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Unreinforced Masonry (URM) Walls Retrofitted with Reinforced Polyurea for Blast Load Protection
No full textUnreinforced masonry (URM) walls are commonly used in the construction of buildings both as external walls and internal partitions because they are cost-effective and meet many functional building requirements. However, URM walls have low flexural capacity as they are designed to resist gravity loads. This weakness and their brittle nature make them extremely vulnerable to blast loads when exposed to accidentally or intentionally generated shock waves. Consequently, their failure mode could be catastrophic, collapsing quickly and disintegrating into fragments of high-impact velocities capable of causing severe injury and death to occupants and damage to the contents of buildings. Thus, improving the performance of these walls against explosions has become necessary for public safety. As such, this research attempts to provide a possible way of enhancing the wall resistance to blast attacks using reinforced membrane retrofits.
The research has two phases: experimental and analytical. The experimental work involved tests of six URM walls, consisting of four concrete masonry unit (CMU) and two stone walls. The dimensions of each wall were 2 meters by 2 meters, and it was built with 4-inch hollow blocks for the CMU walls and solid concrete stone blocks for the stone walls. The walls were categorized into non-load bearing and load bearing, with two CMUs and one stone wall in each category. Also, they were retrofitted with the combination of two materials: the XS-350 polyurea and SS304 welded 76.2 mm x 76.2 mm stainless-steel wire mesh with either 4.1 mm or 3.1 mm wire diameters.
The retrofitted walls were exposed to blast loads of varying intensity during the tests using a shock tube. The test parameters included the percentage of stainless-steel mesh, type of URM, axial loading, arching action, and pressure-impulse combinations. The results were assessed using dynamic mid-height deflections, failure mode, ductility, load-carrying capacity, and energy absorption capacity. The analytical research involved developing resistance functions and single-degree-of-freedom (SDOF) dynamic inelastic analyses. The SDOF models predicted the dynamic mid-span deflection of the hardened URM walls reasonably well, illustrating the effectiveness of the analysis technique for blast load analysis. A parametric investigation was carried out with varying wall thickness, axial load, aspect ratio, and percentage of steel. Finally, a design procedure was developed for use in engineering practice
Research priority setting for implementation science and practice: a living systematic review protocol
No full textAbstract Background Research priority setting has the potential to bridge knowledge gaps, optimize resource allocation, foster collaborations, and inform funding directions for implementation science and practice when these priorities are properly acted upon. This systematic review aims to determine the extent of research in priority setting for implementation science and practice, examine the methodologies employed, synthesize these research priorities, and identify strategies for evaluating and implementing these priorities. Methods We will conduct a living systematic review following the Cochrane guidance. We will search literature from six databases, the website of James Lind Alliance, five implementation science-focused journals and several related journals, Google Scholar, and the reference lists of included studies. Two reviewers will independently screen studies based on the eligibility criteria. The characteristics of the included documents, their prioritization methods, and outcomes, as well as the evaluation and implementation strategies, will be extracted. We will critically appraise these documents using the nine common themes of good practice for research priority setting, and synthesize data using a narrative approach. We will re-run the search 12 months after the original search date to monitor the development of new literature and determine the time to update the review. Discussions By conducting this living systematic review, we will gain a comprehensive and dynamic understanding of the potential research gaps and hotspots in implementation science as perceived by researchers and practitioners. The findings of this review will inform the future research directions of implementation science and practice. Systematic review registration This review has been registered with the Open Science Framework ( https://osf.io/sr69k )
Artificial Intelligence’s Impact on Legal Journals / Incidence de l’intelligence artificielle sur les revues de droit : Challenges and Opportunities for the Ottawa Law Review / Défis et possibilités pour la Revue de droit d’Ottawa
No full textArtificial Intelligence’s Impact on Legal Journals provides an overview of the opportunities and challenges presented by the use of artificial intelligence (AI) and its impact on legal journals, including the Ottawa Law Review (OLR).
Throughout the publication lifecycle of a given piece, AI tools may play a pivotal role in enhancing the editorial and publishing processes. Similarly, authors submitting to legal journals may also leverage AI tools for purposes that range from improving readability to generating content.
