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Introduction: This study presents a global research scenario in the broad domain of osteoarthritis (OA) research, using quantitative and qualitative publication and citation indicators. Methods: The study is based on 45,368 global publications, sourced from the Scopus bibliographical database, covering three decades (1994–2023). We studied the performance of the top 12 developed and top 12 developing countries. The key countries, organizations and authors at national and international levels were identified. The broad subject areas and key journals contributing to global OA research were delineated, besides identifying the broad characteristics of highly cited papers in the field. Results: The United States and China were the most productive countries, while the Netherlands and Canada made the largest citation impact. Harvard Medical School and the University of Sydney made the most contribution, while Boston University and Pfizer Inc., USA registered the highest citation impact. Hunter DJ and Guermazi A were the most productive authors, while Lohmander LS, and Hochberg MC registered the highest citation impact. Osteoarthritis and Cartilage (n = 4879) and Annals of the Rheumatic Diseases (n = 786) published the maximum papers, while Arthritis and Rheumatism and Nature Reviews Rheumatology registered the largest citation impact. The highly cited papers with 100 or more citations constituted 6.25% of the total publications. Conclusions: There has been a systematic growth of publications on OA. The research on OA was mainly done in developed countries, with the maximum publications coming from the United States of America, China and Canada. The most impactful publications on OA were from the Netherlands, Canada and the United States of America. [ABSTRACT FROM AUTHOR]

This paper aims to introduce a novel family of probability distributions by the well-known method of the T–X family of distributions. The proposed family is called a "Novel Generalized Exponent Power X Family" of distributions. A three-parameters special sub-model of the proposed method is derived and named a "Novel Generalized Exponent Power Weibull" distribution (NGEP-Wei for short). For the proposed family, some statistical properties are derived including the hazard rate function, moments, moment generating function, order statistics, residual life, and reverse residual life. The well-known method of estimation, the maximum likelihood estimation method is used for estimating the model parameters. Besides, a comprehensive Monte Carlo simulation study is conducted to assess the efficacy of this estimation method. Finally, the model selection criterion such as Akaike information criterion (AINC), the correct information criterion (CINC), the Bayesian information criterion (BINC), the Hannan–Quinn information criterion (HQINC), the Cramer–von-Misses (CRMI), and the ANDA (Anderson–Darling) are used for comparison purpose. The comparison of the NGEP-Wei with other rival distributions is made by Two COVID-19 data sets. In terms of performance, we show that the proposed method outperforms the other competing methods included in this study. [ABSTRACT FROM AUTHOR]

Risk prediction models hold great promise to reduce the impact of cancer in society through advanced warning of risk and improved preventative modalities. These models are evolving and becoming more complex, increasingly integrating genetic screening data and polygenic risk scores as well as calculating risk for multiple types of a disease. However, unclear regulatory compliance requirements applicable to these models raise significant legal uncertainty and new questions about the regulation of medical devices. This paper aims to address these novel regulatory questions by presenting an initial assessment of the legal status likely applicable to risk prediction models in Canada, using the CanRisk tool for breast and ovarian cancer as an exemplar. Legal analysis is supplemented with qualitative perspectives from expert stakeholders regarding the accessibility and compliance challenges of the Canadian regulatory framework. While the paper focuses on the Canadian context, it also refers to European and U.S. regulations in this domain to contrast them. Legal analysis and stakeholder perspectives highlight the need to clarify and update the Canadian regulatory framework for Software as a Medical Device as it applies to risk prediction models. Findings demonstrate how normative guidance perceived as convoluted, contradictory or overly burdensome can discourage innovation, compliance, and ultimately, implementation. This contribution aims to initiate discussion about a more optimal legal framework for risk prediction models as they continue to evolve and are increasingly integrated into landscape for public health. [ABSTRACT FROM AUTHOR]

In this paper we propose a machine learning-based approach to predict a multitude of insurance claim categories related to canine diseases. We introduce several machine learning approaches that are evaluated on a pet insurance dataset consisting of 785,565 dogs from the US and Canada whose insurance claims have been recorded over 17 years. 270,203 dogs with a long insurance tenure were used to train a model while the inference is applicable to all dogs in the dataset. Through this analysis we demonstrate that with this richness of data, supported by the right feature engineering, and machine learning approaches, 45 disease categories can be predicted with high accuracy. [ABSTRACT FROM AUTHOR]

