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Assessing the Quality of World Health Organisation Guidelines during Health Emergencies: A Domain-Based Analysis
No full textBackground: Effective response during global health emergencies hinges on the quality of guidelines provided by authoritative organisations like the World Health Organisation (WHO). This study assessed the quality of WHO emergency guidelines disseminated through the Disease Outbreak News (DONs) platform between 2023 and 2024 to identify strengths and weaknesses across established quality domains. Methods: A total of 115 WHO guidelines issued within DONs were analysed using the AGREE II framework, which evaluates six domains: Scope and Purpose; Stakeholder Involvement; Rigour of Development; Clarity of Presentation; Applicability, and Editorial Independence. Descriptive statistics and one-way repeated measures ANOVA were conducted to determine significant differences among domain scores. Results: The analysis revealed statistically significant differences across domains, F(2.40, 552.34) = 739.09, p \u3c .001, ηp² = 0.866. The highest mean scores were recorded for Scope and Purpose (M = 6.46) and Clarity of Presentation (M = 6.27), indicating strengths in goal articulation and user accessibility. Conversely, Editorial Independence (M = 2.74) and Rigour of Development (M = 3.26) scored the lowest, pointing to persistent gaps in transparency and methodological robustness. Conclusions: While WHO guidelines during emergencies perform well in clarity and scope, critical weaknesses remain in transparency, stakeholder engagement, and methodological rigour. These findings indicate the need for more balanced and inclusive guideline development processes to enhance trust and utility during public health emergencies
HAWC Performance Enhanced by Machine Learning in Gamma-hadron Separation
Get PDFImproving gamma-hadron separation is one of the most effective ways to enhance the performance of ground-based gamma-ray observatories. With more than a decade of continuous operation, the High-Altitude Water Cherenkov (HAWC) Observatory has contributed significantly to high-energy astrophysics. To further leverage its rich data set, we introduce a machine learning approach for gamma-hadron separation. A multilayer perceptron shows the best performance, surpassing traditional and other machine learning-based methods. This approach shows a notable improvement in the detector’s sensitivity, supported by results from both simulated and real HAWC data. In particular, it achieves a 19% increase in significance for the Crab Nebula, commonly used as a benchmark. These improvements highlight the potential of machine learning to significantly enhance the performance of HAWC and provide a valuable reference for ground-based observatories, such as the Large High Altitude Air Shower Observatory and the upcoming Southern Wide-field Gamma-ray Observatory
Enhancing the absorption performance of minimal surface-based bulk absorbers via symmetry-breaking
No full textA Proposed Study of Tone Indicators in Sentimental Analysis and Emotion Detection
No full textSentimental analysis and emotion detection have been an ever-growing field in academic literature in recent years [1,2,3]. There are many methods and techniques to distinguish positive and negative tokens as well as classification of emotions respectively. However, the use of tone indicators has been relatively underexplored within the field.
Tone indicators are a relatively recent trend in social media. Users denote a positive or negative connotation as well as an emotion in a sentence at the moment of conception with syntax such as “/s,” “/pos,” and “/neg.” These annotations often are context-free, or do not depend on previously declared information, and allow for a possible method of study for sentence-level analysis.
