Sentinel satellite data monitoring of air pollutants with interpolation methods in Guayaquil, Ecuador

In Ecuador, there is a limitation on air quality monitoring due to the cost of monitoring networks. Although air quality monitoring stations are instruments for air measurement, they do not cover an entire city due to their scope. Satellite remote sensing is now an effective tool to study atmospheric pollutants and has been applied to continuously assess a region and overcome the limitations of fixed stations. Despite the application of satellite data for air quality monitoring, there are some limitations, such as measurement frequency, cloud cover and wide spatial resolution, which do not allow the assessment of air pollution in cities. Therefore, downscaling, applying interpolation methods, is essential for continuous air quality monitoring at smaller scales. For this research, Nitrogen Dioxide (NO2) data from the Sentinel-5 satellite percussor was used in the city of Guayaquil for January–December 2020, which is considered before, during and after the COVID-19 quarantine. This mid-size port city does not have a permanent monitoring network, which prevents us from knowing the air quality. Due to the limitation of pixel size, this study used satellite data to apply interpolation techniques and reduce pixels to assess air quality. Two categories of interpolation were selected: deterministic and stochastic. The empirical Bayesian kriging (EBK) interpolation obtained a R2 of 0.9546, which was superior to the other methods applied. Therefore, the EBK method had the best accuracy for tropospheric NO2 concentration. Finally, the method used in this research can help monitor air quality in cities lacking continuous monitoring networks, as the reduction of the pixel size gives us a better pattern of pollutants.

IMPACT OF COVID-19 PANDEMIC ON THE AIR QUALITY OF INDIA

Towards the end of 2019, a novel contagious virus (COVID-19) came out of Wuhan, China and turned into a disastrous pandemic. Many countries were locked down;completely or partially. The ongoing pandemic not only affected our economies and routine life, but also the environment. This study was aimed to compare the air quality of the Indian subcontinent prior to and during the COVID-19 pandemic. In this regard, air quality parameters (ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, PM2.5 and PM10) and meteorological parameters (wind speed and relative humidity) were analysed. The data was obtained from 229 monitoring stations in India and satellitebased Aerosol Absorption Index (AAI) during the springs of 2019 and 2020. The result indicated a significant decline in the concentration mean, six air pollutants (i.e., PM2.5, PM10, N2, SO2, O3 and CO) decreased by 36.27, 42.96, 44.62, 28.88, 18.35 and 20.51 %, respectively during April 2020 due to less to no industrial activities and vehicular emissions. The spatial variation of each parameter was simulated using the Inverse Distance Weighted (IDW) interpolation method. An Analytical Hierarchy Process (AHP) model was applied to generate the overall air quality severity zonation map of the country. The zonation map indicated that by adopting cleaner fuel and restriction on biomass burning in the rural and urban sectors can improve the ambient air quality © 2023, Suranaree Journal of Science and Technology.All Rights Reserved.

COVID-19 Impact on the Italian Community-based System of Mental Health Care: Reflections and Lessons Learned for the Future

Surface longwave downward radiation (LWDR) is a key factor affecting the surface energy balance. The daily LWDR and the diurnal variations of LWDR are of great significance for studies of climate change and surface processes. How to obtain LWDR at an averaged temporal scale from instantaneous LWDR is one of the longstanding problems in the field of radiation budget from remote sensing. In this paper, two temporal upscaling methods are introduced, namely, a method based on the diurnal variations of LWDR (diurnal variation based, DVB) and a method based on random forest regression (RFR). The results reveal that: (1) The DVB method has a global hourly and daily LWDR root-mean-square error (RMSE) of less than 21 W/m2 and 15 W/m2, respectively, and the RMSE of the daily LWDR based on RFR is less than 7 W/m2;(2) When compared with four existing statistical interpolation methods, the DVB method can not only ensure the accuracy, but also can overcome the problem of missing samples and/or an abnormal samples during upscaling;(3) Except for directly predict daily LWDR, the DVB methods can also obtain more accurate LWDR diurnal variations such as hourly, half-hourly etc. The RFR method enables high-efficiency and accurate estimation of daily averaged LWDR from instantaneous measurements. Compared with existing methods and products, the proposed methods are not only efficient, but also have a superior applicability and reliable accuracy. The proposed strategies provide new ideas for the community in estimating LWDR at continuous temporal scales from remotely sensed measurements.

An empirical and modelling approach to the evaluation of cruise ships' influence on air quality: The case of La Paz, Mexico.

