Kilian J Murphy11Laboratory of Wildlife Ecology and Behaviour, SBES, University College Dublin, Ireland*Corresponding Author: kilian.murphy@murphyecology.comAbstract1. Agent-based models (ABMs) are powerful tools for exploring ecological systems but have historically been applied predominantly to theoretical research rather than practical management and policy contexts. Traditionally, ABMs rely on simplified agents and environments, often lacking integration with real-world data, limiting their utility in addressing complex, jurisdiction-specific ecological challenges.2. This research presents a novel framework for building applied ABMs that integrate high-resolution spatial, behavioural, and environmental datasets. By leveraging telemetry data, remote sensing, and site-specific monitoring, the framework creates realistic, data-driven emulations of ecological systems. These models enable researchers and policymakers to test management and policy interventions virtually, offering insights into system-level dynamics and emergent trends without the risks, costs, or delays of field-based experiments.3. We highlight this framework’s ability to significantly enhance the realism of ABMs, increasing their robustness, transparency, and trustworthiness through rigorous data input and validation. By uncovering critical data gaps and fostering a circular feedback loop between simulation outputs and field data collection, the framework ensures continuous refinement of models and monitoring programs. We demonstrate the efficacy of this approach using a published workflow examining the role of badger movement in a disturbance landscape and its implications for disease transmission, showcasing how the framework supports evidence-based management and policy decisions.4. This research establishes a repeatable protocol for integrating ABMs into applied ecology, enhancing their capacity to inform evidence-based decisions. By promoting transparency, collaboration, and trust among stakeholders, it positions ABMs as an essential tool within applied ecology for addressing pressing conservation challenges and human-wildlife conflicts in a cost-effective, adaptable, and scientifically rigorous manner.Introduction In the Anthropocene, ecological systems are shaped by human activity, whether that is through positive forces such as conservation and management of ecosystems or negative impacts such as habitat destruction, climate change, overexploitation of natural resources, pollution, and the introduction of invasive species (Young et al. 2016; Von Essen et al. 2023). These impacts have heightened pressures on biodiversity and ecosystem services, often placing wildlife in direct conflict with human interests (Simkin et al. 2022). This tension is particularly evident in shared production landscapes (e.g. agricultural landscapes, forestry and aquaculture), where wildlife species interact frequently with human interests, leading to a wide variety human-wildlife conflicts across the globe (König et al. 2020; Munguia-Carrara, 2020; Göttert & Stark, 2022). Addressing these challenges demands applied ecological approaches that integrate diverse data sources, including monitoring from remote sensing technologies, systematic field surveys, and citizen science initiatives that are transformed into actionable evidence through state-of-the-art modeling techniques, such as agent-based and predictive ecological models, which capture the complexity of interactions between species, environments, and human activity (Silvy, 2020; Murphy et al. 2020; Morera-Pujol et al. 2023). Crucially, applied ecology extends beyond data analysis; it necessitates transdisciplinary collaboration among scientists, policymakers, and stakeholders to co-design conservation and management strategies (Silvy, 2020; Fuller et al. 2020; Murphy et al. 2022a).The field of applied ecology aims to foster win-win scenarios where ecological integrity is preserved while supporting human livelihoods and societal needs (Hopkins et al. 2021; Hegwood et al. 2022). Applied ecology is a cornerstone of evidence-based management and policy, but it remains a time-, resource, and cost-intensive endeavour (Manfredo et al. 2021). Shortages in personnel, data availability, or even appetite for evidence-based decision-making can significantly limit its broader implementation, posing challenges to its role in informing wildlife conservation and management (Mafredo et al. 2021). While applied ecology has tested frameworks for addressing conservation challenges (e.g. adaptive wildlife management), its practical implementation faces significant challenges (Dressel et al. 2018; Mansson et al. 2023). One of the foremost challenges lies in the inherent complexity and variability of ecological systems, where universal problems change often require system-specific solutions. Developing effective solutions requires longitudinal data to evaluate the long-term impacts of interventions, which are costly to establish and maintain long-term (Caughlan & Oakley, 2001; Lindenmayer et al. 2022). Without this iterative feedback process, conservation efforts risk oversimplification, leading to plans that fail to capture the nuance of real-world systems and deliver sustainable outcomes.Another critical challenge lies in the patchy availability and inconsistent quality of ecological data, which can significantly limit applied ecology’s effectiveness. Monitoring efforts are often unevenly distributed, with some regions or taxa receiving extensive attention while others remain poorly understood (Stephenson et al. 2015). This imbalance frequently forces researchers to rely on proxies or indices as substitutes for high-quality data, which may oversimplify ecological complexity and reduce the accuracy of conclusions (Murphy et al. 2022b). Additionally, many ecological datasets are difficult to access or entirely lost—a phenomenon known as ”dark data.” These datasets, often produced by small-scale, investigator-led studies, are rarely curated or shared due to limited funding, lack of incentives, and the complexity of managing heterogeneous data types (Hampton et al. 2013). The issue is compounded by a historical reliance on “long-tail science,” where small projects generate large amounts of data without