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Measuring fecal nucleic acid indicators for data normalization can increase costs during wastewater-based epidemiology (WBE). The efficacy of routinely assayed water quality parameters was assessed as an alternative or complement to fecal nucleic acid viral indicator data for their utility in adjusting measured SARS-CoV-2 gene concentrations to improve the relationship between wastewater molecular data and clinical COVID-19 case data. This research covers two study designs: grab samples collected from sewers serving The Ohio State University campus and composite influent samples collected at five wastewater treatment plants (WWTPs) across the state of Ohio. Weekly mandatory clinical testing was used to monitor infections in the student population, and statewide cases were reported through voluntary clinical testing. Statewide WWTP results showed significant strong correlation between SARS-CoV-2 concentrations in the wastewater and confirmed COVID-19 cases, and correlation increased when normalized by flow and additionally increased when normalized by pH, total suspended solids, and temperature, but correlation decreased when normalized by a nucleic acid fecal viral indicator (PMMoV). On campus, correlations were generally not significant unless normalized by PMMoV and/or UV absorbance parameters. Because water quality parameters are routinely assayed at wastewater treatment plants and some may be collected by autosamplers, incorporating wastewater quality data may improve WBE models and could minimize molecular and cellular testing for fecal indicators to decrease costs. [ABSTRACT FROM AUTHOR]

Many real-world problems can be formulated as a multimodal optimization problem (MMOP), and metaheuristic algorithms used in solving MMOP have to find multiple optimal points simultaneously. The key requirement for dealing with such problems is to balance exploration capability in global space and exploitation in multiple optimal spaces. Artificial bee colony (ABC), a metaheuristic algorithm, is designed to find only a single global optimum and cannot solve the MMOP. In this paper, we propose an ABC variant named "Elitist ABC with Dynamic Population Size" to cope with multimodal optimization problems. It has a dynamic population size strategy and uses a search equation selection strategy powered by elite members. The dynamic population size strategy enhances the exploration capability of the algorithm. The search equation selection strategy determines the appropriate search behavior for a particular problem instance at runtime. Thus, exploitation and exploration behaviors can be adjusted adaptively. In addition, candidate optimum peaks, that are overlooked in the original ABC algorithm, are memorized with elite population members. The proposed algorithm has been tested on multimodal optimization problems presented at CEC 2013. The algorithm has been compared with ten state-of-the-art multimodal optimization algorithms and the top 25 algorithms participating in the CEC competition on multimodal function optimization between 2013 and 2020. Experimental results have shown that the proposed algorithm is superior to many new algorithms and can compete with top-level algorithms. [ABSTRACT FROM AUTHOR]

Training a neural network can be a challenging task, particularly when working with complex models and large amounts of training data, as it consumes significant time and resources. This research proposes a hybrid model that combines population-based heuristic algorithms with traditional gradient-based techniques to enhance the training process. The proposed approach involves using a dynamic population-based heuristic algorithm to identify good initial values for the neural network weight vector. This is done as an alternative to the traditional technique of starting with random weights. After several cycles of distributing search agents across the search domain, the training process continues using a gradient-based technique that starts with the best initial weight vector identified by the heuristic algorithm. Experimental analysis confirms that exploring the search domain during the training process decreases the number of cycles needed for gradient descent to train a neural network. Furthermore, a dynamic population strategy is applied during the heuristic search, with objects added and removed dynamically based on their progress. This approach yields better results compared to traditional heuristic algorithms that use the same population members throughout the search process. [ABSTRACT FROM AUTHOR]

3Environmental Remote Sensing and Geo-Information Laboratory, Institute of Renewable Natural Resources, College of Forestry and Natural Resources, University of the Philippines, Los Baños, Laguna 4031, Philippines 4Early Childhood Education Department, Universitas Pendidikan Indonesia, Indonesia 5Department of Geological Engineering, Institut Teknologi Nasional Yogyakarta, Indonesia *Email: nadisuprapto@unesa.ac.id ABSTRACT Geohazards are often present in highly populated areas. Dynamic changes in population exposure to geohazards have resulted from shifting population numbers, spatial dispersion, and mobility. In addition, the changing vulnerability distribution of the population is a crucial consideration when establishing an effective evacuation strategy. Therefore, exploring the link between geohazards and population exposure is vital to avert disasters. This paper reviewed geohazards around the world based on a dynamic population approach. It identifies and synthesizes evidence of links between certain parts of the dynamic population and geohazard risk management. The major search engine in this investigation was Google Scholar. “Geohazards”, “dynamic population”, and keywords related to geohazards like landslides, floods, drought, earthquake, and tsunami were used, followed by terms related to dynamic population. The review shows that geohazards based on a dynamic population approach include socioeconomic status; gender and gender relations; migration, residency, and mobility; education and knowledge; and religions and beliefs. Understanding those elements is essential to managing geohazard risks because it can assist the government in its financial commitment and allocation in the event of a disaster, constructing effective policies and adaptation plans, and including communities in managing geohazard risks.

