JOURNAL OF BIOENGINEERING, TECHNOLOGIES AND HEALTH

Automated Identification of Ectatomma edentatum (Hymenoptera: Formicidae) using Supervised Algorithms

Thu, 21 Mar 2024 00:00:00 +0000

Taxonomy constantly seeks alternatives to simplify and enhance the identification of living organisms. This study focuses on developing new tools for identifying ant species, aiming to address gaps in determining certain species that pose challenges for naming and study. Species identification can often be a time-consuming and intricate process. We aim to automate the identification process of Ectatomma edentatum (Roger, 1863), utilizing Machine Learning techniques to assess if efficiency can be improved and gaps in ant taxonomy reduced. We applied k-nearest neighbors (KNN) and Support Vector Classification (SVC) algorithms. Adapting these models to the dataset yielded excellent results, with both models demonstrating positive performance in classifying the ants. SVC achieved 100% accuracy, while KNN achieved 96%, affirming the effectiveness of these methods in ant identification. This study highlights the value of supervised algorithms in myrmecology, offering a valuable tool for taxonomy and species classification, ultimately providing accurate synthesis and prediction for species naming.

Functional Groups of Terrestrial Invertebrates in the Leaf Litter of Atlantic Forest (Bahia, Brazil)

Thu, 21 Mar 2024 04:58:53 +0000

The study conducted in a native fragment of the Atlantic Forest in Alagoinhas, Bahia, Brazil, aimed to evaluate the action of functional groups of terrestrial invertebrates and the effect of seasonality on their biodiversity. Over 6 months, researchers captured 166 invertebrates representing 9 orders and 17 families. The findings revealed a greater abundance of invertebrates (77) at the edge of the forest fragment. However, the indices of diversity, equity, and richness were similar between the fragment's edge and interior. Notably, at the edge, detritivores (such as little armadillos) and predators (including ants and spiders) exhibited dominance over other functional groups like phytophagous, saprophagous, coprophagous, and bioturbators. Predators of the orders Hymenoptera and Araneae were highlighted for their ecological importance, particularly in biological control, especially of detritivores. Interestingly, the study did not observe the effect of seasonality during the brief inventory period. This suggests that other factors may significantly influence the biodiversity of terrestrial invertebrates in the Atlantic Forest fragment in Bahia, Brazil.

Evaluation of LSTM and Wavelet Methods for Wind Speed Forecasting in Bahia, Brazil

Thu, 21 Mar 2024 05:01:10 +0000

The pursuit of sustainable development is intricately linked to the effective management of renewable energy resources, with wind energy emerging as a key player on the global stage. However, the inherent volatility and intermittency of wind patterns pose significant challenges to accurate wind speed forecasting, which is crucial for the stable operation of wind turbines. Our study presents a novel forecasting model that integrates cuttingedge data decomposition techniques with Long Short-Term Memory (LSTM) networks in this context. Our approach leverages advanced methodologies such as Wavelet Transform (WT), Empirical Mode Decomposition (EMD), and Enhanced Empirical Mode Decomposition (EEMD) to segment time series data into distinct high and low-frequency components. These segmented signals are then individually forecasted using Bidirectional LSTM (BiLSTM) networks, with the amalgamation of these predictions providing the final forecast output. Our empirical findings demonstrate that the hybrid model, particularly utilizing EMD and EEMD, exhibits superior performance compared to existing forecasting models in terms of both accuracy and stability. By effectively combining sophisticated data decomposition techniques with state-of-the-art deep learning algorithms, our proposed model offers a robust solution for wind speed forecasting. This facilitates the efficient management of renewable energy resources and advances the cause of sustainable development initiatives worldwide.

