Buffer Length Research Articles

Genetic algorithm optimization of langevin thermostat and thermal properties of graphene-aluminum nanocomposites: a molecular dynamics

Abstract The thermal properties of a laminated structure of graphene-coated aluminum composite nanomaterial were investigated through non-equilibrium molecular dynamics (NEMD) simulations to address the problem of temperature deviation in the thermostat volume applied. This paper presents a new insight into the best values of timestep and Langevin thermostat damping parameters for each atom in the nanomaterial with different size configurations using the genetic algorithm (GA) method by considering the timestep and thermostat damping parameters for each atom type, as well as the thickness of the nanomaterial, the thermostat, buffer, and heat flow lengths. The initial population results indicate that the thermostat temperature deviation increases with higher thermostat damping coefficients and timestep. However, the deviation decreases significantly with increased heat flow and thermostat lengths. Variations in buffer length and aluminum thickness do not have a significant effect on temperature. The application of a GA for optimization leads to a decrease in thermostat temperature deviation. The optimized parameters resulted in better thermostat temperature deviations when analyzing the temperature, aluminum thickness, and both buffer and thermostat lengths. Additionally, the thermal conductivity of aluminum-graphene nanomaterial decreases with increasing temperature, buffer length, and aluminum thickness, but increases by up to 9.85% with increasing thermostat length.

Nutrient and sediment retention by riparian vegetated buffer strips: Impacts of buffer length, vegetation type, and season

Farming activities in watersheds can significantly increase sediment and nutrient loads, threatening aquatic ecosystems. Riparian vegetated buffer strips (RVBS) have emerged as a promising strategy to capture and sequester these pollutants. While previous research highlights the effectiveness of woody vegetation in reducing nutrient loads, an essential knowledge gap exists regarding the comparative efficiency of different vegetation types (woody, shrubs and grasses) in trapping nutrients and reducing transport from watersheds to waterbodies. To address this knowledge gap, a multi-year field investigation was carried out with the following objectives: (a) to evaluate the influences of RVBS length (2, 5, 10, 14 and 18 m) and vegetation type (woody, grass, shrub, and no buffer) on reducing nutrient and sediment concentrations in surface runoff across different seasons, and (b) to compare soil microbiological characteristics among the different buffer treatments. The findings reveal significant insights into the effectiveness of different RVBS configurations in mitigating nutrient and sediment concentrations. Woody RVBS demonstrated exceptional resilience, particularly during snowmelt periods, significantly outperforming non-woody vegetation types. In contrast, RVBS with grasses often showed limited effectiveness in reducing nutrient levels in overland flow. Importantly, the results highlight the remarkable capacity of woody vegetation within 18-metre RVBS, exhibiting 100% sediment trapping efficiency (STE), 89.8% total nitrogen (TN) removal, and 92.82% total phosphorus (TP) removal. Comparatively, grass treatments displayed lower efficiencies (STE:86%, TN:72.3%, TP: 77.8%), followed by shrubs (STE: 62%, 43.9% and 48.8%), and the control with no buffer (STE: 27%, TN: 20.6%, TP: 23.17%). The superior efficacy of woody vegetation can be attributed to its deep rooting systems and higher organic matter content in the soil compared to grass and shrub areas. Additionally, woody treatments exhibited higher levels of organic carbon, nutrients, and microbial activities in the soil, indicating greater potential for nutrient cycling. High-throughput sequencing revealed shifts in bacterial community diversity and biomass in vegetation treatments compared to the control bare soil. These findings underscore the potential of RVBS incorporating woody vegetation or grass to mitigate nutrient and sediment concentrations by enhancing infiltration and plant uptake processes. Nevertheless, RVBS effectiveness may vary, particularly during snowmelt periods. Future studies should explore RVBS design and management approaches in natural systems, with a specific focus on optimizing the effectiveness of grass treatments during snowmelt.

Roots of urban equality: Are low-income neighborhoods paying more for street trees?

