Total Network Research Articles

Driving Sustainability: Analyzing the Influence of Road Transportation Infrastructure on Green Economic Growth in Malaysia

Road infrastructure is an important aspect of the green economy. It integrates environmental, social, and economic factors with the aim of generating a positive net environmental balance. The green Solow growth model proposes that road infrastructure can enhance economic growth while improving environmental quality. This paper examines the relationship between transportation infrastructure and green economic growth in Malaysia, with the aim of ascertaining whether road transportation infrastructure has a role in sustaining economic growth. The study makes use of time series data over the period 1970-2021. This study employs the autoregressive distributed lag (ARDL) model and the error correction model (ECM). The study analyses three models of the total road network and segregates them into federal roads and state roads. The findings show that there are negative long-run relationships between transportation infrastructure and green economic growth. The results also showed that Federal Road had a more negative impact on green economic growth. However, in the short run, only Federal Road shows a significant and negative impact on green economic growth. In Malaysia, the federal road is classified as a primary road extensively utilised by the populace for their routine activities. Thus, federal roads have the potential to generate a more immediate and substantial influence on sustained economic development in comparison to state roads. Nevertheless, road transportation infrastructure can also have adverse effects on the sustainability of long-term economic growth. This study sheds light on the importance of sustainable road development that balances economic growth and the preservation of the environment.

Distributed Traffic Signal Optimization at V2X Intersections

This paper presents our research on a traffic signal control system (TSCS) at V2X intersections. The overall objective of the study is to create an implementable TSCS. The specific objective of this paper is to investigate a distributed system towards implementation. The objective function of minimizing queue delay is formulated as the integral of queue lengths. The discrete queueing estimation is mixed with macro and micro traffic flow models. The novel proposed architecture alleviates the communication network bandwidth constraint by processing BSMs and computing queue lengths at the local intersection. In addition, a two-stage distributed system is designed to optimize offsets, splits, and cycle length simultaneously and in real time. The paper advances TSCS theories by contributing a novel analytic formulation of delay functions and their first degree of derivatives for a two-stage optimization model. The open-source traffic simulation engine Enhanced Transportation Flow Open-Source Microscopic Model (ETFOMM version 1.2) was selected as a simulation environment to develop, debug, and evaluate the models and the system. The control delay of the major direction, minor direction, and the total network were collected to assess the system performance. Compared with the optimized TSCS timing plan by the Virginia Department of Transportation, the system generated a 21% control delay reduction in the major direction and a 7% control delay reduction in the minor direction at just a 10% penetration rate of connected vehicles. Finally, the proposed distributed and centralized systems present similar performances in the case study.

Simulating urban scaling with a term linkages network of a university

In this paper, we make an attempt to increase our understanding of the urban scaling phenomenon. The aim is to investigate how superlinear scaling emerges if a network increases in size and how this scaling depends on the occurrence of elements that constitute the network. To this end, we consider a city as a complex network structure and simulate this structure by the network of all publications of a research intensive university. In this simulation, the publications take the role of the city inhabitants and the concepts (terms and keywords) in the publications represent all kinds of abilities and qualities of the inhabitants. We use in this experiment all author- and database-given terms of the scientific publications of Leiden University from 2022. We calculate the co-occurrence of terms, and on the basis of these connections, we create a network and let this network grow by successively adding publications from the total set of publications. In this way, we get a series of networks with different sizes and this simulates a series of cities with different number of inhabitants. This procedure is performed for different values of the term occurrence threshold. We then analyze how four important network parameters, namely, number of terms, number of clusters, number of links, and total link strength increase with increasing size of the network. Particularly the number of network links and the total network linkage strength are in our opinion the parameters that dominate the scaling phenomenon and can be considered as a simulation of the socioeconomic strength of a city, that is, its gross urban product. We find a significant power law dependence of these network parameters on network size and the power law exponents for the lowest occurrence threshold are within the range that is typical for urban scaling. In our approach, the number of clusters can be interpreted as a measure of complexity within the network. Since the occurrence threshold determines the diversity of terms, we may expect a special relation between the occurrence threshold and the number of clusters. This is indeed the case: whereas for the three other network parameters the scaling exponent increases with increasing occurrence threshold, the number of clusters is the only network parameter of which the scaling exponent decreases with increasing occurrence threshold. Finally, we discuss how our publication term network approach relates to scaling phenomena in cities.

Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China

Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China

SEAFLOOR MORPHOLOGY OF THE OB RIVER GULF, KARA SEA

The article presents the results of an extensive study of the bottom topography of the Ob River Gulf in the Kara Sea, which is a follow-up to the long-term studies of the authors. It refines the existing ideas and aims at solving a fundamental problem of reconstructing the conditions for the formation of bottom topography in the shallow bays of the Kara Sea in the Late Pleistocene and Holocene. The work aims to create a regional characteristic of the geomorphologic structure of the Ob River Gulf bottom. The initial data on the bottom topography were taken from marine navigational maps and sounding boards at a scale of 1:50 000 - 1:100 000. We processed and interpreted the collected cartographic material to create a detailed DEM of the bottom and a bathymetric map at a scale of 1:200 000 with an isobaths interval of 1 m. In addition to bathymetric data, the compilation of a geomorphologic map engaged a large amount of literature and stock materials on geomorphology, geology, and geocryology of the region. The original morphogenetic legend was elaborated with due account of current ideas regarding the conditions for the formation of periglacial plains relief at the regressive stage of the Kara Sea shelf evolution. It was found that the relict fluvial relief prevails within the gulf and was partly modified by subaqueous processes during post-LGM transgression and the current epoch. We have indicated the most critical features of the structure and pattern of the flooded pravalley of the Ob River, as well as the total ancient erosion network. The selected individual forms and relief elements take into account the scale of objects and the degree of their generalization on the map and give an idea of the development of relief-forming processes both at the subaerial stage and under subaqueous conditions, including specific features of modern dynamics of the coastal and bottom relief.

Competency of Rural Roads Achieving Targets 3.6 and 11.2 of the Sustainable Development Goals in Bangladesh

Road Transportation is the major mode of transportation and is recognized as a significant contributor to the socio-economic development in many low-and-middle-income countries like in Bangladesh. In Bangladesh, this transport system is now responsible for carrying about 70% of passengers and 60% of freights and increasing the share of modalities day by day with the development of road transport systems. The responsibility for the improvement and maintenance of the total road network of the country is assigned mainly to the Roads and Highways Department (RHD) and the Local Government Engineering Department (LGED). But the road network belongs to LGED termed as “Rural Road” which is about 94% of the total road length and in the meantime, more than 40% is improved by mainly flexible pavements. However, the state of road traffic safety on many improved rural roads is poor due to narrow carriageways and shoulders along with roadside hazards. On the other hand, the overall road traffic safety in Bangladesh is poor compared to the developed and motorized countries. However, according to the police report, about 4000 people die and another 5000 people become injured by road traffic crashes in each year in Bangladesh but the World Health Organization estimates the fatalities are more than 25,000. However, Bangladesh has committed to achieving the targets 3.6 and 11.2 of the Sustainable Development Goals (SDG) assigned by the United Nations (UN) in 2015 concerning the improvement of road traffic safety by halving 50% of fatalities by 2030. Therefore, the achievement of the above two targets depends on the improvement of road traffic safety on highways as well as rural roads. This research highlights the barriers to improving the safety of rural roads to achieve the above two targets by 2030.

Applicability of Kroon network theory for biaxial mechanics in rubber through multiscale approach

In this paper, we address the problem of the multiscale prediction of the multiaxial hyperelastic behavior of rubber. Our goal is to test the predictive capabilities of a hyperelastic model grounded in the principles of continuum mechanics, bridging the gap between continuum-level mechanics and molecular-level descriptions. This constitutive model posits that macroscopic behavior results from the non-affine deformation of the molecular network, influenced by topological constraints. We employ molecular dynamics methods to measure the mechanical behavior of a rubber network structure under various deformation modes. The study includes an analysis of the molecular network deformation during the straining process, shedding light on the deformation mechanisms at the molecular level. By examining how the total network stress in our specifically designed molecular structure responds to biaxial strain ratios, we are able to assess the effectiveness of the hyperelastic constitutive model.

Research on Microgrid Optimal Dispatching Based on a Multi-Strategy Optimization of Slime Mould Algorithm.

