Waveform-Domain Adaptive Matched Filtering: A Novel Approach to Suppressing Interrupted-Sampling Repeater Jamming

The inadequate adaptability to flexible interference scenarios remains an unresolved challenge in the majority of techniques utilized for mitigating interrupted-sampling repeater jamming (ISRJ). Matched filtering system based methods is desirable to incorporate anti-ISRJ measures based on prior ISRJ modeling, either preceding or succeeding the matched filtering. Due to the partial matching nature of ISRJ, its characteristics are revealed during the process of matched filtering. Therefore, this paper introduces an extended domain called the waveform domain within the matched filtering process. On this domain, a novel matched filtering model, known as the waveform-domain adaptive matched filtering (WD-AMF), is established to tackle the problem of ISRJ suppression without relying on a pre-existing ISRJ model. The output of the WD-AMF encompasses an adaptive filtering term and a compensation term. The adaptive filtering term encompasses the adaptive integration outcomes in the waveform domain, which are determined by an adaptive weighted function. This function, akin to a collection of bandpass filters, decomposes the integrated function into multiple components, some of which contain interference while others do not. The compensation term adheres to an integrated guideline for discerning the presence of signal components or noise within the integrated function. The integration results are then concatenated to reconstruct a compensated matched filter signal output. Simulations are conducted to showcase the exceptional capability of the proposed method in suppressing ISRJ in diverse interference scenarios, even in the absence of a pre-existing ISRJ model

Development of Rapid Detection and Genetic Characterization of Salmonella in Poultry Breeder Feeds

Salmonella is a leading cause of foodborne illness in the United States, with poultry and poultry products being a primary source of infection to humans. Poultry may carry some Salmonella serovars without any signs or symptoms of disease and without causing any adverse effects to the health of the bird. Salmonella may be introduced to a flock by multiple environmental sources, but poultry feed is suspected to be a leading source. Detecting Salmonella in feed can be challenging because low levels of the bacteria may not be recovered using traditional culturing techniques. Numerous detection methodologies have been examined over the years for quantifying Salmonella in feeds and many have proven to be effective for Salmonella isolation and detection in a variety of feeds. However, given the potential need for increased detection sensitivity, molecular detection technologies may the best candidate for developing rapid sensitive methods for identifying small numbers of Salmonella in the background of large volumes of feed. Several studies have been done using polymerase chain reaction (PCR) assays and commercial kits to detect Salmonella spp. in a wide variety of feed sources. In addition, DNA array technology has recently been utilized to track the dissemination of a specific Salmonella serotype in feed mills. This review will discuss the processing of feeds and potential points in the process that may introduce Salmonella contamination to the feed. Detection methods currently used and the need for advances in these methods also will be discussed. Finally, implementation of rapid detection for optimizing control methods to prevent and remove any Salmonella contamination of feeds will be considered

Safe Routes to Play? Pedestrian and Bicyclist Crashes Near Parks in the Los Angeles Region

Rationale: Areas near parks may present active travelers with higher risks than in other areas due to the confluence of more pedestrians and bicyclists, younger travelers, and the potential for increased numbers of motor vehicles. These risks may be amplified in low-income and minority neighborhoods due to generally higher rates of walking or lack of safety infrastructure. Objectives: We pursued three research objectives: (1) to determine if pedestrian and bicycle crashes occur at higher rates in park-adjacent neighborhoods compared to the rest of the study area; (2) to identify if demographic characteristics predict active crash risk after controlling for population and the rate of active trips; and (3) to assess if there is an amplified effect of park proximity for active crash risk in low-income and minority neighborhoods after controlling for population and the rate of active trips. Methods: With negative binomial regression modeling techniques, we used ten years of geolocated pedestrian and bicyclist crash data and a quarter mile (~400 meter) buffer around public parks to assess the risk of active travel near parks. We controlled for differential exposures to active travel risks using travel survey data. Measurements: Quarter-mile network buffers were designated around parks from the Green Visions Plan for 21st Century California in 2249 census tracts. Crashes came from the 90,846 pedestrian and bicyclist injuries and fatalities from the Statewide Integrated Traffic Reporting System, and active travel was predicted using travel data from 9135 households that participated in the Southern California Association of Governments 2001 Travel and Congestion Survey. These data were combined with demographic and income data from the U.S. Census and traffic density predictions. Results: The ratio of active crashes per 100,000 population within the quarter-mile park buffer to those outside is 1.52. The increased risk of crash for active travelers near parks remained after adjusting for varying rates of active travel in different census tracts. Minority and low-income residents of the study area are more likely to walk or bicycle than White and higher-income residents. This higher risk near parks is amplified in neighborhoods with high proportions of minority and low-income people. Higher traffic levels are highly predictive of active crashes.Conclusions: Active travelers accessing parks may lack a safe route to places for play. The socioeconomic modification of active crashes near parks found in this study is supported by existing research showing disparities in park access and higher active travel risks in low-income and minority neighborhoods