sufficient resources for data management or sharing (Hampton et al. 2013). As a result, much of the information remains siloed, inaccessible for broader synthesis or reuse. Poor data infrastructure and insufficient collaboration further hinder the development of comprehensive datasets necessary for informing conservation and management decisions. Despite these challenges, applied ecology has made significant strides by integrating cutting-edge technologies and fostering collaboration across disciplines. Addressing these pitfalls is essential to furthering its impact and ensuring that conservation and management efforts are both effective and equitable.Agent-based modelling (ABMs) is a method that holds immense potential to address many of the pitfalls inherent in conducting research in applied ecology, offering a framework to integrate diverse data sources and explore the complexity of ecological systems in unprecedented detail (Murphy et al. 2020). By simulating individual agents (e.g., animals, plants, humans) and their interactions with the environment, ABMs can capture the emergent dynamics of ecosystems, allowing researchers to predict the outcomes of conservation interventions under various scenarios (McClane et al. 2011). These models are particularly well-suited to tackle system-specific challenges, such as species-specific responses to habitat management, or to assess the cascading impacts of management actions across landscapes (McClane et al. 2011; Murphy et al. 2020). However, while their use is increasing in applied ecology (Recio et al. 2020; Gritter et al. 2024; Murphy et al. 2024; Thompson et al. 2024), the application of ABMs to real-world applied ecological management and decision-making has historically been limited relative to the potential of the method.Traditionally, ABMs have been used predominantly in theoretical or ”blue-sky” ecological research, focusing on advancing fundamental ecological theory rather than providing actionable insights for applied contexts (Chivers et al. 2014; Ringelman, 2014; Kane et al. 2016; Teckentrup et al. 2018). This stems, in part, from the perception that ABMs lack the nuance and complexity needed to reflect real-world systems accurately. ABMs in ecology have traditionally not been parameterised with the level of realism required for evidence-based management. Instead, they often use oversimplified agents and environments, aiming to extrapolate patterns and mechanisms that can be generalised add nuanced evidence about the ecology of the species but not necessarily how to management them within a specific jurisdiction (Chivers et al. 2014; Carter et al. 2015). While this approach has provided valuable insights into ecological processes, it has limited the ability of ABMs to address system-specific management questions directly. Many ABMs assess their efficacy based on their ability to recreate patterns observed in the field, yet they often lack validation using independent, external, and longitudinal datasets (Carter et al. 2015). This absence of robust validation, which would confirm that model outputs align with real-world outcomes over time, has been a significant barrier to their application in high-stakes decision-making. Without this level of proof, ABMs struggle to gain the trust needed to inform contentious management scenarios or resolve conflicts involving multiple stakeholders, who often have significant stakes in the outcomes and cannot afford to rely on models that lack the realism to address their specific circumstances.Recent advancements, however, are rapidly transforming the potential of ABMs for applied ecology. The growth of big data repositories (e.g. MoveBank), high-resolution remote sensing (e.g. Google Earth Engine), and satellite imagery (e.g. Copernicus) now provides a wealth of spatially and temporally detailed datasets that can be used to parameterise ABMs with new levels of realism. Furthermore, the integration of artificial intelligence, including machine learning techniques, has enhanced the ability to develop ABMs capable of capturing complex, non-linear dynamics and emergent behaviours in the model. Improvements in computational power and software capabilities have also significantly reduced the time and resource requirements for running large-scale simulations with millions of agents and hundreds of procedures. Crucially, the community-driven, continual development of popular modeling environments such as R (R Core Team, 2024) and NetLogo (Wilensky, 1999) has played a pivotal role in advancing the capabilities of ABMs. These platforms now enable the seamless integration of diverse datasets and sub-models through extensions (e.g. gis, csv) , facilitating the creation of hyper-realistic agents and environments. This progress has allowed ABMs to scale up their applications, testing applied ecological systems in ways that were previously unfeasible. Combined with improvements in computational power and software efficiency, these advancements have made large-scale simulations increasingly accessible and practical for addressing real-world ecological challenges.In this paper, we present a methodological framework for building realistic agent-based models tailored to informing applied ecological challenges, hereafter referred to as “applied ABMs.” We demonstrate how to effectively parameterise agents and their environments using diverse input datasets and outline methods for generating data to feed into multivariable modelling and external validation protocols. This approach aims to deliver evidence-based insights for management and policy while integrating feedback loops that inform field data collection systems through applied ABMs. By fostering an adaptive data economy, this framework addresses issues such as dark data and long-tail science, promoting iterative improvement in applied ecological management. To illustrate the framework’s practical application, we refer to its use in a previously published case study (Murphy et al. 2024), which investigates the role of wildlife hosts in the transmission of a zoonotic disease within a disturbance-driven landscape. This example highlights how applied ABMs can address pressing ecological and management questions with enhanced realism and practical utility.

