Quality Of Maps Research Articles

Enhancing water quality monitoring through the integration of deep learning neural networks and fuzzy method

The escalating growth of the global population has led to degraded water quality, particularly in seawater environments. Water quality monitoring is crucial to understanding the dynamic changes and implementing effective management strategies. In this study, water samples from the southwestern regions of Iran were spatially analyzed in a GIS environment using geostatistical methods. Subsequently, a water quality map was generated employing large and small fuzzy membership functions. Additionally, advanced prediction models using neural networks were employed to forecast future water pollution trends. Fuzzy method results indicated higher pollution levels in the northern regions of the study area compared to the southern parts. Furthermore, the water quality prediction models demonstrated that the LSTM model exhibited superior predictive performance (R2 = 0.93, RMSE = 0.007). The findings also underscore the impact of urbanization, power plant construction (2010 to 2020), and inadequate urban wastewater management on water pollution in the studied region.

Mapping Quality Indicators to Assess Older Adult Health and Care in Community-, Continuing-, and Acute-Care Settings: A Systematic Review of Reviews and Guidelines.

Quality indicators (QIs) play a vital role in enhancing the care of older adults. This study aimed to identify existing QIs relevant to the health and care of older adults in community-care, continuing-care, and acute-care settings, along with available information such as definitions and calculation methods. A systematic review of published review studies, grey literature, and guidelines was undertaken, utilizing six electronic databases searched for materials dated from 2010 to 2 June 2023. To be included in this study, the literature had to provide data on QIs in a setting involving older adults. This study included 27 reviews and 44 grey literature sources, identifying a total of 6391 QIs. The highest number of indicators (37%) were relevant to continuing care; 32% and 28% were pertinent to community- and acute-care settings, respectively. The process domain had the highest number of QIs (3932), while the structure domain had the fewest indicators (521). A total of 39 focus areas were identified, with the five most common areas being, in descending order, orthopedics/hip fractures, end-of-life/palliative care, appropriate prescribing, neurocognitive conditions, and cardiovascular conditions; these areas ranged between 10% and 6%. When mapped against the Quadruple Aim framework, most QIs (85%) were linked to improving health outcomes. This inclusive compilation of QIs serves as a resource for addressing various focus areas pertinent to the Quadruple Aims. However, few quality indicators have been designed to provide a comprehensive and thorough evaluation of a specific aspect, taking into account all three key domains: structure, process, and outcomes. Addressing the description and psychometric properties of QIs is foundational for ensuring their trustworthiness and effective application.

Enhancing latent palmprints using frequency domain analysis

Latent palmprints are integral to crime scene investigations, constituting a significant portion of encountered prints. They often suffer from poor ridge impressions, noise, and pronounced creases, setting them apart from other palmprint types. While progressive enhancement techniques are widely used for fingerprints, palmprints with numerous thick creases and larger sizes benefit more from region-growing techniques. Frequency domain-based palmprint enhancement excels in separating creases from ridges and reshaping ridge structures accurately. The key challenge lies in identifying suitable initial blocks for both region-growing and iterative enhancement techniques. Existing frequency domain-based quality maps, primarily designed for fingerprints, exhibit limited performance when applied to palmprints, especially latent ones. To address these issues, this paper introduces a new approach that combines region-growing and frequency domain-based enhancement techniques to improve latent palmprints. Our method leverages high-quality blocks, employs the orientation field obtained in the frequency domain to correct possible orientation errors in starting blocks, and utilizes varying weights to enhance all block types effectively. The experimental results indicate that the proposed approach surpasses the existing state-of-the-art techniques in terms of recognition accuracy.

