The accelerated urban sprawl of cities around the world presents major challenges for urban planning and land resource management. In this context, it is crucial to have a detailed 3D representation of buildings enriched with accurate alphanumeric information. A distinctive aspect of this proposal is its specific focus on the spatial unit corresponding to buildings. In order to propose a domain model for the 3D representation of buildings, the national standard of Ecuador and the international standard (ISO 19152:2012 LADM) were considered. The proposal includes a detailed specification of attributes, both for the general subclass of buildings and for their infrastructure. The application of the domain model proposal was crucial in a study area located in the Riobamba canton, due to the characteristics of the buildings in that area. For this purpose, a geodatabase was created in pgAdmin4 with official information, taking into account the structure of the proposed model and linking it with geospatial data for an adequate management and 3D representation of the buildings in an open-source Geographic Information System. This application improves cadastral management in the study region and has wider implications. This model is intended to serve as a benchmark for other countries facing similar challenges in cadastral management and 3D representation of buildings, promote efficient urban development and contribute to global sustainable development.

Nature-based solutions that use a counterfactual scenario depend heavily on the methodology used to determine the business as usual (BAU) case, i.e., the \"baseline.\" Reducing emissions from deforestation and forest degradation (REDD+) projects traditionally set baselines using a \"reference area\" as a control for estimating BAU deforestation and emissions in the treatment (project) area. While the REDD+ market is shifting from project-based to nested approaches as countries increase their efforts to meet nationally determined contributions (NDCs) to the Paris agreement\'s global climate target, methodologies for allocating national baselines are not yet formalized and tested, despite an urgent need to scale the market. We present a novel method for mapping deforestation risk and allocating national forest reference emission levels (FREL) to projects: baseline allocation for assessed risk (BAAR). This approach provides a spatial predictor of future deforestation using a dynamic vector, and a method for allocating a FREL to differentiated risk areas at the project level. Here, we present BAAR using 34 REDD+ projects in the Democratic Republic of the Congo (DRC). We demonstrate the importance of risk-based FREL allocations to balance fitness for purpose and scientific rigor. We show how BAAR can be used by governments to focus voluntary carbon market finance in areas at highest risk of imminent deforestation, while maintaining alignment with nationally determined contribution (NDC) goals.

OBJECTIVE: To describe how people living with HIV/AIDS (PLWHA) make decisions using the diffusion of innovation theory model. Decisions occur when individual decision makers engage in activities that guide choices to adopt or reject a particular innovation.
METHODS: This is a descriptive analysis research using a survey method. Data collection was carried out using a decision making questionnaire. The subjects in this research were HIV/AIDS sufferers (PLWHA) who lived in Turen, Indonesia. The number of research subjects was 36 respondents obtained using the purposive sampling technique on January 2023.
RESULTS: The research design used the correlation method with a cross sectional approach and the Spearman correlation coefficient statistical test. The research results show significance (2-tailed) of 0.934 (p>0.05). The correlation coefficient results are negative. Where the direction of the correlation produces showed a very weak relationship with a value of 0.014 and the results of the analysis between variables are not the same. This is because the persuasion stage was not tested in the analysis. Research shows that 8 (22.2%) patients rejected the decision making of PLWHA using the diffusion of innovation theory model at the Western Provident Association Turen Foundation, Turen, Indonesia, while 28 (77.7%) patients accepted it. It can be concluded that the majority of decisions made by HIV/AIDS patients at the ADIS Turen Peduli Warga Foundation are accepted.
CONCLUSIONS: Knowledge of HIV/AIDS sufferers is at a good level, their decision making is mostly accepted and there is a meaningful relationship between knowledge and decision making in using a chatbot innovation. The suggestion from the research is that this chatbot innovation can be a source of further research and help provide education for PLWHA patients in everyday life.