Thyme is widely distributed in the worldwild. In China, there are 15 species, 2 varieties and 1 variant. Thymus quinquecostatus which contains abundant bioactive terpenoids is an important wild medicinal and aromatic plant in Chinese native thymes. Thymus vulgaris ‘Elsbeth’ comes from Europe and is known for its medicinal properties. The terpenoids exist in the glandular trichomes (GTs), a special epidemal structure. In Lamiaceae, glandular trichomes usually include peltate glandular trichomes (PGTs) and capitate glandular trichomes (CGTs). In previous study, we had analysed the molecular mechanisms of GTs but the formation process was not revealed. In this study, we observed the formation of PGTs and CGTs in thyme. The PGT underwent the complex process, including the basal, stalk, and head cells, there were 8-12 head cells. The CGT also had three cells, but its head cell only had one cell. Meanwhile, molecular biology research was carried out and we identified 68 HD-ZIP proteins and selected several key genes related to the formation of GTs according to the expression levels. Then, we cloned an HD-ZIP Ⅳ transcription factor TqHD1 from T. quinquecostatus and characterized it. TqHD1 not only can promote the formation of GTs but also can lead to the changes of volatile components and some relative genes levels. These findings complete the study of cell micromorphology of thyme and lay the foundation for characterization of factors in epidermis-related functions in thyme.

