Extreme functional specialization of fertile leaves in a widespread fern species and its implications on the evolution of reproductive dimorphism.

Resource allocation theory posits that organisms distribute limited resources across functions to maximize their overall fitness. In plants, the allocation of resources among maintenance, reproduction, and growth influences short-term economics and long-term evolutionary processes, especially during resource scarcity. The evolution of specialized structures to divide labor between reproduction and growth can create a feedback loop where selection can act on individual organs, further increasing specializaton and  resource allocation. Ferns exhibit diverse reproductive strategies, including dimorphism, where leaves can either be sterile (only for photosynthesis) or fertile (for spore dispersal). This dimorphism is similar to processes in seed plants (e.g., the production of fertile flowers and sterile leaves), and presents an opportunity to investigate divergent resource allocation between reproductive and vegetative functions in specialized organs. Here, we conducted anatomical and hydraulic analyses on Onoclea sensibilis L., a widespread dimorphic fern species, to reveal significant structural and hydraulic divergences between fertile and sterile leaves. Fertile fronds invest less in hydraulic architecture, with nearly 1.5 times fewer water-conducting cells and a nearly 0.5 times less drought-resistant xylem compared to sterile fronds. This comes at the increased relative investment in structural support, which may help facilitate spore dispersal. These findings suggest that specialization in ferns-in the form of reproductive dimorphism-can enable independent selection pressures on each leaf type, potentially optimizing spore dispersal in fertile fronds and photosynthetic efficiency in sterile fronds. Overall, our study sheds light on the evolutionary implications of functional specialization and highlights the importance of reproductive strategies in shaping plant fitness and evolution.

Does government environmental attention drive green total factor productivity? Evidence from China.

Based on city-level panel data spanning 2008 to 2021, this study investigates the impact of government environmental attention (GEA) on green total factor productivity (GTFP). The findings suggest that increased GEA substantially enhances the growth of GTFP. After conducting robustness and endogeneity tests, the study's results consistently show reliability and robustness. Further analysis elucidates several mechanisms through which GEA influences GTFP, including fostering green technology innovation, optimizing resource allocation, and promoting upgrades in industrial structure. Heterogeneity analyses reveal that the impact of GEA on GTFP is notably pronounced in eastern cities, as well as in cities characterized by low resource dependency, mature industrial development, and high market competition. Conversely, the influence of GEA on GTFP is less discernible in cities prioritizing economic development goals, possibly due to differing policy orientations and resource allocation strategies. This study offers a novel perspective on understanding how GEA shape green development and provides empirical support for policy formulation.

Geriatric trauma triage: optimizing systems for older adults-a publication of the American Association for the Surgery of Trauma Geriatric Trauma Committee.

Background: Geriatric trauma patients are an increasing population of the United States (US), sustaining a high incidence of falls, and suffer greater morbidity and mortality to their younger counterparts. Significant variation and challenges exist to optimize outcomes for this cohort, while being mindful of available resources. This manuscript provides concise summary of locoregional and national practices, including relevant updates in the triage of geriatric trauma in an effort to synthesize the results and provide guidance for further investigation. Methods: We conducted a review of geriatric triage in the United States (US) at multiple stages in the care of the older patient, evaluating existing literature and guidelines. Opportunities for improvement or standardization were identified. Results: Opportunities for improved geriatric trauma triage exist in the pre-hospital setting, in the trauma bay, and continue after admission. They may include physiologic criteria, biochemical markers, radiologic criteria and even age. Recent Trauma Quality Improvement Program (TQIP) Best Practices Guidelines for Geriatric Trauma Management published in 2024 support these findings. Conclusion: Trauma systems must adjust to provide optimal care for older adults. Further investigation is required to provide pertinent guidance.

Using economic analysis to inform health resource allocation: lessons from Malawi.

