Acquisition Dates Research Articles

ESTUDIO DE PERCEPCIONES Y PREFERENCIAS DEL CONSUMIDOR: CASO DE ESTUDIO DE CONSUMO DE PESCADOS Y MARISCOS EN MEXICALI, BAJA CALIFORNIA, MÉXICO

<p><strong>Background:</strong> The agri-food sector, which includes fishing and aquaculture, poses challenges in improving the food quality and restructure markets. Currently, we have the opportunity to understand the food crisis associated with the COVID-19 pandemic, and to promote a paradigm shift in food and markets, which leads to the changes generation in the food economic system, transformation of markets and rapprochement of producers with consumers. One of the ways to bring the producer and the consumer closer to each other, is precisely to know and characterize their consumption, by conducting market studies. A market study is a systematic investigation based on the information collection and data analysis related to the outstanding characteristics of the market to which a company or a sector is directed, which allows us to know and characterize the tastes and preferences in the population consumption. <strong>Objective:</strong> To diagnose the perceptions and preferences on the fish and shellfish consumption in the Mexicali city, Baja California, Mexico. <strong>Methodology:</strong> To get the required information, surveys were applied at four points in Mexicali. The study was carried out with cluster analysis using the statistical package for social sciences. It was obtained that three clusters were separated from each other and are statistically different (98.5% of the analyzed data). <strong>Results: </strong>The first conglomerate comprises 61.77% of those surveyed, while the second 27.94% and the remaining 10.29% were in the third conglomerate. Cluster 2 represents the population that consumes the most fish and shellfish, followed by cluster 1 and those of cluster 3, which consumes less fish and/or shellfish. In order of importance, the most relevant variables to explain on the three clusters are: differences between fishery products vs aquaculture, presentation of consumption and date of acquisition. <strong>Implications: </strong>This study provides a baseline for future market research in Baja California where trends in consumer buying behavior are analyzed, as well as contributing to the recognition of problems, identification of opportunities and improvement in decision-making by consumers, aquaculture producers and fishermen (or fishing sector). <strong>Conclusions:</strong> The results show that there is a lack of knowledge in the population on fishery and aquaculture products. Therefore, there is a tremendous area of opportunity to publicize information about the fishery and aquaculture products in the different media communication. Especially to their nutritional and health benefits.</p>

Obtaining high-resolution synthetic soil imagery for topsoil mapping

ABSTRACT Detailed soil maps contribute to the understanding of agricultural and food value chains. PlanetScope satellite constellations have high spatial and temporal resolution and could be used to develop Synthetic Soil Image (SYSI) at specific dates of field crops calendar. This work aimed to derive a SYSI from PlanetScope time series and estimate soil clay content, an important attribute related to several soil functions. The normalization process was applied to make the radiometries of the different Doves’ images compatible and, subsequently, land cover classification was performed to select the bare soil class, obtaining the SYSI-Planet. The clay content was predicted for 2337 topsoil samples using the SYSI-Planet and Random Forest algorithm. The normalization of PlanetScope images were based on reference images, which mitigated the radiometric differences from acquisition dates and different radiometric calibrations. SYSI-Planet resulted in an excellent clay content prediction, resulting in a coefficient of determination (R 2) equal to 0.93 and Root-Mean-Square Error (RMSE) equal to 70.90. The predicted clay mapped revealed spatial patterns with a high spatial resolution (~3 m), which can enhance precise agricultural management.

Factors that Influence Acquisition of Lower Extremity Braces in the Pediatric Orthopaedic Population.

