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

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

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

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

Factors Affecting the Situational Awareness of Armored Vehicle Occupants.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Factors influencing hospital charges for tonsillectomy to treat obstructive sleep apnea in children.

PURPOSE: This study investigates the impact of patient characteristics and demographics on hospital charges for tonsillectomy as a treatment for pediatric obstructive sleep apnea (OSA). The aim is to identify potential disparities in hospital charges and contribute to efforts for equitable access to care. METHODS: Data from the 2016 Healthcare Cost and Utilization Project (HCUP) Kid Inpatient Database (KID) was analyzed. The sample included 3,304 pediatric patients undergoing tonsillectomy ± adenoidectomy for OSA. Variables such as age, race, length of stay, hospital region, residential location, payer information, and median household income were collected. The primary outcome variable was hospital charge. Statistical analyses, including t-tests, ANOVA, and multiple linear regression, were conducted. RESULTS: Among 3,304 pediatric patients undergoing tonsillectomy for OSA. The average total charges for tonsillectomy were $26,400, with a mean length of stay of 1.70 days. Significant differences in charges were observed based on patient race, hospital region, and payer information. No significant differences were found based on gender, discharge quarter, residential location, or median household income. Multiple linear regression showed race, hospital region, and residential location were significant predictors of total hospital charges. CONCLUSION: This study highlights the influence of patient demographics and regional factors on hospital charges for pediatric tonsillectomy in OSA cases. These findings underscore the importance of addressing potential disparities in healthcare access and resource allocation to ensure equitable care for children with OSA. Efforts should be made to promote fair and affordable treatment for all pediatric OSA patients, regardless of their demographic backgrounds.

A cost-effectiveness analysis of South Africa's COVID-19 vaccination programme.

BACKGROUND: COVID-19 vaccines were rolled out in South Africa beginning in February 2021. In this study we retrospectively assessed the cost-effectiveness of the vaccination programme in its first two years of implementation. METHOD: We modelled the costs, expressed in 2021 US$, and health outcomes of the COVID-19 vaccination programme compared to a no vaccination programme scenario. The study was conducted from a public payer's perspective over two time-horizons - nine months (February to November 2021) and twenty-four months (February 2021 to January 2023). Health outcomes were estimated from a disease transmission model parameterised with data on COVID-19-related hospitalisations and deaths and were converted to disability adjusted life years (DALYs). Deterministic and probabilistic sensitivity analyses (DSA and PSA) were conducted to assess parameter uncertainty. RESULTS: Incremental cost-effectiveness ratio (ICER) was estimated at US$1600 per DALY averted during the first study time horizon. The corresponding ICER for the second study period was estimated at US$1300 per DALY averted. When 85% of all excess deaths during these periods were included in the analysis, ICERs in the first and second study periods were estimated at US$1070 and US$660 per DALY averted, respectively. In the PSA, almost 100% of simulations fell below the estimated opportunity cost-based cost-effectiveness threshold for South Africa (US$2300 DALYs averted). COVID-19 vaccination programme cost per dose had the greatest impact on the ICERs. CONCLUSION: Our findings suggest that South Africa's COVID-19 vaccination programme represented good value for money in the first two years of rollout.

Potential negative effects of genomic selection.

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

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

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

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

Intelligent Resource Allocation Scheme Using Reinforcement Learning for Efficient Data Transmission in VANET.

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.

Eye development influences horn size but not patterning in horned beetles.

Understanding the origin of novel morphological traits is a long-standing objective in evolutionary developmental biology. We explored the developmental genetic mechanisms that underpin the formation of a textbook example of evolutionary novelties, the cephalic horns of beetles. Previous work has implicated the gene regulatory networks associated with compound eye and ocellar development in horn formation and suggested that horns and compound eyes may influence each other's sizes. Therefore, we investigated the functional significance of genes central to visual system formation in the initiation, patterning, and size determination of head horns across three horned beetle species. We find that while the downregulation of canonical eye patterning genes reliably reduces or eliminates compound eye formation, it does not alter the position or shape of head horns yet does result in an increase in relative horn length. We discuss the implications of our results for our understanding of the genesis of cephalic horns in particular and evolutionary novelties in general.

Rethinking dopamine-guided action sequence learning.

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.

Research on the connectivity of urban river network-wetland and water quality simulation.

The connectivity of urban river networks plays an important role in cities in many aspects, such as urban water safety, water quality (WQ), and aquatic ecological balance. This study focuses on the river network and the Majiawan Wetland in the Chaoyang District of Beijing by establishing a two-dimensional hydrological WQ model employing various water allocation schemes between the river network and the wetland. Water circulation and WQ are the main indexes, and the effects of different scenarios on improving water circulation and WQ are simulated and compared. This study demonstrates that the addition of water replenishment at the intersection of river network and internal slow-water zones of the wetland (Scheme 2) has greater effectiveness in improving both hydrology and WQ compared to two other schemes. The water area of the Majiawan Wetland has expanded, and water velocity has increased. Using chemical oxygen demand, total nitrogen, and total phosphorus as the index values for determining the water class, the WQ of about 20% of the wetland area was reached Water Class II (domestic drinking water), with Water Class III (general industrial water) accounting for the other 80%. This study provides valuable evaluation and reference for similar areas of urban river network connectivity.

A Joint Communication and Computation Design for Probabilistic Semantic Communications.

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.