Nipissing University

For psychoneuroendocrinology, accurate measurement of progesterone (P4) and estradiol (E2) in saliva is crucial for understanding the menstrual cycle phase and its impact on physiology and behavior. Although enzyme-linked immunosorbent assay (ELISA) and radioimmunoassay (RIA) are the preferred methods and are more often found in research labs, liquid chromatography-mass spectrometry (LC-MS/MS) may provide a more valid salivary steroids assessment. In this study, we aimed to compare the performance of LC-MS/MS, ELISA, and RIA to measure salivary P4 and E2. Samples were collected from 120 participants, 81 men and 39 women, in the morning and evening. Additionally, women provided samples during the early follicular (cycle days 3-5) and luteal cycle phases (cycle days 21-23) using the forward-count method. The study considered natural hormone fluctuations (e.g., the diurnal and menstrual cycles) and quality control samples as validity criteria for method evaluation. A total of 336 samples and quality control samples were analyzed using one RIA, two ELISA, and two LC-MS/MS methods across four labs. Correlational analyses were performed to assess inter-lab x inter-method reliability, intra-lab x inter-method reliability, and inter-lab x intra-method reliability. For P4, natural hormone fluctuations in menstrual cycles were detected by all methods. However, in contrast to both LC-MS/MS methods, all immunoassays (IAs) detected an unexpected diurnal decline of P4. For E2, they were found only by LC-MS/MS and RIA. In terms of means, for P4 and E2, ELISA and RIA produced higher values compared to LC-MS/MS. For P4, the inter- and intra-method convergence was r ≥ .92. As for E2, inter-method correlations were between r = -.12 and r = .23, while the ELISA intra-method comparison showed a correlation coefficient of r = .85. Our findings suggest that while LC-MS/MS, RIA, and ELISA were capable of meeting most of the specified criteria for P4, only LC-MS/MS and RIA were able to perform similarly sufficiently at low E2 levels. LC-MS/MS provided more accurate and reliable measurements for P4 and E2, especially at low concentrations, but encountered challenges, too. Although RIA showed comparable performance to LC-MS/MS, it still suffered partly from cross-reactivity. ELISA often overestimated hormone levels and exhibited greater divergence. These differences highlight the importance of carefully selecting methods and considering their limitations in research applications.

In collaborative systems such as factory operations and physician rostering, it is crucial to assign agents to multiple roles over time while balancing performance, equity, and individual preferences. Traditional group multirole assignment (GMRA) models prioritize performance optimization but often neglect workload fairness and individual preferences, leading to agent demotivation and suboptimal team outcomes. To address this gap, we propose a time-constrained GMRA (TGMRA) framework that extends the classical GMRA into a 3-D (roles, agents, time) model, explicitly integrating temporal constraints and heterogeneous agent requirements. Based on this framework, we further develop two extended models: TGMRA_E, which ensures the equitable distribution of work periods and task quantities, and TGMRA_EP, which integrates individual preferences via a weighted multiobjective optimization strategy. Extensive simulations across various group sizes confirm the effectiveness and robustness of these models. Compared with the baseline GMRA, TGMRA_E significantly reduces workload disparities, while TGMRA_EP improves preference satisfaction by up to 123% with less than 4% performance loss. Our results provide scalable scheduling strategies that balance team performance and individual needs and offer practical guidance on parameter selection for diverse real-world scenarios.

State re-sharding can reduce cross-shard transaction ratios, which improves the scalability of blockchain systems. However, unavoidable cross-shard transactions and account-locking mechanisms can lead to security risks (read-write conflicts) and performance bottlenecks (low synchronization efficiency). Therefore, this paper proposes a secure and efficient read-write synchronization model for cross-shard transactions in blockchain state re-sharding via a lightweight Global State Tree (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal{GT}$</tex-math></inline-formula>). The model consists of intra-shard and inter-shard state consistency modules. The intra-shard module includes two methods: account state read-write and account record update. The former allows local shard committees to track account state changes and prevent the use of expired account states, while the latter incorporates account records within maximum latency into an account state data structure, thereby enhancing the traceability and verification efficiency of update history. In the inter-shard module, a transaction processing method with a global takeover mechanism is proposed during the re-sharding window. By using validated data in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal{GT}$</tex-math></inline-formula>, the method achieves non-blocking global coordination and account reallocation. Experimental results demonstrate that the proposed model increases transaction throughput and reduces transaction latency compared to bases under Byzantine conditions.

Planetary health challenges, such as climate change, pollution, deforestation, overfishing, and habitat destruction, are disproportionately experienced and act as threat multipliers to the health, welfare, and security of human and more-than-human species. Nurses, by virtue of their position, are increasingly confronting the health implications stemming from environmental change. An awareness that human health is interrelated with planetary health should inform their role as care providers who can develop solutions to face these unprecedented challenges. This paper stems from a study observing the journeys, approaches, activities, and priorities of 14 registered nurses engaged in planetary health initiatives. Using a focused ethnographic methodology with data from semi-structured interviews, participant observations, and arts-informed self-reflections, this research has the potential to inform practice, policy, education, and research within the nursing profession. It also serves to highlight the importance of empowering nurses to engage in planetary health initiatives as advocates for social and environmental justice.

