Coverage Rate Research Articles

Assessing Electronic Health Records for Describing Racial and Ethnic Health Disparities: A Research Note.

The use of data derived from electronic health records (EHRs) to describe racial and ethnic health disparities is increasingly common, but there are challenges. While the number of patients covered by EHRs can be quite large, such patients may not be representative of a source population. One way to evaluate the extent of this limitation is by linking EHRs to an external source, in this case with the American Community Survey (ACS). Relying on a stratified random sample of about 200,000 patient records from a large, public, integrated health delivery system in North Carolina (2016-2019), we assess linkages to restricted ACS microdata (2001-2017) by race and ethnicity to understand the strengths and weaknesses of EHR-derived data for describing disparities. The results in this research note suggest that Black-White comparisons will benefit from standard adjustments (e.g., weighting procedures) but that misestimation of health disparities may arise for Hispanic patients because of differential coverage rates for this group.

Design and Testing of a Fruit Tree Variable Spray System Based on ExG-AABB

This paper addresses the issue of pesticide waste and low utilization rates resulting from traditional plant protection via spraying operations, which apply equal dosages to different targets or to different parts of the same target. To tackle this problem, we designed a variable fruit tree spraying system based on the ExG-AABB (excess green and axis-aligned bounding box) algorithm. We used a Kinect depth camera to capture information about the fruit tree canopy and constructed a spray flow model using pulse width modulation and variable spray control technology. Variable multi-nozzle spraying was guided by combining this canopy data. We evaluated the accuracy of each model in calculating canopy volume by comparing the coefficient of determination (R2) and root mean square error (RMSE) of the ExG-AABB with the slice convex hull method, voxel method, three-dimensional alpha-shape method, and QuickHull method. The ExG-AABB algorithm had the highest R2 value (0.9334) and the lowest RMSE value (0.0353 m3) among the five models, indicating that it most accurately reflects the true volume of the fruit tree canopy. This validates the effectiveness of the ExG-AABB algorithm in calculating canopy volume. We established a correlation model between canopy volume and spray volume, designed a canopy-adaptive layering method based on point cloud processing, and achieved precise calculation of nozzle flow. Comparative field experiments were conducted to analyze the spray coverage rate and observed flow, thereby evaluating the spraying effect of this variable spraying system. The experimental results showed that compared to conventional continuous spraying, this variable spraying system not only achieves more uniform spray coverage but also significantly reduces pesticide usage by 48.1%. Furthermore, through system optimization, the average coverage rate of the middle layer of the canopy decreased by 17.53%, effectively reducing the phenomenon of overlapping spraying from multiple nozzles and improving spraying efficiency.

Contrasting and combining transcriptome complexity captured by short and long RNA sequencing reads.

Mapping transcriptomic variations using either short- or long-reads RNA sequencing is a staple of genomic research. Long reads are able to capture entire isoforms and overcome repetitive regions, while short reads still provide improved coverage and error rates. Yet how to quantitatively compare the technologies, can we combine those, and what may be the benefit of such a combined view remain open questions. We tackle these questions by first creating a pipeline to assess matched long and short reads data using a variety of transcriptome statistics. We find that across datasets, algorithms, and technologies, matched short reads data detects roughly 30% more splice junctions such that 10-30% of the splice junctions included at 20% or more by short reads are missed by long reads. In contrast, long reads detect many more intron retention events and can detect full isoforms, pointing to the benefit of combining the technologies. We introduce MAJIQ-L, an extension of the MAJIQ software to enable a unified view of transcriptome variations from both technologies and demonstrate its benefits. Our software can be used to assess any future long-read technology or algorithm, and combine it with short reads data for improved transcriptome analysis.

Enhancing seniors' dental care access: Analyzing the impact of government insurance in Canada.