Despite making remarkable strides in improving health outcomes, Malawi faces concerns about sustaining the progress achieved due to limited fiscal space and donor dependency. The imperative for efficient health spending becomes evident, necessitating strategic allocation of resources to areas with the greatest impact on mortality and morbidity. Health benefits packages hold promise in supporting efficient resource allocation. However, despite defining these packages over the last two decades, their development and implementation have posed significant challenges for Malawi. In response, the Malawian government, in collaboration with the Thanzi la Onse Programme, has developed a set of tools and frameworks, primarily based on cost-effectiveness analysis, to guide the design of health benefits packages likely to achieve national health objectives. This review provides an overview of these tools and frameworks, accompanied by other related analyses, aiming to better align health financing with health benefits package prioritization. The paper is organized around five key policy questions facing decision-makers: (i) What interventions should the health system deliver? (ii) How should resources be allocated geographically? (iii) How should investments in health system inputs be prioritized? (iv) How should equity considerations be incorporated into resource allocation decisions? and (v) How should evidence generation be prioritized to support resource allocation decisions (guiding research)? The tools and frameworks presented here are intended to be compatible for use in diverse and often complex healthcare systems across Africa, supporting the health resource allocation process as countries pursue Universal Health Coverage.

Examining geographical inequalities for malaria outcomes and spending on malaria in 40 malaria-endemic countries, 2010-2020.

BACKGROUND: While substantial gains have been made in the fight against malaria over the past 20 years, malaria morbidity and mortality are marked by inequality. The equitable elimination of malaria within countries will be determined in part by greater spending on malaria interventions, and how those investments are allocated. This study aims to identify potential drivers of malaria outcome inequality and to demonstrate how spending through different mechanisms might lead to greater health equity. METHODS: Using the Gini index, subnational estimates of malaria incidence and mortality rates from 2010 to 2020 were used to quantify the degree of inequality in malaria burden within countries with incidence rates above 5000 cases per 100,000 people in 2020. Estimates of Gini indices represent within-country distributions of disease burden, with high values corresponding to inequitable distributions of malaria burden within a country. Time series analyses were used to quantify associations of malaria inequality with malaria spending, controlling for country socioeconomic and population characteristics. RESULTS: Between 2010 and 2020, varying levels of inequality in malaria burden within malaria-endemic countries was found. In 2020, values of the Gini index ranged from 0.06 to 0.73 for incidence, 0.07 to 0.73 for mortality, and 0.00 to 0.36 for case fatality. Greater total malaria spending, spending on health systems strengthening for malaria, healthcare access and quality, and national malaria incidence were associated with reductions in malaria outcomes inequality within countries. In addition, government expenditure on malaria, aggregated government and donor spending on treatment, and maternal educational attainment were also associated with changes in malaria outcome inequality among countries with the greatest malaria burden. CONCLUSIONS: The findings from this study suggest that prioritizing health systems strengthening in malaria spending and malaria spending in general especially from governments will help to reduce inequality of the malaria burden within countries. Given heterogeneity in outcomes in countries currently fighting to control malaria, and the challenges in increasing both domestic and international funding allocated to control and eliminate malaria, the efficient targeting of limited resources is critical to attain global malaria eradication goals.

What does "urgency" mean when prioritizing cancer treatment? Results from a qualitative study with German oncologists and other experts during the COVID-19 pandemic.

PURPOSE: Cancer care in Germany during the COVID-19 pandemic was affected by resource scarcity and the necessity to prioritize medical measures. This study explores ethical criteria for prioritization and their application in cancer practices from the perspective of German oncologists and other experts. METHODS: We conducted fourteen semi-structured interviews with German oncologists between February and July 2021 and fed findings of interviews and additional data on prioritizing cancer care into four structured group discussions, in January and February 2022, with 22 experts from medicine, nursing, law, ethics, health services research and health insurance. Interviews and group discussions were digitally recorded, transcribed verbatim and analyzed using qualitative content analysis. RESULTS: Narratives of the participants focus on "urgency" as most acceptable criterion for prioritization in cancer care. Patients who are considered curable and those with a high level of suffering, were given a high degree of "urgency." However, further analysis indicates that the "urgency" criterion needs to be further distinguished according to at least three different dimensions: "urgency" to (1) prevent imminent harm to life, (2) prevent future harm to life and (3) alleviate suffering. In addition, "urgency" is modulated by the "success," which can be reached by means of an intervention, and the "likelihood" of reaching that success. CONCLUSION: Our analysis indicates that while "urgency" is a well-established criterion, its operationalization in the context of oncology is challenging. We argue that combined conceptual and clinical analyses are necessary for a sound application of the "urgency" criterion to prioritization in cancer care.

