Valid Points Research Articles

RELIABILITY AND RELEVANCE OF 24H AMBULATORY BLOOD PRESSURE MONITORING IN DAY TO DAY TREATMENT OF ARTERIAL HYPERTENSION

Objective: Blood pressure measurements are the basis of diagnosis, treatment and research of arterial hypertension and its sequelae. All decisions hinge on the validity of the measurement method. Here, the ABPM is considered best practice. Evaluation of ABPM, notwithstanding known issues like availability and economy, regarding its validity, accuracy and applicability to clinical practice was investigated. Design and method: We retrospectively consider 450 ABPMs executed using validated devices from Nephrology Center Göttingen (ESH-Excellence-Center). This work evaluates the measurements’ adherence to diagnostic guidelines (ESH) with respect to sources of error, count of valid measurements and cancellation by patient. Criteria for validity are: - Minimum 24 h recording duration.- At least 20 valid day and 7 valid night measurements- Minimum of 70 percent valid measurements in total Cancellation by the patient is assumed if: - Recording duration is below 22h without indication of technical fault or measurement is stopped prior to 7 30 a.m. Results: 23 % (n = 106) of 450 ABPM considered to meet all guideline criteria. Remaining 77 % of measurements failed at least one criterion. In 27 % (n = 120), measurement duration was below 24 h and for 22 % (n = 98) it was below 22 h. In 2.6 % (n = 12) minimum amount of daytime (n = 3) and night-time (n = 9) data points was not met. Requirement for at least 70 % of valid data points was failed by 23.5 % (n = 105). Here the source of error were motion artefacts in about 50 % of cases, pressurisation issues during the day in 17 % (n = 15) and 27 % (n = 11) at night and kinks in tubing in 14 % (n = 12) during daytime. About 20.6 % (n = 93) of ABPM were preliminary cancelled by the patient which amounts to approximately a quarter (25.4 %) of all measurements failing the guideline criteria. Conclusions: Value of ABPM hinges on guideline conforming execution of measurements and should therefore be explicitly discussed with the patient. Quality of data obtained by an ABPM needs to be examined for every case to ensure its validity to justify a course of action.

Borderlands of Slavery: The Struggle over Captivity and Peonage in the American Southwest by William S. Kiser

Reviewed by: Borderlands of Slavery: The Struggle over Captivity and Peonage in the American Southwest by William S. Kiser Lauren Brand (bio) Borderlands of Slavery: The Struggle over Captivity and Peonage in the American Southwest. By William S. Kiser. (Philadelphia: University of Pennsylvania Press, 2017. Pp. 280. Cloth, $47.50; paper, $24.95.) Could slavery in any form expand into the American West? Was it realistically possible? Did the U.S. government have the right to regulate the expansion of slavery? These are some of the massive questions that dominated the political discourse of the antebellum United States. William S. Kiser’s Borderlands of Slavery explores how the reality of Native captivity and debt peonage in New Mexico played a crucial role in the answers that both northern and southern politicians offered to these questions. New Mexico not only shaped national conversations about slavery’s expansion in the antebellum era, but also influenced national policy makers’ decisions regarding the definition of unfree labor during the Reconstruction era. Kiser argues that the existence of a different form of slavery in New [End Page 248] Mexico “forced Americans to think more broadly about slavery and brought awareness to the fact that involuntary labor was not limited to the chattel system in the South” and that this realization “informed profound political debates during the 1850s regarding the future role of unfree labor in the country” (21). The prologue provides a concise overview of the history of New Mexico from the Spanish colonial era through the territory’s integration into the United States in the mid-nineteenth century in order to quickly familiarize readers with this complex period. Indeed, scholars from many subfields of nineteenth-century United States history will find much of interest in this short book. For example, historians studying the various forms of enslavement endured or practiced by the Indigenous peoples of North America (or “the other slavery,” as Andrés Reséndez describes it)1 will appreciate Kiser’s description of the widespread prevalence of Native captivity in New Mexico from the Spanish colonial era through the Civil War. Additionally, historians of the antebellum U.S. South will be interested in Kiser’s dissection of how southern politicians used New Mexico and the various forms of captivity there as a foil to compare to the southern system of enslavement. Although debt peonage was not a racially based system of enslavement, it was a structure that “nonetheless operated upon manipulated conditions of dependency that typically ensured perpetual bondage” (16), leading many southern congressmen to view New Mexico as a valid point of comparison to demonstrate the compatibility of slavery with peonage in the American West. Kiser shows throughout that such comparisons were not unwarranted. For example, he explores comparisons between the ways captive women in the Southwest were described as “likely girl[s]” and valued for their attractiveness and sexual availability in the same way that African American women were in the South (61). However, despite some similarities that prompted these comparisons, Kiser notes that, in the end, there remained “widespread confusion about the differences between Southern slavery and New Mexico’s traditional forms of bondage . . . among federal lawmakers” (52). State and local historians will also appreciate Kiser’s attention to the perspectives of people actually living in New Mexico during this period. Kiser explains how residents of New Mexico themselves thought about these issues. For example, he notes that local people were somewhat ambivalent about chattel slavery and the comparison to the U.S. South but remained just as concerned about retaining their rights to captives and peons as they always had. Kiser also explores how, after 1846, local New Mexico law-makers purposely kept quiet about the issue of debt peonage, because they often personally benefited from it. Thus Kiser notes how challenging it can [End Page 249] be for historians to study the ways captivity and debt peonage impacted the lives of people in New Mexico. The sources have to be carefully mined to reveal what was really going on behind the surface. Kiser is very open about these challenges and how he reads the available sources. As time passed and sectionalist sentiment increased in both the United...