Abstract Background The negative effect of air pollution on human health is widely reported in recent literature. It typically involves urbanized areas where the population is concentrated and where most primary air pollutants are produced. A comprehensive health risk assessment is therefore of strategic importance for health authorities. Methods In this study we propose a methodology to perform an indirect and retrospective health risk assessment of all-cause mortality associated with long-term exposure to particulate matter less than 2.5 microns (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in a typical Monday to Friday working week. A combination of satellite-based settlement data, model-based air pollution data, land use, demographics and regional scale mobility, allowed to examine the effect of population mobility and pollutants daily variations on the health risk. A Health Risk Increase (HRI) metric was derived on the basis of three components: hazard, exposure and vulnerability, utilizing the relative risk values from the World Health Organization. An additional metric, the Health Burden (HB) was formulated, which accounts for the total number of people exposed to a certain risk level. Results The effect of regional mobility patterns on the HRI metric was assessed, resulting in an increased HRI associated with all three stressors when considering a dynamic population compared to a static one. The effect of diurnal variation of pollutants was only observed for NO2 and O3. For both, the HRI metric resulted in significantly higher values during night. Concerning the HB parameter, we identified the commuting flows of the population as the main driver in the resulting metric. Conclusions This indirect exposure assessment methodology provides tools to support policy makers and health authorities in planning intervention and mitigation measures. The study was carried out in Lombardy, Italy, one of the most polluted regions in Europe, but the incorporation of satellite data makes our approach valuable for studying global health.

Geohazards are often present in highly populated areas. Dynamic changes in population exposure to geohazards have resulted from shifting population numbers, spatial dispersion, and mobility. In addition, the changing vulnerability distribution of the population is a crucial consideration when establishing an effective evacuation strategy. Therefore, exploring the link between geohazards and population exposure is vital to avert disasters. This paper reviewed geohazards around the world based on a dynamic population approach. It identifies and synthesizes evidence of links between certain parts of the dynamic population and geohazard risk management. The major search engine in this investigation was Google Scholar. "Geohazards", "dynamic population", and keywords related to geohazards like landslides, floods, drought, earthquake, and tsunami were used, followed by terms related to dynamic population. The review shows that geohazards based on a dynamic population approach include socioeconomic status; gender and gender relations; migration, residency, and mobility; education and knowledge; and religions and beliefs. Understanding those elements is essential to managing geohazard risks because it can assist the government in its financial commitment and allocation in the event of a disaster, constructing effective policies and adaptation plans, and including communities in managing geohazard risks. [ABSTRACT FROM AUTHOR]

Measuring fecal nucleic acid indicators for data normalization can increase costs during wastewater-based epidemiology (WBE). The efficacy of routinely assayed water quality parameters was assessed as an alternative or complement to fecal nucleic acid viral indicator data for their utility in adjusting measured SARS-CoV-2 gene concentrations to improve the relationship between wastewater molecular data and clinical COVID-19 case data. This research covers two study designs: grab samples collected from sewers serving The Ohio State University campus and composite influent samples collected at five wastewater treatment plants (WWTPs) across the state of Ohio. Weekly mandatory clinical testing was used to monitor infections in the student population, and statewide cases were reported through voluntary clinical testing. Statewide WWTP results showed significant strong correlation between SARS-CoV-2 concentrations in the wastewater and confirmed COVID-19 cases, and correlation increased when normalized by flow and additionally increased when normalized by pH, total suspended solids, and temperature, but correlation decreased when normalized by a nucleic acid fecal viral indicator (PMMoV). On campus, correlations were generally not significant unless normalized by PMMoV and/or UV absorbance parameters. Because water quality parameters are routinely assayed at wastewater treatment plants and some may be collected by autosamplers, incorporating wastewater quality data may improve WBE models and could minimize molecular and cellular testing for fecal indicators to decrease costs.

Training a neural network can be a challenging task, particularly when working with complex models and large amounts of training data, as it consumes significant time and resources. This research proposes a hybrid model that combines population-based heuristic algorithms with traditional gradient-based techniques to enhance the training process. The proposed approach involves using a dynamic population-based heuristic algorithm to identify good initial values for the neural network weight vector. This is done as an alternative to the traditional technique of starting with random weights. After several cycles of distributing search agents across the search domain, the training process continues using a gradient-based technique that starts with the best initial weight vector identified by the heuristic algorithm. Experimental analysis confirms that exploring the search domain during the training process decreases the number of cycles needed for gradient descent to train a neural network. Furthermore, a dynamic population strategy is applied during the heuristic search, with objects added and removed dynamically based on their progress. This approach yields better results compared to traditional heuristic algorithms that use the same population members throughout the search process.