MLP Neural Networks in Sex Classification of a High Commercial Fish on the Artisanal Fisheries in Brazil

Tailon Carvalho de Cerqueira, Iramaia de Santana — Thu, 21 Mar 2024 05:01:37 +0000

The scarcity of data on artisanal fishing, particularly in tropical South American countries, presents an additional hurdle for fisheries management and marine conservation. While artificial intelligence (AI) has found applications in various domains, including marine sciences, most AI models primarily focus on species identification. Unfortunately, these models often overlook data-limited scenarios. This article examines the potential of an MLPtype ANN model in addressing this gap. The model aims to classify the sex of a dioecious (separate sexes) fish species of high commercial importance, shedding light on its implications for fisheries management. Evaluation of the model's classification performance using precision, recall, f1-score, and accuracy reveals promising results exceeding 80% for both sexes across both training and testing phases. These findings underscore the potential of MLP models in aiding Brazil's fishing sector management in grappling with challenges stemming from data scarcity. By providing efficient information essential for decision-making regarding the management of specific fishing stocks, such models offer valuable insights into effective fisheries management strategies.

A Model that Establishes a Parallel Behave Between Varying Chemicals and the Presence of Microplastics in the Ocean

Rebeca Souza dos Santos, Marley Oliveira de Souza, Emanuel Brasilino de Santana — Thu, 21 Mar 2024 05:02:00 +0000

The box model is a commonly utilized tool for understanding oceanic phenomena, particularly regarding the spatial distribution of chemicals. In our study, we aimed to establish parallels between existing models that account for the input of various chemicals and the potential flux of microplastics within the ocean. Our objective was to bridge the gap between conventional chemical dynamics and the emerging concern of microplastic pollution. To achieve this, we examined the accumulation of microplastics within the ocean and their uptake by marine organisms while also considering processes such as mineralization and demineralization. Our model treated microplastics as constituents akin to traditional chemical elements like phosphorus (P) and oxygen (O), viewing the ocean as a dynamic system with inputs and losses. Through this modeling framework, we sought to identify our approach's strengths and weaknesses. One key observation was the realization that the ocean receives a significant influx of microplastics, surpassing its capacity for degradation or bioaccumulation. This recognition underscores the urgency of addressing microplastic pollution and highlights the need for effective management
strategies to mitigate its impact on marine ecosystems.

Finite Element Analysis of Polymeric Matrix Composites with Sisal Fiber Reinforcement

Thu, 21 Mar 2024 05:02:19 +0000

Recently, there has been a growing interest in investigating the mechanical properties of plant fibers, particularly in developing nations. This surge is primarily fueled by the escalating environmental concerns, which are becoming more evident and alarming. Against this backdrop, various sectors of materials engineering are actively seeking alternatives to mitigate the environmental impact across the entire lifecycle of products - from production to use and disposal. This study aims to conduct a computational analysis to examine the influence of fiber quantity on the tensile strength of composites containing sisal fibers. It compares the findings with existing experimental test results and demonstrates the outcomes using the finite element method. The research reveals that greater tensile strength in the composites is achieved with higher volumetric fractions of sisal fiber, as observed in both experimental and computational analyses. These results underscore a strong correlation between finite element analysis and experimental tests documented in the literature.

Applications of the Information Dimension in Detecting Border Perturbations

Soraia Bitencourt Carvalho, Antônio Teófilo A. Nascimento — Thu, 21 Mar 2024 05:02:37 +0000

This study utilizes the concept of information dimension based on Shannon and Tsallis' entropy to analyze the contours of flat objects. Our objective is to employ the information dimension for detecting perturbations in borders. We create examples of squares with slight border perturbations, possessing the same mass and sharing identical box-counting dimensions yet exhibiting distinct information dimensions. This construction was devised with the understanding that entropy is responsive to the image frequency within each box. Consequently, the information dimension provides a more precise index of fractal shapes when compared to the box-counting dimension.