Modeling defensive resource allocation in multilayered systems under probabilistic and strategic risks.

Confronting the continuing risk of an attack, security systems have adopted target-hardening strategies through the allocation of security measures. Most previous work on defensive resource allocation considers the security system as a monolithic architecture. However, systems such as schools are typically characterized by multiple layers, where each layer is interconnected to help prevent single points of failure. In this paper, we study the defensive resource allocation problem in a multilayered system. We develop two new resource allocation models accounting for probabilistic and strategic risks, and provide analytical solutions and illustrative examples. We use real data for school shootings to illustrate the performance of the models, where the optimal investment strategies and sensitivity analysis are presented. We show that the defender would invest more in defending outer layers over inner layers in the face of probabilistic risks. While countering strategic risks, the defender would split resources in each layer to make the attacker feel indifferent between any individual layer. This paper provides new insights on resource allocation in layered systems to better enhance the overall security of the system.

Cost Evaluation of the Ontario Virtual Urgent Care Pilot Program: Population-Based, Matched Cohort Study.

BACKGROUND: In 2020, the Ministry of Health (MoH) in Ontario, Canada, introduced a virtual urgent care (VUC) pilot program to provide alternative access to urgent care services and reduce the need for in-person emergency department (ED) visits for patients with low acuity health concerns. OBJECTIVE: This study aims to compare the 30-day costs associated with VUC and in-person ED encounters from an MoH perspective. METHODS: Using administrative data from Ontario (the most populous province of Canada), a population-based, matched cohort study of Ontarians who used VUC services from December 2020 to September 2021 was conducted. As it was expected that VUC and in-person ED users would be different, two cohorts of VUC users were defined: (1) those who were promptly referred to an ED by a VUC provider and subsequently presented to an ED within 72 hours (these patients were matched to in-person ED users with any discharge disposition) and (2) those seen by a VUC provider with no referral to an in-person ED (these patients were matched to patients who presented in-person to the ED and were discharged home by the ED physician). Bootstrap techniques were used to compare the 30-day mean costs of VUC (operational costs to set up the VUC program plus health care expenditures) versus in-person ED care (health care expenditures) from an MoH perspective. All costs are expressed in Canadian dollars (a currency exchange rate of CAD $1=US $0.76 is applicable). RESULTS: We matched 2129 patients who presented to an ED within 72 hours of VUC referral and 14,179 patients seen by a VUC provider without a referral to an ED. Our matched populations represented 99% (2129/2150) of eligible VUC patients referred to the ED by their VUC provider and 98% (14,179/14,498) of eligible VUC patients not referred to the ED by their VUC provider. Compared to matched in-person ED patients, 30-day costs per patient were significantly higher for the cohort of VUC patients who presented to an ED within 72 hours of VUC referral ($2805 vs $2299; difference of $506, 95% CI $139-$885) and significantly lower for the VUC cohort of patients who did not require ED referral ($907 vs $1270; difference of $362, 95% CI 284-$446). Overall, the absolute 30-day costs associated with the 2 VUC cohorts were $18.9 million (ie, $6.0 million + $12.9 million) versus $22.9 million ($4.9 million + $18.0 million) for the 2 in-person ED cohorts. CONCLUSIONS: This costing evaluation supports the use of VUC as most complaints were addressed without referral to ED. Future research should evaluate targeted applications of VUC (eg, VUC models led by nurse practitioners or physician assistants with support from ED physicians) to inform future resource allocation and policy decisions.

Impact of the COVID-19 pandemic on mechanical ventilation cases and mortality rates in non-SARS-CoV-2 patients: A nationwide analysis in Spain.