MODELING OF GROUNDWATER LEVEL CHANGES DUE TO DEEP WELL EXTRACTION USING MODFLOW IN THE NORTH OF BANDAR LAMPUNG

The growth rate of urban areas in the north of Bandar Lampung has caused the extraction of groundwater resources through deep well pumping to increase. Therefore, it is necessary to study the impact of pumping to prevent groundwater depletion. This study performs numerical simulation modeling to identify the impact of deep well pumping on changes in groundwater levels in three sub-districts north of Bandar Lampung city. Modeling is done using MODFLOW-6 and ModelMuse as a graphical user interface (GUI). The simulation method uses steady-state and transient models with four stress periods in 2000, 2010, 2020, and 2030. A total of 30 data wells are used for model setup, and 15 of them as observation and validation points. The simulation results show that hydraulic head changes occur at depths of 10 m to more than 40 m. Drawdown and groundwater head changes are concentrated at deep well points with a radius of up to 1 km. The model also successfully identified a decrease in groundwater level to more than 8 m in the deepest well area DW1. Thus, it is necessary to take action that can overcome the impact of changes in groundwater level due to pumping from deep wells. Furthermore, groundwater storage engineering can be one solution by utilizing rainwater harvesting technology to recharge the groundwater aquifer system.

Exploring the usability of a chatbot-based conversational dietary assessment tool among cardiovascular patients

Abstract Funding Acknowledgements Type of funding sources: None. Background Dietary intervention in cardiac rehabilitation (CR) plays an integral role in health promotion and improving the quality of life for those with cardiovascular diseases (CVDs). Innovative techniques offer great potential to deliver cost-effective dietary modifications by providing dietary intake self-tracking and personalized advice. Nevertheless, the time-consuming inconvenience of manual food logging has led to the development of conversational agents that alleviate the tracking burden and promote self-reflection. However, the usability of nutrition chatbots among CVD patients has not yet been investigated in clinical practice. Purpose This study aimed to examine the usability of a chatbot-based dietary assessment tool among CVD patients in a prospective observational trial and discuss the preliminary results. Methods In the clinical trial, patients who are scheduled for or recently have undergone specific surgical procedures are selected for participation. Data collection starts one week before the intervention and continues until one year after discharge. Participants collect self-reported food intake data via the chatbot several times per day for a maximum of 4 non-consecutive days every other week. Quarterly assessments are scheduled to gather feedback on the use of the system. This paper focuses on the data collected in the first quarter of this trial. The usability was measured through chatbot usage and qualitative input through interviews. Results Participant recruitment started in December 2021. Thirteen out of sixteen participants (1 female and 15 males, mean_age=62.6) completed their first quarter of the trial. We included 1267 valid data entry points by evaluating the reported items and participants' self-perceived tracking accuracy. The average response rate is 87.83%, with the average self-perceived accuracy as 8.05. Although there's no significant difference in the time they spent reporting each meal (mean 110.79s, p>0.05), we observed a significant increase in the number of reported items for each meal (mean 3.79, p<0.05) and a significant decrease in time they spent reporting each item (mean 29.69s, p<0.01). Participants explained that while getting familiar with the chatbot, they found it increasingly easier to report more items if needed, and the self-reporting became less time-consuming. During the interview, the participants shared their enthusiasm about using the chatbot for food tracking and praised its simplicity and learnability. Meanwhile, they gave suggestions to improve the chatbot: 1) involving their partners when using the chatbot; 2) knowing the dietary behaviour of their peers; 3) receiving recognition with anonymous competitions. Conclusion The chatbot was found efficient to use with reasonable high learnability among participants. With insights into the usability issues and patients' expectations from the chatbot, we will conduct further research on developing personalized recommendations.

