Mapping Method Research Articles

Multivariate Bayesian variable selection for multi-trait genetic fine mapping

Abstract Genome-wide association studies (GWAS) have identified thousands of single-nucleotide polymorphisms (SNPs) associated with complex traits, but determining the underlying causal variants remains challenging. Fine mapping aims to pinpoint the potentially causal variants from a large number of correlated SNPs possibly with group structure in GWAS-enriched genomic regions using variable selection approaches. In multi-trait fine mapping, we are interested in identifying the causal variants for multiple related traits. Existing multivariate variable selection methods for fine mapping select variables for all responses without considering the possible heterogeneity across different responses. Here, we develop a novel multivariate Bayesian variable selection method for multi-trait fine mapping to select causal variants from a large number of grouped SNPs that target at multiple correlated and possibly heterogeneous traits. Our new method is featured by its selection at multiple levels, incorporation of prior biological knowledge to guide selection and identification of best subset of traits the variants target at. We showed the advantage of our method over existing methods via comprehensive simulations that mimic typical fine-mapping settings and a real-world fine-mapping example in UK Biobank, where we identified critical causal variants potentially targeting at different subsets of addictive behaviours and risk factors.

Health economics and services research in symbiosis: STREAMLINEing mental health services in Austria

Abstract Objectives The cost of mental diseases exceeds 4% of GDP in Europe. In Austria, every 5th person lives with mental ill-health (MIH). Ongoing epidemiological, demographic and societal changes increase the need for easily accessible, better integrated and good value services. In STREAMLINE, we combine, harmonize and implement the latest methods in human service mapping and economic costing to optimize future service provision and financing for MIH in Austria. Methods We comprehensively assess the availability, activity and affordability of human services targeting MIH in Vienna within the health and social care, education, labour and justice sectors based on a conceptual mind map. We identify services through extensive literature, document and web searches plus expert consultations. Services are mapped and costed according to the European PECUNIA methods: classification of the main service types using the international DESDE taxonomy of human services; development of Reference Unit Costs using the PECUNIA RUC templates with primary data; validation of the results with secondary data; compilation of a comprehensive multi-sectoral service catalogue; and synthesis for spatial and policy analyses. Results We identified 756 sources, 75% individual organisations, 5% multi-organisations and 20% existing provider lists. 84% relate to health and social care, 10% labour, and 3% education and justice sectors each. 58% deliver care to adults, 37% to children & adolescents, and 5% support carers. Further service mapping and RUCs will be presented. Discussion STREAMLINE pioneers in coordinated public mental health services and economics research at scale. Assessed levels of fragmentation call for better integration, improved transition, reduced spatial inequity and increased support for carers. Results increase the feasibility of service evaluations from a societal perspective. Planned public service atlas facilitates information and access for service users and carers. Funding: WWTF Key messages • New methods for harmonized health services and economics research allow comprehensive information for optimized future mental health service planning and financing. • Assessed levels of fragmentation highlight the need for better integration, improved transition, reduced spatial inequity and increased support for carers.

Usefulness of visual classification for cardiac pathophysiology using count-washout rate map in iodine-123-beta-methyl-P-iodophenyl-pentadecanoic acid scintigraphy