Lower extremity brace-wear compliance has been studied in pediatrics, but failure to acquire a prescribed brace has not been included. The purpose of this study was to evaluate brace acquisition as a component of brace-wear compliance. Records of patients (0 to 21 y) prescribed lower extremity braces from 2017 to 2019 were reviewed. Diagnoses included cerebral palsy, spina bifida, short Achilles tendon, clubfoot, and other. Brace type was categorized as clubfoot foot abduction orthosis, ankle-foot orthosis, knee, hip, or custom/other braces. Brace prescription and acquisition dates were recorded. Insurance was classified as government, private, or uninsured. Patient demographics included age, sex, race, and calculated area deprivation index. Of the 1176 prescribed lower extremity braces, 1094 (93%) were acquired while 82 (7%) were not. The odds ratios (OR) of failure to acquire a prescribed brace in Black and Hispanic patients were 1.64 and 2.71 times that in White patients, respectively (95% confidence interval: 1.01-2.71, P=0.045; 1.23-5.6, P=0.015); in patients without insurance, the OR was 8.48 times that in privately insured patients (95% confidence interval: 1.93-31.1, P=0.007). The ORs of failure to acquire were 2.12 (P=0.003) in patients 4 years or more versus 0 to 3 years, 4.17 (P<0.0001) in cerebral palsy versus clubfoot, and 4.12 (P=0.01) in short Achilles tendon versus clubfoot. There was no significant association between sex or area deprivation index and failure of brace acquisition. In our cohort, 7% of prescribed braces were not acquired. Black or Hispanic race, lack of insurance, and older age were associated with failure to acquire prescribed braces. Braces prescribed for clubfoot were acquired more often than for cerebral palsy or short Achilles tendon. Brace-wear compliance is an established factor in treatment success and recurrence. This study identified risk factors for failed brace acquisition, a critical step for improving compliance. These results may help effect changes in the current system that may lead to more compliance with brace wear. Level III-retrospective cohort study.

A machine learning-based approach for picture acquisition timeslot prediction using defective pixels

Estimating the acquisition time of digital photographs is a challenging task in temporal image forensics, but the application is highly demanded for establishing temporal order among individual pieces of evidence and deduce the causal relationship of events in a court case. The forensic investigator needs to identify the timeline of events and look for some patterns to gain a clear overview of activities associated with a crime. This paper aims to explore the presence of defective pixels over time for estimating the acquisition date of digital pictures. We propose a technique to predict the acquisition timeslots of digital pictures using a set of candidate defective pixels in non-overlapping image blocks. First, potential candidate defective pixels are determined through related pixel neighbourhood and two proposed features, called the local variation features to best fit in a machine learning model. The machine learning approach is used to model the temporal behaviour of camera sensor defects in each block using the scores obtained from individually trained pixel defect locations and fused in a majority voting method. Interestingly, timeslot estimation using individual blocks has been shown to be more accurate when virtual sub-classes corresponding to halved timeslots are first considered prior to the reconstruction step. Finally, the last stage of the system consists of the combination of block scores in a second majority voting operation to further enhance performance. Assessed on the NTIF image dataset, the proposed system has been shown to reach very promising results with an estimated accuracy between 88% and 93% and clear superiority over a related state-of-the-art system.

P-018: Automatic analysis of magnetic resonance imaging in multiple myeloma patients: deep-learning based pelvic bone marrow segmentation and radiomics analysis for prediction of plasma cell infiltration

<h3>Background</h3> Advances in deep learning have made automatic biomedical image segmentation feasible. Additionally, radiomics analysis now allows computer based, in-depth tissue analysis from medical images. The goal of this work was to establish a full-automatic framework combining automatic pelvic bone marrow (BM) segmentation and radiomics analysis of the pelvic BM to predict BM plasma cell infiltration (PCI) directly and automatically from whole-body magnetic resonance imaging (wb-MRI). <h3>Methods</h3> A total of 541 MRIs acquired at 5 different MRI scanners from 270 patients with all stages of monoclonal plasma cell disorders were included. One-hundred fifty-eight patients who had received MRI at the standard clinical 1.5T MRI scanner and had information on PCI from concomitant BM biopsy at the iliac crest available were split by date into a training set (n=116) for both, nnU-Net and radiomics model, and an independent test set for the framework (n=42). All MRIs without biopsy data were used for training of the nnU-Net only, which is state of the art deep learning framework for medical image segmentation. Manual BM segmentations of the right and left pelvic bone on coronal T1tse images on the training cohort were used for training of the nnU-Net. A random forest classifier was trained on the pelvic radiomics features to predict PCI. The framework was then tested on the independent test set. Dice scores report accuracy of segmentations. Mean absolute error (MAE) in [%PCI] reports the accuracy of the PCI prediction. For comparison, two radiologists rated the diffuse infiltration according to 3 levels of severity (none-to-mild vs. moderate vs. severe). The mean PCI within each severity level from the training set was determined and assigned as a prediction to patients with the same level diffuse infiltration in the test set (the radiologists' PCI prediction). This study was approved by the institutional review board. <h3>Results</h3> The mean Dice scores of the nnU-Net segmentation for right / left pelvic BM on 8 cases of the test set (last 8 by acquisition date) were 0.94 and 0.94, respectively, the mean Dice scores between manual pelvic BM segmentation of two radiologists on these 8 cases were 0.87 and 0.88. The MAE of the prediction of PCI by the automated framework was 14.3 [%PCI]. The MAE of the radiologists PCI predictions were 16.1 [%PCI] (rater 1) and 16.7 [%PCI] (rater2). <h3>Conclusion</h3> We established automatic pelvic BM segmentation with radiologist level precision for all stages of monoclonal plasma cell disorders, which is a crucial step towards fully automated analysis of wb-MRI. Radiomics analysis of these segmentations can predict PCI with considerable accuracy. Further improvement of the PCI prediction model is necessary and is currently in progress, by adding additional MRI sequences into the model, enhancing the amount of training data by adding multi-institutional data, and learning about multi-scanner radiomics feature stability.