Background: The negative effect of air pollution on human health is widely reported in recent literature. It typically involves urbanized areas where the population is concentrated and where most primary air pollutants are produced. A comprehensive health risk assessment is therefore of strategic importance for health authorities. Methods: In this study we propose a methodology to perform an indirect and retrospective health risk assessment of all-cause mortality associated with long-term exposure to particulate matter less than 2.5 microns (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in a typical Monday to Friday working week. A combination of satellite-based settlement data, model-based air pollution data, land use, demographics and regional scale mobility, allowed to examine the effect of population mobility and pollutants daily variations on the health risk. A Health Risk Increase (HRI) metric was derived on the basis of three components: hazard, exposure and vulnerability, utilizing the relative risk values from the World Health Organization. An additional metric, the Health Burden (HB) was formulated, which accounts for the total number of people exposed to a certain risk level. Results: The effect of regional mobility patterns on the HRI metric was assessed, resulting in an increased HRI associated with all three stressors when considering a dynamic population compared to a static one. The effect of diurnal variation of pollutants was only observed for NO2 and O3. For both, the HRI metric resulted in significantly higher values during night. Concerning the HB parameter, we identified the commuting flows of the population as the main driver in the resulting metric. Conclusions: This indirect exposure assessment methodology provides tools to support policy makers and health authorities in planning intervention and mitigation measures. The study was carried out in Lombardy, Italy, one of the most polluted regions in Europe, but the incorporation of satellite data makes our approach valuable for studying global health. [ABSTRACT FROM AUTHOR]

The phenomenon of owning and visiting a second home is broadening beyond tourism due to the increase in remote working and multi-local living – people living in multiple residences. To understand people, places, and mobility linked to second homes and its implications to society better, new complementary data sources are needed to provide timely and adequate information on temporal patterns and changes in second home use. Big data sources have been used in tourism research, but less often in studies about second homes. This article aims to propose a perspective in describing the potential of utility consumption data, transaction data from mobile positioning and smart cards, social media data, and data from smartphone applications for second home tourism research. By focusing on six key questions relevant to second home tourism research, we exemplify how these data sources could provide new knowledge to the field and propose four axioms for future research. [ABSTRACT FROM AUTHOR]

In this paper, we develop an extension of compartmental epidemiological models which is suitable for COVID-19. The model presented in this paper comprises seven compartments in the progression of the disease. This model, named as the SINTRUE (S usceptible, I nfected and pre-symptomatic, Infected and Symptomatic but N ot Tested, T ested Positive, Recorded R ecovered, U nrecorded Recovered, and E xpired) model. The proposed model incorporates transmission due to asymptomatic carriers and captures the spread of the disease due to the movement of people to/from different administrative boundaries within a country. In addition, the model allows estimating the number of undocumented infections in the population and the number of unrecorded recoveries. The associated parameters in the model can help architect the public health policy and operational management of the pandemic. The results show that the testing rate of the asymptomatic patients is a crucial parameter to fight against the pandemic. The model is also shown to have a better predictive capability than the other epidemiological models. [ABSTRACT FROM AUTHOR]

Escalating heatwaves induced by climate change are giving rise to a range of health issues. Traditionally, government policies focus on residential vulnerability, neglecting dynamic population shifts during workday heat peaks away from their residences. This study explores the influence of spatiotemporal population dynamics on the distribution of heatwave damage. Utilizing the data from Seoul, South Korea between 2017 and 2019, we assessed the relative risk of emergency visits for heat-related illnesses during heatwaves across hospital service areas. A multilevel regression approach was employed to examine the contributions of daytime and residential populations of vulnerable groups, while controlling for established heat vulnerability indicators. The results revealed a significant correlation between the relative risk of heat-related emergencies and the proportion of the daytime elderly population. These findings suggest that the daytime population, as compared to the residential population, can better capture vulnerable areas and individuals in the context of an urban heatwave. Consequently, the study proposes that local heat response policies consider the dynamic locations of vulnerable populations. • Daytime population influences spatial heat vulnerability in an urban context. • Analysis units are set considering the proximity of hospitals to heatwave patients. • Areas with intensified daytime activities of elderly increase heat health risks. • Local heat response policy should consider dynamic locations of vulnerable groups. [ABSTRACT FROM AUTHOR]

The objective of the study was to determine the growth pattern of clam Potamocorbula faba inhabited the estuarine of Permisan Bay, Indonesia by using the Linear Allometric Model (LAM). Its also evaluated by using the length-weight relationships (LWR) and Fulton’s condition factor (K–value). A total of 7 996 specimens with length intervals of 3.5 mm to 17 mm and weight intervals of 0.013 g to 0.636 g were collected from nine sites in Permisan Bay. Based on the length-weight relationships (LWR), equations in ST–1 (0.000886870 L2.17379); ST–2 (0.000884219 L2.15471); ST–3 (0.000832626 L2.18971); ST–4 (0.000746758 L2.21150); ST–5 (0.000687148 L2.28593); ST–6 (0.000765876 L2.23830); ST–7 (0.000657461 L2.31064); ST–8 (0.000379673 L2.52229); and ST–9 (0.000799816 L 2.22922) were obtained. Regarding the b–value, almost all sites showed b < 3, and thus the growth pattern could be categorized as negative allometric. Condition factor (K-value) in all nine sites ranged between 1.1101 to 1.1552. The highest K-value was from ST–7 while ST–2 showed the lowest. During the study period (September 2014 to April 2015), Fulton’s condition factor (K–value) of all nine sites was not significantly different (P > 0.05).