Abstract Background Myocardial washout rate (WR) analysis of radiopharmaceuticals is utilized in nuclear cardiology to evaluate the clinical pathophysiology of the heart. WR is defined as the ratio of the difference between the counts in the early image and time-decay-corrected delayed image divided by the former. The myocardial WR of iodine-123-β-methyl iodophenyl-pentadecanoic acid (BMIPP) is an indicator of myocardial lipolysis, and a decreased WR (<10%) of BMIPP is one of the diagnostic criteria for triglyceride deposit cardiomyovasculopathy (TGCV). However, decreased BMIPP uptake in the early images, as in old myocardial infarction (OMI), would also cause a markedly decreased WR. Since the distinction between these clinical conditions cannot be made based on WR alone, simultaneous evaluation of the counts in the early image and WR is needed to properly understand the cardiac pathophysiology. Purpose To differentiate between TGCV and non-TGCV with OMI, both of which show decreased BMIPP WR, a new Count-WR Map (CWRM) method was evaluated. The purpose is to visually and easily distinguish between the two conditions using CWRM. Methods We introduced a CWRM consisting of two axes: counting in the early image, and WR. The count window was set to a maximum of the mean plus two standard deviations and a minimum of 50 counts, and the WR window was set to a maximum of 30 % and a minimum of -20%. CWRM was visually evaluated as normal, TGCV, non-TGCV with OMI, or TGCV with OMI. Results In normal cases, sufficient counts were observed in the early images, and WR did not decrease; CWRM showed light blue. In TGCV, sufficient counts were observed in the early image, but WRs markedly decreased; CWRM showed even orange. In non-TGCV with OMI, regions with decreased and preserved counts coexisted; CWRM showed light blue in the normal and black in the OMI region. In TGCV with OMI, CWRM showed orange in the TGCV myocardium and black in the OMI region (figure). Conclusions CWRM is useful for visually and easily differentiating TGCV from non-TGCV with OMI. CWRM can be applied to other cardiovascular diseases with count and WR variations for visual classification.

Lowering the PAPR of the optical OTFS-based 6G radio with a hybrid PTS-PSO genetic approach

Orthogonal Time Frequency Space (OTFS) is regarded as one of the best contenders for sixth-generation (6G) radio systems due to its ability to obtain excellent performance in high-speed scenarios. Peak-to-average power ratio (PAPR) significantly impacts and degrades the efficiency of the power amplifier used in OTFS-based 6G systems. The proposed article presents a genetic hybrid algorithm, Partial transmission sequence-particle swarm optimization (PTS-PSO), obtaining an optimal PAPR performance with low computation complexity. The PSO generates the best phase factor compared to the conventional phase generation method of PTS, making PTS-PSO more effective. The impacts such as PAPR, bit error rate (BER), power spectral density (PSD), and complexity of the proposed PTS-PSO are compared with the conventional selective mapping (SLM) and PTS methods for 64, 256, and 512 OTFS sub-carriers. The projected PTS-PSO attained a PAPR and PSD gain of 1–6 dB and − 540 while preserving the BER performance. The experimental results demonstrate that the projected PTS-PSO outperforms the conventional SLM and PTS algorithms with trivial intricacy.

Unveiling Scholarly Insights: Quality Assurance in Open and Distance Education

Open and distance education (ODE) has continuously evolved, significantly influencing educational, daily, and professional spheres, thereby prompting interest in its sustainability and quality. This study explored global scientific perspectives on quality assurance in ODE using the science mapping method. Search terms centred on open education, distance education, and quality assurance; data was gathered from 4,224 scientific texts in the Web of Science Core Collection. Analyses were conducted using VOSviewer software. Co-authorship analyses explored scientific collaboration structures at the country level. Globally shared concepts of interest to the scientific community were addressed using co-occurrence analyses. A detailed examination of co-occurrence outputs led to classification related to general and emerging key concepts. Results depicted a widespread global interest in quality assurance in ODE, fostering connections based on new cultural similarities. The concept of quality assurance in ODE continues to be enriched and developed, gravitating towards focused learning and instruction, establishing strong ties with various components of regular education as well as human elements. However, the prevailing view of quality assurance has yet to encompass this diversity. Rather than consider the nature and current potential of ODE, it has maintained an externalized and technical perspective.

Tribomechanical characterization of ceramic reinforced composite coatings for modification of cylinder liner of heavy-duty diesel engines

