Canada

As the global economy expands, its environmental consequences intensify. Atmospheric CO2 now exceeds 420 ppm and continues rising. Greenhouse gases use CO2 enrichment to boost crop yields, but most rely on fossil fuel-derived CO2. This study models a Direct Air Capture (DAC) system using Zeolite 13X to recycle CO2 for greenhouse enrichment. Regeneration via solar, electricity, and natural gas was compared to determine which energy source offers the most sustainable outcome. A custom gas chamber was packed with Zeolite 13X and outfitted with four thermocouples. Following calibration, the device was connected to a carbon dioxide pump to measure and analyze the total adsorption level. Zeolite 13X effectively captured and released CO2 using all regeneration methods. The highest CO2 adsorption was observed using electric heat wrap (0.275 mmol CO2/g) and solar treatment (0.201 mmol CO2/g). Adsorption was greater in heat-assisted trials with Zeolite 13X compared to controls. Energy inputs remained consistent across sources: grid electricity (0.2653 MJ/kg CO2), solar (0.2642 MJ/kg CO2), and natural gas (0.2681 MJ/kg CO2). Grid electricity produced the lowest emissions per kilogram of CO2 captured (0.003 kg CO2 emitted/kg CO2), followed by solar (0.008 kg/kg), and natural gas (0.013 kg/kg). The experimental findings validate the energy-efficient nature of Zeolite 13X carbon capture technology which shows promise for agricultural and clean energy sector applications.

Indicium 2025

CO₂ Adsorption and Regeneration of Zeolite 13X: Energy Resource Comparison

greenhouse agriculture

co2 enrichment

direct air capture

carbon capture

zeolite 13x

technical paper

This annual scholarly program is designed to provide undergraduate students with a structured introduction to laboratory research within their home institution. Participants are matched with principal investigators and mentored by graduate students, enabling them to engage directly in ongoing research projects and develop foundational skills in scientific inquiry.

This annual scholarly program is designed to provide undergraduate students with a structured introduction to laboratory research within their home institution.
This year’s cohort focused on projects in the field of longevity science, exploring diverse topics such as bioinformatics, molecular biology, and cognitive science.

Heart disease remains the leading global cause of death, highlighting the urgent need for accurate and scalable early detection methods. This study investigates the effectiveness of supervised machine learning techniques in predicting heart disease from routine clinical data. The objective is to determine which classification model provides the most reliable predictions in a clinical setting. We combined four public heart disease datasets from the UCI Machine Learning Repository and selected 14 relevant clinical features. Missing values were filled in using standard statistical methods (mean, median, or mode as appropriate). We then trained and evaluated four supervised machine learning models: logistic regression, support vector machine, extreme gradient boosting, and a multi-layer neural network. Each model was tuned for optimal performance and assessed using accuracy, precision, recall, and the F1-score. All models achieved higher than 80% accuracy. The support vector machine was the most accurate with 83.2% on the test data and also attained the highest F1-score (73%) for identifying heart disease cases. The neural network and gradient boosting models each reached about 82% accuracy, with the neural network identifying slightly more actual positive cases (higher recall). The logistic regression model trailed slightly at 80.7% accuracy but still showed competitive precision and recall. Machine learning models can effectively predict heart disease, providing a valuable tool for early detection. Among the models tested, the support vector machine performed best. These results indicate that support vector machines may offer stronger predictive performance for clinical applications of heart disease detection.