A stage-structured population model with unknown parameters is considered. Our purpose is to study the identifiability of the model and to develop a parameter estimation procedure. First, we analyze whether the parameter vector can or cannot uniquely be determined with the knowledge of the input-output behavior of the model. Second, we analyze how the information in the experimental data is translated into the parameters of the model. Furthermore, we propose a process to improve the recursive values of the parameters when successive observation data are considered. The structure of the state matrix leads to an analysis of the inverse of a sum of rank-one matrices.

In South Korea, one-dose varicella vaccination was introduced to the National Immunization Program in 2005, but varicella outbreaks have continued to occur. Therefore, a two-dose vaccination strategy is considered. We developed an age-structured deterministic compartment model using Korean population projection data. The impact of adding a second dose of varicella vaccine on varicella and herpes zoster (HZ) epidemiology was assessed under four different vaccine effectiveness (VE) scenarios (base, moderate, lowest, highest) and the optimal timing of the second vaccine dose (18 months, 4, 5, or 6 years of age) was examined over the period 2020–2065. A two-dose vaccination schedule reduced the cumulative varicella incidence by > 90% compared to no vaccination, regardless of the VE. The additional reduction attributable to a second dose compared to a single dose was greatest (82%) with the lowest VE scenario. A second dose at 6 years of age reduced the varicella incidence at a population level, whereas a second dose at 18 months of age reduced the varicella incidence primarily in the target birth cohorts. Routine vaccination at the age of 18 months with a catch-up vaccination of 6-year-olds was the optimal strategy for birth cohort and population-level control. HZ incidence continued to increase under no vaccination scenario, which represents the effect of aging population. Under a two-dose scenario, the additional increase in HZ incidence attributable to the reduced exogenous boosting was small relative to a one-dose scenario and a further reduction in HZ cases was observed. A two-dose varicella vaccination schedule would significantly reduce varicella and HZ incidence in the long term. A second dose at the age of 18 months with a catch-up vaccination of 6-year-olds would be optimal for controlling varicella in South Korea. [ABSTRACT FROM AUTHOR]

ABSTRACT: Italian ryegrass (Lolium multiflorum Lam.) is a troublesome weed widely found in temperate Brazilian fields with several herbicide-resistant populations. Soil seed bank is the main source of future infestations and can be strongly affected by agricultural practices, such as crop rotation and tillage system. Therefore, this study evaluated the long-term effects of different crop rotation and tillage systems on the seed bank of Italian ryegrass. Data were collected in 2012 and 2013 in a field managed since 1985 under conventional-tillage and no-tillage and subjected to three cropping systems (monoculture, total, and partial rotation) compared to the control plot. The samples were collected at three soil depths (0-5, 5-10, and 10-20 cm) using a cylindrical soil sampler, and infestations in the field were evaluated with a sample frame of 1 m2. All cropping systems reduced the soil seed bank of Italian ryegrass, regardless of soil management and sampling depths comparing to the control plot. Total and partial rotation were more effective than soil tillage to reduce the seed bank. A larger number of seeds was observed on the surface layer (0-5 cm) for all cropping systems with more uniform distribution in the soil profile in conventional-tillage. Infestation of Italian ryegrass was greater for monoculture under no-tillage in both years, whereas in total and partial rotation, regardless of soil tillage, infestations remained below 40 seedlings m−2. Thus, strategies to reduce the soil seed bank and infestations of Italian ryegrass should focus on cropping systems with crop rotation and no-tillage.

Urban heat waves pose a significant risk to the health and safety of city dwellers, with urbanization potentially amplifying the health impact of extreme heat. Accurate assessments of population heat exposure hinge on the interplay between temperature, population spatial dynamics, and the epidemiological effects of temperature on health. Yet, many past studies have over-simplified the matter by assuming static populations, leading to substantial inaccuracies in heat exposure assessments. To address these issues, this study integrates dynamic population data, fluctuating temperature, and the exposure-response relationship between temperature and health to construct an advanced heat exposure assessment framework predicated on a population dynamic model. We analyzed urban heat island characteristics, population dynamics, and heat exposure during heat wave conditions in Beijing, a major city in China. Our findings highlight significant intra-day population movement between urban and suburban areas during heat wave conditions, with spatial population flow patterns showing clear scale-dependent characteristics. These population flow dynamics intensify heat exposure levels, and the disparity between dynamic population-weighted temperature and average temperature is most pronounced at night. Our research provides a more comprehensive understanding of real urban population heat exposure levels and can furnish city administrators with more scientifically rigorous evidence. [Display omitted] • Urban heat waves pose a significant risk to the health and safety of city dwellers • Population movement between urban and suburban areas is obvious in megacities • Movement of people between places of work and residence modifies the heat exposure • A dynamic population risk assessment index for heat exposure was developed [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) crisis resulted in unprecedented changes in the spatial mobility of people across societies due to the restrictions imposed. This also resulted in unexpected mobility and population dynamics that created a challenge for crisis preparedness, including the mobility from cities during the crisis due to the underlying phenomenon of multi-local living. People changing their residences can spread the virus between regions and create situations in which health and emergency services are not prepared for the population increase. Here, our focus is on urban–rural mobility and the influence of multi-local living on population dynamics in Finland during the COVID-19 crisis in 2020. Results, based on three mobile phone datasets, showed a significant drop in inter-municipal mobility and a shift in the presence of people—a population decline in urban centres and an increase in rural areas, which is strongly correlated to secondary housing. This study highlights the need to improve crisis preparedness by: (1) acknowledging the growing importance of multi-local living, and (2) improving the use of novel data sources for monitoring population dynamics and mobility. Mobile phone data products have enormous potential, but attention should be paid to the varying methodologies and their possible impact on analysis.