Cognitive-affective maps (CAMs) as measurement tool – Elaboration of quantitative and qualitative test-retest reliability

Cognitive-Affective Mapping is a novel mind-map like technique enabling to visually represent existing belief systems or any declarative knowledge and can therefore be used in empirical social research. It can be applied broadly, for example to assess technology acceptance, and the obtained data can be analyzed with quantitative and/or qualitative approaches. Here, we aimed for the first time to assess the data quality of Cognitive-Affective Maps (CAMs). To assess whether the findings of CAM studies are due to measurement errors or due to a real effect, we aimed for a quantitative as well as qualitative test-retest reliability approach. Participants (62 in total) drew a CAM online on their cognitions, emotions and experiences regarding the topic "Universal Basic Income" twice with delays of the two measurement time points ranging from 7 to 24 days. Assuming that the evaluation of this topic is driven by values, a stable psychological measurement construct, we presume a high test-retest reliability. Pearson's Product-Moment-Correlations and Spearman's Rank Correlations of CAM parameters show quantitative test-retest reliabilities up to 0.78. Furthermore, two raters identified on average 52 % of repeated or at least semantically similar concepts drawn by the participants between the two measurement time points. Taken together, these findings are promising for a method with this amount of degrees of freedom.

Unravelling reference bias in ancient DNA datasets.

The alignment of sequencing reads is a critical step in the characterization of ancient genomes. However, reference bias and spurious mappings pose a significant challenge, particularly as cutting-edge wet lab methods generate datasets that push the boundaries of alignment tools. Reference bias occurs when reference alleles are favoured over alternative alleles during mapping, whereas spurious mappings stem from either contamination or when endogenous reads fail to align to their correct position. Previous work has shown that these phenomena are correlated with read length but a more thorough investigation of reference bias and spurious mappings for ancient DNA has been lacking. Here, we use a range of empirical and simulated palaeogenomic datasets to investigate the impacts of mapping tools, quality thresholds, and reference genome on mismatch rates across read lengths. For these analyses, we introduce AMBER, a new bioinformatics tool for assessing the quality of ancient DNA mapping directly from BAM-files and informing on reference bias, read length cut-offs and reference selection. AMBER rapidly and simultaneously computes the sequence read mapping bias in the form of the mismatch rates per read length, cytosine deamination profiles at both CpG and non-CpG sites, fragment length distributions, and genomic breadth and depth of coverage. Using AMBER, we find that mapping algorithms and quality threshold choices dictate reference bias and rates of spurious alignment at different read lengths in a predictable manner, suggesting that optimized mapping parameters for each read length will be a key step in alleviating reference bias and spurious mappings. AMBER is available for noncommercial use on GitHub (https://github.com/tvandervalk/AMBER.git). Scripts used to generate and analyse simulated datasets are available on Github (https://github.com/sdolenz/refbias_scripts).

Akreditasi Sekolah Atau Madrasah Dalam Meningkatkan Mutu Pendidikan Islam

School accreditation is an effort to guarantee school quality and an effort to achieve the expected quality of education and the results are realized in the form of recognition and suitability rankings issued by the National School/Madrasah Accreditation Board. This research aims to determine and describe the implementation of school accreditation in improving the quality of education in Islamic Education Institutions by using school accreditation mechanisms and accreditation components. This research method uses library research methods which are related to library data collection methods. The data collection technique in this research uses documentation and the data analysis technique for this research uses critical analysis, where this analysis is critical in nature, generally based on views or values. certain things that the researcher believes are related to the problem of improving the quality of education through documents related to the problem that the researcher is studying. The results of this research are that accreditation is an effort to improve the quality of education in educational units. One of the objectives of implementing accreditation is to map the quality of education based on the National Education Standards (SNP) which consists of 8 SNPs so that there is unity and equality of education throughout the territory of the Republic of Indonesia. By implementing the accreditation concept, education quality mapping and education quality improvement plans can be carried out.

On Alpha-Expansion-Based Graph-Cut Optimization for Decoder-Side Depth Estimation

In order to achieve high realism an acceptable level of user experience in immersive videos, it is crucial to provide both the best possible quality of depth maps and minimize computational time. In this paper, we propose a new approach to the decoder-side depth estimation (DSDE) problem, which uses the hierarchical alpha-expansion algorithm with additional improvements for the estimation designed to be more robust to compressed video artifacts and limited computational resources. As shown by the experimental results, the proposal simultaneously results in reduction of computation time of the estimation process (by almost 40%) and an improvement of quality of estimated depth maps. The increased quality is demonstrated by more than 6% Bjøntegaard delta gain compared to the Moving Picture Experts Group (MPEG) immersive video state-of-the-art DSDE method.

