Effective lead qualification is essential for maintaining a consistent pipeline of qualified opportunities in sales. Traditional frameworks like BANT (Budget, Authority, Need, and Timing) rely on subjective expert assessments, often affected by inconsistent and incomplete information—particularly common in early sales stages—leading to unreliable decisions and missed opportunities. Machine learning-based automated models often lack transparency, which can hinder their adoption in real-world decision-making. This study integrates Paraconsistent Annotated Evidential Logic Eτ (Logic Eτ) into the BANT framework by proposing the ParaBANT model. The goal is to reduce subjectivity and bias while enhancing decision performance and accuracy in a deterministic and transparent manner—even under conflicting or insufficient data. Findings show that the proposed model improves qualification consistency and supports lead segmentation through an innovative Logic Eτ-based clustering approach, optimizing sales pipeline efficiency with transparency and clarity. The study contributes to academic research by introducing a formal and explainable alternative to both subjective frameworks and opaque AI systems. It expands the use of paraconsistent logic in business intelligence and delivers a low-cost, interpretable solution particularly suitable for small and medium-sized enterprises. More broadly, it aligns with the United Nations’ Sustainable Development Goals—specifically SDG 8, SDG 9, and SDG 12—by promoting innovation, improving resource allocation, and encouraging more ethical, transparent, and accountable decision-making practices.

Understanding how global systemic shocks disrupt long-term development trajectories requires more than economic indicators—it demands a multidimensional perspective grounded in human capabilities and structural resilience. The COVID-19 pandemic has disrupted key dimensions of human development worldwide, amplifying pre-existing inequalities and challenging national capacities to sustain progress in health, education, and income. While numerous studies have examined how countries with different Human Development Index (HDI) levels responded to the pandemic, few have investigated the inverse: how the pandemic itself disrupted the HDI. This gap exists in part due to the limited availability of post-pandemic data until recent years. This study addresses that gap by analyzing how the COVID-19 crisis affected the evolution of HDI across the 15 countries with the highest GDP between 2012 and 2022. We apply Interrupted Time Series Analysis (ITSA) and K-means clustering to detect structural breaks in national HDI trajectories and to classify countries into behavioral profiles based on their post-pandemic recovery patterns. The analysis isolates the impact on each HDI component —life expectancy, expected and mean years of schooling, and gross national income per capita— and identifies five distinct clusters: resilient growth, rapid recovery, slow recovery, stagnation, and sustained decline. The integrated ITSA’s and K-means logic-based structure supports causal reasoning under temporal disruption, enabling interpretable cross-country comparisons. The findings highlight the uneven nature of human development recovery and underscore the value of reasoning-driven approaches for policy design under uncertainty and systemic shock. This work contributes to scientific understanding of development resilience by demonstrating how logic-based temporal analysis and structured reasoning techniques can be applied to quantify and interpret systemic disruptions in human development, and supports societal efforts aligned with the UN Sustainable Development Goals, particularly SDG 3 (Health), SDG 4 (Education), and SDG 10 (Reduced Inequalities).

Considering the growing technological innovation with the use of the Metaverse as an environment for educational, corporate, and governmental interaction, in contrast to the risk of cyberattacks, there is an urgent need to strengthen its security, especially when there is the possibility of transacting assets with NFTs—Non-Fungible Tokens—which are high-value objects acquired and traded through blockchain technology. The objective of this article is to propose a research framework to optimize the security of these NFT assets using DLP—Data Loss Prevention—and Paraconsistent Logic to identify threats not only preventively but also by actively detecting loss, theft, misuse, and leakage of these types of assets during the use of the Metaverse. With a literature review on the Metaverse, DLP—Data Loss Prevention, Evidential Annotated Paraconsistent Logic Eτ, Artificial Intelligence techniques, NFTs—Non-Fungible Tokens, and data protection, the study will employ a Python program to conduct applied research using data from a transportation company, which shows a 37% data loss rate in its analysis. Through this Artificial Intelligence process and statistical concepts, compared to the minimization of data loss in the analysis using Evidential Annotated Paraconsistent Logic, Eτ resulted in 23%, indicating a significant difference of 15%, complemented as a tool to improve decision-making accuracy