• Optimal maintenance in dynamic populations of statistically identical items is discussed. • Systems that generate an output/gain/profit that has a cost-wise interpretation are considered. • Preventive maintenance for dynamic populations of items is studied. • Illustrative examples which confirm the theoretical findings are provided. This is the first study in the reliability literature that discusses optimal maintenance in dynamic populations of statistically identical items. Items that generate an output/gain with a value interpretation, e.g., the performed amount of work, the manufactured product, etc., are considered. The expectation of the overall gain on a cycle of performance defines the corresponding objective function for the preventive maintenance (PM) optimization for a single item, which is also used to develop theory for dynamic populations of items. It is shown that the optimal PM time for dynamic populations of items is larger than that for a single item. Generalizations to the cases when an item is characterized by the major and minor failures and when the output rate is a function of time are considered. It is shown how the framework developed here is relevant to an industrial setting using an example of a fleet of wind turbines. Some numerical results illustrate our findings. [ABSTRACT FROM AUTHOR]

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The use of proper management strategies for citrus huanglongbing ( HLB), caused by ' Candidatus Liberibacter asiaticus' (Las) and transmitted by Asian citrus psyllid ( ACP) ( Diaphorina citri), is a priority issue. HLB control is based on healthy seedlings, tolerant rootstock cultivars and reduction of ACP populations. Here, dynamic populations of Las in different citrus hosts and each instar of ACP were studied, together with the seasonal growth and distribution of Las in different tissues, using conventional and TaqMan real-time PCR. Different levels of susceptibility/tolerance to HLB were seen, resulting in different degrees of symptom severity and growth effects on hosts or rootstocks. Troyer citrange, Swingle citrumelo and wood apple were highly tolerant among 11 rootstock cultivars. Regarding distribution and seasonal analysis of Las, mature and old leaves contained high concentrations in cool temperatures in autumn and spring. Las was detected earlier through psyllid transmission than through graft inoculation, and the amounts of Las ( AOL) varied in different hosts. Thus, different AOL (104-107 copy numbers μL−1) and Las-carrying percentages ( LCP; 40-53.3%) were observed in each citrus cultivar and on psyllids, respectively. Furthermore, both AOL and LCP were lower in nymphs than in adult psyllids, whereas the LCP of psyllids were not affected by increasing the acquisition-access time. The present study has significant implications for disease ecology. The combination of early detection, use of suitable rootstocks and constraint of psyllid populations could achieve better management of HLB disease. [ABSTRACT FROM AUTHOR]

In designing evacuation plans, it is critical for the responsive agencies to consider the dynamic change of human population within impact areas and understand social perception from local residents. Although a large number of evacuation models has been reported in the literature, many used census survey data which represent only the nighttime population distribution. To fill this research gap, this paper introduces a novel data integration framework for developing an evacuation decision support system for wildfire, Integrated Wildfire Evacuation Decision Support System (IWEDSS). IWEDSS integrates multiple data sources including social media, census survey, geographic information systems (GIS) data layers, volunteer suggestions, and remote sensing data. The integration is based on multi-disciplinary theoretical and modeling approaches including Geographic Information Science, civil and transportation engineering, computer science, social media and communication. IWEDSS includes four core modules: dynamic population estimation, stage-based robust evacuation planning, social perception analysis, and web-based geomatical analytic platform. It offers tools for evacuation planers and resource managers to make better decisions that can reduce the evacuation time and potential number of injuries and deaths. This paper also presents a case study to demonstrate the suitability of incorporating social media data to estimate the dynamic change of human population. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a population-based stochastic search algorithm. This algorithm is utilized to solve the optimization problem. It suffers the problems of trapping in local optimum and premature convergence. Nowadays, both problems can be solved by mutation and reposition techniques apply with PSO (MRPSO). However, this technique use over evaluation calls for solution searching. This research paper proposed a technique to minimized evaluation call. This proposed is called DPM-MRPSO. The concept of adaption evaluation call depends on results from improvement solution of mutation technique and PSO technique. The proposed technique is tested on twenty-four benchmark functions and obtains satisfied search results. [ABSTRACT FROM AUTHOR]

The study on population dynamic of Etawah crossed goats was carried out in Hegarmanah Village, Sukabumi, West Java. Thirty famers were reviewed and their animals consisted of 52 adult does, 10 adult bucks, 21 young does, 24 young bucks and 58 kids (31 males, 27 females), were studied. Goats were housed continuously. The result showed that the average age of weaning was 5,22 months and weaning weight was 17,63 kg. The age of first matings were 9,83 month for bucks and 7,50 months for does. First kidding was 12,52 months of age and dry period was 5,20 months. Prolificacy was 1,83 kids per does. Litter size was 29,78% singles, 61,70% twins, 4,26% triplets and 4,20% quadruplets. Kidding intervals were 10,20 months. Kid mortality was 9,30%. The population increased 53,70% during six years.

