Authors - Khumbelo Difference Muthavhine, Mbuyu Sumbwanyambe Abstract - Inadequate Resource Issues (IRI) are one of the challenges in Strategic Management (SM). This study concentrated on applying System Dynamics (SD) modeling to solve IRI. Strategic standard tools like SWOT analysis, PESTEL analysis, and the Resource-Based View have proven effective in addressing IRI; unfortunately, developments like digital transformation, long-term sustainability, and the rise of emerging market multinational corporations are poised to shape the future of SM in these regions. These traditional methods are no longer coping with new technology; hence, the authors implemented a new SD model to tackle the IRI in SM. Additionally, most strategic managers are incapable of developing an SD model due to mathematical and scientific complexity. Although SD is a reliable technique for handling complex issues in management, most managers reject SD because of the implementation’s need for scientific and mathematical requirements. To solve IRI mathematically and make scientific predictions about what would happen if variables were altered in the upcoming five years (2025–2035) and the impact on customers, the study created an SD model.
Authors - Arosha de Silva Abstract - The COVID-19 pandemic disrupted educational systems worldwide and required schools to adopt online learning within a short period. In Sri Lanka, secondary school teachers encountered numerous difficulties while adapting to virtual teaching environments. This study examines the challenges experienced by teachers when conducting online instruction during the pandemic. A mixed-methods approach was adopted, combining qualitative interviews with quantitative survey data collected from secondary school teachers and educational professionals. The findings revealed that teacher motivation, technological infrastructure, and increased workload significantly influenced the effectiveness of online teaching. Difficulties related to internet access, digital resources, and professional demands affected teachers’ ability to deliver lessons efficiently. The study highlights the importance of institutional support, professional training, and improved access to technology in strengthening online education. The findings may assist policymakers and educational institutions in developing effective strategies to support online and blended learning initiatives in the future.
Authors - Bharg Trivedi, Chaitaili Chandankhede Abstract - There is a continuous change in Android malware because it is obfuscated, polymorphic, and structural. Such changing methods diminish the performance of conventional signature-based detection methods. In an effort to defeat this challenge, the present paper provides a model that uses CNN and GNN models. It is an integration of spatial byteplot representations and structural call graph representations to successfully identify Android malware. Our study was based on a dataset of 1,159 real Android applications, and the used extraction technique was based on the static features. The CNN element of the structure Recognized robust spatial attributes of the grayscale images of the byteplot data with a ResNet-50 network. Meanwhile, the GNN component of the structure used a GraphSAGE network to derive structural representations of automatically generated function call graphs. The fused representations are combined into a 2304 dimensional feature vector. It is also optimized by making use of different methods such as Mutual Information. In this study, an Extreme Gradient Boosting Classifier on the fused representations to achieve successful Android malware detection. The assessment indicates that the framework attains a classification accuracy of more than 99% with businesses across the cross-validation holding the same accuracy.
Authors - Khumbelo Difference Muthavhine, Mbuyu Sumbwanyambe Abstract - Knowledge management (KM) is an essential company training process that incorporates four sequential factors: non-knowledgeable professionals, training to become knowledgeable professionals, new knowledgeable professionals, and knowledgeable and experienced professionals. Because the aforementioned variables are interconnected and make it extremely difficult to produce a measured solution, they must be thoroughly analyzed using mathematical formulas and reliable techniques. These issues impact businesses of all sizes, necessitating a versatile instrument for flexible KM analysis. Additionally, most KM managers dislike SD modeling due to its complexity, especially those without scientific training. This study recommended using system dynamic (SD) modeling rather than conventional tools to address the aforementioned issues. The use of SD modeling stems from three factors: (a) the examination of complicated dependencies; (b) the requirement for mathematical formulas; and (c) the graphical results in contrast to traditional methods. The study’s SD model included the four sequential factors and their relationships. KM managers should focus especially on the graph’s data when necessary modifications are needed.
Authors - A Aruna kumari, Tamminana Visweswari Abstract - Lung cancer is a fatal illness that causes several deaths worldwide and detection of lung cancer remains a challenge for medical professionals. Detection of cancer in early stages is difficult as the size of the tumor is very small making it difficult for medical professionals to detect. Cancer detected in the early stages can be treated with proper techniques which can save the lives of the patients. Due to excessive information in the CT scans, MRIs, X-rays, and PET scans the manual detection of lung tumor becomes extremely difficult. The methodology helps in detecting the presence of cancerous tissues in the lungs and predicting which stage of lung cancer is present. The methodology mainly includes image preprocessing, training the model, extracting features using deep learning algorithms and classifying the stage of cancer present as Normal, Benign, Malignant Stage 1, Malignant Stage 2, and Malignant Stage 3. Proper image processing techniques like image augmentation, image normalization and image resizing are applied on the IQ-OTH/NCCD dataset for extracting the necessary features which will be used while training the model. A hybrid model is created by combining two deep learning models, the Xception and MobileNetV2 architectures which can accurately distinguish between the different lung cancer stages and predict the stage of cancer. The performance metrices which include accuracy, precision, recall, f1-score and confusion matrix were also calculated to determine the accuracy of the proposed hybrid model. The proposed model helps in accurate and reliable diagnosis of lung cancer at early stages.
Authors - Erika Haydee Rubio-Camara, Oscar May Tzuc, Elsy Maria Rosales-Uc, Fran-cisco Gilberto Herrera-Chale, Roman A. Canul-Turriza, M. Jimenez Torres Abstract - Mechanical vibration energy harvesting has emerged as a promising strategy for supporting sustainable energy generation in industrial environments, where machinery and transportation systems continuously produce recoverable vibrational energy. This study presents the development and evaluation of predictive models based on deep multilayer perceptrons (DMLP) and Convolutional Neural Networks (CNNs) for estimating the energy potential associated with mechanical vibrations under industrial operating conditions. A simulation frame-work was implemented using experimentally reported operational ranges, including vibration frequencies between 10 and 50 Hz, amplitudes from 0.01 to 0.03 m, and temperatures between 25 and 45 °C. The analysis considered piezoelectric, electromagnetic, and triboelectric harvesting mechanisms to evaluate model adaptability under different scenarios. The predictive framework was implemented using TensorFlow and validated through a 10-fold cross-validation strategy combined with hyperparameter optimization. Results indicate that both architectures achieve high predictive capability for estimating harvested energy; however, CNN models consistently outperformed Deep MLP models, obtaining lower prediction errors and higher stability across validation folds. The superior performance of CNNs is associated with their ability to capture localized patterns and structured relationships within vibration-related data. The proposed method-ology demonstrates the feasibility of integrating artificial intelligence techniques into vibration-based energy harvesting systems for industrial applications. Furthermore, the study provides a computational framework for evaluating operational conditions, optimizing harvesting performance, and supporting the design of sustainable self-powered monitoring systems.
