Science and Technology Journal (STJ) is a peer-reviewed, open-access journal publishing research articles, reviews, perspectives, and news across science, technology, and interdisciplinary fields. The journal focuses on life sciences, physical sciences, environmental studies, engineering, and innovation, with an emphasis on knowledge that addresses global challenges and regional development.
© The Author(s) 2025. Published by the Science & Technology Journal (STJ), Mizoram University. Articles published in this journal are open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are properly credited. Authors retain copyright and grant the journal the right of first publication, with the work simultaneously licensed under the CC BY 4.0 license. License link: Creative Commons Attribution 4.0 International License (CC BY 4.0)
Evaluating Ensemble-Based Classifiers for Khasi Named Entity Recognition with Data Imbalance Constraints
Named Entity Recognition (NER), a core task of NLP, has been the subject of much research in resource-rich languages. However, little has been attempted with respect to low-resource languages such as Khasi. Based on our previous efforts, the existing works on Khasi NER applied transformer models such as RoBERTa, XLM-RoBERTa, and BERT-base-multilingual-cased using transfer learning (TL) and achieved an F1 score of 91.25% with RoBERTa. In this paper, ensemble methods are investigated to improve performance even further. We used a novel Khasi named entity recognition dataset annotated in span-based and converted to IOB2 formats for token-level predictions for both TL and ensemble methods. Ensemble methods, including majority voting, weighted voting, and stacking with logistic regression and XGBoost, were employed. Two settings were tested, one where two models are implemented (RoBERTa and XLM-RoBERTa) and the other with three models, with the addition of BERT-base-multilingual-cased, where these models act as learners. The ensemble of three models with XGBoost scored the highest F1 score of 94.06%, which is an improvement of +2.81% over the previous best. It also achieved significant improvements on rare entities, such as WORK_OF_ ART, LANGUAGE, ORDINAL, and EVENT, although the dataset was skewed toward the PERSON class. The results show how ensemble methods can improve NER performance on imbalanced datasets and low-resource languages such as Khasi.
Copyright (c) 2026 Amitabha Nath, Ransly Hoojon, Saralin Lyngdoh https://creativecommons.org/licenses/by/4.0
LFGA–ADENN Hybrid Feature Extraction and Oversampling for Type 2 Diabetes Mellitus Detection in Mizoram
Early detection of Type 2 Diabetes Mellitus using genomic characteristic is inspiring due to large dimensionality, huge missing values, and large number of class imbalance. Bioinformatics has made it easier to analyse complicated biological data, and the fact that an increasing number of this data is available has made machine learning techniques far more beneficial. The study proposes a hybrid system by integrating preprocessing, hybrid oversampling and feature extraction for the Mizoram population. Missing value filtering, K-Nearest Neighbours imputation, and categorical encoding are performed for curating data. The combination of the ADASYN and Edited Nearest Neighbors approach is used to address class imbalance. Feature extraction uses a genetic algorithm to choose useful representations after combining leaf indices from Decision Tree, Random Forest, and XGBoost models. Minority class detection is constantly improved by machine learning models and the hybrid LFGA–ADENN pipeline, reaching up to 0.921 F1-score and 0.920 MCC using XGBoost. LFGA–ADENN shows strong performance in imbalanced genomic T2DM prediction, improving recall while preserving precision when compared to no oversampling or single-method approaches.
Copyright (c) 2026 Vanlalawmpuia Ralte, Lalhmingliana; Senthil, Brindha , Freda, John , Vanlalhruaii https://creativecommons.org/licenses/by/4.0
VALORIZATION OF DRAGON FRUIT (Hylocereus polyrhizus) PEEL: COMPARATIVE EXTRACTION OF BETACYANIN AND HIGH-METHOXYL PECTIN USING CONVENTIONAL AND EMERGING TECHNOLOGIES
Dragon fruit peel represents a valuable functional biomass rich in natural pigments and gelling agents, yet it remains largely discarded despite its significant phytochemical potential. Addressing this gap is essential for promoting sustainable resource utilization and reducing agro-waste. In this study, betacyanin and pectin were extracted using multiple techniques to compare their efficiency and the quality of recovered compounds. Methanol extraction yielded a higher betacyanin content and superior retention of bioactive compounds compared to ethanol, with notable levels of DPPH activity (88.37% ± 0.001 GAE/100 mL), ABTS activity (0.29 ± 0.02 TEAC), total phenolics (0.253 ± 0.02 mg/mL), flavonoids (0.38 ± 0.01%), alkaloids (5.33 ± 0.03 g), and terpenoids (2.451 ± 0.02 g). Betacyanin stability was strongly influenced by temperature and storage conditions. For pectin extraction, ultrasonic treatment produced the highest yield (7.50%) and a methoxy content of 11.8% (w/w) at 70°C for 30 minutes, outperforming solvent and microwave methods. Key extraction parameters including time, temperature, solvent concentration, pH, and microwave power substantially affected pectin yield and quality. The findings underscore the potential of fully matured dragon fruit peel as a sustainable source of natural colorants and pectin, a bio-polymer for functional food applications, highlighting the broader importance of valorizing fruit-processing waste for eco-friendly product development and circular bioeconomy initiatives.