Heavy-duty diesel engines are essential mechanic power generation tools for industry and transporting freight and passengers. Therefore, increasing engine efficiency and minimizing maintenance and repair costs is crucial. This study examines and characterizes three thermal spray coatings (Cr3C2/NiCr, NiCr, and Al2O3/TiO2) utilized on cylindrical gray cast iron substrate specimens. The coatings were tested for their microstructural (optical microstructure, SEM, EDS, XRD, and AFM topography), mechanical (nanoindentation and nano scratch), and tribological (short and long-distance wear test) properties. Microstructural tests showed that an average coating thickness of 120 to 170 µm was successfully achieved on the cast iron base substrate using different thermal coating methods. The Cr3C2/NiCr coating, observed through the AFM mapping method, achieved the most uniform roughness distribution on the surface. The elasticity of the coatings was measured with a nanoindentation test, and Al2O3/TiO2 coating exhibited high rigidness (177 GPa). The nano scratch test showed that the highest load-carrying capacity was achieved with the Cr3C2/NiCr coated sample. NiCr-coated sample exhibited approximately similar rigidness (61 GPa) to cast iron (67 GPa). Short- and long-distance wear tests showed that the coatings provided significantly better wear resistance than the cast iron base. The coatings’ efficacy was evident. At the long-distance tests, while the cast iron base sample had a wear rate of 19.874 (10−7mm3/Nm), the wear rates of Al2O3/TiO2, Cr3C2/NiCr, and NiCr coatings were 3.835, 4.020, and 6.466 (10−7mm3/Nm), respectively. The Al2O3/TiO2 and Cr3C2/NiCr coatings had superior tribological performance than the NiCr coating, yet the NiCr coating showed much higher wear performance than the uncoated base. The Cr3C2/NiCr coating exhibited superior tribomechanical performance in all the coatings.

A Comparison of the Efficacy of Fuzzy Overlay and Random Forest Classification for Mapping and Shaping Perceptions of the Post-Mining Landscape of Gauteng, South Africa

Post-mining landscapes are multifaceted, comprising multiple characteristics, more so in big metropolitan regions such as Gauteng, South Africa. This paper evaluates the efficacy of Fuzzy overlay and Random Forest classification for integrating and representing post-mining landscapes and how this influences the perception of these landscapes. To this end, this paper uses GISs, MCDA, Fuzzy overlay, and Random Forest classification models to integrate post-mining landscape characteristics derived from the literature. It assesses the results using an accuracy assessment, area statistics, and correlation analysis. The findings from this study indicate that both Fuzzy overlay and Random Forest classification are applicable for integrating multiple landscape characteristics at varying degrees. The resultant maps show some similarity in highlighting mine waste cutting across the province. However, the Fuzzy overlay map has higher accuracy and extends over a larger footprint owing to the model’s use of a range of 0 to 1. This shows both areas of low and high memberships, as well as partial membership as intermediate values. This model also demonstrates strong relationships with regions characterised by landscape transformation and waste and weak relationships with areas of economic decline and inaccessibility. In contrast, the Random Forrest classification model, though also useful for classification purposes, presents a lower accuracy score and smaller footprint. Moreover, it uses discrete values and does not highlight some areas of interaction between landscape characteristics. The Fuzzy overlay model was found to be more favourable for integrating post-mining landscape characteristics in this study as it captures the nuances in the composition of this landscape. These findings highlight the importance of mapping methods such as Fuzzy overlay for an integrated representation and shaping the perception and understanding of the locality and extent of complex landscapes such as post-mining landscapes. Methods such as Fuzzy overlay can support research, planning, and decision-making by providing a nuanced representation of how multiple landscape characteristics are integrated and interact in space and how this influences public perception and policy outcomes.

Quantum data encoding: a comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy

This study explores the integration of quantum data embedding techniques into classical machine learning (ML) algorithms; to assess performance enhancements and computational implications across a spectrum of models. We explored various classical-to-quantum mapping methods; ranging from basis encoding and angle encoding to amplitude encoding; for encoding classical data. We conducted an extensive empirical study encompassing popular ML algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines, and ensemble methods like Random Forest, LightGBM, AdaBoost, and CatBoost. Our findings reveal that quantum data embedding contributes to improved classification accuracy and F1 scores, particularly notable in models that inherently benefit from enhanced feature representation. We observed nuanced effects on running time, with low-complexity models exhibiting moderate increases and more computationally intensive models experiencing discernible changes. Notably, ensemble methods demonstrated a favorable balance between performance gains and computational overhead.This study underscores the potential of quantum data embedding to enhance classical ML models and emphasizes the importance of weighing performance improvements against computational costs. Future research may involve refining quantum encoding processes to optimize computational efficiency and explore scalability for real-world applications. Our work contributes to the growing body of knowledge on the intersection of quantum computing and classical machine learning, offering insights for researchers and practitioners seeking to harness the advantages of quantum-inspired techniques in practical scenarios.