This study investigates the potential influence of street trees on the single-family housing market in Seattle between 2010 and 2020, specifically examining how the relationship between the number of street trees and housing prices varies across neighborhoods with different income levels. It further discusses the policy implications for promoting environmental justice in urban tree-planting endeavors. We used Seattle’s street tree planting data from the 1950s to 2020 and housing transaction records between 2010 and 2020, covering 61,055 housing transactions. Four buffer lengths were applied to associate street trees with individual housing parcels and transactions. The relationship between street trees and the single-family housing market was evaluated through linear regressions and spatial lag models using Spatial Two-stage Least Squares (S2SLS). An interaction term between the number of street trees and neighborhood income was also used to examine the potential varying associations across neighborhoods with different income levels. Subsequently, we calculated the marginal implicit price (MIP) across the city. The results indicate that street trees are valued more in the housing market of lower-income areas. The findings underscore the importance of community-based intervention in the tree-planting process, especially in lower-income areas, to promote environmental justice and enhance neighborhood greenery.

Understanding neural network tuned Langevin thermostat effect on predicting thermal conductivity of graphene-coated copper using nonequilibrium molecular dynamics simulations

Copper has always been used in thermoelectric applications due to its extensive properties among metals. However, it requires further improving its heat transport performance at the nanosized applications by supporting another high thermal conductivity material. Herein, copper was coated with graphene, and the neural network fitting was employed for the nonequilibrium molecular dynamics simulations of graphene-coated copper nanomaterials to predict thermal conductivity. The Langevin thermostat that was tuned with a neural network fitting (NNF), which makes up the backbone of deep learning, generated the temperature difference between the two ends of the models. The NNF calibrated the Langevin thermostat damping constants that helped to control the temperatures precisely. The buffer and thermostat lengths were also analyzed, and they have considerable effects on the thermostat temperatures and a significant impact on the thermal conductivity of the graphene-coated copper. Regarding thermal conductivity, the four different shapes of vacancy defect concentrations and their locations in the graphene sheets were further investigated. The vacancy between the thermostats significantly decreases the thermal conductivity; however, the vacancy defect in thermostats does not have a similar effect. When the graphene is placed between two copper blocks, the thermal conductivity decreases drastically, and it continues to drop when the sine wave amplitude on the graphene sheet increases.

A Trust and Node Capability Model for Reliable and Secure MANET Communication

The Mobile Ad-hoc Network (MANET) is a rapidly deployable network. That is valuable for industrial and domestic applications due to flexible, mobile, and wireless communication. But the network is constrained with resources and security. In this paper, we are presenting a node capability based trusted routing named TNC-AODV for MANET. It is a hybrid approach for maintaining route reliability and security. The model is composed of the property of node capability and Trust. The Node capability is defined by the quality of service parameters like remaining energy, available bandwidth, buffer length, and mobility pattern. The aim is to ensure the discovery of reliable routes. Additionally, the trust is implemented by using a local and global trust for securing the network. The TNC-AODV is implemented through modification of AODV routing. That routing technique has been tested on three security threats namely Black-hole, wormhole, and DOS flooding attack. The simulation has been carried out using the NS2 simulator. The experimental results demonstrate that TNC-AODV provides security against attacks. Additionally, improve the packet delivery ratio, and throughput. Finally, the possible and feasible future extension of the work has also been proposed.

A fast permutation entropy for pulse rate variability online analysis with one-sample recursion.

Pulse rate variability (PRV) signals are extracted from pulsation signal can be effectively used for cardiovascular disease monitoring in wearable devices. Permutation entropy (PE) algorithm is an effective index for the analysis of PRV signals. However, PE is computationally intensive and impractical for online PRV processing on wearable devices. Therefore, to overcome this challenge, a fast permutation entropy (FPE) algorithm is proposed based on the microprocessor data updating process in this paper, which can analyze PRV signals with single-sample recursive. The simulation data and PRV signals extracted from pulse signals in "Fantasia database" were utilized to verify the performance and accuracy of the improved methods. The results show that the speed of FPE is 211 times faster than PE and maintain the accuracy of algorithm (Root Mean Squared Error=0) for simulation data with a length of 10,000 samples and embedded dimension m=5, time delay τ=5, buffer length Lw=512. For the RRV signals with 3000∼5000 samples, the result show that the consumption of FPE is less than 0.2s, which is 175 times faster than PE. This indicates that FPE has better application performance than PE. Furthermore, a low-cost wearable signal detection system is developed to verify the proposed method, the result show that the proposed method can calculate the FPE of PRV signal online with single-sample recursive calculation. Subsequently, entropy-based features are used to explore the performance of decision trees in identifying life-threatening arrhythmias, and the method resulted in a classification accuracy of 85.43%. It can therefore be inferred that the proposed method has great potential in cardiovascular disease.