Evaluating Supervised Machine Learning Models for Heart Disease Prediction

Optical Coherence Tomography (OCT) is a widely adopted imaging technique in ophthalmology, yet its clinical interpretation remains prone to inconsistency and subjectivity. This study presents a retrieval-augmented generation (RAG) framework aimed at improving both diagnostic accuracy and interpretability in OCT-based analysis. The pipeline integrates a dual-headed convolutional neural network with a case-based retrieval system and a language model capable of generating structured clinical summaries. A ResNet-50 model was trained to classify diabetic retinopathy (DR), diabetic macular edema (DME), and key retinal biomarkers using a public OCT dataset. Feature embeddings generated by the model were indexed into a similarity-search database to enable retrieval of clinically relevant cases. Retrieved scans, along with classification outputs, were used to prompt a large language model to generate concise diagnostic reports that reflect both the model’s predictions and visually aligned precedents. The system achieved 89% accuracy in distinguishing DR from DME in a hold-out test set. Interpretability was also demonstrated through consistent retrieval of semantically similar cases. Generated reports were found to be readable and aligned with clinical reasoning. By combining classification performance with transparent, case-grounded explanations, the RAG approach enhances clinical confidence and supports decision-making. This work highlights the potential of retrieval-augmented pipelines to reduce variability in OCT interpretation and to serve as a scalable, explainable foundation for future AI-driven diagnostic support in ophthalmology.

Improving Optical Coherence Tomography Interpretation With Retrieval-Guided Diagnosis and Text Generation

As global energy demand rises and climate concerns intensify, solar energy systems or photovoltaic systems are becoming increasingly vital sources of renewable energy. However, their potential remains limited by the inability to operate naturally at the Maximum Power Point during hours of fluctuating sunlight. This study addresses this challenge by improving photovoltaic system efficiency through advanced Maximum Power Point Tracking techniques. We hypothesized that real-time duty cycle control would allow actual power to consistently rise towards Pmax, regardless of fluctuations. Using simulation software PowerSIM (PSIM), we analyzed the impact of varying duty cycles on power output across several trial runs. We conducted simulations on four PV panels, each with a distinct maximum power value, by modifying the duty cycle under five controlled power fluctuations designed to mimic variable sunlight. A code was developed and implemented to dynamically adjust the duty cycle based in response to changes in output power. The results showed that the PV panels with the highest (395W) and lowest (225W) Pmax values responded effectively, with actual power rising towards Pmax after each fluctuation. To validate our findings, we implemented the algorithm on a digital signal processing board using PSIM’s Processor-in-the-Loop module. The test completed in 4 minutes and 27 seconds, with the system's duty cycle varying with changes in actual power within a 0.03 range. The broader significance of this project lies in its contributions to developing responsive and cost effective PV systems that align with the goals of economic resilience and sustainable chronic disease management.

Improving Photovoltaic Systems for Renewable Energy Efficiency and Affordably Powering Aging Communities

This paper illustrates the process of transforming an initial mathematical permutation of numbers into a final permutation through a series of conditional swaps. The goal is to explore various approaches for modeling and analyzing all valid swap sequences that can achieve such a transformation. To address this and to better understand these transformations, different mathematical and combinatorial representations need to be studied, in order to gain useful insight into the underlying patterns of these transformations. This paper outlines a framework for understanding permutation transformations, solving problems modeled by similar rules, and provides grounds for further research in combinatorics and optimization.

Modelling Mathematical Permutation Transformations: Using Canonical Chains and Combinatorial Objects

Osteoarthritis (OA) is a prevalent and disabling condition affecting over one third of Canadians aged 65 and older, with the hand and wrist being among the most impacted joints. This chronic, degenerative joint disease severely limits physical independence by causing synovial inflammation and cartilage degradation, leading to joint stiffness. As a result, seniors diagnosed with OA often experience persistent joint pain, progressive joint immobility, and diminished quality of life. Current treatments such as pharmacotherapy and physiotherapy often provide limited or inconsistent relief due to side effects, accessibility, or patient adherence. In response, this project introduces a novel, multimodal therapeutic glove designed to deliver lasting non-pharmacological relief for individuals with hand and wrist OA. The glove integrates thermal stimulation and localized vibration to reduce pain and joint immobility, improving hand function. The design is grounded in an evidence-based understanding of osteoarthritis pain responses, with therapeutic principles derived from case studies and meta-analyses of both thermotherapy and massage therapy. The glove features vibrating mechanisms that simulate massage movements, providing relief and promoting circulation in affected areas. Furthermore, the glove incorporates heat therapy, which helps reduce stiffness and alleviates pain by increasing blood flow. To evaluate the glove’s effectiveness and usability, a pilot test was conducted with nine participants, where two-thirds of the participants reported hand or wrist pain. Guided sessions were used to measure pain and assess mobility using a numerical rating scale (0–5). The average reported pain relief score was 3.89, while the ease-of-movement score averaged 4.56, suggesting moderate to significant improvement.

