Abstract
Fertilization is paramount for agriculture productivity and food security. Plant nutrition pre-established recipes and nutrient uptake are rarely managed by changing the fertilizer composition at the different stages of the plant life cycle. Herein we perform a literature review analysis - since the year 2000 and onwards - of the state-of-the-art capabilities of Nitrogen, Phosphorous, and Potassium (NPK) sensors for liquid fertilizers ( e.g. , hydroponics). From the initial search hits of 1660 results, only 53 publications had relevant information for this topic; from these, only 9 had NPK quantitative information. Qualitative analysis was performed by determining the number of publications for each nutrient, according to sample complexity and existing single, multiplexed or hybrid technologies. Quantitative assessment was performed by extracting the bias and linearity, the limit of detection and concentration ranges of sensor operation, framed into the context of the sensor technology development stage and sample compositional complexity. The most common technologies are colorimetry, ionselective electrodes, optrodes, chemosensors, and optical spectroscopy. The most abundant technologies are for nitrate quantification, from which ion-selective electrodes are the most widely used technology, and sensors for phosphate quantification are the less developed. Most are at low technological levels of development, not dealing with the complexity of agriculture samples due to matrix effects and interference. Measuring the fertilizer composition, nutrient uptake, the state of the chemical network, and controlling the release of nutrients using new functional materials, is one of the most important challenges ahead for the existence of precision fertilization. Intelligent sensing and smart materials are today the most successful strategy for dealing with matrix effects and interferences, being led by ion-selective electrodes and spectroscopy technologies.

Abstract
This paper presents a new type of phase-shifted Fiber Bragg Grating (FBG): the sliced-FBG (SFBG). The fabrication process involves cutting a standard FBG inside its grating region. As a result, the last grating pitch is shorter than the others. The optical output signal consists of the overlap between the FBG reflection and the reflection at the fiber-cleaved tip. This new fiber optic device has been studied as a vibration sensor, allowing for the characterization of this sensor in the frequency range of 150 Hz to 70 kHz. How the phase shift in the FBG can be controlled by changing the length of the last pitch is also shown. This device can be used as a filter and a sensing element. As a sensing element, we will demonstrate its application as a vibration sensor that can be utilized in various applications, particularly in monitoring mechanical structures.

Abstract
Traffic forecasting problems involve jointly modeling the non-linear spatio-temporal dependencies at different scales. While graph neural network models have been effectively used to capture the non-linear spatial dependencies, capturing the dynamic spatial dependencies between the locations remains a major challenge. The errors in capturing such dependencies propagate in modeling the temporal dependencies between the locations, thereby severely affecting the performance of long-term predictions. While transformer-based mechanisms have been recently proposed for capturing the dynamic spatial dependencies, these methods are susceptible to fluctuations in data brought on by unforeseen events like traffic congestion and accidents. To mitigate these issues we propose an improvised spatio-temporal parallel transformer (STPT) based model for traffic prediction that uses multiple adjacency graphs passed through a pair of coupled graph transformer- convolution network units, operating in parallel, to generate more noise-resilient embeddings. We conduct extensive experiments on 4 real-world traffic datasets and compare the performance of STPT with several state-of-the-art baselines, in terms of measures like RMSE, MAE, and MAPE. We find that using STPT improves the performance by around 10 - 34% as compared to the baselines. We also investigate the applicability of the model on other spatio-temporal data in other domains. We use a Covid-19 dataset to predict the number of future occurrences in different regions from a given set of historical occurrences. The results demonstrate the superiority of our model for such datasets.

Abstract
Explainable Artificial Intelligence (XAI) is an area of growing interest, particularly in medical imaging, where example-based techniques show great potential. This paper is a systematic review of recent example-based XAI techniques, a promising approach that remains relatively unexplored in clinical practice and medical image analysis. A selection and analysis of recent studies using example-based XAI techniques for interpreting medical images was carried out. Several approaches were examined, highlighting how each contributes to increasing accuracy, transparency, and usability in medical applications. These techniques were compared and discussed in detail, considering their advantages and limitations in the context of medical imaging, with a focus on improving the integration of these technologies into clinical practice and medical decision-making. The review also pointed out gaps in current research, suggesting directions for future investigations. The need to develop XAI methods that are not only technically efficient but also ethically responsible and adaptable to the needs of healthcare professionals was emphasised. Thus, the paper sought to establish a solid foundation for understanding and advancing example-based XAI techniques in medical imaging, promoting a more integrated and patient-centred approach to medicine.

Abstract

Abstract
Electric vehicles (EVs) are key to a sustainable future, but extending battery life is essential to reduce costs and environmental impact. Thus, this paper presents the development of an Adaptive Nonlinear Predictive Model (ANLPM), integrated with a Third Order Generalized Integrator (TOGI) flux observer, which enhances induced torque estimation and stator reactance in Permanent Magnet Synchronous Motor (PMSM) systems. The model employs a Sequential Quadratic Programming (SQP) algorithm, ensuring numerical stability and efficiency within the Model Predictive Control (MPC) framework to handle nonlinear constraints effectively. Moreover, simulation results demonstrate that the ANLPM significantly outperforms classical Adaptive Linear Predictive Models (ALPM), Seven-Dimensional LPM (SDLPM), and Proportional-Integral (PI) control strategies. It achieves marked reductions in battery discharge current and energy consumption rates. Therefore, simulation comparisons, across different scenarios, show that ANLPM reduces battery discharge current by 3% over ALPM and 44.7% over PI, while cutting energy consumption by 12.2% and 28.2%, and decreasing parallel battery cells by 14.2% and 28%, respectively. Under high temperatures, ANLPM cuts battery consumption by 45.3% and reduces cells by 43.7% compared to SDLPM, highlighting its efficiency in managing energy and extending battery life in EVs.