Augmenting Landsat time series with Harmonized Landsat Sentinel-2 data products: Assessment of spectral correspondence

An increase in the temporal revisit of satellite data is often sought to increase the likelihood of obtaining cloud- and shadow-free observations as well as to improve mapping of rapidly- or seasonally-changing features. Currently, as a tandem, Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and −8 Operational Land Imager (OLI) provide an acquisition opportunity on an 8-day revisit interval. Sentinel-2A and -2B MultiSpectral Instrument (MSI), with a wider swath, have a 5-day revisit interval at the equator. Due to robust pre- and post-launch cross-calibration, it has been possible for NASA to produce the Harmonized Landsat Sentinel-2 (HLS) data product from Landsat-8 OLI and Sentinel-2 MSI: L30 and S30, respectively. Knowledge of the agreement of HLS outputs (especially S30) with historic Landsat surface reflectance products will inform the ability to integrate historic time-series information with new and more frequent measures as delivered by HLS. In this research, we control for acquisition date and data source to cross-compare the HLS data (L30, S30) with established Landsat-8 OLI surface-reflectance measures as delivered by the USGS (hereafter BAP, Best Available Pixel). S30 and L30 were found to have high agreement (R = 0.87–0.96) for spectral channels and an r = 0.99 for Normalized Burn Ratio (NBR) with low relative root-mean-square difference values (1.7%–3.3%). Agreement between L30 and BAP was lower, with R values ranging from 0.85 to 0.92 for spectral channels and R = 0.94 for NBR. S30 and BAP had the lowest agreement, with R values ranging from 0.71 to 0.85 for spectral channels and r = 0.90 for NBR. Comparisons indicated a stronger agreement at latitudes above 55° N. Some dependency between spectral agreement and land cover was found, with stronger correspondence for non-vegetated cover types. The level of agreement between S30 and BAP reported herein would enable integration of HLS outputs with historic Landsat data. The resulting increased temporal frequency of data allows for improvements to current cloud screening practices and increases data density and the likelihood of temporal proximity to target date for pixel compositing approaches. Furthermore, additional within-year observations will enable change products with a higher temporal fidelity and allow for the incorporation of phenological trends into land cover classification algorithms.

Estimation of soil texture using Sentinel-2 multispectral imaging data: An ensemble modeling approach

Soil texture is an important compositional property where the separate estimates of sand, silt, and clay often does not add up to 100 % when predicted independently using digital soil mapping approach. An alternative is to use Remote sensing (RS) data, however, varying soil surface conditions, satellite pass day, and cloud cover on imagery often limit its estimation accuracies. Here, we examined an ensemble modeling approach to improve the estimation accuracy of soil textural fractions using Sentinel-2 (S2) data. Multispectral images (cloud free) from S2 mission for 63 different acquisition dates (between Jan. 2017 to Dec. 2020) were used as the RS data source. Spectral reflectance data over visible to near-infrared region (350−2500 nm at 1 nm interval) were also collected for each soil sample under laboratory conditions for comparison. Partial-least-square regression (PLSR), random forest (RF) and support vector regression (SVR) models were used to estimate sand, silt, and clay contents for both the laboratory-measured and S2 spectra. Resulting coefficient of determination (R2) and root-mean-squared error (RMSE) values for the laboratory-measured spectra were 0.44 and 14.9 % for sand contents, 0.32 and 16.23 % for silt contents, and 0.69 and 5.5 % for clay contents, respectively. The S2 datasets collected during Apr. 13, 2019 and Nov. 24, 2019 yielded best performance statistics for sand (R2 = 0.50, RMSE = 11.6 %) and silt (R2 = 0.46, RMSE = 5.8 %) contents, respectively. Similarly, the dataset collected on Jan. 3, 2018 yielded best performance for the clay contents (R2 = 0.44, RMSE = 6.9 %). Compared to S2 data of a single acquisition date, combination of S2 data from multiple acquisition dates through ensemble modeling could significantly improve the prediction accuracies for sand (R2 = 0.62, RMSE = 9.9 %), silt (R2 = 0.54, RMSE = 5.4 %), and clay contents (R2 = 0.54, RMSE = 6.5 %). These developed models allowed for the estimation of textural fractions at a resolution of 30 m.

