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As a university's information resource center, the library has always provided services for teachers and students. However, there is a lack of in-depth research on the relationship between the allocation of library resources and the competitiveness of universities in innovation and entrepreneurship (CUIE). Clarifying that relationship could help optimize library resources to enhance CUIE and enrich the empirical research system of library science. Based on clarifying the composition of library resources, correlation and regression analyses were used to verify the effect of library resource allocation on CUIE. The results show that human, academic, and spatial resources of the library can improve CUIE. The quality of human resources is more important than quantity for CUIE. For academic resources, electronic resources play a greater role than paper resources in CUIE, and spatial resources have the least effect. To maximize CUIE, the university library must optimize its resources. [ABSTRACT FROM AUTHOR]

Microbial factories lacking the ability of dynamically regulating the pathway enzymes overexpression, according to in situ metabolite concentrations, are suboptimal, especially when the metabolic intermediates are competed by growth and chemical production. The production of higher alcohols (HAs), which hijacks the amino acids (AAs) from protein biosynthesis, minimizes the intracellular concentration of AAs and thus inhibits the host growth. To balance the resource allocation and maintain stable AA flux, this work utilizes AA‐responsive transcriptional attenuator ivbL and HA‐responsive transcriptional activator BmoR to establish a concentration recognition‐based auto‐dynamic regulation system (CRUISE). This system ultimately maintains the intracellular homeostasis of AA and maximizes the production of HA. It is demonstrated that ivbL‐driven enzymes overexpression can dynamically regulate the AA‐to‐HA conversion while BmoR‐driven enzymes overexpression can accelerate the AA biosynthesis during the HA production in a feedback activation mode. The AA flux in biosynthesis and conversion pathways is balanced via the intracellular AA concentration, which is vice versa stabilized by the competition between AA biosynthesis and conversion. The CRUISE, further aided by scaffold‐based self‐assembly, enables 40.4 g L−1 of isobutanol production in a bioreactor. Taken together, CRUISE realizes robust HA production and sheds new light on the dynamic flux control during the process of chemical production. [ABSTRACT FROM AUTHOR]

This paper discusses the operation of a massive multiple‐input multiple‐output (MIMO) system from a different perspective. The system is performed in the presence of an eavesdropper who is trying to disrupt the message being transmitted between the base station (BS) and the users. An attempt has been made to maximize the secure energy efficiency (EE) of the system by allocating appropriate power to each user. The channel state information (CSI) between users and the BS is considered imperfect and the employed precoding scheme is the zero forcing (ZF). The CSI related to the eavesdropper is considered to be perfect and its precoding is assumed to be the maximum ratio transmission (MRT). The problem of optimization that is for maximizing the EE has two constraints including the maximum transmission power and the minimum user data rate. To improve the performance of system, the cell division technique is applied, which results in approaching the performance of system to the without eavesdropper scenario. Five various scenarios have been investigated in this research, and it is proved that the proposed method improves the system performance in all cases. The numerical and simulation results obtained from implementation of the proposed algorithm confirm the claim. [ABSTRACT FROM AUTHOR]

This paper investigates an intelligent reflecting surface (IRS) assisted downlink short packet transmission system, where an access point sends short packets to multiple devices with the help of an IRS. Specifically, a performance comparison between the frequency division multiple access and time division multiple access is conducted for the considered system, from the perspective of average age of information (AoI). To minimize the maximum average AoI among all devices, the resource allocation and passive beamforming are jointly optimized. However, the formulated problem is difficult to solve due to the non‐convex objective function and coupled variables. Thus, an alternating optimization based algorithm is proposed by exploiting the semidefinite relaxation and bisection search techniques. Simulation results show that time division multiple access can achieve lower AoI by exploiting the time‐selective passive beamforming of IRS for maximizing the signal to noise ratio of each device consecutively. Moreover, it also shows that as the length of information bits becomes sufficiently large as compared to the available bandwidth, the proposed frequency division multiple access transmission scheme becomes more favourable due to more flexible power allocation. [ABSTRACT FROM AUTHOR]

Background: In China, economic, urbanization, and policy differences between the eastern and western regions lead to uneven healthcare resources. This disparity is more pronounced in the west due to fewer healthcare personnel per thousand individuals and imbalanced doctor-to-nurse ratios, which exacerbates healthcare challenges. This study examines the spatial distribution of human resources in maternal and child healthcare from 2016 to 2021, highlighting regional disparities and offering insights for future policy development. Methods: The data were sourced from the "China Health and Family Planning Statistical Yearbook" (2017) and the "China Health and Health Statistics Yearbook" (2018–2022). This study utilized GeoDa 1.8.6 software to conduct both global and local spatial autocorrelation analyses, using China's administrative map as the base dataset. Results: From 2016 to 2021, there was an upward trend in the number of health personnel and various types of health technical personnel in Chinese maternal and child healthcare institutions. The spatial distribution of these personnel from 2016 to 2021 revealed clusters characterized as high-high, low-low, high-low and low-high. Specifically, high-high clusters were identified in Guangxi, Hunan, Jiangxi, and Guangdong provinces; low-low in Xinjiang Uygur Autonomous Region and Inner Mongolia Autonomous Region; high-low in Sichuan province; and low-high in Fujian and Anhui provinces. Conclusions: From 2016 to 2021, there was evident spatial clustering of health personnel and various health technical personnel in Chinese maternal and child healthcare institutions, indicating regional imbalances. [ABSTRACT FROM AUTHOR]

Family and cultural contexts can constrain the effectiveness of evidence-based interventions designed to improve the health and wellbeing of women and their children. Unequal power relationships within the household may underlie the failure of many programs targeting women to achieve their intended impact. To reduce these unequal power dynamics within the households, many programs or interventions aim to both assess and improve the gender dynamics between husbands and wives within the household. Decision-making is one important facet of these dynamics and has been linked to health outcomes for women and children. However, household decision-making is rarely observed and often difficult to capture. This study aimed to use qualitative research to further understand one aspect of decision-making, namely on how to spend money. In two regions of Tanzania, we used surveys and interviews to explore different perspectives on spending and allocation of resources among 58 couples in rural farming households. While many men and women initially reported that they made decisions jointly, most women stated they would often concede if there was a disagreement or argument around spending. These results highlight the different perceptions of joint decision-making between men and women. We compared these results to survey responses on decision-making and found differences within and between couples across interview and survey responses. Based on the differences in qualitative and survey responses within couples and how they reported dealing with disagreement, our study found households were on a spectrum from no cooperation in decision-making to full cooperation. Our results highlight challenges for assessing decision-making on spending and ultimately improving these decision-making dynamics within the household. These challenges are especially important for maternal and child behavioral change and provide insights on why many interventions aimed at improving women's decision- making power on money may not reach their full potential. [ABSTRACT FROM AUTHOR]

