Starvation Rates Research Articles

Landscape disturbance alters the composition and diversity of the diet of moose, a generalist herbivore

Human-caused landscape disturbance is one of the major threats to large herbivores, globally. Across central British Columbia (BC), Canada, industrial forest harvesting has resulted in rapid change to forest structure and composition. Concurrent with the increase in forest harvesting, moose (Alces alces) populations across central BC have declined, some dramatically. We studied moose diet across three seasons in two regional study areas that represented a range of logging intensity and rate of apparent starvation of moose. We used microhistological analysis of pellets to identify the proportional consumption of major plant groups that were known forage for moose across the study areas, as well as the diversity of the diet of moose. We used regression models to test whether the composition and diversity of moose diet in two study areas were consistent with the Niche Expansion Hypothesis or the Niche Shrinkage Hypothesis. In areas of increased density and size of clearcuts, moose consumed fewer shrubs and fir trees and their diet was more diverse. Where application of herbicides was common, moose consumed fewer forbs and their diet was more diverse. Both responses were consistent with the Niche Expansion Hypothesis. In areas of increased disturbance by wildfire, moose consumed less fir and more shrubs. Our results suggest that landscape disturbance in the form of extensive and rapid forest harvesting can influence the composition and diversity of the diet of moose.

Thermal limits of Africanized honey bees are influenced by temperature ramping rate but not by other experimental conditions

Interest in assessing the critical thermal limits of bees is rapidly increasing, as these physiological traits are good predictors of bees' potential responses to extreme temperature changes, which is relevant in the context of global climate change. However, estimates of thermal limits may be influenced by several factors and published studies differ in experimental methods and conditions, such as the rate of temperature change (ramping rate) and feeding status, which might yield inaccurate predictions and limit comparisons across taxa and regions. Using Africanized honey bees as a model organism, we assessed the effect of ramping rate (0.25, 0.5, 0.75, 1.0 and 1.5 °C min−1) and length of starvation (recently fed vs. fasted for 6, 12, and 18 h) on foragers’ lower (CTMin) and upper (CTMax) thermal limits, as well as the effect of cold stress on CTMax. In addition, we evaluated the two approaches currently used to assess CTMax with a water bath: floating or submerging the testing vials in the bath. We found that critical thermal limits were influenced by ramping rates but not by the other assessed experimental conditions. On average, at ramping rates faster than 0.5 °C min−1, bees displayed a CTMin 1.1–2.6 °C lower and a CTMax 5.3–6.9 °C higher than those of the slowest ramping rate. We discuss the implications of these results and provide suggestions for future thermal studies on bees.

Enhanced time-constraint aware tasks scheduling mechanism based on predictive optimization for efficient load balancing in smart manufacturing

Smart manufacturing has great potential for developing customized products to meet the dynamic needs of customers and a collaborative network to enhance production efficiency. It is an emerging area with the revolution of business strategies, such as Industry 4.0 and Industrial Internet of the United States of America. IIoT and data-driven technologies, such as AI and ML, have leveraged the production environment by facilitating mass personalized customization of products and improving manufacturing processes collaboratively. However, these technologies are segregated and dispersed in the digitization of manufacturing products and automation of machines in existing manufacturing processes. Therefore, it is a leverage challenge to develop an integrated solution based on IIoT and data-driven technologies for an autonomous manufacturing environment. This research study presents an integrated solution using an enhanced TCA tasks scheduling mechanism based on a predictive optimization approach to improve smart manufacturing production efficiency. The proposed enhanced TCA tasks scheduling mechanism is an improved variant of FEF scheduling that considers accurate decision (prediction) measures and tasks’ minimal (optimal) time to schedule tasks efficiently. This study aims to efficiently plan task execution sequence to increase smart manufacturing productions and efficiency of resource utilization in real-time by maximizing utilization of smart machines, minimizing tasks idle time, and autonomously controlling the smart manufacturing environment through installed sensors and actuators. Furthermore, different evaluation methods are used to analyze the significance of the proposed PO-TCA scheduling mechanism, such as response time, tasks drop rate, tasks starvation rate, and machine utilization rate. The experimental analysis shows that the proposed enhanced PO-TCA scheduling mechanism reduces dropout and starvation rates by 21 % and 17 %, respectively. Our proposed mechanism also improves machine utilization by an average of 18 % compared to the baseline scheduling strategy. Moreover, experimental results signify that the proposed PO-TCA scheduling mechanism improves the utilization of smart machines and minimizes the task’s idle time to achieve a trade-off between tasks response and waiting time.

