Fit-for-purpose land administration (FFPLA) seeks to simplify cadastral mapping via lowering the costs and time associated with conventional surveying methods. The approach can be applied to both initial establishment and on-going maintenance of system. In Ethiopia, cadastral maintenance remains an on-going challenge, especially in rapidly urbanizing peri-urban areas, where farmers' land rights and tenure security are often jeopardized. Automatic Feature Extraction (AFE) is an emerging FFPLA approach, proposed as an alternative for mapping and updating cadastral boundaries. This study explores the role of the AFE approach for updating cadastral boundaries in the vibrant peri-urban areas of Addis Ababa. Open-source software solutions are utilized to assess the (semi-) automatic extraction of cadastral boundaries from orthophotos (segmentation), designation of 'boundary' and 'non-boundary' outlines (classification), and delimitation of cadastral boundaries (interactive delineation). Both qualitative and quantitative assessments of the achieved results (validation) are undertaken. A high-resolution orthophoto of the study area and a reference cadastral boundary shape file are used, respectively, for extracting the parcel boundaries and validating the interactive delineation results. Qualitative (visual) assessment verified the completed extraction of newly constructed cadastral boundaries in the study area, although non-boundary outlines such as footpaths and artefacts are also retrieved. For the buffer overlay analysis, the interactively delineated boundary lines and the reference cadastre were buffered within the spatial accuracy limits for urban and rural cadasters. As a result, the quantitative assessment delivered 52% correctness and 32% completeness for a buffer width of 0.4m and 0.6m, respectively, for the interactively delineated and reference boundaries. The study further demonstrated the potentially significant role AFE could assist in delivering fast, affordable, and reliable cadastral mapping. Further investigation, based on user input and expertise evaluation, could help to improve the approach and apply it to a real-world setting.

Low-Level Virtual Machine (LLVM) compiler infrastructure is a useful tool for building Just-in-time (JIT) compilers, besides its reliable front-end represented by clang compiler and its elaborated middle-end containing different optimizations that improve the runtime performance. This paper addresses specifically the part of building a JIT compiler using LLVM with the scope of getting the hardware architecture details of the underlying machine such as the number of cores and the number of logical cores per processing unit and providing them to NUMA-BTLP static thread classification algorithm and to NUMA-BTDM static thread mapping algorithm. Afterwards, the hardware-aware algorithms are run by the JIT compiler within an optimization pass. JIT compiler in this paper is designed to run on a parallel C/C++ application (which creates threads using Pthreads), before the first time the application is executed on a machine. To do that, the JIT compiler takes the native code of the application, gets the corresponding LLVM IR (Intermediate Representation) for the native code and executes the hardware-aware thread classification and the thread mapping algorithms on the IR. The NUMA-Balanced Task and Loop Parallelism (NUMA-BTLP) and NUMA-Balanced Thread and Data Mapping (NUMA-BTDM) are expected to optimize the energy consumption up to 15%, on NUMA systems.

Coal is an important resource that is closely related to people’s lives and has an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. To this end, this paper proposes a coal mine environmental safety early warning model to detect abnormalities and ensure worker safety in a timely manner by assessing the underground climate environment. In this paper, the support vector machine (SVM) parameters are optimized using an improved artificial hummingbird algorithm (IAHA), and its safety level is classified by combining various environmental parameters. To address the problems of insufficient global exploration capability and slow convergence of the artificial hummingbird algorithm during iterations, a strategy incorporating Tent chaos mapping and backward learning is used to initialize the population, a Levy flight strategy is introduced to improve the search capability during the guided foraging phase, and then the simplex method is introduced to replace the worst value before the end of each iteration of the algorithm. The IAHA-SVM safety warning model was established using the improved algorithm to classify and predict the safety of the coal mine environment into four classes. Finally, the performance of the IAHA algorithm and the IAHA-SVM model are simulated separately. The simulation results show that the convergence speed and the search accuracy of the IAHA algorithm are improved, and the performance of the IAHA-SVM model has a significant improvement.

This study investigates how Electronic Livestock Health Recording Systems (ELHRs) facilitates the detection of disease burden and make cluster analysis by applying data analytics tools and techniques. A sample size of 18333 livestock disease cases reported from 2007-2015 by the Ministry of Agriculture of the Federal Democratic of Ethiopia was used for data collection. The results showed that ELHRs are important as livestock disease data preservers, saving costs, and facilitating the extraction of up-to-date and complete information. Euclidean and Manhattan distance performed well at 98%, while cosine distance measurement metrics performed poorly. Finally, with the application of the selected clustering techniques, metrics, tools, and dataset, it has been attempted to successfully detect an optimal number of disease clusters and meet the objectives of the study.

We sought to compare native T1 mapping to conventional Late gadolinium enhancement (LGE) and T2-STIR techniques in a cohort of consecutive patients undergoing cardiac MRI (CMR).. CMR was performed in 323 patients, 206 males (64%), mean age 54±8 years and in 27 age- and sex- matched healthy controls. At T2-STIR images myocardial hyperintensity suggesting edema was found in 41 patients (27%). LGE images were positive in 206 patients (64%). T1 mapping was abnormal in 171 (49%). In 206 patients (64%) a matching between LGE and native T1 was found. T1 was abnormal in 32 out of 41 (78%) with edema at T2-STIR. Overall, LGE and/or T2-STIR were abnormal in 209 patients, whereas native T1 in 154(52%). Conventional techniques and T1 mapping were concordant in 208 patients (64%). In 39 patients T1 mapping was positive despite negative conventional techniques (12%). T1 mapping was able in conditions with diffuse myocardial damage as cardiac amyloidosis, scleroderma and Fabry disease (additive role in 42%). On contrast, T1 mapping was less effective in cardiac disease with regional distribution of myocardial damage as myocardial infarction, HCM, myocarditis. In conclusion, conventional LGE/T2-STIR and T1 mapping are complementary techniques and should be used together in every CMR examination.

Urban environments are frequently populated by different insect species. Some of them are very beautiful and appealing to the people around them, such as ladybugs and butterflies, while some others are molestants and even dangerous to public health. Mosquitoes often inhabit urban environments by staying close to their hosts (humans, birds, etc.), while phytophagous species such as stinkbugs find hosts among ornamental plants and shelters during the winter around humans. This article describes the early discoveries and distribution of two invasive mosquito species, Aedes albopictus, and Ae. japonicus (Diptera: Culicidae), which were recorded in Serbia in 2009 and 2018, respectively. Both species are important for human health, due to their nuisance behavior and because they are vectors of many viruses. On the other hand, in 2015 and 2008, two invasive stinkbug species, Halyomorpha halys and Nezara viridula (Hemiptera: Pentatomidae) were observed to spread, respectively. Since then, these species have disrupted human population in urban areas, but also damaged a range of crops and ornamental plants. All species have been monitored in the territory of Serbia, and establishment and distribution have been confirmed so far.

This manuscript presents a system that can generate a 2D grid map and a 3D voxel model of an environment by fusing multiple sensors integrated into a robot. The fusion of these sensors is done by utilizing Robot Operating System (ROS) and some of its libraries. The sensors that are fused in the robot are a Light Detection and Ranging (LIDAR), Inertial Measurement Units (IMU), wheel encoders, and a Time-of-Flight camera. With the fusion of the point cloud data information obtained with the Time-of-Flight camera, the laser information of the LIDAR sensor, in addition to the odometry generated with the other sensors, a 2D grid map and a 3D model of two different environments were created, thus testing the performance of the system.

Urban spatial perception critically influences human behavior and emotional reactions, emphasizing the necessity of aligning urban spaces with human needs for enhanced urban living. However, functionality-based categorization of urban architecture is prone to biases, stemming from disparities between objective mapping and subjective perception. These biases can result in urban planning and designs that fail to cater adequately to the needs and preferences of city residents, negatively impacting their quality of life and the city's overall functionality. In this study, we apply machine learning to elucidate these biases within urban spatial perception research, utilizing a three-step methodology: objective mapping, subjective perception analysis, and perception deviation assessment. Our findings reveal that machine learning can expose hidden patterns within this research field, bearing substantial implications for urban planning and design. Of particular note, the study revealed significant discrepancies in the distribution centroids between commercial buildings and residential or public buildings. This result illuminates the spatial organization characteristics of urban architectural functions, serving as a valuable reference for urban planning and development. Moreover, it uncovers the advantages and disadvantages of different data sources and techniques in interpreting urban spatial perception, paving the way for a more comprehensive understanding of the subject. These findings underscore the importance of integrating both objective mapping and subjective perspectives in urban architectural functionality classification.

In 1937,German mathematician L.Collatz proposed a conjeture:for any positive integer,if it is even,divide it by 2;if it is odd,multiply it by 3 and 1 to get an even number;keep going on and on, the final result is 1.This paper gives a sequence classification of odd numbers,and gives 1-1 mapping of odd numbers and odd number sequences,and uses these results to give a proof of this conjecture.

For the water bodies of the Lena Delta Nature Reserve, 413 taxa were known. In 14 small tundra reservoirs in its vicinity, we identified 385 taxa, which in the compilatory list made up a significant diversity (666 taxa including definitions to the genus level) of diatoms. Thus, the species composition of diatoms in the reserve and adjacent territories was replenished by 278 species. The predominance of species of the genera Pinnularia (57) and Eunotia (51) at the family and generic levels was revealed. Index of intraspecific variability Ssp./Sp. for the lakes of the reserve was 1.11, and for the lakes of the Tiksi region 1.14, which is typical for high-latitude and high-mountain communities. The number of rare or endangered species varied in different lakes within 1-10, totaling 42 species for the entire study area. Bioindication has shown that potentially threatened species prefer moderate temperatures, slightly acidic or neutral environments free from organic pollution. A comparative analysis of the species composition of diatoms in the vicinity of the Lena Delta and other northern water bodies of Yakutia and the Arctic Islands showed that the species composition of each lake in the Arctic is strictly individual. The indicator characteristics show a certain response of the species composition of diatoms to changes in salinity, pH, and organic pollution. A number of regularities in the spatial distribution of diatoms in the study area were found in connection with the physicochemical parameters of their habitat, height above sea level, and relation to a particular catchment basin. Statistical mapping of indicators of community and habitat diversity revealed a strong reaction to point one-time pollution and made it possible to assume the influence of summer northeast winds on the species composition. We suggest that the high diversity inherent in the diatom lakes of the Tiksi coastal zone, which can even be replenished in further studies, can be considered as a property of coastal biota inherent in ecotones. Since it is in the coastal Tiksi region that a surge in the number of species is observed, this region can be considered not only an ecotone, but also a hotspot of diatom diversity. The results of the study are important for developing the basis for monitoring biodiversity under the conditions of anthropogenic and climatic changes in the Arctic.

Ensuring safe and continuous autonomous navigation in long-term mobile robot applications is still challenging. To ensure a reliable representation of the current environment without the need for periodic re-mapping, updating the map is recommended. However, in case of incorrect estimation of robot pose, updating the map can lead to errors that prevent the robot localisation and jeopardize map accuracy. In this paper, we propose a safe LIDAR-based occupancy grid map updating algorithm for dynamic environments taking into account the uncertainties in the estimation of the robot’s pose. The proposed approach allows robust long-term operations as it can recover the robot’s pose, even when it gets lost, to continue the map update process providing a coherent map. Moreover, the approach is robust also to temporary changes in the map due to the presence of dynamic obstacles such as humans and other robots. Results highlighting map quality, localisation performance, and pose recovery, both in simulation and experiments, are reported.

We propose an algorithm for generating a panoramic image of a pipe’s inner surface based on inverse perspective mapping (IPM). For efficient crack detection, we used only perspective images of the pipe interior that can be obtained with a single lens camera instead of those taken with a 360-degree camera or a 3D depth camera. Frontal images taken while passing through the pipe were converted to images of the inner surface of the pipe using IPM. We derived a generalized IPM formula that considers the slope of the image plane to correct image distortion caused by the tilt of the plane; this IPM formula was derived based on the vanishing point of the perspective image, which was detected using optical flow techniques. Finally, the multiple transformed images with overlapping areas were combined via image stitching to create a panoramic image of the inner pipe surface. To validate our proposed algorithm, we restored images of pipe inner surface using a 3D pipe model and used these images for crack detection.

Fusarium wilt of banana is a devastating disease that has decimated banana production worldwide. Host resistance to Fusarium oxysporum f. sp. cubense (Foc), the causal agent of this disease, is genetically dissected in this study using two Musa acuminata ssp. malaccensis segregating populations segregating for Foc Tropical (TR4) and Subtropical (STR4) race 4 resistance. Marker loci and trait association using 11 SNP-based PCR markers allowed the candidate region to be delimited to a 12.9 cM genetic interval corresponding to a 959 kb region on Chromosome 3 of ‘DH-Pahang’ reference assembly v4. Within this region, there is a cluster of pattern recognition receptors, namely leucine rich repeat ectodomain containing receptor-like protein kinases, cysteine-rich cell wall associated protein kinases, and leaf rust 10 disease-resistance locus receptor-like proteins positioned in an interspersed arrangement. Their transcript levels were rapidly upregulated in the resistant but not in susceptible F2 progenies at the onset of infection. This suggests that one or several of these genes may control resistance at this locus. To confirm the segregation of single-gene resistance, we generated an inter-cross between the resistant parent ‘Ma850’ and a susceptible line ‘Ma848’, to show that the STR4 resistance co-segregated with marker ‘28820’ at this locus. Finally, an informative SNP marker 29730 allowed the locus specific resistance to be assessed in a collection of diploid and polyploid banana plants. Out of the 60 lines screened, 22 lines were predicted to carry resistance at this locus, including lines known to be TR4 resistant, such as ‘Pahang’, ‘SH-3362’, ‘SH-3217’, ‘Ma-ITC0250’, and ‘DH-Pahang/CIRAD 930’. Additional screening in the International Institute for Tropical Agriculture’s collection suggests that the dominant allele is common among in the elite ‘Matooke’ NARITA hybrids, as well as in other triploid or tetraploid hybrids derived from East African highland bananas. Fine-mapping and candidate gene identification will allow characterization of molecular mechanisms underlying TR4 resistance. The markers developed in this study can now aid the marker-assisted selection of TR4 resistance in breeding programs around the world.