Intercomparison of UAV platforms for mapping snow depth distribution in complex alpine terrain

Unmanned Aerial Vehicles (UAVs) offer great flexibility in acquiring images in inaccessible study areas, which are then processed with stereo-matching techniques through Structure-from-Motion (SfM) algorithms. This procedure allows generating high spatial resolution 3D point clouds. The high accuracy of these 3D models allows the production of detailed snow depth distribution maps through the comparison of point clouds from different dates. In this way, UAVs allow monitoring of remote areas that were not achievable previously. The large number of works evaluating this novel technique has not, to date, conducted a systematic evaluation of concurrent snowpack observations with different UAV devices. Taking into account this, and also bearing in mind that potential users of this technique may be interested in exploiting ready-to-use commercial devices, we conducted an evaluation of the snow depth distribution maps with different commercial UAVs. During the 2018–19 snow season, two multi-rotors (Parrot Anafi and DJI Mavic Pro2) and one fixed-wing device (SenseFly eBee plus) were used on three different dates over a small test area (5 ha) within Izas Experimental Catchment in the Central Pyrenees. Simultaneously, snowpack distribution was retrieved with a Terrestrial Laser Scanner (TLS, RIEGL LPM-321) and was considered as ground truth. Three different georeferencing methods (Ground Control Points, ICP algorithm over snow-free areas and RTK-GPS positioning) were tested, showing equivalent performances under optimum illumination conditions. Additionally, for the three acquisition dates, both multi-rotors were flown at two distinct altitudes (50 and 75 m) to evaluate impact on the obtained snow depth maps. The evaluation with the TLS showed an equivalent performance of the two multi-rotors, with mean RMSE below 0.23 m and maximum volume deviations of less than 5%. Flying altitudes did not show significant differences in the obtained maps. These results were obtained under contrasted snow surface characteristics. This study reveals that under good illumination conditions and in relatively small areas, affordable commercial UAVs provide reliable estimations of snow distribution compared to more sophisticated and expensive close-range remote sensing techniques. Results obtained under overcast skies were poor, demonstrating that UAV observations require clear-sky conditions and acquisitions around noon to guarantee a homogenous illumination of the study area.

Forecasting uncertainty: Cindy March at Veracity

Study level/applicability This case is appropriate for use in undergraduate and MBA courses. Subject area This case can be used in courses in business ethics, leading teams and organizations or business strategy. The focus of the case aligns well with discussions of managing up, navigating changes in top leadership and conflicts between executive vision and future company growth. Instructors that choose to emphasize the ethical approach could assign this case to explore tradeoffs between loyalty to current and future bosses. Case overview Associate Director of Forecasting Cindy March faces a multi-faceted dilemma as biotech firm Veracity’s acquisition date by pharmaceutical giant Makhola approaches. After a new competitor enters the market, March expects Veracity drug Sangren’s future revenue to drop to $600m in 2019, but the outgoing Veracity CEO refuses to accept a forecast of less than $700m. March suspects that the CEO is intent on handing over a financially successful company and is overly optimistic about Sangren’s ability to maintain market share. In two weeks, March is due to present a 2019 Sangren forecast to incoming Makhola leadership, who she anticipates becoming her direct boss after the acquisition. Should March present the inflated forecasts and accept the poor reflection on her professional abilities or should she refuse to present numbers she does not believe in? Expected learning outcomes By analyzing and discussing the case, students should be able to:Evaluate the potential business and ethical conflicts arising from decision-making based on both data and intuition. Synthesize an appropriate strategy for navigating tradeoffs between current and future leadership.Analyze the gender dynamics of male-dominated executive leadership structures and strategies for female employees to combat gender biases. Supplementary materials The Behavioral Science Guys, 2015. One Simple Skill to Curb Unconscious Gender Bias. YouTube. https://www.youtube.com/watch?v=SEHi4yauhu8&amp;ab_channel=VitalSmartsVideoTeaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code CSS 6: Human resources.

