Node Density Research Articles

A self-adaptive attraction and repulsion-based naked mole-rat algorithm for energy-efficient mobile wireless sensor networks

Naked mole-rat algorithm (NMRA) is a swarm intelligence-based algorithm that draws inspiration from the mating behaviour of mole rats (workers and breeders). This approach, which is based on the ability of breeders to reproduce with the queen, has been utilized to tackle optimization problems. The algorithm, however, suffers from local optima stagnation problem and a slower rate of convergence in order to provide gobal optimal solution. This study suggests attraction and repulsion strategy based NMRA (ARNMRA) along with self-adaptive properties to avoid trapping of solution in local optima. This strategy is utilized to create new breeder rat solutions and mating factor (λ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\\lambda )$$\\end{document} is made self-adaptive using simulated annealing (sa) based mutation operator. ARNMRA is evaluated on CEC 2005 numerical benchmark problems and found to be superior to other algorithms, including well-known ones like selective operation based GWO (SOGWO), opposition based laplacian equilibrium optimizer (OB-L-EO), improved whale optimization algorithm (IWOA), success-history based adaptive DE (SHADE) and original NMRA. Further, according to experimental results, the performance of ARNMRA is likewise superior to the NMRA for the CEC 2019 and CEC 2020 numerical problems. Convergence profiles and statistical tests (rank-sum test and Friedman test) are employed further to validate the experimental results. Moreover, this article extends the application of ARNMRA to address the data gathering aspect in mobile wireless sensor networks (MWSNs) with the goal of prolonging network lifetime and enhancing energy efficiency. In this MWSN-based protocol, a sensor node is elected as a cluster head based on factors like mobility, residual energy, and connection time. The protocol aims to maximize the system lifetime by efficiently collecting data from all sensors and transmitting it to the base station. The study emphasizes the significance of considering dynamic node densities and speed when designing effective data-gathering protocols for MWSNs.

Optimization Techniques in Cooperative and Distributed MAC Protocols

The tremendous increase in wireless network application finds distributed allocation of resources allocation very useful in the network. Packet delivery ratio and delay can be improved by concentrating on payload size, mobility, and density of nodes in the network. In this article, a survey is carried out on different cooperative and distributed MAC protocols for communication and optimization algorithms for various applications and the mathematical issues related to game theory optimizations in MAC protocol. Spatial reuse of channel improved by (3-29) % and multi-channel improves throughput by 8% using distributed MAC protocol. The energy utility of individual players can be focused to get better network performance with NASH equilibrium. Fuzzy logic improves channel selection by 17% and secondary users' involvement by 8%. Jamming, interference problems can be addressed using cross layer approach in the MAC and simultaneous data, voice transmissions in IoT; WSN applications can be attained using hybrid distributed MAC protocol.

Analyzing the Effectiveness of Ensemble Based Analysis in Wireless Sensor Networks

The usefulness of ensemble-based total time series analysis in Wi-Fi sensor networks is examined in this paper. A device to uses an ensemble approach combines multiple strategies to enhance overall predictive performance. This research assesses various tactics using unique metrics, such as robustness and accuracy. It contrasts the effectiveness of traditional time series methods with ensemble-based total fashions. An experimental approach focusing mostly on exceptional Wi-Fi sensor network scenarios is employed to evaluate the overall effectiveness of the suggested methods. Additionally, this study looks into how changes to community features like energy delivery, conversation range, and node density affect how effective the suggested methods are. The study's findings maintain the capacity to create effective Wi-Fi sensor networks with improved predicted overall performance. The usefulness of ensemble-based time collecting and analysis techniques for wireless sensor networks is investigated in this research. This study primarily looks upon function extraction and seasonality discounting of time series records in WSNs. In this analysis, seasonality is discounted using an ensemble median filter, and feature extraction is accomplished by primary component assessment. To assess the performance of the suggested ensemble technique on every simulated and real-world international WSN fact, multiple experiments are carried out. The findings suggest that the ensemble approach can improve the exceptional time-gathering records within WSNs and reduce seasonality. Furthermore, when compared to single-sensor strategies, the ensemble technique further improves the accuracy of the function extraction system. This work demonstrates the applicability of the ensemble approach for the investigation of time collection data in WSNs

Routing Selection Algorithm for Mobile Ad Hoc Networks Based on Neighbor Node Density.