In order to cope with the problems of energy shortage and environmental pollution, carbon emissions need to be reduced and so the structure of the power grid is constantly being optimized. Traditional centralized power networks are not as capable of controlling and distributing non-renewable energy as distributed power grids. Therefore, the optimal dispatch of microgrids faces increasing challenges. This paper proposes a multi-strategy fusion slime mould algorithm (MFSMA) to tackle the microgrid optimal dispatching problem. Traditional swarm intelligence algorithms suffer from slow convergence, low efficiency, and the risk of falling into local optima. The MFSMA employs reverse learning to enlarge the search space and avoid local optima to overcome these challenges. Furthermore, adaptive parameters ensure a thorough search during the algorithm iterations. The focus is on exploring the solution space in the early stages of the algorithm, while convergence is accelerated during the later stages to ensure efficiency and accuracy. The salp swarm algorithm's search mode is also incorporated to expedite convergence. MFSMA and other algorithms are compared on the benchmark functions, and the test showed that the effect of MFSMA is better. Simulation results demonstrate the superior performance of the MFSMA for function optimization, particularly in solving the 24 h microgrid optimal scheduling problem. This problem considers multiple energy sources such as wind turbines, photovoltaics, and energy storage. A microgrid model based on the MFSMA is established in this paper. Simulation of the proposed algorithm reveals its ability to enhance energy utilization efficiency, reduce total network costs, and minimize environmental pollution. The contributions of this paper are as follows: (1) A comprehensive microgrid dispatch model is proposed. (2) Environmental costs, operation and maintenance costs are taken into consideration. (3) Two modes of grid-tied operation and island operation are considered. (4) This paper uses a multi-strategy optimized slime mould algorithm to optimize scheduling, and the algorithm has excellent results.

Tracing the origins of plastics in biosolids: The role of sewerage pipe materials and trade waste

Plastics are ubiquitous in virtually every environment on earth. While the specific sources of plastics entering wastewater are not well known, growing evidence suggests sewage sludge (biosolids) can be a sink for plastics. One potential source could be the sewerage pipe materials used to transport sewage between premises and wastewater treatment plants (WWTPs). To evaluate the significance of sewerage piping as a source of biosolids plastics concentrations, we compared the proportion of the total network (by length and surface area) of polyethylene (PE), polyvinylchloride (PVC), and polypropylene (PP) pipes from 10 WWTPs against their biosolids mass concentrations (mg plastic/g biosolid). Among the 10 catchments, the percentage of the network consisting of PP piping ranged from 0 to 1 %, with 0.8–21 % for PE, and 8–73 % for PVC. Biosolids plastics concentrations ranged from 0.09 to 8.62 mg/g (mg plastic/g biosolid) for PP and PE, respectively. For all three plastics, there was no significant Pearson correlation (r < 0.4) between the biosolids concentration (dry weight mg/g) and the proportion of the network material of the sewerage piping as plastic (either length or surface area). A comparison of trade waste entering a subset of 6 WWTP showed the highest biosolid principal components analysis (PCA) associations between loads of plastics (g/day) and automotive wash bays, general manufacturing, hospitals, laboratories, food manufacturing, laundry and dry cleaning, and cooling towers. A stepwise regression analysis indicated pipe length and surface area, as well as automotive wash bays and food manufacturing may be significant. While our data gave mixed results on the attribution of the sources of plastics entering WWTPs, it suggests that sewerage infrastructure and trade waste may play some role. Future studies should investigate the leachability of sewerage infrastructure and contributions from specific trade waste categories to determine their significance in plastics entering WWTPs.

The simultaneous use of CRISPR/Cas9 to knock out the PI3Kca gene with radiation to enhance radiosensitivity and inhibit tumor growth in breast cancer.

