Connectivity Scenarios Research Articles

Impacts of River Network Connectivity on Flood Signatures and Severity Regulated by Flood Control Projects

The operation of hydraulic projects within plain river networks to mitigate floods can alter river network connectivity patterns, subsequently affecting flood processes. This study employed the MIKE 11 model to simulate flood processes under three different river network connectivity scenarios. Based on the simulations, we propose a method to evaluate flood intensity severity by integrating three flood characteristic indices: Slope of the Flow Duration Curve (SFDC), Rising Climb Index (RCI), and Flashiness Index (FI). These indices assess the overall magnitude of change, the rate of rise, and process fluctuations, respectively. Results indicate that changes in river network connectivity significantly impact RCI and SFDC, more than FI. Compared to the natural river network connectivity mode, changes in urban or watershed river network connectivity resulted in a significant decrease in RCI values by 3–37% or 18–38% across various return periods, with the rate of change in RCI values increasing as the return period lengthened. The impact of urban river network connectivity changes on SFDC within the Changzhou urban area was more pronounced under high-magnitude storm conditions, causing a 61% reduction. Furthermore, changes in watershed river network connectivity had a larger effect on SFDC under low-magnitude storm conditions than under high-intensity storms. Over 80% of the rivers under natural connectivity conditions exhibited flood intensity severity of Level III or higher, particularly in the Chenshu–Qingyang area. The alterations in connectivity significantly decreased flood intensity severity, with 85% to 91% of rivers showing the lowest flood intensity severity of Level I. Under a 100-year rainstorm scenario, flood risk shifted from within the flood protection envelope to outside it in the Changzhou urban area. The results will provide an important scientific basis for regional flood management in plains with dense rivers.

Exploiting stream scheduling in QUIC: Performance assessment over wireless connectivity scenarios

The advent of wireless technologies has led to the development of novel services for end-users, with stringent needs and requirements. High availability, very high throughput, low latency, and reliability are all of them crucial performance parameters. To address these demands, emerging technologies, such as non-terrestrial networks or millimeter wave (mmWave), are being included in 5G and Beyond 5G (B5G) specifications. mmWave enables massive data transmissions, at the expense of a more hostile propagation, typical for high frequency bands. Consequently, the inherent instability of the physical channel significantly affects the upper layers of the protocol stack, resulting in congestion and data losses, which might strongly hinder the overall communication performance. These challenges can be addressed not only at the link layer, but at any affected layer. QUIC is a new transport protocol designed to reduce communications latency in many ways. Among other features, it enables the use of multiple streams to effectively manage data flows sent through its underlying UDP socket. This paper introduces an implementation of priority-based stream schedulers along with the design of a flexible interface. Exploiting the proposed approach, applications are able to set the required scheduling scheme, as well as the stream priorities. The feasibility of the proposed approach is validated through an extensive experiment campaign, which combines Docker containers, the ns-3 simulator and the Mahimahi framework, which is exploited to introduce realistic mmWave channel traces. The results evince that an appropriate stream scheduler can indeed yield lower delays for time-sensitive applications by up to 36% under unreliable conditions.

Quantifying the Effects of Interwell Communication Using Dynamic Fluid-in-Place Calculations