Copyright (c) 2026 Dr. Baby Zaithanpuii Hmar, Ramngaihzuala, H.Lalnunfeli, Laishram Basantarani, Pontianus Nongrum https://creativecommons.org/licenses/by/4.0
From the Editor: Unearthing the Deep — The Discovery of Iridogorgia chewbacca and the Spirit of Scientific Exploration
This editorial reflects on the recent description of the deep-sea coral Iridogorgia chewbacca and what such finds reveal about the vast, still-hidden architectures of life beneath our oceans. Moving from taxonomy to meaning, it considers how living “archives” like deep-sea corals preserve signals of past ocean conditions, informing science in a warming world. In the same spirit of exploration, Science & Technology Journal (Vol. 12, No. 2, June 2024) curates eighteen contributions spanning computational methods, nanomaterials, mathematical modeling, environmental monitoring, healthcare research, and cultural astronomy. Together, they affirm a simple proposition: discovery thrives where disciplines meet, and where access—through open, digital publication—widens the circle of inquiry.
Copyright (c) 2025 Surya Kant Mehta https://creativecommons.org/licenses/by/4.0
Empowering Mizoram State Museum Applying Virtual Reality on Linked Data and Smart Objects
-Augmented Reality (AR) and Virtual Reality (VR) can be combined to create Mixed Reality (MR) experiences that significantly benefit India's tourism department. These technologies enhance higher-order cognitive learning by immersing users in realistic environments, making them invaluable complements to traditional museum techniques. This Full Research explores the implementation of various algorithms, including Rendering Algorithm, pathfinding navigation meshes Algorithm, convolutional neural networks Algorithm (CNNs) and Simultaneous Localization and Mapping Algorithm (SLAM), to develop a 3D model of Advanced Mizoram State Museum Environment in Unity Hub. This model was then converted into a VR experience for the Meta Quest 2. First, I go to Mizoram State Museum to Collect the information and take the picture for Creating Dataset and Converting the 3D Image to the Unity Hub Application for advanced Museum Environment Creating Purpose. This Paper Only Explain the two Algorithms only Rendering Algorithm and Convolutional Neural Network Algorithm (CNN). The rendering algorithm was employed to optimize the Animals, Birds, Insects and Leaf's to get the information and Create the Dataset to upload the code. The Unity Hub application wants to them Object Assets. I go to Download to the Various Assets stores Example Unity Hub Assets Store, Sketch Fab and Blender Assets Store Websites. The Convolutional Neural Network Algorithm (CNN) have explained the Object Detection, Object Recognition and Object Identification to the information for the Visitors peoples. The 3D model of Mizoram State Museum effectively showcases biological animals, plants, insects, and human features, offering users the convenience of exploring the museum anytime and anywhere.
Copyright (c) 2025 V.D. Ambeth Kumar, Rajesh Ramasamy, Gurram Ramakrishna, R. Ramesh Prabhakaran https://creativecommons.org/licenses/by/4.0
Refinements of Generalized Jacobi Method: A Higher-Order Approach
This study presents a comparative analysis of the pth Refinement of Generalized Jacobi method, focusing on its derivation, convergence properties, and numerical performance. We first derive the Third Refinement of Generalized Jacobi method and establish a general formula applicable to any pth Refinement. The analysis rigorously proves convergence for various types of matrices, including Strictly Diagonally Dominant (SDD) and M-matrices. Additionally, we demonstrate that the convergence rate of the (p + 1)th Refinement surpasses that of the pth Refinement, assuming the Generalized Jacobi method converges. Numerical examples are provided to support our theoretical findings, showcasing the improved convergence rates of the (p + 1)th Refinement compared to the pth Refinement. These results underline the advantages of higher-order refinements in the Generalized Jacobi method.