IoT-Orchestration based nanogrid energy management system and optimal time-aware scheduling for efficient energy usage in nanogrid

Internet of Things (IoT) has brought an immense revolution in diverse fields related to mission-critical systems including healthcare and navigation systems. We argue that the potential of IoT has not been fully exploited in the energy sector. There is a dire need to shift the traditional paradigm of mission-critical electric power architectures to IoT-enabled fully orchestrated architectures to enhance the overall performance. In this study, we present a novel IoT task orchestration architecture for efficient energy management of a nanogrid system that focuses on minimizing the use of non-renewable energy resources and maximizing the use of renewable energy resources. Since network orchestration deals with automating the interaction between multiple components involved to execute a particular service, therefore, scheduling the relevant processes within strict deadlines becomes the core pillar of the system's performance. The mission-critical systems with urgent task execution often suffer from missing task deadlines issues. To overcome this issue, we present a task scheduling algorithm that incorporates the optimized surplus time, and efficiently executes the energy management-related tasks contemplating to their types. The study utilizes sensors to obtain data from physical IoT devices, including photovoltaic (PV), Energy Storage System (ESS), and diesel generator (DG). The performance of the proposed model is evaluated using data set of nanogrid houses. The outcomes revealed that IoT-task orchestration has played a pivotal role in efficient energy management for nanogrid missioncritical systems. Furthermore, the comparison showed that the task starvation rate is reduced to 16% and 12% when compared with state-of-the-art scheduling algorithms.

Sedimentation strategies provide effective but limited mitigation of relative sea-level rise in the Mekong delta

The Mekong delta is experiencing rapid environmental change due to anthropogenic activities causing accelerated subsidence, sea-level rise and sediment starvation. Consequentially, the delta is rapidly losing elevation relative to sea level. Designating specific areas for sedimentation is a suggested strategy to encourage elevation-building with nature in deltas. We combined projections of extraction-induced subsidence, natural compaction and global sea-level rise with new projections of fluvial sediment delivery to evaluate the potential effectiveness of sedimentation strategies in the Mekong delta to 2050. Our results reveal that with current rates of subsidence and sediment starvation, fluvial sediments alone can only preserve elevation locally, even under optimistic assumptions, and organic sedimentation could potentially assume a larger role. While sedimentation strategies alone have limited effectiveness in the present context, combined with enhanced organic matter retention and interventions reducing anthropogenic-accelerated subsidence, they can considerably delay future relative sea-level rise, buying the delta crucial time to adapt.

IMPROVABILITY OF THE FABRICATION LINE IN A SHIPYARD

The ship production process is a complex manufacturing system involving numerous working stations mutually interconnected by transport devices and buffers. Such a production system can be efficiently modeled using the stochastic system approach and Markov chains. Once formulated, the mathematical model enables analysis of the governing production system properties like the production rate, work-in-process, and probabilities of machine blockage and starvation that govern the production system bottleneck identification and its continuous improvement. Although the continuous improvement of the production system is a well-known issue, it is usually based on managerial intuition or more complex discrete event simulation yielding sub-optimal results. Therefore, a semi-analytical procedure for the improvability analysis using the Markov chain framework is presented in this paper in the case of the shipyard’s fabrication lines. Potential benefits for the shipyards are pointed out as the main gain of the improvability analysis.