Summary Multifractured horizontal wells (MFHWs) completed in the same reservoir layer, or different reservoir layers, commonly experience interwell communication through hydraulic fractures. For example, after a well is placed on production, its production performance can be impacted by communication with an offsetting well placed on production after it. The degree of communication between wells is important to quantify for the purposes of well production forecasting, reserves estimation, completions, and well spacing design optimization. In this study, dynamic fluid-in-place calculations, performed using the impacted producing well rates and flowing pressures, are applied to quantify the effect of communication with an offset-producing well on the impacted well-contacted fluid-in-place estimates. Agarwal (2010) demonstrated that pressure transient analysis theory can be used to derive the volume of fluid in place contacted by a well (CFIP) over time during constant rate, transient production. The method was later extended to variable-rate/pressure scenarios. However, all previous applications of Agarwal’s method were for single, isolated wells and assumed single-phase flow of oil and gas. To evaluate the usefulness of the method for modern development scenarios, it is extended to allow for quantification of interwell communication during flowback, for which single-phase flow of water before the breakthrough of formation fluids may precede multiphase flow of formation and fracturing fluids, and for analysis of multiphase data. Analysis of flowback data enables early-time identification and quantification of interference effects. Multiple numerical simulation cases are generated to simulate different degrees of communication for the case of a two-phase flow of oil and water. Wells are assumed to be communicating through a hydraulic fracture with a specified transmissibility multiplier (Tmult) used to adjust the amount of interwell communication. Corrections for multiphase flow in the CFIP method are performed using two different methods—the total volumetric flow rate (combined phase) approach and the multiphase pseudovariable approach. The CFIP diagnostic plot (i.e., log-log plot of CFIP vs. material balance time) is applied to the impacted producing well to evaluate the CFIP trend before and after offset well production and the magnitude of CFIP change. The practical application of the method is demonstrated with field cases. From the simulation cases, it is observed that, after the offset well is placed on production, a reduction of CFIP for the impacted producing well occurs (rapidly decreasing at first and then stabilizing after a transition period) proportional to productivity index reduction. The loss in CFIP for the impacted producing well can be determined simply by estimating its CFIP immediately before and after offset well production. For high connectivity (Tmult> 0.25) scenarios, application of the combined phase approach resulted in estimates of the impacted well CFIP reduction of ~46–50%, whereas application of the modified pseudovariable approach resulted in estimates of ~49–51%. For the low connectivity case (Tmult = 0.001), these estimates were ~11% and ~9%, respectively, for the two approaches. Therefore, for the simulation cases studied herein, the two approaches agreed within acceptable error. Numerical simulation was also used to verify the absolute change in CFIP using these two approaches for correcting for multiphase flow. The practical application of the modified CFIP method was demonstrated using two field cases with early-time production. Both field cases demonstrated that changes in CFIP for the impacted well can be unambiguously interpreted. In the first field case corresponding to early-time production data (gas and water) associated with Well 23 of the SPE data repository, the reduction in CFIP of the impacted producing well was estimated to be ~37% using the combined phase approach. In the second field case, for which a producing well completed in a low-permeability gas condensate reservoir is impacted by placing multiple offset wells on production at the same time, the reduction in CFIP of the impacted well was estimated to be ~20% using the combined phase approach. In this study, we demonstrate for the first time that CFIP calculations can be applied to quantify interwell communication between two wells during flowback or early-time production when multiphase flow occurs in the reservoir.

The spatiotemporal profile and adaptation determine the joint effects and interactions of multiple stressors

BackgroundBiodiversity is declining worldwide as ecosystems are increasingly threatened by multiple stressors associated with anthropogenic global change. Stressors frequently co-occur across scales spatially and temporally, resulting in joint effects that are additive or non-additive, i.e., antagonism or synergism. Forecasting and counteracting threats from intensifying stressors requires improved mechanistic understanding of joint effects, which is currently relatively low. To date, research on multiple stressors has been biased toward simplified scenarios, emphasized classification of interactions over realized joint effects, and mostly ignored adaptation (i.e., phenotypic plasticity or evolving life-history traits) of organisms. To investigate if more a realistic scenarios design incorporating complex spatiotemporal stressor profiles and adaption change joint effects and interactions of multiple stressors compared to simplified scenarios, we modified a spatially explicit meta-population model for a generic freshwater insect. We used the model to simulate different, hypothetical spatiotemporal profiles of a continuous and a discrete stressor and evaluated their joint effects and interactions. Agricultural land use represented the continuous stressor impacting meta-population patch quality and network connectivity and related scenarios implied different trajectories. Climatic events represented the discrete stressor impacting all patches simultaneously by temporary mortality events, with related scenarios implying different event severity. Adaptation mitigated the effects of climatic events based on previous events.ResultsExcluding adaptation, we found that at higher levels of the discrete stressor (i.e., strong and frequent climatic events) it strongly dominates the joint effects, while at a low level (i.e., weak and infrequent climatic events) of the discrete stressor, the continuous stressor (i.e., land use) dominates. Yet, the continuous stressor always defined the interaction type, with decreasing land use stress leading to antagonism, and increasing land use stress leading to synergism. Adaptation reduced joint effects under decreasing land use stress, yet had little compensatory influence under increasing land use stress. Moreover, adaptation changed interaction sizes inconsistently across the different land use and climate scenarios, with change depending on the climate scenario. Here, interactions decreased in the moderate scenario but increased in the severe and intense scenarios.ConclusionsWe highlight that realistic stressor scenarios accounting for potential adaptation are critical for a mechanistic understanding of how species respond to global change. To our knowledge, this is the first modeling study to show that stressor interactions depend on complex spatiotemporal stressor profiles and adaptation, following general principles.