In the process of data transmission in mobile ad hoc networks, it is essential to establish optimal routes from source nodes to destination nodes. However, as network density increases, this process is often accompanied by a significant rise in network overhead. To address this issue, the ND-AODV (neighborhood density AODV) protocol has been introduced, which reduces the probability of transmitting control information in high-density node environments to mitigate network overhead. Nevertheless, this may come at the cost of reduced routing accuracy, potentially leading to unnecessary resource wastage in certain scenarios. Furthermore, ND-AODV does not comprehensively consider the location of the receiving nodes, which limits its ability to reduce network overhead effectively. To overcome these limitations, this paper introduces a novel routing approach, known as CND-AODV (common neighborhood density AODV). In comparison to ND-AODV, CND-AODV offers a more comprehensive solution to the challenges posed by high-density network environments. It intelligently processes control information based on the special positioning of the receiving nodes, thereby significantly reducing unnecessary network overhead. Through simulation experiments comparing performance metrics such as throughput, packet delivery rate, and latency, the results clearly indicate that CND-AODV substantially decreases network overhead, enhancing network performance. Compared to ND-AODV, this innovative routing approach exhibits significant advantages. It provides a more efficient and reliable solution for ad hoc networks in high-density environments.

OCC-MP: An optimal cluster based congestion aware technique multipath routing protocol in WSN using hybrid evolutionary techniques

Due to massive data traffic, wireless network congestion is inevitable, causing network performance deterioration and data transmission quality reduction. By adjustingthe traffic flow at the source node along the optimal path, Wireless sensor networks strive to evade and overcome congestion. Recently, numerous evolutionary approaches for congestion detection and avoidance have been presented, although they do not improve WSN performance. A hybrid evolutionary approach is attempted to avoid congestion in WSN and improve network performance. We present an optimum cluster-based congestion aware multipath routing protocol (OCC-MP). An improved atom search optimization (IASO) approach for efficient clustering is introduced initially in OCC-MP. Then the HSIPO method is used for decision making, which computes each node's trust degree. The HSIPO algorithm selects the cluster head (CH) from a group of nodes. Then a deep recurrent neural network (DRNN) is used to monitor congestion and provide congestion aware routing. Finally, multiple simulation situations like various node densities andat various simulation rounds supported our proposed OCC-MP techniqueoutperformedcurrent methodologies with respect to energy consumption, throughput, traffic load overflow, delivery ratio, and number of nodes alive.

Performance evaluation of proactive, reactive, and hybrid routing protocols for small, medium, and large mobile ad hoc networks

The popularity of wireless ad hoc networks is increasing daily. Examples of such networks are MANETs, VANETs, and Sensor networks. These types of wireless networks are dynamic and have different working features in common, and it is not always obvious to know which network type should be used given the needs of users. Determining the best protocols for a particular sort of wireless network’s problems is frequently another challenge. We evaluate the above-mentioned networks to fill this gap and ultimately demonstrate that MANET is the best option since it can be easily deployed anywhere, at any time, to meet the needs of the majority of users. For evaluation purposes, we used prominent network evaluation parameter metrics, i.e., End-to-end Delay, Network Throughput, and Packet delivery ratio. We compare three different MANET routing protocol types—PROACTIVE, REACTIVE, and HYBRID—one from each type; DSDV [Proactive], TORA [Hybrid], and AODV [Reactive]. The simulations and their results for both small and large systems were done on the OPNET simulator. According to MANET, DSDV outperforms other protocols in networks with high node densities in terms of the same metrics, while TORA performs better in networks with low node densities in terms of packet delivery ratio, end-to-end delay, and throughput.