Climate Resilient Vegetable Farming: A Review

Climate change is one of the global challenges faced by the mankind today with the continuously rising temperature, triggering a host of extreme weather events such as heat waves, drought, and flooding. These climate induced challenges are manifesting themselves rapidly, causing socio-economic insecurities and health challenges, particularly in marginalized communities. There is increasing evidence of indirect associations between climate change and the rise in the rates of malnutrition, poor health, hunger and starvation, as well as food and water insecurity. In addition, climate-change impacts have put an additional pressure on already stressed natural resource base, reducing the resilience of agro-ecosystems that are, in part, providing food and nutritional security in rural communities. Tackling these challenges requires a paradigm shift from the current incremental adaptation strategies towards transformative alternatives that also place an equal emphasis on human nutrition and health, as well as environmental sustainability. In the context of marginalized farming communities, a transformative adaptation strategy is defined as one that causes a disruptive, but desirable and sustainable change to the social– ecological state of the system. In the context of this paper, the inclusion of adaptable nutrient dense vegetable crops into marginalized agricultural systems and dominant food systems is considered part of transformative adaptation.

Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory

The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer’s experience based on learnings from past trends. The implementation of analytics tools to predict the production and consumer patterns plays a vital rule. In this paper, we put our efforts to find integrated solutions for smart factory concerns by proposing an efficient task management mechanism based on learning to scheduling in a smart factory. The learning to prediction mechanism aims to predict the machine utilization for machines involved in the smart factory, in order to efficiently use the machine resources. The prediction algorithm used is artificial neural network (ANN) and the learning to prediction algorithm used is particle swarm optimization (PSO). The proposed task management mechanism is evaluated based on multiple scenario simulations and performance analysis. The comparisons analysis shows that proposed task management system significantly improves the machine utilization rate and drastically drops the tasks instances missing rate and tasks starvation rate.

Optimal Control Based on Scheduling for Comfortable Smart Home Environment

Smart home environments account to a major portion of the total energy consumption in today’s world. The residents of smart home environments wish to find solutions that reduce the energy costs along with providing an optimal indoor environment for the residents. Another significant aspect in smart home systems is efficiency of tasks management and control commands’ execution for smart home actuators. In this paper, we propose an optimal control solution for smart home environment based on smart home energy optimization and control tasks’ load dispatching and scheduling. Optimal control is achieved by first defining an objective function for minimizing energy cost which is implemented using VB-PSO (velocity boost particle swarm optimization) algorithm. Next, the control tasks are generated using rule set implemented in fuzzy logic; defined based on optimal values achieved from VB-PSO. A Markov model based mechanism dispatches control tasks at scheduler, for efficient scheduling and optimal control. The results show that the proposed optimization scheme saves up to 29.73% energy costs on average, in comparison to baseline scheme. The proposed tasks’ load dispatching scheme of admission control, makes the job of load balancing among the processors efficient while giving priority to the urgent tasks. The results for scheduler evidently show the low dropping probabilities for urgent tasks along with showing 34.9% reduction in tasks’ starvation rate and 36.82% reduction in average tasks’ instances missing rates.

A Hybrid Scheduling Mechanism Based on Agent Cooperation Mechanism and Fair Emergency First in Smart Factory

Smart factory also known as smart manufacturing is an emerging field with the revolution of industry 4.0. With the help of all these concepts, the smart factory integrates the manufacturing assets and represents industrial networks. In this paper, we focus on integrated solutions for smart factory concerns; by proposing an efficient task management mechanism based on an efficient and resource-aware scheduling scheme named as ACM-FEF. The scheduling algorithm used for the efficient task management is hybrid of the two scheduling approaches as agent cooperation mechanism (ACM) and fair emergency first (FEF) scheduling scheme. ACM is a decentralized scheduling approach which focuses on the production maximization goals per machine, and also pays attention to the production goals of all the machine networks involved in the smart factory. FEF scheduling scheme focuses on minimizing the tasks starvation rate and maximizing the machine utilization by efficiently using the machine slots. The proposed hybrid mechanism aims to efficiently plan tasks execution, maximize machines’ resource utilization, maximize productivity, minimize production delays, efficiently handle exceptions and efficiently control smart factory actuators.