Safe Routes to Play? Pedestrian and Bicyclist Crashes Near Parks in the Los Angeles Region

Rationale: Areas near parks may present active travelers with higher risks than in other areas due to the confluence of more pedestrians and bicyclists, younger travelers, and the potential for increased numbers of motor vehicles. These risks may be amplified in low-income and minority neighborhoods due to generally higher rates of walking or lack of safety infrastructure. Objectives: We pursued three research objectives: (1) to determine if pedestrian and bicycle crashes occur at higher rates in park-adjacent neighborhoods compared to the rest of the study area; (2) to identify if demographic characteristics predict active crash risk after controlling for population and the rate of active trips; and (3) to assess if there is an amplified effect of park proximity for active crash risk in low-income and minority neighborhoods after controlling for population and the rate of active trips. Methods: With negative binomial regression modeling techniques, we used ten years of geolocated pedestrian and bicyclist crash data and a quarter mile (~400 meter) buffer around public parks to assess the risk of active travel near parks. We controlled for differential exposures to active travel risks using travel survey data. Measurements: Quarter-mile network buffers were designated around parks from the Green Visions Plan for 21st Century California in 2249 census tracts. Crashes came from the 90,846 pedestrian and bicyclist injuries and fatalities from the Statewide Integrated Traffic Reporting System, and active travel was predicted using travel data from 9135 households that participated in the Southern California Association of Governments 2001 Travel and Congestion Survey. These data were combined with demographic and income data from the U.S. Census and traffic density predictions. Results: The ratio of active crashes per 100,000 population within the quarter-mile park buffer to those outside is 1.52. The increased risk of crash for active travelers near parks remained after adjusting for varying rates of active travel in different census tracts. Minority and low-income residents of the study area are more likely to walk or bicycle than White and higher-income residents. This higher risk near parks is amplified in neighborhoods with high proportions of minority and low-income people. Higher traffic levels are highly predictive of active crashes.Conclusions: Active travelers accessing parks may lack a safe route to places for play. The socioeconomic modification of active crashes near parks found in this study is supported by existing research showing disparities in park access and higher active travel risks in low-income and minority neighborhoods

Artificial Plant Root System Growth for Distributed Optimization: Models and Emergent Behaviors

Plant root foraging exhibits complex behaviors analogous to those of animals, including the adaptability to continuous changes in soil environments. In this work, we adapt the optimality principles in the study of plant root foraging behavior to create one possible bio-inspired optimization framework for solving complex engineering problems. This provides us with novel models of plant root foraging behavior and with new methods for global optimization. This framework is instantiated as a new search paradigm, which combines the root tip growth, branching, random walk, and death. We perform a comprehensive simulation to demonstrate that the proposed model accurately reflects the characteristics of natural plant root systems. In order to be able to climb the noise-filled gradients of nutrients in soil, the foraging behaviors of root systems are social and cooperative, and analogous to animal foraging behaviors

A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems

There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell’s pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm