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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]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Iheringia. Série Zoologia is the property of Fundacao Zoobotanica do Rio Grande do Sul 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

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.