This method may help achieve significantly higher certainty and a way to analyze emotions users on social media could be sharing. This paper will argue the benefits of the possible exploration of utilizing tone indicators in sentiment analysis and emotion detection with advantages, accessibility, and possible applications
Gamification of a Bimanual Coordination Task
No full textVideo games are a promising future for research and development. A new way to measure motor skills is to use robots to test individuals. This technology is incredible and has helped the medical field, but there must be a way to allow individuals to use this technology in a similar and affordable manner. Space Trash is a game that was developed with the intent to gamify the object hit detection that is used in the Kin Arm Robot with the intent to see if a person will show signs of Alzheimer’s. The goal is to not only be able to test an individual\u27s motor skills and cognitive abilities but to also be a fun game that can be enjoyed by people. The game will keep track of which objects the player hits and how many times they will hit a correct and incorrect object. The process to do this is to connect two motion controllers for the game so it can imitate the arms for the Kin Arm Robot and program the game to imitate the robot. The game is being programmed in JavaFX along with using tools such as Maven in order to connect the controllers with the game along with being familiar with the programming language. There are more features that will be added for the tracking so more variables can be measured and more varieties of testing can be done to test if a person will have Alzheimer’s. The future of the project will include adding more levels along with new effects to test players with challenging tasks. The creation of the game has gone through some struggles, but the development has been progressing and the hope for this game to be released into the public is still bright
Direct and Indirect Effects of Water-Table Levels on Redox-Active Organic Matter Reduction in an Alaskan Rich Fen
No full textRedox-active organic matter (RAOM) reduction is an important control on methane production in northern peatlands, but it is unclear how global climate change will affect RAOM reduction. We investigated the effects of water-table levels on RAOM reduction by leveraging a long-term water-table manipulation experiment in an Alaskan fen, which includes Lowered and Raised treatment plots relative to a Control. Common substrate peat was incubated in each plot during one summer of experimental manipulation and another summer of site-wide flooding. During experimental manipulation, common substrate RAOM was more reduced in the Raised plot than the Lowered plot at both 10–20 cm (19.1 ± 0.8 vs. 0.7 ± 0.3 μmol e− g−1 dw peat, p = 0.003) and 30–40 cm (18.0 ± 0.5 vs. 3.6 ± 1.2 μmol e− g−1 dw peat, p = 0.011). During site-wide flooding, differences in common substrate RAOM persisted with greater RAOM reduction in the Raised plot than both Control and Lowered plots (p \u3c 0.05) and greater methane production from Raised plot common substrate. A comparison of the chemical composition of Raised and Control peat during an anaerobic laboratory incubation showed that the compounds removed during microbial processing differed between plots with a higher double bond equivalence to carbon ratio for the Raised plot (0.54 ± 0.13) compared to the Control plot (0.44 ± 0.17). Together, these field and laboratory results suggest that long-term increases in water-table levels can have complex effects on RAOM beyond oxygen availability with the potential to impact methane production from northern peatlands
A Lightweight Microchained Architecture for Unmanned Aerial Vehicle Network Reputation Systems
No full textUnmanned Aerial Vehicle (UAV) networks are widely adopted for diverse applications in modern Internet of Things (IoT) ecosystems, ranging from last-mile deliveries to infrastructure inspections. The growing reliance on UAVs makes a secure and trustworthy network environment essential. However, deploying a practical reputation framework in resource-constrained UAV networks necessitates a lightweight, high-throughput, and scalable solution. This paper introduces a Lightweight Microchained ARchitecture (LiMAR) for UAV network reputation systems. LiMAR presents a novel lightweight microchained blockchain model optimized for UAV networks, addressing key blockchain overheads such as storage bloat, high validation costs, and consensus delays. By separating operational data from on-chain transactions, LiMAR reduces computational complexity while maintaining security through reputation-driven consensus. LiMAR is designed to minimize overhead while maintaining key blockchain properties such as immutability and auditability. By incorporating a streamlined consensus mechanism and modular block structure, LiMAR significantly reduces computational and communication costs. This enables UAV nodes to efficiently store and validate reputation scores, even under dynamic flight conditions and limited resource availability. We demonstrate the effectiveness of LiMAR through extensive simulations, evaluating parameters such as latency, throughput, and energy consumption. The results show that LiMAR outperforms conventional blockchain-based solutions in UAV network environments, supporting real-time, decentralized reputation management with minimal resource overhead. This work paves the way for more secure, resilient, and scalable UAV service delivery, underscoring the potential of lightweight blockchain solutions in next-generation IoT deployments