A crucial policy question for the government is whether publicly funded insurance programs effectively improve access to care. Using 2015 and 2018 Canadian Community Health Survey (CCHS) data, we first estimated the effect of government dental insurance for seniors on promoting regular care access and lowering cost barrier. When controlling for individual heterogeneity, we found that having government coverage is associated with significantly lower probability of reporting avoidance of dental care due to cost compared to having no coverage. This effect is comparable with other types of insurance. However, the impact of the government program on regular access to dental care is modest. Secondly, using a portion of data collected in Alberta, we found that the government plan does not increase the overall coverage rate. Moreover, switching from an employer-based plan to government-provided coverage for seniors reduces the probability of regular access to care and increases the probability of experiencing cost barrier. This finding indicates that without expansion of overall coverage rate, the current government dental program may not be generous enough to offset the negative impact of leaving the employer-based plan.

The Impact of Vaccination Programs on the Prevention of Infectious Disease Tuberculosis: A Case Study with Gasabo District in Rwanda

Introduction Tuberculosis (TB) remained a significant public health challenge globally, including in Gasabo District, Rwanda. Vaccination programs, particularly Bacille Calmette-Guérin (BCG) vaccination, played a crucial role in TB prevention. This study aimed to assess the impact of vaccination programs on TB prevention in Gasabo District, Rwanda, by analyzing vaccination coverage, TB incidence rates, demographic characteristics, and healthcare system factors.  Methods A retrospective cohort study design was employed, utilizing secondary data sources from healthcare facilities in Gasabo District. Data on vaccination coverage, TB incidence rates, demographic characteristics, and healthcare system factors were collected and analyzed. Statistical methods, including regression analysis and descriptive statistics, were used to assess the association between vaccination programs and TB prevention outcomes.  Findings The study found a significant association between BCG vaccination coverage and lower TB incidence rates in Gasabo District. Higher vaccination coverage was associated with reduced TB incidence rates, particularly among children. However, challenges such as incomplete vaccination data, potential confounding factors, and socio-economic disparities were identified. Despite these challenges, vaccination programs were found to be effective in reducing TB transmission and protecting vulnerable populations from TB infection.  Conclusion The findings highlighted the importance of vaccination programs in TB prevention efforts in Gasabo District, Rwanda. Strengthening vaccination coverage, enhancing healthcare system capacity, addressing socio-economic determinants, fostering collaborations, promoting research and innovation, and strengthening surveillance and monitoring systems are essential for optimizing TB prevention efforts. By implementing these recommendations, we can accelerate progress towards ending the TB epidemic and improving public health outcomes in Gasabo District and beyond.

Load-balanced offloading of multiple task types for mobile edge computing in IoT

The continuous development of mobile networks poses new challenges for end devices with limited computing power. Mobile or multi-access edge computing (MEC) has been proposed for providing the computing resources close to the end devices that need them. However, in real network environments, MEC servers also have limited computing resources that need to be shared among many devices and efficient resource allocation is critical to ensure that the limited resources are optimally used. In view of this, we propose the Balanced Offload for Multi-type Tasks (BOMT) algorithm. The tasks to be offloaded are first prioritized according to their type, size and maximum tolerable delay; then different offloading algorithms are executed for different priority tasks according to the level of the priority and the current load on the MEC server. Following which, the optimal offloading policy is determined iteratively. Simulation results show that BOMT can effectively reduce system delay, increase user coverage and offload task completion rates.

Innovations in Providing HIV Index Testing Services: A Retrospective Evaluation of Partner Elicitation Models in Southern Nigeria.