BACKGROUND: The COVID-19 pandemic has presented unprecedented challenges for healthcare systems globally, impacting critical care resources and patient outcomes. Understanding its multifaceted effects is crucial for future crisis response. OBJECTIVE: Analyze the repercussions of the COVID-19 pandemic on mechanical ventilation cases and mortality among non-SARS-CoV-2 patients. METHODS: A nationwide database encompassing all patients receiving mechanical ventilation in Spain was used to compare the number of cases and clinical outcomes during COVID-19 (March 2020 - December 2021) to pre-pandemic cases (May 2018 - February 2020). Univariate and multivariate analyses were employed. RESULTS: COVID-19 significantly reduced access to ventilation for non-COVID-19 patients. A 16 % decrease (12,099 fewer patients) was observed during the pandemic compared to pre-pandemic times. This reduction affected all analyzed conditions except self-inflicted injuries, coinciding with a rise in overall mortality risk (34.5% vs 35.6 %, OR 1.09, 95 %CI 1.06-1.12). The increased mortality was consistent across diverse admission types, including cancer (37.1% vs. 41.5 %, OR 1.18, 95 %CI 1.09-1.29), hemorrhagic strokes (55.4% vs. 56.6 %, OR 1.07, 95 %CI 1.02-1.20), acute myocardial infarction (35.6% vs. 38 %, OR 1.11, 95 %CI 1.01-1.21), non-SARS-CoV-2 pneumonia (44.5% vs. 45.8 %, OR 1.12, 95 %CI 1.02-1.24), septic shock (54.7% vs. 56.3 %, OR 1.10, 95 %CI 1.06-1.15), and prolonged ventilation (≥96 h) (37% vs. 38.2 %, OR 1.10, 95 %CI 1.06-1.10). CONCLUSIONS: The findings underscore the profound impact of the COVID-19 pandemic on critical care utilization and patient outcomes among non-SARS-CoV-2 patients. As healthcare systems strive to mitigate future crises, these insights emphasize adaptable strategies for equitable access to life-saving treatments.

Projecting the Potential Clinical and Economic Impact of Human Immunodeficiency Virus Prevention Resource Reallocation in Tennessee.

BACKGROUND: In 2023, Tennessee replaced $6.2 M in US Centers for Disease Control and Prevention (CDC) human immunodeficiency virus (HIV) prevention funding with state funds to redirect support away from men who have sex with men (MSM), transgender women (TGW), and heterosexual Black women (HSBW) and to prioritize instead first responders (FR), pregnant people (PP), and survivors of sex trafficking (SST). METHODS: We used a simulation model of HIV disease to compare the clinical impact of Current, the present allocation of condoms, preexposure prophylaxis (PrEP), and HIV testing to CDC priority risk groups (MSM/TGW/HSBW); with Reallocation, funding instead increased HIV testing and linkage of Tennessee-determined priority populations (FR/PP/SST). Key model inputs included baseline condom use (45%-49%), PrEP provision (0.1%-8%), HIV testing frequency (every 2.5-4.8 years), and 30-day HIV care linkage (57%-65%). We assumed Reallocation would reduce condom use (-4%), PrEP provision (-26%), and HIV testing (-47%) in MSM/TGW/HSBW, whereas it would increase HIV testing among FR (+47%) and HIV care linkage (to 100%/90%) among PP/SST. RESULTS: Reallocation would lead to 166 additional HIV transmissions, 190 additional deaths, and 843 life-years lost over 10 years. HIV testing reductions were most influential in sensitivity analysis; even a 24% reduction would result in 287 more deaths compared to Current. With pessimistic assumptions, we projected 1359 additional HIV transmissions, 712 additional deaths, and 2778 life-years lost over 10 years. CONCLUSIONS: Redirecting HIV prevention funding in Tennessee would greatly harm CDC priority populations while conferring minimal benefits to new priority populations.

Factors Affecting the Situational Awareness of Armored Vehicle Occupants.

In the field of armored vehicles, up to 70% of accidents are associated with low levels of situational awareness among the occupants, highlighting the importance of situational awareness in improving task performance. In this study, we explored the mechanisms influencing situational awareness by simulating an armored vehicle driving platform with 14 levels of experimentation in terms of five factors: experience, expectations, attention, the cueing channel, and automation. The experimental data included SART and SAGAT questionnaire scores, eye movement indicators, and electrocardiographic and electrodermal signals. Data processing and analysis revealed the following conclusions: (1) Experienced operators have higher levels of situational awareness. (2) Operators with certain expectations have lower levels of situational awareness. (3) Situational awareness levels are negatively correlated with information importance affiliations and the frequency of anomalous information in non-primary tasks. (4) Dual-channel cues lead to higher levels of situational awareness than single-channel cues. (5) Operators' situational awareness is lower at high automation levels.