Biological invasions are a human-induced environmental disturbance that can cause major changes in ecosystem structure and functioning. Located in the northeastern Mediterranean basin, the Aegean Sea is a hotspot of biological invasions. Although the presence of alien species in the Aegean has been studied and monitored, no assessment has been conducted on their cumulative impacts on native biodiversity. To address this gap and identify the most highly impacted areas and habitats and the most impactful invasive species in the Aegean, we applied the CIMPAL index, a framework developed for mapping the cumulative impacts of invasive species. Coastal habitats showed stronger impacts than the open sea. The highest CIMPAL scores were four times more frequent in the South than in the North Aegean. Shallow (0-60 m) hard substrate was the most heavily impacted habitat type, followed by soft substrates and seagrass meadows. We identified Caulerpa cylindracea, Lophocladia lallemandii, Siganus spp. and Womersleyella setacea as the most im-pactful species across their range of occurrence in the Aegean but, ranking varied depending on the habitat and impact indicator applied. Our assessment can support marine managers in prioritizing decisions and actions to control biological invasions and mitigate their impacts in the Aegean Sea.

To understand the evolution of the human norovirus GII.P6-GII.6 and GII.P7-GII.6 strains, we analyzed both the RdRp region and VP1 gene in globally collected strains using authentic bioinformatics technologies. A common ancestor of the P6- and P7-type RdRp region emerged approximately 50 years ago and a common ancestor of the P6- and P7-type VP1 gene emerged approximately 110 years ago. Subsequently, the RdRp region and VP1 gene evolved. Moreover, the evolutionary rates were significantly faster for the P6-type RdRp region and VP1 gene than the P7-type RdRp region and VP1 genes. Large genetic divergence was observed in the P7-type RdRp region and VP1 gene compared with the P6-type RdRp region and VP1 gene. The phylodynamics of the RdRp region and VP1 gene fluctuated after the year 2000. Positive selection sites in VP1 proteins were located in the antigenicity-related protruding 2 domain, and these sites overlapped with conformational epitopes. These results suggest that the GII.6 VP1 gene and VP1 proteins evolved uniquely due to recombination between the P6- and P7-type RdRp regions in the HuNoV GII.P6-GII.6 and GII.P7-GII.6 virus strains.

Mineral prospectivity mapping (MPM), aiming to outline and prioritize mineral exploration targets, has been spurred by data-driven machine learning algorithms. Supervised data-driven MPM is a typical few-shot task, suffering from the scarcity of labeled data, over-fitting of models and uncertainty of predictions. The main objective of this contribution is to propose a robust framework of few-shot learning (FSL) combining data augmentation and transfer learning, which enables generation of prospectivity models with excellent predictive efficiency and low uncertainty. The mineral systems approach was used to transfer a conceptual mineral system into mappable exploration criteria. Synthetic minority over-sampling technique (SMOTE) was employed to augment and balance the labeled dataset, allowing for model pre-training with a large synthetic training dataset of source domain. The knowledge derived from pre-trained models was then transferred to the target domain by fine-tuning, and the prospectivity model was generated in light of over-fitting and uncertainty assessment. The proposed FSL framework was applied to tungsten prospectivity mapping in southern Jiangxi Province. The results indicate that the SMOTE-ed balanced dataset boosts the classification accuracy in the training process. The FSL models yield an arch-shaped prediction point pattern favorable for focusing potential targets with high probability and low uncertainty. The FSL models achieve a high predictive performance (test AUC=0.9172) and the lowest quantitative over-fitting value, compared to the models derived from the benchmark algorithms of random forest and support vector machine. Four levels of potential targeting zones, considering both predictive efficiency and uncertainty, are extracted from the resulting FSL prospectivity map. The final high-potential and low-risk exploration targets only cover 4.27% of the area, but capture 41.53% known tungsten deposits, achieving superior predictive performance. This study highlights the capability of FSL framework for controlling over-fitting and generating high-confidence exploration targets with low uncertainty.

Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard names. As these names vary widely, the standardization of the nonstandard names in the Organs at Risk (OARs), Planning Target Volumes (PTVs), and 'Other' organs inside the area of interest is a vital problem. Prior works considered traditional machine learning approaches on structure sets with moderate success. This paper presents integrated deep learning methods applied to structure sets by integrating the multimodal data compiled from the radiotherapy centers administered by the US Veterans Health Administration (VHA) and the Department of Radiation Oncology at Virginia Commonwealth University (VCU). The de-identified radiation oncology data collected from VHA and VCU radiotherapy centers have 16,290 prostate structures. Our method integrates the heterogeneous (textual and imaging) multimodal data with Convolutional Neural Network (CNN)-based deep learning approaches like CNN, Visual Geometry Group (VGG) network, and Residual Network (ResNet). Our model presents improved results in prostate (RT) structure name standardization. Evaluation of our methods with macro-averaged F1 Score shows that our deep learning model with single-modal textual data usually performs better than the previous studies. We also experimented with various combinations of multimodal data (masked images, masked dose) besides textual data. The models perform well on the textual data alone, while the addition of imaging data shows that deep neural networks can achieve improved performance using information present in the other modalities. Additionally, using masked images and masked doses along with text leads to an overall performance improvement with the various CNN-based architectures than using all the modalities together. Undersampling the majority class leads to further performance enhancement. The VGG network on the masked image-dose data combined with CNNs on the text data performs the best and establishes the state-of-the-art in this domain.

The biodiversity loss in urban areas has attracted public concern, which is one of the urgent global environmental issues. This study used bibliometric methods to analyze 3351 publications from 1995-2021 that retrieved from the Web of Science and visually represented the state-of-the-art of researches in city biodiversity field. The prolific authors, journals(sources), institutions, and countries are clearly identified. The most cited and influential paper proposed a conceptual framework of associations between urban green space, and ecosystem and human health, and then concluded that green infrastructure could physically and psychologically benefits people by ecosystem services it provides, and make a better socio-economic benefit. The theme hotspots are urbanization, urban ecology, ecosystem services, urban planning, green infrastructure, urban forest and urban park et al. Ingo Kowarik is the most productive author in terms of number of publications. Michael L. McKinney is the most cited authors by the publications in analyzed corpus, who identified how urbanization harms native ecosystems, however, a well ecologically educated population could greatly enhance species richness in all ecosystems. Urban park, garden, fragmented green spaces and green corridor networks could help enhance city biodiversity. In general, the city biodiversity research presents a trend that involves intensive global cooperation, and become more comprehensive.

The history of agricultural terraces dates back to agricultural society; and throughout history, they have enabled agricultural activities to be carried out on lands with irregular relief. They have also served as an essential element of rural landscapes due to their architectural features, building materials, and techniques, as well as the landscapes they create. Furthermore, terraces also contribute to soil conservation by preventing erosion, adding organic matter to the soil, and harvesting rainwater. The significance of agricultural terraces in the cultural landscape has triggered the need to define and classify them. In this regard, agricultural terraces across many different locations have found their place on the List of Globally Important Agricultural Heritage Sites (GIAHS) and UNESCO cultural heritage lists. In the present study, the agricultural terraces of the Erikli neighborhood of the Uzundere district were mapped through surveys, drone shots, and orthophotos, and analyzed based on the size and density indices previously used in the classification of Alpine terraces. The study is important in terms of demonstrating the use of drone technology in mapping agricultural terraces. The results of the study revealed that 51.6% of the agricultural terraces in the study area had very steep slopes and 22195 meters of total stone wall length, the average density index was 523.29 m/ha, and 76.4% of the terraces were meso and macro in character. Moreover the study shows that the terraces in the region are more susceptible to deterioration.

Acoustic energy mapping provides the functionality to obtain characteristics of acoustic sources, such as: presence, localization, type and trajectory of sound sources. Several beamforming-based techniques can be used for this purpose, however, they rely on the difference of arrival times of the signal at each capture node (or microphone), so it is of major importance to have synchronized multi-channel recordings. A Wireless Acoustic Sensor Network (WASN) can be very practical to install when used for mapping the acoustic energy of a given acoustic environment. However, they are known for having low synchronization between the recordings from each node. The objective of this paper is to characterize the impact of current popular synchronization methodologies as part of the WASN to capture reliable data to be used for acoustic energy mapping. The two evaluated synchronization protocols are: Network Time Protocol (NTP) y Precision Time Protocol (PTP). Additionally, three different audio capture methodologies were proposed for the WASN to capture the acoustic signal: two of them, recording the data locally and one sending the data through a local wireless network. As a real-life evaluation scenario, a WASN was built using nodes conformed by a Raspberry Pi 4B+ with a single MEMS microphone. Experimental results demonstrate that the most reliable methodology is using the PTP synchronization protocol and audio recording locally.

This research is focused on two key areas. The first is mapping the 2022 flood in the Red River of the North near Grafton, North Dakota, US, and the second is evaluating the scour potential of the Grafton Bridge. Local scour of bridge piers can cause hydraulic structures such as bridge piers and abutments to fail during floods, making it a crucial area of investigation. To collect bathymetry and discharge data during low and high flow conditions, including a flood event with a 16.5-year return period in 2022, an Autonomous Surface Vehicle (ASV) incorporated with LiDAR DEM (Digital Elevation Model) data obtained from the US Geological Survey (USGS) National Map was used. Flood mapping and evaluation of local scour around the bridge pier were conducted using the HEC-RAS 6.0.0 software, which utilizes the Colorado State University method as a default equation. This research demonstrates the potential of ASVs in collecting critical data and LiDAR DEM data is an efficient method for flood mapping and determining scour potential, as it integrates bathymetry, flow velocity, and flood prediction.

The article presents a new scalable iterative method for linear programming called the “apex method”. The key feature of this method is constructing a path close to optimal on the surface of the feasible region from a certain starting point to the exact solution of linear programming problem. The optimal path refers to a path of minimum length according to the Euclidean metric. The apex method is based on the predictor-corrector framework and proceeds in two stages: quest (predictor) and target (corrector). The quest stage calculates a rough initial approximation of linear programming problem. The target stage refines the initial approximation with a given precision. The main operation used in the apex method is an operation that calculates the pseudoprojection, which is a generalization of the metric projection to a convex closed set. This operation is used both in the quest stage and in the target stage. A parallel algorithm using a Fejér mapping to compute the pseudoprojection is presented. An analytical estimation of the parallelism degree of this algorithm is obtained. Also, an algorithm implementing the target stage is given. The convergence of this algorithm is proven. An experimental study of the scalability of the apex method on a cluster computing system is described. The results of applying the apex method to solve problems from the Netlib-LP repository are presented.

Mobile Mapping System (MMS) plays a crucial role in generating high-precision 3D maps for various applications. However, the traditional MMS that uses tilted LiDAR (light detection and ranging) has limitations in capturing complete information of the environment. To overcome these limitations, we propose a panoramic vision-aided Cartographer simultaneous localization and mapping (SLAM) system for MMS, named "PVL-Cartographer". The proposed system integrates multiple sensors to achieve accurate and robust localization and mapping. It contains two sub-systems, early fusion and middle fusion. In the early fusion, range-maps are created from LiDAR points in a panoramic image space, facilitating the incorporation of visual features. The SLAM system works with both visual features with and without augmented ranges. In the middle fusion, a pose graph combines camera and LiDAR nodes, with IMU (Inertial Measurement Unit) data providing constraints between each node. Extensive experiments in challenging outdoor scenarios demonstrate the effectiveness of the proposed SLAM system in producing accurate results, even in conditions with limited features. Overall, our proposed PVL Cartographer system offers a robust and accurate solution for MMS localization and mapping.

Interpretive Structural Modelling (ISM) is widely used to understand the complex connections between different components. This study presents a bibliometric overview of ISM research with a focus on its linkages to the Sustainable Development Goals (SDG) and the impact of COVID-19. The study analyzed 1988 publications on ISM published between 2012 and 2021, of which 1202 were directly mapped to the SDG and 59 were related to COVID-19. The study identified key authors, institutions, countries, and journals involved in the research and their linkages to the SDG. The results showed that ISM research is strongly linked to SDG 12 (Responsible Consumption and Production) and SDG 9 (Industry, Innovation and Infrastructure). We also identified influential SDG based on centrality measures like betweenness and eigenvector. The top four countries contributing to ISM publications were India, China, the United Kingdom, and the United States. The most frequently cited journals were Benchmarking: An International Journal, Sustainability, Journal of Modelling in Management, and Journal of Cleaner Production. Four main clusters were identified in the ISM research, including (1) integration with AHP & Fuzzy Logic for promoting sustainability alignment, (2) ISM-based strategy development for various stakeholders, (3) ISM-based decision-making in various fields, and (4) ISM-based risk evaluation. For the first time studies that used ISM approach to understand the epidemiological characteristics of COVID-19 were identified and their key findings were discussed. The study also identified several emerging topics for future ISM research, such as blockchain & IoT, environmental management systems, climate change adaptation, smart cities, and humanitarian logistics and their potential linkages to SDG.

This paper presents a simple drone survey and image processing approach to identifying St. John's wort (Hypericum perforatum) on grazing land in the Hunter Valley NSW, Australia. St John’s wort is an invasive species (weed) that competes with pasture, poisons livestock, can downgrade wool with ‘vegetable fault’, and decreases property values. Identifying the locations of St John wort from the ground can be difficult due to topography, limited access, and/or larger land areas of mixed vegetation. In this study, a drone was used to survey a 174-ha grazing property in the Hunter Valley NSW (Australia). The images were stitched together using commercially available software. A unique Visual Atmospheric Resistance Index (VARI) attribute was identified and used to highlight the presence of St John’s wort in the survey area. These sub-areas were then digitised onto high resolution maps for future planning use by land managers.