Investigating the Impact of Network Structure on Road Traffic Crashes: A Macro-level Analysis

Road traffic crashes (RTCs) are one of the significant externalities associated with transportation that pose a serious public health risk. While addressing the safety concerns of road transport, road network structure is often overlooked. Among the high-risk locations within a road network, intersections pose a particular concern due to the intricate and conflicting traffic movements. Intersection skewness (the angle at which an intersection deviates from 90 degrees) and intersecting road categories are considered to influence intersection safety significantly. While previous research has extensively explored the impact of skew angles and intersecting road categories at the intersection level, there is a lack of investigation at the ward/zonal level. This study aims (a) to estimate the skewness and heterogeneity of intersecting leg categories at the ward level and (b) to model the RTCs and the street network characteristics such as skewness, hierarchy, connectivity, and centrality along with covariates like ward-level population density and economic status. Utilizing data from OpenStreetMap, this study conducted the ward-level analysis for Delhi, India. This study, along with the proposed metrics, also quantified various network structure metrics that are commonly used in safety analysis, such as betweenness centrality, meshedness coefficient, average edge length, node density, and average circuity. Using three years of crash data from 2017-19, this study modelled the crash rate at the ward level for Delhi (289 wards) by developing a negative binomial regression model. The explanatory variables include the network structure metrics, nightlight intensity (indicative of economic status), and population density. It was observed that wards with a higher deviation from the conventional right-angle intersection experienced a higher rate of crashes. Moreover, the wards with more heterogeneous intersections had more crash rates. However, wards with more central (higher betweenness) road network structure had fewer crashes. Furthermore, wards with more circuitous roads (more network distance to travel from one point to another) had more crash rates. However, a negative association was found between the RTCs and ward economic status, indicating that people who are economically disadvantaged continue to be the most at risk for fatal crashes.

Routing Methods For Data Transmission In Large Networks: A Review

A wireless sensor network (WSN) is a network consisting of miniaturized smart sensors communicating the information gathered or collected from a monitored environment via a wireless link. The sensors are capable of sensing the events within their environment, process the data, and transmit the data to the base station (BS). The entire processing of data and subsequent transmission to BS requires high energy consumption. The operation of WSN is limited by repeated dead nodes, which results in energy depletion. Hence, to prolong the life-span of the network, several routing protocols have been developed. However, the effectiveness of these protocols has not been well examined in terms of increasing node population for a given WSN field or area. Therefore, in this work the effect of increasing node density on cluster based energy-efficient routing protocols in wireless sensor network was analysed. The implemented routing protocols were Low Energy Adaptive Clustering Hierarchy LEACH, stable election protocol (SEP), and zone-SEP (Z-SEP). The dimension of the WSN was 100 × 100 square metre area with varying number of sensor nodes: 50, 60, 70, 80, 90, and 100. The results of the simulation conducted in MATLAB revealed that increasing node density resulted in increased alive node and throughput (measures in terms of transmitted number of packets). On the contrary, the protocol with the best performance was ZSEP.

Topology optimization design of microstructures with zero Poisson's ratio

Mechanical metamaterials are materials that possess unconventional mechanical properties that are not found in homogeneous materials, achieved through specific artificial microstructures. Topology optimization is an effective design method for such materials. In this paper, a topology optimization-based method for designing zero Poisson's ratio mechanical metamaterials is proposed. Firstly, a zero Poisson's ratio topology optimization objective function is constructed based on the energy homogenization method, and the optimal microstructure topology configuration and elastic coefficient matrix under different initial topologies and volume fractions are obtained through the boundary density evolution topology optimization method. To improve the saw tooth effect of the boundaries, the node density level set method is used to smooth the boundaries of the microstructure. Then, in finite element simulation analysis, it is demonstrated that the proposed method can effectively design microstructures with zero Poisson's ratio properties. It is also shown that microstructures with expected stiffness and zero Poisson's ratio properties can be obtained by changing the volume fraction and selecting materials with different stiffnesses. Finally, the unit cell is periodically arranged to form a multi-lattice metamaterial, and its zero Poisson's ratio mechanical performance in both X and Y directions is verified.