Fuel cell (FC) systems offer promising solutions for the production of energy that is both efficient and environmentally friendly and are being developed for various applications such as residential, mobile, and vehicle. This advanced technology’s effectiveness, efficiency, and durability depend on a deep understanding, precise prediction, and effective management of the unique transient behaviors exhibited by the FC system. This study focuses on the development and analysis of a machine learning-based maximum power point tracking (ML-MPPT) controller for proton exchange membrane fuel cell (PEMFC) systems. The efficiency of the proposed controller was performed using MATLAB/Simulink, a powerful platform for dynamic system modeling and simulation. Firstly, a model was created using MATLAB/Simulink to evaluate the behavior of PEMFC. Subsequently, data collected from FC operating under diverse conditions were employed to train machine learning algorithms. Based on the simulation outcomes, it was observed that the proposed MPPT controller provides precise and fast MPPT performance. The simulation results demonstrate that the suggested MPPT controller achieves precise results in MPPT performance, exhibiting reduced power fluctuations and enhanced production efficiency. This emphasizes its potential to address the challenges encountered in PEMFC systems effectively.

This paper presents a novel method for exploring Goldbach's Conjecture, which asserts that every even integer greater than 2 can be expressed as the sum of two prime numbers. The proposed approach involves organizing all natural numbers within a binary tree structure, enabling the identification of intricate relationships between even numbers and prime numbers. By leveraging the unique properties of the tree's hierarchy and connections, this method provides a new perspective on the conjecture and its potential proof. The paper includes a detailed demonstration of the method, highlighting its effectiveness in uncovering insights into the interplay between primes and even integers.

POSTTRAUMATIC EPIDERMOID INCLUSION CYST FOLLOWING UNTREATED ORBITAL-ZYGOMATICOMAXILLARY FRACTURE: CASE REPORTDavid Kiwango Deoglas 1, §, Paulo Joseph Laizer1 Shaban Daudi Shaban 21 Department of Oral and Maxillofacial Surgery, School of Dentistry, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania

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A document by Betty Anane-Fenin. Click on the document to view its contents.

The aim of this research is to improve the speed of the wind turbine blade optimization process while consuming less computation power. The traditional process using the constraint optimization technique and metaheuristics (MHs) cannot simultaneously handle many design variables, which is usually addressed by simplifying the problem. In this study, a novel encoding/decoding algorithm for constraint handling was constructed to handle all design variables prior to the application of metaheuristics. The performance enhancement of a 5-MW horizontal-axis wind turbine blade optimization process by the novel algorithm was tested in which 53 design variables, including chord length, twist angle, chord distribution slope, twist distribution slope, and airfoil shape, were treated and then optimized for the maximum power coefficient with several up-to-date metaheuristics. The performance gains of the metaheuristics were indicated by the highest convergence rate and minimum fitness value. Moreover, the optimal rotor had a power coefficient of up to 0.4987, 7.28% higher than that of the National Renewable Energy Laboratory offshore 5-MW baseline wind turbine. The encoding/decoding algorithm together with MHs significantly outperformed the traditional method with respect to optimization and wind turbine blade performance, thereby enabling us to complete the optimization more quickly and obtain better results.

Prepulse inhibition (PPI) of acoustic startle reflex (ASR) measures sensory-motor gating. Further, it provides a platform to assess tinnitus using a gap as a prepulse in the middle of background noise (GPIAS). Despite its validity and debate on neural circuits, GPIAS is being tested on various species, such as rats, mice, guinea pigs, and gerbils. However, there is a considerable lack of evidence of PPI on chinchillas which is an ideal animal model in auditory studies. When optimizing parameters, we used the set parameters to assess blast-induced tinnitus using GPIAS. In this study, before subjecting animals to GPIAS, inhibition of startle using an NBPIAS paradigm was measured using a noise-burst as a prepulse stimulus and was presented 200 ms before the startle stimuli (broadband noise bursts at 120 dB SPL, 20 ms with rise/fall time of 1 ms). The prepulse: no prepulse ratio was calculated based on the Preyer response from the trials with and without prepulse. However, whole-body movement responses were also collected. The animals that robustly exhibited PPI were subjected to the GPIAS paradigm to measure inhibition of startle response using gap detection as a cue. In GPIAS, the background noise is either a narrow-band noise (one-third of octave bandwidth) centered at 1, 2, 4, 8 & 10 kHz or broadband noise presented at 75 dB SPL for 30 s before the presentation of startle stimuli. A gap duration of 100 ms was introduced into background noise in 50% of the trials at 200 ms before the startle stimuli. Both startle response magnitude and pinna displacement were measured. The gap startle ratio was calculated for a given frequency. Before blast exposure, tinnitus was measured by gap detection using prepulse inhibition. The flexion of the pinna was tracked using four IR cameras with reflective markers on the pinna and the center of the body. X, Y & Z coordinates over time were exported to MATLAB, and the magnitude of Preyer’s reflex was calculated for all error-free trials using pinna displacement. Importantly, in addition to the Preyer reflex, for analysis, we used logarithmized ASR ratios with a normal distribution instead of raw PPI values of non-normal distribution to infer GPIAS deficits. The parametric study of GPIAS using logarithmized ASR ratios provides a better interpretation of GPIAS deficits. Using the Preyer reflex instead of whole-body movement provides a better interpretation of GPIAS deficits in chinchillas. These results set the platform for using Chinchilla as a model to study tinnitus using the GPIAS paradigm.