Adapted Intelligent Driver Model for Improved Vehicle Following and Assessment of Energy Impacts
No full textCentral to predicting the impacts of individual vehicle operations within microscopic traffic simulation is the driver model. A driver model determines a vehicle\u27s velocity profile in various driving scenarios and interactions with other vehicles. Characteristics including driver behavior and interactions with stop signs, traffic signals, and with a lead vehicle can be modeled and assessed with a representative driver model. This paper presents the application of an existing intelligent driver model (IDM) with an adaptation for vehicle following dynamics and the interaction with the lead vehicle to be more representative of driver assist systems concerning the relative distance between the lead and simulated ego vehicle. The method uses an additional control term to augment the existing IDM and reduce the inter-vehicle distance to the time gap. The impact on vehicle dynamics is compared and validated with real-world ego vehicle data recorded through driver-assist systems. The adapted IDM is then employed to simulate real-world driving for an ego vehicle and assess the impact on energy consumption of the ego vehicle using a Reduced Order Energy Model (ROE)
Low-Carbon Materials and Practices for Asphalt Pavement Decarbonization
No full textIn recent years, the asphalt industry has significantly increased its emphasis on utilizing green additives and low-carbon materials to improve sustainability and minimize environmental impact and carbon footprint associated with paving materials and operations. The life cycle of asphalt pavements encompasses multiple stages, each contributing to the overall environmental impact and carbon footprint. These stages include production (A1–A3), construction (A4, A5), use (B1, B6, B7), maintenance and rehabilitation (B2–B5), and end-of-life (C1–C4) within a cradle-to-grave framework. This chapter will primarily focus on materials and practices aimed at reducing carbon emissions during production, construction, and maintenance stages. For construction stages, some of the most promising recycled materials such as reclaimed asphalt pavement (RAP), bio-rejuvenators and bio-asphalt, ground tire rubber (GTR), and warm mix asphalt (WMA) technology, as well as some of the practices for lowering the carbon emissions associated with plant operations during heating and drying of materials, will be discussed, alongside related case studies. On the other hand, for construction and maintenance stages, some of the key aspects such as equipment efficiency, fuel consumption, scheduling strategies, and selection of suitable practices based on their carbon footprint will be discussed
Waste wood wool derived cellulose-based adsorbent for removal of heavy metal and bacterial contaminants: double-edged sword
No full textThe present study demonstrates the isolation of pure cellulose (CU) from Cedrus deodara (C. deodara) wood wool. Further, CU and modified mercaptobenzothiazole-functionalized CU (CU-MBT) were evaluated for adsorptive removal of Hg(II), Pb(II), Cd(II) ions and bacterial strains from water. Advanced characterizations through TGA, HR-TEM, FE-SEM, FTIR, XRD, EDX, BET and XPS were performed to analyze the adsorbents. The optimal pH for Pb(II) adsorption observed was 6, and for Cd(II) and Hg(II) was 7 with adsorption capacities 89.22, 103.09, 80.64 mg/g for Pb(II), Cd(II), and Hg(II) with CU, while CU-MBT exhibited 185.18, 178.57, and 140.84 mg/g for Pb(II), Cd(II), and Hg(II) respectively. CU and CU-MBT followed pseudo-second-order kinetics (R2 = 0.99) with equilibrium achieved in 90 min, showing high regeneration efficiency (9898.71%, 98.8% and 96.21%) and minimal loss over five cycles. Thermodynamic studies confirmed favourable adsorption (ΔG° values of -2.364, -2.338 and -12.781 kJ/mol), while DFT analysis revealed CU-MBT’s superior stability, lower energy gap (5.09 eV), and enhanced reactivity. Later, in the antibacterial analysis, the developed adsorbents revealed good antibacterial properties. In conclusion, CU-MBT has considerable calibre to adsorb Pb(II), Cd(II), and Hg(II) as well as bacterial species from water, demonstrating its potential in water treatment