Summary Space information network (SIN) is difficult to fully utilize the limited on‐board resource due to its dynamic and heterogeneous nature, while the traditional resource management methods cannot adapt to the increasingly diverse task requirements. Space cloud network architecture is an effective technology to reduce the pressure on satellite resources. To effectively manage the space cloud network resources, we design a resources allocation strategy based on resource clustering. Firstly, we propose the space cloud network architecture. Then, we propose a genetic algorithm to cluster the space cloud resources. Finally, we propose a dynamic resource allocation method based on reinforcement learning for the dynamic characteristics of space cloud resources. The method improves the reinforcement learning algorithm through dynamic objective optimization to complete the optimization of multiple objectives in the process of space cloud resources allocation. The simulation results show that the algorithm proposed in this paper reduces the task execution delay by an average of 10.5% compared with the original DQN algorithm and increases the execution success rate by 2.17%. [ABSTRACT FROM AUTHOR]

Two distemper paint samples taken from decorative boards in Uvdal stave church, Norway, were analysed using palaeoproteomics, with an aim of identifying their binder and possible contaminants. The results point at the use of calfskin to produce hide glue as the original paint binder, and are consistent with the instructions of binder production and resource allocation in the historical records of Norway. Although we did not observe any evidence of prior restoration treatments using protein-based materials, we found abundant traces of human saliva proteins, as well as a few oats and barley peptides, likely deposited together on the boards during their discovery in the 1970s. This work illustrates the need to fully consider contamination sources in palaeoproteomics and to inform those working with such objects about the potential for their contamination. [ABSTRACT FROM AUTHOR]

In different modern and future wireless communication networks, a large number of low-power user equipment (UE) devices like Internet of Things, sensor terminals, and smart modules have to be supported over constrained power and bandwidth resources. Therefore, wireless-powered communication (WPC) is considered a promising technology for varied applications in which the energy harvesting (EH) from radio frequency radiations is exploited for data transmission. This requires efficient resource allocation schemes to optimize the performance of WPC and prolong the network lifetime. In this paper, harvest-then-transmit-based WP non-orthogonal multiple access (WP-NOMA) system is designed with time-split (TS) and power control (PC) allocation strategies. To evaluate the network performance, the sum rate and UEs' rates expressions are derived considering power-domain NOMA with successive interference cancellation detection. For comparison purposes, the rate performance of the conventional WP orthogonal multiple access (WP-OMA) is derived also considering orthogonal frequency-division multiple access and time-division multiple access schemes. Intensive investigations are conducted to obtain the best TS and PC resource parameters that enable maximum EH for higher data transmission rates compared with the reference WP-OMA techniques. The achieved outcomes demonstrate the effectiveness of designed resource allocation approaches in terms of the realized sum rate, UE's rate, rate region, and fairness without distressing the restricted power of far UEs. [ABSTRACT FROM AUTHOR]

Existing healthcare systems face finite resource allocation and budgetary constraints, resulting in a substantial need for innovative solutions to enhance service delivery at reduced costs. A novel, user-friendly on-body delivery system (OBDS) was developed which enables administration of large-volume subcutaneous (SC) drugs in both clinical and home-based settings (at-home healthcare professional [HCP] administration or at-home self-administration). This research sought to evaluate the potential economic impact of at-home self- or HCP- administration with the OBDS through a comprehensive review of published literature and semi-structured interviews with 17 US payers representing approximately 227 million covered lives. Published literature on OBDS remains limited, but available research highlights the cost-savings of SC administration due to reduced healthcare resource utilization, particularly with home-based care, and improved patient compliance. In interviews, payers identified several attributes that would help address unmet clinical and economic needs. Clinically, the hidden needle and ease-of-use compared to SC syringe pumps was deemed valuable to improve patient compliance and, as OBDS required minimal training, reduce the risk of administration errors. The flexibility to administer drugs at home (self-administration or HCP-administration) or in-clinic was identified as the most impactful attribute on coverage decision making as it has the greatest potential to reduce costs associated with HCP administration for several therapeutic areas. Given the ability to help address critical unmet needs for the patient and healthcare system, a large proportion of the payers stated that the novel OBDS would warrant a price premium versus the cost of the standalone SC vial and certainly over the IV counterpart. Future research to quantify the value that OBDS efficiencies could bring to healthcare delivery are warranted. [ABSTRACT FROM AUTHOR]