Reading proficiency is requisite in our read-to-learn educational system, yet two-thirds of American students are not proficient readers. Assuring educational equity means supporting all learners with multiple component reading interventions that individually scaffold students while remediating weak literacy skills and providing intensive and sustainable intervention early. This study (N = 855) measured the efficacy of two different multiple component reading programs for students in grades three, four, and five. Grade levels of students were assigned to either the treatment intervention or the typical practice condition; and all students were pre-and post-tested using EasyCBM Reading Benchmarks. Students scoring at/below the 30^th percentile on either benchmark were also assessed with the WRMT-3 Passage Reading Comprehension and Oral Reading Fluency measures. Students in the treatment condition received Readable English and students in typical practice condition continued to receive Amplify CKLA during their regular ELA times for 45—60 hours. Students receiving Readable English significantly outperformed students in the typical practice condition on measures of oral reading fluency, reading rate, accuracy, and passage comprehension. Raw scores, growth scale values, and grade equivalents are reported, and implications for practice are discussed. In a school year fraught with pandemic instructional interruptions and learning loss, elementary students in the intervention condition averaged a year’s worth of growth in reading fluency and nine months of growth in reading comprehension compared to three- and five-months fluency and comprehension growth in the typical practice condition. Students in the Readable English condition experienced meaningful gains in reading rate and accuracy that will give exponential word reading volume dividends to students able to read text faster and more accurately going forward. This study adds to accumulating evidence that multiple component reading programs designed to reinforce fluency skills also support reading comprehension gains for all students.

The current COVID19 pandemic has raised huge concerns for outdoor air quality due to expected lungs deterioration. These concerns include the challenges in the scalable prediction of harmful gases like carbon dioxide, iterative/repetitive inhaling due to mask and environmental temperature harshness. Even in the presence of air quality sensing devices, these challenges lead to failed planning and strategy against respiratory diseases, epidemics, and pandemics in severe cases. In this work, a dual time-series with bi-cluster sensor data-stream-based novel optimized regression algorithm was proposed with optimization predictors and optimization responses that use automated iterative optimization of the model based on the similarity coefficient index. The algorithm was implemented over SeReNoV2 sensor nodes data, i.e. multi-variate dual time-series of environmental and US Environmental Protection Agency standard sensor variables for air quality index measured from air quality sensors with geospatial profiling. The SeReNoV2 systems were placed at four locations that were 3 km apart to monitor air quality and their data was collected at Ubidots IoT platform over GSM. Results have shown that the proposed technique achieved a root mean square error (RMSE) of 1.0042 with a training time of 469.28 seconds for normal and RMSE of 1.646 in the training time of 28.53 seconds for optimization. The estimated R-Squared error of 0.03 with Mean-Square Error for temperature 1.0084 ᵒC and 293.98 ppm for CO2 was observed. Furthermore, the Mean-Absolute Error (MAE) for temperature 0.66226 ᵒC and 10.252 ppm for CO2 at a prediction speed of ~5100 observations/second for temperature 45000 observations/second for CO2 due to iterative optimization of the training time 469.28 seconds for temperature and 28.53 seconds for CO2 was very promising in forecasting COVID19 countermeasures before time.

Hydraulic relationships are important for water resource management, hazard prediction, and modelling. Since Leopold first identified power law expressions that could relate streamflow to top-width, depth, and velocity, hydrologists have been estimating ‘At-a-station Hydraulic Geometries’ (AHG) to describe average flow hydraulics. As the amount of data, data sources, and application needs increase, the ability to apply, integrate and compare disparate and often noisy data is critical for applications ranging from reach to continental scales. However, even with quality data, the standard practice of solving each AHG relationship independently can lead to solutions that fail to conserve mass. The challenge addressed here is how to extend the physical properties of the AHG relations, while improving the way they are hydrologically addressed and fit. We present a framework for minimizing error while ensuring mass conservation at reach - or hydrologic Feature - scale geometries’(FHG) that complies with current state-of-the-practice conceptual and logical models. Through this framework, FHG relations are fit for the United States Geological Survey’s (USGS) Rating Curve database, the USGS HYDRoacoustic dataset in support of the Surface Water Oceanographic Topography satellite mission (HYDRoSWOT), and the hydraulic property tables produced as part of the NOAA/Oakridge Continental Flood Inundation Mapping framework. The paper describes and demonstrates the accuracy, interoperability, and application of these relationships to flood modelling and presents this framework in an R package.

Melasma is a challenging chronic skin condition associated with hyperpigmentation and un-known etiology. This scoping review mapped evidence of available treatments and their effectiveness in darker skin types. A comprehensive, systematic online search was conducted in Scopus, PubMed, CINAHL Complete, Cochrane, ScienceDirect, and Web of Science Core Collection. All eligible titles were exported to an Endnote20 library. Thematic content analysis was performed to summarise data on current melasma treatments for darker skin types. The quality of included articles was appraised using the Mixed Methods Appraisal Tool (MMAT) 2018 version. A total of 2863 articles were retrieved from the databases, and 10 met the eligibility criteria following abstract and full-text screening. Our findings demonstrate that topical treatments, chemical peels, lasers, and tranexamic acid are common treatment modalities used in darker skin types. Although these treatments may be effective in the short term, they bring about un-desirable side effects and sometimes worsen or result in reoccurrences of melasma. Based on the evidence mapped, current treatment modalities are not suitable for darker skin types. There are very few studies conducted on individuals of African descent. Further research is necessary to investigate treatment interventions which may be user-friendly when dealing with darker skin types.

C-C chemokine receptor 9 (CCR9) is a receptor for C-C-chemokine ligand 25 (CCL25). CCR9 is crucial in the chemotaxis of immune cells and inflammatory responses. Moreover, CCR9 is highly expressed in tumors including several solid tumors and T-cell acute lymphoblastic leukemia. Several preclinical studies have shown that anti-CCR9 monoclonal antibodies (mAbs) exert antitumor activity. Therefore, CCR9 is an attractive target for tumor therapy. In this study, we conducted the epitope mapping of an anti-mouse CCR9 (mCCR9) mAb, C9Mab-24 (rat IgG2a, kappa), using a 1 × alanine (1 × Ala) and 2 × alanine (2 × Ala)-substitution method via enzyme-linked immunosorbent assay. We first performed the 1 × Ala-substitution method using one alanine-substituted peptides of the mCCR9 N-terminus (amino acids 1-19). C9Mab-24 did not recognize two peptides (F14A and F17A), indicating that Phe14 and Phe17 are critical for C9Mab-24-binding to mCCR9. Furthermore, we conducted the 2 × Ala-substitution method using two consecutive alanine-substituted peptides of the mCCR9 N-terminus, and showed that C9Mab-24 did not react with four peptides (M13A–F14A, F14A–D15A, D16A–F17A, and F17A–S18A), indicating that 13-MFDDFS-18 is involved in C9Mab-24-binding to mCCR9. Overall, combining, the 1 × Ala or 2 × Ala scanning methods could be useful for understanding for target-antibody interaction.

Desertification and dune progression over vegetation is quantified using remote sensing data, but vegetation, eventually temporarily, buried under sand blowout may escape such assessment, and to estimate the extent of buried vegetation, a GPR campaign was conducted over the coastal sand-dune of Tottori Prefecture (Japan) in combination with a high-resolution topographic UAV-based survey of the topography. The result shows that buried vegetation exists underneath sand-blowout, especially near the dune ridges, and that this vegetation can extend 20 – 30 m further than the estimation made from airborne remote sensing. Furthermore, the presence of palaeo-vegetation in palaeodune layers also provide information on the long-term evolution of sand dunes (with periods of stability vs rapid change), which can be used to reconstruct Quaternary coastal environments.

Big data has revolutionised science and technology leading to the transformation of our societies. High Performance Computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditionally HPC and big data had focused on different problem domains and had grown into two different ecosystems. Efforts have been underway for the last few years on bringing the best of both paradigms into HPC and big converged architectures. Designing HPC and big data converged systems is a hard task requiring careful placement of data, analytics, and other computational tasks such that the desired performance is achieved with the least amount of resources. Energy efficiency has become the biggest hurdle in the realisation of HPC, big data, and converged systems capable of delivering exascale and beyond performance. Data locality is a key parameter of HPDA system design as moving even a byte costs heavily both in time and energy with an increase in the size of the system. Performance in terms of time and energy are the most important factors for users, particularly energy, due to it being the major hurdle in high performance system design and the increasing focus on green energy systems due to environmental sustainability. Data locality is a broad term that encapsulates different aspects including bringing computations to data, minimizing data movement by efficient exploitation of cache hierarchies, reducing intra- and inter-node communications, locality-aware process and thread mapping, and in-situ and in-transit data analysis. This paper provides an extensive review of the cutting-edge on data locality in HPC, big data, and converged systems. We review the literature on data locality in HPC, big data, and converged environments and discuss challenges, opportunities, and future directions. Subsequently, using the knowledge gained from this extensive review, we propose a system architecture for future HPC and big data converged systems. To the best of our knowledge, there is no such review on data locality in converged HPC and big data systems.

Antibiotic resistance is a pressing global and planetary health challenge. Links between climate change, antibiotic use and the emergence of antibiotic resistance have been well documented, but less attention has been given to the impact(s) of earth systems on specific bacterial livestock diseases at a more granular level. Understanding the precise impacts of climate change on livestock health – and in turn the use of antibiotics to address that ill-health – is important in providing an evidence base to tackle such impacts and to develop practical, implementable and locally acceptable solutions within and beyond current antibiotic stewardship programmes. In this paper, we set out the case for better integration of earth scientists and their specific disciplinary skill set (specifically, problem-solving with incomplete/fragmentary data; the ability to work across four dimensions and at the interface between the present and deep/geological time) into planetary health research. We then discuss a methodology that makes use of risk mapping, a common methodology in earth science but less frequently used in health science, to map disease risk against changing climatic conditions at a granular level. This will enable predictions of future disease risk and risk impacts based on predicted future climate conditions, and thus provide an evidence base for planetary health activists to influence policy and develop mitigations. Our case study – of climate conditions’ impact on livestock health in Karnataka, India – clearly evidences the benefit of integrating earth scientists into planetary health research.

The COVID-19 pandemic first observed in December 2019 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has influenced every individual on the planet. The virus has influenced our lifestyle, education, economy and the environment. Though the vaccines against COVID-19 have provided protection against the disease; new strains of the virus have lowered the efficiency of the vaccines. There is still no effective therapy for the treatment of the disease. Understanding the protein structure of the virus may lead to the development of effective therapies for the disease. We recently mapped the structural proteins and non-structural proteins of SARS-CoV-2. The accessory proteins (Open reading frames, Orfs) of SARS-CoV-2 modulate the host environment to favor virus replication. This paper reports mapping the accessory proteins (Orfs) of SARS-CoV-2.

The CC chemokine receptor 3 (CCR3) is a receptor for CC chemokines, including CCL5/RANTES, CCL7/MCP-3, and CCL11/eotaxin. CCR3 is expressed on the surface of eosinophils, basophils, a subset of Th2 lymphocytes, mast cells, and airway epithelial cells. CCR3 and its ligands are involved in airway hyperresponsiveness in allergic asthma, ocular allergy, and cancers. Therefore, CCR3 is an attractive target for those therapies. Previously, anti-mouse CCR3 (mCCR3) monoclonal antibodies (mAbs), C3Mab-3 (rat IgG2a, kappa), and C3Mab-4 (rat IgG2a, kappa) were developed using the Cell-Based Immunization and Screening (CBIS) method. In this study, the binding epitope of these mAbs was investigated using flow cytometry. The CCR3 extracellular domain-substituted mutant analysis showed that C3Mab-3, C3Mab-4, and a commercially available mAb (J073E5) recognized the N-terminal region (amino acids 1–38) of mCCR3. Next, the alanine scanning was conducted in the N-terminal region. The results revealed that Ala2, Phe3, Asn4, and Thr5 of mCCR3 are involved in C3Mab-3 binding, whereas Ala2, Phe3, and Thr5 are essential to C3Mab-4 binding, and Ala2 and Phe3 are crucial to J073E5 binding. These results reveal the involvement of the N-terminus of mCCR3 in the recognition of C3Mab-3, C3Mab-4, and J073E5.

Spinal Cord (SC) Anatomy is often assimilated to a morphologically encapsulated neural entity, but its functional anatomy remains only partially understood. We hypothesized that it could be possible to reexplore SC neural networks by performing live electrostimulation mapping, based on “super-selective” Spinal Cord Stimulation (SCS), originally designed as a therapeutical tool to address chronic refractory pain. As a starting point, we initiated a systematic SCS lead programming approach using live electrostimulation mapping on a chronic refractory perineal pain patient, previously implanted with multicolumn SCS at the level of the conus medullaris (T12-L1). It appeared possible to (re)explore the classical anatomy of the conus medullaris using statistical correlations of paresthesia coverage mappings, resulting from 165 different electrical configurations tested. We highlighted that sacral dermatomes were not only located more medially but also deeper than lumbar dermatomes at the level of the conus medullaris, in contrast with classical anatomical descriptions of SC somatotopical organization. As we were finally able to find a morpho-functional description of ‘Philippe-Gombault’s triangle’, in 19th century historical textbooks of neuroanatomy, matching remarkably with these conclusions, the concept of “Neuro-Fiber-Mapping” was introduced.

Fabry disease (FD) is a X-linked inheritable storage disease caused by deficiency of al-pha-galactosidase causing lysosomal overload of sphingolipids. FD cardiomyopathy is character-ized by left ventricular (LV) hypertrophy and should be considered in differential diagnosis with all the other causes of LV hypertrophy. An early diagnosis of FD is very important because the enzyme replacement therapy (ERT) may change the fate of patients by blocking both cardiac and systemic involvement and improving prognosis. Diagnosis may be relatively easy in young patients with the typical signs ans symptoms of FD, but in male patients with late onset of disease and in females, diagnosis may be very challenging. Morphological and functional aspects are not specific for FD, which cannot be diagnosed or excluded by echocardiography. Cardiac magnetic resonance (CMR) with tissue characterization capability, is the preferred technique for the differential diagnosis of LV hypertrophy. The finding of decreased myocardial T1 value in LV hypertrophy is very specific for FD. Late gadolinium enhancement (LGE) is found in late stage of disease but it is useful to predict the cardiac response to ERT and to stratify the prognosis.