Process Mining in Sepsis Treatment Management: A Step-by-Step Approach to Discovery

Eugênio Rocha da Silva Júnior, Uanderson Lima Santos, Marcos Batista Figueredo — Thu, 21 Mar 2024 05:03:00 +0000

This article explores the management of sepsis treatment processes, a prominent contributor to hospital morbidity and mortality on a global scale. The research delves into the application of process mining techniques during the discovery phase, utilizing anonymized data from Mannhardt and Blinde (2017). The impetus for this investigation stems from the imperative to pinpoint inefficiencies within sepsis treatment protocols, with the overarching goal of enhancing care quality while curbing expenses. A primary challenge lies in the need for a comprehensive overview of hospital processes for sepsis management. The study employs discovery algorithms such as Alpha Miner, Heuristic Mining, and DFG (Direct Flow Chart) to address this challenge. By leveraging these methodologies, the research endeavors to foster efficient allocation of costs and resources, elevate the standard of patient care, and foster operational efficacy within public health institutions.

Environmental Impacts of Inadequate Disposal of Heavy Metals in Soil, Water, and Air

Nalisson de Jesus Santos, Flávio Pietrobon Costa, Alexandre do Nascimento Silva — Thu, 21 Mar 2024 05:03:37 +0000

The improper disposal of electronic waste poses significant risks to human health through soil contamination and subsequent water pollution. This text aims to shed light on the detrimental effects of such actions. The primary objective is to identify the impacts of irresponsible disposal of electronic waste and batteries on soil and, by extension, water sources. Specific objectives include exploring how heavy metals from these waste products can be correctly disposed of to prevent harm to animals and humans. Additionally, the text seeks to underscore the necessity of discussing existing public policies for proper waste disposal. A comprehensive literature review was conducted to gather insights from experts in the field. Researchers have proposed various approaches to address this issue, emphasizing the urgency of adopting sustainable practices for electronic waste disposal. In conclusion, there is an urgent need to reconsider current practices and implement sustainable methods for discarding electronic devices. By doing so, we can mitigate water, soil, and air contamination, thereby reducing the risk of disease transmission to humans.

Water Temperature Model: A Case Study in Alagoinhas-BA

Carmen Gonçalves de Macêdo e Silva, Marcos Batista Figueredo — Thu, 21 Mar 2024 05:04:23 +0000

In recent years, climate change has generated disturbances at both local and global scales, directly impacting the water temperature of rivers. Temperature fluctuations have become a common occurrence in river water, where after experiencing heating, the return to its natural temperature is gradual. Consequently, temperature changes affect various internal processes within rivers, including nutrient consumption, food availability, and dissolved oxygen concentration. These alterations subsequently impact the growth and distribution of aquatic organisms. This article focuses on the physical variables derived from a model characterized by limited computational complexity. The final structure of the model consists of a single ordinary differential equation, which is linearly dependent on air and water temperature, as well as flow rate. Additionally, to handle data sequences such as time series more efficiently than traditional Recurrent Neural Networks (RNNs), we employ a Long Short-Term Memory (LSTM) neural network.

Route Optimization with a Traveling Salesperson Tool in Python Language: A Case Study in a Vegetable Oil Collection Company

Eliomar de Almeida Santana, Alexandre do Nascimento Silva — Thu, 21 Mar 2024 05:04:39 +0000

Transportation constitutes a significant portion of organizational expenses, amounting to 60% of total costs, thereby underscoring its pivotal role in company operations. Effective transportation management is paramount for enhancing efficiency and fostering organizational growth. The primary objective of this article is to showcase the viability of utilizing free software solutions to address the traveling salesperson problem, mainly targeting companies with limited financial resources. Our method entails researching to identify and evaluate free software tools capable of addressing the traveling salesperson problem in the context of route optimization for a vegetable oil collection company. Through case studies and performance analyses, we aim to assess the effectiveness of these tools in comparison to commercial solutions. Moreover, we will consider factors such as ease of implementation and accessibility to determine the practicality of open-source tools. This study anticipates demonstrating the feasibility and efficacy of leveraging free software to solve the traveling salesperson problem, thereby providing smaller companies with a cost-effective way to optimize transportation costs. By facilitating the reduction of transport expenses and enhancing operational efficiency, these solutions are poised to bolster the growth and competitiveness of companies in the market landscape.