A Study of Digital Communication Paths in Contemporary Politics and Marxist Thought

Abstract In the context of digital technological change and media convergence, how to quantitatively analyze the digital communication path of contemporary politics and Marxist thought has become a new problem to be solved. The objective of this paper is to create a model for analyzing the digital communication path of political information and Marxist thought. The degree correlation in the social network algorithm is used to analyze the correlation between the information nodes of contemporary politics and Marxist thought, and the clustering coefficients are used to predict the behavior and evolution of the nodes in the dissemination network. The dissemination model of contemporary politics and Marxist ideology can be explored by combining the infectious disease information dissemination model on the network platform. After the construction has been completed, the model is employed to evaluate the relevant information of a network platform. The early stages of the spread of political and Marxist-related information resulted in 180,000 crowd contacts becoming information exposers and quickly reaching the peak point. The peak point of 119,800 people is quickly reached when both intentional and unintentional communicators of political messages increase. Node density reaches 0.94 and then declines roughly 24 hours after the political message is released. The model employed in this paper is capable of effectively analyzing the digital dissemination path of politics and Marxist ideology and providing a reference for exploring the digital dissemination of political information.

Modifikasi Protokol Routing DSDV Menggunakan Algoritma Dynamic-power transmission untuk Mengurangi Interferensi Sinyal dalam Pengiriman Data Berdasarkan Tingkat Kepadatan Node di Jaringan MANET

Mobile Ad-Hoc Network (MANET) is a decentralized collection of nodes that exchange information temporarily via wireless transmission. When nodes communicate, all nodes in the communication range will be affected by signal interference from other nodes. The problem that arises due to this signal interference is that it will affect or interfere with the signal transmitted to the receiver, resulting in a decrease in network performance which will impact the quality of service (QoS). To overcome the problem of signal interference, use interference management. This study applies the Dynamic-Power Transmission (DPT) algorithm to the Destination Sequence Distance Vector (DSDV) routing protocol framework. DPT-DSDV will adjust the communication range automatically based on the node density level. When a node sends data packets to neighbouring nodes, the communication range will be adjusted automatically based on the selected level. The performance of DPT-DSDV will be compared with standard DSDV and standard AODV by looking at performance based on three parameters, namely throughput, packet delivery ratio (PDR), and end-to-end delay. Based on the results of research trials, applying the dynamic-power transmission algorithm can improve the performance of the standard DSDV routing protocol. Throughput results on DPT-DSDV increased by 0.90%. The packet delivery ratio (PDR) increased by 0.50%, and end-to-end delay decreased by 2.80%.
 Key words: MANET, Interference, Quality of Service, Routing, DSDV, Dynamic-Power Transmission.

RDVFF- Reliable Data Dissemination in Vehicular Ad Hoc Networks Based on Validation of Far to Farthest Zone

<p>The rise of connected and autonomous vehicles has resulted in the development of vehicular ad hoc networks (VANETs) as a means of improving road safety, traffic efficiency, and passenger comfort. However, VANETs present challenges to achieving reliability and service quality in networks suffered highly by broadcast storms due to their mobility, scalability, and high node density. By addressing such challenges, this paper seeks to contribute to solving the problem of efficient information dissemination by diluting the broadcast storm and coverage issues by proposing a novel relay selection scheme based on the validation of the far-to-farthest zone technique. A flag control suppression mechanism is presented to overcome repeated information problems. RDVFF includes a separate algorithm for the optimization of participants for relay inclusion to address the latency issue during communication based on a multi-directional multiple-selection scheme. Compared with state-of-art protocols in terms of throughput, delivery ratio, collision, and latency experimental results show improvement of an average of 21.27%, 18.34%, 36.76%, and 38.04% respectively.</p> <p> </p>

