The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classification. A pre-trained MobileNetV2 network is employed as a fixed feature extractor to obtain discriminative visual representations, while Principal Component Analysis (PCA) is applied to reduce feature dimensionality by approximately 76%, retaining 95% of the original variance and improving computational efficiency. The compressed features are subsequently classified using a Radial Basis Function Support Vector Machine (RBF-SVM), optimized via grid search and cross-validation. Experiments conducted on a four-class maize leaf disease dataset (Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Healthy), with class imbalance handled during training, demonstrate that the proposed MobileNetV2–PCA–SVM pipeline achieves 97.58% accuracy, 96.60% precision, 96.59% recall, and 96.59% F1-score, outperforming the DenseNet201 + Bayesian-optimized SVM baseline (94.60%, 94.40%, 94.40%, and 94.40%, respectively). This improvement corresponds to a 2.98% accuracy gain, a 55% reduction in error rate, an 86% reduction in model parameters (20.31M to 2.75M), and an 85% reduction in model size (81 MB to 12 MB). These results indicate that the proposed framework provides a compact and efficient solution with strong potential for deployment in resource-constrained agricultural environments.

This research focuses on analyzing the global company market capitalization dataset using the FP-Growth algorithm combined with a load-balancing constraint approach. The main objective is to identify association patterns among different market capitalization categories Small, Medium, Large, Mega, and Ultra to understand their distribution and interrelationships. The study begins with data preprocessing, cleaning, and categorization of companies based on their market values. The FP-Growth algorithm is applied with a minimum support threshold of 0.02, and a load balancing constraint is introduced by filtering rules with support ≥ 0.05 and lift > 1, ensuring balanced and significant association patterns. The analysis results show that the most dominant categories are Medium and Small, representing the majority of companies worldwide, while Large, Mega, and Ultra categories are relatively rare. The strongest rule indicates that countries with “Large” companies are very likely to also have “Small” and “Medium” companies. Evaluation metrics show an average lift of 1.171 and an average confidence of 1.000, confirming strong and reliable associations. Overall, this study provides insights into global market capitalization patterns and demonstrates the effectiveness of FP-Growth with constraints in revealing meaningful, balanced relationships within large-scale business data.
 
Keywords – FP-Growth, Load Balancing Constraint, Market Capitalization, Association.

Tomato crop yields face significant threats from plant diseases, with existing deep learning solutions often computationally prohibitive for resource-constrained agricultural settings; to address this gap, we propose Efficient Disease Attention Network (EDANet), a novel lightweight architecture combining depthwise separable convolutions with hybrid attention mechanisms for efficient Tomato disease recognition. Our approach integrates channel and spatial attention within hierarchical blocks to prioritize symptomatic regions while utilizing depthwise decomposition to reduce parameters to only 104,043 (multiple times smaller than MobileNet and EfficientNet). Evaluated on ten tomato disease classes from PlantVillage, EDANet achieves 97.32% accuracy and exceptional (~1.00) micro-AUC, with perfect recognition of Mosaic virus (100% F1-score) and robust performance on challenging cases like Early blight (93.2% F1) and Target Spot (93.6% F1). The architecture processes 128×128 RGB images in ~23ms on standard CPUs, enabling real-time field diagnostics without GPU dependencies. This work bridges laboratory AI and practical farm deployment by optimizing the accuracy-efficiency tradeoff, providing farmers with an accessible tool for early disease intervention in resource-limited environments.

Agricultural yields have significantly decreased over the years due to soil degradation and weather variability. Inefficient utilization of these farming resources has worsened the situation. To address this challenge, this study developed an AI-driven crop recommendation framework based on structured soil and climate data. The dataset comprises key features such as temperature, humidity, and rainfall, as well as soil pH, nitrogen, phosphorus, and potassium, collected from the Wannune axis of Tarka Local Government Area in Benue State, Nigeria. To handle the data imbalance in the dataset, the Synthetic Minority Over-Sampling Technique (SMOTE) was applied. Recursive Feature Elimination (RFE) was employed to optimize feature selection. Five tree-based models, including Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, and Extreme Gradient Boost (XGBoost), were evaluated. The XGBoost model yielded the best performance, with an accuracy of 99.65%. The use of indigenous data bridges the gap between reliability and the relevance of localization in precision agriculture. Future work includes integrating dynamic environmental variables and conducting field validation to improve scalability and adoption

Biochemical pathways are the complex pathways of chemical reactions vital to maintain cellular homeostasis, control metabolism and modulate responses to physiological stimuli. Recent developments in the omics technologies, gene editing tools, and systems biology have significantly deepened our understanding of these pathways, changing the scientific paradigm from linear reactions to complex and interrelated regulatory networks. This review examines the changing face of metabolic and signaling pathways including but not limited to glycolysis, TCA cycle, MAPK, PI3K/AKT and JAK/STAT and their role in health and disease. Particular attention is paid to pathway analysis innovations, including CRISPR/Cas9, single-cell and spatial transcriptomics, and computational modelling and their revolutionary effect on discovery of new drug targets and pathway specific therapeutics. In reviewing the most recent advances in cancer metabolism, immune signaling, and cross-pathway interactions, this paper emphasizes the translational promise of pathway-centric research for personalized medicine, especially in oncology, neurodegeneration, cardiovascular, and autoimmune diseases. The review attempts to bridge basic biochemical research with clinical applications, and provides a window into the manner in which pathway-based interventions are influencing the future of precision therapeutics.