Spodoptera frugiperda is an invasive pest that has spread in various parts of the world. These pests have the ability to spread and adapt highly to new habitats. Until now, it is not known with certainty the distribution of these invasive pests in Eastern Indonesia, especially Bali and Nusa Tenggara. This study aims to map the spatial distribution and genetic distribution of S. frugiperda which damages maize in the areas of Bali and Nusa Tenggara. This research was conducted using a survey method from May to September 2022 covering the islands of Bali, Lombok, Sumbawa, Sumba, Flores, and Timor. The results showed that S. frugiperda had spread evenly in Bali and Nusa Tenggara. The results of PCR amplification in the COI gene from 9 sample isolates from all research locations showed the similarity of DNA bands leading to the Spodoptera frugiperda species with a banding pattern length of 683 – 697. These results indicated that the distribution of genetic variants of corn caterpillars in Bali, NTB, and NTT was confirmed as S. frugiperda species. However, the isolated gene S. frugiferda, which was shown by the alignment results of the sequences from Lombok, was confirmed as a different strain from strains from Bali, Sumba, Sumbawa, Flores, and Timor. This incident can be seen from the difference in the protein base composition of S. frugiperda from Bali, Sumba, Sumbawa, Timor, and Flores. The results of phylogenetic analysis in this study confirmed 3 clusters of the genetic closeness of S. frugiperda. Cluster-1 comes from the results of the search for specimens of JB FAW and KB FAW from Bali, SB FAW and SB FAW Sorghum from Sumba, SW FAW from Sumbawa, KP FAW from Timor, and FL FAW from Flores. Cluster-2 is an isolate outside of our species. Cluster-3 comes from the search for LT and LS FAW specimens from Lombok. The genetic distance between cluster-1 and cluster-3 is quite far, which is 0.20 mu.

The national-scale evaluation and modelling of the impact of agricultural management and cli-mate change on soils, crop growth, and the environment require soil information at a spatial res-olution addressing individual agricultural fields. This manuscript presents a data science ap-proach which agglomerates the soil parameter space into a limited number of functional soil pro-cess units (SPUs) which may be used to run agricultural process models. In fact, two unsupervised classification methods were developed to generate a multivariate 3D data product consisting of SPUs, each being defined by a multivariate parameter distribution along the depth profile from 0 to 100 cm. The two methods account for differences in variable types and distributions and in-volve genetic algorithm optimization to identify those SPUs with the lowest internal variability and maximum inter-unit difference with regards to both, their soil characteristics and landscape setting. The high potential of the methods was demonstrated by applying them to the agricultural German soil landscape. The resulting data product consists of twenty SPUs. It has a 100 m raster resolution in the 2D mapping space, and its resolution along the depth profile is 1 cm. It includes the soil properties texture, stone content, bulk density, hydromorphic properties, total organic carbon content, and pH.

The accumulation of process waste in the production line causes fluctuations, bottlenecks, and increased inventory in workstations disrupting process flow. In this paper, the aim is the use a simulated value stream mapping (SVSM) as a lean assessment tool for decision-making in the continuous improvement process to influence and provide consideration and consistency on productivity improvement in the production system. The proposed methodology applied discrete event simulation for production process operations improvement to eliminate non-value adding times and provides good quality products at the lowest cost and highest efficiency. The results are the analysis of the current state of the production system in a South African truck manufacturing industry small and medium enterprise (SMEs) as a potential solution for the production system future state. The identified non-value adding times in the 6 most critical workstations was eliminated by SVSM which resulted in a productivity improvement of 4%, most importantly bringing the productivity to 95% and total cycle time improvement to 451 for small units and 466 for large units. The results proposed combined VSM and Simulation techniques which enhance the LEAN application by DES to increase productivity and performance improvement to remain competitive in the global economy.

We study C*-algebraic versions of following conjectures/theorems: (1) Bieberbach conjecture (de Branges theorem) (2) Robertson conjecture (3) Lebedev-Milin conjecture (4) Zalcman conjecture (5) Krzyz conjecture (6) Corona conjecture (Carleson theorem). We prove that the C*-algebraic Bieberbach Conjecture for invertible coefficients is true for second degree C*-algebraic polynomials.

Over the last few years, Earth Observation (EO) data has shifted towards increased use to produce official statistics, particularly in the agriculture sector. National statistics offices worldwide, including in Asia and the Pacific, are expanding their use of EO data to produce agricultural statistics such as crop classification, yield estimation, irrigation mapping, and crop loss estimation. The advances in image classification, such as pixel-based and phenology-based classifications, and machine learning create new opportunities for researchers to analyze EO data applied to agriculture statistics. However, it requires the ground truth (GT) data because classification result mainly depends on the quality of GT. Therefore, in this study, we introduced a random sampling approach to design and collect GT data using EO imagery and ancillary data. As a result of data collection, GT data improve the algorithms and validates classification results. Nevertheless, despite the importance of GT data, they are rarely disseminated as a data product in themselves. Thus, this results in an untapped opportunity to share GT data as a global public good, and improved use of survey and census data as a source of GT data.

Spinal Cord Stimulation (SCS) is an effective and validated treatment to address chronic refractory neuropathic pain in Persistent Spinal Pain Syndrome-Type 2 (PSPS-T2) patients. Surgical SCS lead placement is traditionally performed under general anesthesia due to its invasiveness. In parallel, recent works have suggested that Awake Anesthesia (AA), consisting in Target Controlled Intra-Veinous Anesthesia (TCIVA), could be an interesting tool to optimize lead anatomical placement using patient intra-operative feedback. We hypothesized that combining AA with Minimal Invasive Surgery (MIS) could improve SCS outcomes. The goal of this study was to evaluate SCS lead performance (defined by the area of pain adequately covered by paraesthesia generated via SCS), using an intraoperative objective quantitative mapping tool, and secondarily to assess pain relief, functional improvement and change in quality of life with a composite score. We analyzed data from a prospective multicenter study (ESTIMET) to compare the outcomes of 115 patients implanted with MIS under AA (MISAA group) or General Anesthesia (MISGA group), or by Laminectomy under General Anesthesia (LGA group). All in all, MISAA appears to show significantly better performance in terms of patient pain coverage, as well as improved secondary outcomes. One step further, our results suggest that MISAA combined with intra-operative hypnosis could potentialize patient intraoperative cooperation and could be proposed as a personalized package offered to PSPS-T2 patients eligible for SCS implantation in highly dedicated neuromodulation centers.

Land cover mapping provides spatial information on the physical properties of the Earth’s surface, for various classes of wetlands, artificial surface and constructions, vineyards, water bodies, etc. Having reliable information on land cover is crucial to developing solutions to a variety of environmental problems such as destruction of important wetlands/forests, and loss of fish and wildlife habitats. This has made land cover mapping one of the most widespread application areas in remote sensing computational imaging. However, due to the differences between modalities in terms of resolutions, content, and sensors, integrating complementary information that multi-modal remote sensing imagery exhibits into a robust and accurate system still remains challenging, and classical segmentation approaches generally do not give satisfactory results for land cover mapping. In this paper, we propose a novel dynamic deep network architecture, AMM-FuseNet, that promotes the use of multi-modal remote sensing images for the purpose of land cover mapping. The proposed network exploits the hybrid approach of the Channel Attention mechanism and Densely Connected Atrous Spatial Pyramid Pooling (DenseASPP). In the experimental analysis, in order to to verify the validity of the proposed method, we test AMM-FuseNet applied to four datasets whilst comparing it to the 6 state-of-the-art models of DeepLabV3+, PSPNet, UNet, SegNet, DenseASPP, and DANet. In addition, we also demonstrate the capability of AMM-FuseNet under minimal training supervision (reduced number of training samples) compared to the state-of-the-art, achieving less accuracy loss even for the case with 1/20 of the training samples.

Impervious surface area (ISA) is a crucial indicator for quantitative urban studies. It is also important for land use land cover classification, groundwater recharge, sustainable development, urban heat island effects, and more. Spectral ISA mapping suffers from mixed pixel problems, especially with bare soil. This study aims to develop an ISA index for spatiotemporal urban mapping from common multispectral bands by reducing soil signature better than in previous studies. This study proposed a global impervious surface area index (GISAI) enhancing ISA mapping accuracy using a temporal parameter of the remote sensing (RS) dataset. Bare soil spectral reflectance shows more fluctuation than urban ISA. Therefore, the study uses minimum composites of earlier urban indices to compile minimum soil signature. It is later improved by removing water, increasing the contrast between bare soil and urban ISA and reducing bright bare soil area. This study maps the ISA of all 12 megacities using the annual RS image collection from 2021. GISAI reduced the bare soil signature and achieved an overall accuracy of 87.29%, F1-score of 0.84, and Kappa coefficient of 0.75. However, it has some limitations with grey bare soil and barren hilly areas. By limiting bare soil signature, GISAI broadens the scope of inter-urban studies globally and lengthens potential urban time-series analysis using common multispectral bands.

COVID-19 national spikes had been reported at varying temporal scales as a result of differences in the driving factors. Factors affecting case load and mortality rates have varied between countries and regions. We investigated the association between various socio-economic, demographic and health variables with the spread on COVID-19 cases in Eswatini using the maximum likelihood estimation method for count data. A generalized Poisson regression (GPR) model was fitted with the data comprising of fifteen covariates to predict COVID-19 risk in Eswatini. The results showed that variables that were key determinants in the spread of the disease were those that included the proportion of elderly above 55 years at 98% (95% CI: 97%-99%) and the proportion of youth below 35 years at 0.08% (95% CI: 0.017%-38%) with a pseudo R-square of 0.72. However, in the early phase of the virus when cases were fewer, results from the Poisson regression showed that household size, household density and poverty index were associated with COVID-19. We produced a risk map of predicted COVID-19 in Eswatini using the variables that were selected at 5% significance level. The map could be used by the country to plan and prioritize health interventions against COVID-19. The identified areas of high risk may be further investigated in order to find out the risk amplifiers and assess what could be done to prevent them.

If one considers chemical-biology toolsets that have had the greatest impact on numerous fields of life sciences over the most recent years, proximity-labeling tools, such as APEX, and Bio-ID arguably lead the way. This article reflects upon the current state-of-the-art and discusses key limitations underlying these emerging approaches, in particular, the limited functional knowledge they provide in understanding local proteomes / interactomes. This limitation is directly linked to the use of non-biologically- or non-pharmaceutically-relevant reactive intermediates in the course of covalently labeling the local proteomes. As such, these methods cannot report on specific functions of localized protein players, nor can they scrutinize whether the specific functions of such proteins/interactomes can be directly manipulated by pharmacologically-relevant small-molecule ligands. The latest data hint that precision localized electrophile delivery concept ushers a means to address this limitation with high spatiotemporal resolution, and ultimately, in relevant live animals.

In this paper, we present a method to map information regarding service activity provision residing in governmental portals across European Commission. In order to perform this, we used as a basis the enriched Greek e-GIF ontology, modeling concepts, and relations in one of the two data portals (i.e., Points of Single Contacts) examined, since relevant information on the second was not provided. Mapping consisted in transforming information appearing in governmental portals in RDF format (i.e., as Linked data), in order to be easily exchangeable. Mapping proved a tedious task, since description on how information is modeled in the second Point of Single Contact is not provided and must be extracted in a manual manner.

The development of antibody therapies against SARS-CoV-2 remains a challenging task during the ongoing COVID-19 pandemic. All approved therapeutic antibodies are directed against the receptor binding domain (RBD) of Spike and lost neutralization efficacy against continuously emerging SARS-CoV-2 variants, which especially mutate in the RBD region. Previously, phage display has been used to identify epitopes of antibody responses against several diseases. Such epitopes have been applied to design vaccines or neutralizing antibodies. Here, we constructed an ORFeome phage display library for the SARS-CoV-2 genome. Open reading frames (ORFs) representing the SARS-CoV-2 genome were displayed on the surface of phage particles in order to identify enriched immunogenic epitopes from COVID-19 patients. Library quality was assessed by both, NGS and epitope mapping of a monoclonal antibody with known binding site. The most prominent epitope captured represented parts of Spike´s fusion peptide (FP). It is associated with the cell entry mechanism of SARS-CoV-2 into the host cell and the serine protease TMPRSS2 cleaves Spike within this sequence. Blocking of this mechanism could be a potential target for non-RBD binding therapeutic anti-SARS-CoV-2 antibodies. As mutations within the FP amino acid sequence were rather rare among SARS-CoV-2 variants so far, this may be an advantage in the fight against future virus variants.

We are all similar, but a bit different. These differences are partially due to variations in our genomes and are related to the heterogeneity of symptoms and responses to treatments that patients exhibit. Most animal studies are performed in one single strain with one manipulation. However, due to the lack of variability, therapies are not always reproducible when treatments are translated to humans. Panels of already sequenced organisms are valuable tools for mimicking human phenotypic heterogeneities and gene mapping. This review summarizes the current knowledge of mouse, fly and yeast panels with insightful applications for translational research.

Background: Immunoglobulin A nephropathy (IgAN) is a complex autoimmune disease, and the exact pathogenesis remains to be elucidated. Methods: We conducted summary data-based Mendelian randomization (SMR) analysis and performed functional mapping and annotation using FUMA to explore genetic loci that are po-tentially involved in the pathogenies of IgAN. Both analyses used summarized data of a recent genome-wide association study (GWAS) on IgANs, which included 477,784 Europeans (15,587 cases and 462,197 controls) and 175,359 East Asians (71 cases and 175,288 controls). We performed separate SMR analysis using CAGE and GTEx eQTL data. Results: Using the CAGE eQTL data, our SMR analysis identified 32 probes tagging 25 unique genes that were pleiotropically/potentially causally associated with IgAN, with the top three probes being ILMN_2150787 (tagging HLA-C, PSMR=2.10×10-18), ILMN_1682717 (tagging IER3, PSMR=1.07×10-16) and ILMN_1661439 (tagging FLOT1, PSMR=1.16×10-14). Using GTEx eQTL data, our SMR analysis identified 24 probes tagging 24 unique genes, with the top three probes being ENSG00000271581.1 (tagging XXbac-BPG248L24.12, PSMR=1.44×10-10), ENSG00000186470.9 (tagging BTN3A2, PSMR=2.28×10-10), and ENSG00000224389.4 (tagging C4B, PSMR=1.23×10-9). FUMA analysis identified 3 independent, significant and lead SNPs, 2 genomic risk loci and 39 genes. Conclusion: We identified many genetic variants/loci that are potentially involved in the patho-genesis of IgAN.