Maritime activity has diverse environmental consequences impacts in port areas, especially for air quality, and the post-COVID-19 cruise tourism market's potential to recover and grow is causing new environmental concerns in expanding port cities. This research proposes an empirical and modelling approach for the evaluation of cruise ships' influence on air quality concerning NO2 and SO2 in the city of La Paz (Mexico) using indirect measurements. EPA emission factors and the AERMOD modelling system coupled to WRF were used to model dispersions, while street-level mobile monitoring data of air quality from two days of 2018 were used and processed using a radial base function interpolator. The local differential Moran's Index was estimated at the intersection level using both datasets and a co-location clustering analysis was performed to address spatial constancy and to identify the pollution levels. The modelled results showed that cruise ships' impact on air quality had maximum values of 13.66 µg/m3 for NO2 and 15.71 µg/m3 for SO2, while background concentrations of 8.80 for NOx and 0.05 for SOx (µg/m3) were found by analysing the LISA index values for intersections not influenced by port pollution. This paper brings insights to the use of hybrid methodologies as an approach to studying the influence of multiple-source pollutants on air quality in contexts totally devoid of environmental data.

Recent Urban Issues Related to Particulate Matter in Ploiesti City, Romania

The present work aimed to assess the ambient levels of air pollution with particulate matter for both mass concentrations and number of particles for various fractions in Ploiesti city during the lockdown period determined by the COVID-19 pandemic (March–June 2020). The PM10 continuously monitored data was retrieved from four air quality automatic stations that are connected to the Romanian National Network for Monitoring Air Quality and located in the city. Because no other information was available for other more dangerous fractions, we used monitoring campaigns employing the Lighthouse 3016 IAQ particle counter near the locations of monitoring stations assessing size-segregated mass fraction concentrations (PM0.5, PM1, PM2.5, PM5, PM10, and TPM) and particle number concentration (differential Δ) range between 0.3 and 10 microns during the specified timeline between 8.00 and 11.00 a.m., which were considered the morning rush hours interval. Interpolation maps estimating the spatial distribution of the mass concentrations of various PM fractions and particle number concentration were drawn using the IDW algorithm in ArcGIS 10.8.2. Regarding the particle count of 0.5 microns during the lockdown, the smallest number was recorded when the restriction of citizens' movement was declared (24 March 2020), which was 5.8-times lower (17,301.3 particles/cm3) compared to a common day outside the lockdown period (100,047.3 particles/cm3). Similar results were observed for other particle sizes. Regarding the spatial distribution of the mass concentrations, the smaller fractions were higher in the middle of the city and west (PM0.5, PM1, and PM2.5) while the PM10 was more concentrated in the west. These are strongly related to traffic patterns. The analysis is useful to establish the impact of PM and the assessment of urban exposure and better air quality planning. Long-term exposure to PM in conjunction with other dangerous air pollutants in urban aerosols of Ploiesti can lead to potential adverse effects on the population, especially for residents located in the most impacted areas.

Video Compression using Deep Neural Networks

Advanced video compression is required due to the rise of online video content. A strong compression method can help convey video data effectively over a constrained bandwidth. We observed how more internet usage for video conferences, online gaming, and education led to decreased video quality from Netflix, YouTube, and other streaming services in Europe and other regions, particularly during the COVID-19 epidemic. They are represented in standard video compression algorithms as a succession of reference frames after residual frames, and these approaches are limited in their application. Deep learning's introduction and current advancements have the potential to overcome such problems. This study provides a deep learning-based video compression model that meets or exceeds current H.264 standards. © 2022 IEEE.

Spatio-temporal dynamics of aerosol distribution in an urban environment recorded in situ by means of a bike based monitoring system