Multimodal Wearable Devices: A Non-Pharmacological Treatment of Hand and Wrist Osteoarthritis

Arthritis is a leading cause of joint pain and reduced mobility, particularly among older adults. Current treatments still rely on reactive treatments and often do not offer personalized monitoring or early intervention. This paper addresses emerging wearable technology that could address these shortcomings by enabling more immediate and proactive management of joint pain. The primary objective was to design a low-cost, wearable knee brace system that integrates motion and pressure sensors with machine learning analytics to deliver real-time, personalized monitoring data for gait and joint stress. To achieve this, we conducted a comprehensive review of existing wearable sensor technologies and their applications in joint monitoring. Insights from this review guided the design of a prototype device featuring motion and pressure sensors embedded in an adjustable knee brace. We developed a companion mobile application to collect data, analyze joint health, and provide user-friendly feedback. Results demonstrated that the multi-sensor approach improved accuracy in tracking knee movement and pressure changes compared to single-sensor models. The device successfully streamed real-time data and classified gait patterns with promising accuracy. Our findings support the feasibility of this integrated system to enhance arthritis monitoring beyond current commercial options. This research contributes new knowledge by addressing gaps in existing wearable technologies, particularly for aging populations with arthritis. The proposed system offers a foundation for more personalized, preventative arthritis care that may improve quality of life and alleviate healthcare burdens for those with knee joint arthritis.

Proactive Arthritis Care with a Multi-Sensor Wearable Knee Brace System

Introduction: Acute interventions allow researchers to examine and compare the influence of a single exercise session. While researchers assume the washout period will minimize carryover effects between exercise bouts, early exercise adaptations may still take place, as observed within the repeated-bout effect. Rarely are these early adaptations assessed. This study aimed to assess pre-to-post acute intervention changes in body composition, strength, and cycling peak power output (Max PO) following an acute intervention of four identical exercise bouts. Methods: Eighteen untrained, healthy adults participated in both pre- and post-study testing. Weight and percent body fat were measured using a digital scale and air displacement plethysmography, respectively, pre- and post-study. Estimated 1 repetition maximums (e1RM) for leg press, chest press, seated row testing, and an incremental cycling test was conducted pre- and post-study. Paired t-tests were used to compare pre- and post-study outcomes in the overall group and stratified by sex. Results: Body weight increased overall (p=0.001), but sex-based analyses revealed an increase only in females (p=0.002). There was no change in percent body fat (p>0.05). e1RM increased for leg press, chest press and seated row as did cycling Max PO (p<0.05 for all), with increases in both sexes for leg press and chest press, and males only for seated row. Conclusions: The acute exercise intervention induced strength and performance improvements, indicating possible early adaptations to exercise. These adaptations may have implications on physiological outcomes measures and should be assessed in other acute interventions.

Strength and Body Composition Changes Following Repeated Acute Exercise in Untrained Adults