A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks

Genome-scale metabolic models (GEMs) cover the entire list of metabolic genes in an organism and associated reactions, in a tissue/condition non-specific manner. RNA-seq provides crucial information to make the GEMs condition-specific. Integrative Metabolic Analysis Tool (iMAT) and Integrative Network Inference for Tissues (INIT) are the two most popular algorithms to create condition-specific GEMs from human transcriptome data. The normalization method of choice for raw RNA-seq count data affects the model content produced by these algorithms and their predictive accuracy. However, a benchmark of the RNA-seq normalization methods on the performance of iMAT and INIT algorithms is missing in the literature. Another important phenomenon is covariates such as age and gender in a dataset, and they can affect the predictivity of analysis. In this study, we aimed to compare five different RNA-seq data normalization methods (TPM, FPKM, TMM, GeTMM, and RLE) and covariate adjusted versions of the normalized data by mapping them on a human GEM using the iMAT and INIT algorithms to generate personalized metabolic models. We used RNA-seq data for Alzheimer’s disease (AD) and lung adenocarcinoma (LUAD) patients. The results demonstrated that RNA-seq data normalized by the RLE, TMM, or GeTMM methods enabled the production of condition-specific metabolic models with considerably low variability in terms of the number of active reactions compared to the within-sample normalization methods (FPKM, TPM). Using these models, we could more accurately capture the disease-associated genes (average accuracy of ~0.80 for AD and ~0.67 for LUAD) for the RLE, TMM, and GeTMM normalization methods. An increase in the accuracies was observed for all the methods when covariate adjustment was applied. We found a similar accuracy trend when we compared the metabolites of perturbed reactions to metabolome data for AD. Together, our benchmark study shows that the between-sample RNA-seq normalization methods reduce false positive predictions at the expense of missing some true positive genes when mapped on GEMs.

Carbon stocks in Norwegian eelgrass meadows across environmental gradients

Seagrass meadows are well-known for their capacity to capture and store blue carbon in sediments. However carbon stocks vary significantly between meadows, spanning more than three orders of magnitude on both local and global scales. Understanding the drivers of seagrass carbon stocks could help improve strategies for incorporating blue carbon into management plans. Here, we measured sediment carbon stocks in eelgrass (Zostera marina) meadows and unvegetated areas along the Norwegian coast, spanning wide gradients in temperature, wave exposure, water depth, salinity, and eelgrass biomass. Carbon stocks were generally higher in eelgrass meadows than in adjacent unvegetated areas, yet they displayed considerable variation (400 − 30 000 g C m−2 at 50 cm sediment depth) even among nearby sites. Overall, the highest carbon stocks were found in deeper, muddier, sheltered meadows near river mouths. These sites likely have the highest input and retention of carbon from different sources. Consequently, they should be prioritized as conservation targets for preserving coastal blue carbon stocks. Despite ever-increasing efforts to quantify seagrass blue carbon globally, high uncertainties still persist, partly due to differing methodologies, processes, and environmental context. Blue carbon stock estimates could be improved through the coordination of standardised mapping and sampling methods.

The Effect of a Parcel-Aggregated Cropping Structure Mapping Method in Irrigation-Water Estimation in Arid Regions—A Case Study of the Weigan River Basin in Xinjiang Province

