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Antibacterial Efficacy of Light-Activated Graphene Oxide Nanoparticles and Nanochitosan in Water
No full textWater quality is a crucial aspect of public health, and microbial contamination remains a significant challenge. The conventional water treatment methods have certain drawbacks, necessitating the exploration for innovative water treatment methods. This study investigated the inactivation of Escherichia coli AW 1.7 in water by the application of light pulses with different wavelengths, including ultraviolet-A (UV-A, 365 nm), near UV-visible (NUV-Vis, 395 nm), and blue (455 nm) light, emitted from light-emitting diodes (LED) in combination with graphene oxide (GO) nanoparticles (NP) and nanochitosan (NC). E. coli inoculum was added to NP solutions (0.2 and 0.3 % of GO and NC) and treated with UV-A, NUV-Vis, and blue light emitted from LED for 10 and 20 min. Results demonstrated that all the LED treatments for 10 and 20 min with GO NP concentrations of 0.2% and 0.3% resulted in the inactivation of E. coli, below the limit of detection (LOD) (>5 log CFU/mL reduction). In the case of NC (0.2 and 0.3%), LED treatment using UV-A was more effective on the photocatalytic inactivation with >5 log CFU/mL reduction in the E. coli population. For the individual LED treatments, UV-A was more effective compared to NUV-Vis and blue light treatments on the inactivation of E. coli. The higher oxidation-reduction potential, electrical conductivity, and lower pH of water contributed to the greater E. coli inactivation when GO was used in combination with LED treatments. The Fourier-transform infrared spectroscopy analysis of LED-treated GO showed partial photoreduction of the oxygen-containing functional groups, whereas the structure of NC remained relatively unchanged. Significant E. coli inactivation (p-value <0.05) was observed in water with GO NP, showing their antimicrobial properties even without LED treatment. The study suggests the photocatalytic antibacterial potential of GO and NC, highlighting their application in water treatment
@Risk North 3 Report
No full textThe Canadian Association of Research Libraries (CARL) Digital Preservation Working Group (DPWG) and partners organized the *@Risk North 3 (@RN3)* summit in Gatineau, Québec from November 21-22, 2024. @RN3 addressed current challenges in the digital preservation arena through presentations by key stakeholders, guided discussions and lightning talks. Partners included LAC, the Canadian Research Knowledge Network (CRKN), Bibliothèque et Archives nationales du Québec (BAnQ), Internet Archive Canada (IAC), the Digital Research Alliance of Canada (Alliance), and the Digital Preservation Coalition (DPC). Areas under discussion included broad themes such as education and training, people strategy, funding and resource allocation, and more focused ones like web archiving, research data, benchmarking, and tools and technologies.
Across the summit, participants emphasized the need for continued and deepened collaboration between institutions, organizations and practitioners for the purposes of advocacy, knowledge-sharing and skills development. Identifying sustainable solutions, shared infrastructure among them, addressing growing storage requirements and budgetary constraints was a priority. The community could benefit from establishing and sharing best practices, methodologies and workflows around key activities such as benchmarking, and greater mechanisms to facilitate access to resources. Varied and more extensive training, both formal and informal and at all career stages, would benefit individual practitioners and organizations.
Moving forward, a follow-up webinar to share findings, the creation of a community of practice, and planning for future iterations of the @Risk North summit would continue the momentum and encourage the relationship-building the summit fostered. Further, the DPWG will work towards developing a multi-year action plan, implementing a national benchmarking exercise, and creating a CARL Visiting Program Officer position in Digital Preservation to aid in coordination and planning.