Background: This analysis aimed to evaluate the effectiveness of eliciting sexual partners from HIV-positive clients using the elicitation box model (where an HIV-positive index can report sexual contacts on paper and insert in a box for a health care provider to contact at a later time) compared to the conventional model (in which a health care provider elicits sexual contacts directly from clients) in Akwa Ibom, Southern Nigeria.Methods: Between March 2021 and April 2022, data were collected from index testing registers at 4 health facilities with a high volume of HIV clients currently on treatment in 4 local government areas in Akwa Ibom State. Primary outcome analyzed was the elicitation ratio (number of partners elicited per HIV-index offered index testing services). Secondary outcomes were the index testing acceptance (index HIV-positive clients accepted index testing service), testing coverage (partners tested for HIV from a list of partners elicited from HIV-index accepted index testing services), testing yield (index partners identified HIV positive from index partners HIV-tested), and linkage rate (index partners identified HIV positive and linked to antiretroviral therapy).Results: Of the total 2,705 index clients offered index testing services, 91.9% accepted, with 2,043 and 439 indexes opting for conventional elicitation and elicitation box models, respectively. A total of 3,796 sexual contacts were elicited: 2,546 using the conventional model (elicitation ratio=1:1) and 1,250 using the elicitation box model (elicitation ratio=1:3). Testing coverage was significantly higher in the conventional compared to the elicitation box model (P<.001). However, there was no significant difference in the testing yield (P=.81) and linkage rate using the conventional compared to elicitation box models (P=.13).Conclusion: The implementation of the elicitation box model resulted in an increase in partner elicitation compared to the conventional model. Increasing the testing coverage by implementing the elicitation box model should be considered.

Runoff and sediment reduction effects of different Paspalum wettsteinii‐planting measures on the slopes of Masson pine plantation in the red soil region of southern China

AbstractGrass‐planting measure is a crucial vegetation approach to mitigate understory soil erosion and improve ecological environment in the red soil region of southern China. This study aimed to quantify the effects of grass (Paspalum wettsteinii Hackel.)‐planting measures on runoff and sediment reduction on slopes of Masson pine plantations under rainstorm conditions. We conducted a rainfall simulation experiment at a rainfall intensity of 2.0 mm/min, comparing single strip (MT1, strip spacing: 145 cm), double strips (MT2, strip spacing: 70 cm), and triple strips (MT3, strip spacing: 45 cm) grass‐planting measures on slope surface runoff generation and soil erosion processes of the young Masson pine (MT0, no grass strip) plantation, and the bare slope (CK) was selected as the control. Results revealed that grass‐planting measures significantly decreased slope erosion parameters compared to CK and MT0. As the average grass coverage increased (MT1 from 10% to 25%, MT2 from 7.5% to 22.5%, MT3 from 7.3% to 25%), the slope surface erosion parameters under the grass‐planting measures decreased, resulting in significantly improved runoff and sediment reduction benefits. The runoff reduction effect could reach 32%, while the sediment reduction effect could reach 88%. Moreover, MT3 demonstrated superior performance over MT2 and MT1, with minimal runoff and sediment reduction effects observed for the MT0. Overall, this study suggests that grass‐planting measures, coupled with the increasing of grass coverage rates, significantly improve runoff and sediment reduction benefits on slopes in regions experiencing heavy rainfall. Among the tested configurations, MT3 emerged as most effective measure for controlling understory soil erosion in Masson pine plantations, especially when its average grass coverage rate reached 25%. These findings provide valuable insights for selecting appropriate grass‐planting strategies, as well as for understanding the underlying mechanisms of how these measures mitigate soil erosion. This scientific reference will aid in the design and implementation of soil and water conservation measures in the region.

Jamming avoidance trajectory planning and load balancing user association in mmWave UAV-assisted HetECN

Emergency communication network (ECN) can provide fast, efficient and high-capacity communication services for specific areas by using mmWave transmission and unmanned aerial vehicles (UAVs) serving as aerial base stations (ABSs) or relay nodes. Now, in order to satisfy diverse demands, ECN should support different types of nodes, access methods, and traffic distributions, which is referred to as heterogeneous ECN (HetECN). Therefore, inappropriate trajectory planning and unbalanced traffic loading can lead to UAV flight collisions and network congestion. In this article, we jointly optimize UAV jamming avoidance trajectory and user association strategy aimed to load balancing, to maximize the utilization of HetECN. Specifically, an improved artificial potential field (APF) method along with mmWave beam forming technology is used to obtain the jamming avoidance trajectory of UAVs, and the optimal deployment location of UAVs are determined based on the distribution of ground users (GUs). Subsequently, the matching game and alliance game are comprehensively used to determine the load balancing based GU-UAV associated strategy under various GU demands, thereby ensuring traffic load balancing and resource optimization allocation. In addition, altitude fine-tuning have been made to further power consumption, thereby improving overall network efficiency. Simulation results demonstrate that the proposed method can achieve the expected performance in network utilities such as coverage rate, network capacity, load balancing effect of mmWave UAV-assisted HetECNs.