Effective management of agricultural water resources in arid regions relies on precise estimation of irrigation-water demand. Most previous studies have adopted pixel-level mapping methods to estimate irrigation-water demand, often leading to inaccuracies when applied in arid areas where land salinization is severe and where poorly growing crops cause the growing area to be smaller than the sown area. To address this issue and improve the accuracy of irrigation-water demand estimation, this study utilizes parcel-aggregated cropping structure mapping. We conducted a case study in the Weigan River Basin, Xinjiang Province, China. Deep learning techniques, the Richer Convolutional Features model, and the bilayer Long Short-Term Memory model were applied to extract parcel-aggregated cropping structures. By analyzing the cropping patterns, we estimated the irrigation-water demand and calculated the supply using statistical data and the water balance approach. The results indicated that in 2020, the cultivated area in the Weigan River Basin was 5.29 × 105 hectares, distributed over 853,404 parcels with an average size of 6202 m2. Based on the parcel-aggregated cropping structure, the estimated irrigation-water demand ranges from 25.1 × 108 m3 to 30.0 × 108 m3, representing a 5.57% increase compared to the pixel-level estimates. This increase highlights the effectiveness of the parcel-aggregated cropping structure in capturing the actual irrigation-water requirements, particularly in areas with severe soil salinization and patchy crop growth. The supply was calculated at 24.4 × 108 m3 according to the water balance approach, resulting in a minimal water deficit of 0.64 × 108 m3, underscoring the challenges in managing agricultural water resources in arid regions. Overall, the use of parcel-aggregated cropping structure mapping addresses the issue of irrigation-water demand underestimation associated with pixel-level mapping in arid regions. This study provides a methodological framework for efficient agricultural water resource management and sustainable development in arid regions.

Methods for Detection and Mapping of Methylated and Hydroxymethylated Cytosine in DNA

The chemical modifications of DNA are of pivotal importance in the epigenetic regulation of cellular processes. Although the function of 5-methylcytosine (5mC) has been extensively investigated, the significance of 5-hydroxymethylcytosine (5hmC) has only recently been acknowledged. Conventional methods for the detection of DNA methylation frequently lack the capacity to distinguish between 5mC and 5hmC, resulting in the combined reporting of both. The growing importance of 5hmC has prompted the development of a multitude of methods for the qualitative and quantitative analysis of 5hmC in recent years, thereby facilitating researchers’ understanding of the mechanisms underlying the onset and progression of numerous diseases. This review covers both established and novel methods for the detection of cytosine modifications, including 5mC, 5hmC, 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC), with a particular focus on those that allow for accurate mapping and detection, particularly with third-generation sequencing. The review aims to help researchers choose the most appropriate methods based on their specific research goals and budget.

Streamlining Complex Intervention Evaluation Through Participatory Systems Mapping and Contribution Analysis: A Comprehensive Framework for Actionable Complexity Evaluation

The use of complexity-based evaluation methods remains relatively underexplored in the field of evaluation. While increasingly employed to assess complex interventions, Contribution Analysis (CA) continues to suffer from a lack of operationalization. In this article, we propose enhancing the implementation of CA by leveraging Participatory Systems Mapping as a tool to delineate and scrutinize contribution narratives. The research has a dual objective. It aims to experiment with the recently introduced Participatory Systems Mapping method in the field of evaluation while providing a tool for examining contribution narratives. The combination of CA and Participatory Systems Mapping was tested by evaluating a youth employability project. This approach enabled a collaborative and in-depth examination of impact stories and alternative explanations. It proved valuable not only in managing the complexity of the intervention but also in strengthening the implementation of CA.

Based on improved crayfish optimization algorithm cooperative optimal scheduling of multi-microgrid system

In order to solve the influence of the complex interaction relationships among subjects on the system solution accuracy and speed of the Multi-Microgrid system under the high penetration rate of new energy. Firstly, the paper establishes the bi-level optimal scheduling Stackelberg game model based on shared energy storage, considering the inter-subject interaction in MMG. Subsequently, based on the four improvement methods of Chaotic Map, Quantum Behavior, Gaussian Distribution, and Nonlinear Control Strategy, the Chaotic Gaussian Quantum Crayfish Optimization Algorithm is proposed to solve the optimization scheduling model. The improved algorithm exhibits superior initial solutions and enhanced search capability. In comparison to the original algorithm, the relative errors of the CGQCOA optimization outcomes are 98%, 20.96%, 98.74% and 16.55%, respectively, enhancing the model-solving accuracy and the speed of convergence to the optimal solution. Finally, the simulation demonstrates that the revenue of Microgrid 1, Microgrid 2, and Microgrid 3 have increased by 0.73%, 1.17%, and 1.04%, respectively. Concurrently, the penalty cost of pollutant emission has decreased by 5.9%, 11.5%, and 12.68%, respectively. Furthermore, the revenue of the shared storage have increased by 1.91%. These findings validate the efficacy of the methodology proposed in enhancing the revenue of the various subjects and reducing pollutant gas emission.