Development of high-resolution gridded data for water availability identification through GRACE data downscaling: Development of machine learning models

Estimation of total water availability has paramount importance in planning sustainable development of a region, particularly in arid water-scarce areas. Coarse-resolution of existing total water availability or terrestrial water storage anomaly (TWSA) data is the major limitation of their applications in different sectors. An attempt has been made to downscale Gravity Recovery and Climate Experiment (GRACE) TWSA data to develop a high-resolution gridded data product of the total water availability of Iraq. European reanalysis (ERA5) precipitation, evapotranspiration, surface runoff, subsurface runoff and soil water contents data were used to downscale GRACE 1.0° spatial resolution monthly TWSA to 0.1° spatial resolution for the period 2002–2020. A machine learning (ML)-based recursive feature elimination algorithm was used to identify the optimum input combination according to the nonlinear relationship of ERA5 variables with GRACE water equivalence data. The selected subset of inputs was used to develop the downscaling models using three classical ML algorithms for the available GRACE measurement points over Iraq. The models were calibrated at 70% of GRACE grid point locations and validated in the rest of the points. Finally, the model was used to predict TWSA at each ERA5 grid point to generate Iraq's high-resolution water availability dataset. The results showed higher performance of random forest in downscaling TWSA compared to other algorithms. The model estimated the TWSA at validation points with Kling-Gupta Efficiency (KGE) in the range of 0.5–0.91 and Nash-Sutcliff Efficiency (NSE) between 0.54 and 0.88. The modelled high-resolution TWSA data shows higher availability of water resources in the north, particularly northeast of Iraq, and the least in the southeast. The technique developed in this study can be implemented in developing a high-resolution gridded water availability dataset from satellite GRACE data in the region where in-situ estimation is very limited.

Implementation of a Real-Time Object Pick-and-Place System Based on a Changing Strategy for Rapidly-Exploring Random Tree

An object pick-and-place system with a camera, a six-degree-of-freedom (DOF) robot manipulator, and a two-finger gripper is implemented based on the robot operating system (ROS) in this paper. A collision-free path planning method is one of the most fundamental problems that has to be solved before the robot manipulator can autonomously pick-and-place objects in complex environments. In the implementation of the real-time pick-and-place system, the success rate and computing time of path planning by a six-DOF robot manipulator are two essential key factors. Therefore, an improved rapidly-exploring random tree (RRT) algorithm, named changing strategy RRT (CS-RRT), is proposed. Based on the method of gradually changing the sampling area based on RRT (CSA-RRT), two mechanisms are used in the proposed CS-RRT to improve the success rate and computing time. The proposed CS-RRT algorithm adopts a sampling-radius limitation mechanism, which enables the random tree to approach the goal area more efficiently each time the environment is explored. It can avoid spending a lot of time looking for valid points when it is close to the goal point, thus reducing the computing time of the improved RRT algorithm. In addition, the CS-RRT algorithm adopts a node counting mechanism, which enables the algorithm to switch to an appropriate sampling method in complex environments. It can avoid the search path being trapped in some constrained areas due to excessive exploration in the direction of the goal point, thus improving the adaptability of the proposed algorithm to various environments and increasing the success rate. Finally, an environment with four object pick-and-place tasks is established, and four simulation results are given to illustrate that the proposed CS-RRT-based collision-free path planning method has the best performance compared with the other two RRT algorithms. A practical experiment is also provided to verify that the robot manipulator can indeed complete the specified four object pick-and-place tasks successfully and effectively.