The objective of this article is to propose a research structure to optimize this security of assets with NFT - Non- fungible Token with the use of DLP - Data Loss Prevention and Paraconsistent Logic for the identification not only preventively, but actively of the loss, theft, misuse and leakage of this type of assets during their use in the Metaverse. A bibliographic review was carried out on Metaverse, DLP, Paraconsistent Logic, Artificial Intelligence techniques [10][27][28], NFT [49][50], and Data Protection [4] with a focus on the LGPD (Brazilian Data Protection Law) [2][23], in conjunction with exploratory research. With a DLP and a database provided by the transport company with 200 articles analyzed. It was verified that a significant amount of data would be discarded in the first stage of the process (37%) since they do not present an active definition on the status of these assets. Considering the growing technological innovation with the use of the Metaverse, as an environment for educational, business and governmental interaction against the risk of cyber-attacks, there is an urgent need to strengthen its security, even more so when in this environment, where there is the possibility of moving assets with NFTs that are objects of great value acquired and traded in this medium. With the use of the Python program in the DLP, it was observed that it presented a 37% data loss in its analysis with this Artificial Intelligence [11] process only with the performance of the DLP, compared to the optimization of this analysis with the use of Paraconsistent Logic at 23%, that is, a use of more than 15% of the data.

Currently, many patients in psychological therapy clinics have phobias, noting that this personal information is sensitive, both from the company’s headquarters and its branches, and is recorded in a cloud environment, leaving it exposed and subject to data theft by hackers or data leaks by malicious users. Companies around the world are subject to this type of risk and, therefore, seek not only to protect their customers’ data when dealing with sensitive health data, but also preventive monitoring to avoid the disclosure of information. This study presents the use of SIEM - Security Information Event Monitoring, that is, a tool for collecting information on risk analysis of events that occur with patients using Evidential Annotated Paraconsistent Logic Eτ. This identifies threats that exploit their vulnerabilities, in case of leaking of this data, through the dissemination of malware, or phishing through spam. Automated market tools could have been used, but in them, the risk analysis is only at the basic or initial level, with no secondary levels. However, when a customized SIEM is used, suspicious situations or situations that require further evaluation are detected, at which point an alert is automatically triggered for the DPO – Data Protection Officer who calls on a psychologist to understand the impact of the leaked data. Thus, the article presents exploratory research using SIEM tool with paraconsistent logic, focusing on risk analysis based on artificial intelligence on vulnerabilities related to sensitive data of clinic patients, with a focus on the protection and privacy of their sensitive personal data. Therefore, compared to security regarding data loss minimization in the analysis using SIEM with the aid of Evidential Annotated Paraconsistent Logic, Eτ resulted in 27%, indicating a significant difference as a complementary tool in improving assertiveness at the time of decision making.

Background: Cargo theft remains a persistent and critical challenge in last-mile logistics, particularly within high-crime urban peripheries such as those in São Paulo, Brazil. These incidents compromise transportation security, disrupt supply chains, and impose considerable financial and operational burdens on logistics providers. Traditional security measures often prove inadequate in addressing the dynamic and complex nature of such criminal activities, highlighting the need for adaptive, intelligent decision-support systems capable of managing uncertainty, contradictions, and real-time risk assessments. Methods: This study synthesizes findings from three prior investigations to propose a comprehensive decision-making framework for cargo security, underpinned by a Paraconsistent Expert System (PES) based on Paraconsistent Annotated Evidential Logic Eτ. Employing a mixed-methods approach, the study integrates qualitative evaluations from logistics professionals with quantitative analyses of six targeted security interventions. A panel of nine experts-ncluding logistics managers, operational coordinators, and frontline delivery personnel-assigned degrees of certainty and uncertainty to each intervention. The PES processed these inputs to reconcile conflicting assessments and support strategic decision-making under conditions of informational ambiguity. Results: The analysis revealed that five of the six evaluated strategies-such as GPS tracking, armed escorts, optimized delivery scheduling, and the recruitment of local drivers-substantially reduced cargo theft incidents. The use of the Paraconsistent Expert System enabled more accurate risk assessments and enhanced the responsiveness of security operations. Notably, security-related costs decreased by 58% in Arujá and 75% in Cajamar, two critical logistics hubs in the São Paulo region. Conclusions: The proposed framework demonstrates the viability of a scalable, data-driven model for improving cargo security in high-risk urban logistics. By incorporating Paraconsistent Logic Eτ, the system offers a robust analytical tool for managing contradictory and uncertain data, thereby strengthening risk mitigation and operational resilience. The findings advance the literature on AI-supported decision-making in logistics and offer actionable insights for industry practitioners, policymakers, and security agencies seeking to enhance cargo protection in vulnerable urban areas.