The road to implementation of competency-based medical education (CBME) in the medical curriculum in India has both challenges and windows of opportunities in its folds. The hindrances identified were reluctance to change, capacity building of faculties, lack of infrastructural support, and methods of assessment. Notwithstanding, CBME has the potential to ensure that the Indian medical graduates are equipped with better clinical skills, and learner-centric education, that aligns well with individual competence, and community healthcare needs. Effective navigation through this transition calls for collaborative efforts among academicians, regulatory bodies, and related stakeholders while drawing from relevant successful models of our country itself. It is imperative to address the challenges concerning capacity building of faculties, resource allocation, and assessment methodology for successful implementation. Given appropriate adoption, the CBME-based curriculum can go a long way to deliver quality healthcare. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Mission scheduling is an essential function of the management control of remote-sensing satellite application systems. With the continuous development of remote-sensing satellite applications, mission scheduling faces significant challenges. Existing work has many inherent shortcomings in dealing with dynamic task scheduling for remote-sensing satellites. In high-load and complex remote sensing task scenarios, there is low scheduling efficiency and a waste of resources. The paper proposes a scheduling method for remote-sensing satellite applications based on dynamic task prioritization. This paper combines the and Bound methodologies with an onboard task queue scheduling band in an active task prioritization context. A purpose-built emotional task priority-based scheduling blueprint is implemented to mitigate the flux and unpredictability characteristics inherent in the traditional satellite scheduling paradigm, improve scheduling efficiency, and fine-tune satellite resource allocation. Therefore, the Branch and Bound method in remote-sensing satellite task scheduling will significantly save space and improve efficiency. The experimental results show that comparing the technique to the three heuristic algorithms (GA, PSO, DE), the BnB method usually performs better in terms of the maximum value of the objective function, always finds a better solution, and reduces about 80% in terms of running time. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the impact of the National Institute for Health and Care Excellence's (NICE) new severity modifier, which has replaced the end-of-life (EoL) premium, on future NICE recommendations, considering past decision-making patterns. NICE technology appraisals (TAs) published between January 2020 and December 2022 were reviewed. Summary statistics were generated to assess how the new severity modifier might affect hypothetical decision making in historical TAs. A total of 138 data points were identified from 132 TAs. Although the EoL premium was applied in 46 appraisals (33%), 57 (39%) qualify for a severity-based quality-adjusted life-year (QALY) multiplier. Only 19 appraisals (14.6%) not receiving an EoL premium met the severity criteria, the majority (17) qualifying for a 1.2× multiplier. In appraisals predicted to meet the severity criteria, 45 (79%) were in oncology, making them 4.04 times (95% CI 1.91-9.02) more likely to qualify for a severity modifier than nononcology indications. Among historically EoL indications, 42 (91%) were predicted to meet the severity criteria, making them 14.8 times (95% CI 6.37-37.6) more likely to qualify for a severity modifier. The new severity modifier will predominantly benefit oncology indications, continuing their previous explicit prioritization under the EoL decision modifier. However, the new severity modifier is harder to achieve and less generous; only a fraction of appraisals qualify for the highest effective £51 000 per QALY threshold. The vast majority of indications previously approved at £50 000 per QALY would now need to meet a cost-effectiveness threshold of <£36 000. This may necessitate greater pricing flexibility from manufacturers and increase the likelihood of negative recommendations. • The National Institute for Health and Care Excellence has made major changes to the adjustment of their cost-effectiveness thresholds, replacing the "end-of-life" premium for life-extending treatments with a severity-based, tiered approach to valuing new technologies. • Based on historical decision making, very few technologies appraised by the National Institute for Health and Care Excellence are likely to be eligible for the highest cost-effectiveness threshold, with the new severity modifier continuing to predominantly benefit oncology technologies. • The vast majority of indications previously qualifying for the £50 000 per quality-adjusted life-year gained end-of-life premium would now need to meet a cost-effectiveness threshold of either £30 000 or £36 000. This more restrictive policy environment may present challenges to the introduction of new technologies into the United Kingdom, but may improve the allocation of resources to where they provide the greatest benefits. [ABSTRACT FROM AUTHOR]

Addressing the formidable challenges posed by multiple jammers jamming multiple radars, which arise from spatial discretization, many degrees of freedom, numerous model input parameters, and the complexity of constraints, along with a multi-peaked objective function, this paper proposes a cooperative jamming resource allocation method, based on evolutionary reinforcement learning, that uses joint multi-domain information. Firstly, an adversarial scenario model is established, characterizing the interaction between multiple jammers and radars based on a multi-beam jammer model and a radar detection model. Subsequently, considering real-world scenarios, this paper analyzes the constraints and objective function involved in cooperative jamming resource allocation by multiple jammers. Finally, accounting for the impact of spatial, frequency, and energy domain information on jamming resource allocation, matrices representing spatial condition constraints, jamming beam allocation, and jamming power allocation are formulated to characterize the cooperative jamming resource allocation problem. Based on this foundation, the joint allocation of the jamming beam and jamming power is optimized under the constraints of jamming resources. Through simulation experiments, it was determined that, compared to the dung beetle optimizer (DBO) algorithm and the particle swarm optimization (PSO) algorithm, the proposed evolutionary reinforcement learning algorithm based on DBO and Q-Learning (DBO-QL) offers 3.03% and 6.25% improvements in terms of jamming benefit and 26.33% and 50.26% improvements in terms of optimization success rate, respectively. In terms of algorithm response time, the proposed hybrid DBO-QL algorithm has a response time of 0.11 s, which is 97.35% and 96.57% lower than the response times of the DBO and PSO algorithms, respectively. The results show that the method proposed in this paper has good convergence, stability, and timeliness. [ABSTRACT FROM AUTHOR]

In the context of the COVID-19 pandemic, there has been a scarcity of resources with various effects on the care of cancer patients. This paper provides an English summary of a German guideline on prioritization and resource allocation for colorectal and pancreatic cancer in the context of the pandemic. Based on a selective literature review as well as empirical and ethical analyses, the research team of the CancerCOVID Consortium drafted recommendations for prioritizing diagnostic and treatment measures for both entities. The final version of the guideline received consent from the executive boards of nine societies of the Association of Scientific Medical Societies in Germany (AWMF), 20 further professional organizations and 22 other experts from various disciplines as well as patient representatives. The guiding principle for the prioritization of decisions is the minimization of harm. Prioritization decisions to fulfill this overall goal should be guided by (1) the urgency relevant to avoid or reduce harm, (2) the likelihood of success of the diagnostic or therapeutic measure advised, and (3) the availability of alternative treatment options. In the event of a relevant risk of harm as a result of prioritization, these decisions should be made by means of a team approach. Gender, age, disability, ethnicity, origin, and other social characteristics, such as social or insurance status, as well as the vehemence of a patient's treatment request and SARS-CoV-2 vaccination status should not be used as prioritization criteria. The guideline provides concrete recommendations for (1) diagnostic procedures, (2) surgical procedures for cancer, and (3) systemic treatment and radiotherapy in patients with colorectal or pancreatic cancer within the context of the German healthcare system. [ABSTRACT FROM AUTHOR]