The main goal of this paper is to present results of preliminary archaeological research on the south side of Lake Issyk-Kul in Kyrgyzstan. We test the hypothesis that agropastoral land use changed over four millennia from the Bronze Age through the ethnographic Kirghiz period due to economic, socio-political, and religious changes in the prehistoric and historic societies of this region. Our research objectives are to: (1) describe and analyze survey results from Lower Kizil Suu Valley; (2) discuss the results of radiometric and archaeobotanical samples taken from three stratigraphic profiles from three settlements from the Juuku Valley, including these chronological periods: the Wusun period (200 to 400 CE), the Qarakhanid period (1100 to 1200 CE), and the ethnographic Kirghiz period (1700 to 1900 CE); and (3) conduct preliminary GIS spatial analyses on the Iron Age mortuary remains (Saka and Wusun period). This research emerges out of the first archaeological surveys conducted in 2019 - 2021 and includes the Lower Kizil Suu alluvial fan; it is an initial step toward developing a model for agropastoral land use for upland valleys of the Inner Tian Shan Mountains.

Japan Aerospace Exploration Agency (JAXA) is going to launch Advanced Land Observing Satellite 3 (ALOS-3) after 2022. ALOS-3 satellite is capable of observing global land areas with wide swath (4000 km along-track direction and 70 km cross-track direction) at high spatial resolution (panchromatic: 0.8m, multispectral: 3.2m). Maintenance and updating of land cover and vegetation information at national level is one of the major goals of the ALOS-3 mission. This paper presents the potential of simulated ALOS-3 images for the classification and mapping of land cover and vegetation types at Genus-Physiognomy-Ecosystem (GPE) level. We acquired and simulated WorldView-3 images according to the configuration of the ALOS-3 satellite sensor and the simulated ALOS-3 images were utilized for the classification and mapping of land cover and vegetation types in three sites (Hakkoda, Zao, and Shiranuka) in northern Japan. This research dealt with 17 land cover and vegetation types in Hakkoda site, 25 land cover and vegetation types in Zao site, and 12 land cover and vegetation types in Shiranuka site. Ground truth data were newly collected in three sites, and we employed eXtreme Gradient Boosting (XGBoost) classifier with the implementation of 10-fold cross-validation method for assessing the potential of ALOS-3S images. The classification accuracies obtained in Hakkoda, Zao, and Shiranuka sites in terms of f1-score were 0.810, 0.729, and 0.805 respectively. The fine scale (3.2m) land cover and vegetation maps produced in the study sites showed clear and detailed view of the distribution of plant communities. Regardless of the limited number of the temporal images, ALOS-3S images showed high potential (at least 0.729 F1-score) for the land cover and vegetation classification in all three sites. The availability of more cloud free temporal scenes is expected for improved classification and mapping in the future.

The easternmost Mediterranean is a distinct transition zone from the ocean to the continent located at the junction between the largest Earth's lithospheric segments: Eurasian and African. The methodology of paleomagnetic mapping of such transition zones is based on integrating the mapping techniques for both continental and oceanic platforms: paleomagnetic reconstructions, results of radiometric dating of magnetized rocks, tectonic-structural reconstructions, biogeography, and utilization of the results of various geophysical surveys. The geodynamic-paleomagnetic mapping makes it possible to reveal the multilevel structural heterogeneity and display complex elements of the geodynamics of different ages inherent in this transition zone. Northern Israel is obviously the most complex area in the easternmost Mediterranean. For the combined paleomagnetic mapping, well-studied paleomagnetically and radiometrically areas were selected: (1) the Carmel area, (2) the Atlit area (internal part of the Carmel area), (3) the Sea of Galilee with the adjoining zones (primarily, the Kinnarot Valley), and (4) the area of the Hula Basin with adjacent areas of the Golan Plateau, Hermon Mt., and Galilea uplift. The constructed paleomagnetic profiles for the Carmel area (on the top of the accumulative surface of the Lower Cretaceous traps), and the Kinnarot Valley – Sea of Galilee – Hula Basin, evidently indicate the complex history of the paleogeodynamic evolution of the region. These studies demonstrate the effectiveness of paleomagnetic mapping integrated with paleomagnetic profiling, crossing these geologically complex areas.

Flood events and their associated damages have escalated significantly in the last few decades. To add to the gruesome situation, many reports and studies warn that flood risk would aggravate significantly in future periods due to significant alterations in the climate patterns and socio-economic dynamics. Floodplain mapping is looked upon as a viable option to tackle this global issue as it provides both quantitative and qualitative information on flood dynamics. Moreover, with the increasing availability of global data and enhancement in computational simulations, it has become easier to simlate flooding patterns at large scales. This study deter-mines the usability of publicly available datasets in capturing flood hazards over Canada. Run-off data set from the North American Regional Reanalysis (NARR), along with a few other rele-vant inputs are fed to CaMa-Flood, a robust global hydrodynamic model to generate flooding patterns for 1 in 100 and 1 in 200-yr return period events over Canada . The simulated maps are compared and validated with the existing maps of a few flood-prone regions in Canada, thereby establishing their performance over both regional and country-scale. Later, the simulated flood-plain maps are used in conjunction with property related information at 34 cities (within the top 100 populous cities in Canada) to determine the degree of exposure due to flooding in 1991, 2001, and 2011. The results indicate that around 80 percent of inundated spots belong to high and very-high hazard classes in a 200-yr event, which is roughly 4 percent more than simulated for 100-yr event. NARR derived floodplain maps perform very well while compared over the six flood-prone regions. While analyzing the exposure of properties to flooding, we notice an in-crease in the number during the last three decades, with the maximum rise observed in Toronto, followed by Montreal, and Edmonton. To disseminate the extensive flood-related information, a web-based public tool, Flood Map Viewer (http://www.floodmapviewer.com/) is developed. The development of the tool was motivated by the commitment of the Canadian government to provide $63 M over the next three years for the completion of flood maps for higher-risk areas. The study reaches out to demonstrate how publicly available datasets can be utilized with a lesser degree of uncertainty in representing flooding patterns over large regions. The flood re-lated information derived from the study can be used along with vulnerability for quantifying flood risk, which will help in developing appropriate pathways for resilience building for long-term sustainable benefits.

This research introduces Genus-Physiognomy-Ecosystem (GPE) mapping at a prefecture level through machine learning of multi-spectral and multi-temporal satellite images at 10m spatial resolution, and later integration of prefecture wise maps into country scale for dealing with 88 GPE types to be classified from a large size of training data involved in the research effectively. This research was made possible by harnessing entire archives of Level-2A product, Bottom of Atmosphere reflectance images collected by MultiSpectral Instruments onboard a constellation of two polar-orbiting Sentinel-2 mission satellites. The satellite images were pre-processed for cloud masking and monthly median composite images consisting of 10 multi-spectral bands and 7 spectral indexes were generated. The ground truth labels were extracted from extant vegetation survey maps by implementing systematic stratified sampling approach and noisy labels were dropped out for preparing a reliable ground truth database. Graphics Processing Unit (GPU) implementation of Gradient Boosting Decision Trees (GBDT) classifier was employed for classification of 88 GPE types from 204 satellite features. The classification accuracy computed with 25% test data varied from 65-81% in terms of F1-score across 48 prefectural regions. This research produced seamless maps of 88 GPE types first time at a country scale with an average 72% F1-score. In addition, mapping of solar panels and vegetation disturbance are added.

The quantification of sea surface temperature (SST) through space platforms has revolutionized how we obtain information at a global level. However, the main disadvantage of obtaining SST with satellite images consists of its inherent coarse spatial resolution. One solution could be the use of downscaling algorithms to create sequences of matrices at a higher resolution. We used the same SST data source from the MODIS-Aqua sensor at three spatial resolutions of 9 km, 4.5 km, and 1 km in the Gulf of California. Based on an open-source algorithm, the original SST images were downscaled to 4.5 km, 1 km, 500 m, 250 m, and 125 m per pixel scales. Results indicate a strong linear relationship between the original SST-MODIS data and the modeled data for all spatial resolutions. This study demonstrates the feasibility of an open-source downscaling algorithm to enhance the spatial resolution of SST images in a marginal sea.

Remote sensing technology, especially using satellite images, has become essential support in many aspects of decision-making, particularly in disaster risk management. It requires a shorter period of data updates and less cost compared to conventional field observations and surveys. Yet, the intensive processing and high-powered computing resources are necessary to analyze satellite imagery data through Geographic Information System (GIS). In this paper, we introduce the identification and mapping of natural disaster impact in Indonesia using the open-source collaborative tool of Google Earth Engine (GEE) application which analyzes the relative temporal difference of Earth surface from three major satellite images: Sentinel-1, Sentinel-2, and Landsat-8. Taking the advantage of the geographical, geological, and demographic conditions of Indonesia's disaster-prone areas, we analyze relative difference from normalized difference vegetation index (NDVI) out of months before and after natural disaster occurrence to measure the impact of natural disaster in focus study areas. Given the high-vegetation nature of three main natural disaster impacted areas in Indonesia: Aceh, Palu, and Yogyakarta, we are able to simplify the analysis by highlighting areas with vegetative loss or gain after the event. Using an open-source GEE application, namely HazMapper, we identify and visualize the aftermath of the tsunami disaster in Aceh and Palu as well as the earthquake in Yogyakarta. Our study is potentially beneficial for government and decision-makers to utilize publicly available satellite images for disaster recovery and mitigation policy.

Crop-type mapping is an important intermediate step for cost-effective crop management at the field level, as an overview of all fields with a particular crop type can be used for monitoring or yield forecasting, for instance. Our study used a data set with 2400 fields and corresponding satellite observations from the federal state of Bavaria, Germany. The study classified corn, winter wheat, winter barley, sugar beet, potato, and winter rapeseed as the main crops grown in Upper Bavaria. We additionally experimented with a rejection class "Other", which summarised further crop types. Corresponding Sentinel-2 data included the normalised difference vegetation index (NDVI) and raw bands from 2016 to 2018 for each selected field. The influence of raw bands compared to NDVI was analysed and the classification algorithms, i.e. support vector machine (SVM) and random forest (RF), were compared. The study showed that the use of an index should be critically questioned and that raw bands provided a wider spectral bandwidth, which significantly improved the mapping of crop types. The results underline the use of RF with raw bands and achieved overall accuracies (OA) of up to 92%. We also predicted crop types in an unknown year with significantly different weather conditions and several months before the end of the growing season. Thus, the influence of climate anomalies and the accuracy depending on the time of prediction were assessed. The crop types of a test site and year without labels could be determined with an OA of up to 86%. The results demonstrate the usefulness of the proof-of-concept and its readiness for use in real applications.

Manufacturing corporations has the acceptance of the Outsourcing Process (OP) to improve industrial activities as well as to archive the revenue objectives, and with this, Risk Analysis (RA) tools are constantly used to assure expected results. Failure Mode and Effect Analysis (FMEA) is one of preferred RA tools, moreover, it is proven that FMEA adds uncertainty because of the human participation at the RA, afterward it is demonstrated that Pythagorean Fuzzy Dimensional Analysis – FMEA – Value Stream Mapping (PFDA-FMEA-VSM) method removes the uncertainty in RA, likewise it aids to the stakeholders for decision making, giving more advantages improving the use of the resources on the project. This document exhibits a real case scenario in a manufacturing firm applying PFDA-FMEA-VSM method adapted for manufacturing OP. The application of PFDA-FMEA-VSM shows solid RA results, removing the human intervention uncertainty added to the risk ranking, gives advantages to the stakeholders for visualize the main risks in detailed diagram, as well as make easier to take better decisions on where to apply resources and mitigate risks during OP.

Classification and mapping of plant communities is an essential step for conservation and management of ecosystems and biodiversity. We adopt the Genus-Physiognomy-Ecosystem (GPE) system developed in previous study for satellite-based classification of plant communities. This paper assesses the potential of multi-spectral and multi-temporal images collected by Sentinel-2 satellites. This research was conducted in five representative study sites in a temperate region. It consists of 44 types of plant communities including a few land cover types as well. The plant community types were enumerated in the study sites and ground truth data were prepared with reference to extant vegetation surveys, visual interpretation of high-resolution images, and onsite field observations. We acquired all Sentinel-2 Level-1C product images available for the study sites between 2017-2019 and generated monthly median composite images consisting of ten spectral and twelve spectral-indices. Gradient Boosting Decision Trees (GBDT) classifier was employed as an efficient and distributed gradient boosting technique for the supervised classification of big datasets involved in the research. The cross-validation accuracy in terms of kappa coefficient varied from 87% in Oze site with 41 land cover and plant community types to 95% in Hakkoda site with 19 land cover and plant community types; with average performance of 91% across all sites. In addition, the resulting maps demonstrated a clear distribution of plant community types involved in all sites, highlighting the potential of Sentinel-2 multi-spectral and multi-temporal images with GPE classification system for operational and broad-scale mapping of land cover and plant communities.

The current and projected climate change that is represented by increasing temperatures, humidity levels and irregular rainfall patterns, promotes the occurrence of preharvest sprouting (PHS) in wheat. PHS results in significant economic losses, globally, which necessitates the need for high-yielding cultivars with increased PHS tolerance, hence this study was conducted. The current study evaluated two doubled-haploid (DH) wheat populations of Tugela-Dn × Elands and Elands × Flamink across six environments in the Free State Province of South Africa to select genotypes with increased PHS tolerance and further map the underlying loci. Significant effects of DH lines (194) and environments (6) were observed for PHS tolerance. The results of this study validate previous findings that PHS is only expressed when environmental conditions are conducive. Quantitative trait loci (QTL) mapping using single nucleotide polymorphism (SNP) and silicoDArT markers revealed three additive QTL with major effects on chromosomes 5B and 7B, and these QTL were detected more than once, when conditions were favourable. These QTL explained a phenotypic variation (PVE) varying between 10.08% and 20.30% (LOD = 2.73 – 3.11). About 16.50% of DH lines performed to the level of Elands (the PHS-tolerant parent) and are recommended for further selection in a pre-breeding or breeding programme. The findings of the study are expected to facilitate the on-going breeding efforts for PHS tolerance in winter wheat.

This paper considered the issue of agricultural fields boundary recognition in satellite images. A novel algorithm based on the aggregated history of vegetation index data obtained via open satellite data, Sentinel-2, was proposed. The proposed algorithm included several basic steps, namely the detection of parcel regions on aggregated index data; the calculation of aggregated edge maps; the segmentation of parcel regions using the edges obtained; the computation of connected components and their contour extraction. In this paper, we showed that the use of aggregated vegetation index data and boundary maps allow for much more accurate agricultural field segmentation compared to the instant vegetation index approach. The quality of segmentation within regions of Russia and the Ukraine was estimated. The dataset that was used and Python implementation of the proposed algorithm were provided.

