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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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

With the proliferation of the Internet of Things and smart devices, there exists an urge to address the critical computation demands of end users for several real-time applications. Fog computing (FC) targets to address the computation requirements nearer to the end user in almost real-time. Mainly, the fog environments (FE) exhibit characteristics of resource constraint and wide heterogeneity. In addition, the complex fog environment enforces the fog resources (FR) deployment either within fixed or distributed over random geographical locations. In the literature, there exist several solutions for the optimization of FR allocation. However, the location-dependent FR allocation has not been addressed thoroughly. Hence, this paper proposes a novel hybrid clustering-based deep Q-network for location-based optimal resource allocation (HCDQN-ORA) model. Here, the first objective is to minimize the search operation for identifying suitable FR and to meet the quality of service (QoS) demand using an enhanced fog resource clustering (E-FRC) algorithm. The second objective aims to achieve optimal FR allocation among the set of location-dependent FRs using reinforcement learning techniques. Problem formulation has been modelled using Markov decision process. A deep Q-network algorithm with two variants, namely enhanced optimal resource allocation using deep Q-network (EORA-DQN) and enhanced optimal resource allocation using deep Q-network with experience replay (EORA-DQN-ER), has been incorporated. To analyse the statically located and randomly distributed FR environment, the experimental results exhibit that, in the case of clustering of FRs, E-FRC is more efficient than conventional clustering algorithms. Next, the location-dependent optimal FR allocation problem incorporates the performance analysis based on the convergence of loss function and the parametric analysis based on batch size, respectively. Results exhibit that EORA-DQN performs better only for statically allotted resources. However, the proposed EORA-DQN-ER outperforms both types of FR with a success rate of 89.6% of optimal allocation with the batch size of 200. [ABSTRACT FROM AUTHOR]

We study the problem of sharing the revenues from broadcasting sports leagues axiomatically. Our key axiom is anonymity, the classical impartiality axiom. Other impartiality axioms already studied in broadcasting problems are equal treatment of equals, weak equal treatment of equals and symmetry. We study the relationship between all impartiality axioms. Besides we combine anonymity with other axioms that have been considered in the literature. Some combinations give rise to new characterizations of well-known rules. The family of generalized split rules is characterized with anonymity, additivity and null team. The concede-and-divide rule is characterized with anonymity, additivity and essential team. Other and combinations characterize new rules that had not been considered before. We provide three characterizations in which three axioms are the same (anonymity, additivity, and order preservation) and the fourth one is different (maximum aspirations, weak upper bound, and non-negativity). Depending on the fourth axiom we obtain three different families of rules. In all of them concede-and-divide plays a central role. [ABSTRACT FROM AUTHOR]

