Brandon University

Advances in peatland ecohydrological modelling require higher resolution depth profiles of important soil physical properties, which exist as a continuum from Sphagnum-dominated surface cover to highly decomposed peat at depth. We determined the bulk and particle density, porosity, saturated hydraulic conductivity (Ksat ), and von Post score at 5 locations in a northern bog to a depth of ~ 200 cm in 5-cm intervals. The bulk and particle densities and von Post scores increased, and porosity decreased with depth. The particle density had a relatively abrupt shift near ~ 75 cm changing from ~ 0.8 g cm− 3 to a relatively consistent ~ 1.4 g cm− 3. The variability measured was small in the upper ~ 25 cm, larger at depths of ~ 25–125 cm, and became more moderate at depths > ~ 125 cm (but not particle density). The variability of bulk density at the deeper depths results in the observed variability of porosity. The larger variability in physical properties roughly coincides with the abrupt shift in the magnitude of measured properties suggesting that contemporary processes and/or past events (e.g., wildfire, or vegetation succession and peat botanical type) could be responsible for this pattern. Bulk and particle density and porosity exhibited a relationship with the von Post score with a shift in values between von Post scores of 3 and 4. Detailed examination of peatland soil properties, in particular particle densities which are not commonly reported, will improve the robustness and reliability of models and may reveal additional information on the history and processes of formation.

Currently, few empirical studies of agile project management methods used in analytics projects exist. That, combined with a lack of theoretical conceptualization of Business Intelligence and Analytics (BI&A) project team agility, motivates this study. We aim to address these research gaps by conducting an exploratory case study of project team agility in the context of analytics projects. First, we introduce the research context and the Complex Adaptive Systems (CAS) theory as a theoretical framework for the study. Next, we describe the research methodology and data analysis used. Finally, we introduce and discuss the findings.

The agriculture sector contributes significantly to the overall development of the Indian economy. This sector can be revamped by modern technological interventions like the Internet of Things (IoT) and Machine Learning (ML) along with traditional processes. To improve sustainable growth in the agriculture field, monitoring of parameters like temperature, light, smoke, and flame is given top priority in crop yields. In this work, a smart IoT-based device (Machine Learning-based Smart Fire Detection Device (MLSFDD)) is designed for smart agriculture. The proposed MLSFDD has gathered data from agricultural crop fields through sensors and sensed data are analyzed by employing state-of-the-art ML algorithms like Random Forest (RF), Support Vector Regression (SVR), K-Nearest Neighbours (KNN), and Decision Tree (DT) to detect the fire status by sending the notification through Android phones during strange hours. This model has been realized and examined using raw data received from different sensors. The accuracy, Root Mean Square Error (RMSE), Coefficient of Determination (R ² ), and Ratio of Prediction to Deviation (RPD) of the proposed model have been calculated via extensive simulation of four ML algorithms. The accuracy of the prediction model of 94% for DT, 93% for RF, 90% for SVR, and 92% for KNN have been achieved. This suggested that mapping the field area’s agricultural fire content can be accomplished using the DT ML model. The study’s findings provide a valuable resource for accurate fire prediction in precision agriculture.

Integrating Natural Language Processing (NLP) with Generative Pre-trained Transformer (GPT) models plays a pivotal role in enhancing the accuracy and efficiency of healthcare software, which is essential for patient safety and providing high-quality care. The precision of healthcare software is fundamental to protecting the well-being of the patient. In addition, it can ensure the delivery of superior care, maintain the integrity of healthcare systems, and promote trust and cost-effectiveness. It is necessary to emphasize the importance of software reliability in its development and deployment. Symbolic execution serves as a vital technology in automated vulnerability detection. However, symbolic execution often faces problems such as path explosion, which seriously affects efficiency. Although there have been several studies to reduce the number of computational paths in symbolic execution, this problem remains a major obstacle. Therefore, more efficient solutions are urgently needed to ensure the software security. This paper proposes a large-scale language model(LLM) induction method mitigating path explosion applied to symbolic execution engines. In contrast to traditional symbolic execution engines, which often result in timeout or out-of-memory detection, our approach achieves the task of detecting vulnerabilities in seconds. Furthermore, our proposal improves the scalability of symbolic execution, allowing more extensive and complex programs to be analyzed without significant increases in computational resources or time. This scalability is crucial to tackling modern software systems and improving the efficiency and effectiveness of automated defect verification in healthcare software.

