University of Moncton

Background Preventing and addressing social isolation and loneliness among older adults is important because of the known associations with negative health outcomes. The Canadian Coalition for Seniors’ Mental Health (CCSMH) took on the task of creating clinical guidelines. Method A multidisciplinary working group was established. The process was guided by an initial rapid scoping review of the literature focused on older adults. An adapted GRADE approach was utilized. Results CCSMH has produced first-ever clinical guidelines on social isolation and loneliness in older adults. Prevention, including recognition of risk factors and educational approaches focused on clinicians and students, is recommended. Targeted screening with validated tools is recommended. A comprehensive assessment is optimal to treat any underlying conditions and to identify contributing factors that may be responsive to psychosocial interventions. An individualized approach to interventions with shared decision-making is recommended. A variety of possible interventions include social prescribing, social activity, physical activity, psychological therapies, animal-assisted therapies and ownership, leisure skill development and activities, and the use of technology. Conclusion The problem of social isolation and loneliness is a “geriatric” giant that needs to be recognized and addressed. Because of its complexity, it will require the collective attention of many individuals and organizations working together at multiple levels of society, to raise awareness and find solutions. We recommend that health-care and social service providers use these guidelines as a comprehensive tool to identify, assess, and implement strategies to reduce the negative impact of social isolation and loneliness.

Loss of chromosome 3p and loss of heterogeneity of the von Hippel–Lindau (VHL) gene are common characteristics of clear cell renal cell carcinoma (ccRCC). Despite frequent mutations on VHL, a fraction of tumors still grows with the expression of wild‐type (WT) VHL and evolve into an aggressive subtype. Additionally, mutations on chromatin‐modifying genes, such as the gene coding for the histone methyltransferase SET containing domain 2 (SETD2), are essential to ccRCC evolution. We previously identified STF‐62247, a small molecule first discovered as a synthetically lethal molecule for VHL‐deficient cells by blocking late stages of autophagy. This study investigated how other commonly mutated genes in ccRCC could impact the response to STF‐62247. We showed that SETD2 inactivation in ccRCC cells expressing WT‐VHL became vulnerable to STF‐62247, as indicated by decreases in cell proliferation and survival. Furthermore, activation of the DNA damage response pathway leads to the loss of M‐phase inducer phosphatase 1 (CDC25A) and cell cycle arrest in S phase. Cleavage of both caspase‐3 and gasdermin E suggests that STF‐62247 eliminates WT‐VHL ccRCC cells through pyroptosis specifically when SETD2 is inactivated.

Integrating climate change into strategic forest management is crucial for predicting forest dynamics and maintaining resource availability. However, there is no scientific consensus on how to integrate their climate-sensitive outputs into strategic forest planning. In this study, we propose a novel approach to incorporate climate change into a strategic forest planning model, Woodstock, using a parsimonious set of climate-sensitive stand yield tables and transition rules derived from the PICUS stand simulation model, calibrated for the Acadian forest region. Climate-sensitive yield tables were generated for the baseline and two climate forcing scenarios to dynamically capture climate effects on forest growth and composition. Initial forest conditions were grouped into four age classes for each stand type, with future conditions determined by three transition periods. Simulations predict a significant shift toward warm-adapted species, with red maple, white pine, and yellow birch becoming dominant, while cold-adapted species like balsam fir and spruce decline by 2120. Under high climate forcing, the merchantable wood volume is projected to decrease by 50%, indicating potential shortages and economic risks. The incorporation of climate change uncertainty into strategic forest planning is essential to reduce the risk of overutilization of forest resources. This research offers a novel and straightforward approach to integrating climate change into forest planning models.

In smart homes, industrial automation, healthcare, agriculture, and environmental monitoring, IoT antenna systems improve communication efficiency and dependability. IoT antenna systems affect network performance and connection by affecting gain, directivity, bandwidth, efficiency, and impedance matching. Dipole, patch, spiral, metamaterial-based, and other antenna types are tested in IoT settings to identify their applicability, benefits, and downsides. Current antenna technology has challenges with frequency, bandwidth, size, weight, material choices, and energy efficiency, requiring new solutions. According to the study, interference control, power consumption, and dynamic IoT adaptation research are inadequate. Metamaterials, nanomaterials, and 3D printing may circumvent these antenna design limitations. AI and machine learning can improve antenna design real-time optimization and performance in complex settings. The paper explores how standards and regulatory frameworks affect IoT antenna system development to ensure future designs meet a fast-growing market. For the growing range of IoT applications, this research suggests more flexible and reconfigurable antennas that can function across numerous frequency bands. The report emphasizes antenna material and design innovation to improve durability, cut costs, and scale manufacturing. This research tackles these key elements to enable the next generation of antenna systems to meet IoT technology's expanding needs and increase networked devices' functionality, efficiency, and integration across industries. This comprehensive approach helps identify current trends and concerns and prepares for future IoT antenna system advancements, enabling smarter, more connected, and more efficient technologies.

