Volume 21 Issue 6 2026

Circadian dysfunction plays a critical role in the onset and progression of hepatic and renal cancers, driven by the dysregulation of key circadian clock genes, including ARNTL, PER2, and PER3. In hepatocellular carcinoma, disrupted circadian rhythms affect cell cycle control, DNA repair, and metabolism, facilitating tumor growth and metastasis. ARNTL, a central circadian regulator, is often silenced in HCC due to promoter hypermethylation, impairing pathways related to cell proliferation and differentiation. Tumor-suppressor genes PER2 and PER3 are also downregulated, promoting resistance to therapy and poor prognosis through disrupted apoptosis and altered metabolic regulation. Research suggests restoring circadian function could inhibit HCC progression, with chronotherapy emerging as a potential strategy to enhance treatment outcomes. In renal cancers, especially clear cell renal cell carcinoma, circadian dysfunction involves altered ARNTL expression, which interacts with hypoxia-inducible factors (HIFs) to drive angiogenesis and metabolic changes. ARNTL dysregulation fosters tumor development, while downregulation of PER2 and PER3 contributes to unchecked cell proliferation, genomic instability, and disrupted circadian regulation of pathways like Wnt/β-catenin signaling. These disturbances exacerbate oxidative stress and inflammation, fueling tumor progression. Targeting circadian pathways offers promising opportunities for diagnosis, prognosis, and treatment, with the potential to improve patient outcomes by restoring circadian homeostasis.

Radiators play a vital role in diesel generators and industrial machinery by removing excess heat and maintaining safe operating temperatures. Effective cooling is important for improving system performance, ensuring operational safety, and extending equipment lifespan. This project presents the design and performance analysis of a smart multilayer industrial radiator system integrated with digital monitoring technology.The proposed system uses a multilayer radiator structure to increase the heat transfer surface area and enhance cooling efficiency. It is equipped with digital temperature and pressure sensors that enable real-time monitoring of coolant temperature and pressure during operation. In the system, the coolant continuously circulates between the diesel generator and the radiator, carrying heat away from the engine. The absorbed heat is then released into the atmosphere through conduction and forced convection with the assistance of a cooling fan. The study examines important parameters such as coolant flow rate, airflow, temperature variation, and heat dissipation under different operating conditions. Experimental observations, heat transfer calculations, and graphical analyses were conducted to evaluate the overall cooling performance of the radiator system.The results indicate that improved airflow, efficient coolant circulation, increased surface area, and the multilayer radiator configuration significantly enhance heat dissipation and overall thermal performance. In addition, the integration of digital sensors improves system monitoring and enables more accurate performance analysis.Overall, this project provides practical knowledge of smart radiator systems, thermal management, and advanced cooling methods used in diesel generator and industrial applications.

Many PDEs can be cast into infinite-dimensional Hamiltonian system. In this paper, the Klein-Gordon Schr¨odinger (KGS) is considered as a finite dimensional Hamiltonian system. The KGS equation is discretized by a compact scheme and a finite dimensional Hamiltonian system is obtained. Then, we propose and study several accurate numerical methods for solving one-dimensional KGS equation. Discrete conservation laws of the proposed schemes are analyzed. Numerical examples are given to show the accuracy, stability and the efficiency of the new methods.

The systematic integration of Generative AI (GenAI) in Philippine higher education is critically hindered by the absence of validated tools to assess its effective scholarly embedding. While GenAI promises significant enhancements to research, writing, and learning, current evaluation mechanisms fail to determine whether its application genuinely fosters deeper engagement, creativity, and ethical practice, or inadvertently promotes dependency, integrity breaches, and inequity. This gap obstructs the development of evidence-based policies and robust quality assurance. Addressing this need, this study developed and validated the Generative AI Integration Scale (GAIS), a psychometrically robust instrument specifically designed for the Philippine context. Employing a quantitative research design grounded in the Input-Process-Output (IPO) framework, which integrates TAM, UTAUT, and TPACK theories, and adhering to rigorous scale development protocols, initial content validity assessment (S-CVI/Ave = 0.96) guided item refinement. Exploratory Factor Analysis (EFA) conducted on data from 200 graduate students across three State Universities in Region IX revealed a stable three-factor structure: Societal Equity & Critical Vigilance (18 items, measuring equity advocacy, bias awareness, and risk management), Functional Productivity (7 items, assessing efficiency gains and AIsupported creativity), and Institutional Support for Ethical AI (4 items, evaluating trust in transparent, ethical governance). Demonstrating excellent reliability (Overall α = 0.977; subscales > 0.7), the validated GAIS equips Philippine Higher Education Institutions (HEIs) to effectively measure GenAI integration, enabling data-driven decisions to foster responsible, equitable, and impactful adoption aligned with scholarly excellence.

Phishing attacks represent one of the most prevalent and economically damaging threats in contemporary cybersecurity, exploiting counterfeit websites to harvest sensitive user credentials. This paper introduces a machine learning-based phishing website detection framework constructed upon the PhiUSIIL Phishing URL Dataset, encompassing 235,795 labelled URL samples. The original dataset comprises 56 features derived from URL structure, HTML content, and webpage metadata. To enhance model efficiency and reduce computational overhead, a feature selection methodology grounded in Mutual Information (MI) scoring was applied, contracting the feature space from 56 to 20 URL-extractable features with negligible performance degradation. Four machine learning algorithms were systematically evaluated: Random Forest, Decision Tree, Gradient Boosting, and Logistic Regression. The Random Forest classifier configured with 200 estimators delivered superior performance, attaining an accuracy of 97.38%, an AUC-ROC of 0.9973, and robust generalisation through 5-fold cross-validation yielding a mean accuracy of 97.36% ± 0.04%. A deterministic rule-based override layer was further incorporated to manage unambiguous phishing or legitimate signals with high confidence. The complete system is deployed as an interactive Streamlit web application enabling real-time URL classification. These findings affirm that a compact suite of URL-based features, paired with a robust ensemble classifier, yields an effective and practically deployable phishing detection solution.