Wood density (WD) is a key functional trait for its importance in tree performance and in biomass calculations of forests. Yet, the variation of WD among different woody tree parts, how this varies across ecosystems, and how this influences estimates of forest carbon stocks remains little understood, particularly for diverse tropical forests such as the Amazon. We assembled a dataset on stem and twig wood density from 119 tree species in three different Amazonian ecosystem types that differ considerably in soil nutrition and flooding: non‐flooded forest (Terra Firme), white‐water floodplain forest (Várzea) and black‐water floodplain forest (Igapó) to investigate (i) variation of stem and twig wood density across ecosystems, (ii) the relationships between stem and twig wood density and how these relationships vary across ecosystems. Wood density varied substantially across ecosystems. Várzea species showed lower mean WD for stems compared to Terra firme, while Igapó species showed higher WD for twigs compared to the other ecosystems. Twig and stem wood density were positively related (R2adj = 0.47) with similarly increasing rates across ecosystems, although average WD values differed between Terra firme and Igapó. For any given twig density, stem density tends to be lower in floodplain environments but higher in Terra firme, a habitat‐specific pattern of wood density variation within trees that may emerge from differences in the function of stem and twig wood for growth and survival in ecologically differentiated environments. Our results show how ecosystem has strong impacts on how trees allocate resources to different woody tissues, suggesting contrasting ecological strategies linked to ecosystem constraints. Our results suggest that greater consideration of the variation of WD within trees and how these changes across ecosystems might lead to more accurate estimates of above‐ground biomass in Amazonia. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Taxus chinensis (Taxus cuspidata Sieb. et Zucc.) is a traditional medicinal plant known for its anticancer substance paclitaxel, and its growth age is also an important factor affecting its medicinal value. However, how age affects the physiological and metabolic characteristics and active substances of T. chinensis is still unclear. In this study, carbon and nitrogen accumulation, contents of active substances and changes in primary metabolites in barks and annual leaves of T. chinensis of different diameter classes were investigated by using diameter classes instead of age. The results showed that leaves and barks of small diameter class (D1) had higher content of non-structural carbohydrates and C, which were effective in enhancing defense capacity, while N content was higher in medium (D2) and large diameter classes (D3). Active substances such as paclitaxel, baccatin III and cephalomannine also accumulated significantly in barks of large diameter classes. Moreover, 21 and 25 differential metabolites were identified in leaves and barks of different diameter classes, respectively. The differential metabolites were enhanced the TCA cycle and amino acid biosynthesis, accumulate metabolites such as organic acids, and promote the synthesis and accumulation of active substances such as paclitaxel in the medium and large diameter classes. These results revealed the carbon and nitrogen allocation mechanism of different diameter classes of T. chinensis, and its relationship with medicinal components, providing a guidance for the harvesting and utilization of wild T. chinensis. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer (PCa) causes a substantial health and financial burden worldwide, underscoring the need for efficient mass screening approaches. This study attempts to evaluate the Net Cost-Benefit Index (NCBI) of PCa screening in Iran to offer insights for informed decision-making and resource allocation. Method: The Net Cost-Benefit Index (NCBI) was calculated for four age groups (40 years and above) using a decision-analysis model. Two screening strategies, prostate-specific antigen (PSA) solely and PSA with Digital Rectal Examination (DRE), were evaluated from the health system perspective. A retrospective assessment of 1402 prostate cancer (PCa) patients' profiles were conducted, and direct medical and non-medical costs were calculated based on the 2021 official tariff rates, patient records, and interviews. The monetary value of mass screening was determined through Willingness to Pay (WTP) assessments, which served as a measure for the benefit aspect. Result: The combined PSA and DRE strategy of screening is cost-effective, yields up to $3 saving in costs per case and emerges as the dominant strategy over PSA alone. Screening for men aged 70 and above does not meet economic justification, indicated by a negative Net Cost-Benefit Index (NCBI). The 40–49 age group exhibits the highest net benefit, $13.81 based on basic information and $13.54 based on comprehensive information. Sensitivity analysis strongly supports the cost-effectiveness of the combined screening approach. Conclusion: This study advocates prostate cancer screening with PSA and DRE, is economically justified for men aged 40–69. The results of the study recommend that policymakers prioritize resource allocation for PCa screening programs based on age and budget constraints. Men's willingness to pay, especially for the 40–49 age group which had the highest net benefit, leverages their financial participation in screening services. Additionally, screening services for other age groups, such as 50–54 or 55–59, can be provided either for free or at a reduced cost. [ABSTRACT FROM AUTHOR]