India's vast and diverse population strains its healthcare system. Amidst these complexities, Artificial Intelligence (AI) emerges as a beacon of hope. This transformative technology promises to revolutionize healthcare, starting with early disease detection and accurate diagnoses. AI, driven by vast medical data, paints a deeper picture of individual health. By analyzing health patterns, it can detect hidden cancers and tuberculosis early, saving lives through proactive treatment. AI's power extends beyond individual diagnoses. It can scan populations, identifying risk factors and predicting outbreaks before they erupt. This foresight allows for targeted resource allocation and preventive measures, mitigating outbreak impact. AI can even personalize healthcare, shaping treatment plans based on a patient's unique lifestyle and medical history. This maximizes treatment efficacy, minimizes adverse reactions, and improves patient's well-being. Imagine AI as a trusted medical advisor, suggesting the most effective treatment options for each individual. However, AI's promise comes with challenges. Data privacy, reliable infrastructure, and biased algorithms need effective solutions. India, with its strong tech ecosystem and commitment to innovation, is well-positioned to tackle these challenges. By investing in AI research, strengthening data infrastructure, and establishing ethical frameworks, India can unlock AI's immense potential to revolutionize its healthcare landscape. This will be a dividend for millions, ensuring India's healthcare system transforms with the brushstrokes of AI, leading to a healthier and more affordable future for all. [ABSTRACT FROM AUTHOR]

This narrative explores the impact of deinstitutionalization policies on the quality of life and care outcomes for individuals with Alzheimer's disease and related dementias. We offer a historical perspective on these policies, their implications on dementia care, and the barriers to deinstitutionalization. The potential benefits of deinstitutionalization, such as improved quality of life and access to community-based support and services, are highlighted. Challenges and controversies surrounding safety, caregiver burden, and resource allocation are also examined. Ethical considerations related to the autonomy and decision-making capacity of people living with dementia are discussed. We present best practices and innovative models in dementia care that balance deinstitutionalization with appropriate care. We further put forth recommendations for future research and policy development in dementia care and deinstitutionalization, emphasizing the need for a balanced approach that respects the autonomy and preferences of people living with dementia while ensuring their safety and well-being. [ABSTRACT FROM AUTHOR]

Introduction Agricultural production involves a series of tasks including tillage, planting, and harvesting, which must be done at the right time for each region and type of product. Failing to complete these tasks on time can lead to a decrease in yield. Farmers may wrongly attribute this to factors such as infertile land, pests, diseases, and uneven rainfall distribution. However, this decrease in yield may not always be evident or tangible. To avoid such losses and unforeseen expenses, it is crucial to plan agricultural mechanization projects using the principles of project control. Agricultural projects, like industrial projects, must be carried out in the correct order and at the right time to achieve optimal results. Given the limited availability of resources for mechanization projects, it is imperative to meticulously plan activities to ensure that they are carried out on time and with maximum utilization of resources. To address these challenges, researchers have used meta-heuristic methods in project control, such as the colonial competition algorithm, which has been proven effective in solving the issue of scheduling projects with limited resources. The algorithm has been tested across various industrial activities and projects, and its performance in scheduling the Resource-Constrained Project Scheduling Problem (RCPSP) has been validated by researchers globally. Materials and Methods There is a scheduling issue regarding limited resources in agriculture, and this study presents a novel approach using the imperialist competitive algorithm (ICA). The algorithm not only explores a wider solution space but also strives to minimize deviation from the optimal solution, thereby improving the success rate of the proposed method. This research focuses on two dominant products, wheat and rapeseed, produced in Moghan Agriculture and Industry located in Northwest Iran. To evaluate the effectiveness of ICA, we compared it with other well-known meta-heuristic algorithms. We successfully resolved the problem of project scheduling problem with limited resources by implementing the imperialist competitive algorithm. Our findings have shown that this approach not only significantly increased efficiency but also outperformed other algorithms. Results and Discussion In this study, we assessed the efficiency of meta-heuristic methods in solving the RCPSP, which can be useful in optimizing the timeliness of project execution, especially for large-scale projects. Some meta-heuristic methods are only useful for smaller problems, while others can provide near-optimal solutions for larger problems, making them suitable for RCPSP. The algorithm explores a wide range of solutions and avoids premature convergence and getting stuck in local optima, unlike other algorithms such as the genetic algorithm. Optimization reduced the required budget and shortened the duration by 42 days for wheat and 25 days for rapeseed. Conclusion We utilized the colonial competition algorithm to address the RCPSP problem in agricultural mechanization projects for two agricultural products in Moghan. Our results show that the proposed algorithm converged and reached the optimal solution. The proposed algorithm was compared with other algorithms and it outperformed them. [ABSTRACT FROM AUTHOR]

As intelligent manufacturing expands globally, efficient resource allocation strategies are critical for optimizing production efficiency, costs, and quality. Although multi-objective optimization algorithms can handle conflicting objectives, traditional approaches struggle with complex manufacturing systems. This research proposes an optimization model integrating an enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the Fishbone layout for intelligent manufacturing resource allocation. The Fishbone-based model provides efficient decision support, while the enhanced NSGA-II improves solution efficiency and quality. Flexsim simulation demonstrates the practical value of the proposed method in optimizing resource allocation. This work extends the application of multi-objective optimization in intelligent manufacturing and offers a novel tool for resource allocation optimization in the manufacturing industry. [ABSTRACT FROM AUTHOR]

The ability to balance the allocation of resources between growth and reproduction as a response to stress factors, can be an advantage for plants in disturbed environments. Invasive alien plants (IAPs) often show high levels of phenotypic variability in resource allocation, a key trait that plays a crucial role in their success to invade new areas. Control management for IAPs must consider this capacity in the development of effective strategies. In this study, we performed continuous measures of leaf growth and reproductive traits of Cortaderia selloana, an IAP of global concern, and applied generalised linear models (GLMs) to evaluate trade‐offs between vegetative growth, leaf composition and reproductive success at different cutting moments. Cutting moment, but not flowering, affected the length of the vegetative growth period (VGP) and average growth rate (AGR), and the interaction with flowering affected AGR and final leaf length (vegetative growth total, VGT). Specific leaf area (SLA), leaf nitrogen (N) content and the isotopic value of δ13C were affected by cutting, and N was also affected by flowering and the interaction with cutting time. Silica also showed a negative correlation with leaf carbon (C) depicting a trade‐off between both structural components. Cortaderia selloana successfully adapted its leaf growth and composition to cutting moment, but this was also modulated by flowering. Moreover, the species is dioecious, and its response may differ between female and hermaphroditic plants. This suggests flexible trade‐offs in resource allocation, therefore the time for cutting must be precisely scheduled to suppress flowering. [ABSTRACT FROM AUTHOR]