Emotion is a complex mental experience often accompanied by physiological changes such as rapid heartbeat, altered breathing, sweating, and shifts in facial expressions. Detecting emotions through the physiological signals poses a significant challenge but offers valuable applications, including developing wearable assistive devices and intelligent human-computer interactions. With the proliferation of wearable devices like smartwatches and wristbands, emotion detection in natural environments has gained prominence as a research focus. This study investigates automated emotion detection through physiological signals, specifically electrocardiograms (ECG) and galvanic skin responses (GSR), captured by wirelessly connected wearable devices. By integrating the Time Series Feature Extraction Library (TSFEL) and Recursive Feature Elimination (RFE), the rich set of temporal, statistical, spectral, and fractal features are mined and selected from these non-stationary signals. These features are then analyzed using machine learning classifiers, including “Artificial Neural Networks”, “k-Nearest Neighbor”, “Support Vector Machine”, “Random Forest”, “Adaboost”, and “gradient-boosted decision Trees” (XGBoost), for an automated emotion classification. A benchmark dataset is used to validate the methodology. The devised method secured the average classification accuracy of 79.46% for the case of eight-class problem while using the XGBoost classifier. The findings underscore the potential of intelligently combining the TSFEL, RFE, and ensemble of machine learning classifiers to enable an effective emotion detection via wearable devices.

Northern elephant seals (Mirounga angustirostris) exhibit some of the strongest anatomical and behavioral sexual dimorphism of any mammalian species. The degree of dimorphism at the microbial level, especially in young individuals, is still relatively unknown. Here, we investigated the interplay between sex, county of stranding, rehabilitation environment, and host genetics on the gut microbiomes of 44 northern elephant seal pups that were stranded along the California coastline and brought to a rehabilitation facility. Using a metabarcoding approach, we characterized microbial communities shortly after admission to the facility and found that both sex and county of stranding contributed to variation in microbial composition. Through population genetic analyses, we showed that the effect of county of stranding on microbial composition was not driven by underlying genetic structure. More broadly, we did not find any correlation between host genetics and microbiome dissimilarity, perhaps related to the extremely low genetic diversity of this bottlenecked species. Finally, we analyzed paired samples from a subset of 24 seals at two time points: shortly after admission to the rehabilitation facility and a month post-acclimation in the facility. Although microbiome compositions became more similar over time, sex continued to contribute to variation. Sex had a weaker effect on microbiome variation at the second time point in comparison to the first, potentially due to the homogenizing effects of rehabilitation. Our findings ultimately help shape our understanding of how environment and sex shape the gut microbiomes of young NES during an understudied period of development.

Recent advances in modeling 21st-century sea-level rise (SLR) and its associated societal outcomes have demonstrated that the spatial pattern of SLR combined with highly variable population density along global coastlines exert a strong control on its impacts. Here, we extend this research by examining differential costs arising from two sources of SLR that exhibit distinct spatial “fingerprints” — mass flux from the Antarctic (AIS) and Greenland (GrIS) Ice Sheets. To do this, we employ the DSCIM-Coastal data and modeling platform to quantify flood extents and population exposure to inundation from sea-level changes associated with an ensemble of Ice Sheet Model Intercomparison Project projections between 2015 and 2100 CE. We also introduce the Social Cost of Ice Sheet Mass loss (SC-ISM) metric and calculate this for both AIS and GrIS mass loss scenarios. Due to the distinct sea-level fingerprints of the two ice sheets, we find that mass flux from the AIS floods a larger area and would inundate a greater (present-day) population than an equivalent mass flux from the GrIS and yields a substantially higher SC-ISM. Across a suite of future climate scenarios, the global SC-ISM associated with AIS mass loss is ~30% higher than that of GrIS, driven largely by differential SLR rates along North Atlantic coastlines. Additionally, across both ice sheet mass loss scenarios and a uniform sea-level rise scenario, the SC-ISM exhibits disproportionate impacts. In other words, when normalized by local GDP, low-income regions experience a greater economic burden than high-income regions, regardless of sea-level rise source.