The construction and development of the metro have reshaped the activity spaces of residents and provided them with abundant opportunities to access various resources. However, existing literature often overlooks the impact of increased access to resources due to the metro system on housing prices. This study, based on residential transaction data from 2017 to 2019 obtained from Beike and metro line and station data from Chengdu, employed network analysis to define a 15-minute activity space for 56,999 residential samples in 2,609 neighborhoods around metro stations, serving as the basis for decomposing resource allocation. Subsequently, an XGBoost model was developed to explore the nonlinear impacts of accessibility and resource allocation at the neighborhood and cross-regional levels on housing prices. The results show that 1) the closeness centrality of the nearest metro station primarily affected housing prices. 2) Additionally, the relative importance of resource allocation on housing prices at the neighborhood and cross-regional levels was 34.60% and 19.55%, respectively, highlighting the significance of resource allocation at the cross-regional level. The development of the metro has reshaped residents' activity spaces, increasing access to various resources. The value of resources obtained through the metro has significantly affected residents' willingness to pay. Furthermore, resource allocation at the two levels reflected a different impact on housing prices. At the neighborhood level, the relative importance of factories and comprehensive hospital facilities was 8.02% and 7.47%, respectively. Meanwhile, the relative importance of parks and comprehensive hospital facilities were 4.85% and 3.86%, respectively, at the crossregional level. 3) The relationship between accessibility, resource allocation characteristics, and housing prices is complex and nonlinear. Regarding accessibility, the travel time to the nearest metro station and housing prices exhibited a roughly U-shaped relationship, whereas the closeness centrality of the nearest metro station positively affected housing prices. In terms of resource allocation characteristics, different facilities had varying degrees of impact on housing prices at the neighborhood and cross-regional levels. Specifically, parks and primary and secondary schools at the neighborhood level showed an overall positive impact on housing prices, while factories, comprehensive hospitals, and shopping services at the neighborhood level generally suppressed housing prices. Additionally, business and financial service facilities at the neighborhood level showed an inverted U-shaped relationship with housing prices. At the cross-regional level, comprehensive hospital facilities and primary and secondary school facilities had opposite effects on housing prices compared to park facilities and shopping services. The impact of factories at the cross-regional level on housing prices was unstable. Business and financial service facilities at the cross-regional level demonstrated an inverted U-shaped relationship with housing prices, with a suppressive effect when the count of these facilities exceeded 423. The study findings provide valuable insights into sustainable metro development and rational resource allocation. [ABSTRACT FROM AUTHOR]

There are massive data and rapidly changing battlefield situations in modern electronic warfare, which is a challenge to jamming resource allocation. It is difficult for the existing optimization algorithms to balance optimization capability and calculation speed at the same time. Above all, this study designs a fuzzy multiattribute evaluation model based on behavioral characteristics. The model comprehensively integrates the characteristics of signal behavior, motion behavior, and external intelligence and can effectively deal with unknown threat signals. Then, this study proposes an improved genetic selection electronic warfare operator (EWO) hyperheuristic (GAEWHH) algorithm. As an emergent optimization algorithm, the HH framework has not previously been applied to the problem of jamming resource allocation. This is a two-level algorithm framework that can isolate problem domains. The high level uses an improved genetic algorithm to search the heuristic space, and four EWOs based on the problem domain are designed for the low level to search the solution space. Combining different EWOs can change the population diversity, evolution direction, and algorithm complexity of the GAEWHH algorithm, which improves the algorithm performance to meet battlefield situation requirements. The experiment shows that for large-scale problems, the GAEWHH algorithm is better than the mainstream evolutionary algorithm in terms of optimization capability and better than Google OR-Tools in terms of calculation speed. In this way, the GAEWHH algorithm achieves a balance between optimization capability and calculation speed. [ABSTRACT FROM AUTHOR]