*The @Risk North 3 Summit Report is available in both English and French.*
Le Groupe de travail sur la préservation numérique (GTPN) de l'Association des bibliothèques de recherche du Canada (ABRC) et ses partenaires ont organisé *le sommet @Risk North 3 (@RN3)* à Gatineau, au Québec, les 21 et 22 novembre 2024. @RN3 a abordé les défis actuels de la préservation numérique au moyen de présentations d'intervenants clés, de discussions guidées et de conférences éclair. Parmi les partenaires figuraient BAC, le Réseau canadien de documentation pour la recherche (RCDR), Bibliothèque et Archives nationales du Québec (BAnQ), Internet Archive Canada (IAC), l'Alliance de recherche numérique du Canada (Alliance) et a Digital Preservation Coalition (DPC). Les domaines discutés comprenaient des thèmes généraux, tels que l’éducation et la formation, la stratégie en matière de personnel, le financement et l’allocation des ressources, ainsi que des thèmes plus ciblés, comme l’archivage web, les données de recherche, l’analyse comparative, les outils et les technologies.
Au cours du Sommet, les personnes qui ont participé ont souligné la nécessité d’une collaboration continue et approfondie entre les établissements, les organisations et les gens sur le terrain aux fins de sensibilisation, de partage des connaissances et de développement des compétences. L’identification de solutions durables, la mise en commun de l’infrastructure, la réponse aux besoins croissants en matière de stockage et aux contraintes budgétaires étaient des priorités. La communauté pourrait tirer profit de l’établissement et du partage des meilleures pratiques, méthodologies et flux de travail autour d’activités clés, telles que l’analyse comparative, et de mécanismes plus efficaces pour faciliter l’accès aux ressources. Une formation variée et plus complète, à la fois formelle et informelle et à tous les stades de carrière, profiterait aux personnes individuellement ainsi qu’aux organisations.
Pour la suite des choses, un webinaire de suivi pour partager les conclusions, la création d’une communauté de pratique et la planification des prochaines iterations du sommet @Risk North permettraient de poursuivre sur la lancée et d’encourager le maintien des relations que le sommet a favorisées. De plus, le GTPN travaillera à l’élaboration d’un plan d’action pluriannuel, à la mise en œuvre d’un exercice national d’analyse comparative et à la création d’un poste d’agente ou d’agent de programme invité à l’ABRC en préservation numérique pour aider à la coordination et à la planification
Predicting Axial Force and Bending Moment in Pipelines Affected by Geohazard Using Machine Learning Techniques
No full textPipelines are vital to the safe and efficient transportation of energy resources, playing a critical role in meeting global energy demands and supporting economic stability. However, these critical infrastructures face significant risks from geohazards, particularly landslides, which can lead to sudden ground displacement and severe damage to pipelines. Such events not only compromise the structural integrity of pipelines but also pose environmental, economic, and public safety risks. Understanding the effects of landslides on pipeline design and safety is essential to developing robust strategies for mitigating these risks and ensuring the reliable transport of energy resources under challenging geohazard-induced conditions.
To address these challenges, this research focuses on predicting the structural responses of pipelines, including axial force and bending moment, under geohazard-induced conditions, such as landslides. Employing machine learning models, this study aims to provide a robust and efficient alternative to numerical methods. Specifically, Support Vector Regression (SVR), Neural Networks, and Random Forest models are developed and systematically evaluated for their ability to predict these responses, offering insights into the performance and applicability of each technique.
The dataset used in this study was generated through Python-based numerical simulations, leveraging theoretical models grounded in the Euler-Bernoulli beam theory. Parameters such as axial displacement (u′), lateral displacement (v′), and curvature (v′′) were sampled over ranges reflective of real-world pipeline deformation scenarios. This comprehensive dataset captures a realistic spectrum of elastic, plastic, and strain-hardening behaviours, ensuring accurate modelling of pipeline responses under diverse loading scenarios.
The generated dataset was used to train and evaluate the machine learning models, ensuring a comprehensive representation of diverse pipeline deformation scenarios. Model performance was assessed through key metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of determination (R²), alongside computational efficiency metrics such as training times. These metrics and comparisons were crucial in verifying that the models did not overfit or underfit the data, ensuring their ability to generalize effectively across unseen scenarios and diverse geohazard-induced conditions.
Recall performance and trend comparison were conducted to evaluate the models’ consistency and their ability to generalize across diverse scenarios. The recall comparison assessed the efficiency of each model in sequential and batch tasks, providing insights into their suitability for different operational requirements. Trend analysis examined the models' ability to capture theoretical relationships between input parameters and pipeline responses, validating their alignment with established frameworks.
The results demonstrated that Neural Networks provided the best balance of accuracy and computational efficiency, achieving high R² values (0.999 for axial force and 0.997 for bending moment) and moderate training times (37 seconds for axial force and 13 seconds for bending moment). SVR exhibited the highest R² values (0.999 for axial force and 0.996 for bending moment), indicating exceptional predictive accuracy; however, this came at the cost of significantly higher training times, particularly for bending moment predictions (3473 seconds). Random Forest, while computationally efficient in sequential recall tasks, lagged in predictive accuracy (R² values of 0.992 for axial force and 0.983 for bending moment) and struggled to capture complex trends, limiting its applicability to the studied scenarios.
This study is subject to several limitations. The dataset was generated using numerical simulations based on predefined parameter ranges, which may not fully capture the variability of real-world pipeline deformation scenarios. Additionally, the reliance on synthetic data and the lack of validation against experimental or field data limit the ability to confirm the models’ robustness in practical applications.
This research opens several avenues for future studies. Expanding the range of input parameters, such as u′, v′, and v′′, could enhance the generalizability of the predictive models, allowing them to handle a wider variety of deformation scenarios. Customizing material and geometric properties, such as pipe diameter, wall thickness, and soil characteristics, would provide deeper insights into the influence of these factors on axial force and bending moment predictions. Additionally, validating the findings with real-world data, instead of relying solely on synthetic datasets, would test the robustness of the models under practical conditions and increase their applicability to real-world engineering challenges. These efforts could further refine the models and broaden their relevance in pipeline safety and reliability studies
Influence of growth implants on beef calf immunity and growth
No full textGrowth implants have been used in beef production to improve growth performance, attempting not to compromise animal welfare and health status. However, their effect on the health status and vaccine response of suckling and weaned calves has not been deeply researched. In these phases, bovine respiratory disease (BRD) remains the most impactful disease because animals are exposed to several stressors. Although vaccines have been an effective mechanism to prevent BRD, improving their efficacy is essential to reduce its incidence and improve overall health status. Therefore, these studies aimed to evaluate the growth performance and immunity of beef steers under two implant treatments and timing of booster vaccination. In experiment one, ninety beef calves (39 ± 2.3 kg of body weight [BW]; 80 d of age) were blocked by age, stratified by birth weight, and assigned to one of three treatments (30 per treatment) in a completely randomized block design: 1) Synovex C (SC), 2) Synovex One Grower (SG); or 3) no implant (CON). After implantation (d 0) until weaning (d 94), calves were raised with their dams in a single pasture and were vaccinated on d 0 and 94 against main BRD pathogens (Bovi-Shield Gold One Shot® plus Ultrabac® 7). At weaning, calves were allocated in a pen receiving a total mixed ration ad libitum until d 60 post-weaning (d 154). Blood and BW were collected to analyze insulin-like growth factor (IGF-1), cortisol, and β-hydroxybutyric acid (BHBA), serum antibody titers and complete blood cell count on d 0, 94, 108, and 154. In experiment two, fifty-nine beef calves were stratified by birth weight and age (41 ± 5.2 kg and 70 ± 8 d of age) and randomly assigned in a split-plot design with implant treatment (assigned as in experiment one) as the main plot and timing of vaccination as the subplot. The treatments were timing of vaccination booster at weaning (WV) or 7-d post-weaning (PWV). After weaning (d 187), calves were allocated in a pen receiving ad libitum hay until d 63 post-weaning. Blood and BW were collected to perform analyses as in experiment one, plus non-esterified fatty acids and leptin. In experiment one, infectious bovine rhinotracheitis (IBR) titers were greater (P = 0.022) in SG compared to SC and CON (16.3 vs 12.7, and 13.3 ± 0.45) while IGF-1 was greater (P = 0.045) in SC and SG compared to CON (119.9 and 111.1 vs 99.1 ± 6.60 