Purpose In recent years, road trips have become a popular travel mode in China. However, existing attraction recommendation methods often overlook conflicting preferences among tourists and key factors specific to road trips, resulting in suboptimal recommendations. To address this issue, we propose a knowledge-enhanced non-compensatory group decision-making approach to improve the accuracy of road trip attraction recommendations. Design/methodology/approach First, our approach constructs a comprehensive tourism knowledge graph by integrating information about both tourists and attractions. Second, an 11-dimensional modeling framework is proposed to better portray tourist preferences and attraction characteristics in the road trip context. Finally, the non-compensatory group decision-making algorithm, Elimination Et Choix Traduisant la REalité III (ELECTRE-III), is applied to model each tourist's preferences within the group and rank attractions for the tourist group. Findings Experimental results demonstrate the effectiveness of the proposed method in road trip attraction recommendation. Compared to existing approaches, our method maintains a recommendation failure rate below 15% across varying levels of conflict rates and group sizes, consistently outperforming all baseline methods. Statistical analysis further confirms that the non-compensatory mechanism effectively identifies attractions that align with the collective preferences of tourist groups. Originality/value This paper proposes a novel method for recommending attractions to road trip tourist groups by integrating non-compensatory group decision-making with knowledge integration. The approach effectively incorporates individual preferences while maintaining a low recommendation failure rate, thereby enhancing both recommendation performance and overall tourist satisfaction.

Together For Us (T4Us) is a newly developed and evidence-informed social identity intervention for youth sport. Drawing on theoretical underpinnings of social identity theory and previous shared leadership intervention research in sport, T4Us leverages athlete leaders to foster a shared sense of social identity within the team. The purpose of this study was to conduct an initial feasibility study (Study 1) and a proof-of-concept evaluation (Study 2) to determine whether T4Us enhanced social identity and assessed the intervention implementation. In Study 1, five competitive youth ice hockey teams (mean age = 13.0years) completed T4Us at midseason. Overall, athlete leaders (n = 19) and coaches (n = 4) expressed support for the acceptability and feasibility of the initial T4Us workshop in a youth sport setting, including the creation of a unique team "trademark" and the shared leadership mapping exercise. Participants also recommended possible improvements to T4Us (e.g., intervention timing and follow-up booster sessions to reinforce content). In Study 2, a total of 14 competitive youth soccer teams (mean age = 14.7years) completed the revised T4Us at midseason. Descriptive results highlight that athletes' perceptions of social identity were higher at post-intervention in comparison with pre-intervention. Post-intervention implementation evaluation results revealed that the teams used the game plan to support the team trademark, and athlete leaders and coaches encouraged team members to act according to the team game plan (scores > 5 on a 7-point scale). Interviews with athlete leaders described the different ways in which T4Us enhanced social identity within the team. Overall, the initial feasibility evidence and the proof-of-concept evaluation support the further development of T4Us, including a randomized-controlled T4Us protocol.

Prostate cancer is the most common malignancy among Australian men, with over 20 000 new diagnoses each year. Accurate forecasts of its incidence and mortality inform stakeholder decision-making and help mitigate its public health impact. In this context, we introduce cutting-edge lightweight neural networks into the domain of prostate cancer data forecasting with edge intelligence for the first time. To address the issue of overfitting in coarse-grained and small-scale prostate cancer datasets, we employ structurally streamlined models: the Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN), representing two predominant branches of neural networks. The GRU’s simplified gating mechanism maintains excellent long-term dependencies capturing capability while drastically reducing parameter count, and the TCN combines sparse connections, parameter sharing, and causal dilated convolutions for efficient temporal modeling. To further bolster generalization, we integrate multiple regularization strategies, including the snapshot ensemble method. Comparative experiments on three real-world prostate cancer datasets demonstrate that our improved lightweight, high-performance neural networks achieve over 40% higher accuracy than linear time series forecasting suitable for small-scale datasets.

Abstract Zero-dimensional structural numbers Z 0 ind and Z 0 dim $\mathcal{Z}_0^{\text {ind }} \text { and } \mathcal{Z}_0^{\text {dim }}$ w.r.t. dimensions ind and dim were introduced by Georgiou, Hattori, Megaritis, and Sereti. Somewhat similarly, we define structural numbers Sn 𝒜 for different subclasses 𝒜 of the class of hereditarily normal T 1 -spaces. In particular, if ℳ dim denotes the class of metrizable spaces Z with dim Z = 0 ; we show that: for any metrizable space X with dim X = n ≥ 0, we have 1 ≤ Sn M dim X ≤ n + 1 ; $1 \leq \operatorname{Sn}^{\mathcal{M}_{\operatorname{dim}}} X \leq n+1 ;$ for any countable-dimensional metrizable space Y , we have 1 ≤ Sn M dim Y ≤ ℵ 0 . $1 \leq \operatorname{Sn}^{\mathcal{M}_{\operatorname{dim}}} Y \leq \aleph_0 .$