Recreational fishing in the lower basin of the Santa Cruz river(Patagonia, Argentina) in the face of damming scenarios

The Steelhead ecotype (Oncorhynchus mykiss) and chinook salmon (Oncorhynchus tshawytscha) have promoted a recreational fishery in the Santa Cruz river, the last large free flowing river of Patagonia. However, two dams are being built upstream in the middle course, potentially affecting the life cycle of these anadromous salmonids. The study describes the dynamics of the recreational fishing of the Santa Cruz river around Piedrabuena city and anglers perceptions, discussing its trends according to possible river connectivity scenarios. The fishery was assessed through a monthly year-round field sampling from April 2018 to April 2019, developing instantaneous angler counts, discriminating fishing methods, effort, and catch per unit effort; also, in situ interviews were used to collect information on anglers’ motivations and perceptions. In addition, information on fishing tournaments was used. Three fishing zones (FZ) were identified near Piedrabuena city: FZ-1, located downstream where anglers used baited hook and line to catch the native marine Patagonian blenny (Eleginops maclovinus); FZ-2, located in the town where baited hook and line, spinning, and fly-cast are used to catch Patagonian blenny and salmonids; FZ-3 located upstream the town, where fishing effort was mainly directed to salmonids employing spinning and fly-cast. Fishing effort showed a seasonal pa�ern with high values from March to May and September to October, agreeing with Steelhead and Chinook salmon runs, respectively. The most relevant motivations for anglers were nonoriented catch variables. Anglers preferred the capture of salmonids over native species, considering dams to be a major threat to fishery. Although a postdamming scenario could allow the adult salmonids to migrate upstream, the current reproduction grounds of Steelhead will be flooded, and juveniles and adults of both species will be hindered in their downstream migration to sea. These aspects seem to seriously compromise the future of these unique fisheries.

Robust Energy Management Policies for Solar Microgrids via Reinforcement Learning

As the integration of renewable energy expands, effective energy system management becomes increasingly crucial. Distributed renewable generation microgrids offer green energy and resilience. Combining them with energy storage and a suitable energy management system (EMS) is essential due to the variability in renewable energy generation. Reinforcement learning (RL)-based EMSs have shown promising results in handling these complexities. However, concerns about policy robustness arise with the growing number of grid intermittent disruptions or disconnections from the main utility. This study investigates the resilience of RL-based EMSs to unforeseen grid disconnections when trained in grid-connected scenarios. Specifically, we evaluate the resilience of policies derived from advantage actor–critic (A2C) and proximal policy optimization (PPO) networks trained in both grid-connected and uncertain grid-connectivity scenarios. Stochastic models, incorporating solar energy and load uncertainties and utilizing real-world data, are employed in the simulation. Our findings indicate that grid-trained PPO and A2C excel in cost coverage, with PPO performing better. However, in isolated or uncertain connectivity scenarios, the demand coverage performance hierarchy shifts. The disruption-trained A2C model achieves the best demand coverage when islanded, whereas the grid-connected A2C network performs best in an uncertain grid connectivity scenario. This study enhances the understanding of the resilience of RL-based solutions using varied training methods and provides an analysis of the EMS policies generated.

Landscape connectivity in extensive livestock farming: an adaptive approach to the land sharing and land sparing dilemma

This study investigates the “land sharing” versus “land sparing” dilemma in the context of extensive cattle ranching in Chiapas, Mexico. Employing a comprehensive methodology that synthesizes various systems and uses a normalized matrix for relative priority assessment, we identified several geographic variables as zoning criteria. These criteria encompass the hemerobic index, proximity to structurally intact forests, fire frequency, and terrain slope, aiming to identify areas optimal for conservation. Our results highlight properties with high conservation potential and propose two distinct connectivity scenarios, both excluding currently preserved areas. The analysis focuses on the interplay between connectivity and hemeroby, identifying human-influenced regions within the landscape and emphasizing the importance of tree conservation in agricultural contexts for biodiversity preservation. By tackling the “land sharing” vs. “land sparing” debate, the study underscores the necessity of sustainable livestock practices and the critical role of connectivity in ranching landscapes for ecosystem preservation.