Objective: This scoping review maps the existing global evidence on the met and unmet cognitive, environmental, financial, physical, personal, psychological, and social needs of community-dwelling Black older adults. Methods: Following Arksey and O’Malley’s framework and the Joanna Briggs Institute methodology, we conducted a comprehensive search across 12 databases for peer-reviewed articles published between 2000 and March 2025. Eligible studies reported on care needs within seven domains. Data were extracted, categorized, and summarized descriptively using PRISMA-ScR guidelines. Results: From 30,905 citations, 552 studies across 28 countries (83.5% in the U.S.) identified 5,023 needs, primarily in the personal and physical domains, based on mostly small, urban, quantitative studies. The most unmet needs within their domain were processing speed, transportation by bike, financial status, coordination, apathy,caregiving and assisted burden, and social isolation. In contrast, the most met need within their domains were nationality/birthplace, muscle power, insurance, social capital, self-efficacy, language and land use mix. Conclusion: This scoping review highlights that Black older adults face widespread unmet needs, particularly in physical, cognitive, financial, psychological, and environmental domains, largely due to structural inequities and the erosion of traditional support systems. Addressing these issues requires culturally grounded, equity-driven approaches that center the lived experiences of Black older adults and inform inclusive, responsive aging policies globally.

The Met and Unmet Needs of Black Older Adults: A Scoping Review

Introduction: Breast cancer is predominantly a disease of aging, with most cases occurring in women over the age of 50. A key biological contributor to this age-related risk is “inflammaging”—a chronic, low-grade inflammatory state driven by immunosenescence and dysregulated cytokine signalling. This persistent inflammatory milieu promotes tumour initiation, growth, and progression. Dietary factors, particularly omega-3 polyunsaturated fatty acids (n-3 PUFA), may modulate inflammaging and influence breast cancer development. Methods: This narrative review synthesizes findings from epidemiological, preclinical, and mechanistic studies examining the role of n-3 PUFA, particularly EPA and DHA, in modifying inflammaging and breast cancer risk. Literature was identified through PubMed and reference screening using keywords related to inflammation, aging, dietary fats, and breast cancer. Results: Preclinical and mechanistic studies demonstrate that n-3 PUFA exert anti-inflammatory, anti-proliferative, and pro-apoptotic effects through modulation of NF-κB, cytokine signaling, lipid raft integrity, and immune cell function. Notably, these mechanisms directly target biological processes underlying inflammaging. Early-life exposure to n-3 PUFA appears especially protective against breast cancer. Epidemiologic evidence suggests a modest protective association, particularly in populations with higher marine n-3 PUFA intake and lower n-6/n-3 PUFA ratios. Conclusion: n-3 PUFA may attenuate inflammaging and represent a promising dietary strategy to reduce age-related breast cancer risk. Their integration into the diet, particularly during key windows of susceptibility, could represent a preventive approach targeting the inflammation–aging–breast cancer axis.

The Role of Omega-3 Polyunsaturated Fatty Acids in Inflammaging and Breast Cancer: A Narrative Literature Review

Alzheimer’s disease (AD) is a neurodegenerative disorder driven by diverse mechanisms, including cognitive decline and disrupted gut-brain communication, resulting in a mortality increase of over 145% in North America. Emerging evidence highlights the critical role of gut-derived metabolites, G protein, and short-chain fatty acids, which modulate neuroinflammation, tau phosphorylation, and Aβ aggregation via G protein-coupled receptors (GPCRs), including C3AR, GPR84, and GPR27. These effects are mediated through signalling pathways such as STAT3 and cAMP/PKA, with orphan GPCRs emerging as promising, disease-specific targets. Through examining literature research, we assessed machine learning models, such as support vector machines and random forest models. Insight-generating AI methods and tools such as LIME and SHAP were used to clarify the model decisions, which pinpoint the critical biomarkers and brain regions that are connected to AD progression. We propose a personalized treatment that integrates microbiome sequencing, AI-informed GPCR targeting, and therapeutic strategies such as probiotics, metabolite mimics, and drug repurposing in elderly patients who are living with AD. This system level supports precision neurology by customizing treatments to individual gut and molecular profiles, leading AD patient care towards targeted personalized therapies. Future directions include validation via CRISPR-edited organoids, fecal transplant models, and microbiome-guided clinical trials. This approach holds promise for reducing cognitive decline, long-term care reliance, and healthcare costs, while advancing therapeutic equity and economic resilience in aging populations.

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Evaluating Supervised Machine Learning Models for Heart Disease Prediction

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