What Is Antagonistic Pleiotropy?

Antagonistic Pleiotropy (AP) is today the best-accepted theory for the evolutionary origin of aging. According to AP theory, aging is a side effect of genes that are selected for their contribution to fertility and other essential components of individual fitness. In this standard view, aging exists because the benefits of enhanced fertility early in life are linked logically or physically to the long-term deterioration of the body, and evolution has been compelled to accept the latter as a cost of the former. What is the evidence for this proposition? Indeed, biological aging has sometimes been observed to be genetically associated with enhancement of fertility or survivability early in life, but the association is not universal or consistent. There are known examples of mutations that lead to longer lifespan without apparent cost. This is a major problem for AP theory, because AP theory supposes that senescence could never have evolved on its own, and (according to AP theory) there should be a substantial fitness dividend for any individual that manages to extend its lifespan without curtailing its fertility. How can we understand the observed fact that genetic mechanisms of aging are sometimes pleiotropic and sometimes not? I propose herein an alternative interpretation, in which antagonistic pleiotropy is not a precondition imposed on evolution, but an evolved adaptation in its own right. My hypothesis is that aging is selected because of important long-term benefits for the community, but in the short term, individual selection is stronger than group selection. Therefore, the communal benefits are in danger of being lost. Antagonistic pleiotropy evolves as an evolvability adaptation that protects long term group-level benefits from being lost to short term individual selection. The mechanism of selection is indirect, the benefit long-term, and it accrues only to the group at the expense of the individual. By standard population genetic reasoning, selection for pleiotropy should be too weak to matter; but in our individual-based simulation, pleiotropy evolves consistently under a broad swath of parameter space. A key feature of the model contributing to the viability of group adaptations is the assumption of a predator species in a geographically structured population, such that any population that evolves a reproduction rate exceeding the local prey population is punished by high rates of starvation. The combination of high fertility and long lifespan is a temptation for individuals, but a danger for the stability of populations. Once a sustainable mix of fertility and longevity has been established by multilevel natural selection, pleiotropy can help to assure that it is not lost. The population is free to shift from (high fertility/short lifespan) to (lower fertility/longer lifespan) as varying environmental conditions demand, without risking population overshoot and collapse.

Effects of human-induced prey depletion on large carnivores in protected areas: Lessons from modeling tiger populations in stylized spatial scenarios.

Prey depletion is a major threat to the conservation of large carnivore species globally. However, at the policy‐relevant scale of protected areas, we know little about how the spatial distribution of prey depletion affects carnivore space use and population persistence. We developed a spatially explicit, agent‐based model to investigate the effects of different human‐induced prey depletion experiments on the globally endangered tiger (Panthera tigris) in isolated protected areas—a situation that prevails throughout the tiger's range. Specifically, we generated 120 experiments that varied the spatial extent and intensity of prey depletion across a stylized (circle) landscape (1,000 km2) and Nepal's Chitwan National Park (~1,239 km2). Experiments that created more spatially homogenous prey distributions (i.e., less prey removed per cell but over larger areas) resulted in larger tiger territories and smaller population sizes over time. Counterintuitively, we found that depleting prey along the edge of Chitwan National Park, while decreasing tiger numbers overall, also decreased female competition for those areas, leading to lower rates of female starvation. Overall our results suggest that subtle differences in the spatial distributions of prey densities created by various human activities, such as natural resource‐use patterns, urban growth and infrastructure development, or conservation spatial zoning might have unintended, detrimental effects on carnivore populations. Our model is a useful planning tool as it incorporates information on animal behavioral ecology, resource spatial distribution, and the drivers of change to those resources, such as human activities.