Breast neoplasm is a malignancy that can have a poor prognosis. The PI3K/AKT signaling pathway is frequently activated in various tumor types, including breast cancer, leading to alterations in the tumor microenvironment and radioresistance. Selective inhibition of PI3Kca (p110α) has been considered an alternative approach to overcome radioresistance, owing to concerns surrounding the excessive side effects of pan-PI3K inhibitors tested in clinical trials. This investigation aimed to evaluate the efficacy of co-administering PI3Kca knocking out with radiation therapy in mitigating radioresistance and suppressing tumor growth in the MDA-MB-231 cell line. The present investigation utilized the CRISPR/Cas9 technique to induce a knockout of the PI3Kca gene. Subsequently, after 24 hr of transfection, gene expression, cell proliferation, apoptosis rate, and angiogenesis were assessed. We demonstrated that knocking out PI3Kca, in combination with radiation, increased apoptosis, reduced the expression of PI3Kca and AKT1 genes, and decreased cell proliferation. The CAM assay analysis has demonstrated that knocking out the PI3Kca gene and radiotherapy substantially reduced the total vessel network length and the number of junctions. The findings of our investigation indicate that the integration of radiation therapy with PI3Kca yielded enhanced radiosensitivity, leading to a marked retardation of tumor progression and an increased survival rate.

Investigating the effect of dynamic traffic distribution on network-wide traffic emissions: An empirical study in Ningbo, China.

Urban road traffic is one of the primary sources of carbon emissions. Previous studies have demonstrated the close relationship between traffic flow characteristics and carbon emissions (CO2). However, the impact of dynamic traffic distribution on carbon emissions is rarely empirically studied on the network level. To fill this gap, this study proposes a dynamic network carbon emissions estimation method. The network-level traffic emissions are estimated by combining macroscopic emission models and recent advances in dynamic network traffic flow modeling, namely, Macroscopic Fundamental Diagram. The impact of traffic distribution and the penetration of battery electric vehicles on total network emissions are further investigated using the Monte Carlo method. The results indicate the substantial effect of network traffic distribution on carbon emissions. Using the urban expressway network in Ningbo as an example, in the scenario of 100% internal combustion engine vehicles, increasing the standard deviation of link-level traffic density from 0 to 15 veh/km-ln can result in an 8.9% network capacity drop and a 15.5% reduction in network carbon emissions. This effect can be moderated as the penetration rate of battery electric vehicles increases. Based on the empirical and simulating evidence, different expressway pollution management strategies can be implemented, such as petrol vehicle restrictions, ramp metering, congestion pricing, and perimeter control strategies.

Asymptotic analysis of a closed G-network with rewards

A closed exponential G-network with positive and negative customers is studied. In addition, the G-network under study generates a sequence of rewards or earnings associated with network transitions from one state to another. The total network reward is a random process governed by the probabilistic relations of the Markov process that determines the number of customers in the network nodes. The purpose of this paper is to asymptotically study the G-network with rewards under the assumption of a large number of customers. The main objective is to calculate the expected total reward of the G-network in the asymptotic case. It is proved that reward density function satisfies the generalized Kolmogorov backward equation. An ordinary differential equation for the expected reward that the G-network will earn in a time t if it starts in a given initial state, is derived.

Deep Reinforcement Learning-Based Scheduling for NR-U/WiGig Coexistence in Unlicensed mmWave Bands

This paper investigates the coexistence of the New Radio-based access to Unlicensed spectrum (NR-U) network and the Wireless Gigabit (WiGig) network in unlicensed millimeter-wave (mmWave) bands. To enable the NR-U network to achieve equitable and harmonious spectrum sharing with WiGig systems, we develop two new classes of user equipment (UE) scheduling schemes by exploiting the deep reinforcement learning (DRL) technique. Specifically, we first propose the distributed deep reinforcement learning scheduling (DeepDS) scheme, wherein multiple deep neural networks (DNNs) are used to make decisions for different panels in an independent fashion. Thereafter, to reduce the computational cost of adopting multiple DNNs, we design the centralized deep reinforcement learning scheduling (DeepCS) scheme that introduces the shared DNN framework to perform decisions for all panels in parallel at one time. The objective of both DeepDS and DeepCS is to maximize the total data rate of the NR-U network with as little interference to WiGig systems as possible, while satisfying the quality of service (QoS) requirement for each UE. We first formulate this problem into the constrained Markov decision process framework. To address the multi-constraint issue, we put forth a new DRL algorithm that incorporates the Lagrangian primal-dual optimization into the deep Q-network framework, referred to as adaptive multi-constraint deep Q-network (AMC-DQN). With AMC-DQN, both DeepDS and DeepCS can achieve their goals even without acquiring prior operations about the WiGig network. Simulation results show that compared with the state-of-the-art omniLBT and dirLBT, both DeepDS and DeepCS yield significant performance benefits in terms of the total network data rate. We also demonstrate the ability of DeepDS and DeepCS to satisfy the QoS requirements of different UEs and their robustness against various simulation setups. Furthermore, compared with DeepDS, DeepCS can save a large amount of computational cost although at the expense of a slightly lower data rate.

Geographical Disparities in the Distribution of Community Pharmacies Providing Aseptic Preparation Services in Japan.

Providing medical, nursing, and welfare services in each community is becoming increasingly important as population ages in Japan, and the demand for aseptic preparation in community pharmacies is expected to increase. In this study, the disparity in the distribution of community pharmacies providing aseptic preparation services were examined to explore how the unequal distribution of the pharmacies can be improved in the future. The regional inequality of community pharmacies providing aseptic preparation services was evaluated using Gini coefficients. The regional distribution was evaluated using a geographic information system application. Only 8.0% of all insurance community pharmacies in Japan provided aseptic preparation services. The Gini coefficient of pharmacies providing aseptic preparation services for the total population of each municipality was 0.410. The population coverage, within 16 km of pharmacies providing aseptic preparation services, was 96.5% of the total population. The residential grid coverage, within 16 km of the pharmacies, was 75.2% of the total network, approximately one quarter not covered. It is estimated that the coverage ratios will improve by some percentage by 2050, although the projected population in 2050 is expected to decrease by approximately 20%. The current number and distribution of pharmacies providing aseptic preparation services are inadequate, and measures need to be taken to avoid future problems.

تحسين استهلاك الطاقة لشبكات الهاتف الخلوي ذات الجيل الخامس باستخدام خوارزمية تحسين مدمجة

The 5G network technology is a promising technology that can successfully meet the demand for network capacity growth, due to its high-speed, low-latency, and wide connectivity capabilities. Adapting 5G will increase the energy consumption and CO2 emissions caused by this high consumption. Base stations (BSs) consume the most power in mobile networks accounting for approximately 57% of total energy consumption. This study proposes an integrated meta-heuristic optimization algorithm that combines the Arithmetic Optimization Algorithm (AOA) and the Particle Swarm Optimization (PSO) method. This combination aims to leverage the strengths of both approaches improving the speed and accuracy of the optimization process, to achieve better energy efficiency (EE) in 5G network technology. The proposed algorithm performance is evaluated based on a set of benchmarks functions and compared with other optimization methods. And to demonstrate the applicability of the proposed algorithm in terms of network energy efficiency problem, it was tested against a real-world case study involving power allocation in 5G, the results showed that the proposed algorithm outperforms more recent meta-heuristics state-of-the-art methods in solving a wide range of benchmark functions and obtaining a fair power allocation for multiple users in 5G network. Moreover, it maximizes energy efficiency while maintaining users service quality and availability and reducing total network energy consumption.

Volatility spillover features in financial industries and identification of systemically important financial institutions: A new perspective

We study the spillover effects between financial industries and the identification of systemically important financial institutions (SIFIs) from a new perspective. The total volatility is decomposed into two categories, i.e., good volatility triggered by positive returns and bad volatility triggered by negative returns, which are applied to the volatility spillover index (the DY spillover index) model (Diebold and Yilmaz, 2012; 2014) to study the volatility spillover effects between different financial industries based on different types of volatility. We construct the total, good, and bad volatility spillover networks and use PageRank to measure the systemic importance of financial institutions based on the three networks and compare them with the risks of financial institutions obtained by the three risk measurement methods of ΔCoVaR, MES, and SRISK. By decomposing total volatility into good volatility triggered by positive returns and bad volatility triggered by negative returns, the positive and negative impacts of different financial industries during different periods can be effectively identified to accurately evaluate the risk levels of different financial industries. The results indicate that the PageRank rankings of financial institutions calculated by the bad volatility spillover network have the highest correlation with the risk rankings of financial institutions calculated by ΔCoVaR, MES, and SRISK. The findings suggest the necessity of decomposing the spillover effects between financial industries into good spillover effects triggered by positive returns and bad spillover effects triggered by negative returns, providing a new way of identifying SIFIs.