To address the formidable challenge of optimizing and accurately solving the extensive scale of train timetables for high-speed railways, this study adopts a directed space-time network to depict the train timetable. By introducing incompatible arc sets, constraints such as minimum headway time and train overtaking are consolidated into mutually exclusive arc segment constraints, forming an integer programming model. The model is processed using the Lagrangian relaxation method, coupled with fuzzy theory, and an enhancement is made to the key iterative algorithm--the subgradient optimization algorithm--within the Lagrangian relaxation algorithm. The aim is to eliminate potential conflicts in the allocation of transportation resources among different train operation lines. The improved fuzzy subgradient optimization algorithm effectively leverages historical subgradient information and updates the subgradient reasonably. Finally, using the Beijing-Shanghai high-speed railway as a case study, experiments are conducted to optimize and compile the train timetables of 82 train lines in the segment. The computational performance of the standard subgradient algorithm and the fuzzy subgradient algorithm is compared. The results demonstrate that, while ensuring computational accuracy, the Lagrangian relaxation algorithm based on fuzzy subgradient optimization significantly enhances the quality of the optimal solution, reducing the dual gap value from 8.51% to 7.18%. This refined Lagrangian relaxation algorithm serves as an effective approach to obtain a higher-quality train timetable for high-speed railway trains. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) use multiple channels to communicate using wireless access in vehicular environment (WAVE) standards to provide a variety of vehicle-related applications. The current IEEE 802.11p WAVE communication channel structure is composed of one control channel (CCH) and several service channels (SCHs). SCHs are used for non-safety data transmission, while the CCH is used for broadcasting beacons, control, and safety. WAVE devices transmit data that alternate between CCHs and SCHs, and each channel is active for a duration called the CCH interval (CCHI) and SCH interval (SCHI), respectively. Currently, both intervals are fixed at 50 ms. However, fixed-length intervals cannot effectively respond to dynamically changing traffic loads. Additionally, when many vehicles are simultaneously using the limited channel resources for data transmission, the network performance significantly degrades due to numerous packet collisions. Herein, we propose an adaptive resource allocation technique for efficient data transmission. The technique dynamically adjusts the SCHI and CCHI to improve network performance. Moreover, to reduce data collisions and optimize the network's backoff distribution, the proposed scheme applies reinforcement learning (RL) to provide an intelligent channel access algorithm. The simulation results demonstrate that the proposed scheme can ensure high throughputs and low transmission delays. [ABSTRACT FROM AUTHOR]

Effective resource allocation is a crucial aspect in improving the performance of visible light communication (VLC)/hybrid WiFi networks. This study presents a dynamic resource allocation algorithm for multiuser scenarios. A fuzzy logic technique is employed to select the network resources with higher scores for communication. In this sense, to ensure fairness in the hybrid VLC-WiFi network, an enhanced proportional fairness (PF) algorithm is utilized. The algorithm considers the access delay and the distance to the access point as users move, striking a reasonable balance between fair allocation of user resources and maximizing system resource utilization. Therefore, to enhance the user experience in real-time scheduling, a compensation factor is introduced to compensate users with higher latency, increasing their priority. A thorough analysis is provided for the throughput, latency, utility value, and fairness models of the VLC-WiFi heterogeneous network system. The algorithm proposed in this paper is simulated and compared with four traditional algorithms, namely the Round-Robin algorithm, maximum carrier to interference ratio (Max C/I) algorithm, modified largest weighted delay first algorithm, and PF algorithm, under different metrics. Simulation results show that the proposed method is effective, and the improved algorithm yields better fairness and higher throughput compared to the conventional algorithm. As the number of frames increases, the fairness index of the proposed algorithm increases the fastest and gradually converges to 1. The throughput of this method is significantly greater than that of previous algorithms, reaching a stable rate of roughly 1.3 Mbps. The suggested algorithm is approximately 150% higher than the throughput achieved by conventional PF algorithms. The algorithm improves user satisfaction in different regions, and the utility value remains around 0.975. The packet loss rate is approximately 75% lower than traditional typical algorithms, and the average latency is approximately 60% lower. Simulation results show that the proposed algorithm has good application prospects in heterogeneous networks. [ABSTRACT FROM AUTHOR]