A deep learning based latency aware predictive routing model for network‐on‐chip architectures

SummaryNetwork on Chip (NoC) is an evolving platform for communications related applications, which are executed on a single silicon chip. There are several routing models in NoC architectures, but the accuracy of these models is limited, and the existing models are degraded because of over and under fitting issues. This research introduces the new deep learning‐based latency aware predictive routing model for on‐chip networks to route packets with better performance and power efficiency. The deep learning model used in this research is a new convolutional residual gated recurrent unit (CRGRU) with queuing theory. Moreover, the source and channel queuing delay is comprised of features to learn spatial and sequential information that improves the overall prediction accuracy. This router is modified by the intrusion of the Router States Monitor unit and the CRGRU hardware engine. The work is executed using the Xilinx platform, and the performance measures like latency and throughput are obtained by varying the network size as , , and and also varying the buffer space and length as , , and , respectively. In addition, the squared correlation coefficient (SCC) and normalized root mean square error (NRMSE) are evaluated and compared with existing learning models to validate the proposed model.

Realistic assessment of transport protocols performance over LEO-based communications

We study the performance exhibited by the transport protocols, Transport Control Protocol (TCP) and QUIC, over realistic satellite networks. We propose a novel methodology, which combines real implementation (exploiting virtualization techniques) and simulation, to carry out systematic and repetitive experiments. We modify the default operation of the ns-3 framework and we integrate the dynamism that characterizes satellite communication links, particularly Low Earth Orbit (LEO). We carry out a thorough assessment over different setups, changing the operating frequency band and packet buffer lengths. In addition, we ascertain the impact of using the multi-streaming feature that QUIC integrates. The results show that QUIC yields lower delays than TCP, although it might suffer from higher jitter in particular setups. In addition, the results evince that using multiple streams in QUIC does not yield a relevant gain for the default Round-Robin (RR) scheduler. We propose more appropriate scheduling strategies, which are able to yield better performances with unbalanced traffic. Even if the behavior of transport protocols over non-terrestrial-networks might not be always appropriate, the obtained results evince that QUIC can definitively bring benefits when compared to TCP. Furthermore, we have shown that optimal scheduling policies yields a fairer performance when using multiple flows, having unbalanced traffic loads.

Improving UE Energy Efficiency Through Network-Aware Video Streaming Over 5G

Adaptive Bitrate (ABR) Streaming over cellular networks has been well studied in the literature; however, existing ABR algorithms primarily focus on improving the end-user Quality of Experience (QoE) while ignoring the resource consumption aspect of the underlying device. Consequently, proactive attempts to download video data to maintain the user’s QoE often impact the battery life of the underlying device unless the download attempts are synchronized with the network’s channel condition. In this work, we develop EnDASH-5G – a wrapper over the popular DASH-based ABR streaming algorithm, which establishes this synchronization by utilizing a network-aware video data download mechanism. EnDASH-5G utilizes a novel throughput prediction mechanism for 5G mmWave networks by upgrading the existing throughput prediction models with a transfer learning-based approach, leveraging publicly available 5G datasets. It then exploits deep reinforcement learning to dynamically decide the playback buffer length and the video bitrate using the predicted throughput. This ensures that the data download attempts get synchronized with the underlying network condition, thus saving the device’s battery power. From a thorough evaluation of EnDASH-5G, we observe that it achieves a near 30.5% decrease in the maximum energy consumption than the state-of-the-art Pensieve ABR algorithm while performing almost at par in term of QoE.

Mapping stream and floodplain geomorphometry with the Floodplain and Channel Evaluation Tool

AbstractBroad‐scale mapping of stream channel and floodplain geomorphic metrics is critical to improve the understanding of geomorphic change, biogeochemical processes, riverine habitat quality, and opportunities for management intervention. The Floodplain and Channel Evaluation Tool (FACET) was developed to provide an open‐source tool for automated processing of digital elevation models (DEMs) to generate regional‐scale estimates of bank height, channel width, floodplain width, and a suite of other fluvial geomorphic dimensions that can be summarized at the stream reach‐ or catchment‐scale. FACET was tested on 3‐m DEMs covering the Delaware River watershed and 85% of the Chesapeake Bay watershed in the United States (U.S.) and on 1‐m DEMs for a subset of the study area. Accuracy was assessed from data collected at 67 field sites in the study area. FACET successfully measured geomorphometry for over 270,000 stream reaches (88% of streams attempted) in the study area. Factors that reduced the ability of FACET to accurately estimate geomorphic metrics included errors in DEM hydro‐conditioning, gradually sloping banks, incised stream channels, and the use of fixed input parameters to define buffer lengths. Even with these limitations, FACET was able to map regional patterns in stream and floodplain geomorphometry providing a robust dataset that can enhance modeling and management efforts throughout the mid‐Atlantic region, U.S.