Background: Modern sports nutrition has evolved through discoveries in muscle metabolism and dietary supplementation. Advances in muscle biopsy techniques revealed how diet influences muscle energetics and exercise performance. The establishment of the Metabolic Research Laboratory provided a platform for further investigation, leading to the identification of creatine monohydrate (CrM) as an effective ergogenic aid. This review outlines the historical development of sports nutrition research from the 1960s to the early 1990s, highlighting key breakthroughs in muscle glycogen metabolism, dietary interventions, and creatine supplementation. Methods: We conducted a narrative review that combined personal accounts with seminal research studies. This approach allowed us to examine the contributions of Drs. Jonas Bergström and Eric Hultman-founders of the Metabolic Research Laboratory-as well as the early work of their postdoctoral colleague, Dr. Roger Harris. Results: Muscle biopsy techniques enabled direct analysis of muscle metabolism, leading to insights into glycogen depletion and recovery. The Metabolic Research Laboratory advanced our understanding of muscle energetics and informed dietary strategies for enhancing performance. In 1992, the rediscovery of CrM supplementation demonstrated its capacity to increase intramuscular creatine levels, significantly improving exercise performance and recovery. These breakthroughs reshaped sports nutrition and expanded its relevance to clinical and aging populations. Conclusion: The progression from early muscle metabolism research to the validation of CrM supplementation underscores how foundational laboratory discoveries have shaped modern sports nutrition. The work of the Metabolic Research Laboratory and its key investigators continues to inform applications in both performance enhancement and clinical health.

When radiation from a background source passes through a cloud of cold plasma, diverging lensing occurs if the source and observer are well-aligned. Unlike gravitational lensing, plasma lensing is dispersive, increasing in strength with wavelength. The Drude model is a generalization of cold plasma, including absorbing dielectric dust described by a complex index of refraction. The Drude lens is only dispersive for wavelengths shorter than the dust characteristic scale (λ≪λd). At sufficient photon energy, the dust particles act like refractive clouds. For longer wavelengths λ≫λd, the optical properties of the Drude lens are constant, unique behavior compared to the predictions of the cold plasma lens. Thus, cold plasma lenses can be distinguished from Drude lenses using multi-band observations. The Drude medium extends the applicability of all previous tools, from gravitational and plasma lensing, to describe scattering phenomena in the X-ray regime.

Position Statement: The International Society of Sports Nutrition (ISSN) presents this position based on a critical examination of the literature surrounding the effects of long-chain omega-3 polyunsaturated fatty acid (ω-3 PUFA) supplementation on exercise performance, recovery, and brain health. This position stand is intended to provide a scientific foundation for athletes, dietitians, trainers, and other practitioners regarding the effects of supplemental ω-3 PUFA in healthy and athletic populations. The following conclusions represent the official position of the ISSN: Athletes may be at a higher risk for ω-3 PUFA insufficiency. Diets rich in ω-3 PUFA, including supplements, are effective strategies for increasing ω-3 PUFA levels. ω-3 PUFA supplementation, particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), has been shown to enhance endurance capacity and cardiovascular function during aerobic-type exercise. ω-3 PUFA supplementation may not confer a muscle hypertrophic benefit in young adults. ω-3 PUFA supplementation in combination with resistance training may improve strength in a dose- and duration-dependent manner. ω-3 PUFA supplementation may decrease subjective measures of muscle soreness following intense exercise. ω-3 PUFA supplementation can positively affect various immune cell responses in athletic populations. Prophylactic ω-3 PUFA supplementation may offer neuroprotective benefits in athletes exposed to repeated head impacts. ω-3 PUFA supplementation is associated with improved sleep quality. ω-3 PUFA are classified as prebiotics; however, studies on the gut microbiome and gut health in athletes are currently lacking.

We calculate the leading and next-to-leading corrections to the real-time QCD static potential in a high-temperature medium in the region where bound states transit from narrow resonances to wide ones. We find sizable contributions to both the real and the imaginary part of the potential. The calculation involves both loop diagrams calculated in the hard thermal loop effective theory and power corrections to the hard thermal loop Lagrangian calculated in QCD. We compare our results with recent lattice data and check the consistency of different methods used in lattice calculations. We also discuss the usefulness of our results to guide lattice inputs. Published by the American Physical Society 2025

How situations are defined is a social process. This paper examines how users on YouTube make sense of the alleged sexual assault perpetrated by shock rocker Marilyn Manson in the 2007 “Heart Shaped‐Glasses (When the Heart Guides the Hand)” music video. Actor Evan Rachel Wood revealed in a 2022 documentary that she had been “essentially raped” by Manson in the video. Using qualitative media analysis, we collected and analyzed a total of 5466 user‐generated comments on YouTube posted in response to the “Heart‐Shaped Glasses” video after the publication of Wood's allegations. The research question that we explore is: How do users on YouTube understand the “Heart‐Shaped Glasses” video in light of Wood's allegations? Does the video depict a consensual simulated sex scene or is it documentation of a criminal sexual assault? Our analysis and findings reveal that users' interpretations of social cues provided in the video are subject to external forces of narration. Specifically, users draw explicitly and implicitly on both rape myths and on counter‐narratives stemming from the #MeToo movement to justify their support for Manson or for Wood, respectively. Media narratives about the “Heart‐Shaped Glasses” video and the user's orientation to the problem of sexual violence appear to be more salient social cues than the video footage itself in determining how commenters defined the video. These findings offer some insights specifically into how definitional processes, with respect to sexual violence, draw on socially established narratives, like rape myths or pro‐survivor activism. More generally, the findings provide a lens to consider how definitional processes operate in other kinds of situations in which the definition of actions recorded on video is contested. Video Abstract: https://www.youtube.com/watch?v=Uo7qxmTwA‐U .