Identification and Ranking of Binding Sites from Structural Ensembles: Application to SARS-CoV-2

Target identification and evaluation is a critical step in the drug discovery process. Although time-intensive and complex, the challenge becomes even more acute in the realm of infectious disease, where the rapid emergence of new viruses, the swift mutation of existing targets, and partial effectiveness of approved antivirals can lead to outbreaks of significant public health concern. The COVID-19 pandemic, caused by the SARS-CoV-2 virus, serves as a prime example of this, where despite the allocation of substantial resources, Paxlovid is currently the only effective treatment. In that case, significant effort pre-pandemic had been expended to evaluate the biological target for the closely related SARS-CoV. In this work, we utilize the computational hot spot mapping method, FTMove, to rapidly identify and rank binding sites for a set of nine SARS-CoV-2 drug/potential drug targets. FTMove takes into account protein flexibility by mapping binding site hot spots across an ensemble of structures for a given target. To assess the applicability of the FTMove approach to a wide range of drug targets for viral pathogens, we also carry out a comprehensive review of the known SARS-CoV-2 ligandable sites. The approach is able to identify the vast majority of all known sites and a few additional sites, which may in fact be yet to be discovered as ligandable. Furthermore, a UMAP analysis of the FTMove features for each identified binding site is largely able to separate predicted sites with experimentally known binders from those without known binders. These results demonstrate the utility of FTMove to rapidly identify actionable sites across a range of targets for a given indication. As such, the approach is expected to be particularly useful for assessing target binding sites for any emerging pathogen, as well as for indications in other disease areas, and providing actionable starting points for structure-based drug design efforts.

An Automatic Mapping Method of Navigation Map for Outdoor Road Scenes

Abstract. In the process of autonomous navigation of outdoor mobile robots, four modules are involved: perception, localization, planning, and controlling. The perception module utilizes sensors such as cameras and radars based on various principles to analyze the robot's surroundings in real-time. The localization module uses GPS (Global Positioning System), IMU (Inertial Measurement Unit), and prior maps for real-time positioning analysis. The planning module plans optimal path based on the outputs of the first two modules, and the controlling module directs the robot's chassis to move along the planned optimal path. For the localization module, the accuracy of GPS positioning results heavily depends on weather conditions and GPS signal receptions. Even if the positioning results of imu are integrated, the positioning accuracy still cannot meet the needs of robot navigation. Therefore, using prior maps for repositioning can compensate for this accuracy deficiency. The planning module also requires path planning based on prior maps. If the a priori map storage is large, it will lead to difficulties in usage, maintenance, and updates. Therefore, it is crucial to research lightweight navigation map mapping methods. In this paper, an automatically mapping method of lightweight navigation maps is proposed, combining cameras and LiDAR (Light Detection and Ranging), including semantic informations necessary for outdoor navigation positioning, such as pole-like objects and traffic signs for robot longitudinal positioning, and lane line elements for robot lateral positioning. This method automatic generates robot navigation maps in Lanelet2 format, providing support for subsequent positioning and path planning modules.

Dynamic Coastal Mapping Using Sentinel-1 and Sentinel-2 Data Through Digital Earth Africa

Abstract. Coastal erosion poses a continuous threat to ecosystems, infrastructure, and property. To address these challenges and mitigate the effects of coastal changes, effective and current monitoring is essential. It is particularly important to monitor coastlines and coastal changes in Africa, where a significant portion of the population resides in coastal regions. While optical satellite imagery has been used for large-scale annual coastlines and change monitoring for Africa, its availability and quality are largely limited by the presence of cloud and cloud shadow. In comparison, using radar satellite observations such as Sentinel-1 data can provide consistent coastal mapping and change detection regardless of cloud presence. This paper outlines a fully automated supervised machine learning workflow using Sentinel-1 data and training samples extracted from Sentinel-2 data. It also explores the performance of the workflow for different coastal morphology types across the African coast. The workflow has proved to perform better and produced results that were visually more consistent with Sentinel-2 data compared to thresholding methods. While challenges exist to distinguish between land and water over smooth sandy beaches and rough near-shore water surfaces, our workflow provides an alternative method for coastal change mapping where optical satellites provide insufficient observations free from clouds. Python code of the proposed methodology has been made publicly available.