Determinants of exclusive breastfeeding among mothers of infants aged 6 to 12\u2009months in Gwanda District, Zimbabwe

BackgroundIn 2016, 98% of children in Zimbabwe received breastmilk, however only 40% of babies under six months were exclusively breastfed 24 h prior to data collection. A 2014 survey revealed that Matabeleland South Province had the country’s highest starvation rates and food insecurities were rife. This study aimed at investigating maternal, infant, household, environmental and cultural factors influencing exclusive breastfeeding (EBF) practice in Gwanda District.MethodsA cross-sectional study was conducted from January to March 2018. Interviews used pretested structured questionnaires for 225 mothers of infants aged between six and twelve months at immunization outreach points and health facilities. Descriptive statistics, bivariate and multivariate analysis estimated the association between the dependent and independent variables. Exclusive breastfeeding was defined as feeding an infant on breast milk only from birth up to the age of six months.ResultsThe majority of mothers (n = 193; 89%) had knowledge about EBF and 189 (84%) expressed a positive attitude towards the practice, however, only 81 (36%) practiced exclusive breastfeeding. The most common complementary food/fluid given to the infants was plain water (n = 85; 59%). Predictors for EBF were: maternal Human Immuno-deficiency Virus positive status (Odds Ratio [OR] 0.30; 95% Confidence Interval [CI] 0.17, 0.56) and being economically independent (OR 0.41; 95% CI 0.21, 0.79). Barriers to practicing EBF were: being a young mother under 25 years of age (OR 3.05; 95% CI 1.67, 5.57), having one or two children (OR 2.49; 95% CI 1.29, 4.79), living in less than two rooms (OR 3.86; 95% CI 1.88, 7.93) and having a baby of low birthweight (OR 1.05; 95% CI 0.40, 2.71). After multivariate analysis, only the mother’s economic independence was associated with practicing EBF (Adjusted OR [AOR] 0.83; 95% CI 0.30, 0.92). Key informants identified traditional family practices as the major barrier to EBF.ConclusionThe exclusive breastfeeding rates were low despite the mothers’ high knowledge levels and positive attitudes towards the practice. In addressing the multiple factors influencing the cost effective practice, there is need to channel supportive measures through a system-wide approach. This can be achieved by realigning breastfeeding policy directives as well as community attitudes and values towards the exclusive breastfeeding.

An Adaptive Emergency First Intelligent Scheduling Algorithm for Efficient Task Management and Scheduling in Hybrid of Hard Real-Time and Soft Real-Time Embedded IoT Systems

Industrial revolution is advancing, and the augmented role of autonomous technology and embedded Internet of Things (IoT) systems is at its vanguard. In autonomous technology, real-time systems and real-time computing are of core importance. It is crucial for embedded IoT devices to respond in real-time; along with fulfilling all the constraints. Many combinations for existing approaches have been proposed with different trade-offs between the resources constraints and tasks dropping rate. Hence, it highlights the significance of a task scheduler which not only takes care of complex nature task input; but also maximizes the CPU throughput. A complex nature task input is when combinations of hard real-time tasks and soft real-time tasks, with different priorities and urgency measures, arrive at the scheduler. In this work, we propose a custom tailored adaptive and intelligent scheduling algorithm for the efficient execution and management of hard and soft real time tasks in embedded IoT systems. The proposed scheduling algorithm aims to distribute the CPU resources fairly to the possibly starving, in overloaded cases, soft real-time tasks while focusing on the execution of high priority hard real-time tasks as its primary objective. The proposal is achieved with the help of two intelligent measures; Urgency Measure (UM) and Failure Measure (FM). The proposed mechanism reduces the rate of tasks missed and the rate of tasks starved, by utilizing the free CPU units for maximum CPU utilization and quick response times. We have performed comparisons of our proposed scheme based on performance metrics as percentage of task instances missed, number of tasks with missed instances, and tasks starvation rate to evaluate the CPU utilization. We first compare our proposed approach with multiple traditional and combined scheduling approaches, and then we evaluate the effect of intelligent modules by comparing the intelligent FEF with non-intelligent FEF. We also evaluate the proposed algorithm in contrast to the most commonly-used hybrid scheduling scheme in embedded systems. The results show that the proposed algorithm out performs the other algorithms, by significantly reducing the task starvation rate and increasing the CPU utilization.

