A highly sensitive temperature sensor based on an all-fiber Sagnac loop interferometer combined with metal-filled side-hole photonic crystal fiber (PCF) is proposed and demonstrated. PCFs containing two side holes filled with metal offer a structure that can be modified to create a change in the birefringence of the fiber by the expansion of the filler metal. Bismuth and indium were used to examine the effect of filler metal on the temperature sensitivity of the fiber-optic temperature sensor. It was found from measurements that a very high temperature sensitivity of $-9.0\mathrm{nm}/°\mathrm{C}$ could be achieved with the indium-filled side-hole PCF. The experimental results are compared to numerical simulations with good agreement. It is shown that the high temperature sensitivity of the sensor is attributed to the fiber microstructure, which has a significant influence on the modulation of the birefringence caused by the expansion of the metal-filled holes.

Machine learning (ML) has revolutionized resource management in agriculture by analyzing vast amounts of data and creating precise predictive models. Precision agriculture improves agricultural productivity and profitability while reducing costs and environmental impact. However, ML implementation faces challenges such as managing large volumes of data and adequate infrastructure. Despite significant advances in ML applications in sustainable agriculture, there is still a lack of deep and systematic understanding in several areas. Challenges include integrating data sources and adapting models to local conditions. This research aims to identify research trends and key players associated with ML use in sustainable agriculture. A systematic review was conducted using the PRISMA methodology by a bibliometric analysis to capture relevant studies from the Scopus and Web of Science databases. The study analyzed the ML literature in sustainable agriculture between 2007 and 2025, identifying 124 articles that meet the criteria for certainty assessment. The findings show a quadratic polynomial growth in the publication of articles on ML in sustainable agriculture, with a notable increase of up to 91% per year. The most productive years were 2024, 2022, and 2023, demonstrating a growing interest in the field. The study highlights the importance of integrating data from multiple sources for improved decision making, soil health monitoring, and understanding the interaction between climate, topography, and soil properties with agricultural land use and crop patterns. Furthermore, ML in sustainable agriculture has evolved from understanding weather data to integrating advanced technologies like the Internet of Things, remote sensing, and smart farming. Finally, the research agenda highlights the need for the deepening and expansion of predominant concepts, such as deep learning and smart farming, to develop more detailed and specialized studies and explore new applications to maximize the benefits of ML in agricultural sustainability.

A petrol adulteration sensor based on a rectangular shaped hollow-core photonic crystal fiber is proposed and numerically analyzed in the terahertz regime. The performance of the proposed sensor was evaluated when it is employed to characterize different kerosene mixtures. In this research, the adulterated fuel sample is filled in the rectangular hollow channel and the electromagnetic signal of the terahertz band is also driven through the same channel. The received signal after the interaction of fuel with the terahertz signal will advise the refractive index of the fuel oil inside the core, which will also bear the information of how much extrinsic component is present in the fuel. The finite element method based simulation shows that the proposed sensor can reach a high relative sensitivity of 89% and presents low confinement losses at 2.8 THz. The reported sensing structure is easily realizable with the conventional manufacturing techniques. Consequently, this proposed fiber may be treated as an essential part of real-life applications of petrol adulteration measurements.

This work presents a non-invasive, reusable and submersible permittivity sensor that uses a microwave technique for the dielectric characterization of liquid materials. The proposed device consists of a compact split ring resonator excited by two integrated monopole antennas. The sensing principle is based on the notch introduced by the resonators in the transmission coefficient, which is affected due to the introduction of the sensor in a new liquid material. Then, a frequency shift of the notch and the Q-factor of the proposed sensor are related with the changes in the surrounding medium. By means of a particular experimental procedure, commercial liquids are employed to obtain the calibration curve. Thus, a mathematical equation is obtained to extract the dielectric permittivity of liquid materials with unknown dielectric properties. A good match between simulated and experimental results is obtained, as well as a high Q-factor, compact size, good sensitivity and high repeatability for use in sensing applications. Sensors like the one here presented could lead to promising solutions for characterizing materials, particularly in determining material properties and quality in the food industry, bio-sensing and other applications.

The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.

A novel compact microstrip resonant dielectric permittivity sensor with dual-band operation is presented in this paper. The circuit makes use of a single stepped-impedance-resonator as the sensing element, working at the 2.45-GHz and 5.8-GHz ISM bands, under differential and common modes, respectively. Sensitivity of the circuit and resonator coupling at each band can be configured independently. Moreover, testing of the sample is done by using a single sensing region without the need of duplicating the sample location. Experimental measurements are carried out using a very small amount of liquids and the results are presented validating the theory. This work delivers a compact, reusable, label-free and non-destructive microwave device and paves a way for sensing dielectric properties of chemicals with accuracy due to the dual-band performance.