Plinio Thomaz Aquino Junior

Understanding human behavior remains a paramount challenge for artificial intelligence in human–machine interaction. This study introduces an integrated approach for developing efficient and noninvasive adaptive systems tailored to digital environments. The method enables real-time inference of user intent by correlating personality traits with navigation patterns, improving processing accuracy, performance, and cost-efficiency. This research bridges the gap between user psychology and system design by incorporating integration variables, such as task completion time and dynamic interface adjustments. The results reveal significant correlations between psychological profiles and navigation behaviors, highlighting personality traits through user actions and decisions. Applications include personalized recommendation systems, online content curation, and adaptive support services. These findings contribute to the development of intelligent systems that address diverse challenges in digital environments, paving the way for adaptive human–machine systems aligned with individual needs and preferences.

The production, storage, and dissemination of information have evolved from ancient communication methods to modern digital technologies, with digital media playing a key role in connecting individuals. While keyboards are common tools for interaction, they present challenges for individuals with motor impairments. Augmentative and Alternative Communication (AAC) techniques, including gesture input, voice commands, and sensor-based systems, have emerged to address these limitations. Eye tracking, used in accessibility systems, offers both opportunities and challenges, such as visual fatigue and inaccuracies that lead to slower typing. To address these challenges, this study proposes an interaction approach integrating eye movement tracking with a virtual keyboard, utilizing an artificial neural network to interpret gaze data and translate intentions within the interface at a low cost for the user. Additionally, a Language Model (LM) aids in predicting next-word suggestions. This research will assess the impact of these technologies on typing speed, error rate, and linguistic predictability, contributing both scientifically and societally to the advancement of accessible communication systems.

Agile project management have gained substantial popularity in the IT sector over the years, particularly among software development organizations. The attainment of success in these endeavors hinges on various factors known as “Critical Success Factors” (CSFs), a subject frequently explored in academia. Nonetheless, there is no consensus regarding their impact on Agile software development. To address this gap, this study aims to recognize and analyze CSFs in software development projects executed using Agile methodologies. The study identified 25 CSFs categorized into five categories based on recent literature findings. A structured survey with 7-point Likert scale questionnaires was conducted among Agile software development professionals, resulting in 121 valid responses. These responses were analyzed using Structural Equation Modeling (PLS-SEM). The findings highlight that all constructs formed within the CSFs significantly contribute to the success of Agile software development projects. Moreover, the study reveals that the positive effects of these CSFs on success follow a descending order: Project Factors, People Factors, Organizational Factors, Process Factors, and Cultural Factors. Intriguingly, when these CSFs are considered as a second-order construct, their impact is even more pronounced, suggesting a synergistic relationship among these elements. This research addresses a crucial scientific aspect in project management literature and also introduces a novel yet theoretically grounded model. The study demonstrates strong validity, reliability, and explanatory capabilities (56.1%) concerning the phenomenon. Additionally, it contributes to the discussion on CSFs by pinpointing statistically significant factors and gauging their individual and collective influence on the success of Agile software development projects.

Energy efficiency has become a significant challenge for manufacturing companies. Although it is possible to improve efficiency by applying new and more efficient machines, decision makers tend to look for less expensive alternatives. Furthermore, the current reality of manufacturing companies, brought about by Industry 4.0, requires more flexibility of production systems and increase the complexity off machine rescheduling without compromising sustainable requirements. This study contributes to the subject by applying machine learning techniques in a job shop to reduce the makespan and estimate the total energy consumption. First, an artificial neural network (ANN) was trained to estimate the total electrical energy consumption in the system. A new input variable for the network was defined to assist in energy estimation. This variable is called the Priority Factor (PF) and helps capture the different patterns in the job shop. Second, as the ANN was trained, a Genetic Algorithm (GA) was used to reduce the makespan. Therefore, it is possible to reduce the makespan and know in advance the total electricity consumption in production. This solution supports a more sustainable manufacturing process, and is completely developed in a digital manufacturing environment.

This study aims to identify the key factors that contribute to successful outcomes in agile projects. A survey was conducted with 121 experts in the field, and confirmatory factor analysis was used to establish a construct related to project success. Multiple linear regression analysis was then employed to determine the significant CSF that influences success. The results indicate that 59.7% of success in agile projects can be explained by the identified factors. These factors include aligning work processes with agile methodologies, providing training and education on agile practices, prioritizing interpersonal skills and communication over processes and tools, co-locating project teams, engaging customers in the development process, and ensuring staff expertise.

In recent years, the concept of Agriculture 4.0 has emerged as an evolution of precision agriculture (PA) through the diffusion of the Internet of things (IoT). There is a perception that the PA adoption is occurring at a slower pace than expected. Little research has been carried out about Agriculture 4.0, as well as to farmer behavior and operations management. This work explores what drives the adoption of PA in the Agriculture 4.0 context, focusing on farmer behavior and operations management. As a result of a multimethod approach, the factors explaining the PA adoption in the Agriculture 4.0 context and a model of irrigation operations management are proposed. Six simulation scenarios are performed to study the relationships among the factors involved in irrigation planning. Empirical findings contribute to a better understanding of what Agriculture 4.0 is and to expand the possibilities of IoT in the PA domain. This work also contributes to the discussion on Agriculture 4.0, thanks to multidisciplinary research bringing together the different perspectives of PA, IoT and operations management. Moreover, this research highlights the key role of IoT, considering the farmer’s possible choice to adopt several IoT sensing technologies for data collection.

A key point to decrease water usage in agriculture is to employ precision agriculture by adopting technologies such as the Internet of Things (IoT). Many solutions using the IoT rely on the use of soil moisture sensors to gather data, in real-time, from the plant’s root zone. This study presents a linear calibration curve, obtained by the thermogravimetric method, for a capacitive soil moisture sensor for a silty clay soil sampled at the FEI University Center. The curve obtained has an adjusted R ² of 0.86 and a residual standard error of 2.8%. The soil sensor was tested in the field during the end of February revealing that the sensor is capable of indicating changes in soil moisture within the error obtained from the calibration procedure and that it can be used to indicate changes by rain or irrigation procedures.

Given the need to reduce water consumption in irrigation to face the challenge of guaranteeing the supply of this resource to the population in the coming decades, this work presents a fuzzy inference system based irrigation control system to determine water irrigation volume for a sweet pepper (capsicum annuum) crop. The system is composed of a Mamdani fuzzy control algorithm which receives data from soil moisture, air humidity and air temperature sensors from the crop field. Also, the rain forecast is gathered from a personal weather station located near to the crop field. Data gathered are used by the control algorithm to ensure that the soil moisture at the root zone is readily available to the plant. After 126 days from seeding, the results indicated that the system was able to reduced irrigation by 18% through controlling soil moisture levels at the plant root zone, saving water content on the irrigation process.