As web applications become increasingly central to digital infrastructure, they also represent prime targets for sophisticated adversarial attacks. Traditional Web Application Firewalls, which rely on binary or statistical classification mechanisms, often fail under conditions of ambiguity—particularly when facing mutated payloads designed to evade detection. This study proposes Mogura Taiji Eτ , a model that applies Paraconsistent Annotated Evidential Logic Eτ to enhance detection by explicitly managing contradictory and uncertain inputs. The model was evaluated using a labeled synthetic dataset. Results demonstrate significant improvements in detection reliability, with marked reductions in classification errors and increased consistency in decision-making. Beyond quantitative gains, the model exhibits a qualitative shift in behavior, enabling more resilient and interpretable threat classification under adversarial conditions. This work contributes to the scientific field by advancing non-classical logic in computational security, offers a practical solution for the cybersecurity industry, and supports societal trust in digital systems. It aligns with the United Nations Sustainable Development Goals, particularly SDG 9 (Industry, Innovation and Infrastructure) and SDG 16 (Peace, Justice and Strong Institutions).

As the poultry industry grows, maintaining effective environmental control presents increasing challenges, particularly in tropical climates where variability undermines data reliability. Temperature, relative humidity, air velocity, and gas concentrations directly affect broiler health, productivity, and welfare. Although IoT-based monitoring technologies have enhanced data collection, conventional systems remain limited in managing inconsistent, incomplete, or contradictory sensor readings caused by abrupt fluctuations, equipment failures, or animal interference. This study presents a novel, mathematically grounded model for environmental monitoring in broiler production, leveraging Paraconsistent Annotated Evidential Logic Eτ to process conflicting data without discarding potentially valuable information. The model assigns dynamic degrees of favorable and unfavorable evidence to each reading and integrates a temporal decay function to reduce the influence of outdated data. This approach enables consistent inference even when faced with contradictory or delayed sensor data inputs. The results demonstrate that the proposed model effectively manages inconsistent sensor data, enhancing decision-making in dynamic and uncertainty-prone production environments. The approach improves the robustness and accuracy of environmental assessments, supports timely interventions, and contributes to better animal welfare, productivity, and operational efficiency. Beyond its practical contributions, the model illustrates the applicability of non-classical logic in smart agriculture, offering a flexible framework for decision support in farming contexts. The research also supports SDGs 3, 9, and 12, promoting sustainable, innovative, and welfare-focused poultry production.

Poultry farming faces significant challenges, including increasing productivity, improving animal welfare, and promoting sustainability. Smart technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), Big Data, cloud computing, and robotics, offer transformative potential for this sector. However, implementing these technologies involves challenges, such as high initial costs, proper infrastructure, and adaptation to adverse environmental conditions. Innovative approaches, including government subsidies, cooperative models, and adaptive technologies, are being explored to reduce costs and improve accessibility. Furthermore, there is a lack of comprehensive research on the applicability of these technologies in poultry farming. This study aims to provide an overview of the applications of smart technologies in optimizing productivity, improving animal welfare, and promoting sustainability across various stages of poultry production, including feeding, health monitoring, and environmental control. The study details key use cases and challenges encountered using methods such as systematic literature review and bibliometric analysis in databases like Web of Science and Scopus. The results highlight a significant increase in global interest in research. The study offers a global overview of scientific production and significant contributions to the advancement of scientific research, specifically in integrating smart technologies across different stages of poultry farming and introducing new approaches, such as non-classical logic, to handle inconsistent and incomplete data. The study provides a better understanding of the potential of smart technologies in poultry farming and critically evaluates their impact on productivity, animal welfare, and sustainability.

Demand generation is crucial for organizations, supplying sales teams with well-qualified commercial opportunities. Despite the wide variety of existing opportunity qualification methodologies, the subjective nature of experts’ final evaluation remains an obstacle to efficiency and productivity in the business process. This research investigated how Fuzzy Set Theory and Fuzzy Logic could be applied to the BANT methodology for qualifying commercial opportunities, aiming to replace these deliberative evaluations by experts to increase the sales cycle performance. A fuzzy inference system was developed to emulate the assessments of the experts. The analysis of the ratings obtained after processing a sample of commercial opportunities from 2022 and 2023 confirmed the system’s effectiveness in aligning with expert perceptions. While the study indicated room for refinements in the model, the findings underscore the potential to streamline the qualification of opportunities and improve sales cycle performance.

