Abstract
Human resources play a crucial role in the success of small- and medium-sized enterprises (SMEs), and in today’s competitive recruitment landscape, leveraging technology can be instrumental in enhancing these processes. Organizations and HR departments increasingly adopt artificial intelligence solutions to streamline recruitment and selection procedures. By doing so, SMEs can improve operational efficiency while enabling human resource (HR) specialists to focus on crucial tasks, enhancing candidate experience throughout the recruitment process. However, adopting artificial intelligence (AI) in recruitment remains limited among SMEs. We can attribute this to various factors, including a need for more capacity among SME managers to evaluate and leverage AI’s potential and concerns related to costs and risks associated with its implementation. Given that SMEs constitute 90% of businesses and contribute over 50% of global employment, it is crucial to address this issue and research ways to enhance recruitment processes specifically tailored for SMEs. Our research aims to explore the benefits, challenges, and necessary organizational resources for SMEs to adopt AI effectively in recruitment processes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
Robotics has emerged as a cornerstone of modern society, significantly impacting diverse sectors including industry, healthcare, and defense. Among its varied applications, one of the most crucial fields is the control of rigid-structure robotic manipulators. However, conventional robotic arms are typically highly specialized and rigid in design, which limits their adaptability to different tasks and environments. One promising solution to this challenge is the development of modular robotic manipulators. This work proposes a cost-effective approach for implementing a n-Degrees-of-Freedom (DoF) manipulator. It introduces a design consisting of 3D printable links that allow for flexible assembly into custom configurations. A reconfigurable software architecture is presented, enabling automated generation of description and configuration files. This facilitates visualization, planning, and control of various custom configurations. The solution leverages the open-source Robot Operating System (ROS) as a digital twin for the modular setups. Additionally, it explores the development of hardware modules accompanying each link, facilitating independent joint control irrespective of motor type. Communication with ROS software is achieved via a CAN-based OpenCyphal network.

Abstract
After the pandemic we experienced, companies have felt the need to reinvent themselves and adapt to the present moment. The Internet and social networks have developed and increased their activity substantially. Users spend more time on social networks, shop more online, and feel more than ever a need for information and to view content. The main objective of this research is to define and implement a content marketing strategy for the social networks, through a quarterly content plan in the marketing services company Naive. In the first part of the research, presented in this paper, the work consisted of designing and implementing a questionnaire, obtaining a sample of 200 respondents to assess their perceptions and habits regarding social networks and the content offered on social networks, to study the results. The results obtained and analysis done will be used to develop a content strategy for Naive, which include studying the specific objectives for the company's different social networks, the actions to be developed and the content to be implemented.

Abstract
As many automated algorithms find their way into the IT systems of the banking sector, having a way to validate and interpret the results from these algorithms can lead to a substantial reduction in the risks associated with automation. Usually, validating these pricing mechanisms requires human resources to manually analyze and validate large quantities of data. There is a lack of effective methods that analyze the time series and understand if what is currently happening is plausible based on previous data, without information about the variables used to calculate the price of the asset. This paper describes an implementation of a process that allows us to validate many data points automatically. We explore the K-Nearest Neighbors algorithm to find coincident patterns in financial time series, allowing us to detect anomalies, outliers, and data points that do not follow normal behavior. This system allows quicker detection of defective calculations that would otherwise result in the incorrect pricing of financial assets. Furthermore, our method does not require knowledge about the variables used to calculate the time series being analyzed. Our proposal uses pattern matching and can validate more than 58% of instances, substantially improving human risk analysts' efficiency. The proposal is completely transparent, allowing analysts to understand how the algorithm made its decision, increasing the trustworthiness of the method.

Abstract
Quadruped robots hold immense potential for navigating in unknown environments due to their ability to use individual footholds as well as their increased stability in uneven terrain. However, legged robots often experience limitations due to weight shifts during gait transitions. These weight shifts can cause torque peaks that exceed the capacity of the jointmotors (overdrive torque), which lead to an increased risk of mechanical failure. Through the optimization of gait parameters, it is possible to reduce these risks while maximizing performance. This paper presents the use of multi-objective optimization algorithms for gait optimization in a simulated quadruped mammal robot within the Pybullet physics engine. The main focus of the study was to compare the performance of NSGA-II, NSGA-III and U-NSGA-III in minimizing overdrive torque while maximizing travel distance. The results showed that the three algorithms solve this problem, although the NSGA-III consistently yields better results in comparison to the other versions of the NSGA algorithm.

Abstract
Understanding and accurately predicting stomatal conductance in almond orchards is critical for effective water-management strategies, especially under challenging climatic conditions. In this study, machine-learning (ML) regression models trained on multispectral (MSP) and thermal infrared (TIR) data acquired from unmanned aerial vehicles (UAVs) are used to address this challenge. Through an analysis of spectral indices calculated from UAV-based data and feature-selection methods, this study investigates the predictive performance of three ML models (extra trees, ET; stochastic gradient descent, SGD; and extreme gradient boosting, XGBoost) in predicting stomatal conductance. The results show that the XGBoost model trained with both MSP and TIR data had the best performance (R2 = 0.87) and highlight the importance of integrating surface-temperature information in addition to other spectral indices to improve prediction accuracy, up to 11% more when compared to the use of only MSP data. Key features, such as the green-red vegetation index, chlorophyll red-edge index, and the ratio between canopy temperature and air temperature (Tc-Ta), prove to be relevant features for model performance and highlight their importance for the assessment of water stress dynamics. Furthermore, the implementation of Shapley additive explanations (SHAP) values facilitates the interpretation of model decisions and provides valuable insights into the contributions of the features. This study contributes to the advancement of precision agriculture by providing a novel approach for stomatal conductance prediction in almond orchards, supporting efforts towards sustainable water management in changing environmental conditions.