Ma Chung University

Purpose This study proposes a deep learning-based decision support system for stress induction management in tangerine cultivation. While current techniques depend heavily on subjective visual information, we offer an objective, data-driven mechanism to determine whether a branch is likely to respond favorably to stress induction.Design/methodology/approach Leaf images were used as input, as color reflects photosynthetic pigment composition linked to readiness for stress-induced flowering. A dataset of 516 images, acquired under controlled conditions, was used to train the model. The system predicts flowering success into three categories: low, moderate and high. Four convolutional neural networks architectures (VGG16, ResNet50, MobileNet and Inception) were evaluated using Adam and Adamax with 10-fold cross-validation. Beyond standard transfer learning, the modeling pipeline integrates task-specific fine-tuning and red-, green- and blue-based colorimetric interpretation. Performance analysis includes receiver operating characteristic and gradient-weighted class activation mapping, with metrics summarized using accuracy, precision, recall and F1-score. Model selection was based on class-wise robustness and model size.Findings We found that architectural choice strongly affects predictive performance. The inception provides the highest accuracy-efficiency ratio (accuracy = 0.90, F1-Score = 0.89, precision = 0.92, recall = 0.88 and model size = 57 Megabytes), beating larger models and being lightweight enough for mobile application. The trained models were implemented into a mobile application prototype for real-time inference. The low flowering-success category remains difficult to detect due to its high intra-class variability.Originality/value This decision support system provides a practical approach to use an artificial intelligence-driven flowering analysis straight in stress induction workflows for more accurate and efficient cultivation management.

This study aims to analyze the effect of profitability, leverage, and good corporate governance on tax avoidance , as well as to examine the role of good corporate governance as a mediating variable in the relationship between profitability and leverage on tax avoidance in Food and Beverage subsector companies listed on the Indonesia Stock Exchange for the 2021–2024 period. This study uses a quantitative approach with secondary data obtained from the companies' annual financial reports. Data analysis was conducted using descriptive statistics, classical assumption tests, multiple linear regression analysis, path analysis , and the Sobel test. The results show that profitability has no significant effect on tax avoidance. Leverage has a negative and significant effect on tax avoidance, while good corporate governance also has a negative and significant effect on tax avoidance. The mediation test shows that good corporate governance is unable to mediate the effect of profitability on tax avoidance, but is able to mediate the effect of leverage on tax avoidance. The results of this study indicate that tax avoidance practices in Food and Beverage subsector companies are more influenced by the company's funding structure and the effectiveness of corporate governance mechanisms than by the company's level of profitability.

This article presents the design of the User Interface (UI) for the DSL Global Partnership (DSLGP) system, a Web-GIS–based educational platform developed to support disaster literacy and digital learning. The study focuses on translating the Software Requirements Specification into structured UI components, including the Home page, role-based dashboards, and key transactional workflows such as guest registration, assessment distribution, resource validation, and data import operations. The UI is designed using a user-centered approach, emphasizing clarity, accessibility, and role separation to ensure intuitive navigation for research teams, teachers, students, administrators, and public users. This work provides a concise and systematic overview of the UI structure that can guide subsequent implementation and evaluation stages within the DSLGP development cycle.

Breast cancer progression is driven by uncontrolled proliferation and metastatic dissemination, processes in which matrix metalloproteinases (MMPs) and apoptosis resistance play critical roles. This study evaluated three isolates from Sterculia quadrifida for their anti-invasive and anticancer mechanisms, with emphasis on MMP inhibition and apoptosis induction. In vitro assays demonstrated that aurone exhibited the strongest and broadest inhibition of MMP-2, MMP-3, and MMP-9, suppressing each isoform by approximately 69-70% at 200 µg/mL, whereas the phenylpropanoid showed moderate inhibition (54-56%) and the phenolic compound displayed weaker activity (36-41%). Fluorescence-based assays confirmed enzymatic blockade, with aurone-treated wells approaching baseline relative fluorescence units (RFU), while the pan-MMP inhibitor NNGH achieved approximately 95% inhibition. Mechanistic analyses revealed that aurone robustly induced intrinsic mitochondrial apoptosis across multiple breast cancer cell lines, as evidenced by Bax upregulation, Bcl-2 downregulation, an increased Bax/Bcl-2 ratio, activation of caspase-9, processing of executioner caspases (caspase-3 or caspase-7), and enhanced PARP cleavage, including in p53-mutant backgrounds. Aurone further enforced G1 phase arrest through suppression of Cyclin D1, CDK4/6, and phosphorylated Rb, accompanied by upregulation of p21^Cip1 and p27^Kip1. Concurrently, decreased phosphorylation of Akt and ERK1/2 indicated attenuation of pro-survival signaling pathways. Collectively, these findings demonstrate that S. quadrifida aurone exerts dual anti-invasive and antiproliferative effects through coordinated MMP inhibition, reactivation of mitochondrial apoptosis, and G1 checkpoint regulation, highlighting its potential as a multi-target anticancer candidate.

Indole alkaloids are a structurally diverse class of natural compounds known for their potent anticancer properties, including the ability to induce apoptosis and inhibit cell proliferation and metastasis. This study aimed to evaluate the cytotoxic potential of 20 indole alkaloids against breast cancer cell lines, investigate their molecular interactions with five key cancer-related proteins (Bcl-2, Caspase-3, CDK4, NF-κB, and MMP-9), and assess their pharmacokinetic and toxicity profiles. Cytotoxicity was tested using the MTT assay across five breast cancer cell lines: MCF-7, T47D, MDA-MB-231, BT-474, and 4T1. Vincristine exhibited the most potent cytotoxicity (IC₅₀: 0.05–0.10 µM), followed by vinblastine and camptothecin. Triple-negative breast cancer (TNBC) cells demonstrated higher resistance compared to luminal subtypes, indicating subtype-specific drug responses. Structure–activity relationship analysis revealed that the presence of lactone rings and hydrophobic side chains enhanced cytotoxic activity. Molecular docking using AutoDock 4.2 identified irinotecan, sanguinarine, and piperine as top binders, with binding affinities ranging from –8.5 to –10.9 kcal/mol. Molecular dynamics simulations confirmed the stability of ligand–target interactions (RMSF < 3 Å). ADMET predictions using SwissADME and pkCSM indicated that all three compounds possessed favorable drug-like properties and high gastrointestinal absorption, though sanguinarine and piperine showed potential mutagenicity or carcinogenicity. The integrated in vitro and in silico approach supports the further exploration of irinotecan, sanguinarine, and piperine as promising multi-target anticancer agents. Notably, tylophorine and vincristine also emerged as potent candidates with favorable pharmacological profiles, warranting further preclinical validation for breast cancer therapy.