Previous studies have characterized spatial patterns of air pollution with land-use regression (LUR) models. However, the spatiotemporal characteristics of air pollution, the contribution of various factors to them, and the resultant health impacts have yet to be evaluated comprehensively. This study integrates machine learning (random forest) into LUR modeling (LURF) with intensive evaluations to develop high spatiotemporal resolution prediction models to estimate daily and diurnal PM 2.5 and NO 2 in Seoul, South Korea, at the spatial resolution of 500 m for a year (2019) and to then evaluate the contribution of driving factors and quantify the resultant premature mortality. Our results show that incorporating the random forest algorithm into our LUR model improves the model performance. Meteorological conditions have a great influence on daily models, while land-use factors play important roles in diurnal models. Our health assessment using dynamic population data estimates that PM 2.5 and NO 2 pollution, when combined, causes a total of 11,183 (95% CI: 5837-16,354) premature mortalities in Seoul in 2019, of which 64.9% are due to PM 2.5 , while the remaining are attributable to NO 2 . The air pollution-attributable health impacts in Seoul are largely caused by cardiovascular diseases including stroke. This study pinpoints the significant spatiotemporal variations and health impact of PM 2.5 and NO 2 in Seoul, providing essential data for epidemiological research and air quality management.

Floods are one of the most common natural disasters that can cause considerable economic damage and loss of life in many regions of the world. Urban flood risk assessment is important for urban flood control, disaster reduction, and risk management. In this study, a novel approach for assessing urban flood risk was proposed based on the dynamic population distribution, improved entropy weight method, fuzzy comprehensive evaluation method, and the principle of maximum membership, and the spatial distribution of flood risk in four different sessions or daily time segments (TS1-TS4) in the northern part of the Shenzhen River Basin (China) was assessed using geographic information system technology. Results indicated that risk levels varied with population movement. The areas of highest risk were largest in TS1 and TS3, accounting for 7.03% and 7.07% of the total area, respectively. The areas of higher risk were largest in TS2 and TS4, accounting for 4.54% and 4.64% of the total area, respectively. The findings of this study could provide a theoretical basis for assessing urban flood risk management measures in Shenzhen (and even throughout China), and a scientific basis for development of disaster prevention and reduction strategies by flood control departments.

Highlights: [•] We studied the dynamics of Pyrenean newt using population dynamics P-system model. [•] We applied the model to some hypothetical scenarios of extreme rainfall. [•] Pyrenean newt populations are adapted to flooding. [•] The extinction of the population is plausible. [Copyright &y& Elsevier]

Consider a uniformly mixing population which grows as a super-critical linear birth and death process. At some time an infectious disease (of SIR or SEIR type) is introduced by one individual being infected from outside. It is shown that three different scenarios may occur: (i) an epidemic never takes off, (ii) an epidemic gets going and grows but at a slower rate than the community thus still being negligible in terms of population fractions, or (iii) an epidemic takes off and grows quicker than the community eventually leading to an endemic equilibrium. Depending on the parameter values, either scenario (i) is the only possibility, both scenarios (i) and (ii) are possible, or scenarios (i) and (iii) are possible. [ABSTRACT FROM AUTHOR]

Natural populations are dynamic in both time and space. In biological populations such as insects, spatial distribution patterns are often studied as the first step to characterize population dynamics. In nature, the spatial distribution patterns of insect populations are considered as the emergent expression (property) of individual behaviors at population levels and are fine-tuned or optimized by natural selection. This inspiration prompts us to investigate the possibly similar mechanisms in Genetic Algorithms (GA) populations. In this study, we introduce the mathematical models for the spatial distribution patterns of insect populations to GA with the conjecture that the emulation of biological populations in nature may lead to computational improvement. In particular, we introduce three modeling approaches from the research of spatial distribution patterns of insect populations: (i) probability distribution modeling approach, (ii) aggregation index approach, and (iii) Taylor's (1961, 1977) Power Law, Iwao's (1968, 1976) Mean Crowding Model and Ma's (1991c) population aggregation critical density (PACD), to characterize populations in GA. With these three approaches, we investigate four mappings from the research field of insect spatial distribution patterns to GA populations in order to search for possible counterpart mechanisms or features in GA. They are: (i) mapping insect spatial distribution patterns to GA populations or allowing GA populations to be controlled by stochastic distribution models that describe insect spatial distributions; (ii) mapping insect population distribution to GA population fitness distribution via Power Law and PACD modeling; (iii) mapping population aggregation dynamics to GA fitness progression across generations (or fitness aggregation dynamics in GA) via insect population aggregation index; (iv) mapping insect population sampling model to optimal GA population sizing. With regard to the mapping (i), the experiment results show the significant improvements in GA computational efficiency in terms of the reduced fitness evaluations and associated costs. This prompts us to suggest using probability distribution models, or what we call stochastic GA populations, to replace the fixed-size population settings. We also found the counterpart for the second mapping, the wide applicability of Power Law and Mean Crowding model to the fitness distribution in GA populations. The testing of the third and fourth mappings is very preliminary; we use example cases to suggest two further research problems: the potential to use fitness aggregation dynamics for controlling the number of generations iterated in GA searches, and the possibility to use fitness aggregation distribution parameters [(obtained in mapping (ii)] in determining the optimum population size in GA. A third interesting research problem is to investigate the relationship between mapping (i) and (iii), i.e., the controlling of both population sizes and population generations. [ABSTRACT FROM AUTHOR]

