutic potential for halofuginone in CEI-induced oxidative stress.