The rapid growth of RT/RW Net-based internet services among the community has driven the need for an integrated, efficient, and easy-to-use billing system. The self-help RT/RW Net model often faces challenges in terms of administrative management, especially related to customer recording, billing, and financial reporting which are still done manually. Common problems faced by partners are payment systems that have not been digitized and minimal transparency in data collection and tracking of customer transactions. The purpose of this activity is to design and implement a web-based billing application built using the Laravel framework. This application is designed to facilitate the process of managing customer bills, monitoring internet service usage, and preparing financial reports automatically and in a structured manner. The method of implementing the activity includes the system design stage, training partners in using the application, and assistance in direct implementation in the RT/RW Net operational environment. The results of the activity show that the developed system has succeeded in automating most of the billing process and increasing the efficiency of administrative management by up to 80%. In addition, this system also has a positive impact on increasing transparency and accuracy of customer data. Suggestions for further development include the addition of automatic payment features through integration with payment gateways, as well as improving the user interface to make it more responsive and user-friendly.

This research aims to review the teaching tools used at Madrasah Aliyah Negeri (MAN) 2 Kota Padang in order to support the implementation of the Independent Curriculum. Observations were carried out for two months, focusing on analysis of learning documents, interviews with teachers, and direct observation of the learning process in the classroom. Observation results show that the teaching tools at MAN 2 Padang City have been prepared in accordance with the principles of the Independent Curriculum, including teaching modules, student worksheets, and formative assessments that support differentiated learning. The teachers at this madrasa show good adaptability to curriculum changes, by applying varied and contextual learning methods. Students also show a positive response to learning that is more interactive and relevant to everyday life. However, several challenges were found, such as the need for further training for teachers and the provision of more diverse learning resources. Overall, the teaching tools at MAN 2 Padang City support a more meaningful and student-centered learning process, in line with the objectives of the Independent Curriculum.
 

Rapid advances in information technology have fuelled the emergence of digital currencies such as Bitcoin as an increasingly popular means of transaction in Indonesia. However, behind the convenience and speed offered, the use of Bitcoin also poses a high risk of fraud in online transactions. The main objective of this research is to analyse the nature of legal protection for Bitcoin users in Indonesia. This research applies normative juridical method with statutory approach, conceptual approach, case study analysis, and refers to legal protection theory, online transaction theory, and legal economic theory. One of the case studies studied is the High Court Decision 1240/Pid.Sus/2022/PN Tng which reflects the existence of a vacuum and vagueness of legal norms in the protection of Bitcoin users. The analysis shows that although Bitcoin has been regulated under the legal framework of digital asset trading, there are still inefficiencies in the application of legal protection in a comprehensive and effective manner. This research emphasises the need for more progressive regulatory reforms, as well as strengthening the role of law enforcement agencies and financial technology supervisors to ensure fair, certain and comprehensive protection for Bitcoin users in the territory of Indonesia. It is hoped that the results of this research can strengthen theoretical contributions in enriching the development of digital economy law and become a practical reference for policy makers.

Aromatherapy candles made from used cooking oil represent an eco-friendly and economically valuable entrepreneurial innovation. This proposal presents the utilization of used frying oil as a base material in producing aromatherapy candles, aiming to support the circular economy concept and provide an alternative solution to environmental pollution. The product formulation combines paraffin, beeswax, and natural essential oils, offering relaxation and comfort while also opening up new business opportunities. SWOT analysis and marketing strategies based on the 4Ps (Product, Price, Place, Promotion) serve as the foundation for business development. The results of the study show that aromatherapy candle businesses have strong potential to attract consumer interest, especially among young people, and can deliver significant social, economic, and environmental impact.

Economic development in today's world has grown rapidly, leading to numerous changes in human life. By investing in the capital market, it becomes one of the alternatives for financing the community's economy and is easily accessible to the public. One of them is to invest in bond securities in issuer companies. However, by purchasing bonds in the capital market with the issuer company, in addition to providing benefits through interest rates, there are risks, including if the issuer company goes bankrupt. Under these conditions, the holder of the unsecured bond will be positioned as a concurrent creditor, whose repayment is made after the separatist and preferred creditor. Although not guaranteed collateral, bondholders still obtain legal guarantees of their rights through information disclosure, the role of trustees, and arrangements within the applicable legal framework.