INVESTIGATIONS ON FEATURE SIMILARITY AND THE IMPACT OF TRAINING DATA FOR LAND COVER CLASSIFICATION

Abstract. Fully convolutional neural networks (FCN) are successfully used for pixel-wise land cover classification - the task of identifying the physical material of the Earth’s surface for every pixel in an image. The acquisition of large training datasets is challenging, especially in remote sensing, but necessary for a FCN to perform well. One way to circumvent manual labelling is the usage of existing databases, which usually contain a certain amount of label noise when combined with another data source. As a first part of this work, we investigate the impact of training data on a FCN. We experiment with different amounts of training data, varying w.r.t. the covered area, the available acquisition dates and the amount of label noise. We conclude that the more data is used for training, the better is the generalization performance of the model, and the FCN is able to mitigate the effect of label noise to a high degree. Another challenge is the imbalanced class distribution in most real-world datasets, which can cause the classifier to focus on the majority classes, leading to poor classification performance for minority classes. To tackle this problem, in this paper, we use the cosine similarity loss to force feature vectors of the same class to be close to each other in feature space. Our experiments show that the cosine loss helps to obtain more similar feature vectors, but the similarity of the cluster centers also increases.

A novel automatic method on pseudo-invariant features extraction for enhancing the relative radiometric normalization of high-resolution images

ABSTRACT Relative radiometric normalization (RRN) is a critical preprocessing step that is widely applied to remote sensing data. Essential to RRN are the pseudo-invariant features (PIFs) – ground objects with invariant reflectance across the acquisition dates of multiple images. To meet the requirements of RRN for change detection in multi-temporal images, this paper proposed a novel automatic method for PIF extraction: first, we calculated spectral, spatial, and visual features for the images. Next, a set of change vectors were generated to quantify the features’ differences. Then, the initial PIF was obtained by synthesizing similar pixels from each change vector. Finally, an iterative linear regression was performed to generate the final PIFs. Furthermore, we employed the random sample consensus (RANSAC) algorithm to establish the RRN model for normalizing the target image. The methods and workflow of PIF extraction and the RRN model establishment were validated with two TripleSat-2 multi-spectral images with a spatial resolution of 3.2 metres. The applicability of the proposed PIF extraction method was further validated by Landsat-8 OLI, GF-1 WFV, Sentinel-2 MSI, and WorldView-2 images. The results showed that: (1) The proposed PIF extraction method can automatically obtain the PIFs with high precision. Based on the extracted PIFs, the RANSAC algorithm is capable of building the RRN model with a significant linear relationship. (2) The PIF extraction method is applicable to multi-spectral images with different spatial resolutions. (3) Compared with the multivariate alteration detection (MAD) and the iteratively reweighted MAD (IR-MAD), the proposed PIF extraction method achieved a better performance in selecting the pixels with smaller radiometric differences, which is helpful to reach a higher accuracy of the RRN model. (4) Differences in image brightness, spectral domain, and spatial domain are considered in the PIF extraction method with fewer parameters and high portability. (5) Spectral features are dominant to obtain ideal PIFs, and feature combinations can get the advantages to enhance the effectiveness of the PIFs.