Assessment of machine learning classifiers in mapping the cocoa-forest mosaic landscape of Ghana

The absence of clear-cut directives on the optimal classifier for land-use land-cover (LULC) classification of Ghana's cocoa landscape presents a practice gap. This has resulted in monitoring challenges since it is difficult to effectively compare land cover maps because they differ in the classifiers. In this paper, we explored the performance of four commonly used machine learning classifiers in the cocoa landscape to accurately determine the option that best segregates the different vegetation classes. Specifically, the accuracy with which k-Nearest Neighbors (kNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF) classifiers mapped the cocoa landscape in Juaboso, Ghana, was compared. A pre-processed Sentinel-2 image, 352 training and 151 validation points collected through field Global Positioning System survey were used. The image was classified using the kNN, ANN, SVM and RF classifiers. The accuracies of the LULC maps were assessed using overall accuracy (OA), Cohens’ kappa (k). Also, practitioners with practical knowledge of the land cover classes and their distribution in the landscape subjected the map to visual inspection. The OA and k values indicated RF (OA=84.77%, k = 0.801), kNN (OA = 84.11%, k = 0.796), ANN (OA = 76.13%, k = 0.7), and SVM (OA = 81.45%, k = 0.762) all performed well in classifying the landscape with a satisfactory agreement. Additionally, there are no clear-cut classifier that experts in remote sensing should apply while mapping Ghana's cocoa environment. At any point, using the classifier that most accurately represents the landscape is crucial and should be prioritized. Therefore, guidance on the choice of classification algorithms by researchers and practitioners for mapping the cocoa landscape of Ghana must not be limited to the overall accuracies and kappa only. Instead, operationalising a mapping and validation framework that incorporates experts’ review will yield a LULC map that better represents the landscape.

Performance of a simplified nutrition screening tool for outpatient radiotherapy cancer patients.

Public hospitals in Thailand recently adopted a new nutrition screening tool to satisfy documentation requirements for reimbursements through the diagnosis-related group system. However, data on the performance of this instrument remains limited. This study was designed to assess the validity and cutoff points of the Society of Parenteral and Enteral Nutrition of Thailand (SPENT) nutrition screening tool against the patient-generated subjective global assessment (PG-SGA) and malnutrition diagnostic criteria proposed by the global leadership initiative on malnutrition (GLIM) in cancer patients receiving outpatient radiation therapy. A cross-sectional study of 350 patients was conducted from August 2018 to September 2020. All patients were screened for malnutrition using the SPENT nutrition screening tool. The instrument's sensitivity, specificity, positive predictive value, negative predictive value, and agreement were calculated using either the PG-SGA or GLIM malnutrition diagnosis as benchmarks. The cutoff that gave the highest sensitivity and specificity of the SPENT nutrition screening tool was selected. The mean age standard deviation of the 350 cancer patients was 59.9 (13.9) years, and 191 (54.6%) were men. Head and neck cancers were the most common type (35.7%). Against PG-SGA and GLIM malnutrition diagnosis, the SPENT nutrition screening tool demonstrated good sensitivity (85.3% and 82.8%), specificity (84.1% and 59.4%), positive predictive value (90.5% and 64.0%), negative predictive value (76.3% and 79.9%), with moderate strength of agreement (Cohen kappa 0.678, P < .001 and 0.414, P < .001). Using only the first 2 out of 4 questions revealed an acceptable sensitivity and specificity. The SPENT nutrition screening tool is an accurate, sensitive, and specific tool for malnutrition screening in cancer patients receiving outpatient radiotherapy.

UAV Photogrammetry for Soil Surface Deformation Detection in a Timber Harvesting Area, South Korea

During forest operations, canopy removal results in the soil surface being vulnerable to deformation, negatively impacting soil fertility and water quality. This study utilized unmanned aerial vehicle (UAV) photogrammetry to accurately detect soil surface deformation (SSD). Two-dimensional images were safely collected on a steep slope without real-time kinematics by conducting vertically parallel flights (VPFs). A high-resolution digital surface model (DSM) with a &lt;3 cm resolution was acquired for precise SSD detection. Using DSM of difference (DoD), SSDs were calculated from DSMs acquired in June, July, September, and October 2022. By checking spatial distances at ground control points, errors of DSM alignments were confirmed as only 3 cm, 11.1 cm, and 4 cm from July to June, September to June, and October to June, respectively. From the first month of monitoring, erosion and deposition of approximately 7 cm and 9 cm, respectively, were detected at validation points (VPs). However, from total monitoring, cumulative SSD was assessed as having deposition tendencies at all VPs, even compared to ground truths. Although UAV photogrammetry can detect SSDs, spatial distortion may occur during UAV surveys. For vegetation growth issues, UAV photogrammetry may be unable to capture data on the soil surface itself.

“What is critical religion?” A Response to Galen Watts and Sharday Mosurinjohn, “Can Critical Religion Play by Its Own Rules?”