Objective: To investigate the intervention effect of Anemarrhenae Rhizoma (AR) on osteoporosis (OP) in rats and elucidate the potential regulatory network mechanism of AR in ameliorating OP. Methods: RNA-seq technology was employed to detect differential gene expression in the femur transcription profiles of rats. Differential metabolites in rat serum were analyzed using GC-MS. An integrated analysis of transcriptomics and metabolomics was conducted to construct the ” metabolite-gene ” interaction network. The expression of key target genes was verified through polymerase chain reaction (PCR) and western blot analysis to further clarify the regulatory mechanism of AR on OP in rats. Results: AR reversed OVX-induced femoral damage, modulated serum markers, inhibited bone resorption, promoted bone formation, and improved OP. Transcriptomics and metabolomics analysis demonstrated that AR administration altered the expression levels of 698 genes and 27 endogenous metabolites in OP rats. By constructing an interaction network of differential genes and differential metabolites, seven key metabolites and six key genes were identified. These key molecules influence amino acid metabolism, lipid metabolism, and other pathways related to bone metabolism. Conclusion: AR improved OP in rats by positively regulating changes in femoral transcriptional profiles and endogenous metabolites.

A document by lin wang. Click on the document to view its contents.

Context: Manuscript management systems (MMS) serve as centralized platforms for managing the submission, review, and publication of scholarly articles, thereby streamlining the publishing process. The submission process is crucial for usability analysis, as it represents the initial stage of interaction with users’ profiles. While many researchers emphasize the importance of making these systems more user-friendly, there is a notable lack of studies evaluating the user experience of prominent MMS and offering solutions for identified usability issues. Aim: This research aims to identify usability issues faced by authors working with prominent manuscript management systems, measure user satisfaction levels for the evaluated systems, and propose usability guidelines to enhance their user experience. Method: In this research, the authors conducted comparative usability testing to identify usability issues encountered by typical users when interacting with prominent manuscript management systems. Additionally, they utilized the SUS questionnaire to measure user satisfaction with the submission process. Results: The usability testing results reveal that Editorial Manager, ScholarOne, and Open Journal Systems exhibit numerous usability issues in submission, user registration, password recovery, and profile updating. These issues violate several usability heuristics and web design principles. The results indicate low user satisfaction, with Editorial Manager rated as “Awful,” ScholarOne as “Okay,” and Open Journal Systems as “Poor”. Conclusion: Usability analysis identifies key usability issues and proposes usability guidelines for manuscript management systems. These guidelines aim to minimize usability issues and ensure excellent user experience, thereby enabling developers to design more usable manuscript management systems.

Skin cancer, particularly melanoma, poses a significant global health challenge due to its prevalence and mortality rate. Early detection is critical to improving outcomes, as advanced cases become increasingly difficult to treat. The advent of Artificial Intelligence (AI) and Explainable AI (XAI) techniques has revolutionized dermatological diagnostics by offering accurate and interpretable solutions. This systematic review investigates the integration of XAI in skin lesion classification, analyzing 22 recent studies published between 2019 and 2023. The studies encompass diverse approaches, including deep learning models like CNNs, ResNet, DenseNet, and MobileNet, as well as explainability techniques such as Grad-CAM, SHAP, and saliency maps. Results highlight significant advancements in accuracy and interpretability, with some models achieving over 99% accuracy on datasets like ISIC 2018 and HAM10000. However, challenges persist, including dataset imbalances, limited diversity in patient metadata, and generalizability across different skin types and imaging conditions. XAI methods, by visualizing model decision pathways, enhance transparency, fostering trust among clinicians and enabling seamless AI integration into clinical practice. This review underscores the potential of combining state-of-the-art AI models with explainable frameworks to address the complexities of skin lesion diagnostics. It advocates for future research to prioritize diverse, metadata-rich datasets, innovative optimization techniques, and robust architectures to develop reliable, interpretable diagnostic tools. By bridging the gap between advanced AI and user understanding, this work contributes to the creation of clinically applicable, trustable AI-driven healthcare solutions.