Introduction: Exclusive breastfeeding is recognized as the optimal source of nutrition for infants. Although exclusive breastfeeding rates have increased overall in the United States, substantial inequities exist in breastfeeding among individuals of different socioeconomic statuses, races, and ethnicities. The purpose of this study was to examine characteristics associated with exclusive breastfeeding intentions among pregnant women in Arkansas enrolled in a Healthy Start program. Methods: The current study included a cross-sectional design, with a sample of 242 pregnant women in Arkansas enrolled in a Healthy Start program. Results: The majority of the participants (56.6%) indicated their infant feeding intentions included a combination of breastfeeding and formula feeding. There were substantial differences in breastfeeding intentions among women of different races/ethnicities, with 18.5% of Marshallese women indicating they planned to exclusively breastfeed, compared to 42.1% of White women, 47.6% of Black women, and 31.8% of Hispanic women (p < 0.001). Women over the age of 18 and with higher educational attainment were more likely to intend on exclusively breastfeeding. Discussion: This is the first study to examine characteristics associated with exclusive breastfeeding intentions among pregnant women in Arkansas enrolled in a Healthy Start program. The study found that race/ethnicity and age were most strongly associated with breastfeeding intentions. These findings are critical to identifying populations for resource allocation and to developing culturally-tailored interventions to help women in Arkansas achieve their desired infant feeding methods. Significance: What is already known on this subject?: Exclusive breastfeeding is recognized as the optimal source of nutrition for infants, and it has been associated with decreased risk of obesity and cardiometabolic disease for both mother and infant. Although exclusive breastfeeding rates have increased overall in the United States, substantial inequities exist in breastfeeding among individuals of different socioeconomic statuses, races, and ethnicities. What this study adds?: This is the first study to examine characteristics associated with exclusive breastfeeding intentions among pregnant women in Arkansas enrolled in a Healthy Start program. The study found that race/ethnicity and age were most strongly associated with breastfeeding intentions. [ABSTRACT FROM AUTHOR]

The research we fund today will improve the health of people who will live tomorrow. But future people will not all benefit equally: decisions we make about what research to prioritize will predictably affect when and how much different people benefit from research. Organizations that fund health research should thus fairly account for the health needs of future populations when setting priorities. To this end, some research funders aim to allocate research resources in accordance with disease burden, prioritizing illnesses that cause more morbidity and mortality. In this article, I defend research funders' practice of aligning research funding with disease burden but argue that funders should aim to align research funding with future—rather than present—disease burden. I suggest that research funders should allocate research funding in proportion to aggregated estimates of disease burden over the period when research could plausibly start to yield benefits until indefinitely into the future. [ABSTRACT FROM AUTHOR]

This commentary explores historical efforts to diagnose the present and project the future through the specific example of medical manpower planning. To situate this, it draws on work within and across geography exploring the concept of anticipation and considers the discipline's failure to adequately engage with healthcare workers, despite the vibrancy of health geography as a subdiscipline. As it explores, ensuring adequate numbers of staff in a healthcare system has been an issue for as long as there have been healthcare systems. Planning for healthcare system needs is thus a particularly fraught form of anticipation that seeks to project future needs from a contested and often incalculable present as a basis for political decision‐making and resource allocation. As this commentary explores, it is a problem that is global in scope, historically deep and thus rich in analytical possibility. [ABSTRACT FROM AUTHOR]

Summary: The solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation. [ABSTRACT FROM AUTHOR]

In the harsh mobile edge computing (HMEC) environment, there are many dynamic changes such as interference from noise, the impact of extreme environmental conditions, and the mobility of devices. It is a great challenge to the online realtime task offloading scheduling for delay-sensitive applications. However, the dynamic changes in HMEC environment have been ignored in almost all previous studies. Therefore, we propose the federated deep reinforcement learning-based online task offloading and resource allocation (FD-OTR) algorithm to address the task offloading in HMEC. Additionally, the FD-OTR algorithm performs resource allocation for offloaded tasks. The task offloading part of FD-OTR algorithm can be divided into two layers: the deep reinforcement learning (DRL) layer and the federated learning (FL) layer. The online algorithm in the DRL layer can adapt to the dynamic HMEC environment and make real-time task offloading decisions. In the FL layer, federated learning with low communication overhead is used for model aggregation to form a better global model. Resource allocation is done by using a new meta-heuristic algorithm: the Sparrow Search Algorithm (SSA). Finally, the simulation results demonstrate that the FD-OTR algorithm performs well in HMEC. The convergence speed of FD-OTR is three times faster than the centralized method. Compared to the baseline algorithms, FD-OTR reduces costs by 14.3%, 11.2% and 9.28%, respectively. [ABSTRACT FROM AUTHOR]

For the global-scale multimedia live streaming services, both of the cost-efficiency at the service provider side and the Quality of Experience (QoE) satisfaction at the viewer side need to be achieved. This is a difficult challenge because the request patterns of global live-streaming services are highly dynamic. In this paper, we solve this issue by cloud-based adaptive resource allocation. We first present a cloud-based multi-tier architecture, called MaaS (Media as a Service), which consists of four types of modules. The main issue that we focus on is the deployment of properly dimensioned MaaS modules in proper geographical regions. We take the QoE of the Dynamic Adaptive Streaming over HTTP viewers into account for this decision. We propose a combination of deep-learning based demand prediction scheme and a dynamic-programming based heuristic to make a good tradeoff between viewers' QoE and the cloud resource cost. Extensive evaluation shows that the proposed scheme clearly outperforms the existing schemes. [ABSTRACT FROM AUTHOR]