THE CONTRIBUTION OF REMOTE SENSING AND AEROMAGNETISM TO GOLD PROSPECTING: THE CASE OF THE MEIGANGA ZONE, CAMEROON

In order to optimize gold prospecting in the Meiganga zone located in the Adamaoua region of Cameroon, aeromagnetic and remote sensing prospecting was carried out in the eastern and southern parts. The remote sensing approach on a Landsat 8 OLI/TIRS image highlighted areas of maximum gold concentration. Thus, ferric ion bearing minerals are located in the North-West, silicate minerals bearing ferrous ions are in the Centre while clay minerals are in the North-East and East. The principal component analysis revealed important structural information. The PCA Spatial Map (PC1, PC2, PC3) showed the plutonic formations composed of anatexis and anatexis granites, vegetation cover (at the date of image acquisition: February 22, 2019), areas of permanent water circulation or accumulation, and metamorphic and sedimentary formations namely gneisses, quartzites, schists and superficial clay formations. A Landsat SRTM (Shuttle Radar Topography Mission) image was also used to enhance the lineaments through the Sobel filter to highlight the geomorphological (cliffs, valleys, ...) and topographic (river network, ridge and drainage segment) structures. The aeromagnetic approach was also important. The study of the modified magnetic field (CM) showed 4 ranges: very high, high, medium and low. The Total Magnetic Anomalies (TMI) of the area are subdivided into 2 ranges large positive anomalies (221.1-103.0 nT) located in the lower part of NE-SW orientation, small positive anomalies (103.0-(-)89.7 nT) located in the upper part of NE-SW orientation. The reduced total magnetic anomaly at the equator shows a fairly similar distribution to the total magnetic anomaly with the large positive anomalies in almost the entire lower part. Superimposed on the geological map, Neoproterozoic pre- to syn-tectonic granitoids (C) are superimposed on the large positive anomalies and Neoproterozoic conglomerates, quartzites, sedimentary shales and volcanosedimentary rocks (A) and Neoproterozoic syn-tectonic granitoids (B) are superimposed on the large and small positive anomalies. The grid of the reduced residual equatorial anomaly (ARRE) confirms that the local geology is strongly magnetic (gneiss and quartzite). The filters of the derivatives allowed to establish a map of magnetic lineaments of major orientation N045° and minor orientation N130°. The horizontal gadient superimposed on the local maxima showed the presence of deep structures oriented NE-SW. The analytical signal superimposed on the local maxima highlights the metamorphic basement consisting of rocks with strong magnetism. The application of Euler deconvolution localizes the depth of the sources of linear anomalies.

Effects of integrase inhibitor-based antiretroviral therapy on brain outcomes according to time since acquisition of HIV-1 infection

Integrase strand transfer inhibitors (INSTI) are a main component of the current antiretroviral regimens recommended for treatment of HIV infection. However, little is known about the impact of INSTI on neurocognition and neuroimaging. We developed a prospective observational trial to evaluate the effects of INSTI-based antiretroviral therapy on comprehensive brain outcomes (cognitive, functional, and imaging) according to the time since HIV-1 acquisition. We recruited men living with HIV who initiated antiretroviral therapy with INSTI < 3 months since the estimated date of HIV-1 acquisition (n = 12) and > 6 months since estimated date of HIV-1 acquisition (n = 15). We also recruited a group of matched seronegative individuals (n = 15). Assessments were performed at baseline (before initiation of therapy in HIV arms) and at weeks 4 and 48. Baseline cognitive functioning was comparable between the arms. At week 48, we did not find cognitive differences between starting therapy with INSTI earlier than 3 months or later than 6 months after acquisition of HIV-1 infection. Functional status was poorer in individuals diagnosed earlier. This effect recovered 48 weeks after initiation of therapy. Regarding brain imaging, we found that men living with HIV initiating antiretroviral therapy later experienced a greater decrease in medial orbitofrontal cortex over time, with expected negative repercussions for decision-making tasks.

A Sentinel-2 Image-Based Irrigation Advisory Service: Cases for Tea Plantations

In this study, we aim to develop an inexpensive site-specific irrigation advisory service for resolving disadvantages related to using immobile soil moisture sensors and to the differences in irrigation needs of different tea plantations affected by variabilities in cultivars, plant ages, soil heterogeneity, and management practices. In the paper, we present methodologies to retrieve two biophysical variables, surface soil water content and canopy water content of tea trees from Sentinel-2 (S2) (European Space Agency, Paris, France) images and consider their association with crop water availability status to be used for making decisions to send an alert level. Precipitation records are used as auxiliary information to assist in determining or modifying the alert level. Once the site-specific alert level for each target plantation is determined, it is sent to the corresponding farmer through text messaging. All the processes that make up the service, from downloading an S2 image from the web to alert level text messaging, are automated and can be completed before 7:30 a.m. the next day after an S2 image was taken. Therefore, the service is operated cyclically, and corresponds to the five-day revisit period of S2, but one day behind the S2 image acquisition date. However, it should be noted that the amount of irrigation water required for each site-specific plantation has not yet been estimated because of the complexities involved. Instead, a single irrigation rate (300 t ha−1) per irrigation event is recommended. The service is now available to over 20 tea plantations in the Mingjian Township, the largest tea producing region in Taiwan, free of charge since September 2020. This operational application is expected to save expenditures on buying irrigation water and induce deeper root systems by decreasing the frequency of insufficient irrigation commonly employed by local farmers.