Aerosol pollution in urban areas is highly variable due to numerous single emission sources such as automobiles, industrial and commercial activities as well as domestic heating, but also due to complex building structures redirecting air mass flows, producing leeward and windward turbulences and resuspension effects. In this publication, it is shown that one or even few aerosol monitoring sites are not able to reflect these complex patterns. In summer 2019, aerosol pollution was recorded in high spatial resolution during six night and daytime tours with a mobile sensor platform on a trailer pulled by a bicycle. Particle mass loadings showed a high variability with PM10 values ranging from 1.3 to 221 µg m-3 and PM2.5 values from 0.7 to 69.0 µg m-3. Geostatistics were used to calculate respective models of the spatial distributions of PM2.5 and PM10. The resulting maps depict the variability of aerosol concentrations within the urban space. These spatial distribution models delineate the distributions without cutting out the built-up structures. Elsewise, the overall spatial patterns do not become visible because of being sharply interrupted by those outcuts in the resulting maps. Thus, the spatial maps allow to identify most affected urban areas and are not restricted to the street space. Furthermore, this method provides an insight to potentially affected areas, and thus can be used to develop counter measures. It is evident that the spatial aerosol patterns cannot be directly derived from the main wind direction, but result far more from an interplay between main wind direction, built-up patterns and distribution of pollution sources. Not all pollution sources are directly obvious and more research has to be carried out to explain the micro-scale variations of spatial aerosol distribution patterns. In addition, since aerosol load in the atmosphere is a severe issue for health and well-being of city residents more attention has to be paid to these local inhomogeneities.

Changes in per capita wheat production in China in the context of climate change and population growth.

To address challenges associated with climate change, population growth and decline in international trade linked to the COVID-19 pandemic, determining whether national crop production can meet populations' requirements and contribute to socio-economic resilience is crucial. Three crop models and three global climate models were used in conjunction with predicted population changes. Compared with wheat production in 2000-2010, total production and per capita wheat production were significantly (P < 0.05) increase in 2020-2030, 2030-2040 and 2040-2050, respectively, under RCP4.5 and RCP8.5 due to climate change in China. However, when considering population and climate changes, the predicted per capita production values were 125.3 ± 0.3, 127.1 ± 2.3 and 128.8 ± 2.7 kg during the 2020-2030, 2030-2040, 2040-2050 periods under RCP4.5, or 126.2 ± 0.7, 128.7 ± 2.5, and 131.0 ± 4.1 kg, respectively, under RCP8.5. These values do not significantly differ (P > 0.05) from the baseline level (127.9 ± 1.3 kg). The average per capita production in Loess Plateau and Gansu-Xinjiang subregions declined. In contrast, per capita production in the Huanghuai, Southwestern China, and Middle-Lower Yangtze Valleys subregions increased. The results suggest that climate change will increase total wheat production in China, but population change will partly offset the benefits to the grain market. In addition, domestic grain trade will be influenced by both climate and population changes. Wheat supply capacity will decline in the main supply areas. Further research is required to address effects of the changes on more crops and in more countries to obtain deeper understanding of the implications of climate change and population growth for global food production and assist formulation of robust policies to enhance food security. Supplementary Information: The online version contains supplementary material available at 10.1007/s12571-023-01351-x.

ESA-FedGNN: Efficient secure aggregation for federated graph neural networks.

Graph Neural Network (GNN) architecture is a state-of-the-art model, which can obtain complete node embedding features and rich data information by aggregating the information of nodes and neighbors. Therefore, GNNs are widely used in electronic shopping, drug discovery (especially for the treatment of COVID-19) and other fields, promoting the explosive development of machine learning. However, user interaction, data sharing and circulation are highly sensitive to privacy, and centralized storage can lead to data isolation. Therefore, Federated Learning with high efficiency and strong security and privacy enhancement technology based on secure aggregation can improve the security dilemma faced by GNN. In this paper, we propose an Efficient Secure Aggregation for Federated Graph Neural Network(ESA-FedGNN), which can efficiently reduce the cost of communication and avoid computational redundancy while ensuring data privacy. Firstly, a novel secret sharing scheme based on numerical analysis is proposed, which employs Fast Fourier Transform to improve the computational power of the neural network in sharing phase, and leverages Newton Interpolation method to deal with the disconnection and loss of the client in reconstruction phase. Secondly, a regular graph embedding based on geometric distribution is proposed, which optimizes the aggregation speed by using data parallelism. Finally, a double mask is adopted to ensure privacy and prevent malicious adversaries from stealing model parameters. We achieve O ( log N log ( log N ) ) improvements compared to O N 2 in state-of-the-art works. This research helps to provide security solutions related to the practical development and application of privacy-preserving graph neural network technology.

Spatial interpolation of regional PM2.5 concentrations in China during COVID-19 incorporating multivariate data.