Enhancing Reliability in Rural Networks Using a Software-Defined Wide Area Network

Due to limited infrastructure and remote locations, rural areas often need help providing reliable and high-quality network connectivity. We propose an innovative approach that leverages Software-Defined Wide Area Network (SD-WAN) architecture to enhance reliability in such challenging rural scenarios. Our study focuses on cases in which network resources are limited to network solutions such as Long-Term Evolution (LTE) and a Low-Earth-Orbit satellite connection. The SD-WAN implementation compares three tunnel selection algorithms that leverage real-time network performance monitoring: Deterministic, Random, and Deep Q-learning. The results offer valuable insights into the practical implementation of SD-WAN for rural connectivity scenarios, showing its potential to bridge the digital divide in underserved areas.

On rate performance of M‐ary amplitude shift keying compact ultra massive array systems for massive connectivity

AbstractCompact ultra massive array (CUMA) is a new form of the emerging fluid antenna system where a huge number of flexible‐position antennas are selected to produce the output signal. By making sure that the in‐phase channels (similarly for the quadrature channels) of the desired signal at the selected antenna ports align, it builds an advantage of the desired signal over the interference. It is known that CUMA as a multiple access scheme is able to deal with hundreds of users on the same channel use, in the case of rich scattering, if binary phase shift keying is considered. It is nevertheless unclear if higher‐level modulation can bring even greater network rate in this extreme massive connectivity scenario. This letter investigated this situation by presenting the average data rate expression of CUMA when M‐ary amplitude shift keying is used, assuming a binary symmetric channel. Numerical results reveal that M‐ary amplitude shift keying can indeed raise the rate performance considerably.

Digital enabled innovation ecosystems: a dual case study of knowledge flows in intellectual property platforms

PurposeThe embedding of digital technology and the fuzzy organizational boundary have changed the operation of platform innovation ecosystem (PIE). Specifically, as an important energy of PIE, the internal logic of knowledge flow needs to be reconsidered in the context of digital age, which will be helpful to select the cultivation and governance strategy of PIE.Design/methodology/approachA dual case-analysis is applied to open the “black box” of knowledge flow in the PIE from the perspective of enabled by digital technology, by taking the intellectual property (IP) operation platform as cases.FindingsThe research findings are as follow: (1) The knowledge flow mechanism of PIE is mainly demonstrated through the processes of knowledge acquisition, knowledge integration and knowledge spillover. During this process, connectivity empowerment and scenario empowerment realize the digital empowerment of the platform. (2) Connectivity empowerment provides a channel of knowledge acquisition for the digital connection between participants in PIE. In the process of knowledge integration, scenario empowerment improves the opportunities for accurate matching and collaborative innovation between knowledge supplier and demander, and enhance the value of knowledge. The dual effect of connectivity empowerment and scenario empowerment has accelerated the knowledge spillover in PIE. Particularly, connectivity empowerment expands the range of knowledge spillover, and scenario empowerment affects the generativity of the platform, resulting in the enhancement of platform’s capability to embed and expand its value network. (3) Participants have been benefitted from the PIE enabled by digital technology through three key modules (knowledge acquisition, knowledge integration and knowledge spillover), as the result of knowledge flow.Originality/valueThis study focuses on the knowledge flow mechanism of PIE enabled by digital technology, which enriches the PIE theory, and has enlightenments for the cultivation of digital platform ecosystem.

The role of connectivity in conservation planning for species with obligatory interactions: Prospects for future climate scenarios

AbstractClimate change may lead to range shifts, and barriers to such displacements may result in extirpations from previously suitable habitats. This may be particularly important in freshwater ecosystems that are highly fragmented by anthropogenic obstacles, such as dams and other smaller in‐stream barriers. Conservation planning in freshwaters should consider the dynamic effects of climate change and the ability of species to cope with it. In this study, we developed a framework for incorporating climate‐driven dispersal barriers into conservation planning taking into account the medium and long‐term impacts of climate change and species with obligatory interactions. Given that freshwater mussels (Bivalvia: Unionida) are a group of highly threatened organisms dependent on fish hosts to complete their larval development and dispersal, we used Marxan to prioritize areas for their joint conservation in the Iberian Peninsula as a case study. We tested two connectivity scenarios between current and future habitats, (i) unlimited dispersal capacity and (ii) dispersal constrained by artificial barriers, and also identified priority translocation areas for species that were unable to disperse. Accounting for the effects of climate change on species distributions allowed the identification of long‐term conservation areas, but disregarding artificial barriers to dispersal may lead to unrealistic solutions. Integrating the location of barriers allowed the identification of priority areas that are more likely to be colonized in the future following climatic shifts, although this resulted in an additional loss of six to eight features (~5%–7%) compared to solutions without dispersal constraints. Between 173 and 357 artificial barriers (~1.6%–3.3%) will potentially block species dispersal to irreplaceable planning units. Where removal of artificial barriers is unfeasible, conservation translocations may additionally cover up to eight additional features that do not meet conservation targets due to dispersal constraints. This study highlights the challenge of identifying protected areas to safeguard biodiversity under climate change.