ADGEO: A new shore‐based approach to improving spatial accuracy when mapping water bodies using low‐cost drones

AbstractOver the last three decades, satellite imagery has been instrumental in mapping and monitoring water quality. However, satellites often have limitations due to image availability and cloud cover. Today, the spatial resolution of satellite images does not provide finer detail measurements essential for small‐scale water pollution management. Drones offer a complimentary platform capable of operating below cloud cover and acquiring very high spatial resolution datasets in near real‐time. Studies have shown that drone mapping over water can be done via the Direct Georeferencing approach. However, this method is only suitable for high‐end drones with accurate GNSS/IMU. Importantly, this limitation is exacerbated because of the difficulty in placing targets over water, which can be used to improve the accuracy after the survey. This study explored a new method called Assisted Direct Georeferencing which combines the benefits of traditional Bundle Adjustment with Direct Georeferencing. The performance of the approach was evaluated over a variety of different scenarios, demonstrating significant improvement in the planimetric accuracy. From the results, the method reduced the error in XY of drone imagery from MAE of 18.9 to 3.4 m. The result shows the potential of low‐cost drones with Assisted Direct Georeferencing in closing the gap to high‐end drones.

Automated Characterization of Macroscale Tissue 3D Spatial Material Properties via Robotically-Directed Impedimetric Sensing

Here, we report a novel method for automated characterization of bulk tissue 3D spatial properties based on reverse engineering-driven non-planar tool path planning and robotically-directed sensing. The method incorporates information on object (e.g., tissue) and inspection tool (e.g., sensor) geometry for automated inspection of tissue mechanical and dielectric properties across macroscopic nonplanar domains as large as 44 cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{2}$</tex-math> </inline-formula> . The process avoids the need for manual sensor-tissue integration processes. The impact and the utility of the method were demonstrated by automated mapping of 3D spatial distributions of mechanical and dielectric properties of plant and animal tissues using multiple complementary impedimetric-based sensors of varying types and form factor, including rigid micro-electromechanical systems (MEMS) and flexible multi-functional fibers. Applications to automated characterization of food quality (e.g., type and age) are provided, including 3D spatial mapping of plant and animal tissue mechanical and dielectric property distributions. Ultimately, automated methods for 3D spatial inspection of plant and animal tissue properties are critical to agriculture, food processing, organ transplantation, and biomanufacturing industries. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the need for automating the inspection of soft 3D biological objects. Here, we present a method that generates 3D quality maps of tissue properties using several sensors. The proposed tool path planning program outputs a customized tissue-conforming path for inspection based on the topographical features of the tissue. Hence, this method enables high-throughput, spatially-resolved, minimally-invasive, and reliable inspection of soft 3D biological objects. Applications to inspection of food quality were provided using two impedimetric-based sensors. Practitioners can directly apply the framework to inspection of other tissue properties and soft objects. This work provides an advance in automated methods for inspection, and real-time monitoring of tissue 3D property distributions, which reduces the need for manual tissue handling and requirement for sensor-product interface prior to characterization. This work can be implemented in various biomanufacturing applications and industries, including food safety and quality control, tissue engineering, and organ transplantation, to ensure the quality and safety of macroscopic tissue-engineered medical and food products.

A Novel intelligent SAV oriented QL-based task offloading in mobile edge environments