Cross-layer cooperative offloading in vehicular edge computing networks

Vehicular edge computing (VEC) uses the computing resources of edge devices to complete tasks for complex calculations and time-sensitive requirements in vehicular networks. However, there are some problems with the current task offloading, such as simplifying the communication mechanism at the media access control (MAC) layer and ignoring the traffic characteristics at the application layer. To address the problems, we first model the transmission mechanism of MAC and the packet queuing delay for IEEE 802.11p. Then, we propose a cross-layer cooperative offloading (CLCO) algorithm based on rate matching between the application layer and the MAC layer. The algorithm evaluates the network load by detecting the queue length of the vehicular sending buffer. The application rate adopts the multiplicative-decrease strategy to reduce the end-to-end packet delay when the queue length exceeds a queue threshold. Alternatively, the application rate adopts the additive-increase strategy to use MAC bandwidth adequately when the queue length is lower than the queue threshold. NS simulations show that the proposed CLCO algorithm reduces the delay in task offloading, while the amount of offloading data and the packet delivery rate are satisfactory.

Dynamic force identification in milling based on IRLS using acceleration signals

Measurement of cutting force is important for in-process monitoring, control and optimization. However, it is very difficult to measure the cutting force directly using invasive sensor in industrial scene. This paper develops an infinite-time recursive identification method of cutting force under finite buffer length condition, called the improved recursive least squares (IRLS) method. A cutting force identification framework based on time-domain convolutional theory is introduced, where the cutting force is identified using the acceleration signal easily obtained from industrial scene. Then, the decay characteristic of the impulse response function and the recursive characteristic of the transfer matrix are used to improve the recursive least squares method. The IRLS method overcomes the data saturation problem of the recursive least squares method and can identify the cutting force in infinite time. To reduce the error amplification of the proposed cutting force identification method, the averaged transfer function is used to determine the length of the buffer. In addition, the compensated values of the Kistler 9129AA dynamometer measurement results are used as a reference. Finally, several sets of milling experiments are performed to verify the effectiveness and rationality of the IRLS method.

Occipital artery-posterior inferior cerebellar artery bypass: a cadaveric feasibility study

PurposeTo demonstrate that occipital artery (OA)-p1 posterior inferior cerebellar artery (PICA) bypass can be an alternative for complex posterior circulation aneurysms.MethodsA far-lateral approach to craniotomy was performed on 20 cadaveric specimens, and the OA was obtained 'in-line.' Its length, diameter, and the number of p1/p2 and p3 segmental perforators were determined, and the relationship between the caudal loop and cerebellar tonsil position was also assessed. The distance between the PICA’s origin and the cranial nerve XI (CN XI), the buffer length above the CN XI after dissection, the OA length required to complete the OA-p1/p3 PICA bypass, and the p1 and p3 segment diameters were all measured. A bypass training practical scale (TSIO) was used to evaluate the quality of the anastomosis.ResultsAll specimens underwent OA-p1 PICA end-to-end bypass and had favorable results for the TSIO score, 15 sides underwent OA-p3 PICA end-to-side bypass, and the other bypass protocols were less common. The buffer length above the CN XI after dissection, the distance between the PICA’s origin and the CN XI, and the first perforator were all of sufficient length. The direct length of the OA needed to complete the OA-p1 PICA end-to-end bypass was significantly less than the available length and the OA-p3 PICA end-to-side bypass, with the OA matching the p1 segment diameter. The number of p1 perforators was less than that of p3, and the OA diameter was equal to that of the p1 segment.ConclusionOA-p1 PICA end-to-end bypass is a feasible alternative in cases in which p3 segment has high caudal loops or anatomic anomalies.