The purpose of this article is to present in elementary mathematical and statistical terms a simple way to quickly and effectively teach and understand case–control studies, as they are commonly done in dynamic populations—without using the rare disease assumption. Our focus is on case–control studies of disease incidence (‘incident case–control studies’); we will not consider the situation of case–control studies of prevalent disease, which are published much less frequently. [ABSTRACT FROM PUBLISHER]

To describe how often a disease or another health event occurs in a population, different measures of disease frequency can be used. The prevalence reflects the number of existing cases of a disease. In contrast to the prevalence, the incidence reflects the number of new cases of disease and can be reported as a risk or as an incidence rate. Prevalence and incidence are used for different purposes and to answer different research questions. In this article, we discuss the different measures of disease frequency and we explain when to apply which measure. Copyright © 2010 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the annual dynamic population of Boophilus microplus in two cattle genotypes. The study was carried out in Martinez de la Torre, Veracruz, Mexico (tropical humid region). Twenty steers of 10 months of age, 185 kg of body weight and naturally infested with B. microplus ticks were used. Two groups of 10 animals were used according to their genotype (¾ Bos taurus [½ Holstein, ¼ Simmental, ¼ Zebu], and ½ Bos taurus [½ Holstein, ½ Zebu]). Animals grazed together in a pasture of five hectares. No ixodicidal treatment was used during the experiment. The number of B. microplus female ticks (> 4.5 mm length) was assessed every 28 days. Temperature (T), relative humidity (RH) and pluvial precipitation (PP) were registered during the study. The U Mann Whitney test was used to analyze differences between both animal genotypes (P < 0.05). Correlations between the number of B. microplus of both genotypes, PP accumulated and average of T (minimum and maximum) regis tered four weeks prior measurement were determined. Throughout the studied period, tick infestation in both animal genotypes was registered. The genotype ¾ Bos taurus had higher B. microplus infestation in nine of the 12 evaluated months (P < 0.05). PP accumulated and minimum T were correlated with the number of ticks in both genotypes. Natural infestation of Boophilus microplus Canestrini, 1887 (Acari: Ixodidae) in two cattle genotypes in the humid tropic of Veracruz, Mexico. [ABSTRACT FROM AUTHOR]

One of the outstanding problems of palaeoclimate reconstruction from physico-chemical properties of terrigenous deep-sea sediments stems from the fact that most basin fills are mixtures of sediment populations derived from different sources and transported to the site of deposition by different mechanisms. Conventional approaches to palaeoclimate reconstruction from deep-sea sediments do not distinguish between provenance and dispersal-related variations, and therefore often fail to recognise the true significance of variations in sediment properties. We formulate a set of requirements that each proposed palaeo-environmental indicator should fulfil, and focus on the intrinsic coupling between grain size and chemical composition. A critical review of past achievements in grain-size analysis serves to introduce a conceptual model of spatio-temporal grain-size variation based on dynamic populations. Each dynamic population results from a characteristic combination of production and transport mechanisms that corresponds to a distinct subpopulation in the data analysed. The mathematical–statistical representation of the conceptual model can be obtained by means of the end-member-modelling algorithm EMMA. Applications of the model to several ocean basins are discussed, as well as methods to examine the validity of grain-size-based palaeoclimate reconstructions. Palaeoclimate reconstructions of a high- and low-latitude basin illustrate the common degree of complexity of deep-sea grain-size records. [Copyright &y& Elsevier]