Copyright (c) 2025 Urboshi Hazarika, Samira Behera https://creativecommons.org/licenses/by/4.0
Artificial Intelligence Techniques for Rainfall Prediction in Mizoram
Rainfall prediction plays a vital role in managing hydrological events. An accurate predictive model can provide timely information to reduce the affects of extreme events such as, drought and flood. In Mizoram, agriculture is a key component of the economy. Accurate and early rainfall predictions are crucial to mitigate the adverse affects of prolonged dry periods or heavy rains on crop yield and the overall economy of the state. Artificial intelligence (AI) models can achieve precise rainfall predictions by uncovering concealed patterns within historical rainfall data. In the present study, Al models viz. extreme learning machine (ELM), artificial neural network (ANN) and support vector machine (SVM) have been used for predicting rainfall in Mizoram state of India. The meteorological data from 2002 to 2021 is used to train and test the predictive accuracies of the models. The study demonstrates reasonably accurate predictive capability of all the aforesaid AI models considered in the study. The performance of ELM was found superior, exhibiting smaller error and higher coefficient of determination than ANN and SVM.
Copyright (c) 2025 Abul Kashim Md Fujail, Jarita Das https://creativecommons.org/licenses/by/4.0
Shadowed and Redundant Rules Removal in the Cloud Firewall Policy: A Modified Tree Rule Firewall Approach
Firewalls are essential for security and are used to secure the majority of private networks. A firewall's goal is to examine every incoming and outgoing data before granting permission. One common type of conventional firewall is the rule-based firewall. But when it pertains to task performance, traditional listed-rule firewalls are limited, and they don't operate well on some networks with extremely big firewall rule sets. This study proposes a model firewall architecture called "Tree-Rule Firewall," which has benefits and functions well on large-scale networks like "cloud." In order to improve cloud network security, this study suggests an improved tree firewall that eliminates shadowing and redundant rules. Initially, this effort creates a tree rule. The proposed revised tree rule firewall effectively locates the shadow rules while avoiding the creation of redundant rules. Next, a cloud-based test was conducted on an altered Tree-Rule firewall that controls firewall rules. It is demonstrated that increased network security and quicker processing are offered by the modified Tree-Rule firewall. Large networks, such as cloud networks, are easier to build using a modified Tree-Rule firewall since it effectively eliminates shadow and redundant rules.
Copyright (c) 2025 Dhwani Hakani, Palvinder Singh Mann https://creativecommons.org/licenses/by/4.0
Patient Satisfaction with Nursing Care: A Comparative Study on Communication and Psychological Support given by the Nurse in Medical and Orthopedic Ward
The present study aimed to compare patient satisfaction with nursing care in the areas of communication and psychological support given by the nurse during the patient admitted in the medical ward and Orthopedic ward. The study was conducted at a civil hospital in Aizawl, Mizoram, India, from August 2023 to September 2023. A questionnaire regarding patient satisfaction in line with nursing theory by Virginia Henderson's basic nursing care model was administered. Out of 17 areas of nursing care, communication and psychological support given by the nurse on patient satisfaction were compared in the Medicine and Orthopedic wards. A total of 66 patients admitted to the medical and orthopedic ward were interviewed. Out of these, 40 (60.6%) participants were from the medicine ward, and 26 (39.3%) participants were from the orthopedic ward. The comparison of the mean scores of the two groups indicates that patients admitted to the Orthopedic ward are more satisfied than patients admitted to the Medical ward.
Copyright (c) 2025 M. Lalhlimpuii, A.K. Trivedi, G. Konwar https://creativecommons.org/licenses/by/4.0
Celestial Harmony: Exploring the Analogy of Greek, Indian, South East Asian and Mizo Astronomy
This brief research explores the historical accounts and traditions of astronomy in Greece, India, Southeast Asia, and the Mizo people. We search to discover the common intellectual fabric that these civilisations share across space and time by examining their astronomical works. This research investigates the impact of Indian mathematical expertise on astronomical computations, from the discovery of zero to the development of trigonometric functions. As we shift our focus to Southeast Asia, we learn about the lesser-known but no less fascinating astrological traditions that were common in this area. Through the combination of Buddhism, Hinduism, and indigenous belief systems, a unique system emerged. The sky temples of Angkor and the ancient observatories are two instances of how Southeast Asian astronomy merges religious and scientific constituents. Most importantly, this research includes the perception of the Mizo people, an indigenous group with a rich cultural history. Following Mizo's historical astrological customs, we find interesting similarities and divergences with the larger Greek, Indian, and Southeast Asian traditions. The way that the stars are connected throughout Mizo rites, agriculture, and folklore provides an insight into the relationship between cosmic and everyday life. The investigation goes past listing commonalities to investigate how the Mizo people have modified and customised astronomical knowledge. The Mizo contribution offers a lively notion of humanity's relationship with the skies, whether through cosmic narratives, celestial navigation methods, or traditional star lore.
Copyright (c) 2025 T. Malsawmtluanga https://creativecommons.org/licenses/by/4.0