Microsurgical anatomical vascular study of the PICA-PICA bypass

ObjectiveTo assess the posterior inferior cerebellar artery (PICA)-PICA bypass possibility. MethodsFifteen adult cadaver heads were used for surgical simulation, and the far-lateral approach was used to expose the surgical field. The bilateral PICA course, diameter, and perforators were observed and measured to evaluate the possibility of a PICA-PICA bypass. ResultsThe PICA-PICA bypass was performed in seven (46.7 %) of the 15 specimens; the procedure was performed easily in three specimens, a little difficult in two, and was difficult in two specimens because of the relationship between the tonsil and the short parallel length of the bilateral tonsillomedullary (p3) segment. In eight (53.3 %) of the 15 specimens, PICA-PICA bypass was not feasible for reasons including 1) the caliber of the bilateral p3 was unmatched for bypass, 2) the distance of bilateral p3 in the midline was > 7 mm, 3) the middle segment of p3 perforating direct arteries limited the buffer length, and 4) single caudal loops. ConclusionThe possibility of PICA-PICA bypass was determined by the proximity of the bilateral p3, caliber match, and mobilization of the bilateral caudal loop due to the perforators. The difficulty of the PICA-PICA bypass mainly depends on the relationship between the cerebellar tonsil and the parallel length of the bilateral PICA in the midline.

Effectiveness of vegetative filter strips for sediment control from steep construction landscapes

Sediment carried off-site by runoff poses a significant water quality threat to receiving water bodies. Best management practices (BMPs) are commonly applied to reduce the impact of runoff on surface water quality. Vegetative filter strips (VFS) are low-cost BMP that often provide significant runoff mitigation in agricultural and urban settings with moderate slopes (<15 %). However, the runoff mitigation effectiveness of VFS under steep, short slopes like those typical of roadway construction is unknown. This study aimed to systematically investigate the efficacy of short (<5 m) and sparse VFS on steep slopes (>15 %). Field experiments were conducted on a steep hillslope (3H:1V, 33.3 %) where three VFS lengths (1.52 m, 3.05 m, and 4.57 m) were monitored during runoff events (10-y, 6-hr) to measure runoff delivery ratio (RDR) and sediment delivery ratio (SDR). The experimental results showed reductions of 60–98 % in runoff and 73–98 % in sediment, confirming the potential efficiency of this BMP in high-sloped conditions like a roadway construction site under certain conditions. The Vegetative Filter Strips Modeling System (VFSMOD) was successfully tested against the observed field values (NSE = 0.98 and 0.89 for RDR and SDR, respectively) and used to explore more scenarios. A wide range of roadway construction settings (VFS size, slope, vegetation density) was investigated with VFSMOD, and two simplified statistical regression equations and nomographs were developed to quantify RDR and SDR based on the simulation results. The proposed equations simplify the complexity of VFS design and can be readily generalized to other locations (rainfall/runoff, soil, vegetation) after reparametrizing coefficients in regression equations based on VFSMOD simulations to obtain new nomographs as a design aid to determine the minimum buffer length for a particular site.

Usage of LDPC Codes in a Gilbert Channel

Although low-density parity-check (LDPC) codes in modern communication standards have been extensively studied over a memoryless channel, their burst error correction capacity in channels with memory has yet to be thoroughly analyzed. The conventional approach to transmission in channels with memory uses interleaving within a buffer of several codewords. However, such an approach reduces the efficiency of the redundancy embedded by the error-correcting code. It is known from information theory that considering channel memory during decoding allows the transmission rate to be increased. An evaluation of the decoding error probability of different types of low-density parity-check codes in channels with memory is presented along with estimates of minimum distance and burst error correction capability for the considered codes. The decoding error probability is estimated for conventional decoding with deinterleaving and decoding taking channel memory into account. The decoding error probability is estimated for several parameters of a channel with memory and different buffer lengths. The obtained results reveal the absence of the unique decoding approach for all parameters of the channel with memory. The best decoding error probability is determined by the degree of channel memory correlation.

Buffer scheme for aero-performance deterioration caused by trains passing bilateral vertical noise barriers with crosswinds

Bilateral vertical noise barriers have been widely used along high-speed railway lines in coastal cities where typhoons are frequent. When a high-speed train (HST) enters (or exits) a noise barrier under strong crosswind conditions, its running safety will be more severely tested because of the instantaneous switching of aerodynamic environment. Installing a buffer structure at the end of the noise barrier is necessary to ensure the running safety of HSTs. In this study, two types of aerodynamic buffer structures (triangle and fence types) for the end of the noise barrier are proposed. The buffering effects of the two structures on the sudden change amplitude of the aerodynamic load of the carriage are compared by using an improved delayed detached eddy simulation method. The difference in the influence of the two buffer structures on the aerodynamic responses of the carriage is discussed by using a wind–train–bridge coupling dynamic response calculation method. The buffer mechanisms of the two structures are revealed in terms of the flow field. Results show that the buffering effect of fence type is superior triangle type, and the buffer length of 4 L is the most reasonable.