Blockchain, a promising technology, has been extensively applied in numerous fields, such as network security, finance, and medical care. However, due to the low power consumption and weak computing power of the mobile environment, the application of blockchain in this environment still faces many challenges. Therefore, edge computing has been introduced to improve the computing power of mobile devices and encourage more mobile edge devices to join the blockchain network. In this paper, we propose a double auction model to address the issue of edge computing resource allocation in blockchain networks. Based on this auction model, we first propose a truthful double auction mechanism based on breakeven (TDAMB) to determine matched pairs of edge computing service providers (ECSPs) and miners. Furthermore, to improve the system efficiency, we propose a double auction mechanism based on a critical value (DAMCV). We also theoretically analyze the individual rationality, budget balance and truthfulness of the proposed mechanisms. Extensive experiments show that TDAMB and DAMCV have good effects on edge computing resource allocation in blockchain networks. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communication is an emerging technology for beyond fifth-generation (B5G) networks to support significant features, such as spectrum efficiency, high data rate, and reduced power consumption. Efficient spectrum allocation and optimum power control in D2D networks is one of the major issues that arise due to dynamic traffic arrivals. This article proposes a novel approach to maximize the spectral efficiency of the D2D network. The formulated problem is a mixed-integer nonlinear programming problem (MINLP). However, due to its complexity, the global optimal solution is difficult to solve directly. A two-stage optimization algorithm is presented: optimal resource allocation algorithm (ORAA) by utilizing the concept of queue dynamics and optimal power control algorithm by adapting the Lyapunov stability method. The proposed method achieves higher spectral efficiency up to 21.6% and latency is minimized up to 39.89% over the benchmark schemes. The proposed method shall find immense use in smart traffic management to support Intelligent transportation systems. [ABSTRACT FROM AUTHOR]

This paper examines the development of indicators measuring attacks on education through a case study of the Global Coalition to Protect Education from Attack (GCPEA). As GCPEA and its partners have brought the problem of attacks on education to the attention of global civil society, they have engaged in contestation to define attacks on education and construct indicators to track the relevant violations. These debates are significant in that indicators are a tool of global governance that shape policymaking and resource allocation. The discussion draws on the author's decade of experience working among groups focused on the protection of education, including direct involvement developing indicators on attacks on education, and on three sets of qualitative interviews. It analyses how resource limitations, organisational agendas, challenges of measurement and verification, and global power dynamics exert pressure towards a more narrow understanding of attacks on education. This limits the transformative potential of the protecting education agenda. The discussion illustrates that EiE actors must consider the ways that they measure their work in ongoing conversations about creating a decolonial and more equitable field of practice. [ABSTRACT FROM AUTHOR]

Facing the exponential demand for massive connectivity and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets. Particularly, the operators have to cope with the continuous prosperity of the Internet of things (IoT) along with the ever-increasing deployment of machine-type devices (MTDs). In this regard, due to its compelling benefits, non-orthogonal multiple access (NOMA) has sparked a significant interest as a sophisticated technology to address the above-mentioned challenges. In this paper, we consider a hybrid NOMA scenario, wherein the MTDs are divided into different groups, each of which is allocated an orthogonal resource block (RB) so that the members of each group share a given RB to simultaneously transmit their signals. Firstly, we model the densely deployed network using a mean field game (MFG) framework while taking into consideration the effect of the collective behavior of devices. Then, in order to reduce the complexity of the proposed technique, we apply the multi-armed bandit (MAB) framework to jointly address the resource allocation and the power control problem. Thereafter, we derive two distributed decision-making algorithms that enable the users to autonomously regulate their transmit power levels and self-organize into coalitions based on brief feedback received from the base station (BS). Simulation results are given to underline the equilibrium properties of the proposed resource allocation algorithms and to reveal the robustness of the proposed learning process. [ABSTRACT FROM AUTHOR]

As opposed to those requiring a single action for reward acquisition, tasks necessitating action sequences demand that animals learn action elements and their sequential order and sustain the behaviour until the sequence is completed. With repeated learning, animals not only exhibit precise execution of these sequences but also demonstrate enhanced smoothness and efficiency. Previous research has demonstrated that midbrain dopamine and its major projection target, the striatum, play crucial roles in these processes. Recent studies have shown that dopamine from the substantia nigra pars compacta (SNc) and the ventral tegmental area (VTA) serve distinct functions in action sequence learning. The distinct contributions of dopamine also depend on the striatal subregions, namely the ventral, dorsomedial and dorsolateral striatum. Here, we have reviewed recent findings on the role of striatal dopamine in action sequence learning, with a focus on recent rodent studies. [ABSTRACT FROM AUTHOR]

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness. [ABSTRACT FROM AUTHOR]

The planning of human resources and the management of enterprises consider the organization's size, the amount of effort put into operations, and the level of productivity. Inefficient allocation of resources in organizations due to skill-task misalignment lowers production and operational efficiency. This study addresses organizations' poor resource allocation and use, which reduces productivity and the efficiency of operations, and inefficiency may adversely impact company production and finances. This research aims to develop and assess a Placement-Assisted Resource Management Scheme (PRMS) to improve resource allocation and usage and businesses' operational efficiency and productivity. PRMS uses expertise, business requirements, and processes that are driven by data to match resources with activities that align with their capabilities and require them to perform promptly. The proposed system PRMS outperforms existing approaches on various performance metrics at two distinct levels of operations and operating levels, with a success rate of 0.9328% and 0.9302%, minimal swapping ratios of 12.052% and 11.658%, smaller resource mitigation ratios of 4.098% and 4.815%, mean decision times of 5.414s and 4.976s, and data analysis counts of 6387 and 6335 Success and data analysis increase by 9.98% and 8.2%, respectively, with the proposed strategy. This technique cuts the switching ratio, resource mitigation, and decision time by 6.52%, 13.84%, and 8.49%. The study concluded that PRMS is a solid, productivity-focused corporate improvement method that optimizes the allocation of resources and meets business needs. [ABSTRACT FROM AUTHOR]

