Canada

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.

Indicium 2025

Modelling Mathematical Permutation Transformations: Using Canonical Chains and Combinatorial Objects

k-bruhat

combinatorial object

canonical chain

inversion

permutations

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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.

Large language models are a fascinating and powerful technology, but are not faultless. Iteratively selecting the most probable next word can result in a sentence that sounds coherent, but contains content that is fabricated, taken out of context, or in self-conflict. This can create particular danger when end users rely on LLMs for information on critical issues. Many countries lack equal or affordable access to healthcare. This can lead to some viewing popular LLM chatbots such as ChatGPT as being an online doctor; available to answer their healthcare needs. People with chronic diseases are particularly vulnerable to this, as the chronicity of their disease presents a consistent healthcare cost. Our research seeks to take the first step of many in creating a framework within which LLMs can self-check for hallucinations, resulting in more reliable information. We worked in reference to an existing paper to create a dataset containing pairs of questions and hallucinated answers, specifically in reference to chronic disease. We also conduct an experiment on our dataset in which we determine whether LLMs can accurately detect their own hallucinations. We find that, while some LLMs can identify a hallucinated answer with incredible accuracy, it is highly inconsistent from model to model. We endorse the future research of hallucination detection technologies in LLMs in order to mitigate risk of harm caused by the dissemination of inaccurate information by LLMs.

A Reflection on Hallucinations in LLM Responses to Chronic Disease Queries

Fragile X syndrome (FXS) is a neurodevelopmental disorder caused by the expansion of cysteine-guanine-guanine (CGG) repeats in the fragile X messenger ribonucleoprotein 1 (FMR1) gene. The increase in repeats silences the transcription of fragile X messenger ribonucleoprotein (FMRP), resulting in intellectual and developmental disabilities. Despite the development of symptom management therapies, there is no cure for FXS. In the past decades, several studies have demonstrated the importance of age-related alterations in the processing of amyloid precursor protein (APP) in FXS pathology. Hence, this systematic review was conducted to summarize and evaluate FXS treatments targeting components of the APP pathway. Using PubMed, a literature search was performed and uncovered 88 results. 9 articles related to the APP pathway were selected, and they were further categorized by their targets: glutamate, metabotropic glutamate receptor 5 (mGluR5), brain cytoplasmic RNA (BC1), and A disintegrin and metalloproteinase 10 (ADAM10). The majority of these results focused on treating the symptoms in young patients, corresponding with peak symptomatology. However, FXS individuals also display age-dependent premature cognitive decline. Since APP processing is altered with age, we suggest these treatments for FXS in younger individuals may be translatable to the aging FXS population. Further research is required to understand the mechanism of age-dependent alternation in APP processing in FXS patients and to investigate the efficiency of treatments with increased sample size and different tissues.

Aging with Fragile X Syndrome: Prospective Treatments Targeting the Amyloid Processing Pathway

People from around the world experience mobility impairments, leading to significant impacts on independence and quality of life. While conventional mobility assistive devices such as wheelchairs, walkers, and prosthetics offer essential support, they often lack adaptability and user responsiveness. Artificial intelligence redefines mobility devices by advancing autonomy, personalization, and user experience. This scoping review maps the current landscape of AI-powered mobility aids for adults with physical impairments. Using the Arksey and O’Malley framework and PRISMA-ScR guidelines, we systematically reviewed 39 peer-reviewed studies, which were identified through specific search strategies developed for each database. Screening and data extraction were conducted in pairs using a predefined criterion. The included studies spanned 17 countries and focused primarily on smart wheelchairs (n=22), followed by walkers (n=8), prosthetics (n=3), rollators (n=2), and individual studies on canes, wheelchairs/scooters, lower-limb braces, and body-weight support systems (n=1 each). AI models were then categorized by learning type. AI-powered mobility devices offer individual benefits like enhanced navigation, motivation, and emotional well-being, but users face challenges such as device complexity, usability issues, and fatigue. At the administrative level, these technologies reduce caregiver workload and improve efficiency, yet are hindered by insufficient training and limited user input, while policy-level barriers include inadequate funding and lack of regulatory support. These findings affirm the potential of AI to enhance mobility and autonomy and promote the urgent need for inclusive, real-world research, standardized evaluation, and equitable access across diverse populations.

AI-powered Mobility Devices for Adults With Impairments: A Scoping Review

As populations age globally, older adults are increasingly affected by chronic physical and mental health challenges, including frailty, cognitive impairment, depression, and social isolation. Artificial intelligence (AI) tools such as chatbots, wearable monitors, predictive models, and socially assistive robots have been proposed as scalable solutions to support aging populations. This literature review systematically examined 28 peer-reviewed studies to evaluate the efficacy and usability of current AI interventions designed to enhance health and well-being in adults aged 65 and older. Methods included supervised machine learning for diagnosis and prediction, large language models for health communication, and co-designed systems for home-based care and emotional support. Results suggest that AI applications can improve physical activity, emotional regulation, and early detection of cognitive decline and depression, with user feedback indicating positive perceptions of usability and engagement. However, most studies had small, homogeneous samples, lacked long-term follow-up, and were conducted in simulated or non-clinical settings. Ethical and implementation challenges were common, including data privacy concerns, unclear algorithmic decision-making, limited digital literacy, and underrepresentation of diverse older adult populations. Despite these limitations, the evidence indicates that AI holds promise in augmenting and streamlining older adult care when tools are transparent, inclusive, and tailored to user needs. Broader adoption will require rigorous evaluation in real-world environments, equitable design practices, and sustained attention to ethical risks.

Artificial Intelligence for Care for Older Adults with Chronic Health Conditions

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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A Reflection on Hallucinations in LLM Responses to Chronic Disease Queries

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