The use of machine learning techniques to predict yield based on remote sensing is a no-return path and studies conducted on farm aim to help rural producers in decision-making. Thus, commercial fields equipped with technologies in Mato Grosso, Brazil, were monitored by satellite images to predict cotton yield using supervised learning techniques. The objective of this research was to identify how early in the growing season, which vegetation indices and which machine learning algorithms are best to predict cotton yield at the farm level. For that, we went through the following steps: 1) We observed the yield in 398 ha (3 fields) and eight vegetation indices (VI) were calculated on five dates during the growing season. 2) Scenarios were created to facilitate the analysis and interpretation of results: Scenario 1: All Data (8 indices on 5 dates = 40 inputs) and Scenario 2: best variable selected by Stepwise regression (1 input). 3) In the search for the best algorithm, hyperparameter adjustments, calibrations and tests using machine learning were performed to predict yield and performances were evaluated. Scenario 1 had the best metrics in all fields of study, and the Multilayer Perceptron (MLP) and Random Forest (RF) algorithms showed the best performances with adjusted R2 of 47% and RMSE of only 0.24 t ha^-1, however, in this scenario all predictive inputs that were generated throughout the growing season (approx. 180 days) are needed, so we optimized the prediction and tested only the best VI in each field, and found that among the eight VIs, the Simple Ratio (SR), driven by the K-Nearest Neighbor (KNN) algorithm predicts with 0.26 and 0.28 t ha^-1 of RMSE and 5.20% MAPE, anticipating the cotton yield with low error by ±143 days, and with important aspect of requiring less computational demand in the generation of the prediction when compared to MLP and RF, for example, enabling its use as a technique that helps predict cotton yield, resulting in time savings for planning, whether in marketing or in crop management strategies.

The Mediterranean basin is a hot spot of climate change where the Posidonia oceanica (L.) Delile (PO) and other seagrass are under stress due to its effect on marine habitats and the rising influence of anthropogenic activities (tourism, fishery). The PO and seabed ecosystems, in the coastal environments of Pantelleria and Lampedusa, suffer additional growing impacts from tourism in synergy with specific stress factors due to increasing vessel traffic for supplying potable water, fossil fuels for electrical power generation. Earth Observation (EO) data, provided by high resolution (HR) multi/hyperspectral operative satellite sensors of the last generation (i.e. Sentinel 2 MSI and PRISMA) have been successfully tested, using innovative calibration and sea truth collecting methods, for monitoring and mapping of PO meadows under stress, in the coastal waters of these islands, located in the Sicily Channel, to better support the sustainable management of these vulnerable ecosystems. The area of interest in Pantelleria was where the first prototype of the Italian Inertial Sea Wave Energy Converter (ISWEC) for renewable energy production was installed in 2015, and sea truth campaigns on the PO meadows were conducted. The PO of Lampedusa coastal areas, impacted by ship traffic linked to the previous factors and tropicalization effects of Italy southernmost climate change transitional zone, was mapped through a multi/hyper spectral EO-based approach, using training/testing data provided by side scan sonar data, previously acquired. Some advanced machine learning algorithms (MLA) were successfully evaluated with different supervised regression/classification models to map seabed and PO meadow classes and related Leaf Area Index (LAI) distributions in the areas of interest, using multi/hyperspectral data atmospherically corrected via different advanced approaches.

While paresthesia-based spinal cord stimulation (SCS) has been proven effective to treat chronic neuropathic pain, initial benefits may lead to the development of “Failed SCS Syndrome’ (FSCSS) defined as decrease over time related to Loss of Efficacy (LoE) with or without Loss of Coverage (LoC). Development of technologies associating new paresthesia-free stimulation waveforms and implanted pulse generator adapters provide opportunities to manage patients with LoE. The main goal of our study was to investigate salvage procedures, through neu-rostimulation adapters, in patients already implanted with SCS and experienced LoE. We retro-spectively analyzed a cohort of patients who were offered new SCS programs/waveforms through an implanted adapter between 2018 and 2021. Patients were evaluated before, and at 1, 3, 6 and 12-month follow-up. Outcomes included pain intensity rating with Visual Analog Scale (VAS), pain/coverage mappings and stimulation preferences. Last follow-up evaluations (N=27) showed significant improvement in VAS (p = 0.0001), ODI (p = 0.021) and Quality of Life (p=0.023). In the 11/27 patients with LoC, SCS efficacy on pain intensity (36.89%) was accompa-nied by paresthesia coverage recovery (55.57%) and pain surface decrease (47.01%). At 12-month follow-up, 81.3% preferred to keep tonic stimulation in their waveform portfolio. SCS conver-sion using adapters appears promising as a salvage solution, with an emphasis on paresthesia recapturing enabled by spatial retargeting. In light of these results, adapters could be integrated to SCS rescue algorithms or should be considered in SCS rescue.

While Spinal Cord Stimulation (SCS) provides satisfaction to almost 2/3 of Persistent Spinal Pain Syndrome-Type 2 (PSPS-T2) patients implanted for refractory chronic back and/or leg pain when not adequately addressed the back pain component, leaves patients in a therapeutic cul-de-sac. Peripheral Nerve field Stimulation (PNfS) has shown interesting results addressing back pain in the same population. Far from placing these two techniques in opposition, we suggest that these approaches could be combined to better treat PSPS-T2 patients. We designed a RCT (CUMPNS), with a 12-month follow-up, to assess the potential added value of PNfS, as a salvage therapy, in PSPS-T2 patients experiencing a “Failed SCS Syndrome” in the back pain component. Fourteen patients were included in this study and randomized into 2 groups (“SCS + PNfS” group/n=6 vs “SCS only” group/n=8). The primary objective of the study was to compare the percentage of back pain surface decrease after 3 months, using a computerized interface to obtain quantitative pain mappings, combined with multi-dimensional SCS outcomes. Back pain surface decreased significantly greater for the ”SCS+PNfS” group (80.2% ± 21.3%) compared to the “SCS only” group (13.2% ± 94.8%) (p=0.012), highlighting the clinical interest of SCS+PNfS, in cases where SCS fails to address back pain.

The multidimensionality of chronic pain forces us to look beyond isolated pain assessment such as pain intensity, which does not consider multiple key parameters, particularly in patients suffering from post-operative Persistent Spinal Pain Syndrome (PSPS-T2). Our ambition was to provide a novel Multi-dimensional Clinical Response Index (MCRI), including not only pain intensity but also functional capacity, anxiety-depression, quality of life and objective quantitative pain mapping assessments, the objective being to capture patient condition instantaneously, using machine learning techniques. Two hundred PSPS-T2 patients were enrolled in a real-life observational prospective PREDIBACK study with 12-month follow-up and received various treatments. From a multitude of questionnaires/scores, specific items were combined using exploratory factor analyses to create an optimally accurate MCRI; as a single composite index, using pairwise correlations between measurements, it appeared to better represent all pain dimensions than any other classical score. It appeared to be the best compromise among all existing indexes, showing the highest sensitivity/specificity related to Patient Global Impression of Change (PGIC). Novel composite indexes could help to refine pain assessment by changing the physician’s perception of patient condition on the basis of objective and holistic metrics, and by providing new insights to therapy efficacy/patient outcome assessments, before ultimately being adapted to other pathologies.

MzSTools is a plugin for QGIS developed by the National Research Council (CNR) as part of the activities concerning the coordination of seismic microzonation studies in Italy. It train from the need to create a practical and easy-to-use tool to carry out seismic microzonation (SM) studies by producing standards compliant geographic database and maps, thus making them accurate, homogeneous and uniform for all municipalities in Italy. A geodatabase based on SQLite/SpatiaLite Relational Database Management System (RDBMS). It has been designed to collect and store data related to elements such as: geognostic surveys; bedrocks and cover terrains; superficial and buried geomorphological elements; tectonic-structural elements; elements of geological instability such as landslide zones, liquefaction zones and zones affected by active and capable faults; homogeneous microzones in seismic perspective, microzones characterized by a seismic amplification factor. The QGIS plugin provides tools such as data entry forms designed with Qt Designer; a QGIS project template with layers, symbol libraries and graphic styles; layouts for the SM Maps. MzSTools assembles in a single software environment a set of useful tools for those who work in. The plugin is open source, whose code hosted on the GitHub platform, and is published via the official QGIS plugins repository (https://plugins.qgis.org/plugins/MzSTools/).

Automated extraction of buildings from earth observation (EO) data is important for various applications, including updating of maps, risk assessment, urban planning, policy making. Combining data from different sensors such as high-resolution multispectral (HRI) and light detection and ranging (LiDAR) has shown great potential in building extraction. Deep learning (DL) is increasingly used in multimodal data fusion and urban object extraction. However, DL-based multimodal fusion networks may underperform due to insufficient learning of “joint features” from multiple sources and oversimplified approaches to fusing multimodal features. Recently, an hybrid attention-aware fusion network (HAFNet) has been proposed for building extraction from a dataset including co-located Very-High-Resolution (VHR) optical images and Light Detection And Ranging (LiDAR) joint data. The system reported good performances thanks to the adaptivity of the attention mechanism to the features of the information content of the three streams but suffered from model overparametrization, which inevitably leads to long training times and heavy computational load. In this paper the authors propose a restructuring of the scheme, which involved replacing VGG-16-like encoders with the recently proposed EfficientNet, whose advantages counteract exactly the issues found with the HAFNet scheme. The novel configuration was tested on multiple benchmark datasets, reporting great improvements in terms of processing times, and also in terms of accuracy. The new scheme, called HAFNetE (HAFNet with EfficientNet integration), appears indeed capable of achieving good results with less parameters, translating into better computational efficiency. Based on these findings, we can conclude that, given the current advancements in single-thread schemes, the classical multi-thread HAFNet scheme could be effectively transformed by the HAFNetE scheme by replacing VGG-16 with EfficientNet blocks on each single thread. The remarkable reduction achieved in computational requirements moves the system one step closer to on-board implementation in a possible, future “urban mapping” satellite constellation.

This paper presents a theoretically and methodologically grounded GIS-based model for the measurement and mapping of an index of living conditions in urban residential areas across Sweden. Further, the model is compared and evaluated using the Swedish Police’s assessment of crime-exposed areas. The results indicate that geographically measured vulnerable living conditions overlap to a large extent with the areas assessed to be crime-exposed by the Swedish Police. Over 61% of the police-defined crime-exposed areas are characterized by vulnerable living conditions. The results also show that the overlap is not perfect and that there are vulnerable areas that are not included in the police’s assessment of crime-exposed areas, but which are nonetheless characterized by vulnerable living conditions that could negatively affect the development of crime. It is also proposed that the model and the mapped index of living conditions provide a more well-grounded scientific basis for the police's assessment work. As a first step, the Swedish police have implemented the model and the mapped index in the work process employed in their annual identification of crime-exposed or at-risk areas. In addition to assisting the police, the model and the mapped index could also be used to support other societal actors working with vulnerable areas.

Novel cultivation technologies demand the adaptation of existing analytical concepts. Metabolic flux analysis (MFA) requires stable-isotope labeling of biomass-bound protein as the primary information source. Obtaining the required protein in cultivation set-ups where biomass is scarce or inaccessible due to low cell densities and cell immobilization is difficult to date. We developed a non-disruptive analytical concept for 13C-based metabolic flux analysis based on secreted protein as an information carrier for isotope mapping in the protein-bound amino acids. This "metabolic flux probe" (MFP) concept was investigated in different cultivation set-ups with a recombinant, protein-secreting yeast strain. The obtained results grant insight into intracellular protein turnover dynamics. Experiments under metabolic but isotopically nonstationary conditions in continuous glucose-limited chemostats at high dilution rates demonstrated faster incorporation of isotope information from labeled glucose into the recombinant reporter protein than in biomass-bound protein. Our results suggest that the reporter protein was polymerized from intracellular amino acid pools with higher turnover rates than biomass-bound protein. The latter aspect might be vital for 13C-flux analyses under isotopically nonstationary conditions for analyzing fast metabolic dynamics.

Gender and identity issues permeate society as a whole. Therefore, the matters involving transgender individuals should be analised in order to understand the difficulties experienced by this population and the social practices implemented. In this sense, the objective of this study was to investigate the strategic themes and their evolution in relation to the theme. For this, a bibliometric performance and network analysis (BPNA) was carried out with the existing data in the Web of Science database between 1954 and march 2021. Twenty-three thousand and four hundred and seventy-one (23,471) articles were identified, which were included in the SciMAT software to perform a bibliometric analysis, resulting in the graph of the thematic evolution structure and the strategic diagram, in which 8 motor themes and a cross-cutting theme of great magnitude are highlighted, which are discussed in depth. The results show the relation between the transgender theme and gender, identity, sexual orientation, hormone therapy and gender-affirming surgery. It is concluded that, despite the large number of associated researches, some areas of study are still incipient, such as the inclusion of transgender people in the formal labor market and in the prison context, thus opening field for further studies.

This work proposes a systematic topology optimization approach to simultaneously design the morphing functionality and actuation in three-dimensional wing structures. The actuation is assumed to be a linear strain-based expansion in the actuation material and a three-phase material model is employed to represent structural and actuating materials, and void. To ensure both structural stiffness with respect to aerodynamic loading and morphing capabilities, the optimization problem is formulated to minimize structural compliance while morphing functionality is enforced by constraining a morphing error between actual and target wing shape. Moreover, a feature mapping approach is utilized to constrain and simplify actuator geometries. A trailing edge wing section is designed to validate the proposed optimization approach. Numerical results demonstrate that three-dimensional optimized wing sections utilize a more advanced structural layout to enhance structural performance while keeping morphing functionality than two-dimensional wing ribs. The work presents the first step towards systematic design of three-dimensional morphing wing sections.