In response to increasingly complex social emergencies, this study realizes the optimization of logistics information flow and resource allocation by constructing the Emergency logistics information Traceability model (ELITM-CBT) based on alliance blockchain technology. Using the decentralized, data immutable and transparent characteristics of alliance blockchain technology, this research breaks through the limitations of traditional emergency logistics models and improves the accuracy and efficiency of information management. Combined with the hybrid genetic simulated Annealing algorithm (HGASA), the improved model shows significant advantages in emergency logistics scenarios, especially in terms of total transportation time, total cost, and fairness of resource allocation. The simulation results verify the high efficiency of the model in terms of timeliness of emergency response and accuracy of resource allocation, and provide innovative theoretical support and practical scheme for the field of emergency logistics. Future research will explore more efficient consensus mechanisms, and combine big data and artificial intelligence technology to further improve the performance and adaptability of emergency logistics systems. [ABSTRACT FROM AUTHOR]

Optimizing downlink coordinated multipoint (CoMP) performance through advanced scheduling algorithms enhances V2X communication technology, enabling efficient resource allocation, minimizing interference, and maximizing data rates for reliable and synchronized communication between vehicles and infrastructure. In this paper, several scheduling algorithms were compared, including support vector machine (SVM) linear, SVM Radial Basis Function (RBF), SVM Sigmoid, Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Graph Convolutional Networks (GCN). The performance metrics used in this comparison included CoMP decision, throughput, and cell edge throughput. The results showed that the out-rated GCN algorithm had the best-triggering composition for 5G radio networks, outperforming the other algorithms in terms of CoMP decision accuracy and overall throughput. In particular, the GCN algorithm demonstrated significant improvements in cell edge throughput, which is critical for ensuring reliable communication in areas with weaker signal strength. The reported results proves that the integration of advanced scheduling algorithms in the downlink CoMP framework enhances the efficiency of V2X communication, enabling optimized resource allocation, interference mitigation, and maximized throughput, thereby improving system efficiency, reducing latency, and ensuring reliable and seamless information exchange for connected vehicles, smart cities, and industrial automation. [ABSTRACT FROM AUTHOR]

The digital economy is now the expected norm for economic development, warranting strategic importance for enterprise digital transformation. Nonetheless, enterprises have a lengthy journey to embark upon for digital transformation. On the one hand, resource-based demands pose a significant challenge due to the development characteristics of the initiative; on the other hand, excessive emphasis on economic gains may result in severe environmental issues. Therefore, this paper examines whether green innovation, which combines environmental and economic benefits, can effectively address the above dilemma. The study includes all A-share listed companies from 2010 to 2020 as the research sample, and empirically investigates the impact of green innovation on enterprise digital transformation and its mechanism based on resource-based view. The study concluded that (i) green innovation has a significant positive impact on corporate digital transformation performance, exhibiting asymmetric effects. The robustness tests confirmed the validity of the findings. (ii) Enterprises that actively engage in green innovation can effectively reduce their financial constraints, enhance their operational capacity, and enable the efficient allocation of resources, thereby promoting digital transformation within the enterprise. (iii) There is a regional imbalance in the conversion of green innovation performance into economic performance. The aforementioned results offer fresh insights for investigating the connection between green innovation and digital transformation. Additionally, these findings hold significant implications for the discourse on the synergistic advancement of the environment and economy. [ABSTRACT FROM AUTHOR]

In our article titled, "How WEIRD and androcentric is sex research? Global inequities in study populations," we showed that the published sex research is dominated by male and WEIRD (Western, Educated, Industrialized, Rich, and Democratic) samples. The commentary on our article by Sakaluk and Daniel critiqued the dichotomous coding of WEIRD and non-WEIRD contexts. After acknowledging how the androcentric bias finding was disregarded in the whole discussion, we used this critique as an opportunity to expand our argument about the epistemic exclusion and invisibility of researchers and samples from the majority of the world in sex research. We think having this debate between two groups of researchers located at Western universities is at odds with our intention. Thus, we invited researchers from Global South countries to join the debate via a short survey, and expanded our recommendations from the original paper with the help of these voices. [ABSTRACT FROM AUTHOR]

Objective To analyze the influencing factors and improvement paths of the service capacity of standardized township health centers in Guangxi under the background of "high-quality service at the grassroots level", providing useful reference for further deepening reform and promoting the healthy development of rural medical and health systems. Methods By the end of 2021, township health centers that have reached the recommended standard of "quality service primary line" service ability were selected as the research samples, and the coupled coordination degree model was combined with fuzzy set qualitative comparative analysis method to carry out empirical analysis. Results Single factors cannot constitute a necessary condition for improving or decreasing the service capacity of township health centers. There are a total of four configuration paths for improving the service capacity of township health centers in Guangxi, among which the core sufficient conditions for playing a leading role are the number of high-level practicing (assistant) physicians, the number of registered nurses, and coupling coordination scheduling. The overall consistency of the solution is 0.920 (≥0.8), and the model has strong explanatory power. Conclusion The improvement of service capacity in township health centers in Guangxi is facilitated by the synergistic effect of multiple factors both internally and externally. The optimization and expansion of grassroots talent teams is the core combination element for the improvement of service capacity in township health centers, and the coupling and coordinated development of medical resource allocation and economy is the key element for the improvement of service capacity. [ABSTRACT FROM AUTHOR]

Summary: Phosphorus (P) for carbon (C) exchange is the pivotal function of arbuscular mycorrhiza (AM), but how this exchange varies with soil P availability and among co‐occurring plants in complex communities is still largely unknown.We collected intact plant communities in two regions differing c. 10‐fold in labile inorganic P. After a 2‐month glasshouse incubation, we measured 32P transfer from AM fungi (AMF) to shoots and 13C transfer from shoots to AMF using an AMF‐specific fatty acid. AMF communities were assessed using molecular methods.AMF delivered a larger proportion of total shoot P in communities from high‐P soils despite similar 13C allocation to AMF in roots and soil. Within communities, 13C concentration in AMF was consistently higher in grass than in blanketflower (Gaillardia aristata Pursh) roots, that is P appeared more costly for grasses. This coincided with differences in AMF taxa composition and a trend of more vesicles (storage structures) but fewer arbuscules (exchange structures) in grass roots. Additionally, 32P‐for‐13C exchange ratios increased with soil P for blanketflower but not grasses.Contrary to predictions, AMF transferred proportionally more P to plants in communities from high‐P soils. However, the 32P‐for‐13C exchange differed among co‐occurring plants, suggesting differential regulation of the AM symbiosis. [ABSTRACT FROM AUTHOR]