Noise-aware resource allocation with integrated key generation and consumption for CV-QKD over WDM networks

Continuous-variable quantum key distribution (CV-QKD) has been regarded as a promising approach to secure optical communications over wavelength division multiplexing (WDM) networks. Conventionally, the key generation and the key consumption were considered separately, thus potentially leading to a possible mismatch between them and significantly degrading the network efficiency. A few studies considered these two parts jointly but assumed the worst noise condition, which may lead to an over-supply of quantum keys. In this paper, we propose a noise-aware resource allocation (NARA) scheme and design a framework to balance the generation and the consumption of quantum keys for CV-QKD over WDM networks. We formulate the NARA problem as a mixed-integer linear programming (MILP) model. An efficient heuristic algorithm with three allocation schemes, including threshold-based noise loading (TNL), stress-based noise loading (SNL), and noise margin reservation (NMR), has been designed. Simulation results show that NARA with the NMR scheme can significantly reduce the total number of wavelengths used and the total network cost under all system parameters by more than 10% and 20%, respectively, compared to the other two schemes.

Gigantic animal cells suggest organellar scaling mechanisms across a 50-fold range in cell volume.

The largest cells are orders of magnitude bigger than the smallest cells. Organelle content scales to maintain cell function, with different organelles increasing in volume, length, or number as cells increase in size. Scaling may also reflect functional demands placed on organelles by increased cell size. Amphibians exhibit exceptional diversity in cell size. Using transmission electron microscopy, we analyzed 3 species whose enterocyte cell volumes range from 228 to 10,593 μm3. We show that nuclear volume increases by an increase in radius while mitochondrial volume increases by an increase in total network length; the endoplasmic reticulum and Golgi apparatus, with their complex shapes, are intermediate. Notably, all 4 organelle types increase in total volume proportional to cell volume, despite variation in functional (i.e., metabolic, transport) demands. This pattern suggests that organellar building blocks are incorporated into more or larger organelles following the same rules across species that vary ~50-fold in cell sizes, consistent with a "limited precursor" model for organellar scaling that, in turn, assumes equivalent cytoplasmic concentrations of organellar building block proteins. Taken together, our results lead us to hypothesize that salamanders have evolved increased biosynthetic capacity to maintain functional protein concentrations despite huge cell volumes.

Optimal Power Allocation and Delay Minimization Based on Conflict Graph Algorithm for Device-to-Device Communications

Device-to-device (D2D) technology is a promising technique in terms of being capable of providing efficiency, decreased latency, improved data rate, and increased capacity to cellular networks. Allocating power to users in order to reduce energy consumption and maintain quality of service (QoS) remains a major challenge in D2D communications. In this paper, we aim to maximize the throughput of D2D users and cellular users subject to QoS requirements and signal-to-interference-plus-noise ratio (SINR). To this end, we propose a resource and power allocation approach called optimal power allocation and delay minimization based on the conflict graph (OP-DMCG) algorithm that considers optimal power allocation for D2D multi-users in the cellular uplink channels and minimization of the total network delay using conflict graphs. Based on the simulations presented in this paper, we show that the proposed OP-DMCG algorithm outperforms the greedy throughput maximization plus (GTM+), delay minimization conflict graph (DMCG), and power and delay optimization based uplink resource allocation (PDO-URA) algorithms in terms of both total network throughput and total D2D throughput.

Stochastic model for active distribution networks planning: An analysis of the combination of active network management schemes

The widespread adoption of distributed generation (DG) is changing the operation of distribution networks. To increase the control of power flows and avoid the detriments of DG intermittency, active distribution networks (ADN) emerge as a feasible solution. In this paper, we propose a convex model for ADN expansion planning. Four active network management (ANM) schemes are considered: DG active power curtailment, DG reactive power control, on-load tap changer (OLTC) tap adjustment, and shunt compensation device control. The problem is formulated as a mixed integer second-order cone programming problem (MISOCP) using the distflow equations and second-order cone relaxations (SOCR). By scenario analysis, we address the uncertainty associated with power demand, renewable generation, and electricity prices. The proposed model is used to study the benefits of different combinations of ANM schemes to a modified IEEE 33-bus system and compare them with the individual benefits of each scheme. The results indicate that ANM schemes can reduce total network costs, reduce losses, delay network reinforcement, and improve the voltage profile. In addition, some combinations of schemes develop synergies in which the combined economic benefits are greater than the sum of the individual benefits.