Dynamic population estimation - counting people in a special event (e.g. rally, sport events, evacuation) has been challenging because a large crowd is difficult to acquire an accurate count manually as people can join and leave the crowd at any time and place. In the context of a rally where people are moving on the streets over an extended period, it is often difficult to answer some questions with regards to confine the phenomenon, including but not limited to: When and where does the rally start/end precisely? Who are the attendees (e.g. activists, spectators, organizers, police, opposing crowd(s), etc.)? How does the confining environment affect stochastic human behaviours, interactions and movements over time and space? What is the count (e.g. peak attendance, total count)? Nevertheless, estimating the attendance of these dynamic events could be emotionally and politically charged. Due to these practical uncertainties of dynamic population estimation, the attendance of annual July 1st Rally in Hong Kong reported by the police and organizers could be very different as it often became a bluffing game to promote hidden political agenda. For example, the police estimated a peak attendance of 9,800 in the 2017 July 1st Rally, whereas the organizers reported five times the attendance at about 50,000. Independent researchers from the University of Hong Kong estimated the total attendance of 14,170 (Yip, 2017) and 29,000 (HKUPOP, 2017) respectively. With limited field observations, it is hard to examine the accuracy and confident level of these reported counts. This research aims to estimate the total attendance of 2017 July 1st Rally in Hong Kong and examine the counts reported by various stakeholders. Based on many stories reported by the public and social media, the timeline of the rally event was reconstructed to trace the observed rally time of the head and tail crowds participated in the rally event. Important geospatial features, including the street network, approved protest areas and entry/exit gateways along the main rally route, were reconstructed in the Geographic Information System (GIS). This study adopted a crowdsourcinggeocomputation approach to simulate how a dynamic crowd would have navigated in such as rally event (Chow, 2019). Using a mobile application that tracks individual trajectory, volunteers were recruited to contribute valuable in-situ data of dynamic human movements and behaviours attending the rally event. These data were used to formulate and calibrate the parameters of a computational cartographic model where each rally attendees were represented as a moving agent (x, y, t) confined in a micro spatial environment where the protesters marched from Victoria Park to the Government Headquarter in Hong Kong. Hence, individual GPS (Global Positioning System) trajectory during the rally was collected and converted into GIS data format for further analysis. More details about the data collection and processing can be found on the project website (https://chowte. wixsite.com/dynamicpop). By leveraging a large number of observations volunteered by crowdsourcing, this study attempted to answer the research question: What might be the simulated crowd size that reasonable range of model parameters can be converged? Using this crowdsoucing-geocomputation model, a sensitivity analysis was conducted to simulate varying model parameters, including maximum walking speed, maximum crowd density, early departure and late entry rates. Based on the reported count reported by the police, organizers and independent researchers, various crowd sizes were simulated to be compared against the observed rally time of 209 minutes (i.e. about 3.5 hours) from start to finish. Using the crowdsourced data for calibration, most rally models simulated an arrival time of head crowd between 106 - 108 minutes, which was very close to the observed rally time of head crowd of 107 minutes. In this study, crowd sizes were considered to be acceptable based on a 95% confidence interval of arrival time of tail crowd (i.e. 174 - 192 minutes) and total rally time (i.e. 199 - 219 minutes). For example, a crowd size of 9,800 people was simulated matching the total rally time to examine the credibility of calibrated model parameters (Figure 1). Within the tested range of calibrated model parameters, the results indicated that it was possible to tweak the model parameters of varying crowd size to match the observed rally time (Table 1). Despite the simulated rally time of some accepted models were within ±5% of the observed rally time, the parameters used to simulate such a model were not necessary reasonable in reality. The simulated count of 9,800, for example, would require a cap of maximum walking speed of 0.5 m/s, which seemed to be unreasonably slow under normal circumstance and incompatible with crowdsourced data (Figure 1). Given the observed rally time, it was found that the crowd sizes of 14,000 - 29,000 could be simulated with reasonable model parameters, whereas the crowd sizes of 9,800 and 50,000 would yield unreasonable model parameters. Taking the median within the range of 14,000 - 29,000, this study also found that a crowd size of 21,000 could yield eight matching simulations with varying reasonable model parameters that may be better simulate the actual rally attendance. This paper provided empirical evidences to examined the credibility of various crowd sizes of the 2017 July 1st Rally in Hong Kong reported by the stakeholders. The research also presents a transparent, repeatable and verifiable approach to explore, quantify and simulate human movements in a rally event, such as the early departure and late arrival, to better understand dynamic crowd behaviours and interactions. [ABSTRACT FROM AUTHOR]

This study presents an estimate of the total NO 2 exposure in a polluted and densely populated region in Malta, the Northern Harbour district. To estimate the population mobility, we follow a dynamic approach whereby four microenvironments are mapped onto defined Copernicus Urban Atlas 2012 land use classifications. These include the home (ME_home), work (ME_work), traffic (ME_traffic), and other outdoor activities (ME_other) microenvironments. In addition, generic time-activity profiles are used to estimate the hourly mobility in the different microenvironments depending on weekday or weekend profiles. Measured hourly NO 2 ambient concentrations from the air quality network run by the Environment and Resources Authority (ERA) are used in conjunction with the estimated time-activity profiles to calculate the total exposure. The highest NO 2 population exposure is estimated for ME_home (71%) as people spend the majority of the time in this microenvironment followed by ME_other (15%), ME_work (10%) and ME_traffic (4%), respectively. In addition, we test the sensitivity of the total NO 2 exposure to changes in NO 2 concentrations for different microenvironments. The total NO 2 exposure using infiltration rates to estimate infiltrated outdoor NO 2 concentrations in indoor microenvironments, is up to 25% lower compared to the NO 2 exposure estimated using outdoor NO 2 concentrations for all microenvironments. Results also suggest a decrease of 3% in the estimated NO 2 exposure if a static population is assumed for each microenvironment as opposed to a dynamic one. Exposure assessments such as that presented in this study are essential to aid the development of targeted policies to limit such exposures. Image 1 • The first study to estimate NO 2 exposure considering population mobility in Malta. • The highest NO 2 exposure is noted in the home microenvironment. • The lowest NO 2 exposure is estimated for the traffic microenvironment. • The NO 2 exposure is 25% higher when using a constant NO 2 concentration for MEs. • The total NO 2 exposure is 3% less if a static population is used for each time step. [ABSTRACT FROM AUTHOR]