Abstract The authors should be congratulated for writing about ‘critical religion’. Whatever else critical religion might or might not be, nothing could be more important than a critical inquiry into the categories that powerfully organise our knowledge and our institutions, including our universities. By publishing in a major journal, the JAAR, they bring into the mainstream significant topics that are habitually marginalised. They raise many valid points for public debate. Their article, however, is marred by reification and contradiction. To squeeze their generalisations into one journal article, the authors set up Russell McCutcheon, Craig Martin and Timothy Fitzgerald as the core of an imaginary school, and then when they stumble on a possible disagreement between us, they accuse us of inconsistency. Speaking for myself, I explain why I have habitually used the term ‘critical religion’ to refer to my own work, and why I have recently considered abandoning it. I point out that the authors never properly discuss the genesis of the discourse on the non-religious secular, which is fundamental to any serious attempt to understand ‘critical religion’. They ignore my work on India and Japan. They nowhere discuss a central core of my own position, that religion is a member of a configuration of empty categories, including politics, nature, economy, and nation, a signalling system that is the source of hegemonic power and the illusions of enlightenment modernity. However, these shortcomings should not deter us from taking forward their work as a positive opportunity.

CONCEPTUAL MODEL FOR SCHEDULING AND CONTROL OF PRODUCTION AND LOGISTICS OPERATIONS USING MULTI-AGENT ROBOTIC SYSTEMS AND BLOCKCHAIN

The work proposes a conceptual model called MASBLOCK for scheduling and control of production and logistics operations, the study incorporates multi-agent robotic systems and blockchain technologies applied to industry. The problem identified in the research is the lack of coordination between fixed and mobile robots to develop activities and fulfill tasks in work areas, coupled with the lack or non-existence of secure data management. For this purpose, 22 scientific articles related to the subject have been considered considering three criteria (L), (Q) and (L-Q). A methodology proposed in a traditional model has been analyzed, which has served as a basis to define 5 steps for the creation of the new model, multi-agent system technologies have been incorporated for the coordination of robots and blockchain to perform secure data recording, decentralization and verifiable pose. Thus, the MASBLOCK model has been proposed, which shows an industrial environment where inputs, technologies, fixed and mobile robots interact; which perform multiple tasks, until reaching a validation point that allows to continue with the flow of activities until ending with an output, benefiting the interaction, self-programming and self-regulation of robots and all agents involved in productive and logistic operations, and data security in information management. Keywords: Conceptual model, multi-agent robotic systems, blockchain, industry.

A 3D method called mdMRS for post-processing Magnetic Resonance Spectroscopy data

The data obtained from a scanner for Magnetic Resonance Spectroscopy (MRS) is three-dimensional (3D) since the FID data from the scanner has spectrum values which are 2D complex numbers and 1D time values. This paper describes an MRS frequency-based post-processing method that has the advantage that it quickly determines the values needed for metabolite intensities and concentrations, even for most overlapping spectral terms.The method of this paper does not depend on area under a curve or a user-chosen basis set. As few as three valid points corresponding to a peak on the spectral curve are all that is needed, other than similar information on a reference metabolite, for concentration determination of the associated metabolite. All four of the key values of peak location, amplitude, phase angle, and damping constant are determined simultaneously with high accuracy, dependent upon noise, and with computational simplicity from only three complex-valued constants. The theory behind mdMRS uses the knowledge that the projection of the 3D spectrum to the complex plane is a simple circle. Several concepts from complex variables theory are important, such as the Linear Fractional Transformation (LFT) that maps the frequency axis to that circle. The duality linking an LFT with a 2 × 2 Moebius matrix enables a fast iteration process that sharpens the four key value estimates on each iteration. The iteration removes all other terms when considering one of them. Applied to initial estimates, this leads to increasingly accurate output.To be useful to clinicians and to researchers developing treatments based on metabolite concentrations, the goal for post-processing of the data include both fast and accurate computations, with speed sufficient to provide results “on console” and output provided as concentrations. Besides the number of protons associated with a metabolite, concentrations are determined using both amplitude and damping constant values. Since the methods of mdMRS provide both of those characteristics simultaneously, the time from data collection to metabolite concentration output is minimized. The name of this new post-processing method was chosen since the attributes of the method help bring that goal closer to reality for Medical Doctors.

A Method Using LSTM Networks to Impute Missing Temperatures in Temperature Datasets and to Predict Future Temperatures

With the growth of population and the development of industry, global warming has become a serious problem. It is necessary to focus on temperature change now. This paper designed and tested methods to impute the missing temperature in global temperature datasets and predict the future temperature. Imputation was divided into two steps, the first step is filling small gaps with equally spaced points for efficiency, and the second step is filling larger gaps with Long Short-Term Memory (LSTM) neural networks. This combination of imputation methods gave valid points with both accuracy and efficiency and successfully made some datasets continuous. Another LSTM network was used to predict future temperatures. The χ2 test for the goodness of prediction with real data in Australia showed that this prediction method using LSTM network is highly accurate.

