Abstract
The wavelength sensitivity and spectral resolution of Mach-Zehnder fiber interferometers obtained through a combination of two identical uncoated and titanium dioxide (TiO2) coated long period fiber gratings (LPFGs) is presented and compared with single LPFGs-based refractometric sensors. A set of LPFGs were fabricated in single mode fiber with the resonance band having an amplitude of 3 dB in order to split in half the optical power between the core and the specific cladding modes. The separation between the pair of LPFG written in the fiber was varied between 1 and 3 cm and the thickness of the TiO2 coating around the fiber ranged from 20 to 40 nm. A wavelength shift sensitivity of 216 nm/refractive index units (RIU) was achieved for the device with 3 cm and a 30-nm thick TiO2 coating, which presented a spectral resolution of 1.1 x 10(-4 )Rill Despite the lower wavelength shift sensitivity of 142 nm/RIU, attained for a 2-cm long device and 30-nm thick TiO2 coating, a spectral resolution of 1.8 x 10(-5) RIU was measured, which is one order of magnitude lower than a single LPFG.

Abstract
In the last few decades, the alkali-silica reaction (ASR) has been reported as one of the major concrete concerns regarding durability, leading to high maintenance and reconstruction costs. The occurrence of ASR in numerous concrete infrastructures all over the world points to the need for research regarding measures for its detection in an initial stage (and further mitigation) either in new or existing structures. Furthermore, the chemical and physical mechanisms for ASR remain poorly understood. This lack of knowledge leads to incapacity to assess risk, cost-effectively predict service life, and efficiently mitigate the deterioration process due to ASR in concrete structures. This manuscript aims to review the most recent and relevant achievements and the existing knowledge concerning the reaction mechanisms of ASR. Additionally, this manuscript is focused on the conditioning factors, diagnostic and prognostic methodologies, preventive measures and test methods (including their limitations) of ASR conducted at an academic level. The perspectives for future research challenges are also identified and debated.

Abstract
Colloids and suspensions are part of our daily routines. Even the blood is considered a "naturally" occurring colloid. However, the majority of colloids are complex and composed by a diversity of nano to microparticles. The characterization of both synthetic and physiological fluids in terms of particulate types, size and surface characteristics plays a vital role in products formulation, and in the early diagnosis through the identification of abnormal scatterers in physiological fluids, respectively. Several methods have been proposed for characterizing suspensions, including imaging, electrical sensing counters, hydrodynamic or field flow fractionation. However, the Dynamic Light Scattering (DLS) has evolved as the most convenient method from these. Based also on the scattering signal, we propose a novel, simple and fast method able to determine the number of different scatterers type present in a suspension, without any previous information about its composition (in terms of particle classes). This is achieved by collecting features from a 980 nm laser back-scattered signal acquired through a polymeric lensed optical fiber tip dipped into the solution. Unlike DLS, this technique allows the trapping of particles whose diameter >= 1 mu m. For smaller particles, despite not guaranteeing their immobilization, it is also able to determine the number of different nanoparticles classes in an ensemble. The number of particle types was correctly determined for suspensions of synthetic particles and yeasts; different bacteria; and 100 nm nanoparticles types, using both Principal Component Analysis and K-means algorithms. This method could be a valuable alternative to complex and time-consuming methods for particles separation, such as field flow fractionation.

Abstract
Metabolomics is paramount for precision agriculture. Knowing the metabolic state of the vine and its implication for grape quality is of outermost importance for viticulture and wine industry. The MetBots system is a metabolomics precision agriculture platform, for automated monitoring of vineyards, providing geo-referenced metabolic images that are correlated and interpreted by an artificial intelligence self-learning system for aiding precise viticultural practices. Results can further be used to analyze the plant metabolic response by genome-scale models. In this research, we introduce the system main components: (i) robotic platform; (ii) autonomous navigation; (iii) sampling arm manipulation; (iv) spectroscopy systems; and (v) non-invasive, real-time metabolic hyper-spectral imaging monitoring of vineyards. The full potential of the Metbots system is revealed when metabolic data and images are analyzed by big data AI and systems biology vine plant models, establishing a new age of molecular biology precision agriculture. © Springer Nature Switzerland AG 2019.

Abstract
An optical fiber anemometer based on a flexible multi-FBG curvature sensor is reported. The probe is comprised of a structured polymer shell with embedded single-mode fibers with written fiber Bragg gratings. When the sensor is bent, the different spectral shift of the Bragg wavelengths allows the determination of the mechanical stimulus. Moreover, the probe was also used as a cantilever sensor for assessing the airflow speed in a wind tunnel. The sensor presented sensitivities of 0.8 nm/m(-1) and 1.05 pm/(m/s) for curvature and square speed measurements, respectively, and the sensing characteristics can be improved by simply changing the material and the geometry of the bulk polymer shell, providing a versatile and feasible probe for the mechanical and flow measurements.

Abstract
The feasibility of a compact, modular sensing system able to quantify the presence of nitrogen, phosphorus and potassium (NPK) in nutrient-containing fertilizer water was investigated. Direct UV-Vis spectroscopy combined with optical fibers were employed to design modular compact sensing systems able to record absorption spectra of nutrient solutions resulting from local producer samples. N, P, and K spectral interference was studied by mixtures of commercial fertilizer solutions to simulate real conditions in hydroponic productions. This study demonstrates that the use of bands for the quantification of nitrogen with linear or logarithmic regression models does not produce analytical grade calibrations. Furthermore, multivariate regression models, i.e., Partial Least Squares (PLS), which consider specimens interference, perform poorly for low absorbance nutrients. The high interference present in the spectra has proven to be solved by an innovative self-learning artificial intelligence algorithm that is able to find interference modes among a spectral database to produce consistent predictions. By correctly modeling the existing interferences, analytical grade quantification of N, P, and K has proven feasible. The results of this work open the possibility of real-time NPK monitoring in Micro-Irrigation Systems.