Multiple anthropogenic interventions drive puma survival following wolf recovery in the Greater Yellowstone Ecosystem.

Humans are primary drivers of declining abundances and extirpation of large carnivores worldwide. Management interventions to restore biodiversity patterns, however, include carnivore reintroductions, despite the many unresolved ecological consequences associated with such efforts. Using multistate capture–mark–recapture models, we explored age‐specific survival and cause‐specific mortality rates for 134 pumas (Puma concolor) monitored in the Greater Yellowstone Ecosystem during gray wolf (Canis lupus) recovery. We identified two top models explaining differences in puma survivorship, and our results suggested three management interventions (unsustainable puma hunting, reduction in a primary prey, and reintroduction of a dominant competitor) have unintentionally impacted puma survival. Specifically, puma survival across age classes was lower in the 6‐month hunting season than the 6‐month nonhunting season; human‐caused mortality rates for juveniles and adults, and predation rates on puma kittens, were higher in the hunting season. Predation on puma kittens, and starvation rates for all pumas, also increased as managers reduced elk (Cervus elaphus) abundance in the system, highlighting direct and indirect effects of competition between recovering wolves and pumas over prey. Our results emphasize the importance of understanding the synergistic effects of existing management strategies and the recovery of large, dominant carnivores to effectively conserve subordinate, hunted carnivores in human‐dominated landscapes.

Different Material States of Pub1 Condensates Define Distinct Modes of Stress Adaptation and Recovery

How cells adapt to varying environmental conditions is largely unknown. Here, we show that, in budding yeast, the RNA-binding and stress granule protein Pub1 has an intrinsic property to form condensates upon starvation or heat stress and that condensate formation is associated with cell-cycle arrest. Release from arrest coincides with condensate dissolution, which takes minutes (starvation) or hours (heat shock). Invitro reconstitution reveals that the different dissolution rates of starvation- and heat-induced condensates are due to their different material properties: starvation-induced Pub1 condensates form by liquid-liquid demixing and subsequently convert into reversible gel-like particles; heat-induced condensates are more solid-like and require chaperones for disaggregation. Our data suggest that different physiological stresses, as well as stress durations and intensities, induce condensates with distinct physical properties and thereby define different modes of stress adaptation and rates of recovery.

Starvation rates in larval and juvenile Atlantic silversides (Menidia menidia) are unaffected by high CO2 conditions

Over the past decade, laboratory experiments on fish early life stages have found many traits that are evidently sensitive to elevated CO2 levels. With respect to larval growth, high CO2 environments are commonly assumed to increase acid–base regulation and other plastic responses, thus incurring additional metabolic costs that reduce the scope for growth. This assumption is not well supported by empirical evidence. One reason might be that experiments often provide unrestricted feeding conditions, which could allow larvae to compensate for higher costs by increased food intake. To remove potentially confounding effects of larval feeding, we conducted a series of starvation trials on offspring of the Atlantic silverside (Menidia menidia), predicting faster starvation at high compared to ambient CO2 treatments. We compiled observations from five separate experiments spanning different years, laboratories, temperatures (17–26 °C), life stages (newly hatched larvae, previously fed larvae, early juveniles), and CO2 levels (300–6500 µatm). Contrary to expectation, we found that starvation rates were largely independent of the CO2 environment in this fish species. The one exception occurred at the lowest temperature and most extreme CO2 treatment, which resulted in slower not faster starvation in newly hatched larvae at high compared to ambient CO2 treatments. The apparent failure of starvation rate as a proxy for CO2 effects on larval fish metabolism may have several reasons, including potential CO2 tolerance of offspring, observed large stochasticity in early life survival masking small metabolic costs of high CO2, and the general depression and reconfiguration of fish metabolism in response to food deprivation.