An accurate renewable energy output forecast is essential for energy efficiency and power system stability. Long Short-Term Memory(LSTM), Bidirectional LSTM(BiLSTM), Gated Recurrent Unit(GRU), and Convolutional Neural Network-LSTM(CNN-LSTM) Deep Neural Network (DNN) topologies are tested for solar and wind power production forecasting in this study. ARIMA was compared to the models. This study offers a unique architecture for Deep Neural Networks (DNNs) that are specifically tailored for renewable energy forecasting, optimizing accuracy by advanced hyperparameter tuning and the incorporation of essential meteorological and temporal variables. The optimized LSTM model outperformed others, with MAE (0.08765), MSE (0.00876), RMSE (0.09363), MAPE (3.8765), and R² (0.99234) values. The GRU, CNN-LSTM, and BiLSTM models predicted well. Meteorological and time-based factors enhanced model accuracy. The addition of sun and wind data improved its prediction. The results show that advanced deep neural network (DNN) models can predict renewable energy, highlighting the importance of carefully selecting characteristics and fine-tuning the model. This work improves renewable energy estimates to promote a more reliable and environmentally sustainable electricity system.

Background Language and cultural discordance refer to when a physician and patient do not share the same language or culture. This can create barriers to providing high-quality care at the end-of-life (EoL). This study explores the intersections of language, culture, geography, and care model in EoL care from the perspectives of palliative care physicians. Methods In this exploratory-descriptive qualitative study, semi-structured interviews (1-h) were conducted virtually between July and November 2023. We interviewed 16 family physicians with experience providing linguistic and/or culturally discordant palliative/EoL care in various urban, suburban, and rural regions of Ontario, who practiced at community and hospital outpatient clinics, home-based care, or long-term care homes. We used reflexive thematic analysis to identify themes across the interviews guided by the intersectionality theoretical framework. Results We identified three themes 1) Visible barriers to care access due to the inability to communicate accurate information and insufficient time spent during appointments with patients; 2) Invisible barriers to care access, shaped by the Eurocentric approach to palliative care and physicians’ lack of awareness on cultural discordance; 3) Workplace supports that currently exist and interventions that physicians would like to see. Community physicians following fee-for-service models were less likely to have access to professional interpreter services. Physicians in long-term care emphasized resource limitations to providing culturally-appropriate care environments. Conclusion Cultural discordance required awareness of personal biases, while language discordance hindered basic communication. These findings will be useful in informing clinical practice guidelines and mobilizing policy-level change to improve palliative/EoL care for patients from linguistic and cultural minority groups.

Aims: This study examines the contribution of top management’s transformational leadership behaviors on two targets of nurses’ turnover intention (organization and occupation) by focusing on the indirect (through vigor and dedication) and conditional indirect associations (involving autonomous motivation as a moderator). Background: Although the issue of nurse turnover has received growing scientific attention, the research is currently silent about the specific targets of turnover intention and more importantly, the potential pathways through which top management’s transformational leadership behaviors relate to each target. Method: Cross-sectional data from a sample of 426 French–Canadian nurses and structural equation modeling were used to test the proposed model. Results: Top management’s transformational leadership behaviors distinctly predicted organizational and occupational turnover intention through specific nurses’ states of engagement. While perceived transformational leadership positively predicted vigor, its indirect associations (via dedication) with organizational and occupational turnover intention depend on nurses’ level of autonomous motivation at work. Conclusion: In times of nurse shortage, the present findings provide insights into how and when top management’s transformational leadership behaviors relate to nurses’ organizational and occupational turnover intention. Implications for Nursing Management: Healthcare organizations are advised to foster top management transformational leadership behaviors and autonomous motivation to sustain the nursing workforce.