What does the way autistics bypass, learn, and eventually master language tell us about humans’ genetically encoded linguistic ability? In this theoretical review, we argue that autistic non-social acquisition of language, as well as autistic savant abilities, provide a strong argument for an innate, human-specific orientation toward (and mastery of) complex embedded structures. Autistic non-social language learning may represent a widening of the material processed during development beyond oral language. Structure detection and manipulation and generative production of non-linguistic embedded and chained material (savant abilities in calendar calculation, musical composition and interpretation, three-dimensional drawing) may thus represent an application of such innate mechanisms to non-standard materials. Typical language learning through exposure to the child’s mother tongue may represent but one of many possible achievements of the same capacity. The deviation from typical language development in autism may ultimately allow access to oral language, sometimes in its most elaborate forms, but also explains the possibility of the absence of its development when applied exclusively to non-linguistic structured material. Such an extension of human capacities beyond, or in parallel to, their usual limits call into question what we consider to be specific or expected in humans and, therefore, does not necessarily represent a genetic “error”. Regardless of the adaptive success or failure of non-social language learning, it is up to science and ethical principles to strive to maintain autism as a human potentiality to further foster our vision of a plural society.

This paper concerns a new methodology for accuracy assessment of global positioning system verified experimentally with LiDAR (Light Detection and Ranging) data alignment at continent scale for autonomous driving safety analysis. Accuracy of GPS (Global Positioning System) positioning of an autonomous driving vehicle within a lane on the road is one of the key safety considerations. Safety is addressed as a geometry of the problem, where the aim is to maintain knowledge that the vehicle (its bounding box) is within its lane. Accuracy of GPS positioning is checked by comparing it with mobile mapping tracks in the recorded high definition source. The aim of the comparison is to see if the GPS positioning remains accurate up to the dimensions of the lane where the vehicle is driving. For this reason, a new methodology is proposed. Methodology is composed of six elements: 1) Mobile mapping system minimal setup, 2) Global positioning data processing, 3) LiDAR data processing, 4) Alignment algorithm, 5) Accuracy assessment confirmation and 6) Autonomous driving safety analysis. The research challenge is to assess positioning accuracy of moving cars taking into account the constraints of the coverage of limited access highways in the United States of America. The available coverage limits the possibility of repeatable measurements and introduces an important challenge being the lack the ground truth data. State-of-the-art methods are not applicable for this particular application, therefore a novel approach is proposed. The method is to align all the available LiDAR car trajectories to confirm the GNSS+INS (Global Navigation Satellite System + Inertial Navigation System) accuracy. For this reason, the use of LiDAR metric measurements for data alignment implemented using SLAM (Simultaneous Localization and Mapping) was investigated, assuring no systematic drift by applying GNSS+INS constraints. SLAM implementation used state-of-the-art observation equations and the Weighted Non-Linear Least Square optimization technique that enables integration of the required constraints. The methodology was verified experimentally using arbitrarily chosen measurement instruments (NovAtel GNSS+INS, LiDAR Velodyne HDL32) mounted onto mobile mapping systems. The accuracy was assessed and confirmed by the alignment of 32785 trajectories with total length of 1,159,956.9~km and of total $186.4*10^{9}$~optimized parameters (six degrees of freedom of poses) that cover the United States region in the 2016--2019 period. It is demonstrated that the alignment improves the trajectories, thus final map is consistent. The proposed methodology extends the existing methods of global positioning system accuracy assessment focusing on realistic environmental and driving conditions. The impact of global positioning system accuracy on autonomous car safety is discussed. It is shown that 99\% of the assessed data satisfies the safety requirements (driving within lanes of 3.6~m) for Mid-Size (width 1.85~m, length 4.87~m) vehicle and 95\% for 6-Wheel Pickup (width 2.03--2.43~m, length 5.32--6.76~m). The conclusion is that this methodology has great potential for global positioning accuracy assessment at global scale for autonomous driving applications. LiDAR data alignment is introduced as a novel approach to GNSS+INS accuracy confirmation. Further research is needed to solve the identified challenges.

Maze navigation using one or more robots has become a recurring challenge in scientific literature and real life practice, with fleets having to find faster and better ways to navigate environments such as a travel hub (e.g. airports) or to evacuate a disaster zone. Many methods have been used to solve this issue, including the implementation of a variety of sensors and other signal receiving systems. Most interestingly, camera-based techniques have increasingly become more popular in this kind of applications, given their robustness and scalability. In this paper, we have implemented an end-to-end strategy to address this scenario, allowing a robot to solve a maze in an autonomous way, by using computer vision and path planning. In addition, this robot shares the generated knowledge to another by means of communication protocols, having to adapt its mechanical characteristics to be able to solve the same challenge. The paper presents experimental validation of the four components of this solution, namely camera calibration, maze mapping, path planning and robot communication. Finally, we present the integration and functionality of these methods applied in a pair of NAO robots.

: The agriculture sector is one of the backbones of many countries’ economies and its processes have been changing in order to enable technological adoption to increase productivity, quality, and sustainable development. In this research, we present a theoretical reflection through a scientific mapping of the adoption of precision techniques and breakthrough technologies in agriculture, the so-called Precision Agriculture (PA) and Agriculture 4.0 (A4.0). To do this, we used 4,694 documents from the Web of Science database to perform a Bibliometric Performance and Network Analysis (BPNA) of the literature with the support of the PICOC protocol and the SciMAT software. Our findings present 22 strategic themes related to Digital Agriculture (DA) such as Internet of Things (IoT) and Climate-smart Agriculture (CSA) among others, and the thematic network structures of the motor themes and the thematic evolution structure of the field of the study over time. In addition, our results discuss the main challenges and opportunities of DA. Our findings have the potential to provide insights for practitioners and researchers in decision-making and pave the way for future works.

Recently, the mapping industry has been focusing on the possibility of large-scale mapping from unmanned aerial vehicles (UAVs) owing to advantages such as easy operation and cost reduction. In order to produce large-scale maps from UAV images, it is important to obtain precise orientation parameters. For this, various techniques have been developed and are included in most of the commercial UAV image processing software. For mapping, it is equally important to select images that can cover a region of interest (ROI) with the fewest possible images. Otherwise, to map the ROI, one may have to handle too many images, and commercial software does not provide information needed to select images, nor does it explicitly explain how to select images for mapping. For these reasons, stereo mapping of UAV images in particular is time consuming and costly. In order to solve these problems, this study proposes a method to select images intelligently. We can select a minimum number of image pairs to cover the ROI with the fewest possible images. We can also select optimal image pairs to cover the ROI with the most accurate stereo pairs. We group images by strips, and generate the initial image pairs. We then apply an intelligent scheme to iteratively select optimal image pairs from the start to the end of an image strip. According to the results of the experiment, the number of images selected is greatly reduced by applying the proposed optimal image–composition algorithm. The selected image pairs produce a dense 3D point cloud over the ROI without any holes. For stereoscopic plotting, the selected image pairs were map the ROI successfully on a digital photogrammetric workstation (DPW), and a digital map covering the ROI is generated. The proposed method should contribute to time and cost reductions in UAV mapping.

Recent studies on seafloor mapping have presented different modelling methods for the automatic classification of seafloor sediments. However, most of these studies have applied these models to seafloor data with appropriate number of ground-truth samples, which raises the question whether these methods are applicable to studies with smaller numbers of ground-truth data. In this study, we aim to address this issue by conducting sediment class-specific predictions using ensemble modelling to map areas with limited or without ground-truth data and combined with hydro-acoustic datasets. The resulting class-specific maps were then assembled into one map, where the most probable class was assigned to the appropriate location. Our approach was able to predict sediment classes without bias to the class with more ground-truth data and produced reliable seafloor sediment distributions maps that can be used for seafloor monitoring. Sediment shifts of a heterogenous seafloor in the Sylt Outer Reef, German North Sea were also assessed to understand the sediment dynamics in the area. The analyses of sediment shifts showed that the western area of the Sylt Outer Reef is highly active, and the results of the analyses assisted in providing recommendations on future seafloor monitoring activities.

The primary purpose of this article is to present a maturity model dealing with environmental manufacturing processes in a company. First, a theoretical approach is presented where evolution from the first industrial revolution to the present (Industry 4.0.) is outlined with a need to implement environmental initiatives. Chapter two contains a detailed literature review, which resulted in the creation of our own maturity model presented in the next chapter. According to some authors, Industry 4.0 is based on characteristics that have already been the focus of “lean and green” concepts. The practical part of the article is a case study that shows which areas of the manufacturing process have “environmental” potential. The goal was to move from resource consumption, pollutant emissions and more extensive manufacturing towards environmentally responsible manufacturing (ERM). Using environmental materials and methods reduces energy consumption, which generates cost savings and higher profits. Here, VSM (Value Stream Mapping) was applied to identify core processes with environmental potential. The final part of the article summarizes the work and presents future possibilities. This paper provides an understanding of the role of environmental manufacturing in the era of the 4th industrial revolution.

In this paper, we introduce a new iteration scheme, named as the S**-iteration scheme, for approximation of fixed point of the nonexpansive mappings. This scheme is faster than Picard, Mann, Ishikawa, Noor, Agarwal, Abbas, Thakur, and Ullah iteration schemes. We show the stability of our instigated scheme and give a numerical example to vindicate our claim. We also put forward some weak and strong convergence theorems for Suzuki's generalized nonexpansive mappings in the setting of uniformly convex Banach spaces. Our results comprehend, improve, and consolidate many results in the existing literature.

Accurately estimating the current state of local traffic scenes is one of the key problems in the development of software components for automated vehicles. In addition to details on free space and drivability, static and dynamic traffic participants, information on the semantics may also be included in the desired representation. Multi-layer grid maps allow to include all this information in a common representation. However, most existing grid mapping approaches only process range sensor measurements such as LIDAR and Radar and solely model occupancy without semantic states. In order to add sensor redundancy and diversity it is desired to add vision based sensor setups in a common grid map representation. In this work, we present a semantic evidential grid mapping pipeline including estimates for eight semantic classes that is designed for straightforward fusion with range sensor data. Unlike in other publication our representation explicitly models uncertainties in the evdiential model. We present results of our grid mapping pipeline based on a monocular vision setup and a stereo vision setup. Our mapping resulsts are accurate and dense mapping due to the incorporation of a disparity- or depth-based ground surface estimation in the inverse perspective mapping. We conclude this paper by providing a detailed quantitative evaluation based on real traffic scenarios in the Kitti odometry benchmark and demonstrating the advantages compared to other semantic grid mapping approaches.

Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. These processes link geological processes at depth to surface features. Consequently, the study of landscapes can reveal essential information about the geochemical footprint of ore deposits at depth. Advances in satellite imaging and computing power have enabled the creation of large geospatial datasets, the sheer size of which necessitates automated processing. We describe a methodology to enable the automated mapping of landscape pattern domains using machine learning (ML) algorithms. From a freely available Digital Elevation Model, derived data, and sample landclass boundaries provided by domain experts, our algorithm produces a dense map of the model region in Western Australia. Both random forest and support vector machine classification achieve about 98\% classification accuracy with reasonable runtime of 48 minutes on a single core. We discuss computational resources and study the effect of grid resolution. Larger tiles result in a more contiguous map, while smaller tiles result in a more detailed, and at some point, noisy map. Diversity and distribution of landscapes mapped in this study support previous results. In addition, our results are consistent with the geological trends and main basement features in the region.

Optical remote sensing can contribute to biodiversity monitoring and species composition mapping in tropical forests. Inferring ecological information from canopy reflectance is complex and data availability suitable to such a task is limiting, which makes simulation tools particularly important in this context. We explored the capability of the 3D radiative transfer model DART to simulate top of canopy reflectance acquired with airborne imaging spectroscopy in complex tropical forest, and to reproduce spectral dissimilarity within and among species, as well as species discrimination based on spectral information. We focused on two factors contributing to these canopy reflectance properties: the horizontal variability in leaf optical properties (LOP) and the fraction of non-photosynthetic vegetation (NPVf). The variability in LOP was induced by changes in leaf pigment content, and defined for each pixel based on a hybrid approach combining radiative transfer modeling and spectral indices. The influence of LOP variability on simulated reflectance was tested by considering variability at species, individual tree crown and pixel level. We incorporated NPVf into simulations following two approaches, either considering NPVf as a part of wood area density in each voxel or using leaf brown pigments. We validated the different scenarios by comparing simulated scenes with experimental airborne imaging spectroscopy using statistical metrics, spectral dissimilarity (within crowns, within species, and among species dissimilarity) and supervised classification for species discrimination. The simulation of NPVf based on leaf brown pigments resulted in the closest match between measured and simulated canopy reflectance. The definition of LOP at pixel level resulted in conservation of the spectral dissimilarity and expected performances for species discrimination. Our simulation framework could contribute to better understand performances for species discrimination and relationship between spectral variations and taxonomic and functional dimensions of biodiversity.

What does the way autistics bypass, learn, and eventually master language tell us about human linguistic ability? Here, we argue that non-social acquisition of language, in addition to representing a strong argument for nativist models of human language, may be encompassed within the human-specific orientation and mastery of complex embedded structures, of which language represents one realization. Non-social language learning could thus represent the extension of available linguistic, and non-linguistic material processed by human genetic constraints, allowing language acquisition. This deviation from typical developmental language acquisition may ultimately allow access to language, sometimes in its most elaborate forms, and also explains the possibility of the absence of its development when applied to primarily non-linguistic structured material. However, such enlargement of material-specificity does not cast doubts about its human nature. Regardless of the adaptive success or failure of non-social language learning, it is up to science, legal policies, and ethical principles to strive to maintain autism as a human potentiality to further foster our vision of a plural society.

Intertidal seagrass plays a vital role in estimating the overall health and dynamics of coastal environments due to its interaction with tidal changes. However, most seagrass habitats around the globe have been in steady decline due to human impacts, disturbing the already delicate balance in environmental conditions that sustain seagrass. Miniaturization of multi-spectral sensors has facilitated very high resolution mapping of seagrass meadows, which significantly improve the potential for ecologists to monitor changes. In this study, two analytical approaches used for classifying intertidal seagrass habitats are compared: Object-based Image Analysis (OBIA) and Fully Convolutional Neural Networks (FCNNs). Both methods produce pixel-wise classifications in order to create segmented maps, however FCNNs are an emerging set of algorithms within Deep Learning with sparse application towards seagrass mapping. Conversely, OBIA has been a prominent solution within this field, with many studies leveraging in-situ data and multiscale segmentation to create habitat maps. This work demonstrates the utility of FCNNs in a semi-supervised setting to map seagrass and other coastal features from an optical drone survey conducted at Budle Bay, Northumberland, England. Semi-supervision is also an emerging field within Deep Learning that has practical benefits of achieving state of the art results using only subsets of labelled data. This is especially beneficial for remote sensing applications where in-situ data is an expensive commodity. For our results, we show that FCNNs have comparable performance with standard OBIA method used by ecologists, while also noting an increase in performance for mapping ecological features that are sparsely labelled across the study site.

