International Conference on Science, Health and Engineering

  Interplay Between Residential Nature Exposure and Walkability: A Path to Better Cardiovascular Health Our modern urban environments are reshaping the way we live, move, and connect with nature. But did you know that where you live—and the accessibility of green spaces—could significantly influence your heart health? Recent research reveals how residential nature exposure, coupled with walkable neighborhoods, plays a pivotal role in cardiovascular (CV) health. The Link Between Green Spaces and Heart Health Green spaces have long been associated with numerous physical and mental health benefits. Studies show that access to natural environments can reduce stress, promote physical activity, and improve overall well-being. More importantly, green spaces may act as a protective factor against cardiovascular diseases (CVD), including hypertension, diabetes, obesity, and atherosclerosis. But what exactly makes green spaces so beneficial? It’s not just the presence of trees and parks. The...

  Title: Rhizosphere Priming Effects and Trade-offs Among Root Traits, Exudation, and Mycorrhizal Symbioses Introduction Plants are extraordinary architects of the soil environment, particularly within the rhizosphere—the narrow zone of soil influenced by root activity. The rhizosphere is a bustling hub of chemical exchanges, microbial activity, and nutrient cycling, with far-reaching implications for plant growth and ecosystem health. One fascinating phenomenon that occurs in this microcosm is the rhizosphere priming effect (RPE). RPE describes the process by which root-derived carbon inputs alter the microbial decomposition of organic matter, thereby influencing soil carbon dynamics and nutrient availability. However, the expression of RPE is not a stand-alone event. It is intricately tied to trade-offs among root traits, the release of root exudates, and the nature of plant-mycorrhizal symbioses. Understanding these trade-offs can unlock new insights into plant-soil interacti...

  Modeling Root Decomposition Effects on Root Reinforcement and Slope Stability Healthy ecosystems and sustainable land management are closely tied to the stability of slopes and the ability of vegetation to protect against soil erosion. Roots play a crucial role in stabilizing slopes by binding soil particles together, reducing the risk of landslides and erosion. But what happens when roots decompose? This post dives into the intriguing dynamics of root decomposition , root reinforcement , and their combined impact on slope stability . We'll explore the mechanisms, implications, and how we can model these processes to predict and mitigate slope instability. Understanding Root Reinforcement Roots anchor vegetation to the soil, increasing its mechanical strength. By forming a natural "web" in the soil matrix, roots reduce the likelihood of mass movement and enhance slope stability. This process, called root reinforcement, is critical in preventing landslides and maintainin...

 Membrane science has come a long way in recent years, evolving from fundamental research to practical applications in water treatment, energy production, and environmental protection. But now, a transformative technology—machine learning (ML)—is reshaping this field. This post explores how ML is advancing membrane science, accelerating discoveries, and unlocking new potentials in this critical area. 1.  The Challenges in Membrane Science Membrane science is primarily concerned with designing materials that selectively separate substances, often at a molecular level. Researchers face challenges in developing materials that achieve optimal permeability, selectivity, and durability. The experimentation required to identify or design these materials can be time-intensive and costly, given the high number of variables that influence membrane performance. 2.  Machine Learning to the Rescue Machine learning can analyze complex datasets to find patterns that may not be visible t...

  Mining Interpretable Rules with MCRM: A Novel Approach to Rule Mining In the world of data analysis, the demand for interpretable machine learning models is growing. While many black-box models like deep neural networks dominate predictive performance, they often fall short in terms of interpretability—a critical aspect for domains such as healthcare, finance, and law. To address this, researchers have been exploring rule-based algorithms that not only deliver reliable results but are also easy to understand. Enter MCRM (Multivariate Comprehensive Rule Mining) , a groundbreaking algorithm designed to mine interpretable rules with inherent feature selection and discretization. This novel approach opens new doors for extracting actionable insights from complex datasets. Let’s dive into what makes MCRM unique and why it stands out in the field of rule mining. What is MCRM? MCRM is a rule mining algorithm that focuses on two core goals: Generating interpretable rules – MCRM creates ...