In Tehran, Iran's most populous province, there has been a notable surge in livestock production in recent years, prompting a critical examination of water resource allocation through the water footprint indicator. This study uniquely concentrates on livestock productions at a provincial level, specifically within four key counties boasting the highest production rates in Tehran province. Employing the methodology introduced by Mekonnen and Hoekstra (2012), a comprehensive analysis of the water footprints (WFs) associated with various livestock productions is conducted. The results underscore that milk production holds the highest average water footprint at 13,007 cubic meters per ton, significantly surpassing other products. Conversely, lamb exhibits the lowest water footprint at 2,266.7 cubic meters per ton among livestock productions. Evaluation of fresh water sources in relation to animal water footprints highlights unsustainable conditions in southern counties, particularly Islamshahr, due to a pronounced disparity between their product water footprint and available water resources. In contrast, northern counties demonstrate lower water footprints, aligning with their abundant water resources. A scrutiny of water resources reveals vulnerability in Tehran's dams, approaching long-term minimum water volume despite the escalating levels of livestock production. While Varamin features a wastewater treatment unit contributing substantial volume (160 million cubic meters) and recycled water to the system, wells and Qanats play a role in water provision but prove inadequate. Given the substantial productions and water resource scarcity, a recommendation is made to discontinue all livestock production; however, if production persists, focusing solely on sheep meat (lamb) in northern counties is deemed a feasible strategy. [ABSTRACT FROM AUTHOR]

The space time-sensitive multi-target collaborative observation scheduling problem extends the traditional Earth Observation Satellite (EOS) scheduling by incorporating the time dimension. Multiple effective and collaborative observations over time are required to collect relevant information on time-sensitive space targets in high-speed motion. Furthermore, changes in target spatial relationships cause conflicts in resource allocation. Due to the urgency of the time-sensitive targets, the observation system must respond promptly and efficiently upon receiving a task. This study proposes a mathematical model and a Time-Sensitive Observation Scheduling (TSOS) algorithm to address these problem characteristics. Firstly, a mathematical model for scheduling is established by introducing the completion quality index and resource switching frequency index, given the requirement for collaborative multiple satellites in this problem. The TSOS algorithm consists of two parts: task pre-processing and a circular scheduling strategy. In the task pre-processing module, the entire scheduling is divided into multiple observation decision intervals. This enables the candidate satellite set corresponding to each target to remain stable, which reduces the complexity of the problem. An action mapping method is used in the circular scheduling module, and a decision network based on Deep Reinforcement Learning (DRL) called the Observation Pair Decision Network is proposed. Experimental results demonstrate that the trained TSOS algorithm can effectively schedule resources in scenarios with different conflict degrees and exhibits strong robustness. [ABSTRACT FROM AUTHOR]

In this paper, the problem of joint transmission and computation resource allocation for a multi-user probabilistic semantic communication (PSC) network is investigated. In the considered model, users employ semantic information extraction techniques to compress their large-sized data before transmitting them to a multi-antenna base station (BS). Our model represents large-sized data through substantial knowledge graphs, utilizing shared probability graphs between the users and the BS for efficient semantic compression. The resource allocation problem is formulated as an optimization problem with the objective of maximizing the sum of the equivalent rate of all users, considering the total power budget and semantic resource limit constraints. The computation load considered in the PSC network is formulated as a non-smooth piecewise function with respect to the semantic compression ratio. To tackle this non-convex non-smooth optimization challenge, a three-stage algorithm is proposed, where the solutions for the received beamforming matrix of the BS, the transmit power of each user, and the semantic compression ratio of each user are obtained stage by stage. The numerical results validate the effectiveness of our proposed scheme. [ABSTRACT FROM AUTHOR]