Objectives Many human growth studies note a trend of differential variation in limb segment lengths, where distal elements show greater variability than their proximal counterparts. This has been attributed to their developmental sequence, where bones further from the head develop later and are more impacted by fluctuating growth conditions. We aimed to explore limb dimensions within this framework, known as the laws of developmental direction, in children (0.09–11.75 years) from a documented skeletal collection of low socioeconomic status. Materials and Methods Z‐scores were generated for diaphyseal length measurements of six limb bones. Differences between mean z‐score values of the limbs, as well as of the proximal and distal segments of each limb, were assessed using paired samples t‐tests. Results The lower limb was significantly more stunted in growth relative to the upper limb (p ≤ 0.001), as was the distal segment of the upper limb relative to the proximal segment (p ≤ 0.001). In contrast, the distal segment of the lower limb was significantly less stunted in growth relative to the proximal segment (p ≤ 0.001). Discussion The findings of increased sensitivity in the lower limb relative to the upper limb and in the distal segment of the upper limb relative to its proximal segment are consistent with the laws of developmental direction. However, the finding of greater sensitivity in the proximal segment of the lower limb relative to the distal segment does not align with the theorized developmental gradient. These results reveal the complexity of human growth and developmental plasticity in response to biocultural factors.

We present a novel approach for addressing computer vision tasks in intelligent transportation systems, with a strong focus on data security during training through federated learning. Our method leverages visual transformers, training multiple models for each image. By calculating and storing visual image features as well as loss values, we propose a novel Shapley value model based on model performance consistency to select the most appropriate models during testing. To enhance security, we introduce an intelligent federated learning strategy, where users are grouped into clusters based on constrastive clustering for creating a global model as well as customized local models. Users receive both global as well as local models, enabling tailored computer vision applications. We evaluated KGVT-ITS (Knowledge Guided Visual Transformers for Intelligent Transportation Systems) on various ITS challenges, including pedestrian detection, abnormal event detection, as well as near-crash detection. The results demonstrate the superiority of KGVT-ITS over baseline solutions, showcasing its effectiveness and robustness in intelligent transportation scenarios. More particularly, KGVT-ITS achieves significant improvements of about 8% against the existing ITS methods.

Sentiment analysis, also known as opinion mining, is an emerging field that involves the automatic identification and categorization of opinions expressed in textual data. This process is referred to as sentiment mining. Sentiment analysis is becoming an increasingly important task in a variety of domains, including business, politics, and social media, due to the growing amount of text data accessible on the Internet. In recent years, this area of research has seen increased traction as well as added methodologies in interdisciplinary domains intersecting with sentiment analysis.

In this work, we introduce a novel hybrid joint optimization framework specifically designed for enhancing the performance of consumer electronics in vehicular networks using a transmissive reconfigurable intelligent surface (T-RIS)-mounted unmanned aerial vehicle (UAV) system. The UAV employs the non-orthogonal multiple access (NOMA) protocol to broadcast data to multiple ground devices, ensuring efficient communication. Our primary objective is to maximize the overall system sum rate while adhering to key constraints such as the rate requirements of ground devices, UAV battery capacity, and UAV coordinate boundaries. The optimization challenge of maximizing the system’s sum rate is inherently non-convex and complex. To address this, we decompose the problem into manageable subproblems. The beamforming optimization problem is tackled using successive convex approximation and semi-definite programming techniques, allowing for effective handling of non-convexity. For power allocation, we employ the Lagrangian dual method along with the sub-gradient technique, ensuring optimal power distribution among devices. To optimize the UAV’s location, we propose a dueling-based double deep reinforcement learning (D3RL) framework. This approach effectively combines all computed solutions, resulting in a comprehensive joint optimization strategy. Simulation results highlight the exceptional performance of the proposed framework. Specifically, optimizing the UAV’s location leads to a substantial performance gain of up to 65.9% compared to a system where only beamforming and power allocation are optimized with the UAV positioned at the center of the service area. These findings underscore the potential of our framework in advancing consumer electronics connectivity in vehicular networks.