While the potential benefits are significant, the use of such tools raises various issues pertaining to the accuracy and quality of publications, as well as broader ethical and legal issues.
Journals have responded to these opportunities and challenges at different speeds and in different ways. Some journals in non-legal disciplines have developed extensive AI policies, while the majority of legal journals appear to be falling behind in this regard.
This book contains several recommendations that will empower the OLR to embrace the transformative potential of AI responsibly while maintaining its commitment to safeguarding privacy, intellectual property, and scholarly rigour.
Central to this endeavour is the adoption of three AI policies: one covering the use of generative AI and AI-assisted technologies in submissions, another addressing AI usage in the peer review process, and a final one relating to the editorial team.
This book ultimately aspires to ensure the OLR upholds its reputation as a reliable contributor to legal scholarship by guiding the organization through this new technological revolution.
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Incidence de l’intelligence artificielle sur les revues de droit discute des possibilités et des défis que présente le recours à l’intelligence artificielle (IA), ainsi que de son incidence sur les revues de droit, notamment la Revue de droit d’Ottawa (RDO).
Tout au long du cycle de publication d’un article donné, les outils d’IA peuvent jouer un rôle clé pour améliorer les processus de révision et de publication. Les auteurs et autrices qui soumettent des articles à des revues de droit peuvent aussi tirer parti d’outils d’IA à des fins allant de l’amélioration de la lisibilité de leurs textes à la génération de contenu.
Bien que les avantages potentiels soient considérables, le recours à de tels outils soulève diverses questions quant à l’exactitude et à la qualité des publications, de même que des questions éthiques et juridiques plus larges.
Les revues ont réagi à ces possibilités et défis à des rythmes différents et de façons diverses. Certaines d’entre elles, spécialisées dans des disciplines autres que le droit, ont élaboré des politiques détaillées quant au recours à l’IA, tandis que la plupart des revues de droit semblent accuser un retard à cet égard.
Cet ouvrage formule plusieurs recommandations qui permettront à la RDO d’exploiter le potentiel transformateur de l’IA de manière responsable, tout en maintenant son engagement à protéger la confidentialité des soumissions ainsi que la propriété intellectuelle et la rigueur scientifique.
Pour assurer l’atteinte de ces objectifs, il est capital que trois politiques relatives à l’IA soient adoptées : une politique sur le recours à l’IA générative et aux technologies assistées par l’IA dans les soumissions, une autre sur l’utilisation de l’IA dans le cadre du processus d’évaluation par les pairs et une dernière en lien avec l’équipe de rédaction.
En définitive, le présent cet ouvrage vise à aider la RDO à maintenir sa réputation à titre de contributrice fiable à l’avancement de la recherche juridique en orientant son approche dans le contexte de cette nouvelle révolution technologique.1. Introduction
1.1. AI and Generative AI
1.2. Increasing Role of AI in Legal Scholarship
1.3. Importance of Understanding AI’s Impact on Legal Journals
1.4. Objectives of the Report
2. How Journals Are Responding to AI
2.1. Legal Journals
2.2. Non-Legal Journals
3. Challenges
3.1. Accuracy and Quality Considerations
3.1.1. Bias and Fairness in AI Algorithms
3.1.2. Hallucination of Facts and Sources
3.1.3. Transparency
3.2. Legal Issues
3.2.1. Privacy Concerns
3.2.2. Intellectual Property Implications
4. Opportunities
4.1. Legal Journals Using AI Tools Internally
4.1.1. Review Process
4.1.1.1. Reviewing Submissions
4.1.1.2 . Detecting the Use of AI
4.1.2. Editorial Process
4.1.2.1. Editing Footnotes
4.1.2.2. Editing Article Text for Syntax or Grammar
4.1.3. Publishing Process
4.2. External Parties Using AI Tools
4.2.1. Literature Review, Drafting, and Analysis
4.2.2. AI Legal Writing Tools
5. Recommendations
5.1. Internal Use of AI Tools
5.1.1. Review Process
5.1.2. Modernizing the Editorial Process
5.1.3. Translation Services
5.1.4. Monitoring Legal Developments
5.1.5. Develop OLR AI Tool
5.2. External Use of AI Tools
5.2.1. Authors
5.2.2. Peer Reviewers
6. Conclusion
7. Appendices
Appendix A: Policy on the Use of Generative AI in the OLR Submission Process
Appendix B: Policy Prohibiting the Use of AI in the OLR Peer Review Process
Appendix C: Policy on the Use of AI in the OLR Editing Process
8. References
9. Acknowledgements