A Novel Dynamic Bit Rate Analysis Technique for Adaptive Video Streaming over HTTP Support

Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nature of wireless communication channels. State-of-the-art techniques, such as Dynamic Adaptive Streaming over HTTP (DASH), support the streaming of stored video, but they suffer from the challenge of live video content due to fluctuating bit rate in the network. In this work, a novel dynamic bit rate analysis technique is proposed to model client-server architecture using attention-based long short-term memory (A-LSTM) networks for solving the problem of smooth video streaming over HTTP networks. The proposed client system analyzes the bit rate dynamically, and a status report is sent to the server to adjust the ongoing session parameter. The server assesses the dynamics of the bit rate on the fly and calculates the status for each video sequence. The bit rate and buffer length are given as sequential inputs to LSTM to produce feature vectors. These feature vectors are given different weights to produce updated feature vectors. These updated feature vectors are given to multi-layer feed forward neural networks to predict six output class labels (144p, 240p, 360p, 480p, 720p, and 1080p). Finally, the proposed A-LSTM work is evaluated in real-time using a code division multiple access evolution-data optimized network (CDMA20001xEVDO Rev-A) with the help of an Internet dongle. Furthermore, the performance is analyzed with the full reference quality metric of streaming video to validate our proposed work. Experimental results also show an average improvement of 37.53% in peak signal-to-noise ratio (PSNR) and 5.7% in structural similarity (SSIM) index over the commonly used buffer-filling technique during the live streaming of video.

Problems of course design management in the formation of professional competencies of IT specialists

The authors analyze the relevance of the object of research, which is dictated by the ever-increasing role of competence-based education both at the stage of training at a higher educational institution and in the learning process throughout the professional path of IT specialists. The article is devoted to the specifics of planning and managing the process of creating course projects by future IT specialists during their studies at a higher educational institution. The emphasis in the article is on the aspect that the process of managing the direction of course design is in many ways similar to the process of creating IT products, which undoubtedly needs to be taken into account when determining the difficulties and features of managing the direction of course design of IT specialists. During the research, the main objective and subjective causes of difficulties during the process of creating a course project for students of IT specialties were identified, and ways of leveling them were determined. As a result of the analysis of project management techniques and practices, the critical chain method is identified as an effective tool for managing the planning and implementation of tasks in project activities. The purpose of the study is to study methods and practices in project activities and adapt their features to the practice of developing future IT specialists course projects as mini IT projects with a minimum set of requirements and participants, which can undoubtedly be considered as a mini model of a real IT project. The article analyzes the factors influencing the unfavorable course of execution and completion of course projects, highlights the main artifacts of course design, determines the length of the project buffer by the critical chain method. Based on the results of the study, practical recommendations are given for the effective planning and management of course design of future IT specialists.

Riparian buffers: Disrupting the transport of E. coli from rural catchments to streams

High levels of E. coli and associated faecal microbes in waterways as a result of agricultural and residential land use can pose environmental, human health, and economic risks. This study aims to understand the impacts of land use, climatic variables, and riparian buffers on in-stream E. coli concentrations. Flow, temperature, and E. coli were monitored during three sampling campaigns within eleven independent catchments. These catchments have varying land use and extents of riparian buffer coverage. Results showed that catchments with predominantly agricultural and residential land uses (average = 349.7 MPN/100 mL) had higher E. coli concentrations than predominantly forested catchments (average = 111.8 MPN/100 mL). However, there were no statistically significant differences in E. coli concentrations between the agricultural and residential land uses. Riparian buffers appear to reduce E. coli concentrations in streams, as indicated by significant negative correlations between in-stream E. coli concentrations with the riparian buffer areal coverage (Pearson's r = -0.95, Spearman's ρ = -0.90) and the ratio of buffer length to stream length (Pearson's r = -0.87, Spearman's ρ = -0.90). We find that riparian buffers potentially disrupt transport pathways that govern E. coli movement, which in-turn can affect the concentration-discharge relationship. This reinforces the importance of protecting and restoring riparian buffers along drainage lines in agricultural and rural-residential catchments to improve downstream microbial water quality.