Rock typing of the Miocene Hammam Faraun alluvial fan delta sandstone reservoir using well logs, nuclear magnetic resonance, artificial neural networks, and core analysis, Gulf of Suez, Egypt

The application of integrated methods to evaluate the quality of reservoir rocks is becoming crucial in petroleum geoscience. The Miocene reservoirs in the El Morgan Oil Field consist mainly of sandstones with interbedded dolomitic silts and shales. Based on the pore scale, the sandstone reservoir of Hammam Faraun Member is a heterogeneous reservoir, and this is the main challenge in this oil field that affects both production and exploration activities. In this article, we have integrated the well logs, nuclear magnetic resonance (NMR), artificial neural networks (ANN) and core analysis to better understand these alluvial fan sandstone reservoirs. The petrographic description indicates arkosic sandstone with varied pore types, texture, cement, and sorting. The studied flow and storage capacity for well#1, indicate four rock types with 24 flow units and eleven barriers, which indicates a high degree of heterogeneity. The artificial neural networks (ANN), that is, the K.mod module, were used to enhance the prediction quality and forecast of FZI in the uncored intervals, where the NMR‐calculated permeability acts as a calibration point for the FZI model validation. This study reveals that the FZI is controlled by permeability, which is mostly influenced by pore throat size. The NMR log showed varied free fluid volume with varied capillaries, clay‐bound volumes and minor streaks of tar along the studied unit. The reservoir pores in the study area are residual pores that have undergone mechanical compaction and cementation that reduce porosities, while dissolution enhances the sandstone porosities. Porosity and permeability were significantly reduced in the presence of clays and carbonate minerals, which indicates poor reservoir quality zones in some parts. The primary factor influencing permeability is pore throat size, which is heavily influenced by the depositional environment and subsequent diagenetic processes. The findings of this study could lead to the development of accurate reservoir static models for future exploration and development plans in the studied area and elsewhere in similar depositional settings.

Accuracy assessment of high-resolution terrain data produced from UAV images georeferenced with on-board PPK positioning

In recent years, unmanned aerial vehicles (UAVs) have increased rapidly and successfully established themselves as a tool for the rapid collection of high-resolution images as baseline data in land cover studies and topographic mapping. In photogrammetry using the SfM-MVS method of processing captured images, indirect georeferencing of the digital data through ground control points (GCPs) is usually applied. But selecting, marking, and coordinating GCPs in hardto-reach terrains is time-consuming and sometimes dangerous or impossible. The main objective of this study is to evaluate the accuracy of high-resolution topographic data (HRTD) products of photogrammetric processing of PPK-directly georeferenced images by SfM-MVS workflow. Direct and indirect methods of georeferencing digital products are compared. The planimetric and vertical root mean square error (RMSE) in the position of the validation points were calculated by the differences between measured coordinates in dense point clouds, orthophoto mosaics, and terrain surfaces (DSM), and precisely measured coordinates of the validation points by GNSSRTK receivers. The analysis is based on a statistical evaluation of experimental data obtained from a TAROT X6-based hexacopter equipped with two different image sensor configurations: 1) Sony RX0 action camera and 2) Sony A6000 mirrorless camera, and 3) DJI Phantom 4 Pro quadcopter with integrated additional L1-GNSS module for direct georeferencing by PPKmethod. HRTD generation was performed with three block control configurations for each UAV: 1) Indirect georeferencing via GCP only, 2) PPK direct georeferencing without GCP, and 3) PPK georeferencing using one GCP. Our research showed that when using L1-GNSS onboard receivers for PPK-georeferencing without any GCPs, the point cloud&amp;#39;s planimetric accuracy (RMSExy) was from 0.125 to 0.231 cm, depending on the UAV/camera configuration. However, two flight missions produced significant vertical offsets, most likely due to ionospheric disturbances affecting the resolution of phase cycle ambiguities in the single-frequency receivers used. When adding one control point in the PPK georeferencing method, the planimetric and vertical accuracy of the data is comparable to the indirect GCP referencing method. Furthermore, our results show that camera properties (i.e., focal length, resolution, sensor quality) affect the quality and accuracy of digital products. The HRTDs were also evaluated according to the ASPRS (American Photogrammetry and Remote Sensing Society) Standards for Accuracy of Digital Geospatial Data. Analyzing the accuracy of the HRTDs obtained with the experimental UAV/camera configurations for the test area, the present study shows that the PPK-SfM-MVS workflow can provide quality data with a centimeters accuracy of the photogrammetric products.