This paper considers a resource allocation problem involving servers and mobile users (MUs) operating in a serverless edge computing (SEC)-enabled Internet of Things (IoT) network. Each MU has a fixed budget, and each server is powered by the grid and has energy harvesting (EH) capability. Our objective is to maximize the revenue of the operator that operates the said servers and the number of resources purchased by the MUs. We propose a Stackelberg game approach, where servers and MUs act as leaders and followers, respectively. We prove the existence of a Stackelberg game equilibrium and develop an iterative algorithm to determine the final game equilibrium price. Simulation results show that the proposed scheme is efficient in terms of the SEC's profit and MU's demand. Moreover, both MUs and SECs gain benefits from renewable energy. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop a scoring platform to be used as a reference for both medical preparedness and research resource allocation in the prioritization of zoonoses. Using a case-control design, a comprehensive analysis of 46 zoonoses was conducted to identify factors influencing disease prioritization. This analysis provides a basis for constructing models and calculating prioritization scores for different diseases. The case group (n = 23) includes diseases that require immediate notification to health authorities within 24 hours of diagnosis. The control group (n = 23) includes diseases that do not require such immediate notification. Two different models were developed for primary disease prioritization: one model incorporated the four most commonly used prioritization criteria identified through an extensive literature review. The second model used the results of multiple logistic regression analysis to identify significant factors (with p-value less than 0.1) associated with 24-hour reporting, allowing for objective determination of disease prioritization criteria. These different modeling approaches may result in different weights and positive or negative effects of relevant factors within each model. Our study results highlight the variability of zoonotic disease information across time and geographic regions. It provides an objective platform to rank zoonoses and highlights the critical need for regular updates in the prioritization process to ensure timely preparedness. This study successfully established an objective framework for assessing the importance of zoonotic diseases. From a government perspective, it advocates applying principles that consider disease characteristics and medical resource preparedness in prioritization. The results of this study also emphasize the need for dynamic prioritization to effectively improve preparedness to prevent and control disease. [ABSTRACT FROM AUTHOR]

Understanding how competition varies with environmental stress is critical to anticipating species and community responses to rapid environmental change. While the stress‐gradient hypothesis predicts the strength of competition to decrease with increasing stress, our understanding of how competition varies with stress is limited by a lack of mechanistic understanding of how resource‐use traits underlying competitive dynamics respond to stress. Here, we use duckweeds in the Lemna species complex to measure how phenotypic and genetic variation in R* (a resource acquisition trait representing the minimum resource requirement for positive population growth) varies with high‐temperature stress to better understand how stress alters competitive ability for essential resources. We found that heat stress increased the R* of Lemna plants for nitrogen acquisition. Because lower R* values predict dominance in competitive dynamics where resources are limiting, this indicates that under stressful, high temperatures, plants could experience reduced competitive ability due to the higher resources required to sustain positive population growth rates. We found minimal genetic variation in R* across 11 local genotypes within the Lemna species complex, indicating that selection on resource acquisition strategies for essential resources such as nitrogen may be constrained in nature. The expression of genetic variation in R* for nitrogen was further reduced under heat stress, suggesting that the response to selection for R* could be particularly constrained under high‐temperature stress. Contrary to predictions drawn from the gleaner–opportunist trade‐off, we did not find evidence for a trade‐off in resource acquisition strategies under benign conditions or high‐temperature stress. Plants with lower R* (i.e. higher growth rates under lower nitrogen levels) were not constrained to have lower growth rates under higher nitrogen levels, possibly because the chosen genotypes have not diverged across resource acquisition strategies or because Lemna spp. has escaped this constraint. Importantly, our work indicates that high‐temperature stress could increase sensitivity to competition through an increased requirement for resources while reducing the evolutionary potential for Lemna species to respond to selection for resource traits. This study acts as a key step to understanding the mechanistic traits behind competitive dynamics in resource‐limited and stressful environments. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

