Exploring Collaborative Frameworks to Assess and Monitor Conservation Outcomes of Indigenous Protected and Conserved Areas (IPCAs)

Within Canada, active strives are being made to achieve Canada’s Target 1 conservation goal. The creation of area-based conservation methods such as Other Effective Conservation Measures (OECMs) and Indigenous Protected and Conserved Areas (IPCAs), provide the means to achieve these goals. However, the current screening tools used to identify and monitor OECMs and IPCAs heavily reflect exclusively western science, thereby creating barriers for Indigenous nations. This research uses the collaborative framework of Two- Eyed Seeing to identify potential criteria indicators that are inclusive of Indigenous traditional knowledge to assess the governance systems, cultural and spiritual outcomes, and conservation outcomes of IPCAs. A rapid literature review was conducted to analyze the current screening metrics used by the Canadian government which revealed the potential for criteria for monitoring metrics. This paper highlights the need for place-based conservation management, co-governance models and wellness indicators in current monitoring tools for OECMs and IPCAs

GDPR employee awareness

The poster was created to make the viewer aware of the Employee awareness needs in the wake of GDPR and impact on NZ businesses

Plant and Plant Associated Microflora: Potential Bioremediation Option of Indoor Air Pollutants

Indoor air pollution is a significant problem today because the release of various contaminants into the indoor air has created a major health threat for humans occupying indoors. Volatile Organic Compounds (VOCs) are pollutants released into the environment and persist in the atmosphere due to its low boiling point values. Various types of indoor activities, sources, and exposure to outdoor environments enhance indoor VOCs. This poor indoor air quality leads to adverse negative impacts on the people in the indoor environment. Many physical and chemical methods have been developed to remove or decompose these compounds from indoors. However, those methods are interrupted by many environmental and other factors in the indoor atmosphere, thus limit the applications. Therefore, there is a global need to develop an effective, promising, economical, and environmentally friendly alternatives to the problem. The use of the plant and associated microflora significantly impact reducing the environmental VOC gases, inorganic gases, particulate matter, and other pollutants contained in the air. Placing potted plants in indoor environments not only helps to remove indoor air pollutants but also to boost the mood, productivity, concentration, and creativity of the occupants and reduces stress, fatigue, sore throat, and cold.  Plants normally uptake air pollutants through the roots and leaves, then metabolize, sequestrate, and excrete them. Plant-associated microorganisms help to degrade, detoxify, or sequestrate the pollutants, the air remediation, and promote plant growth. Further studies on the plant varieties and microorganisms help develop eco-friendly and environmentally friendly indoor air purifying sources

Growing Self Organizing Map with an Imposed Binary Search Tree for Discovering Temporal Input Patterns

In this paper the Binary Search Tree Imposed Growing Self Organizing Map (BSTGSOM) is presented as an extended version of the Growing Self Organizing Map (GSOM), which has proven advantages in knowledge discovery applications. A Binary Search Tree imposed on the GSOM is mainly used to investigate the dynamic perspectives of the GSOM based on the inputs and these generated temporal patterns are stored to further analyze the behavior of the GSOM based on the input sequence. Also, the performance advantages are discussed and compared with that of the original GSOM

Academicians' Acceptance of Online Learning Environments: A Review of Information System Theories and Models

Aim of this paper is to review technology IS acceptance theories and models recognizing empirical evidence available to support the suitability of each theoretical model in explaining academicians acceptance of online learning technology Understanding the factors influencing system usage is crucial for decision-makers to recognize potential user needs and concerns which could be addressed during the development phase of a system Thus for decades researchers have been trying to understand why people accept new technologies As a result a wide variety of theories and models explaining the concept of technology acceptance Some prominent theoretical models explaining technology acceptance are Theory of Reasoned Action Diffusion of Innovation theory Theory of Planned Behavior Social Cognitive Theory Technology Acceptance Model Model of PC Utilization Motivational Model Unified Theory of Acceptance and Use of Technology UTAUT 2 UTAUT 3 The concept of academic s acceptance of online learning technology can be explained through several determinants that are operationalized through above information systems model

Cardiovascular Disease Prediction Modelling: A Machine Learning Approach

The objective of this project is to utilize the UCI Heart Disease dataset to identify physiological biomarkers that are highly correlated with heart disease incidence. A predictive model can then be developed using these biomarkers to estimate the likelihood of someone having or developing a heart-related condition. This study compares the efficacy of predicting cardiovascular disease as an outcome using three machine learning algorithms: Support Vector Machine, Gaussian Naive Bayes, and logistic regression. Support Vector Machine works by creating hyperplanes between data points to conduct classification. Gaussian Naive Bayes works by using the conditional probabilities of events to classify the target. In logistic regression, the independent variables included all features in the data set except for “target,” which is a categorical variable that indicates whether the patient has cardiovascular disease. The dependent variable included the “target” variable. The findings suggest that the logistic regression model had the highest accuracy in predicting cardiovascular disease. The results of this study can be beneficial to healthcare professionals in developing new preventative protocols for assessing and treating cardiovascular disease

Apocynin and ebselen reduce influenza A virus-induced lung inflammation in cigarette smoke-exposed mice

Influenza A virus (IAV) infections are a common cause of acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Oxidative stress is increased in COPD, IAV-induced lung inflammation and AECOPD. Therefore, we investigated whether targeting oxidative stress with the Nox2 oxidase inhibitors and ROS scavengers, apocynin and ebselen could ameliorate lung inflammation in a mouse model of AECOPD. Male BALB/c mice were exposed to cigarette smoke (CS) generated from 9 cigarettes per day for 4 days. On day 5, mice were infected with 1 × 104.5 PFUs of the IAV Mem71 (H3N1). BALF inflammation, viral titers, superoxide production and whole lung cytokine, chemokine and protease mRNA expression were assessed 3 and 7 days post infection. IAV infection resulted in a greater increase in BALF inflammation in mice that had been exposed to CS compared to non-smoking mice. This increase in BALF inflammation in CS-exposed mice caused by IAV infection was associated with elevated gene expression of pro-inflammatory cytokines, chemokines and proteases, compared to CS alone mice. Apocynin and ebselen significantly reduced the exacerbated BALF inflammation and pro-inflammatory cytokine, chemokine and protease expression caused by IAV infection in CS mice. Targeting oxidative stress using apocynin and ebselen reduces IAV-induced lung inflammation in CS-exposed mice and may be therapeutically exploited to alleviate AECOPD