Edge computing is a novel and potential computing model which moves storage and computing capabilities to the network edge, substantially decreasing service latency and network traffic. The existing Internet of Things (IoT) network offloading algorithm faces limitations such as a fixed number of applications, high edge-to-edge delay, and reliance on a single Mobile Edge Computing (MEC) server, posing security and privacy concerns. Moreover, resource-constrained mobile devices need more effective data integration and compression strategies. Addressing these challenges, this study suggests an approach based on deep reinforcement learning (DRL), specifically the SAV (State Action Value) Oriented QL (Q-Learning) based Task Offloading method, to optimise task offloading and resource allocation in edge-cloud computing. The model aims to empower Mobile Devices (MDs) to develop optimal offloading decisions for long-term Quality Perception, utilising a neural network to establish the relationship between MD state and action value. The paper introduces a Recurrent Extended Memory Network (REMN) to capture dynamic workload behaviour at Edge nodes (ENs). It incorporates Quality Mapping, Quality Estimation, and a Quality-Aware DRL Task Offloading Algorithm to improve the accuracy and efficiency of the offloading procedure in MEC systems. This systematic approach improves overall system performance and enables MDs to leverage ENs for neural network training, reducing computational burdens. As a result, it can accomplish a more significant number of tasks, reducing latency from 0.74 ms to 7.168 ms and decreasing energy consumption from 270 J to 1820.39 J for tasks ranging from 10 to 50, respectively.

A new quadrant constellation graph-based water quality index and map

Water quality (WQ) evaluation is very important issue in human health and environment management. Many WQ evaluation (WQE) methods based on statistical methods and multi-criteria decision making (MCDM) methods have been developed and used. The common drawback is that they lack in visualization/intuitiveness of the WQE results. They evaluate the WQ level only quantitatively. To intuitively represent the WQ level, the previous WQE methods have applied some additional graphical methods such as concentration/distribution/contour maps combined with complicated interpolation techniques based on geographic information system. The objective of this research is to develop a new effective approach to evaluate the WQ level not only quantitatively but also intuitively without requiring additional complicated graphical technique. To do this, we proposed a new quadrant constellation graph-based water quality index (QCG-WQI) and map (QCG-WQM). The QCG-WQI evaluates the WQ level quantitatively, and QCG-WQM evaluates the WQ level intuitively. To demonstrate the effectiveness, three illustration cases were considered and compared with the results from some well-known MCDM methods. The results demonstrate that the new approach is very effective intuitive and quantitative WQE tool. It could be widely applied to not only WQE and environmental evaluation but also MCDM problems in many physical research works.

Information System Model and Key Technologies of High-Definition Maps in Autonomous Driving Scenarios.

Background: High-definition maps can provide necessary prior data for autonomous driving, as well as the corresponding beyond-line-of-sight perception, verification and positioning, dynamic planning, and decision control. It is a necessary element to achieve L4/L5 unmanned driving at the current stage. However, currently, high-definition maps still have problems such as a large amount of data, a lot of data redundancy, and weak data correlation, which make autonomous driving fall into difficulties such as high data query difficulty and low timeliness. In order to optimize the data quality of high-definition maps, enhance the degree of data correlation, and ensure that they better assist vehicles in safe driving and efficient passage in the autonomous driving scenario, it is necessary to clarify the information system thinking of high-definition maps, propose a complete and accurate model, determine the content and functions of each level of the model, and continuously improve the information system model. Objective: The study aimed to put forward a complete and accurate high-definition map information system model and elaborate in detail the content and functions of each component in the data logic structure of the system model. Methods: Through research methods such as the modeling method and literature research method, we studied the high-definition map information system model in the autonomous driving scenario and explored the key technologies therein. Results: We put forward a four-layer integrated high-definition map information system model, elaborated in detail the content and functions of each component (map, road, vehicle, and user) in the data logic structure of the model, and also elaborated on the mechanism of the combined information of each level of the model to provide services in perception, positioning, decision making, and control for autonomous driving vehicles. This article also discussed two key technologies that can support autonomous driving vehicles to complete path planning, navigation decision making, and vehicle control in different autonomous driving scenarios. Conclusions: The four-layer integrated high-definition map information model proposed by this research institute has certain application feasibility and can provide references for the standardized production of high-definition maps, the unification of information interaction relationships, and the standardization of map data associations.

Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge.