This work investigates the effectiveness of solar heating using clear polyethylene bags against rice weevil Sitophilus oryzae (L.), which is one of the most destructive insect pests against many strategic grains such as wheat. In this paper, we aim at finding the key parameters that affect the control heating system against stored grain insects while ensuring that the wheat grain quality is maintained. We provide a new benchmark dataset, where the experimental and environmental data was collected based on fieldwork during the summer in Canada. We measure the effectiveness of the solution using a novel formula to describe the amortising temperature effect on rice weevil. We adopted different machine learning models to predict the effectiveness of our solution in reaching a lethal heating condition for insect pests, and hence measure the importance of the parameters. The performance of our machine learning models has been validated using a 10-fold cross-validation, showing a high accuracy of 99.5% with 99.01% recall, 100% precision and 99.5% F1-Score obtained by the Random Forest model. Our experimental study on machine learning with SHAP values as an eXplainable post-hoc model provides the best environmental conditions and parameters that have a significant effect on the disinfestation of rice weevils. Our findings suggest that there is an optimal medium-sized grain amount when using solar bags for thermal insect disinfestation under high ambient temperatures. Machine learning provides us with a versatile model for predicting the lethal temperatures that are most effective for eliminating stored grain insects inside clear plastic bags. Using this powerful technology, we can gain valuable information on the optimal conditions to eliminate these pests. Our model allows us to predict whether a certain combination of parameters will be effective in the treatment of insects using thermal control. We make our dataset publicly available under a Creative Commons Licence to encourage researchers to use it as a benchmark for their studies. [ABSTRACT FROM AUTHOR]

Many sediment attributes have been proposed as proxies for determining salinity conditions under which sediment is deposited, and six attributes (Sr/Ba-HAc, Sr/Ba-NH4Ac, δ13Corg, C/N, and the relative abundances and concentrations of dinoflagellate cysts) are compared here. In this paper, sediment attributes from the Fraser River Delta, Canada and surrounding coastal areas are compared by depositional position along the fluvial-to-marine transition, by salinity, and by sedimentological characteristics. Along the fluvial-to-marine transition, most attributes exhibit distinct trends between parts of the river that experience sustained marine water (saltwater) influence over seasonal and tidal timeframes, and parts that experience only freshwater or periodic saltwater influence. No attributes are reliable indicators of depositional position where saltwater incursion is short lived or where water is fresh. Where marine influence is sustained, Sr/Ba-HAc and Sr/Ba-NH4Ac are the most reliable positional indicators along the fluvial-to-marine transition. When compared strictly to salinity, Sr/Ba-HAc, Sr/Ba-NH4Ac, and δ13Corg all correlate predictably except in delta front and prodelta settings. Our data show that all six sediment attributes are heavily impacted by river-derived sedimentation, and it is not appropriate to compare values from strongly river-influenced settings (e.g., deltas) with those from weakly river-influenced settings (e.g., bays and estuaries). [ABSTRACT FROM AUTHOR]

Sustainable global energy production is back-stopped by hydropower which is responsible for a significant share of the green energy produced worldwide. Hydropower, however, does not come without some environmental impacts but has worked to reduce those impacts. Here, we discuss the historical, legislative, and design configurations of hydropower facilities located in three of the world's most important producers: Brazil, Canada, and Norway. The background is intended to inform the collection of scientific papers from each country aimed at assessing and improving the sustainability of hydropower production that form the core of this special issue on sustainable hydropower. We review the development and key legislative history for hydropower in each country and point out the common backgrounds and interests each nation has in the continued sustainable development of its hydropower resources. [ABSTRACT FROM AUTHOR]

Hydropeaking refers to the mode of hydropower dam operation where sub-daily changes in flow are used to vary the generation of electricity in accordance with demand. A typical pattern produces maximum power during the day (i.e., the peak), and minimal power at night. Hydropeaking is considered necessary to stabilize the energy grid since it is the only reliably flexible method of producing electricity besides fossil fuels. With the planned phase-out of traditional coal-fired electricity production across Canada by 2030, and the increased reliance on intermittent wind and solar generation, the flexibility of hydropeaking will assume an increased importance. However, hydropower generation comes with costs; hydropeaking in particular is considered one of the most ecologically harmful modes of operation since downstream biota are subjected to flows that deviate greatly from typical natural flow regime patterns. The ecological effects of hydropeaking have been examined in a growing body of literature, but mitigation options do exist that include dam operational and/or structural modifications. This paper will explore the importance of hydropeaking in the Canadian electricity system, the ecological consequences of flexible hydropower, and mitigation options that could potentially strike a balance between meeting Canadian energy needs and minimizing ecosystem impacts. [ABSTRACT FROM AUTHOR]