Study on the Characteristics of Community Elderly Care Service Facilities Usage and Optimization Design Based on Life Cycle Theory

Abstract: As the aging population in China increases, home-based elderly care is set to become the main mode of elderly care in the future. The construction of community elderly care service facilities is receiving more and more attention from society. Currently, residential area design standards in China lack clear guidance on the spatial planning of facilities, which will lead to unreasonable facility layout planning and insufficient convenience in the utilization of facilities by the elderly. Fully considering the distribution characteristics of community elderly care service facilities and the needs of the community life circle when planning residential areas can effectively solve this problem. This study takes Xi’an City as the research object, based on the theory of life circle, through questionnaire surveys and cluster analysis, analyzes the current situation of the construction of elderly care service facilities and the characteristics of the use by the elderly. Combined with the community life circle model, the study scope is divided into community life circle levels, and put forward the planning strategies of coverage rate, number, richness, and spatial layout of the facilities, respectively. These strategies are designed to enhance the quality of life for seniors, ensuring that they have access to essential support and resources within their communities. It demonstrated that middle-aged elderly prefer community canteens and activity centers, while those with limited mobility and multiple chronic diseases have a higher demand for health service centers and community canteens. The utilization weight analysis shows that vegetable markets and supermarkets are most frequently used, whereas health service centers are the least utilized. The research proposes strategic planning enhancements, including full coverage of basic facilities, targeted increases in high-demand facilities, and tailored facilities for elderly characteristics. It recommends cross-community layouts to ensure service radius coverage, development reservations for future needs, and quality improvements to address the low satisfaction rates of existing facilities. The study concludes that a targeted approach to facility planning, considering the elderly’s diverse needs, can enhance the community elderly care service system’s sustainability and effectiveness.

Research on Path Planning for Industrial Cleaning Robots in Complex Environments: An Enhanced Immunogenetic Algorithm Approach

As industrial automation advances, cleaning robots in complex environments have demonstrated significant value in enhancing efficiency and reducing labor costs. This paper presents a path planning method based on an improved Immune Genetic Algorithm (IGA) for comprehensive coverage path planning in complex industrial settings. Initially, the Boustrophedon Cellular Decomposition (BCD) method is employed to effectively segment the complex scene, identifying key areas for cleaning tasks. Subsequently, the path planning efficiency and adaptability are enhanced through an optimized genetic algorithm that integrates immune memory, Inver-Over operators, and 3-opt operators to refine the traversal sequence between regions. Moreover, this study incorporates the A* algorithm and the Biologically Inspired Neural Network (BINN) algorithm for efficient inter-regional path transitions and precise intra-regional traversal, respectively. A series of simulation experiments validate the superiority of the proposed algorithm in terms of path length, coverage rate, and computational time. The results demonstrate that this method not only effectively addresses the challenges of path planning in complex industrial environments but also significantly improves cleaning efficiency, offering high practical value and potential for broader application.

Janus GO/BTA/PMMA Microcapsules for Biobased Self-Healing Anticorrosion Coatings with Ultrahigh Performance.