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1. Introduction
1.1. IA et IA générative
1.2. Rôle croissant de l’IA dans le domaine de la recherche juridique
1.3. Importance de comprendre l’incidence de l’IA sur les revues de droit
1.4. Objectifs du rapport
2. Comment les revues répondent-elles à l’IA
2.1. Revues de droit
2.2. Revues non juridiques
3. Défis
3.1. Considérations liées à l’exactitude et à la qualité
3.1.1. Préjugés et discrimination que recèlent les algorithmes des outils d’IA
3.1.2. Hallucinations de faits et de sources
3.1.3. Transparence
3.2. Questions juridiques
3.2.1. Préoccupations en matière de confidentialité
3.2.2. Incidences sur la propriété intellectuelle
4. Possibilités en lien avec l’IA
4.1. Recours aux outils d’IA par les revues de droit dans leurs processus internes
4.1.1. Processus d’évaluation
4.1.1.1. Sélection des soumissions
4.1.1.2. Déceler le recours à l’IA
4.1.2. Processus de révision
4.1.2.1. Révision des notes de bas de page
4.1.2.2. Révision du texte des articles pour leur syntaxe et grammaire
4.1.3. Processus de publication
4.2. Recours aux outils d’IA par les parties externes
4.2.1. Revue de littérature, rédaction et analyse
4.2.2. Outils d’aide à la rédaction juridique par l’IA
5. Recommandations
5.1. Recours aux outils d’IA à l’interne
5.1.1. Processus de révision
5.1.2. Modernisation du processus de révision
5.1.3. Services de traduction
5.1.4. Suivi de l’évolution du droit
5.1.5. Élaboration d’un outil d’IA propre à la RDO
5.2. Recours aux outils d’IA à l’externe
5.2.1. Auteurs
5.2.2. Évaluateurs externes
6. Conclusion
7. Annexes
Annexe A : Politique sur l’utilisation de l’IA générative dans le processus de soumission de la RDO
Annexe B : Politique interdisant l’utilisation de l’IA dans le processus d’évaluation par les pairs de la RDO
Annexe C : Politique sur l’utilisation de l’IA dans le processus de révision de la RDO
8. Références
9. Remerciement
The impact of economic growth and recessions on maternal and child health outcomes in sub-Saharan African countries: a systematic literature review
No full textAbstract Background The discourse surrounding the relationship between economic growth and maternal and child health has extended over several years. While some studies highlight the potential positive impact of economic growth on maternal and child health, others challenge the conventional belief that economic growth invariably translates to improved maternal and child health. Recent findings suggest that its role as a sole determinant of mortality outcomes has declined over time. This systematic review aims to consolidate existing literature and offer a comprehensive overview of this relationship in sub-Saharan African countries. Methods A structured search of Medline, Embase, Web of Science, EconLit, and Global Health was conducted. Inclusion criteria encompassed studies published between 2000 to 2022 that examined national level economic growth and recession in conjunction with health outcomes of mothers and children in sub-Saharan African countries. Results A total of 1167 studies were initially identified from the database searches, of which 18 met the inclusion criteria for data extraction. The review presents a range of findings. Eleven studies underscore the significant impact of economic growth in reducing child mortality and undernutrition, and maternal mortality rate. Conversely, other studies indicated insignificant or inconsistent associations, emphasizing the importance of various socio-economic factors such as female education, equitable resource distribution, effective governance, and comprehensive maternal and child health coverage and interventions. These factors are considered crucial in maximizing the benefits derived from national economic growth. Conclusions Future research should explore alternative economic growth indicators such as, inequality-adjusted Human Development Index and Genuine Progress Indicator, to better capture several socio-economic factors. Additionally, expanding the timeframe could provide a more comprehensive understanding of the impact of economic growth and recession on maternal and child health in sub-Saharan Africa
A comprehensive peri-operative protocol to decrease the risk of infection post coccygectomy: a case series study
No full textAbstract Background Coccygectomy is the definitive treatment of coccygodynia refractory to conservative therapy, but post operative wound infection poses a significant challenge in these patients. We introduce a novel peri-operative technique incorporating a specific pre-operatively dietary regimen, polyethylene glycol enema, and prophylactic antibiotics. Post-operatively, patients adhered to strict hygienic protocols in addition to receiving antibiotics. This technique successfully reduced the incidence of surgical site infection post coccygectomy to a rate of 0.0%. Methods A retrospective review was conducted on 21 patients who underwent partial or complete coccygectomy for coccygodynia refractory to 6 months of conservative therapy. Patients were treated using our novel protocol to minimize the infection risk and significant improvement in their pain. Results All of the patients experienced uneventful post operative recovery except for 1 solitary case of delayed wound healing. This case was treated with a silver impregnated dressing and demonstrated full wound recovery 1 week later. Additionally, pain scores showed a significant reduction of pain before and after surgery. These results highlight the efficacy of our enhanced peri-operative protocol in preventing surgical site infection as well as substantial pain relief. Conclusion Our findings are consistent with the existing literature, demonstrating that an enhanced peri-operative protocol not only effectively prevents post-operative infections but also facilitates significant pain relief in patients undergoing coccygectomy. This novel peri-operative protocol may offer a new standard for managing post-surgical outcomes in coccygectomy, though prospective studies are needed to further validate these results
A hybrid machine learning framework for functional annotation of mitochondrial glutathione transport and metabolism proteins in cancers
No full textAbstract Background Alterations of metabolism, including changes in mitochondrial metabolism as well as glutathione (GSH) metabolism are a well appreciated hallmark of many cancers. Mitochondrial GSH (mGSH) transport is a poorly characterized aspect of GSH metabolism, which we investigate in the context of cancer. Existing functional annotation approaches from machine (ML) or deep learning (DL) models based only on protein sequences, were unable to annotate functions in biological contexts. Results We develop a flexible ML framework for functional annotation from diverse feature data. This hybrid ML framework leverages cancer cell line multi-omics data and other biological knowledge data as features, to uncover potential genes involved in mGSH metabolism and membrane transport in cancers. This framework achieves strong performance across functional annotation tasks and several cell line and primary tumor cancer samples. For our application, classification models predict the known mGSH transporter SLC25A39 but not SLC25A40 as being highly probably related to mGSH metabolism in cancers. SLC25A10, SLC25A50, and orphan SLC25A24, SLC25A43 are predicted to be associated with mGSH metabolism in multiple biological contexts and structural analysis of these proteins reveal similarities in potential substrate binding regions to the binding residues of SLC25A39. Conclusion These findings have implications for a better understanding of cancer cell metabolism and novel therapeutic targets with respect to GSH metabolism through potential novel functional annotations of genes. The hybrid ML framework proposed here can be applied to other biological function classifications or multi-omics datasets to generate hypotheses in various biological contexts. Code and a tutorial for generating models and predictions in this framework are available at: https://github.com/lkenn012/mGSH_cancerClassifiers
Computational development of mushroom-6-glucan/paclitaxel as a synergistic complementary medicine for breast cancer therapy
No full textAbstract Background Breast cancer is chemo-resistant and highly metastatic, often resulting in patient mortality. One of the primary factors contributing to the metastasis and chemotherapy resistance is the presence of cancer stem-like cells. We posited that the natural polysaccharide known as 6-glucans, derived from Pleurotus ostreatus, could effectively counteract the chemotherapy resistance associated with cancer stem-like cells in breast cancer. Methods We computationally developed a specific dual combinatorial therapy involving 6-glucans and Paclitaxel (PTX) and tested on preclinical 3D mammosphere human tumor models representing receptor-positive and receptor-negative breast cancer. Using this preclinical 3D spheroid technology, we tested the anti-cancer properties of these predicted treatment combinations on mammospheres containing human breast cancer stem cells. Results Among the 40 distinct combinations examined, computational prediction revealed that the addition of 2.0 mg/mL of 6-glucans to a low dose of 3.0 µg/mL PTX was the sole combination demonstrating a synergistic effect. This optimized synergistic combination therapy displayed a significant inhibitory impact on human cancer epithelial and stem cell migration, evasion, and colony formation. The inclusion of 6-glucans also augmented apoptosis in both breast cancer cells and stem cells, leading to a six-fold reduction in BrdU labeled cells and an increased arrest of cells in the sub-G0 phase. These effects were mediated through mitochondrial dysfunction and the downregulation of associated oncogenes. Conclusion Our study revealed that the computationally predicted 6-glucans-based binary complementary medicine exhibited sequence- and concentration-dependent anticancer synergistic effects