The modified Benjamin-Bona-Mahony (mBBM) model is utilized in the optical illusion field to describe the propagation of long waves in a nonlinear dispersive medium during a visual illusion (Khater 2021). This article investigates the mBBM equation through the utilization of the rational [Formula: see text]-expansion technique to derive new analytical wave solutions. The analytical solutions we have obtained comprise hyperbolic, trigonometric, and rational functions. Some of these exact solutions closely align with previously published results in specific cases, affirming the validity of our other solutions. To provide insights into diverse wave propagation characteristics, we have conducted an in-depth analysis of these solutions using 2D, 3D, and density plots. We also investigated the effects of various parameters on the characteristics of the obtained wave solutions of the model. Moreover, we employed the techniques of linear stability to perform stability analysis of the considered model. Additionally, we have explored the stability of the associated dynamical system through the application of phase plane theory. This study also demonstrates the efficacy and capabilities of the rational [Formula: see text]-expansion approach in analyzing and extracting soliton solutions from nonlinear partial differential equations.

The Precautionary Approach to Fisheries Management requires an assessment of the impact of uncertainty on the risk of achieving management objectives. However, the main quantities, such as spawning stock biomass (SSB) and fish mortality (F), used in management metrics cannot be directly observed. This requires the use of models to provide guidance, for which there are three paradigms: the best assessment, model ensemble, and Management Strategy Evaluation (MSE). It is important to validate the models used to provide advice. In this study, we demonstrate how stock assessment models can be validated using a diagnostic toolbox, with a specific focus on prediction skill. Prediction skill measures the precision of a predicted value, which is unknown to the model, in relation to its observed value. By evaluating the accuracy of model predictions against observed data, prediction skill establishes an objective framework for accepting or rejecting model hypotheses, as well as for assigning weights to models within an ensemble. Our analysis uncovers the limitations of traditional stock assessment methods. Through the quantification of uncertainties and the integration of multiple models, our objective is to improve the reliability of management advice considering the complex interplay of factors that influence the dynamics of fish stocks.

In this paper, we suggest a mathematical model of COVID-19 with multiple variants of the virus under optimal control. Mathematical modeling has been used to gain deeper insights into the transmission of COVID-19, and various prevention and control strategies have been implemented to mitigate its spread. Our model is a SEIR-based model for multi-strains of COVID-19 with 7 compartments. We also consider the circulatory structure to account for the termination of immunity for COVID-19. The model is established in terms of the positivity and boundedness of the solution and the existence of equilibrium points, and the local stability of the solution. As a result of fitting data of COVID-19 in Ghana to the model, the basic reproduction number of the original virus and Delta variant was estimated to be 1.9396, and the basic reproduction number of the Omicron variant was estimated to be 3.4905, which is 1.8 times larger than that. We observe that even small differences in the incubation and recovery periods of two strains with the same initial transmission rate resulted in large differences in the number of infected individuals. In the case of COVID-19, infections caused by the Omicron variant occur 1.5 to 10 times more than those caused by the original virus. In terms of the optimal control strategy, we formulate three control strategies focusing on social distancing, vaccination, and testing-treatment. We have developed an optimal control model for the three strategies outlined above for the multi-strain model using the Pontryagin\'s Maximum Principle. Through numerical simulations, we analyze three optimal control strategies for each strain and also consider combinations of the two control strategies. As a result of the simulation, all control strategies are effective in reducing disease spread, in particular, vaccination strategies are more effective than the other two control strategies. In addition the combination of the two strategies also reduces the number of infected individuals by 1/10 compared to implementing one strategy, even when mild levels are implemented. Finally, we show that if the testing-treatment strategy is not properly implemented, the number of asymptomatic and unidentified infections may surge. These results could help guide the level of government intervention and prevention strategy formulation.

In this research, we employ the potent technique of Lie group analysis to derive analytical solutions for the (3+1)-extended Kadomtsev-Petviashvili (3D-EKP) equation. The systematic application of this method enables the identification of Lie point symmetries associated with the equation, leading to the derivation of an optimal system of one-dimensional subalgebras relevant to the equation. This optimal system is utilized to obtain several invariant solutions. The Lie group method is subsequently applied to the reduced governing equations derived from the given equation. We complement our findings with Mathematica simulations illustrating some of the obtained solutions. Furthermore, a direct approach is used to investigate local conservation laws. Importantly, our study addresses a gap in the exploration of the 3D-EXP equation using group theoretic methods, making our findings novel in this context.

Excessive anthropogenic phosphorus (P) emissions put constant pressure on aquatic ecosystems. This pressure can be quantified as the freshwater eutrophication potential (FEP) by linking P emissions, P fate in environmental compartments, and the potentially disappeared fraction of species due to increase of P concentrations in freshwater. However, previous fate modeling on global and regional scales is mainly based on the eight-direction algorithm without distinguishing pollution sources. The algorithm fails to characterize the fate paths of point-source emissions via subsurface pipelines and wastewater treatment infrastructure, and exhibits suboptimal performance in accounting for multidirectional paths caused by river bifurcations, especially in flat terrains. Here we aim to improve the fate modeling by incorporating various fate paths and addressing multidirectional scenarios. We also update the P estimates by complementing potential untreated point-source emissions (PSu). The improved method is examined in a rapidly urbanizing area in Taihu Lake Basin, China in 2017 at a spatial resolution of 100 m × 100 m. Results show that the contribution of PSu on FEP (62.6%) is greater than that on P emissions (58.5%). The FEP is more spatially widely distributed with the improved fate modeling, facilitating targeted regulatory strategies tailored to local conditions.

Vaccination against COVID-19 was integral to controlling the pandemic that persisted with the continuous emergence of SARS-CoV-2 variants. Using a mathematical model describing SARS-CoV-2 within-host infection dynamics, we estimate differences in virus and immunity due to factors of infecting variant, age, and vaccination history (vaccination brand, number of doses and time since vaccination). We fit our model in a Bayesian framework to upper respiratory tract viral load measurements obtained from cases of Delta and Omicron infections in Singapore, of whom the majority only had one nasopharyngeal swab measurement. With this dataset, we are able to recreate similar trends in URT virus dynamics observed in past within-host modelling studies fitted to longitudinal patient data.We found that Omicron had higher R0,within values than Delta, indicating greater initial cell-to-cell spread of infection within the host. Moreover, heterogeneities in infection dynamics across patient subgroups could be recreated by fitting immunity-related parameters as vaccination history-specific, with or without age modification. Our model results are consistent with the notion of immunosenescence in SARS-CoV-2 infection in elderly individuals, and the issue of waning immunity with increased time since last vaccination. Lastly, vaccination was not found to subdue virus dynamics in Omicron infections as well as it had for Delta infections.This study provides insight into the influence of vaccine-elicited immunity on SARS-CoV-2 within-host dynamics, and the interplay between age and vaccination history. Furthermore, it demonstrates the need to disentangle host factors and changes in pathogen to discern factors influencing virus dynamics. Finally, this work demonstrates a way forward in the study of within-host virus dynamics, by use of viral load datasets including a large number of patients without repeated measurements.

Daylight saving time (DST) is currently utilized in many countries with the rationale that it enhances the alignment between daylight hours and activity peaks in the population. The act of transitioning into and out of DST introduces disruptions to the circadian rhythm, thereby impacting sleep and overall health. Despite the substantial number of individuals affected, the consequences of this circadian disruption have often been overlooked. Here, we employ a mathematical model of the human circadian pacemaker to elucidate how the biological clock interacts with daytime and evening exposures to both natural and electrical light. This interaction plays a crucial role in determining the adaptation to the 1 hour time zone shift imposed by the transition to or from DST. In global discussions about DST, there is a prevailing assumption that individuals easily adjust to DST transitions despite a few studies indicating that the human circadian system requires several days to fully adjust to a DST transition. Our study highlights that evening light exposure changes can be the main driving force for re-entrainment, with chronobiological models predicting that people with longer intrinsic period (i.e. later chronotype) entrain more slowly to transitions to or from DST as compared to people with a shorter intrinsic period (earlier chronotype). Moreover, the model forecasts large inter-individual differences in the adaptation speed, in particular during the spring transition. The predictions derived from our model offer circadian biology-based recommendations for light exposure strategies that facilitate a more rapid adaptation to DST-related transitions or travel across a single time zone. As such, our study contributes valuable insights to the ongoing discourse on DST and its implications for human circadian rhythms.