Background: L-glutamine (Gln) suppresses inflammation via rapid up-regulation of MAPK phosphatase (MKP)-1, deactivating p38 and JNK mitogen-activated protein kinases (MAPKs). However, the high dosage required for this may cause serious side effects. Objective: To facilitate reduced Gln intake, we developed a less-hydrolysable Gln derivative, α, δ-N-acetyl-glutamine (α, δ-NAG), which is resistant to the hydrolytic action of glutaminase. Methods: We developed α, δ-NAG by substituting the NH 2 group in α-chain and δ-amide group of Gln with acetyl groups. We employed the ovalbumin model, previously developed by us, to examine sequential asthmatic events, including neutrophilia/Th1 and eosinophilia/Th2 responses. MKP-1 was knocked down using small-interfering RNA (siRNA). Gln levels and intracellular calcium concentration ([Ca 2+] i) were analysed using multiple reaction monitoring chromatograms and confocal laser scanning microscopy, respectively. Results: Oral administration of α, δ-NAG and Gln suppressed all the parameters at 0.2 and 2 g/kg body weight, respectively. MKP-1 siRNA abrogated the beneficial effects of α, δ-NAG. α, δ-NAG up-regulated MKP-1 in an ERK MAPK-dependent manner. α, δ-NAG transiently increased [Ca 2+] I, resulting in increased Ras activity. Inhibition of Gα q, a G-protein subfamily, abrogated the effects of α, δ-NAG on [Ca 2+] I and Ras activity. Inhibition of Gα q, Ca 2+, and Ras abrogated the effects of α, δ-NAG, such as signalling pathways (ERK phosphorylation and MKP-1 up-regulation) and clinical signs (neutrophilia/Th1 responses) in asthmatic mice. Conclusion: α, δ-NAG exhibits strong anti-inflammatory activity (~ 10,000-fold stronger than that of Gln), likely attributable to its up-regulation of MKP-1 by activating pathways involving the G protein-coupled receptor (GPCR)/Gα q/Ca 2+/Ras/ERK cascade.

Clustering and routing are effective solutions for addressing the potential design difficulty of energy efficiency in Multimedia assisted Wireless Sensor Networks (WSNs). However, imbalances in the distribution of chosen cluster head (CH) nodes and difficult data transmission routes might lead to unequal energy exhaustion in the network. This work focuses on Energy-Efficient Cluster-based Routing Protocols using the Resilient Honey Badger Optimization Algorithm (EECR-RHBOA) for cluster head (CH) selection in WSN. Inspired by honey badger foraging behaviour, the RHBOA algorithm incorporates resilience and adaptation into cluster formation and routing selections to guarantee optimum network energy usage. RHBOA selects the best cluster head among all the sensors regarding distance to the residual energy, base stations (BS), distance to neighbours, node degree, and centrality. The two main steps of the suggested EECR protocol are cluster formation and data transmission. The RHBOA algorithm arranges sensor nodes into clusters during the cluster formation phase. The method then examines a fitness function considering residual energy, node centrality, and intra-cluster communication cost to choose cluster heads. Extended network lifespan is achieved by reaching balanced energy consumption among multiple nodes. The RHBOA algorithm minimizes energy dissipation during data transmission by optimizing multi-hop routing processes from CHs to BS. These approaches avoid nodes with low energy and help alleviate network congestion. The numerical outcomes show the suggested EECR-RHBOA technique to achieve a lower packet ratio of 0.05, throughput of 104Kbps, packet delivery ratio of 96.2% and coverage rate of 94.8% compared to other methods.

A document by Jiangmin Wan. Click on the document to view its contents.