The giant reduction of the barrier properties due to self-healing microcapsules and the lack of real-time protection during the healing remained the main challenges in self-healing anticorrosion coatings. Herein, a facile strategy using Janus graphene oxide (GO) as a dense and flexible shell has been proposed to synergistically solve these challenges. Benzotriazole (BTA) was used to synthesize Janus GO at the oil-water interface, and Janus GO/BTA/poly(methyl methacrylate) microcapsules were prepared. Energy-dispersive X-ray spectroscopy, Fourier infrared spectroscopy, Raman spectroscopy, and ultraviolet spectrophotometer analysis confirmed the formation of a Janus GO structure with one surface hydrophilic and the other hydrophobic. The surface morphology of J-GO-capsules with a high GO coverage rate was observed by scanning electron microscopy. The high biobased content coating containing J-GO-capsules showed a low-frequency impedance value above 1010 as assessed by electrochemical impedance spectroscopy after being immersed in 3.5 wt % NaCl solution for 60 days. In addition, the low-frequency impedance values of the coating were maintained after being scratched due to the self-healing properties of the J-GO-capsules as well as the real-time protective effect of the BTA. Biobased coatings with the best overall properties among all of the self-healing anticorrosion coatings were prepared.

Lessons learnt from the first two SARS-CoV-2 Omicron waves of the COVID-19 pandemic in six remote Aboriginal and Torres Strait Islander communities in Queensland, Australia: a retrospective epidemiological review.

To describe the preparedness for, epidemiological characteristics of and public health responses to the first and second waves of coronavirus disease 2019 (COVID-19) in six remote Aboriginal and Torres Strait Islander communities in Queensland from late 2021. This was a descriptive epidemiological study. Data were collated by each participating public health unit. Case and outbreak characteristics were obtained from the statewide Notifiable Conditions System. Six discrete remote First Nations communities across Queensland were selected to represent a broad geographic spread across the state: Badu Island, Cherbourg, Lockhart River, Palm Island, Woorabinda and Yarrabah. People with a positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test result recorded between 13 December 2021 and 12 June 2022 who acquired the infection and isolated in one of the six communities. COVID-19 vaccination coverage among First Nations people; number of COVID-19 cases reported; and attack rates for each community. All six First Nations communities led the COVID-19 preparedness and planning. COVID-19 vaccination coverage rates before the first outbreak ranged from 59% to 84% for the first dose and from 39% to 76% for the second dose across the six communities. During the study period, 2624 cases of COVID-19 in these communities were notified to Queensland Health. Attack rates for each community were: Badu Island, 23%; Cherbourg, 34%; Lockhart River, 18%; and Palm Island, Woorabinda and Yarrabah, 35% each. The 2624 cases included 52 cases (2%) involving hospital admission and two cases (< 1%) in which the person died from COVID-19. It is likely that the co-designed, collaborative partnerships between local councils, community-controlled health services, state health services and public health units positively impacted the management and outcomes of COVID-19 in each of the six communities.

Influence of hydrophobic groups in polyglycerol ester emulsifier at oil-polyol interface: Interfacial adsorption behavior and its relationship with emulsification effect

This study investigated the variance in adsorption behavior of polyglycerol emulsifiers at the oil- polyol interface and its consequential impact on emulsion properties and stability. The critical micelle concentration (CMCIFT), saturation adsorption capacity (Γ∞), and Langmuir constant (κ) of the emulsifier were determined through measurements of dynamic interfacial tension between glycerol and mineral oil with varying hydrophobic chains. Results indicated that the CMCIFT exhibits an increase corresponding to the rising number of hydrophobic chains. Emulsifiers with differing initial particle sizes were synthesized, and the actual emulsifying area (SA), theoretical covering area (ST), and emulsifying index (EI) at the interface were calculated. Variations in coverage rate led to disparate adsorption behavior and arrangement of the emulsifier at the interface, thereby impacting emulsion stability. This study establishes a theoretical foundation for predicting the stability trend of polyol/oil (P/O) emulsions.

An Interval Neural Network Method for Identifying Static Concentrated Loads in a Population of Structures

During the design and validation of structural engineering, the focus is on a population of similar structures, not just one. These structures face uncertainties from external environments and internal configurations, causing variability in responses under the same load. Identifying the real load from these dispersed responses is a significant challenge. This paper proposes an interval neural network (INN) method for identifying static concentrated loads, where the network parameters are internalized to create a new INN architecture. Additionally, the paper introduces an improved interval prediction quality loss function indicator named coverage and mean square criterion (CMSC), which balances the interval coverage rate and interval width of the identified load, ensuring that the median of the recognition interval is closer to the real load. The efficiency of the proposed method is assessed through three examples and validated through comparative research against other loss functions. Our research findings indicate that this approach enhances the interval accuracy, robustness, and generalization of load identification. This improvement is evident even when faced with challenges such as limited training data and significant noise interference.