A Modified Energy Enhancement in WSN Using the Shortest Path Transmission Technique

Abstract This study introduced a novel energy enhancement approach for Wireless Sensor Networks (WSNs) by leveraging the shortest path transmission technique to minimize energy consumption and extend the network’s lifetime. Unlike traditional methods that heavily relied on cluster heads (CHs) for data transmission, our model proposed a non-cluster-based routing algorithm, utilizing Dijkstra’s algorithm to identify the most energy-efficient paths for data transmission. Simulation results, based on varying node densities (100, 200, and 300 nodes) within a 200x200 network area, demonstrated the effectiveness of our approach. Our findings indicated a significant reduction in energy consumption, with the network lifetime extending to approximately 100,000 rounds, surpassing traditional LEACH-based and other related protocols. This enhancement not only promised a sustainable WSN deployment but also offered a scalable solution adaptable to different network sizes and configurations.

APPLICATION OF PINN AND THE METHOD OF DIFFERENTIAL CONSTRUCTION OF ADAPTIVE ONE-DIMENSIONAL COMPUTATIONAL GRIDS

This paper presents an approach to constructing adaptive one-dimensional computational grids using the Beltrami equation and Physics-Informed Neural Networks (PINNs). The main focus is on exploring the potential for precise control of grid node density through the control function ω(s), which allows the grid to adapt to the local features of the problem. The Beltrami equation used, being a key component of the method, regulates the distribution of nodes by modifying the function’s derivatives depending on the values of the control function. The effectiveness of this approach is demonstrated through examples involving one and two regions of node clustering. The results showed that the PINN method combined with the Beltrami equation allows for the creation of computational grids with a high degree of adaptation to given conditions, providing detailed modeling in critical regions. This approach has advantages over traditional numerical methods, as integrating physical laws in the grid construction process minimizes numerical errors and improves modeling accuracy. The use of neural networks offers flexibility in model tuning and the ability to account for complex nonlinear dependencies. The discussion of the results highlights the potential of using PINNs for adaptive grid construction in various fields requiring precise and efficient modeling. In conclusion, this study confirms that the combination of the Beltrami equation and PINNs is a powerful tool for adaptive grid construction, opening new possibilities for numerical modeling of complex physical processes.

Enhanced Network QoS in Large Scale and High Sensor Node Density Wireless Sensor Networks Using (IR-DV-Hop) Localization Algorithm and Mobile Data Collector (MDC)

Enhanced Network QoS in Large Scale and High Sensor Node Density Wireless Sensor Networks Using (IR-DV-Hop) Localization Algorithm and Mobile Data Collector (MDC)

Environmental Monitoring with Wireless Sensor Network for Energy Aware Routing and Localization

This paper introduces a novel approach to enhance the efficiency of environmental monitoring in Wireless Sensor Networks (WSNs) through the application of the Centroid Direction of Arrival Routing (CDAR) protocol. WSNs play a pivotal role in collecting and transmitting data for environmental surveillance, yet energy constraints and dynamic deployment scenarios pose challenges to their sustained operation. The CDAR protocol addresses these challenges by optimizing energy consumption, improving data transmission efficiency, and extending network lifetimes. We present comprehensive performance evaluations across ten deployment scenarios, revealing CDAR’s adaptability to varying node densities and environmental conditions. The protocol consistently demonstrates high energy efficiency percentages, showcasing its ability to strike a balance between energy conservation and reliable data transmission. Security measures integrated into CDAR ensure the protection of transmitted environmental data. This paper provides valuable insights into the applicability of CDAR in achieving sustainable and effective WSNs for environmental monitoring, setting the stage for further research and real-world implementations.