Stabilization of Variable Bit Rate Video Streams Using Linear Lyapunov Functions on Wireless Mobile Network

Streaming videos over wireless networks suffers from low video quality due to network ability limitations. The quality of the channel and the characteristics of source play the major role in transmitting video stream over mobile environments. On failure of wireless video transmission, retransmission method was employed to improve the reliability of wireless link. However, retransmission of video leads to significant impact on energy consumption and bounded average waiting time is also increased. The longer waiting time on retransmission results in buffer starvation. Therefore, it is desirable to reduce the variable bit rate of transmitted video signal and increase the stability level when buffer starvation is occurs. In order to overcome such limitation, a technique named Response based Stabilization Analysis (RSA) using Distributed Optimality Bit Rate Allocation (RSA-DOBRA) is proposed in this paper. Initially, video stream is segregated into frames of different classes (i.e., size). Each frame is transmitted based on the variable bit rate response using Optimal Quantization process. Secondly, Linear Lyapunov Functions is employed with RSA to prove the stability of different bit rates on wireless video streaming. The application of Linear Lyapunov Function maintains the stability level of bit rate on different class of frame transmission on wireless link. Finally, Distributed Optimality Bit Rate Allocation uses the time slicing procedure to reduce the bounded average waiting time. RSA performs the time slicing based on multiplexed wireless video transmission on variable bit rate to avoid buffer starvation. RSA at the final stage reduces the energy consumption by improving the reliability of wireless link. Experiment is conducted on factors such as buffered starvation rate, waiting time on video frame transmission, and energy consumption rate.

Transient Performance Analysis of Serial Production Lines With Geometric Machines

A production system is characterized by both its steady state and transient properties. While extensive research efforts have been spent in the analysis of the steady state of production systems, very few results, especially analytical ones, have been reported regarding their transient behavior. Indeed, transient behavior of production systems has significant practical and theoretical implications. A better understanding of the transient properties of production systems is critical to effective utilization of real-time production data for efficient factory floor operation and management. In the framework of serial production lines with geometric machines and finite buffers, this paper develops mathematical models for transient analysis and derives closed-form expressions for evaluating the production rate, consumption rate, work-in-process, and probabilities of machine starvation and blockage, during transients. In addition, a computationally efficient algorithm based on aggregation is developed to approximate the transient performance measures with high accuracy. Numerical experiments show that the methods developed can be applied to systems with time-varying machine parameters as well.

Foraging mode switching: the importance of prey distribution and foraging currency

Foraging methods are highly variable, but can be grouped into two modes: searching and ambush. While research has focused on the functioning of each mode, the question of how animals choose which to use has been largely neglected. Here we consider a forager that exploits prey that are patchily distributed in space and time. This forager can either sit and wait for prey to appear or search for prey, which is more likely to result in encounters with prey but costs more energy and/or exposes the forager to greater predation risk. The currency that natural selection appears to have optimized will be determined by the additional costs of searching and whether there is a risk of starvation. We therefore compare the predictions of models based on currencies that consider only energy and predation risk to state-dependent models in which energy reserves are used to trade off predation rate, starvation rate and investment in growth. The choice of currency qualitatively affects how mode should change when prey abundance and prey patchiness increase. We show how differing prey distributions can explain variation in effects of experimentally increasing prey abundance. Our work has several implications for the study of foraging mode, population dynamics and the methods used to assess population size.