Modified ICP based Framework for 3D Localization and Mapping in GNSS-denied Environments using LiDAR

Abstract. Lidar odometry and mapping has emerged as fundamental technology in various fields, including accurate positioning and mapping, autonomous vehicles, robotics, and environmental monitoring. The ability to create accurate maps and calculate trajectories is essential for localization and navigation in complex environments, particularly in scenarios where GNSS signal are unavailable. In this context, we propose a novel method for simultaneous localization and mapping (SLAM) specifically designed for GNSS-denied environments and without the need for high-accuracy ranging or inertial measurement units (IMUs). Our approach aims to address the challenges posed by GNSS-denied environments and the cost constraints associated with using multiple sensors. By using only lidar sensors, specifically the Velodyne VLP 16 sensor, we offer a cost-effective solution. Our method utilizes a modified version of the Iterative Closest Point (ICP) algorithm to handle consecutive lidar scans seamlessly. In addition to our method, we conducted thorough accuracy assessments on the created map. The reason behind extensive accuracy evaluation is to ensure the quality of registration. By comparing alignment between the features of different frames within the map and determining the error between the registered features and the original, we gain a more comprehensive understanding of registration quality.

Efficient Storage and Analysis of Genomic Data: A k-mer Frequency Mapping and Image Representation Method.

k-mer frequencies are crucial for understanding DNA sequence patterns and structure, with applications in motif discovery, genome classification, and short read assembly. However, the exponential increase in the dimension of frequency tables with increasing k-mer length poses storage challenges. In this study, we present a novel method for compressing k-mer data without information loss, aiming to optimize storage and analysis processes. We employed Chaos Game Representation (CGR) to map k-mers to coordinates and used these components to generate raster images of k-mers. The CGR maps were partitioned and labeled based on substrings, with each substring mapped to a subframe, creating a fractal-like structure. The entire k-mer frequency set of each genomic sequence was represented as a single image, with each pixel corresponding to a specific k-mer and its occurrence. This approach reduced file size by up to 16-fold compared to plain text and 3-fold compared to binary format. Furthermore, we demonstrated the feasibility of performing alignment-free similarity analyses on images derived from k-mer frequencies of whole genome sequences from 14 plant species. Our results highlight the potential of this method as a fast and efficient tool for accessing, processing, and analyzing large biological sequence datasets, enabling the retrieval of k-mer frequencies and image reconstruction.

PAPR reduction of OTFS using an automatic amplitude reduction neural network with vendermonde matrix-based PTS and SLM algorithms

Orthogonal time frequency selectivity is considered one of the most promising advanced waveforms for high-mobility conditions in beyond-fifth-generation networks. A high peak-to-average power ratio in orthogonal time frequency introduces nonlinearity and degrades the power amplifier performance. In this work, we propose a novel automatic amplitude reduction neural network algorithm combined with partial transmission sequence and selective mapping methods using the Vendermonde matrix for generating phase sequences. In a conventional selective mapping methods or partial transmission sequence, selecting an optimal phase increases the complexity, which is overcome via Vendermonde matrix in a proposed framework. The minimization of the peak-to-average power ratio is obtained at the double-stage process: in the first stage, the threshold amplitude is set, and the amplitude exceeding the threshold is reduced by using an automatic amplitude reduction neural network and then the partial transmission scheme or selective mapping methods. The simulation results reveal that, compared with the conventional algorithms, the proposed algorithms yield a significant peak average-to-power ratio, bit error rate, and power spectrum density performance.