Federated Generative-Adversarial-Network-Enabled Channel Estimation

Accurately estimating channel state information is essential for meeting the quality-of-service requirements of modern applications and scenarios. Deep learning techniques have proven effective in acquiring channel conditions with low pilot overhead in massive connectivity scenarios. However, accessing channel data brings new challenges related to transmission overhead, privacy concerns, scalability, heterogeneous network support, and adaptability to dynamic environments. We propose a federated generative-adversarial-network-enabled channel estimator to address these challenges. We refine the coarse least-squares estimation results for their low complexity and fast convergence. To ensure accuracy, we designed a double U-shaped network. The Lipschitz continuous function is applied to discriminators for spectral normalization. We then propose a federated learning framework to utilize the training process. The local generator parameters are updated at the center, reducing communication overhead and privacy concerns. To deal with nonindependent and identically distributed datasets, the discriminators dynamically push away the predictions by dynamic regularization to obtain a more robust aggregated generative model at the center. Furthermore, we propose a motivation scheme that benefits users participating in the training process, encouraging them to join and take advantage of edge/cloud computing capabilities. Numerical results demonstrate that the proposed federated generative adversarial network-enabled channel estimator provides high estimation accuracy and reduces the burden on pilots. The proposed dynamic regularization terms and motivation scheme boost performance efficiently with low communication cost and high participation.

Hybridization and invasive species in a threatened freshwater fish community under environmental pressures: Morphometric and molecular evidence

Abstract Mediterranean freshwater systems are under threat owing to increased drought driven by climate change, intensive human land uses and non‐native species. This is causing increased fish hybridization in isolated watercourses. The genetic and morphological characteristics of hybrids of sympatric native and non‐native fish species were studied in four streams of the Mediterranean Guadalquivir basin (south‐west Spain). Fish morphology was analysed using geometric morphometrics, and molecular determination of parenthood was inferred through one mitochondrial gene (cytb) and one nuclear gene (Beta‐actin) for all hybrids and a subset of pure parental specimens. Molecular analyses confirmed hybrids between the native Squalius alburnoides and non‐native Alburnus alburnus in a stream with continuous flow. Haplotype analyses suggested that they originated from backcrossing of hybrid offspring. Intergeneric crosses between native species S. alburnoides and Pseudochondrostoma willkommii, and S. alburnoides and Iberochondrostoma lemmingii were detected in streams under reduced connectivity scenarios. Morphometrics revealed that hybrid phenotypes were similar to S. alburnoides. In some cases, molecular markers uncovered hybridization events that were neither detected in the field nor by morphometric analyses, potentially supporting a backcrossing/introgression scenario. Hybridization is likely to be increasing in Mediterranean rivers where S. alburnoides are present owing to increased fragmentation caused by summer drought exacerbated by climate change and human land uses and pressures. This can become a problem for these endemic vulnerable species if genetic diversity is lost, morphological homogenization occurs and hybrids cannot be easily detected in the field. The potential risks could be addressed by monitoring and eradication of non‐native species and segregation from natives. To avoid native–native crosses, habitat quality and desiccation risk could be tackled by improved water quality and riparian reforestation to provide shade and reduce evapotranspiration. This would need increased coordination and intervention between the institutions that share conservation responsibilities in the area.

Using social-ecological models to explore stream connectivity outcomes for stakeholders and Yellowstone cutthroat trout.