Laboratory Information Management Systems in Oral Pathology: A Comprehensive Review.

Efficiency in oral pathological laboratory processes is paramount for timely and accurate diagnosis. This review explores various strategies and methodologies that help streamline oral pathological laboratory workflows to enhance productivity and reduce turnaround times. Key focus areas include specimen collection, handling, processing, and analysis. Optimization techniques such as automation, digitalization, and standardization are discussed in detail, emphasizing their role in minimizing errors and maximizing throughput. Additionally, the integration of advanced technologies such as artificial intelligence and machine learning is examined for their potential to improve laboratory operations. Moreover, the importance of quality control measures and compliance with regulatory standards is underscored as essential components of any successful laboratory streamlining initiative. By implementing a comprehensive approach that addresses the entire diagnostic pathway, oral pathological laboratories can achieve significant efficiency, ultimately leading to better patient care and outcomes.

Ecosystem engineers can regulate resource allocation strategies in associated plant species.

Balancing the biomass requirements of different functions for the purpose of population reproduction and persistence can be challenging for alpine plants due to extreme environmental stresses from both above- and below-ground sources. The presence of ecosystem engineers in alpine ecosystems effectively alleviates microenvironmental stresses, hence promoting the survival and growth of other less stress-tolerant species. However, the influence of ecosystem engineers on plant resource allocation strategies remains highly unexplored. In this study, we compared resource allocation strategies, including biomass accumulation, reproductive effort (RE), root fraction (RF), as well as relationships between different functions, among four alpine plant species belonging to Gentianaceae across bare ground, tussock grass-, cushion-, and shrub-engineered microhabitats. Shrub-engineered microhabitats exerted the strongest effects on regulating plant resource allocation patterns, followed by tussock grass- and cushion-engineered microhabitats. Additionally, apart from microhabitats, population background and plant life history also significantly influenced resource allocation strategies. Generally, plants established within engineered microhabitats exhibited higher biomass accumulation, as well as increased flower, leaf and stem production. Furthermore, individuals within engineered microhabitats commonly displayed lower RF, indicating a greater allocation of resources to above-ground functions while reducing allocation to root development. RE of annual plants was significantly higher than that of perennial plants. However, individuals of annual plants within engineered microhabitats showed lower RE compared to their counterparts in bare ground habitats; whereas perennial species demonstrated similar RE between microhabitat types. Moreover, RE was generally independent of plant size in bare-ground habitats but exhibited size-dependency in certain populations for some species within specific engineered microhabitat types. However, size-dependency did exist for absolute reproductive and root biomass allocation in most of the cases examined here. No trade-offs were observed between flower mass and flower number, nor between leaf mass and leaf number. The capacity of ecosystem engineers to regulate resource allocation strategies in associated plants was confirmed. However, the resource allocation patterns resulted synergistically from the ecosystem engineering effects, population environmental backgrounds, and plant life history strategies. In general, such regulations can improve individual survival and reproductive potential, potentially promoting population persistence in challenging alpine environments.

Factors of children's allocation behavior: peer relationship and resource quantity as the main determinants.

This study investigated the resource allocation of Chinese sixth-graders and the role of peer relationship in different resource conditions (N = 132, Mage = 11. 35 years, SD = 0.60). We designed the resource quantity as a between-group variable, with one group participating in a resource-limited experiment and another group in a resource-abundant experiment. Both groups of children allocated token resources to three types of peers relationships: good friends, disliked individuals, and strangers. Based on our experimental hypotheses, we presupposed three experimental outcomes: selfish allocation, equal allocation, and altruistic allocation. To analyze the data, we employed multivariate unordered regression analysis and performed two rounds of regression analyses using both selfish and altruistic allocations as reference categories to enhance the statistical power of regression model. Our results reveal that the resource quantity had a significant hindering effect on children's allocation behaviors, as the amount of available resources for allocation increased, so did their willingness to allocate selfishly. It was also found that an increase in resources led to a decrease in the proportion of children allocating equally. Nonetheless, the results still revealed generalized peer relationship preferences: children tended to allocate more resources to friends than to individuals they disliked. But when faced with disliked individuals, they were relatively more likely to allocate equally. Finally, we observed the proportion of equal allocation and discussed the similar impact of inequality aversion, different allocation contexts, and children's theory of mind on equitable allocation among sixth-graders.