Urban trees provide essential ecosystem services, notably air cooling through transpiration, which helps mitigate the urban heat island effect and enhances cities’ climate resilience. However, the complex spatial variability within urban areas and extreme weather events like droughts can disrupt trees’ ecohydrological dynamics. In a study conducted in Freiburg, Germany, we investigated transpiration processes in Norway maple (Acer platanoides) and small-leaved lime (Tilia cordata) across diverse urban locations, including parks, parking lots, grass verges, and tree pits. We assessed the effects of four distinct drought periods on transpiration and compared differences between tree species and growing sites. Small-leaved lime exhibited a 5% greater reduction in transpiration during drought periods compared to Norway maple, which experienced a 34% decline in transpiration during peak sap flow compared to non-drought periods. Tree pits with 90% surface sealing induced the most significant drought-induced transpiration reduction for small-leaved lime (58%), with both species displaying the lowest transpiration to potential evapotranspiration ratio in these locations. Significant differences were observed in the diurnal sap velocity patterns for both species. We highlighted the site-specific impact of surface sealing on transpiration during droughts, as well as the significant relationship between soil water deficit and relative transpiration rates. This study provides crucial insights into common urban tree species’ responses to drought-induced transpiration across varied urban settings, emphasizing the role of surface sealing. Continuous monitoring of diverse urban tree species is essential for building extensive databases and enhancing our understanding of tree water relations in diverse urban landscapes.

This study evaluated the effects of specimen and coarse aggregate sizes on the mechanical properties (compressive strength, modulus of elasticity, and Poisson’s ratio) of concrete. Cylindrical specimens of three concrete grades (C25, C45, C60) with two different coarse aggregate sizes (16 and 25 mm), and four specimen sizes (0.66 × 10 3, 1.57 × 10 3, 5.30 × 10 3, and 12.56 × 10 3 cm 3) were tested under static loading. The results reveal that smaller specimens consistently exhibited higher compressive strength and modulus of elasticity, with compressive strength reductions ranging from 51.54% to 56.42% as specimen size increased. Lower-grade concrete (C25) was more sensitive to size effects, while higher-grade concrete (C60) exhibited improved resistance. The Modulus of elasticity decreased by up to 30.5%, with smaller coarse aggregates sizes. Concrete bulk density decreased with specimen size, reflecting increased material heterogeneity and void content in larger specimens. Larger aggregates (25 mm) generally resulted in higher densities due to efficient packing, except in C60, where better binder quality offset this effect. The Poisson’s ratio increased slightly with specimen size, ranging from 0.15 to 0.25, reflecting greater lateral strain in larger specimens. Smaller aggregates occasionally exhibited higher Poisson ratios, indicating improved ductility.

This short communication develops an existence result for a general non-linear parabolic equation and concerning eigenvalue problem. The method of proof comprises an application of the Newton's method combined with a proximal approach and a Banach fixed point theorem.

CHAPTER 14 — AWS Well-Architected FrameworkPreprint DOI: https://doi.org/10.22541/au.173679849.92631174/v1 Status: Archived pre-publication draft (2024) — SupersededThis chapter is an early developmental version later finalized and published as part of:Leocadio, Paulo H. (2025). Mastering AWS Cloud. BPB Publications — ISBN: 9789365890617 (Released October 28, 2025).A significantly updated, expanded, and editorially refined version appears in the published edition. This preprint is preserved for scholarly transparency, version tracking, and citation continuity.For academic use:         • Cite this DOI as the preprint record, or         • Cite the published book for the authoritative edition.Readers seeking the most accurate and up-to-date content should refer to the published version.

CHAPTER 13 — Migration and TransferPreprint DOI: https://doi.org/10.22541/au.173679848.85679645/v1 Status: Archived pre-publication draft (2024) — SupersededThis chapter is an early developmental version later finalized and published as part of:Leocadio, Paulo H. (2025). Mastering AWS Cloud. BPB Publications — ISBN: 9789365890617 (Released October 28, 2025).A significantly updated, expanded, and editorially refined version appears in the published edition. This preprint is preserved for scholarly transparency, version tracking, and citation continuity.For academic use:         • Cite this DOI as the preprint record, or         • Cite the published book for the authoritative edition.Readers seeking the most current material should refer to the published version.