With the progression of smart vehicles, i.e., connected autonomous vehicles (CAVs), and wireless technologies, there has been an increased need for substantial computational operations for tasks such as path planning, scene recognition, and vision-based object detection. Managing these intensive computational applications is concerned with significant energy consumption. Hence, for this article, a low-cost and sustainable solution using computational offloading and efficient resource allocation at edge devices within the Internet of Vehicles (IoV) framework has been utilised. To address the quality of service (QoS) among vehicles, a trade-off between energy consumption and computational time has been taken into consideration while deciding on the offloading process and resource allocation. The offloading process has been assigned at a minimum wireless resource block level to adapt to the beyond 5G (B5G) network. The novel approach of joint optimisation of computational resources and task offloading decisions uses the meta-heuristic particle swarm optimisation (PSO) algorithm and decision analysis (DA) to find the near-optimal solution. Subsequently, a comparison is made with other proposed algorithms, namely CTORA, CODO, and Heuristics, in terms of computational efficiency and latency. The performance analysis reveals that the numerical results outperform existing algorithms, demonstrating an 8% and a 5% increase in energy efficiency. [ABSTRACT FROM AUTHOR]

Edge computing provides higher computational power and lower transmission latency by offloading tasks to nearby edge nodes with available computational resources to meet the requirements of time-sensitive tasks and computationally complex tasks. Resource allocation schemes are essential to this process. To allocate resources effectively, it is necessary to attach metadata to a task to indicate what kind of resources are needed and how many computation resources are required. However, these metadata are sensitive and can be exposed to eavesdroppers, which can lead to privacy breaches. In addition, edge nodes are vulnerable to corruption because of their limited cybersecurity defenses. Attackers can easily obtain end-device privacy through unprotected metadata or corrupted edge nodes. To address this problem, we propose a metadata privacy resource allocation scheme that uses searchable encryption to protect metadata privacy and zero-knowledge proofs to resist semi-malicious edge nodes. We have formally proven that our proposed scheme satisfies the required security concepts and experimentally demonstrated the effectiveness of the scheme. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) swarms serve as a dynamic platform for diverse missions, including communication relays, search and rescue operations, and environmental monitoring. The success of these operations crucially depends on the resilience of their electrical support systems, especially in terms of battery management. This paper examines the reliability of electrical support for UAV swarms engaged in missions that require prioritization into high and low categories. The paper proposes a dynamic resource allocation strategy that permits the flexible reassignment of drones across different-priority tasks, ensuring continuous operation while optimizing resource use. By leveraging the Markov chain theory, an analytical model for the evaluation of the resilience of the battery management system under different operational scenarios was developed. The paper quantitatively assesses the impact of different operational strategies and battery management approaches on the overall system resilience and mission efficacy. This approach aims to ensure uninterrupted service delivery for critical tasks while optimizing the overall utilization of available electrical resources. Through modeling and analytical evaluations, the paper quantifies the impact of various parameters and operating strategies on overall system resilience and mission availability, considering the utilization strategies of batteries and their reliability and maintenance metrics. The developed models and strategies can inform the development of robust battery management protocols, resource allocation algorithms, and mission planning frameworks, ultimately enhancing the operational availability and effectiveness of UAV swarms in critical special missions. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoT) devices has given rise to applications that demand real-time responses and minimal delay. Fog computing has emerged as a suitable platform for processing IoT applications, extending cloud computing services to the edge of the network. This enables more cost-effective and time-efficient processing at the network's edge. However, determining how to allocate tasks to fog nodes presents a fundamental challenge, involving factors like energy consumption and limited fog server capacity, impacting quality of service parameters such as delay. This paper introduces a mathematical formula for resource allocation to minimize delay and energy consumption while considering quality of service criteria. The subsequent step involves presenting a hybrid genetic algorithm (GA) and the gray wolf optimization (GWO), constituting an improved hybrid approach where the GA exhaustively explores the solution space to reduce the risk of converging to a locally optimal point. The combination of these algorithms produces multiple solutions. Despite incurring processing costs and computation delays, the implementation of these algorithms is crucial for enhancing the Quality of Service (QoS). In conclusion, the results indicate that the simultaneous use of positive aspects from both algorithms significantly improves execution time, final task completion time compared to the other methods. [ABSTRACT FROM AUTHOR]

Function-as-a-service has reduced the user burden by allowing cloud service providers to overtake operational activities such as resource allocation, service deployment, auto-scaling, and load-balancing, to name a few. The users are only responsible for developing the business logic through event-triggered functions catering to an application. Although FaaS brings about multiple user benefits, a typical challenge in this context is the time incurred in the environmental setup of the containers on which the functions execute, often referred to as the cold-start time leading to delayed execution and quality-of-service violations. This paper presents an efficient scheduling strategy FASE that uses a finite-sized warm pool to facilitate the instantaneous execution of functions on pre-warmed containers. Test-bed evaluations over AWS Lambda confirm that FASE achieves a 40% reduction in the average cold-start time and 1.29 × speedup compared to the baselines. [ABSTRACT FROM AUTHOR]

An auction mechanism is an effective way to allocate resources through market behavior. However, in existing studies, most auction mechanisms are designed based on the maximization of social welfare, and there are few studies on potential revenue. Based on cloud computing resource allocation, this paper studies an auction mechanism with revenue limits under single-dimensional and multidimensional resource allocation. That is, the resource provider proposes the lowest revenue limit B. The mechanism aims to maximize the total social welfare while conforming to the lowest revenue limit of the provider. Specifically, we design a new price-raising auction mechanism based on resource similarity and the user cost-effectiveness value, which unifies the two stages of resource allocation and payment pricing, overcoming the problem of low revenue caused by overallocated resources and the lowest winning price. This mechanism can also achieve truthfulness, individual rationality and computational efficiency. In the experimental section, the proposed mechanism is compared with the optimal VCG mechanism and the monotonic mechanism with critical values in terms of revenue, social welfare, resource utilization, etc., and the results demonstrate the good effects of the mechanism designed in this article. [ABSTRACT FROM AUTHOR]