The pillars of Environment, Social and Corporate Governance at Naval Terminal are of utmost relevance, which it is carries out 70% of the bulk loading of soybean meal in the Santos Harbor, and responsible for 40% in Brazil. This study aims to present the optimization of ESG actions developed by the Compliance area. A project was developed based on financial, social and environmental materiality, implementation of internal audits and monitoring for Certifications. This is a exploratory research carried out by the naval terminal Compliance area. Implementation of a Reporting Channel, Due Diligence for suppliers, and Certifications: ISO 9001 (Quality Management System), ISO 14001 (Environmental Management System), GMP Plus (Good Manufacturing Practices and Food Safety) and ISO 45001 (System of Occupational Health and Safety Management), HACCP Certification - Hazard Analysis & Critical Control Point (Food Safety on Hazard Analysis and Critical Control Points). These actions contributed to ensuring excellent quality in its bulk loading processes.

One of the significant challenges in contemporary robotics concerns navigation systems and autonomous robots’ location, being the detection and avoidance of obstacles an essential part of this system. This article proposed Paraconsistent Annotated Evidential Logic Eτ in the control algorithm for a terrestrial robot, and thus contribute with navigation systems, to avoid possible collisions with obstacles. A prototype terrestrial robot was then built with ultrasonic sensors for obstacle detection and a servomotor in steering control. Several performance tests were performed. It was noticed that by shortening the perception distances of the sensors, the performance of the robot’s displacement proved to be satisfactory.

Medical Medicine specialists are using images more and more as support in decision making in the identification of pathologies with greater complexity and severity in the specific case. Predictive and diagnostic models in images with neoplasia (collagen V) associated with exposure to asbestos fibers were studied. In this article we intend to initially assess machine learning on the basis containing 100 images classified by specialists with characteristics of normality and abnormality to aid diagnosis. Therefore, the objective is to analyze and use the concepts of learning by the technique of artificial intelligence with neural networks that culminated in significant advances of 81% of correct answers in the image diagnostics and to propose the application of the Paraconsistent Standard Analyser Unit of Artificial Neural Networks in order to categorize the degree of abnormality (normal, almost normal, almost abnormal, abnormal) with the use of the extreme and non-extreme states of Paraconsistent Logic and thus support specialists in decision making in the diagnosis of cancer.

In the most varied specialties in the health hospital field, health professionals constantly need the main activity to make decisions in a timely manner and usually do so only in view of the data collected in clinical examinations. This has been the main source to define in an assertive way even in increasingly complex scenarios, notably in pathologies resulting from cancer cells. In this article, there is the possibility to present medical specialists with new aid to complement with inputs that add to their final decision-making. For this, the objective is to use the concepts of artificial intelligence applied in artificial neural networks and propose the use of paraconsistent logic architected in components of paraconsistent artificial neural networks (PANN) to support specialists in decision-making.

Group decision-making may present inconsistencies, resulting in biased decisions. This research presents simulations with the formation of different groups composed of professionals who participate in evaluating a candidate for project manager. The Behavioural Competencies Assessment Instrument and the Para-analyser algorithm were used as the foundation of the simulations based on the Paraconsistent Annotated Evidential Logic Eτ. In a factual scenario, it is necessary, at least two evaluators, in order to obtain equitable results since just one evaluator opinion may influence the result.

Every country in the world was concern about personal privacy data and protection models in Brazil Act promulgated late because LGPD Act (Protection Data Brazilian Law) came into force only in September 2020, otherwise elaborated in August 2018. With this advent the need to raise the awareness of people and companies about the information importance. The population still stays apart from the risk in personal data privacy, which grows every day a lot of companies are not in compliance with this LGPD Act. The study objective first part is a bibliographic review that points to the LGPD Act, activities, and function of the DPO – Data Protection Officer. Second, the application survey with the software of data protection and its approval used Paraconsistent Logic Evidential Annotated Eτ. Then, we detail the methodology used and compare the results with the discussions. Within these discussions, the information from the statistics collected by the ANPPD - National Association of Data Privacy Professionals at the CNPPD National Congress of Data Privacy Professionals in March / 2021 compared with the points of the analysis carried out using the Paraconsistent Logic Evidential Annotated Eτ.