An inversion method of subsurface thermohaline field based on deep learning and remote sensing data

ABSTRACT Seawater temperature and salinity are basic marine environmental parameters, which can be used to calculate other marine environmental parameters. However, most of the on-site observation data have the problems of uneven spatial distribution and time discontinuity, and it is difficult for remote sensing observation methods to obtain subsurface information. In this study, we proposed a deep learning model with combining remote sensing temperature and salinity as well as in-situ measured data by Argo profiles, and the nonlinear relationship was revealed. An effective and direct inversion method was realized for underwater three-dimensional thermohaline structure based on remote sensing temperature and salinity at accurate points on global scale. The SST data were obtained from the FY3C-VIRR daily sea surface temperature product, the SSS data were acquired from the SMAP Level 3 8-day running averages sea surface salinity product, and the Argo scatter data were got from the ‘Global Ocean Argo Scatter Data Set’. Based on the temporal and spatial location information of the data, this paper matched the remote sensing data from 2016 to 2019 with the Argo data, 64751 valid pairing points were obtained. The deep learning model was constructed as a multilayer perceptron model with 5 hidden layers. The RMSE of temperature had a maximum value of 2.106°C in 130 m depth and a minimum value of 0.367°C in 1000 m with an average of 1.174°C for validation dataset. And the RMSE of salinity had a maximum value of 0.356 psu in 0 m and a minimum value of 0.045 psu in 1000 m with an average of 0.202 psu. Compared with other methods based on fixed mesh products, this study realized a direct inversion method at any location in the global ocean and improved the inversion accuracy, which provides a reliable data support for refined marine monitoring.

Wetland mapping at 10 m resolution reveals fragmentation in southern Nigeria

Wetland ecosystems play key roles in global biogeochemical cycling, but their spatial extent and connectivity is often not well known. Here, we detect the spatial coverage and type of wetlands at 10 m resolution across southern Nigeria (total area: 147,094 km2), thought to be one of the most wetland-rich areas of Africa. We use Sentinel-1 and Sentinel-2 imagery supported by 1500 control points for algorithm training and validation. We estimate that the swamps, marshes, mangroves, and shallow water wetlands of southern Nigeria cover 29,924 km2 with 2% uncertainty of 460 km2. We found larger mangrove and smaller marsh extent than suggested by earlier, coarser spatial resolution studies. Average continuous wetland patch areas were 120, 11, 55 and 13 km2 for mangrove, marsh, swamp, and shallow water respectively. Our final map with 10 m pixels captures small patches of wetland which may not have been observed in earlier mapping exercises, with 20% of wetland patches being < 1 km2; these were clustered around urban centres, suggesting anthropogenic wetland fragmentation. Our approach fills a knowledge gap between very local (< 400 km2) studies reliant on field studies and aerial photos, and low resolution (> 250 m pixel dimensions) global wetland datasets and provides data critical for both improving land-surface climate models and for wetland conservation.

A dataset of land cover classifications with a spatial resolution of 30m in Mongolia in 2005 and 2015

The Mongolian Plateau is in the interior of Northeast Asia, and is extremely vulnerable to climate change and the deleterious effects of human activities. Mongolia is an important component unit of the Mongolian Plateau, and its resources, environment and ecological problems are closely related to the ecological barrier and resource security in northern China and the sustainable development of the China-Mongolia-Russia Corridor. However, there is still a lack of high-precision land cover data products suitable for the regional characteristics of Mongolia. In this study, according to the landscape pattern of Mongolia, we constructed a land cover classification system suitable for Mongolia; and based on the object-oriented remote sensing interpretation method, we adopted the split-scene interpretation to select a variety of indexes. According to certain rules and classification thresholds, we obtained a dataset of land cover classifications with a spatial resolution of 30m in Mongolia in 2005 and 2015. The land cover classifications of Mongolia includes 11 categories: forest, meadow steppe, real steppe, desert steppe, bare land, sand, desert, ice and snow, water, cropland and built areas. Based on multi-source validation point information and high-resolution Google Earth images, we completed an overall quality assessment and a single classification quality assessment of land cover classification results in Mongolia. In 2005, the overall classification accuracy is 78.85% and the Kappa coefficient is 0.77. In 2015, the overall classification accuracy is 80.49% and the Kappa coefficient is 0.78. The average annual classification accuracy is 79.67%, which meets the accuracy requirements. The dataset can directly reflect the changes of land cover pattern and trend in Mongolia and provide basic scientific data to support the sustainable development of Mongolia.