The proliferation of Internet of Things (IoT) devices generates vast amounts of data, traditionally stored, processed, and analyzed using centralized systems, making them susceptible to attacks. Blockchain offers a solution by storing and securing IoT data in a distributed manner. However, the low performance and poor scalability of blockchain technology pose significant challenges for its application in IoT networks. The primary obstacle is the distributed consensus protocol, while ensuring data transparency, integrity, and immutability in a decentralized and untrusted circumstances which often compromises scalability. To address this issue, this paper introduces the use of the Delegated Proof of Stake (DPoS) consensus algorithm and sharding techniques to enhance scalability in blockchain-based IoT networks. Experimental results indicate that system throughput increases synchronously with the test load. Our findings reveal a tradeoff between throughput, latency, and up-downstream time on the Inter Planetary File System (IPFS). Given the critical importance of latency and throughput in IoT networks, the results demonstrate that DPoS offers high throughput, parallel processing, and robust security while efficiently scaling the network. Furthermore, at a test load of 500 Transactions Per Second (TPS), the system achieves a maximum throughput of approximately 11.094 ms. However, when the test load exceeds 2000 TPS, the total processing time for transactions extends to 11.205 ms. This method is particularly suitable for constrained IoT networks. Compared to previous edge computing-based approaches, our scheme demonstrates superior throughput performance.

The atmosphere’s fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, the mortality rate is continuously increasing, requiring immediate inclination of the scientific community towards the design and development of advanced predictive models. Conventional statistical approaches have become dormant due to their limitations in capturing the innate relationships between the pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine and deep learning techniques have shown great potential for forecasting air quality, providing more accuracy than its predecessor techniques. The present study investigates the utilization of hybrid approaches by integrating machine learning models with deep learning models to improve the prediction capabilities of PM2.5 concentration. It uses datasets from the World Air Quality Index (WAQI) and the State of Global Air (SOGA) to analyze the performance of the models on both the daily and annual data, respectively. This ensures the model’s effectiveness on a diversified dataset. The present study implements Random Forest (RF), Polynomial Regression (PR), XGBoost, and Extra Tree Regressor (ETR) coupled with Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for obtaining optimized results. Finally, after a thorough investigation, the hybrid PR model coupled with FCNN (PR-FCNN) is found to be the best model with improved R-squared (R2) values, portraying its potential for predicting PM2.5 concentration accurately. Based on the experimentation, the preset study recommends implementing hybrid approaches, offering better predictive accuracy in forecasting air pollutants, especially PM2.5.

Background Midlife and older women who experience intimate partner violence (IPV) often have less access to supports and services than younger women. There is far less focus on research and supports for midlife and older women compared to younger women experiencing IPV, and often, neither elder abuse nor IPV services meet their needs. Few interventions are available to meet the needs of midlife and older women. Objective The goal of this randomized controlled trial is to test the effectiveness of an advocacy intervention for midlife and older women who experience IPV and to learn from the experiences of those who implement and participate in the program. Methods This trial is a 2-arm, unblinded, parallel, pragmatic randomized controlled trial with a qualitative component. Eligible participants will be women who live in the Maritime provinces of Canada (New Brunswick, Nova Scotia, and Prince Edward Island), who are in midlife and older (aged approximately ≥50 years), and who are currently in a relationship with an abusive partner or have recently left an abusive partner. Facilitators will be trained to deliver the intervention. The intervention will be entirely virtual and will consist of 2 components: (1) an empowerment component, which will involve sharing resources and information with the women; and (2) a social support component, which will include providing support and encouragement to women for 12 weeks. Quantitative effectiveness data will be collected from all trial participants at baseline, 3 months after the intervention, and 9 months after the intervention about the incidence and severity of IPV, physical and mental health, and safety behaviors and strategies. Qualitative interviews will be conducted with the facilitators and intervention group participants. Control group participants will receive a static, nontailored version of the advocacy intervention for midlife and older women (AIM) intervention materials after baseline data collection. Results A total of 12 facilitators have been trained to deliver the AIM intervention to trial participants. Participant recruitment and data collection will be completed in January 2025. Data analysis will continue throughout the data collection period, and the results will be disseminated by December 2025. Conclusions This research will result in the adaptation and testing of a program to support and empower midlife and older women in the Maritime provinces of Canada who experience IPV. Trial Registration International Standard Randomized Controlled Trial Registry ISRCTN30646991; https://doi.org/10.1186/ISRCTN30646991 International Registered Report Identifier (IRRID) DERR1-10.2196/57886