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein-nucleic acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: Escherichia coli beta-galactosidase with inhibitor, SARS-CoV-2 virus RNA-dependent RNA polymerase with covalently bound nucleotide analog and SARS-CoV-2 virus ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. The quality of submitted ligand models and surrounding atoms were analyzed by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics and contact scores. A composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

Mapping EU Member States’ Quality of Life during COVID-19 Pandemic Crisis

This study proposes an integrated methodology for the assessment and mapping of quality of life (QoL) among European Union member states in the period before and after the pandemic crisis of COVID-19. The assessment of quality of life was based on the development of composite criteria and Geographical Information Systems or GIS technology, using variables that assess quality of life. The composite criteria relate to the socioeconomic environment, employment conditions, economic conditions and health services. Each criterion was evaluated by a set of variables, and each variable was weighted based on relevant research by Greek experts. Criteria were also weighted and combined to assess overall quality of life. The methodology was applied in 27 EU member countries, and mapping led to the identification of countries with low and high quality of life. The results showed a change in the level of overall quality of life in the EU countries before and after the pandemic period, although on a limited scale, since there is a slight reclassification of the countries’ positions. The analysis also revealed the highest level of quality of life in four EU countries [Sweden, Denmark, the Netherlands and Luxembourg] that show an increased GDP per capita, combining a low level of arrears and a low level of inability to make ends meet, whereas four countries showed the lowest level of quality of life [Greece, Bulgaria, Romania and Croatia] in both periods.

A validation study of the accuracy of the atrial pace map assessed with intracardiac pattern matching: Potential utility of non-pulmonary vein mapping.

Identification of infrequent nonpulmonary vein trigger premature atrial contractions (PACs) is challenging. We hypothesized that pace mapping (PM) assessed by correlation scores calculated by an intracardiac pattern matching (ICPM) module was useful for locating PAC origins, and conducted a validation study to assess the accuracy of ICPM-guided PM. Analyzed were 30 patients with atrial fibrillation. After pulmonary vein isolation, atrial pacing was performed at one or two of four sites on the anterior and posterior aspects of the left atrium (LA, n=10/10), LA septum (n=10), and lateral RA (n=10), which was arbitrarily determined as PAC. The intracardiac activation obtained from each pacing was set as an ICPM reference consisting of six CS unipolar electrograms (CS group) or six CS unipolar electrograms and four RA electrograms (CS-RA group). The PM was performed at 193±107 sites for each reference pacing site. All reference pacing sites corresponded to sites where the maximal ICPM correlation score was obtained. Sites with a correlation score ≥98% were rarely obtained in the CS-RA than CS group (33%vs. 55%, P=.04), but those ≥95% were similarly obtained between the two groups (93%vs. 88%, P=.71), and those ≥90% were obtained in all. The surface areas with correlation scores ≥98% (0[0,10] vs. 10[0,35] mm2, P=.02), ≥95% (10[10,30] vs. 50[10,180] mm2, P=.002) and ≥90% (60[30,100] vs. 170[100,560] mm2, P=.0002) were smaller in the CS-RA than CS group. ICPM-guided PM was useful for identifying the reference pacing sites. Combined use of RA and CS electrograms may improve the mapping quality.

Assessment and mapping of soil salinity and groundwater quality in Nijhum Island, Hatiya Upazila, Noakhali

Crop yield is heavily influenced by soil salinity. The objective of the research is to assess the depth wise soil salinity distribution pattern and groundwater quality by measuring physico-chemical parameters in the agricultural land of Nijhum Island, Noakhali, Bangladesh. This study also explicates the spatial variation of soil salinity in the study area. Soil samples were collected from three different depths to estimate the Electrical Conductivity (EC) using a paste of 1:5 soil and distilled water suspension (1:5 weight-to-volume method). Spatial analyst tool of ArcGIS was used and observed that the land of Nijhum Island was affected with severe salinity in almost 85% of sampling plots and 15% of sampling plots were extremely saline. The soil salinity process has been noticed due to seawater intrusion from the Bay of Bengal through the Meghna estuary which threatens crop production and soil fertility, threatening the population's livelihood. This study provides a baseline understanding of soil salinity in Nijhum Island to help decision-makers and smallholder farmers improve their livelihoods. Thus, groundwater depth did not affect soil salinity significantly. The result contradicts most studies that found a strong influence on soil salinity. Because the study area is coastal, most of the salt comes from seawater inundation and capillary rise, which reduces groundwater availability.