The rapid advancement of modern communication technologies necessitates the development of generalized multi-access frameworks and the continuous implementation of rate splitting, augmented with semantic awareness. This trend, coupled with the mounting pressure on wireless services, underscores the need for intelligent approaches to radio signal propagation. In response to these challenges, intelligent reflecting surfaces (IRS) have garnered significant attention for their ability to control data transmission systems in a goal-oriented and dynamic manner. This innovation is largely attributed to equitable resource allocation and the dynamic enhancement of network performance. However, the integration of rate-splitting multi-access (RSMA) architecture with semantic considerations imposes stringent requirements on IRS platforms to ensure seamless connectivity and broad coverage for a diverse user base without interference. Semantic communications hinge on a knowledge base-a centralized repository of integrated information related to the transmitted data-which becomes critically important in multi-antenna scenarios. This article proposes a novel set of design strategies for RSMA-IRS systems, enabled by reconfigurable intelligent surface synergizing with semantic communication principles. An experimental analysis is presented, demonstrating the effectiveness of these design guidelines in the context of Beyond 5G/6G communication systems. The RSMA-IRS model, infused with semantic communication, offers a promising solution for future wireless networks. Performance evaluations of the proposed approach reveal that, despite an increase in the number of users, the delay in the RSMA-IRS framework incorporating semantics is 2.94% less than that of a RSMA-IRS system without semantic integration. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is proven to be a primary technique for sixth-generation (6G) wireless communication networks. However, the great increase in users and antennas brings challenges for interference suppression and resource allocation for mmWave massive MIMO-NOMA systems. This study proposes a spectrum-efficient and fast convergence deep reinforcement learning (DRL)-based resource allocation framework to optimize user grouping and allocation of subchannel and power. First, an enhanced K-means grouping algorithm is proposed to reduce the multi-user interference and accelerate the convergence. Then, a dueling deep Q-network (DQN) structure is proposed to perform subchannel allocation, which further improves the convergence speed. Moreover, a deep deterministic policy gradient (DDPG)-based power resource allocation algorithm is designed to avoid the performance loss caused by power quantization and improve the system's achievable sum-rate. The simulation results demonstrate that our proposed scheme outperforms other neural network-based algorithms in terms of convergence performance, and can achieve higher system capacity compared with the greedy algorithm, the random algorithm, the RNN algorithm, and the DoubleDQN algorithm. [ABSTRACT FROM AUTHOR]

Evolutionary maintenance of dioecy is a complex phenomenon and varies by species and underlying pathways. Also, different sexes may exhibit variable resource allocation (RA) patterns among the vegetative and reproductive functions. Such differences are reflected in the extent of sexual dimorphism. Though rarely pursued, investigation on plant species harbouring intermediate sexual phenotypes may reveal useful information on the strategy pertaining to sex-ratios and evolutionary pathways. We studied H. rhamnoides ssp. turkestanica, a subdioecious species with polygamomonoecious (PGM) plants, in western Himalaya. The species naturally inhabits a wide range of habitats ranging from river deltas to hill slopes. These attributes of the species are conducive to test the influence of abiotic factors on sexual dimorphism, and RA strategy among different sexes. The study demonstrates sexual dimorphism in vegetative and reproductive traits. The sexual dimorphism index, aligned the traits like height, number of branches, flower production, and dry-weight of flowers with males while others including fresh-weight of leaves, number of thorns, fruit production were significantly associated with females. The difference in RA pattern is more pronounced in reproductive traits of the male and female plants, while in the PGM plants the traits overlap. In general, habitat conditions did not influence either the extent of sexual dimorphism or RA pattern. However, it seems to influence secondary sex-ratio as females show their significant association with soil moisture. Our findings on sexual dimorphism and RA pattern supports attributes of wind-pollination in the species. The observed extent of sexual dimorphism in the species reiterates limited genomic differences among the sexes and the ongoing evolution of dioecy via monoecy in the species. The dynamics of RA in the species appears to be independent of resource availability in the habitats as the species grows in a resource-limited and extreme environment. [ABSTRACT FROM AUTHOR]