Numerical Modelling, Simulations and Experimental Analysis of Quantum Well, Quantum Dot and Quantum Dash Mode-Locked Lasers
No full textMode-locked lasers are essential light sources for generating ultrashort pulses, widely used in high-speed optical communication systems such as Time-division multiplexing and Dense Wavelength Division Multiplexing (DWDM). These lasers are preferred over other light sources for their broad bandwidth, high-peak power and highly coherent light pulses. This work investigates the mode-locking phenomenon in various quantum-sized structures, including multi-quantum wells, quantum dot and quantum dash-based lasers. The primary objective of conducting this research is to gain a deeper understanding of the complex dynamics occurring within the laser cavity. The study employs various numerical modelling, simulations, and experimental analyses to achieve the objective.
Numerical modelling is based on two key approaches: the Time Delay Oscillator (TDO) model and the Delay Differential Equation (DDE) model. Both models use the time-domain lumped element approach to understand the physical processes within the laser cavity. Specifically, the DDE model is developed using the Time Domain Travelling Wave (TDTW) approach to analyze the passive mode-locking phenomenon in lasers. Both models were implemented in the Simulink platform.
The simulation section focuses on the quantum well laser designs due to the current limitation of the Photon Design Tools, Harold and PICWave to support only quantum well-based structures. The parameter values for the quantum well-based simulations from PICWave were extracted to be used in Simulink as PICWave uses a TDTW engine, thus providing a basis for the parameter extraction.
The experimental analysis focuses on quantum dash lasers, examining various aspects of the laser device parameters such as the repetition rate, pulse width, and threshold currents. Additionally, the study investigates the impact of factors such as temperature and injection currents on key parameters like L-I curves, optical spectra, dispersion, and mode-locking behaviour of the laser device
Association of cigarette use with risk of prostate cancer among US males: a cross-sectional study from NHANES 1999–2020
No full textAbstract Background Association of cigarette use with risk of prostate cancer remains unclear. We performed this study to examine whether cigarette use is associated with increased risk of prostate cancer. Methods This cross-sectional study used data from the 1999 to 2020 National Health and Nutrition Examination Survey (NHANES), a population-based nationally representative survey designed to assess the health and nutritional status of US adults and children. Males were eligible if they were aged ≥ 20 years at the time of participation. Cigarette use (ever use, categorized into former use and current use) was defined as having smoked at least 100 cigarettes in life. Smoking duration, cigarettes smoked per day, and smoking pack-years were calculated in former smokers and current smokers. The primary outcome was self-reported diagnosis of prostate cancer by participants. Logistic regression was used to calculate the adjusted odd ratios (aOR) and 95% CI for the associations of cigarette use with risk of prostate cancer, adjusting for demographic characteristics. Subgroup analyses by age group were conducted. Data were analyzed from June 4 to November 30, 2023. Results Of the 107 622 participants in 1999–2020 NHANES, 28 170 were included in the analysis. The mean (SD) age of the 28 170 participants was 46.4 (16.4) years, 68.0% were non-Hispanic White. Compared with never smokers, ever (aOR, 2.41 [95% CI, 1.15–5.06]) and former smokers (aOR, 3.56 [95% CI, 1.62–7.85]) had a higher risk of prostate cancer. This higher risk in former (aOR, 3.82 [95% CI, 1.69–8.64]) and ever smokers (aOR, 2.82 [95% CI, 1.27–6.25]) was also found in participants aged 20–59 years. Dose-response analysis showed a positive association between smoking duration (aOR, 1.07 [95% CI, 1.03–1.11]), cigarettes smoked per day (aOR, 1.03 [95% CI, 1.00-1.07]), smoking pack-years (aOR, 1.02 [95% CI, 1.01–1.03]) and risk of prostate cancer in current smokers. Conclusions This study suggests that cigarette use was associated with an increased risk of prostate cancer in US males, especially among those aged 20–59 years. Further research utilizing prospective study design and modeling family history is needed to confirm the findings