Despite growing interest in conservation and re-establishment of ecological connectivity, few studies have explored its context-specific social-ecological outcomes. We aimed to explore social and ecological outcomes to changing stream connectivity for both stakeholders and native fish species impacted by habitat fragmentation and nonnative species. We (1) investigated stakeholder perceptions of the drivers and outcomes of stream connectivity, and (2) evaluated the effects of stakeholder-identified connectivity and nonnative species scenarios on Yellowstone cutthroat trout (YCT) populations. Our study was conducted in the Teton River, Idaho, USA. We integrated two modeling approaches, mental modeling and individual-based ecological modeling, to explore social-ecological outcomes for stakeholders and YCT populations. Aggregation of mental models revealed an emergent pattern of increasing complexity as more types of stakeholders were considered, as well as gaps and linkages among different stakeholder knowledge areas. These results highlight the importance of knowledge sharing among stakeholders when making decisions about connectivity. Additionally, the results from the individual-based models suggested that the potential for a large, migratory life history form of YCT, in addition to self-preference mating where they overlap with rainbow trout, had the strongest effects on outcomes for YCT. Exploring social and ecological drivers and outcomes to changing connectivity is useful for anticipating and adapting to unintended outcomes, as well as making decisions for desirable outcomes. The results from this study can contribute to the management dialogue surrounding stream connectivity in the Teton River, as well as to our understanding of connectivity conservation and its outcomes more broadly.

Multi-connectivity in 5G New Radio: Optimal resource allocation for split bearer and data duplication

Mobile radio networks have been evolving towards the integration of services and devices with a diverse set of throughput, latency, and reliability requirements. To support these requirements, 3GPP has introduced Multi Connectivity (MC) as a more flexible architecture for 5G New Radio (NR), where multiple radio links can be simultaneously activated to split or duplicate data traffic. Multi connectivity improves single user performance at the cost of higher interference due to the increase of radio transmissions, which negatively affects system throughput.This paper analyzes the problem of admission control and resource allocation in multi connectivity scenarios, considering different requirements and 5G NR features. Specifically, we formulate two optimization problems that leverage the features of the Packet Data Convergence Protocol (PDCP) layer, which controls the flow of data packets of the data radio bearer: the PDCP Split-Bearer Decision (PSD) and the PDCP Duplication Decision (PDD) problems, which are tailored for the enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (uRLLC) services, respectively. We further provide heuristic approaches, specifically designed for the PSD and PDD problems, to effectively solve both these problems.Numerical results in realistic network deployments confirm that our solutions can effectively allocate radio resources increasing admission rate and system throughput, while guaranteeing the required reliability level.

Broadband Connectivity for Handheld Devices via LEO Satellites: Is Distributed Massive MIMO the Answer?

Significant efforts are being made to integrate satellite and terrestrial networks into a unified wireless network. One major aspect of such an integration is the use of unified user terminals (UTs), which work for both networks and can switch seamlessly between them. However, supporting broadband connectivity for handheld UTs directly from low Earth orbit (LEO) satellite networks is very challenging due to link budget reasons. This paper proposes using distributed massive multiple-input multiple-output (DM-MIMO) techniques to improve the data rates of handheld devices with a view to supporting their broadband connectivity by exploiting the ultra-dense deployment of LEO satellites and high-speed inter-satellite links. In this regard, we discuss DM-MIMO-based satellite networks from different perspectives, including the channel model, network management, and architecture. In addition, we evaluate the performance of such networks theoretically by deriving closed-form expressions for spectral efficiency and using extensive simulations based on actual data from a Starlink constellation. The performance is compared with that of collocated massive MIMO connectivity (CMMC) and single-satellite connectivity (SSC) scenarios. The simulation results validate the analytical results and show the superior performance of DM-MIMO-based techniques compared to CMMC and SSC modes for improving the data rates of individual users.

Fluvial connectivity in the Brazilian semi-arid, Araripe sedimentary plateau

This article aims at identifying and describing short-term connectivity scenarios in a semi-arid watershed in northeastern Brazil. The emergence of different decadal connectivity patterns is discussed, addressing the role of recent climatic inputs in triggering cut-and-fill erosive-depositional patterns within a valley bottom. A confluence stretch between a tributary and the stem channel was selected in order to analyze changes in connectivity and dysconnectivity patterns in response to climatic inputs from 2004 to 2014. The climatic behavior was assessed using the Rainfall Anomaly Index, which established dry and wet years, hence highlighting the relationship between connected and unconnected channel phases and a decennial precipitation dynamic. The years of 2004, 2008 and 2011 stand out as the rainiest and with the highest RAI values, in contrast to 2012, with a total rainfall of less than 600 mm and a RAI value below -2. The discrepancy between the 2012 rainfall total and the 2013/2014 biennium points to the relevance of this period for the reworking of alluvial deposits and connectivity resumption in the watershed. In the ephemeral drainages, connectivity is dramatically interrupted every dry season, creating fluvial disconnection. Dysconnectivity is characteristic of tropical semi-arid fluvial environments, but at landscape scale it may attest to the longevity of dry conditions, hence shall be addressed at time intervals ranging from a few days to thousands and tens of thousands of years.