Spatial transformations of light are ubiquitous in optics, with examples ranging from simple imaging with a lens to quantum and classical information processing in waveguide meshes. Multi-plane light converter (MPLC) systems have emerged as a platform that promises completely general spatial transformations, i.e., a universal unitary. However, until now, MPLC systems have demonstrated transformations that are far from general, e.g., converting from a Gaussian to Laguerre-Gauss mode. Here, we demonstrate the promise of an MLPC, the ability to impose an arbitrary unitary transformation that can be reconfigured dynamically. Specifically, we consider transformations on superpositions of parallel free-space beams arranged in an array, which is a common information encoding in photonics. We experimentally test the full gamut of unitary transformations for a system of two parallel beams and make a map of their fidelity. We obtain an average transformation fidelity of 0.85 ± 0.03. This high-fidelity suggests that MPLCs are a useful tool for implementing the unitary transformations that comprise quantum and classical information processing.

Introduction: Frailty is associated with increased hospitalization and mortality and may be exacerbated with physical inactivity. The COVID-19 pandemic has heterogeneously impacted peoples' physical activity level, but the impact on the frail population is unclear. Objective: The objective of this study is to test the hypothesis that higher frailty levels were associated with worse changes in physical activity, ability to move around the home, and do housework during the pandemic. Methods: We included 23,303 Canadians (age: 69.1 ± 9.5 years, 53% females) who participated in the Canadian Longitudinal Study on Aging COVID-19 Questionnaire study. Frailty prior to the pandemic (follow-up 1: 2015-2018) was measured via 52-item frailty index and grouped into 0.00-0.05, 0.05-0.10, 0.10-0.20, and >0.20 scores. Participants were asked whether they changed their levels of physical activity, ability to move at home, and ability to do housework during the pandemic. Results: The average frailty level was 0.09 ± 0.06. Compared with the lowest frailty group, participants with frailty levels >0.20 reported worse physical activity (17% vs. 45%), worse ability to move at home (3% vs. 26%), and perform housework (3% vs. 27%). Covariate-adjusted logistic regression models demonstrated that higher frailty level was associated with a greater odds of reporting worse physical activity (reference: 0.00-0.05; odds ratio for frailty index >0.20:4.03, 95% confidence interval [3.33, 4.87]), worse ability to move in home (odds ratio = 11.16, 95% confidence interval [8.28, 15.03]), and worse ability doing housework (odds ratio = 12.58, 95% confidence interval [9.24, 17.13]). Conclusions: The adverse changes in physical activity levels and ability to move at home among frail older adults during the pandemic document the need for strategies to help vulnerable populations move more.

Background Appropriate use of medication is a key indicator of the quality of care provided in long-term care (LTC). The objective of this study was to determine whether resident-facility language concordance/discordance is associated with the odds of potentially inappropriate prescribing of antipsychotics (PIP-AP) in LTC. Methods We conducted a population-based, retrospective cohort study of LTC residents in Ontario, Canada from 2010 to 2019. We obtained resident language from standardized resident assessments, and derived facility language by determining the proportion of residents belonging to each linguistic group within individual LTC homes. Using linked administrative databases, we identified all instances of PIP-AP during a 1-year follow-up period. PIP-AP was defined using the STOPP-START criteria, which have previously been shown to predict adverse clinical events such as emergency department (ED) visits and hospitalizations. The association between linguistic factors and PIP-AP was assessed using adjusted multivariable logistic regression analysis. Results We identified 198,729 LTC residents consisting of 162,814 Anglophones (81.9%), 6,230 Francophones (3.1%), and 29,685 Allophones (14.9%). The odds of PIP-AP of were higher for both Francophones (aOR 1.15, 95% CI 1.08–1.23) and Allophones (aOR 1.11, 95% CI 1.08–1.15) when compared to Anglophones. When compared to English LTC homes, French LTC homes had greater odds of PIP-AP (aOR 1.12, 95% CI 1.05–1.20), while Allophone homes had lower odds of PIP-AP (aOR 0.82, 95% CI 0.77–0.86). Residents living in language-discordant LTC homes had higher odds of PIP-AP when compared to LTC residents living in language-concordant LTC homes (aOR 1.07, 95% CI 1.04–1.10). Conclusion This study identified linguistic factors related to the odds of PIP-AP in LTC, suggesting that the linguistic environment may have an impact on the quality of care provided to residents.