Adaptive mesh methods on compact manifolds via Optimal Transport and Optimal Information Transport

Moving mesh methods were devised to redistribute a mesh in a smooth way, while keeping the number of vertices of the mesh and their connectivity unchanged. A fruitful theoretical point-of-view is to take such moving mesh methods and think of them as an application of the diffeomorphic density matching problem. Given two probability measures μ0 and μ1, the diffeomorphic density matching problem consists of finding a diffeomorphic pushforward map T such that T#μ0=μ1. Moving mesh methods are seen to be an instance of the diffeomorphic density matching problem by treating the probability density as the local density of nodes in the mesh. It is preferable that the restructuring of the mesh be done in a smooth way that avoids tangling the connections between nodes, which would lead to numerical instability when the mesh is used in computational applications. This then suggests that a diffeomorphic map T is desirable to avoid tangling. The first tool employed to solve the moving mesh problem between source and target probability densities on the sphere was Optimal Transport (OT). Recently Optimal Information Transport (OIT) was rigorously derived and developed allowing for the computation of a diffeomorphic mapping by simply solving a Poisson equation. Not only is the equation simpler to solve numerically in OIT, but with Optimal Transport there is no guarantee that the mapping between probability density functions defines a diffeomorphism for general 2D compact manifolds.In this manuscript, we perform a side-by-side comparison of using Optimal Transport and Optimal Information Transport on the sphere for adaptive mesh problems. We choose to perform this comparison with recently developed provably convergent solvers, but these are, of course, not the only numerical methods that may be used. We believe that Optimal Information Transport is preferable in computations due to the fact that the partial differential equation (PDE) solve step is simply a Poisson equation. For more general surfaces M, we show how the Optimal Transport and Optimal Information Transport problems can be reduced to solving on the sphere, provided that there exists a diffeomorphic mapping Φ:M→S2. This implies that the Optimal Transport problem on M with a special cost function can be solved with regularity guarantees, while computations for the problem are performed on the unit sphere.

Prognostic Significance of Lymph Node Density in Pathological Node Positive Urothelial Carcinoma of the Bladder -Upfront Surgery and Post Neoadjuvant Chemotherapy Cohorts

AimTo validate the role of lymph node density as a prognostic marker in patients undergoing primary surgery and postneoadjuvant therapy in pathological node-positive urothelial bladder carcinoma. Materials and MethodsRetrospective analysis of 503 patients who underwent radical cystectomy from 2006 to 2019 for muscle-invasive urothelial bladder carcinoma, of which 152 patients with pathological node-positive disease were analyzed. Demographic details, pathological findings, treatment details, disease-free, and overall survival were documented. X tile program analysis was used to divide patients with positive lymph nodes into 3 groups: LD1: <= 7, LD2 :>7 to <15, LD3: >15, and the optimal cut-off value obtained was 15%. To evaluate the impact of lymph node ratio, patients with positive lymph nodes into 3 categories for each cut-off point estimation method, the application generates the histogram, Kaplan–Meier plot and calculates hazard ratio, confidence intervals and P-values. Univariate and multivariate cox regression analysis was done with a P-value of <.05, considered significant. ResultsOne hundred fifty-two patients (30.2%) had pathological nodal metastasis, with 87 of them having perinodal extension. Ninety-six underwent primary surgery, and 56 were postneoadjuvant chemotherapy. The median follow-up was 55.42 months. 68 of the 152 node-positive patients died of the disease. Median number of lymph nodes removed was 17.11. Lymph node density divided into tertiles were LD1 <7%, LD2 7-<15%, LD3 >15% showed 5-year RFS 40.5%,29.3%, 22.6% and 5 year OS was 55.5%, 42.4%,32.1% respectively. Cox regression analysis showed that age less than 55 years ,higher tumor stage, lymphovascular invasion, and higher lymph node ratio were significant in univariate and multivariate analysis. The lymph node density cut-off value of 15% was substantial among node-positive patients (P = .027), and subgroup analysis in upfront surgery with the adjuvant treatment group and postneoadjuvant chemotherapy group was also significant (P =.021). ConclusionPathological higher T stage, Age <55 years, Lymphovascular invasion, adjuvant chemotherapy , adjuvant radiation treatment and lymph node density had prognostic significance in both cohorts of patients who underwent upfront surgery and neoadjuvant chemotherapy. Lymph node density cut-off value of <15% was prognostically significant.

Regional lymph node density-based nomogram predicts prognosis in nasopharyngeal carcinoma patients without distant metastases

BackgroundNasopharyngeal carcinoma (NPC) is a relatively common type of cancer in Southern China, with local recurrence or distant metastases even after radical treatment; consequently, it is critical to identify the patients at higher risk for these events beforehand. This study aimed to assess the prognostic value of regional lymph node density (RLND) associated nomograms in NPC and to evaluate the utility of nomograms in risk stratification.MethodsA total of 610 NPC patients without distant metastases (425 in the training and 185 in the validation cohort) were enrolled. The MRI-identified nodal features and clinical characteristics were documented, and the RLND was calculated. Cox analyses were conducted to identify prognostic-associated factors. Nomograms were generated based on the multivariate analysis results. The predictive accuracy and discriminative ability of the nomogram models were determined using the concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve; the results were compared with those of the tumor-node-metastasis (TNM) classification. Decision curve analysis (DCA) and C-index were used to assess the prognostic effect and added discriminative ability of RLND. We also estimated the optimal RLND-based nomogram score cut-off values for survival prediction.ResultsRLND was an independent predictor of overall survival (OS) and disease-free survival (DFS), with hazard ratios of 1.36 and 1.30, respectively. RLND was utilized in the construction of nomograms, alongside other independent prognostic factors. The RLND-based nomogram models presented a more effective discriminative ability than the TNM classification for predicting OS (C-index, 0.711 vs. 0.680) and DFS (C-index, 0.681 vs. 0.669), with favorable calibration and consistency. The comparison of C-index values between the nomogram models with and without RLND provided substantiation of the crucial role RLND plays in these models. DCA confirmed the satisfactory clinical practicability of RLND. Moreover, the nomograms were used to categorize the patients into three groups (high-, middle-, and low-risk), and the Kaplan–Meier curves showed significant differences in prognosis between them (p < 0.05). These results were verified in the validation cohort.ConclusionRLND stands as a robust prognostic factor in NPC. The RLND-based nomograms excel in predicting survival, surpassing the TNM classification.

Multifaceted impacts of moss-dominated biocrusts on dryland soils: From soil pore structure to aeration and water infiltration

Biocrusts are a crucial living cover of dryland soils that alter near-surface soil structure and properties. To date, it is still unclear how biocrusts affect soil pore structure, and subsequently alter gas and water transport processes. In this study, we investigated aeolian sand and loess soil colonized with moss-dominated biocrusts and in bare state of the Chinese Loess Plateau. X-ray computed tomography (CT) was used to quantify and visualize the pore characteristics for all considered scenarios. In addition, the soil air permeabilities and relative gas diffusivities were measured together with infiltration rates. CT imaging and reconstruction showed 102–107%, 56–87%, and 72–203% higher macroporosities, surface area densities, and mean pore volumes, respectively, for soils colonized with biocrusts when compared to their bare counterparts. The biocrusts also increased the ratio of connected and isolated porosities as well as pore node densities, while decreasing Euler numbers and tortuosity when compared to the bare soils. This suggests that the pore networks in biocrust-colonized soils exhibit higher continuity and less tortuosity. In addition, the biocrusts increased air permeability by 277–301% and relative gas diffusivity by 47–59%, which is attributable to more air-filled pores and better pore connectivity. However, when compared with bare soils, the biocrusts reduced steady state infiltration rates by 41–50% and the 60 min cumulative infiltration amount by 33–54%. The decrease in infiltrability is attributable to the higher fine particle content and the enhanced water holding capacity of biocrusts. In summary, the biocrusts improved soil pore structure and soil aeration, and reduced infiltrability when compared to bare soils. This implies that biocrusts play a multifaceted and complex role in the transport of gases and water, and as a consequence significantly impact near-surface hydrological and biochemical processes, such as soil evaporation and respiration in drylands.