Multi-round auction-based resource allocation for edge computing: Maximizing social welfare

5G NR-Lite technology still suffers from inadequate response to low latency, high speed and mass connectivity scenarios in high mobility conditions. Building on 4G and 5G, 6G networks will work to more fully enable the Internet of Things (IoT) paradigm, working to provide secure wireless computing for the digital world. In order to ensure the resource utilization of edge computing, it is usually necessary to allocate resources among mobile devices. Many scholars have applied auction theory to resource allocation in mobile edge networks and achieved specific results in recent years. However, most existing studies unilaterally consider the utility of users or edge servers, and few jointly consider social welfare and user selection priorities, which cannot maximize the overall utility of mobile edge networks. This paper proposes a multi-round auction algorithm that pursues maximizing social welfare in a multi-user–multi-server network. The algorithm combines combinatorial auction with double auction, allowing users and servers to adjust their bids and asking prices in each auction round. To enhance the generalizability of the study, the algorithm takes full advantage of the flexibility of the auction mechanism and considers the users’ demand for different servers, the variability of bidding strategies, and the variability of the total resources of edge servers so that the users can obtain resources with higher priority and improve social welfare. To ensure that social welfare is maximized, we adopt the Vickrey–Clarke–Groves (VCG) payment mechanism and consider the effect of network externalities. Extensive comparative experiments show that the algorithm can effectively improve social welfare and maximize the overall benefits of the mobile edge network.

Integrating Hydrological Connectivity in a Process–Response Framework for Restoration and Monitoring Prioritisation of Floodplain Wetlands in the Ramganga Basin, India

Floodplain wetlands are critical for sustaining various ecological and hydrological functions in a riverine environment. Severe anthropogenic alterations and human occupation of floodplains have threatened these wetlands in several parts of the world. A major handicap in designing sustainable restoration and monitoring strategies for these wetlands is the lack of scientific process-based understanding and information on the basin-scale controls of their degradation. Here, we offer a novel approach to integrate the connectivity of the wetlands with the surrounding landscape along with other attributes such as stream density, hydrometeorological parameters, and groundwater dynamics to explain their degradation and then to prioritise them for restoration and monitoring. We hypothesise that the best possible connectivity scenario for the existence of a wetland would be if (a) the wetland has a high connectivity with its upslope area, and (b) the wetland has a low connectivity with its downslope region. The first condition ensures the flow of water into the wetland and the second condition allows longer water residence time in the wetland. Accordingly, we define four connectivity-based wetland health scenarios—good, no impact, bad, and worst. We have implemented the proposed method in 3226 wetlands in the Ramganga Basin in north India. Further, we have applied specific selection criteria, such as distance from the nearest stream and stream density, to prioritise the wetlands for restoration and monitoring. We conclude that the connectivity analysis offers a quick process-based assessment of wetlands’ health status and serves as an important criterion to prioritise the wetlands for developing appropriate management strategies.

Connectivity modelling in conservation science: a comparative evaluation

Landscape connectivity, the extent to which a landscape facilitates the flow of ecological processes such as organism movement, has grown to become a central focus of applied ecology and conservation science. Several computational algorithms have been developed to understand and map connectivity, and many studies have validated their predictions using empirical data. Yet at present, there is no published comparative analysis which uses a comprehensive simulation framework to measure the accuracy and performance of the dominant methods in connectivity modelling. Given the widespread usage of such models in spatial ecology and conservation science, a thorough evaluation of their predictive abilities using simulation techniques is essential for guiding their appropriate and effective application across different contexts. In this paper, we address this by using the individual-based movement model Pathwalker to simulate different connectivity scenarios generated from a wide range of possible movement behaviours and spatial complexities. With this simulated data, we test the predictive abilities of three major connectivity models: factorial least-cost paths, resistant kernels, and Circuitscape. Our study shows the latter two of these three models to consistently perform most accurately in nearly all cases, with their abilities varying substantially in different contexts. For the majority of conservation applications, we infer resistant kernels to be the most appropriate model, except for when the movement is strongly directed towards a known location. We conclude this paper with a review and interdisciplinary discussion of the current limitations and possible future developments of connectivity modelling.