Journal of Hunan University Natural Sciences

This study presents the development of a standardized process for producing artisanal paper from Gynerium sagittatum (locally known as Caña Flecha) residues, a byproduct of Zenú handicraft production, carried out at the Industrial Engineering Laboratory of Universidad del Sinú Elías Bechara Zainúm. The objective was to obtain a paper with appropriate strength, appearance, and quality using accessible and low-cost techniques. The methodology involved mechanical and chemical fiber treatment, followed by sieving, pressing, and drying. Various process parameters were tested, including cooking time (2–3 hours), chemical agents (NaOH, CaCO₃, CaO), water volume, and drying time (2–4 days). The final product exhibited satisfactory mechanical and esthetic properties. The novelty of this research lies in transforming local agro-industrial waste into a sustainable material for artisanal use, aligning traditional craftsmanship with the principles of innovation, recycling, and environmental responsibility.

Keywords: Gynerium sagittatum, Caña Flecha, Zenú handicrafts, artisanal paper production, sustainable materials, agro-industrial waste, fiber processing, eco-friendly manufacturing, circular economy, traditional crafts and innovation, low-cost paper production, indigenous technologies, natural fiber pulp, chemical treatment of the plant fibers, Colombia’s sustainable development.

https://doi.org/10.55463/issn.1674-2974.52.3.4

This study investigates the relationship between emotional intelligence (EI) and decision-making in the professional choice of eleventh grade students in a Colombian city. A quantitative approach with a descriptive cross-sectional design was used to assess decision-making. The Kuder test for career choice, Bar-On emotional intelligence test, and Bechara and Damasio card games were applied to this end. The findings indicated a positive and significant correlation (p < 0.005) between EI and career decision making, underscoring the pivotal influence of dimensions such as self-awareness (r = 0.42, p < 0.01), social skills (r = 0.44, p < 0.01), and general mood (r = 0.232, p < 0.05). Specifically, heightened self-awareness fosters a more profound comprehension of personal strengths and interests, while augmented social skills and positive moods enhance the capacity for effective interaction and the maintenance of a favorable attitude toward decisions.
One of the novel contributions of the present study is the absence of previous research using the instruments used here, which highlights its relevance within the field. Likewise, the findings obtained allow us to open a path of reflection on the importance of the phenomenon of professional choice, both in youth and society in general. These results suggest that strengthening EI in students not only improves their professional choice process, but can also reduce academic dropout and increase satisfaction in their future careers.

Keywords: Career choice, emotional intelligence, decision making, vocational guidance, secondary education.

https://doi.org/10.55463/issn.1674-2974.52.3.3

This study examines the main challenges in English Language Teaching (ELT) in the academic environment and offers innovative solutions to improve students' learning experiences. Given the importance of English as a global language, effective mastery is crucial. However, obstacles such as language anxiety, lack of exposure to the use of authentic language, less effective teaching methods, and low motivation often hinder students' progress. This study used a literature-based approach to identify and analyze these challenges and formulate actionable solution strategies. The results showed that an interactive and communicative teaching approach, the integration of technology in learning, and the creation of a supportive learning environment can improve student engagement and language retention. Furthermore, this study highlights the need for a holistic approach that combines cultural relevance, student-based teaching practices, and consistent evaluation to improve language skills and academic success in ELT programs. By providing practical recommendations for educators, institutions, and policymakers, this study contributes to the development of more effective strategies to address barriers to English language learning in academic settings.

Keywords: English Language Teaching (ELT), Academic Environments, Language Learning Challenges, Teaching Strategies.

https://doi.org/10.55463/issn.1674-2974.52.3.2

Plant bioactive compounds are typically distributed across leaves, stems, and fruits. The Dungun plant (Heritiera littoralis), a mangrove species, is a promising source of bioactive compounds. However, research on the bioactive properties of H. littoralis seeds remains limited and warrants further investigation. This study aimed to isolate and characterize the bioactive compounds from H. littoralis seed extracts for their antioxidant and anti-inflammatory potential, with the aim of developing functional food applications. The research methodology involved the extraction and fractionation of H. littoralis seeds, antioxidant analysis using the DPPH assay, in vivo evaluation of anti-inflammatory activity using a rat paw edema model, and in silico molecular docking studies. Ethanol extraction yielded secondary metabolites including flavonoids, tannins, saponins, and steroids. Fractionation using Vacuum Liquid Chromatography (VLC) produced three main fractions. Fraction F2 was further purified by Gravity Column Chromatography (GCC), resulting in a pure isolate (F2.3).
The melting point of the isolate (175–177°C) was consistent with that of catechins, suggesting its high purity. Characterization of the isolate using UV-Vis spectrophotometry revealed two absorption peaks at 218 nm and 280 nm, indicative of hydroxyl groups and electron transitions from the carbonyl groups. FTIR analysis supported this identification, with absorption bands at 3346.55 cm⁻¹ (-OH group), 2925.67 cm⁻¹ (C-H alkane), and 1521.35 cm⁻¹ (C=C aromatic), confirming the isolate’s similarity to catechin. The anti-inflammatory activity was assessed using a rat paw edema model. The results showed that the highest inhibition percentage was achieved with Na diclofenac (80.21%), followed by a 15 mg/kg body weight (BW) extract (62.63%), and a 10 mg/kg BW isolate (53.13%). Antioxidant activity testing via the DPPH method revealed that the ethanol fraction exhibited significant activity, with an IC₅₀ value of 26.51 ppm, compared to ascorbic acid (positive control) with an IC₅₀ of 5.68 ppm. These findings demonstrate that H. littoralis seeds have substantial potential as sources of effective antioxidant and anti-inflammatory agents, highlighting their potential for future development in functional foods and therapeutic applications.

Keywords: Antioxidant, Anti-Inflammatory, Bioactive Compouds, Heritoria littoralis.

https://doi.org/10.55463/issn.1674-2974.52.3.1

This study compares the performance of three deep neural network architectures with the goal of searching for robotic navigation by semantic means of recognizing the environment in which it is located. The first is a pre-designed vision transformer network, the second is also a pre-designed convolutional neural network, and the third is a custom-designed convolutional network. These architectures are oriented to machine vision for mobile robots, enabling the recognition of global environments. The novelty exposed in this development consists in being able to identify a place based on its environment, as a human being does, so that a robot can address a place described by name and not by spatial coordinates, as usual. Comparison metrics include the level of recognition accuracy of the network, its size in kilobytes, and identification time. In addition to being able to operate in real time, each network is intended to be at least 90% accurate as an initial design parameter. The proprietary CCN network proved to be the most suitable for use in a mobile robot because it has a size of 22.5 KB, a response time of 0.07 seconds and an accuracy of 95.8%.

Keywords: Convolutional networks, transformer networks, deep learning, pre-trained network architecture, mobile robotics, transfer learning.

https://doi.org/10.55463/issn.1674-2974.52.2.11