Evolutionary Approach to Optimal Oil Skimmer Assignment for Oil Spill Response: A Case Study.

We propose a genetic algorithm for optimizing oil skimmer assignments, introducing a tailored repair operation for constrained assignments. Methods essentially involve simulation-based evaluation to ensure adherence to South Korea's regulations. Results show that the optimized assignments, compared to current ones, reduced work time on average and led to a significant reduction in total skimmer capacity. Additionally, we present a deep neural network-based surrogate model, greatly enhancing efficiency compared to simulation-based optimization. Addressing inefficiencies in mobilizing locations that store oil skimmers, further optimization aimed to minimize mobilized locations and was validated through scenario-based simulations resembling actual situations. Based on major oil spills in South Korea, this strategy significantly reduced work time and required locations. These findings demonstrate the effectiveness of the proposed genetic algorithm and mobilized location minimization strategy in enhancing oil spill response operations.

Resource regulation and green innovation: Evidence from China's water rights trading policy.

Green innovation is the pivotal part connected with achieving urban sustainable development. Resource regulation, represented by water rights trading policy (WRTP), is playing an increasingly important role in supporting urban green innovation (UGI). Therefore, this paper uses the WRTP conducted in 2014 in China as a quasi-natural experiment to evaluate the net effect of resource regulation on green innovation by the identification methods of difference-in-differences (DID) model. The results show that: (1) WRTP promotes UGI significantly, and this finding holds after a series of robustness tests. (2)The influencing mechanisms are mainly by optimizing industrial structure and enhancing the efficiency of resource allocation and information exchange efficiency and the promoting function is moderated by urban competitiveness and population agglomeration. (3) The promotion effects are greater in cities with higher level of government intervention, environmental pollution, water using quantity and local economy.(4) WRTP has the spatial spillover effect on UGI. These findings provide insights into sustainable development of water resources, management of water trading market, urban green innovation and sustainable economic development.

Multi-objective optimization of solar resource allocation in radial distribution systems using a refined slime mold algorithm.

The integration of distributed generation resources in power systems offers various advantages, such as peak load management and reduced transmission line congestion. However, it also introduces challenges related to voltage stability. This paper presents a novel multi-objective model for optimizing the allocation of solar resources in radial distribution systems. The model aims to achieve an optimal voltage profile, minimize losses, and maximize penetration levels. To address the conflicting nature of these objectives, a refined multi-objective slime mold algorithm (MOSMA) is proposed. This algorithm demonstrates exceptional capabilities in finding Pareto fronts, avoiding local optima, and effectively solving multi-objective problems compared to other optimization methods. Additionally, the corrected social hierarchy method is integrated to enhance performance. The proposed method is evaluated using a standard system under various operational conditions, showing superior results in terms of maintaining an acceptable voltage profile and significantly reducing losses. The study reveals that while losses decrease for penetration levels ranging from low to medium, they start to increase for levels exceeding 100 %. Notably, the proposed method achieves approximately 12 % system efficiency improvement, as measured by the voltage profile, at a penetration level of 300 %. These findings highlight the effectiveness of the proposed method, even at high penetration levels, surpassing other optimization approaches based on the inverse generation distance parameter.

Hybrid deep learning and optimized clustering mechanism for load balancing and fault tolerance in cloud computing.

Cloud services are one of the most quickly developing technologies. Furthermore, load balancing is recognized as a fundamental challenge for achieving energy efficiency. The primary function of load balancing is to deliver optimal services by releasing the load over multiple resources. Fault tolerance is being used to improve the reliability and accessibility of the network. In this paper, a hybrid Deep Learning-based load balancing algorithm is developed. Initially, tasks are allocated to all VMs in a round-robin method. Furthermore, the Deep Embedding Cluster (DEC) utilizes the Central Processing Unit (CPU), bandwidth, memory, processing elements, and frequency scaling factors while determining if a VM is overloaded or underloaded. The task performed on the overloaded VM is valued and the tasks accomplished on the overloaded VM are assigned to the underloaded VM for cloud load balancing. In addition, the Deep Q Recurrent Neural Network (DQRNN) is proposed to balance the load based on numerous factors such as supply, demand, capacity, load, resource utilization, and fault tolerance. Furthermore, the effectiveness of this model is assessed by load, capacity, resource consumption, and success rate, with ideal values of 0.147, 0.726, 0.527, and 0.895 are achieved.