Development and validation of a 5K low-density SNP chip for Hainan cattle

BackgroundThis study aimed to design and develop a 5K low-density liquid chip for Hainan cattle utilizing targeted capture sequencing technology. The chip incorporates a substantial number of functional single nucleotide polymorphism (SNP) loci derived from public literature, including SNP loci significantly associated with immunity, heat stress, meat quality, reproduction, and other traits. Additionally, SNPs located in the coding regions of immune-related genes from the Bovine Genome Variation Database (BGVD) and Hainan cattle-specific SNP loci were included.ResultsA total of 5,293 SNPs were selected, resulting in 9,837 DNA probes with a coverage rate of 85.69%, thereby creating a Hainan cattle-specific 5K Genotyping by Target Sequencing (GBTS) liquid chip. Evaluation with 152 cattle samples demonstrated excellent clustering performance and a detection rate ranging from 96.60 to 99.07%, with 94.5% of SNP sites exhibiting polymorphism. The chip achieved 100% gender coverage and displayed a heterozygosity rate between 14.20% and 29.65%, with a repeatability rate of 99.65–99.85%. Analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed the potential regulatory roles of exonic SNPs in immune response pathways.ConclusionThe development and validation of the 5K GBTS liquid chip for Hainan cattle represent a valuable tool for genome analysis and genetic diversity assessment. Furthermore, it facilitates breed identification, gender determination, and kinship analysis, providing a foundation for the efficient utilization and development of local cattle genetic resources.

Mobile Robot Motion Behavior Based on Hopfield Neural Network Dynamics Under Electromagnetic Radiation

The nonlinear characteristic of activation function is a critical aspect that affects the performance of artificial neural networks (ANNs). Despite its importance, the impact of neuronal nonlinearity variations on the dynamics of ANNs has not been thoroughly studied in previous research. This paper aims to fill this gap by exploring the influence of nonlinearity changes in activation function due to memristive electromagnetic induction on the dynamical characteristics of ANNs. We first propose a memristor model with sinusoidal conductance function. Then, the effects on the biological neuron under the electromagnetic induction are simulated by replacing the gradient of hyperbolic tangent activation function with the conductance of the proposed memristor. Furthermore, a Hopfield neural network (HNN) is constructed by taking the nonlinearity changes of activation function into consideration and its dynamical behaviors are analyzed through Lyapunov exponent spectra, bifurcation diagram, biparameter-based dynamic maps and phase portraits. The numerical simulation results show complex dynamical behaviors such as single-scroll chaotic spiral attractor, double-scroll chaotic attractors and initial-value-dependent offset boosted attractors, demonstrating that nonlinearity changes of activation function induced by the electromagnetic induction have a significant impact on the dynamics of HNN. To further verify our findings, we implement the proposed HNN in hardware and find that the results are in line with our numerical simulations. Finally, we apply the chaotic HNN in the mobile robot path planning task, which achieves higher coverage rate than the random number-based random walk method.

Model-Based Prediction for Small Domains Using Covariates: A Comparison of Four Methods