During specific periods when the PM2.5 variation pattern is unusual, such as during the coronavirus disease 2019 (COVID-19) outbreak, epidemic PM2.5 regional interpolation models have been relatively little investigated, and little consideration has been given to the residuals of optimized models and changes in model interpolation accuracy for the PM2.5 concentration under the influence of epidemic phenomena. Therefore, this paper mainly introduces four interpolation methods (kriging, empirical Bayesian kriging, tensor spline function and complete regular spline function), constructs geographically weighted regression (GWR) models of the PM2.5 concentration in Chinese regions for the periods from January-June 2019 and January-June 2020 by considering multiple factors, and optimizes the GWR regression residuals using these four interpolation methods, thus achieving the purpose of enhancing the model accuracy. The PM2.5 concentrations in many regions of China showed a downward trend during the same period before and after the COVID-19 outbreak. Atmospheric pollutants, meteorological factors, elevation, zenith wet delay (ZWD), normalized difference vegetation index (NDVI) and population maintained a certain relationship with the PM2.5 concentration in terms of linear spatial relationships, which could explain why the PM2.5 concentration changed to a certain extent. By evaluating the model accuracy from two perspectives, i.e., the overall interpolation effect and the validation set interpolation effect, the results showed that all four interpolation methods could improve the numerical accuracy of GWR to different degrees, among which the tensor spline function and the fully regular spline function achieved the most stable effect on the correction of GWR residuals, followed by kriging and empirical Bayesian kriging.

Mapping Population Distribution with High Spatiotemporal Resolution in Beijing Using Baidu Heat Map Data

Population distribution data with high spatiotemporal resolution are of significant value and fundamental to many application areas, such as public health, urban planning, environmental change, and disaster management. However, such data are still not widely available due to the limited knowledge of complex human activity patterns. The emergence of location-based service big data provides additional opportunities to solve this problem. In this study, we integrated ambient population data, nighttime light data, and building volume data;innovatively proposed a spatial downscaling framework for Baidu heat map data during work time and sleep time;and mapped the population distribution with high spatiotemporal resolution (i.e., hourly, 100 m) in Beijing. Finally, we validated the generated population distribution maps with high spatiotemporal resolution using the highest-quality validation data (i.e., mobile signaling data). The relevant results indicate that our proposed spatial downscaling framework for both work time and sleep time has high accuracy, that the distribution of the population in Beijing on a regular weekday shows "centripetal centralization at daytime, centrifugal dispersion at night" spatiotemporal variation characteristics, that the interaction between the purpose of residents' activities and the spatial functional differences leads to the spatiotemporal evolution of the population distribution, and that China's "surgical control and dynamic zero COVID-19" epidemic policy was strongly implemented. In addition, our proposed spatial downscaling framework can be transferred to other regions, which is of value for governmental emergency measures and for studies about human risks to environmental issues.

MAPPING TRENDS OF CARBON DIOXIDE AND METHANE DURING MOVEMENT CONTROL ORDER IN INDUSTRIAL AREAS IN PENINSULAR MALAYSIA USING GOSAT, GOSAT-2, OCO-2, OCO-3, AND TROPOMI SATELLITES DATA

The Malaysian government implemented the Movement Control Order (MCO) from March 18 to May 13, 2020, in an effort to curb the coronavirus disease outbreak that had spread throughout the nation. Utilizing data from GOSAT, GOSAT-2, OCO-2, OCO-3, and TROPOMI, the total column-averaged dry-air mole fraction of carbon dioxide and methane (referred as XCO2 and XCH4) is employed to examine the patterns of both gases throughout the MCO as well as from the same period the prior and following year. The Inverse Distance Weighting (IDW) interpolation method is utilized in mapping the XCO2 and XCH4 for the industrial areas in Peninsular Malaysia. The results revealed that even MCO is implemented, the XCO2 and XCH4 in the industrial areas are increasing year by year. By using satellites data, the XCO2 and XCH4 from large areas can be monitored continuously.

Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems

Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify patterns in big data, and make decision around them. Neural networks are now consistently used as universal function approximators for data with underlying mechanisms that are incompletely understood or exceedingly complex. However, neural networks alone ignore the fundamental laws of physics and often fail to make plausible predictions. Here we integrate data, physics, and uncertainties by combining neural networks, physics informed modeling, and Bayesian inference to improve the predictive potential of traditional neural network models. We embed the physical model of a damped harmonic oscillator into a fully-connected feed-forward neural network to explore a simple and illustrative model system, the outbreak dynamics of COVID-19. Our Physics Informed Neural Networks seamlessly integrate data and physics, robustly solve forward and inverse problems, and perform well for both interpolation and extrapolation, even for a small amount of noisy and incomplete data. At only minor additional cost, they self-adaptively learn the weighting between data and physics. They can serve as priors in a Bayesian Inference, and provide credible intervals for uncertainty quantification. Our study reveals the inherent advantages and disadvantages of Neural Networks, Bayesian Inference, and a combination of both and provides valuable guidelines for model selection. While we have only demonstrated these different approaches for the simple model problem of a seasonal endemic infectious disease, we anticipate that the underlying concepts and trends generalize to more complex disease conditions and, more broadly, to a wide variety of nonlinear dynamical systems.

Vaccination control measures of an epidemic model with long-term memristive effect.

COVID-19 is a drastic air-way tract infection that set off a global pandemic recently. Most infected people with mild and moderate symptoms have recovered with naturally acquired immunity. In the interim, the defensive mechanism of vaccines helps to suppress the viral complications of the pathogenic spread. Besides effective vaccination, vaccine breakthrough infections occurred rapidly due to noxious exposure to contagions. This paper proposes a new epidemiological control model in terms of Atangana Baleanu Caputo (ABC) type fractional order differ integrals for the reported cases of COVID-19 outburst. The qualitative theoretical and numerical analysis of the aforesaid mathematical model in terms of three compartments namely susceptible, vaccinated, and infected population are exhibited through non-linear functional analysis. The hysteresis kernel involved in AB integral inherits the long-term memory of the dynamical trajectory of the epidemics. Hyer-Ulam's stability of the system is studied by the dichotomy operator. The most effective approximate solution is derived by numerical interpolation to our proposed model. An extensive analysis of the vigorous vaccination and the proportion of vaccinated individuals are explored through graphical simulations. The efficacious enforcement of this vaccination control mechanism will mitigate the contagious spread and severity.

A fractional mathematical model for COVID-19 outbreak transmission dynamics with the impact of isolation and social distancing

The Covid illness (COVID-19), which has emerged, is a highly infectious viral disease. This disease led to thousands of infected cases worldwide . Several mathematical compartmental models have been examined recently in order to better understand the Covid disease. The majority of these models rely on integer-order derivatives, which are incapable of capturing the fading memory and crossover behaviour observed in many biological phenomena. Similarly, the Covid disease is investigated in this paper by exploring the elements of COVID-19 pathogens using the non-integer Atangana-Baleanu-Caputo derivative. Using fixed point theory, we demonstrate the existence and uniqueness of the model's solution. All basic properties for the given model are investigated in addition to Ulam-Hyers stability analysis. The numerical scheme is based on Lagrange's interpolation polynomial developed to estimate the model's approximate solution. Using real-world data, we simulate the outcomes for different fractional orders in Matlab to illustrate the transmission patterns of the present Coronavirus-19 epidemic through graphs.

A general framework for quantifying uncertainty at scale

In many fields of science, comprehensive and realistic computational models are available nowadays. Often, the respective numerical calculations call for the use of powerful supercomputers, and therefore only a limited number of cases can be investigated explicitly. This prevents straightforward approaches to important tasks like uncertainty quantification and sensitivity analysis. This challenge can be overcome via our recently developed sensitivity-driven dimension-adaptive sparse grid interpolation strategy. The method exploits, via adaptivity, the structure of the underlying model (such as lower intrinsic dimensionality and anisotropic coupling of the uncertain inputs) to enable efficient and accurate uncertainty quantification and sensitivity analysis at scale. Here, we demonstrate the efficiency of this adaptive approach in the context of fusion research, in a realistic, computationally expensive scenario of turbulent transport in a magnetic confinement tokamak device with eight uncertain parameters, reducing the effort by at least two orders of magnitude. In addition, we show that this refinement method intrinsically provides an accurate surrogate model that is nine orders of magnitude cheaper than the high-fidelity model.Ionuţ-Gabriel Farcaş, Gabriele Merlo and colleagues developed a framework for uncertainty quantification and sensitivity analysis at scale by focusing on important input parameters. The framework was demonstrated to reduce computational effort and cost compared to standard methods in a turbulent transport simulation in the context of fusion research.

Polynomial Interpolation and Cubic Spline to Determine Approximate Function of COVID-19 Tweet Dataset

As we know theoretically if we are going to construct a polynomial interpolation function through a mapped base, we create an approximation function. In this study, we try to build an approximation function using all sample data available. The approximation function obtained represents the data whose graph goes through a given set of data points. We determine the value of a function at different points and specific intervals using the interpolation model. The first derivative of the function is obtained to find the growth rate of tweet data. The experimental data is a crawling tweet with the keyword COVID-19. Then we get the amount of data per time duration representing a value of the function at a node. The interpolation includes such as Lagrange, Newton's divided difference, and cubic spline. In this study, we compared polynomial interpolation with cubic splines to obtain optimal results. With the functional approach obtained, a pattern of tweets related to COVID-19 can be seen from its graph that passes through the given data points. The graph and the estimated values obtained show that the cubic spline is the optimal interpolation as an approximation function.