Meta-Analysis of Candidate Surrogate End Points in Advanced Prostate Cancer

BackgroundThe Intermediate Clinical Endpoints in Cancer of the Prostate (ICECaP) working group identified metastasis-free survival as a valid surrogate end point for overall survival (OS) for patients with localized prostate cancer. No comparably validated surrogate end points exist in advanced prostate cancer.MethodsWe searched for trials in advanced prostate cancer, defined as node-positive, metastatic castration-sensitive, nonmetastatic, or metastatic castration-resistant prostate cancer. Eligible randomized trials reported OS and one or more intermediate clinical end points, including biochemical failure (BF), clinical failure, biochemical failure–free survival (BFS), progression-free survival (PFS), and radiographic PFS. Candidacy for surrogacy was assessed by using the second condition of the meta-analytic approach; R2 was weighted by the inverse variance of the log intermediate clinical end point hazard ratio and defined as R2>0.70.ResultsA total of 143 randomized trials (n=75,601 patients) were included. No candidate end points met the criteria for surrogacy (R2 BF [n=28,922], 0.42 [95% confidence interval (CI), 0.18 to 0.64]; BFS [n=25,741], 0.57 [95% CI, 0.37 to 0.73]; clinical failure [n=22,616], 0.31 [95% CI, 0.075 to 0.56]; PFS [n=52,639], 0.50 [95% CI, 0.35 to 0.63]; and radiographic PFS [n=52,548], 0.50 [95% CI, 0.35 to 0.63]). Within preplanned subgroups according to castration-sensitive or castration-resistant disease or according to treatment type, neither BFS nor PFS consistently met criteria for surrogacy. Sensitivity analyses showed that candidacy for surrogacy of all end points tested did not change over time.ConclusionsOur aggregate screening method for surrogate end points in advanced prostate cancer showed that commonly used clinical end points are not clear valid surrogate end points for OS. (Funded by the Prostate Cancer Foundation and the National Cancer Institute.)

The association of asthma duration with body mass index and Weight-Adjusted-Waist index in a nationwide study of the U.S. adults

BackgroundsThe association between obesity and asthma has been of interest, but whether the duration of asthma has an effect on obesity is still limitedly studied.AimThe purpose of this study was to investigate the association between asthma duration and obesity-related indexes, where obesity-related indexes include Body mass index (BMI) and Weight-adjusted-waist index (WWI).MethodsData from National Health and Nutrition Examination Survey (NHANES) 2009–2018 were obtained to conduct this cross-sectional study. Duration of asthma was used as the independent variable and obesity-related indexes as the response variables. Multiple linear regression was used to assess the association between the independent variable and the response variables, and subsequently smoothed curve fitting and threshold effect analysis were performed to clarify whether there was a nonlinear correlation between the independent variable and the response variables. Finally, subgroup analysis was conducted to find sensitive populations.ResultsA total of 9170 participants were included in the analysis. Asthma duration was statistically different between the two groups when all participants were grouped by median WWI (Q1 < 11.65, Q2 ≥ 11.65) (P < 0.001), but not by median BMI (Q1 < 31.8, Q2 ≥ 31.8) (P = 0.130). There was a positive association between asthma duration and WWI [β = 0.016, 95% CI (0.016, 0.017)], but a negative one with BMI [β = − 0.098, 95% CI (− 0.112, − 0.085)], and the correlations between the independent and response variables became more pronounced with increasing asthma duration (P for trend < 0.01). In addition, there were nonlinear relationships between asthma duration with BMI and WWI (log likelihood ratio < 0.001), with the best valid inflection points for asthma duration being 2 years (with WWI as the response variable) and 3 years (with BMI as the response variable), respectively. In the subgroup analysis, the positive association between asthma duration and WWI was more pronounced in the participants who were male, aged less than 40 years, and had asthma onset before 12 years of age. In contrast, when BMI was used as the response variable, the negative association between it and asthma duration was more pronounced among participants of female, aged 60 years or older, and with asthma onset less than 12 years of age.ConclusionsIn US adults, asthma duration might cause changes in obesity-related indexes. Longer asthma duration might cause weight loss, but might increase the risk of abdominal obesity.