Abstract We consider methods for model-based small area estimation when the number of areas with sampled data is a small fraction of the total areas for which estimates are required. Abundant auxiliary information is available from the survey for all the sampled areas. Further, through an external source, there is information for all areas. The goal is to use auxiliary variables to predict the outcome of interest for all areas. We compare areal-level random forests and LASSO approaches to a frequentist forward variable selection approach and a Bayesian shrinkage method using a horseshoe prior. Further, to measure the uncertainty of estimates obtained from random forests and the LASSO, we propose a modification of the split conformal procedure that relaxes the assumption of exchangeable data. We show that the proposed method yields intervals with the correct coverage rate and this is confirmed through a simulation study. This work is motivated by Ghanaian data available from the sixth Ghana Living Standards Survey (GLSS) and the 2010 Population and Housing Census, in the Greater Accra Metropolitan Area (GAMA) region, which comprises eight districts that are further divided into enumeration areas (EAs). We estimate the areal mean household log consumption using both datasets. The outcome variable is measured only in the GLSS for 3 percent of all the EAs (136 out of 5019) and 174 potential covariates are available in both datasets. In the application, among the four modeling methods considered, the Bayesian shrinkage performed the best in terms of bias, mean squared error (MSE), and prediction interval coverages and scores, as assessed through a cross-validation study. We find substantial between-area variation with the estimated log consumption showing a 1.3-fold variation across the GAMA region. The western areas are the poorest while the Accra Metropolitan Area district has the richest areas.

A dual-stage coverage path planning method for bathymetric survey using an AUV in graph-based SLAM framework considering positioning uncertainty

Autonomous underwater vehicles (AUVs) are becoming increasingly popular for seabed coverage survey tasks but suffer from a serious problem of positioning error accumulation, which leads to low path tracking accuracy and a low coverage rate of the target area. A dual-stage AUV path planning (DSPP) approach was proposed within a simultaneous localization and mapping (SLAM) framework for coverage bathymetric surveys. In the first stage, a global coverage path is designed in a lawnmower style. Crossed paths are added to generate periodic loop closures in SLAM, which can improve the efficiency of online replanning. In the second stage, with a probabilistic neural network (PNN) used for feature-rich bathymetric sub-map identification, an entropy-based online path replanning approach was implemented using 20 templates to adjust the global path to produce accurate loop closures for positioning error correction. Two bathymetric datasets were used to test the proposed DSPP method using a self-developed experimental simulation system. Experiments verified that DSPP can significantly improve the positioning accuracy and coverage rate with acceptably longer paths. In a 6000 m× 5000 marea,the DSPP approach reduced the positioning error by 76% compared with the pure dead reckoning method, and the coverage ratio reached 98.15%.

Performance of Different Spray Nozzles in the Application of Defoliant on Cotton Plants (Gossypium hirsutum L.)

Defoliant spraying is an important link in the mechanized cotton harvest because adequate and uniform spraying can improve defoliation quality and reduce cotton trash content. In defoliant application, application volume and spraying technology are extremely important. In this study, the effectiveness of defoliant application to cotton plant that has come to harvest with two different application volumes and three different types of nozzles with a standard field crop sprayer was determined. Application rates were 250 and 400 L/ha and spraying nozzles were: (1) standard flat fan nozzle (TP8006), (2) air induction nozzle (AI 11002-VS) and (3) dual pattern nozzle (AI307003VP). A tracer (BSF) and defoliant were applied to mature cotton with approximately 60% open bolls and samplings for BSF deposition and spray coverage on cotton plant were done at two plant height (upper layer, lower layer) of plant. Before and after spraying, bolls open and leaves rate on cotton plants were calculated and filter papers were used to detect BSF deposition and water sensitive papers (WSP) were used to measure coverage rate of spraying methods used. Spectrofluorophotometer was used to detect the amount of tracer deposition on targets and an image process computer program was used to measure coverage rate on WSP. In analysis conclusions showed that air induction nozzle (AI 11002-VS), achieved better results than the dual pattern and standard flat fan nozzles in terms of higher depositions, coverages and leaf defoliations and boll opening rates. AI nozzles operating at 250 L/ha application rate provide the highest deposition and coverage rate on applications of defoliant, in addition, BSF as an indicator of the defoliant used reached on leaf beneath in merely this spray nozzle. After defoliation boll opining rate was 85-% on the 7th and 12th days after spraying and falling rate of leaves was 76-% at application rate of 250 L/ha with air induction (AI1102) nozzle.