UAV-Assisted Mobile Edge Computing: Dynamic Trajectory Design and Resource Allocation.

The recent advancements of mobile edge computing (MEC) technologies and unmanned aerial vehicles (UAVs) have provided resilient and flexible computation services for ground users beyond the coverage of terrestrial service. In this paper, we focus on a UAV-assisted MEC system in which the UAV equipped with MEC servers is used to assist user devices in computing their tasks. To minimize the weighted average energy consumption and delay in the UAV-assisted MEC system, a LQR-Lagrange-based DDPG (LLDDPG) algorithm, which jointly optimizes the user task offloading and the UAV trajectory design, is proposed. To be specific, the LLDDPG algorithm consists of three subproblems. The DDPG algorithm is used to address the issue of UAV desired trajectory planning, and subsequently, the LQR-based algorithm is employed to achieve the real-time tracking control of UAV desired trajectory. Finally, the Lagrange duality method is proposed to solve the optimization problem of computational resource allocation. Simulation results indicate that the proposed LLDDPG algorithm can effectively improve the system resource management and realize the real-time UAV trajectory design.

Potential negative effects of genomic selection.

Initial findings on genomic selection (GS) indicated substantial improvement for major traits, such as performance, and even successful selection for antagonistic traits. However, recent unofficial reports indicate an increased frequency of deterioration of secondary traits. This phenomenon may arise due to the mismatch between the accelerated selection process and resource allocation. Traits explicitly or implicitly accounted for by a selection index move toward the desired direction, whereas neglected traits change according to the genetic correlations with selected traits. Historically, the first stage of commercial genetic selection focused on production traits. After long-term selection, production traits improved, whereas fitness traits deteriorated, although this deterioration was partially compensated for by constantly improving management. Adding these fitness traits to the breeding objective and the used selection index also helped offset their decline while promoting long-term gains. Subsequently, the trend in observed fitness traits was a combination of a negative response due to genetic antagonism, positive response from inclusion in the selection index, and a positive effect of improving management. Under GS, the genetic trends accelerate, especially for well-recorded higher heritability traits, magnifying the negatively correlated responses for fitness traits. Then, the observed trend for fitness traits can become negative, especially because management modifications do not accelerate under GS. Additional deterioration can occur due to the rapid turnover of GS, as heritabilities for production traits can decline and the genetic antagonism between production and fitness traits can intensify. If the genetic parameters are not updated, the selection index will be inaccurate, and the intended gains will not occur. While the deterioration can accelerate for unrecorded or sparsely recorded fitness traits, GS can lead to an improvement for widely recorded fitness traits. In the context of GS, it is crucial to look for unexpected changes in relevant traits and take rapid steps to prevent further declines, especially in secondary traits. Changes can be anticipated by investigating the temporal dynamics of genetic parameters, especially genetic correlations. However, new methods are needed to estimate genetic parameters for the last generation with large amounts of genomic data.

Initial findings on genomic selection indicated substantial improvement for major traits such as growth or milk yield and even successful selection for secondary traits such as fertility or survival. However, recent unofficial reports indicate an increased frequency of problems in several secondary traits. This study looks at potential sources of those problems and mitigation strategies. Under selection initially carried out for production traits, production improved, but fertility (i.e., a secondary trait) declined, with the decline partially compensated for by improving management. Later, also because the observed deteriorations were becoming too strong, these traits became part of the breeding objectives, and used selection indexes were modified to include secondary traits, halting the deterioration. Under genomic selection, genetic gains accelerate, especially for higher heritability production traits, potentially magnifying the negative responses for secondary traits, and management modifications may not be fast enough to alleviate the decline. The responses can especially decline for unrecorded or sparsely recorded fitness traits. While the decline may be slow and hard to see, it may be serious in the long term and hard to reverse. Changes under genomic selection may be monitored by recalculating genetic parameters every generation. Secondary traits that become more antagonistic with production traits will likely deteriorate more and will need special attention.