The anarchic hand syndrome refers to an inability to control the movements of one’s own hand which acts as if it had a will of its own. The symptoms may differ depending on whether the brain lesion is anterior, posterior, callosal or subcortical, but the relative classifications are not conclusive. This study investigates the role of white matter disconnections in a patient whose symptoms are inconsistent with the mapping of the lesion site. A repeated neuropsychological investigation was associated with a review of the literature on the topic to identify the frequency of various different symptoms relating to this syndrome. Furthermore, an analysis of the neuroimaging regarding structural connectivity allowed us to investigate the grey matter lesions and white matter disconnections. The results indicated that some of the patient’s symptoms were associated with structures that, although not directly damaged, were dysfunctional due to a disconnection in their networks. This suggests that the anarchic hand may be considered as a disconnection syndrome involving the integration of multiple antero-posterior, insular and interhemispheric networks. In order to comprehend this rare syndrome better, the clinical and neuroimaging data need to be integrated with the clinical reports available in the literature on the topic.

(1) Background: The diphtheria toxoid antigen is a major component in pediatric and booster combination vaccines and is known to raise a protective humoral immune response upon vaccination. Although antibodies are considered critical for diphtheria protection, little is known about the antigenic determinants that maintain humoral immunity. (2) Methods: One hundred twelve 15-mer peptides covering the entire sequence of DTx protein were prepared by Spot-synthesis. Membrane-bound peptides immunoreactivity with sera from mice immunized with triple DTP vaccine allowed mapping of continuous B-cell epitopes, topological studies, MAPs synthesis, and ELISA development. (3) Results: Twenty epitopes were identified, being 2 in the signal peptide, 5 in the CD, 7 in the HBFT domain, and 5 in the RBD. Two 17-mer (CB/Tx-2/12 and CB/DTx-4-13) derived bi-epitope peptides linked by a Gly-Gly spacer were chemically synthesized. The peptides were used as antigens to coat ELISA plates and assayed with human (huVS) and mice vaccined sera (miVS) for in vitro diagnosis of diphtheria. The assay proved to be highly sensitive (99.96%) and specific (100%) either for huVS and miVS and when compared with a commercial ELISA test, demonstrate high performance. (4) Conclusions: Our work displayed the complete picture of the linear B cell IgG response epitope of the DTx responsible for the protective effect and demonstrated the specificity and eligibility to enter phase IIB studies of some epitopes to develop new and fast diagnostic assays.

Term vascular dysfunctional refers to a wide spectrum of vascular abnormalities including pathogenesis of tumors, their proliferation and leading to malfunctioned conditions. The treatment of most of the vascular anomalies including peripheral arterial disease and cardiac disease is multi factor procedure which include the embolic therapy, laser-based treatments and coagulation are all plating important role in managing the disease associated with vascular breakdown. The research proposal defines the treatment and diagnosis procedures involved in treatment of vascular abnormalities with a deep emphasis on techniques, efficiency and complications resulted from various other procedure and use of mapping and sequencing techniques based on genetics of variants of selected genes PIk3CA which will ultimately be more effective.

Low- and middle-income country cities face unprecedented urbanization and growth in slums. Gridded population data in small grid squares (e.g., 100x100m) derived from demographic and spatial data are a promising source of current population estimates, but face limitations in slums due to the dynamic nature of this population as well as modelling assumptions. The efficacy of using gridded population data in slum areas remains a question mark especially in the context of UN SDG indicator development. In this study, we use field-referenced boundaries and population counts from Slum Dwellers International (SDI) in Lagos (Nigeria), Port Harcourt (Nigeria), and Nairobi (Kenya) to assess the accuracy of nine gridded population datasets in slums. We also use a modelled map of all slums in Lagos to assess use of gridded population dataset for SDG11.1.1 (percent of population living in deprived areas). We found that all gridded population estimates vastly under-estimated population counts in populous slums, and the calculation of SDG11.1.1 in Lagos was impossibly low; gridded population datasets estimated that just 1-3% of the Lagos population lived in slums, compared to 56% using the UN-Habitat approach. We outline specific steps that might be taken to improve each gridded population dataset in deprived urban areas. While gridded population estimates are not yet sufficiently accurate to estimate SDG11.1.1, we are optimistic that some datasets could be following updates to their modelling approaches.

Karst wetlands are being seriously damaged, and protecting it has become an important matter. Karst vegetation is the essential component of wetland and plays an important role in in the ecological functions of wetland ecosystems. Classifying karst vegetation is important for karst wetlands protection and management. This paper addressed to classify karst vegetation in Huixian National Wetland Park, located in China using the improved SegNet Deep-Learning Algorithm and UAV images. This study proposed a method to fuse single-class SegNet models using the maximum probability algorithm for karst vegetation classification, and compared with object-based RF classification and multi-class SegNet classification, respectively. This paper evaluated the performance of multi-class SegNet model and fusion of single-class SegNet model with different EPOCH values for mapping karst vegetation. A new optimized post-classification algorithm was proposed to eliminate the stitching traces caused by SegNet model prediction. The specific conclusions of this paper include the followings:(1) fusion of four single-class SegNet models produced better classification for karst wetland vegetation than multi-class SegNet model, and achieved the highest overall classification accuracy (87.34%); (2) The optimized post-classification algorithm was able to improve prediction accuracy of SegNet model, and it could eliminate splicing traces; (3) The karst wetland vegetation classifications produced by single-class SegNet model outperformed multi-class SegNet model, and improved classification accuracy(F1-Score) between 10%~25%;(4)The EPCOH values and textural feature important impact on karst wetland vegetation classifications. The SegNet model with EPCOH 15 achieved greater classification accuracy(F1-Score) than the model with EPOCH 5 or 10. The textural feature improved improves the capability of the SegNet model for mapping karst vegetation;(5) Fusion of single-class SegNet models and object-based RF model could provide high classifications results for karst wetland vegetation, and both achieved greater 87% overall accuracy.

Significant efforts in the past decades to teach evidence-based practice (EBP) implementation has emphasized increasing knowledge of EBP and developing interventions to support adoption to practice. These efforts have resulted in only limited sustained improvements in the daily use of evidence-based interventions in clinical practice in most health professions. Many new interven-tions with limited evidence of effectiveness are readily adopted each year - indicating openness to change is not the problem. The selection of an intervention is the outcome of an elaborate and complex cognitive process which is shaped by how they represent the problem in their mind and is mostly invisible processes to others. Therefore, the complex thinking process which support appropriate adoption of interventions should be taught more explicitly. Making the process visible to clinicians increases the acquisition of the skills required to judiciously select one in-tervention over others. The purpose of this paper is to provide a review of the selection process and the critical analysis that is required to appropriately decide to trial or not trial new intervention strategies with patients.

Trichomes are a common morphological defense against pests, in particular, type IV glandular trichomes have been associated with resistance against different invertebrates. Cultivated tomatoes usually lack or have a very low density of type IV trichomes. Thus, specific breeding programs to incorporate these natural defences, that are common within the Solanum genus, might improve a more sustainable management. We have identified a S. pimpinellifolium accession with very high density of this type of trichomes. Two F2 mapping populations using two different parents have been developed, characterized and genotyped using a new genotype methodology, K-seq. We have been able to build an ultra-dense genetic map with 147,326 markers with an average distance between markers of 0.2 cM that has allowed us to perform a detailed mapping. We have used two different families and two different approaches, QTL mapping and QTL-seq, to identify several QTLs implicated in the control of trichome type IV developed in this accession on the chromosomes 5, 6, 9 and 11. The QTL located on chromosome 9 is a major QTL that has not been previously reported in S. pimpinellifolium that increases by a factor of 9 the density of trichomes.

The current study investigated the use of two-dimensional spatial distribution mapping representing the chlorophyll-a level in a river generated via an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV). A domestically developed UAV (Remo-M, Uconsystem Inc., Korea) and a USV developed by our research team were used to collect data from the Nae Seong stream in Korea. An adaptation of the “Data Cleaner” tool was developed and used for USV data processing and analysis. The operation of the autonomous USV was successful. Four previously described indices for quantifying algal blooms in rivers were utilized to create chlorophyll-a images, the normalized difference vegetation index (NDVI), the normalized green red difference index, the green normalized difference vegetation index (GNDVI), and the normalized difference red edge index. The suitability of the linear regression analysis of the correlation between the spectral indices obtained using the UAV and the in situ chlorophyll-a data obtained using the USV was evaluated with the coefficient of determination (R2) at a significance level of p &lt; 0.001. In field application and correlational analysis the NDVI was strongly correlated with chlorophyll-a (R2 = 0.88, p < 0.001), and the GNDVI was moderately correlated with chlorophyll-a (R2 = 0.74, p < 0.001). The map of chlorophyll-a was successfully quantified using the UAV and USV hybrid platforms.

Trail detection in mixed canopy ecosystems has important implications for forest management, monitoring, and conservation, although active sensor technology for sub-canopy trail detection is still developing. In order to assess the effectiveness of UAV(Unmanned Aerial Vehicle)-borne lidar (light detection and ranging) data for small trails (&lt; 2.5m width) in mixed forest canopy cover, we collected lidar data and trail characteristics (canopy cover and trail width) and created a high definition surface model map from the resulting lidar data, and also a high-resolution satellite imagery map using Google Earth. Through participatory mapping methods, seven respondents with limited prior geospatial experience completed a rapid identification of trails on both maps. Respondents’ trails were georeferenced in order to compare the rate of detectability between maps. We found greater detection on the lidar-derived map compared to the Google Earth map. Detectability in Google Earth maps was positively correlated with wider trails and trials with lower canopy. In lidar maps, trail detectability increased with wider trails, but canopy cover had no effect on detection rates. Our data indicate that a mixed-method approach that combines UAV-mounted lidar with high-resolution satellite imagery and participatory mapping increases rapid detection rates of small trails under varying canopy cover and trail widths.

Root rot diseases remain a major global threat to the productivity of agricultural crops. They are usually caused by more than one type of pathogen and are thus often referred to as a root rot complex. Fungal and oomycete species are the predominant participants in the complex, while bacteria and viruses are also known to cause root rot. Incorporating genetic resistance in cultivated crops is considered as the most efficient and sustainable solution to counter root rot; however, resistance is often quantitative in nature. Several genetics studies in various crops have identified quantitative trait loci associated with resistance. With access to whole genome sequences, the identity of the genes within the reported loci is becoming available. Several of the identified genes have been implicated in pathogen response. However, it is becoming apparent that at the molecular level, each pathogen engages a unique set of proteins to either infest the host successfully or be defeated or contained in doing so. In this review, a comprehensive summary of the genes and potential mechanisms underlying resistance or susceptibility against the most investigated root rots of important agricultural crops is presented.

The e-commerce platform in the digital economy era has evolved into a data platform ecosystem built around data resources and data mining technology systems. The most typical applications of big data are also concentrated in the field of e-commerce. E-commerce companies should first grasp the interactive relationship among the three major factors of data, technology and innovation, e-commerce platform operation is a multidisciplinary research field. It is not easy for researchers to obtain a panoramic view of the knowledge structure in this field. Knowledge graph is a kind of graph that shows the development process and structure relationship of knowledge with the field of knowledge as the object. It is not only a visual knowledge mapping, but also a serialized knowledge pedigree, which provides researchers with a quantitative research method for the development trend of statistics and academic status. The purpose of this research is to help researchers understand the key knowledge, evolutionary trends and research frontiers of current research. This study uses Citespace bibliometric analysis to analyze the data of the Science Net database and finds that: 1) The development of the research field has gone through three stages, and some representative key scholars and key documents have been recognized; 2) the common knowledge mapping of literature The co-occurrence of citations and keywords shows research hotspots; 3) The results of burst detection and central node analysis reveal research frontiers and development trends. Today, the visualization of big data brings different challenges. The abstraction between the world and today's data visualization occurs when the data is captured. Every user sees his own visualization data generated by standardized calculations. At the same time, there are still many controversies in the theoretical model, structure and structural dimensions. This is the direction that future researchers need to further study.

Ovarian cancer is the most frequent cause of deaths in gynecologic malignancies. Many possible mechanisms have been proposed via RNAseq and DNAseq technique recently. However, the driving factors are still obscure. The possible reasons are attributed to the incomplete human reference. This study integrated the canonical mapping-based and mapping-free protocols to extract reliable variations and novel events. We eventually obtained 450 reliable SNVs from the WES data and novel events from the RNAseq data, including 154 SNVs, 462 intron events, two repeats and six splice events. We identified six differentially expressed genes and six contigs that are significantly related to survival prognosis. The recurrent SNVs in significantly differentially expressed genes can be validated in an independent cohort of 20 Chinese ovarian cancer patients.

Faba bean (Vicia faba L.), a member of the Fabaceae family, is one of the important food legumes cultivated in cool temperate regions. It holds great importance for human consumption and livestock feed because of its high protein content, dietary fibre, and nutritional value. Major faba bean breeding challenges include its mixed breeding system, unknown wild progenitor, and genome size of ~13 Gb, which is the largest among diploid field crops. The key breeding objectives in faba bean include improved resistance to biotic and abiotic stress and enhanced seed quality traits. Major progress on reduction of vicine-convicine and seed coat tannins, the main anti-nutritional factors limiting faba bean seed usage, have been recently achieved through gene discovery. Genomic resources are relatively less advanced compared to other grain legume species, but significant improvements are underway due to a recent significant increase in research activities. A number of bi-parental populations have been constructed and mapped for targeted traits in the last decade. Faba bean now benefits from saturated synteny‐based genetic maps, along with next-generation sequencing and high-throughput genotyping technologies that are paving the way for marker-assisted selection. Developing a reference genome, and ultimately a pan-genome, will provide a foundational resource for molecular breeding. In this review, we cover the recent development and deployment of genomic tools for faba bean breeding.