Introduction: The lack of attention to transgender and gender diverse (TGD) people in undergraduate medical education (UME) is a point of concern, particularly among medical students. A project was undertaken to develop a UME curriculum framework for teaching the healthcare needs of TGD people. Methods: Using a modified Delphi methodology, four rounds of surveys were presented to an expert stakeholder group that included content experts, generalist physicians, UME teaching faculty, and medical students. Questions covered what content should be taught, who should teach the content, and how much time should be dedicated for this teaching. Once the Delphi process was complete, feedback on the provisional framework was sought from members of the TGD community to ensure it represented their needs and perspectives. Results: 71 panel members and 56 community members participated in the study. Core values included the scope of the framework, and topics such as inclusivity, and safety in practice and in teaching. The framework included terminology, epidemiology, medical and surgical treatment, mental health, sexual and reproductive health, and routine primary care. There was also guidance on who should teach, time to be allocated, and the learning environment. Discussion: There is a clear need to train tomorrow's doctors to provide competent and respectful healthcare services to and for TGD patients. Although local factors will likely shape the way in which this framework will be implemented in different contexts, this paper outlines a core UME-level curriculum framework for Canada and, potentially, for use in other parts of the world. [ABSTRACT FROM AUTHOR]

Logging causes changes in habitat structure, which can potentially lead to variations in taxonomic and functional richness of biodiversity. Studies on how functional traits in birds are affected by logging operations can provide an important element for the understanding of ecosystem processes. In this paper, we examined how logging in subtropical Andean forests influenced taxonomic and functional diversity of cavity-nesting birds. We used these results to compare how logging affected ecosystem functions in temperate and subtropical forests of the Americas. We used point-counts to examine the effects of logging on taxonomic and functional traits in avian communities (Functional Richness, Functional evenness, Functional Divergence, and Community-weighted mean). We found that logging changed bird richness and abundance, although it had no effect on the functional response to the measured traits. The comparison of our results with those of temperate forests of Canada and Chile reveals differences in the functional richness of birds in these habitats, with a lower impact of logging on functional traits. We highlight the importance of including functional traits in the analyses, since the reduction in the species richness and abundance may not be translated into functional changes within the ecosystem. [ABSTRACT FROM AUTHOR]

This paper examines the role of forests in national climate policies of two countries very rich in woods: Russia and Canada. Canada has made efforts to reduce direct CO2 emissions in the national economy, intensify forestry, and increase greenhouse gas sequestration by forests. Russia focuses on the verification and recalculation of the carbon sequestration capacity of its forests. Analysis of the Russian and Canadian stationary models used to assess the carbon sequestration capacity of forests (ROBUL and CBM-CFS, respectively) shows that both the Canadian model and the Russian one derived from it reflect the stationary dynamics of forest stands, which inevitably results in a downward CO2 absorption trend. Even if the forest inventory is updated on a regular basis, the predictive components of such models are unable to take into account the variability of forest ecosystems and their adaptation to climate change. Models that describe global carbon fluxes (e.g., ones using FLUXNET and remote sensing data) provide significantly higher net carbon sequestration values and indicate a nondecreasing net carbon accumulation trend in forests. It is concluded that stationary and remote sensing models should be used together to assess net carbon sequestration and formulate key principles of national climate policies in countries rich in forests. [ABSTRACT FROM AUTHOR]

In response to calls for greater interdisciplinary involvement from the social sciences and humanities in the development, governance, and study of artificial intelligence systems, this paper presents one sociologist's view on the problem of algorithmic bias and the reproduction of societal bias. Discussions of bias in AI cover much of the same conceptual terrain that sociologists studying inequality have long understood using more specific terms and theories. Concerns over reproducing societal bias should be informed by an understanding of the ways that inequality is continually reproduced in society—processes that AI systems are either complicit in, or can be designed to disrupt and counter. The contrast presented here is between conservative and radical approaches to AI, with conservatism referring to dominant tendencies that reproduce and strengthen the status quo, while radical approaches work to disrupt systemic forms of inequality. The limitations of a conservative approach to racial bias are discussed through the specific example of biased criminal risk assessments and Indigenous overrepresentation in Canada's criminal justice system. This illustrates the dangers of treating racial bias as a generalizable problem and equality as a generalizable solution, emphasizing the importance of considering inequality in context. Societal issues can no longer be out of scope for AI and machine learning, given the impact of these systems on human lives. This requires engagement with a growing body of critical AI scholarship that goes beyond biased data to analyze structured ways of perpetuating inequality, opening up the possibility for interdisciplinary engagement and radical alternatives. [ABSTRACT FROM AUTHOR]