ng/mL). A treatment × day interaction was observed for white blood cells (WBC) with greater (P = 0.024) concentration in SC than CON and SG on d 94 (11.4 vs 10.2 and 9.4 ± 0.57 109/L). Pre-weaning average daily gain (ADG) was greater (P = 0.01) in SG compared to CON while SC was intermediate (1.18, 1.04, 1.12 ± 0.03 kg, respectively); from d 94 to 154, ADG was greater (P = 0.03) in SG and SC compared to CON (1.52 and 1.50 vs. 1.37 ± 0.07 kg). In experiment 2, greater concentration of lymphocytes (P < 0.01) was observed in CON compared with SC and SG (8.6 vs. 7.5 and 7.0 ± 0.21 109cells/L) and greater leptin (P = 0.01) in SC compared to SG while CON was intermediate (7.4, 4.6 and 5.6 ± 0.374 ng/mL, respectively). A treatment × timing of booster vaccination interaction (P = 0.003) was observed with greater monocyte counts in SG-PWV compared to SC-PWV and CON-PWV (0.40 vs. 0.16 and 0.07 ± 0.02 109cells/L). A timing of booster vaccination × day interaction (P = 0.01) was observed for cortisol with lower concentration in WV compared with PWV on d 0 a 28 post-vaccination booster (82.0 vs. 35.9 and 107.0 vs. 32.7 ± 2.745 ng/mL respectively). Greater BW (P = 0.049) was observed in SG compared to CON, while SC was intermediate on d 28 (238, 232, and 225 ± 3.59 kg, respectively) and d 35 (230, 228, and 225 ± 3.59 kg, respectively; P = 0.049) post-vaccination booster. In summary, was observed that growth hormonal implants enhance growth performance, with SG showing the best response. Also, SG evidenced an enhanced immune response to the IBR vaccine and increased IGF-1 concentrations. Furthermore, no positive benefits of delaying the timing of booster vaccination were observed
Application of Distributed Dislocation Technique in Heat Conduction and Multiphysical Problems of Cracked Structures
Get PDFThe use of smart materials, particularly piezoelectric materials, has seen significant growth due to their unique capabilities in sensing, actuation, and energy harvesting. However, these materials are frequently subjected to various thermal loads, necessitating robust fracture criteria for ensuring their structural integrity. The primary objective of this research is to develop an analytical framework to analyze the fracture behavior of smart materials, particularly piezoelectric materials, and to provide insights for the design of smart devices subjected to different thermal loading conditions. Using the Distributed Dislocation Technique (DDT), this work aims to provide a deeper understanding of the fracture behavior of structures with multiple cracks of different spatial distribution patterns, investigating how thermal, mechanical, and electrical fields affect crack propagation.
The research begins by examining a single curved crack in a piezoelectric plane under general steady state temperature loading. The effects of heat source location, loading parameters, and crack geometry on stress intensity factors (SIFs) are thoroughly analyzed. Building on these insights, multiple cracks in a finite-sized, piezoelectric half-plane are studied, focusing on crack interaction, boundary effects, and crack angles on the multiphysical response of cracks under thermal loading conditions.
In the subsequent phase, the research extends to study the transient thermal response of multiple cracks in a half-plane or along the interface between a thermal coating and a substrate by considering non-Fourier heat conduction. The comprehensive analysis reveals significant deviations from traditional Fourier models, highlighting the importance of thermal relaxation time, loading parameters, crack dimensions, and spacing in predicting the thermal response of cracked structures. Key findings demonstrate that the presence of thermal relaxation time results in dynamic overshooting, emphasizing the necessity for accurate modeling of transient thermal responses.
This thesis provides a robust framework for analyzing the thermal and multiphysical behavior of cracked structures under various loading conditions. Inclusion of multiphysical effects under dynamic transient loading presents a more complex and challenging scenario for future studies. The insights gained are crucial for the design and optimization of thermal protection systems, particularly in extreme thermal environments, in order to enhance their reliability and performance. The integration of smart materials and advanced heat conduction models offers new perspectives for developing innovative solutions in material science and engineering