In this expository work, we sketch some applications of annotated logics. Such logics were discovered in the late 1980s and nowadays have become one of the most fertile logics for applications. They constitute a two-sorted logic, and they are paraconsistent and in general paracomplete and non-alethic logics.

Object caches are used to minimize data traffic in various areas of Information Technology, including computer networks. In this scenario, they are usually hosted in proxies, storing page objects (texts, figures, among others), and implementing access control policies. Its correct operation can provide a significant gain of performance in data exchange since it allows the immediate response of requested resources. This study aims to discuss the different states of the efficiency of two distinct types of computer network caches and determine the changing dynamics of these states with Logic Eτ.

Automation, and Robotics have experienced incredible improvements over the past decades. Due to the high sophistication more and more elaborated logical tool became necessary to overcome what classical logic offers. In this work, it is shown how a new class of non-classical logics, namely paraconsistent annotated evidential logic Eτ, can be used to deal situations where the presence of conflicting, diffuse and paracomplete must be treated in a non-trivial way. Our purpose is to show its usefulness through examples that have been successfully applied.

The Internet of Things may be a hot topic in the society but it's not a new concept especially in industry. In this paper, we introduce the fundamental concepts of the Internet of Things (IoT) and critical points about how we can build IoT devices. We will also explain about our newly designed IoT hub, "A-Sight" and its hardware design. In particular, we also introduce projects concerning IoT, Vegetable Production and Distribution System and Public Transportation Monitoring System.

In this expository work, we show an application of a new class of ANN, namely the Paraconsistent Artificial Neural Network - PANN. Also, we use an algorithm - the Paraconsistent Extractor - for our studies. It was performed on the attributes of mammographic images. To perform these simulations, we used two different databases. The first one is used to classify calcifications, is composed of 143 samples divided into 64 benign cases and 79 malignant cases represented by form. The second is intended for mammographic masses and tumors classification and is composed of 57 regions of interest divided into 37 malignant and 20 benign cases, represented by form factors, transition edges and texture measures. The results demonstrate the qualities of Paraconsistent classifier when using a small number of samples for training the neural network and its low processing time. The proposed classifier can be rated as a Computer-Aided Diagnosis system (CAD). The Paraconsistent Extractor can obtain image parameters and sends them to the paraconsistent artificial neural network to analyze them.

This study aims to present a compliance analysis tool to Six Sigma by integrating indicative of success and Paraconsistent Method Decision. This way is contributing to a previous scenario analysis that can help the implementation of Six Sigma with higher chances of success.

In this work we discuss complementarity and heterodoxy in non-classical logics, exemplifying with the paraconsistent Cn systems introduced and studied mainly by Costa.

The purpose of this paper is to discuss an automated computational system able to recognize MICR characters commonly used on bank checks based on Paraconsistent Artificial Neural Networks due to their intrinsic ability to deal with imprecise, inconsistent and paracomplete data. The recognition process is carried out from character features chosen in advance based on Graphology and Graphoscopy techniques. The analysis of such features and the character recognition are performed employing Paraconsistent Artificial Neural Networks. Actual checks batches were presented to validate the proposed study and 97.8 percent of the characters were recognized correctly by the system.

In this work we present an overview of some studies made with Paraconsistent Artificial Neural Network – PANN and we discuss its potential applications. PANN is a new type of Artificial Neural Network – ANNs based on a new paraconsistent logics, namely Paraconsistent Annotated Evidential Logic Eτ, which is capable of manipulating concepts like impreciseness, inconsistency, and paracompleteness in a non-trivial manner.

This paper illustrates some applications of the notion of Curry algebras. Formerly introduced as a concept to study algebraic version of some non-classical systems, such structures also generalize some fundamental logical notions as computability, construtibility, and topological spaces. A brief study of Curry algebras Cn and algebra Cω is also discussed.

This paper shows the results of the sensing system which was designed for the autonomous mobile robot Emmy III. The proposed Sensing System has as its mean part the Paraconsistent Neural Network. This artificial neural network is based on the Paraconsistent Evidential Logics — Et. The objective of the Sensing System is to inform the other robot components about the obstacle position. The reached results have been satisfactory.