This paper establishes a coherent framework for delineating the nexus between the digital economy and the subjective efficacy of labor resource allocation. It elucidates the theoretical underpinnings of the digital economy's impact and its channel effects on the efficiency of labor allocation. Within the digital economy landscape, the phenomena of survivorship bias, digital divide, and algorithmic hegemony wield substantial sway over the efficiency of labor market allocation. Empirical analysis, conducted through a cross-sectional data model, validates the theoretical framework. The findings demonstrate that the digital economy markedly diminishes the subjective efficiency of labor allocation. Notably, this inhibitory effect is more pronounced among female workers, households with multiple residences, the non-unmarried demographic, and individuals over the age of 40, with the most pronounced effect observed among those aged over 60. In the examination of the causative mechanisms, it is discerned that the digital economy attenuates the subjective efficiency of labor allocation by workers through three conduits: alterations in social and economic status, shifts in living standards, and modifications in workplace comfort. [ABSTRACT FROM AUTHOR]

Aquatic invasive species (AIS) are one of the greatest threats to the functioning of aquatic ecosystems worldwide. Once an invasive species has been introduced to a new region, many governments develop management strategies to reduce further spread. Nevertheless, managing AIS in a new region is challenging because of the vast areas that need protection and limited resources. Spatial heterogeneity in invasion risk is driven by environmental suitability and propagule pressure, which can be used to prioritize locations for surveillance and intervention activities. To better understand invasion risk across aquatic landscapes, we developed a simulation model to estimate the likelihood of a waterbody becoming invaded with an AIS. The model included waterbodies connected via a multilayer network that included boater movements and hydrological connections. In a case study of Minnesota, we used zebra mussels (Dreissena polymorpha) and starry stonewort (Nitellopsis obtusa) as model species. We simulated the impacts of management scenarios developed by stakeholders and created a decision‐support tool available through an online application provided as part of the AIS Explorer dashboard. Our baseline model revealed that 89% of new zebra mussel invasions and 84% of new starry stonewort invasions occurred through boater movements, establishing it as a primary pathway of spread and offering insights beyond risk estimates generated by traditional environmental suitability models alone. Our results highlight the critical role of interventions applied to boater movements to reduce AIS dispersal. [ABSTRACT FROM AUTHOR]

Network disintegration is a fundamental issue in the field of complex networks, with its core in identifying critical nodes or sets and removing them to weaken network functionality. The research on this problem has significant strategic value and has increasingly attracted attention, including in controlling the spread of diseases and dismantling terrorist organizations. In this paper, we focus on the problem of network disintegration with discrete entity resources from the attack view, that is, optimizing resource allocation to maximize the effect of network disintegration. Specifically, we model the network disintegration problem with limited entity resources as a nonlinear optimization problem and prove its NP-hardness. Then, we design a method based on deep reinforcement learning (DRL), Net-Cracker, which transforms the two-stage entity resource and network node selection task into a single-stage object selection problem. Extensive experiments demonstrate that compared with the benchmark algorithm, Net-Cracker can improve the solution quality by about 8∼62%, while enabling a 30-to-160-fold speed up. Net-Cracker also exhibits strong generalization ability and can find better results in a near real-time manner even when the network scale is much larger than that in training data. [ABSTRACT FROM AUTHOR]

This work investigates a downlink nonorthogonal multiple access (NOMA) scheme with unmanned aerial vehicle (UAV) aided wireless communication, where a single UAV was regarded as an air base station (ABS) to communicate with multiple ground users. Considering the constraints of velocity and maneuverability, a UAV energy efficiency (EE) model was proposed via collaborative design resource allocation and trajectory optimization. Based on this, an EE maximization problem was formulated to jointly optimize the transmit power of ground users and the trajectory of the UAV. To obtain the optimal solutions, this nonconvex problem was transformed into an equivalent convex optimization problem on the basis of three user clustering algorithms. After several alternating iterations, our proposed algorithms converged quickly. The simulation results show an enhancement in EE with NOMA because our proposed algorithm is nearly 99.6% superior to other OMA schemes. [ABSTRACT FROM AUTHOR]