Integrated geophysical exploration, water quality mapping and salinization of the coastal aquifers in Bonaberi, Douala, Cameroon

Integrated geophysical exploration, water quality mapping and salinization of the coastal aquifers in Bonaberi, Douala, Cameroon

Multi-view depth estimation based on multi-feature aggregation for 3D reconstruction

Robust 3D reconstruction depends critically on the accuracy of depth maps, and multi-view depth estimate is greatly aided by Multi-View Stereo (MVS). Nevertheless, challenges still exist, particularly in accurately estimating depth in regions with low texture and occlusion edges. We introduce MFA-MVSNet, a novel Multi-Feature Aggregation Multi-View Stereo Network that aggregates diverse features in order to address these issues. The Local Feature Extractor (LFE) is incorporated into MFA-MVSNet to adaptively capture local features across various receptive fields. It also incorporates a Multi-feature Fusion Module (MFM) to combine local features with global features extracted by the Global Feature Extractor (GFE). We utilize neural edge information in a CNN-based Depth Refinement Module (DRM) to iteratively filter the depth map to address depth errors in low-textured regions and occlusion edges. To further improve network robustness and training efficiency, we also introduce a Region Perception Loss (RPL) to lessen the impact of both easily matched and mismatched areas. On both the DTU and BlendedMVS datasets, experimental results demonstrate that MFA-MVSNet outperforms recent advanced methods in terms of depth map quality. Additionally, we incorporate MFA-MVSNet into a multi-view 3D reconstruction pipeline, utilizing its depth maps to enhance downstream tasks such as point cloud reconstruction and neural radiance fields (NeRF), leading to improved 3D reconstruction performance.

Enhancing robotic grasping with attention mechanism and advanced UNet architectures in generative grasping convolutional neural networks

This research presents a novel approach to robotic grasping by integrating an attention mechanism and advanced U-Net architectures, specifically UNet and UNet++, into the Generative Grasping Convolutional Neural Network (GG-CNN). The proposed method, Attention UNet++ GG-CNN, aims to improve the performance and accuracy of robotic grasping predictions significantly. The attention mechanism allows the model to focus on the most relevant features in the depth image, enhancing the quality of grasp predictions. The UNet and UNet++ structures, renowned for their efficiency in semantic segmentation tasks, are adapted to generate a pixel-wise grasp quality map, providing a one-to-one mapping from depth images. The UNet++ structure, with its nested and dense skip pathways, further improves the model's ability to capture and propagate low-level details to higher-level features. This approach overcomes the limitations of traditional deep learning grasping techniques by avoiding discrete sampling of grasp candidates and reducing computation times. Preliminary results indicate that the Attention UNet++ GG-CNN significantly improves the accuracy and performance of robotic grasping,with the Attention UNet++ GG-CNN achieving Intersection over Union (IoU) scores up to 95.86%, and average performance metrics across 25%, 50%, and 75% thresholds reaching as high as 88.29%, confirming its potential for more effective and reliable robotic manipulation in various settings, paving the way for more efficient and reliable robotic manipulation in dynamic environments.

Does giving students feedback on their concept maps through an on-screen avatar or a humanoid robot make a difference?

Active or engaged learning is often seen as a way to improve students’ performance concerning STEM topics. When following such a form of self-directed learning, students often need to receive feedback on their progress. Giving real-time feedback on an individual basis is usually beyond the teacher’s capacity; in digital learning environments, this opens the door for exploring automated feedback. In the current study, a posttest only design was used to investigate the effect of providing students with different forms of automated feedback while they were creating a concept map about photosynthesis in an online inquiry learning environment. Participants were high school students (N = 138), divided over two experimental groups. In one group, feedback was given by a humanoid robot and in the other group via an avatar. The effects of the different feedback forms were compared for the two groups in terms of the frequency with which students consulted the feedback, concept map quality, and students’ attitudes. Results showed that the robot group consulted feedback more often than the avatar group. Moreover, the robot group had higher scores on a scale measuring enjoyment than the avatar group. Both of these differences were statistically significant. However, the average quality of the concept maps created by both groups was similar.