Introduction Despite the availability of various antihypertensive medications, the response to these medications varies among individuals. Understanding how individual genetic variations affect drugs treatment outcomes is a key area of focus in precision medicine. This study investigated the correlation between single nucleotide polymorphisms (SNPs) in selected genes (CACNA1C, CACNA1D, ABCB1, ACE, ADBR2, and NOS1AP) and the blood pressure (BP) control by amlodipine. Methods Four hundred individuals of Pashtun ethnicity undergoing amlodipine treatment for hypertension were included in the present study and divided into the controlled (BP less than 140/90 mmHg) and uncontrolled (BP greater than 140/90 mmHg) hypertension groups. Blood samples (3 mL) were collected from each participant, and DNA was extracted using the Kit method. Ten SNPs in amlodipine pharmacogenes were selected and genotyped using real-time PCR with the TaqMan® system. Logistic regression model was used to determine the association between SNPs and the amlodipine BP response. Results Notable association were observed between SNP rs2239050/CACNA1C and amlodipine blood pressure response, with GG genotype carriers demonstrating a better response (P=0.004) than individuals carrying CC or CG genotypes. SNP rs312481/CACNA1D also exhibited a positive pharmacogenetic association, Individuals with the GG genotype showing a considerable reduction in BP (P=0.021) compared to participants with AA or GA genotypes. In case of SNP rs429/ACE individuals carrying TA genotype were less likely to achieve BP control (P=0.002) than AA genotype carriers. Conclusion Our finding suggests that the SNPs rs2239050/CACNA1C, rs312481/CACNA1D and rs429/ACE influence amlodipine blood pressure response in patients with hypertension. It is recommended that prior knowledge of amlodipine associated pharmacogenetic variants is important that could improve its treatment outcomes in hypertensive patients.

This paper presents an efficient implementation of the Twofish encryption algorithm on FPGA chips, with a specific focus on its utilization in applications requiring a high level of security. The Twofish algorithm is renowned for its robust security and efficiency, making it suitable for a wide range of security-sensitive applications. The primary objective of this study is to demonstrate how Twofish 1 can be successfully deployed on FPGAs, capitalizing on their parallelism and cus-tomization capabilities to achieve high encryption performance while preserving data security. We provide detailed insights into the hardware design and specific optimizations required to fully harness FPGA technology. Additionally, this study compares the implementation of the Twofish encryption algorithm across four FPGA families: Artix-7, Kintex, Stratix, and Vitex. The efficiency of this FPGA implementation is assessed through performance tests, highlighting its ability to deliver fast and secure encryption and decryption processes. Furthermore, we explore the practical applications of this solution, including securing communications , safeguarding sensitive data, and other scenarios where data security is paramount. This study serves as a valuable resource for researchers, engineers, and practitioners working in fields where data security is a top priority, offering a detailed insight into the successful implementation of Twofish on FPGAs and its potential applications.

Neoliberalism has become much more than a mere economic doctrine; it is a socio-political and economic framework that influences many spheres of society, including immigration and language services. In this chapter, bridging literature from different disciplines, we show how neoliberal agendas concerning immigration and language shape community translation and interpreting services, i.e. services offered to speakers of non-official languages in multilingual societies so that they can access information and participate in society. We use the cases of Australia and Canada, two countries with neoliberal positions and a strong tradition of immigration and language services. We conclude that neoliberal patterns are tangible in language service procurement; productivity and cost considerations have taken precedence over other criteria, including service quality and working conditions. This situation is partly the result of the quantification of services, the decline in the importance given to welfare services, and the promotion of individualistic rather than collective approaches.

This paper investigates how the uncertainty of future expected values influences decision making among non-industrial private woodlot owners. A model of a red spruce plantation in the context of New Brunswick, Canada, is constructed using stochastic dynamic programming, where risks from natural disasters and market fluctuations are modeled as a Markov process. It is used to investigate whether an increase in perceived risk can justify harvesting plantations much earlier than planned. Findings indicate that no plausible natural disaster scenarios can warrant harvesting substantially earlier than optimal under a risk-free scenario for a risk neutral decision maker. However, the observed sensitivity to the discount rate suggests that early harvesting may reflect the behaviour of a risk-averse landowner confronting increased perceived risk. Such behaviour may result in suboptimal value from the viewpoint of plantation subsidy program managers. These results highlight the importance of reassessing subsidy programs to find the right balance between societal objectives and those of non-industrial private woodlot owners.