Efficient Ontology Meta-Matching Based on Interpolation Model Assisted Evolutionary Algorithm

Ontology is the kernel technique of the Semantic Web (SW), which models the domain knowledge in a formal and machine-understandable way. To ensure different ontologies’ communications, the cutting-edge technology is to determine the heterogeneous entity mappings through the ontology matching process. During this procedure, it is of utmost importance to integrate different similarity measures to distinguish heterogeneous entity correspondence. The way to find the most appropriate aggregating weights to enhance the ontology alignment’s quality is called ontology meta-matching problem, and recently, Evolutionary Algorithm (EA) has become a great methodology of addressing it. Classic EA-based meta-matching technique evaluates each individual through traversing the reference alignment, which increases the computational complexity and the algorithm’s running time. For overcoming this drawback, an Interpolation Model assisted EA (EA-IM) is proposed, which introduces the IM to predict the fitness value of each newly generated individual. In particular, we first divide the feasible region into several uniform sub-regions using lattice design method, and then precisely evaluate the Interpolating Individuals (INIDs). On this basis, an IM is constructed for each new individual to forecast its fitness value, with the help of its neighborhood. For testing EA-IM’s performance, we use the Ontology Alignment Evaluation Initiative (OAEI) Benchmark in the experiment and the final results show that EA-IM is capable of improving EA’s searching efficiency without sacrificing the solution’s quality, and the alignment’s f-measure values of EA-IM are better than OAEI’s participants.

Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling

International scientific fishery survey programmes systematically collect samples of target stocks’ biomass and abundance and use them as the basis to estimate stock status in the framework of stock assessment models. The research surveys can also inform decision makers about Essential Fish Habitat conservation and help define harvest control rules based on direct observation of biomass at the sea. However, missed survey locations over the survey years are common in long-term programme data. Currently, modelling approaches to filling gaps in spatiotemporal survey data range from quickly applicable solutions to complex modelling. Most models require setting prior statistical assumptions on spatial distributions, assuming short-term temporal dependency between the data, and scarcely considering the environmental aspects that might have influenced stock presence in the missed locations. This paper proposes a statistical and machine learning based model to fill spatiotemporal gaps in survey data and produce robust estimates for stock assessment experts, decision makers, and regional fisheries management organizations. We apply our model to the SoleMon survey data in North-Central Adriatic Sea (Mediterranean Sea) for 4 stocks: Sepia officinalis, Solea solea, Squilla mantis, and Pecten jacobaeus. We reconstruct the biomass-index (i.e., biomass over the swept area) of 10 locations missed in 2020 (out of the 67 planned) because of several factors, including COVID-19 pandemic related restrictions. We evaluate model performance on 2019 data with respect to an alternative index that assumes biomass proportion consistency over time. Our model’s novelty is that it combines three complementary components. A spatial component estimates stock biomass-index in the missed locations in one year, given the surveyed location’s biomass-index distribution in the same year. A temporal component forecasts, for each missed survey location, biomass-index given the data history of that haul. An environmental component estimates a biomass-index weighting factor based on the environmental suitability of the haul area to species presence. Combining these components allows understanding the interplay between environmental-change drivers, stock presence, and fisheries. Our model formulation is general enough to be applied to other survey data with lower spatial homogeneity and more temporal gaps than the SoleMon dataset. Copyright © 2022 Coro, Bove, Armelloni, Masnadi, Scanu and Scarcella.

Analysis of a COVID-19 model with nonlocal and stochastic behaviors

Systems of nonlinear ordinary differential equations have been employed to model complex behaviors arising in many real-world problems including epidemiology, biology, and many others. Many chaotic behaviors have been modeled using these equations as well as epidemiological problems. To construct these, model differential and integral operators with local and nonlocal features have been used. However, in many instances, it was noted that models with these concepts are unable to replicate accurately complex behaviors with different patterns, thus very recently piecewise operators were suggested and applied in some problems. In this paper, we chose a system of six nonlinear different equations and applied the concept of piecewise derivative. [ FROM AUTHOR] Copyright of Waves in Random & Complex Media is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)