Redesigning Olefin Metathesis Catalysts for Chemical Biology
No full textOlefin metathesis is arguably the most versatile method yet developed to forge carbon-carbon bonds in chemical biology. Its power rests on the formation of an alkene linker that is not only the smallest, simplest, most flexible such connecting group attainable, but one that is inert under physiological conditions. Additional advantages stem from the reduced toxicity of ruthenium, relative to other transition metals in routine use, of which copper is most notoriously problematic. All of these are key strengths over alternative bond-forming methods such as click chemistry and cross-coupling. Beyond merely conjugation, olefin metathesis is uniquely suited to the assembly of conformationally flexible macrocycles. The latter include macrolides now emerging as a major class of antiviral and oncology therapeutics, and as probes for understanding biological processes.
Nevertheless, applications of metathesis in these demanding contexts are largely confined to proof-of-concept studies, owing to facile catalyst degradation. Many view the fundamental challenge as the abundance of functional groups present. This thesis work began with an alternative hypothesis: that water - the native solvent of chemical biology - is potentially the major contributor to catalyst decomposition. By identifying and addressing decomposition events via a mechanism-driven approach, this thesis establishes a robust foundation for understanding in aqueous metathesis, which is anticipated to open up new opportunities in chemical biology.
Decomposition by trace water in organic solvents was examined first, to isolate the problem from the broader complexities presented by aqueous environments. Trace water was shown to significantly reduce metathesis productivity, even for benchmark substrates that deliver turnover numbers in the tens of thousands in anhydrous media. Evidence was presented for accelerated decomposition via β-H elimination and bimolecular coupling of the active species. Ligand design to block these pathways correlated with metathesis productivity, providing a foundation for rational design for enhanced performance in water. Coordination of water to the catalyst to form transient aqua species was posited, with hydrogen-bonding of the water ligand underlying both β-H elimination and bimolecular coupling.
Subsequent studies focused on metathesis in bulk water, using the water-soluble catalyst that overwhelmingly dominates current use. In this catalyst, cationic ammonium groups are used to confer solubility in water. Posited as a key weakness is the charge buildup associated with the cationic ligand, in conjunction with the initial aquation step. Charge buildup drives formation of hydroxide species that participate aggressively in catalyst decomposition. Speciation studies revealed that the aqueous chemistry of the catalyst is governed by dynamic equilibria involving aqua, hydroxide, and chloride species, which are strongly influenced by pH and chloride concentration. Low chloride levels permit hydroxide formation: higher chloride concentrations suppress hydroxide species, but compromise catalyst solubility, owing in part to the common-ion effect.
Building on these insights, a new family of water-soluble catalysts was developed. Anionic sulfonate groups were introduced to improve solubility, to inhibit hydroxide formation, and to limit the affinity of the ruthenium center for negatively-charged biological functionalities, including DNA. The sulfonate catalysts exhibited breakthrough productivity in metathesis of unprotected nucleoside and carbohydrate substrates in water. They also enabled, for the first time, on-DNA metathesis in bulk water, with minimal DNA degradation.
These advances highlight the strengths of mechanistically-guided catalyst design - and, more specifically, the understanding of catalyst decomposition - in addressing challenges that have limited opportunities to date. Olefin metathesis is now poised to open new areas of chemical space. A specific consideration in this thesis are opportunities for drug discovery via DNA-encoded libraries and oligonucleotide-based therapeutics. More broadly, however, transformative advances can be anticipated at the frontiers of biology and medicine