Sentinel-2 Exposed Soil Composite for Soil Organic Carbon Prediction

Pilot studies have demonstrated the potential of remote sensing for soil organic carbon (SOC) mapping in exposed croplands. However, the use of remote sensing for SOC prediction is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non-photosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, recent studies have focused on stabilizing the soil reflectance by building image composites. These composites tend to minimize the disturbing effects by applying sets of criteria. Here, we aim to develop a robust method that allows selecting Sentinel-2 (S-2) pixels with minimal influence of the following disturbing factors: crop residues, surface roughness and soil moisture. We selected all S-2 cloud-free images covering the Belgian Loam Belt from January 2019 to December 2020 (in total 36 images). We then built nine exposed soil composites based on four sets of criteria: (1) lowest Normalized Burn Ratio (NBR2), (2) Normalized Difference Vegetation Index (NDVI) &lt; 0.25, (3–5) NDVI &lt; 0.25 and NBR2 &lt; threshold, (6) the ‘greening-up’ period of a crop and (7–9) the ‘greening-up’ period of a crop and NBR2 &lt; threshold. The ‘greening-up’ period was selected based on the NDVI timeline, where ‘greening-up’ is considered as the last date of acquisition where the soil is exposed (NDVI &lt; 0.25) before the crop develops (NDVI &gt; 0.25). We then built a partial least square regression (PLSR) model with 10-fold cross-validation to estimate the SOC content based on 137 georeferenced calibration samples on the nine composites. We obtained non-satisfactory results (R2 &lt; 0.30, RMSE &gt; 2.50 g C kg–1, and RPD &lt; 1.4, n &gt; 68) for all composites except for the composite in the ‘greening-up’ stage with a NBR2 &lt; 0.07 (R2 = 0.54 ± 0.12, RPD = 1.68 ± 0.45 and RMSE = 2.09 ± 0.39 g C kg–1, n = 49). Hence, the ‘greening-up’ method combined with a strict NBR2 threshold allows selecting the purest exposed soil pixels suitable for SOC prediction. The limit of this method might be its coverage of the total cropland area, which in a two-year period reached 62%, compared to 95% coverage if only the NDVI threshold is applied.

E-Passport Advanced Security Technique Using Biometric Information Watermarking

There has been significant research in security of e-passport. E-passport combine the security technologies such as smartcard, PKI, and biometric, so that these technologies can prevent unauthorized copies and counterfeits. We propose an e-passport security technology in which biometric watermarking is used to prevent the copy of biometric information in the e-passport. The biometric watermarking embeds the invisible date of acquisition into the original data so that the human visual system cannot perceive its invisibly watermarked information. Then the biometric sample, having its unauthorized copy, is retrieved at the moment of reading the e-passport from the issuing database. The previous e-passport security technology placed an emphasis on both access control readers and anti-cloning chip features, and it is expected that the proposed feature, copy protection of biometric information, will be demanded as the cases of biometric recognition to verify personal identity information has increased. This study proposed test results through issuing and deciphering of watermarks for e-passports, and it is expected that it can be used as standards for protecting biometric information.

Sentinel-2 Data for Precision Agriculture?-A UAV-Based Assessment.

An approach of exploiting and assessing the potential of Sentinel-2 data in the context of precision agriculture by using data from an unmanned aerial vehicle (UAV) is presented based on a four-year dataset. An established model for the estimation of the green area index (GAI) of winter wheat from a UAV-based multispectral camera was used to calibrate the Sentinel-2 data. Large independent datasets were used for evaluation purposes. Furthermore, the potential of the satellite-based GAI-predictions for crop monitoring and yield prediction was tested. Therefore, the total absorbed photosynthetic radiation between spring and harvest was calculated with satellite and UAV data and correlated with the final grain yield. Yield maps at the same resolution were generated by combining yield data on a plot level with a UAV-based crop coverage map. The best tested model for satellite-based GAI-prediction was obtained by combining the near-, infrared- and Red Edge-waveband in a simple ratio (R2 = 0.82, mean absolute error = 0.52 m2/m2). Yet, the Sentinel-2 data seem to depict average GAI-developments through the seasons, rather than to map site-specific variations at single acquisition dates. The results show that the lower information content of the satellite-based crop monitoring might be mainly traced back to its coarser Red Edge-band. Additionally, date-specific effects within the Sentinel-2 data were detected. Due to cloud coverage, the temporal resolution was found to be unsatisfactory as well. These results emphasize the need for further research on the applicability of the Sentinel-2 data and a cautious use in the context of precision agriculture.

Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy

Background: Immune checkpoint inhibitor efficacy in advanced cancer patients remains difficult to predict. Imaging is the only technique available that can non-invasively provide whole body information of a patient's response to treatment. We hypothesize that quantitative whole-body prognostic information can be extracted by leveraging artificial intelligence (AI) for treatment monitoring, superior and complementary to the current response evaluation methods.Methods: To test this, a cohort of 74 stage-IV urothelial cancer patients (37 in the discovery set, 37 in the independent test, 1087 CTs), who received anti-PD1 or anti-PDL1 were retrospectively collected. We designed an AI system [named prognostic AI-monitor (PAM)] able to identify morphological changes in chest and abdominal CT scans acquired during follow-up, and link them to survival.Results: Our findings showed significant performance of PAM in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.73 (p < 0.001) for abdominal imaging, and 0.67 AUC (p < 0.001) for chest imaging. Subanalysis revealed higher accuracy of abdominal imaging around and in the first 6 months of treatment, reaching an AUC of 0.82 (p < 0.001). Similar accuracy was found by chest imaging, 5–11 months after start of treatment. Univariate comparison with current monitoring methods (laboratory results and radiological assessments) revealed higher or similar prognostic performance. In multivariate analysis, PAM remained significant against all other methods (p < 0.001), suggesting its complementary value in current clinical settings.Conclusions: Our study demonstrates that a comprehensive AI-based method such as PAM, can provide prognostic information in advanced urothelial cancer patients receiving immunotherapy, leveraging morphological changes not only in tumor lesions, but also tumor spread, and side-effects. Further investigations should focus beyond anatomical imaging. Prospective studies are warranted to test and validate our findings.

부동산 시효취득시 취득세 산정 기산일 고찰

As the local tax laws don’t prescribe the date of acquisition for real property obtained through occupancy acquisition, relevant judgements of the Supreme Court, decisions of the Tax Tribunal and rulings of the tax authorities are all different from each other, which causes the uncertainty in tax practices and possible problems of the loss of tax revenues. In order to improve taxpayers’ right and the clarity of the interpretation of tax laws, this study comprehensively investigates the issue of effective taxation period of acquisition tax for occupancy acquisition, and suggests that the taxation period should accrue on the date when occupancy acquisition completes although some court cases ruled that it accrued on either the date when court’s judgement is finally decided or the date when ownership transfer is registered. Regarding the period of exclusion, to cope with the latent problem that the tax authorities are unable to impose acquisition tax, considering the difficulty in recognizing the completion of occupancy acquisition, it should be remedied by legislating on “special” effective taxation period and reporting period of acquisition tax for occupancy acquisition.

Opodatkowanie nabycia nieruchomości w drodze działu spadku. Glosa do wyroku Naczelnego Sądu Administracyjnego z dnia 13 października 2015 r. (II FSK 2133/13, LEX nr 1808541)

&lt;p&gt;The commented judgement concerns the taxation of acquisition of immovable property by inheritance division. In the applicant’s opinion dissolution of co-ownership is not an acquisition of assets if it is within the share which the former co-owners had in the common property and was carried out without any repayments or surcharges. If the acquisition of one-half of the property occurred upon the death of the testator, but through the subsequent abolition of co-ownership, the applicant acquired nothing more than what he already owned, the abolition of co-ownership does not constitute an acquisition and has no tax consequences. According to the tax authority, this position is incorrect. The applicant acquired half of the share in the property by way of inheritance and division of inheritance and a half share in the property by way of abolition of co-ownership. The administrative courts have held that the tax authority’s position is incorrect. In administrative cases, it may be necessary to use external systemic interpretation and reference by the administrative authorities to the relevant provisions of civil law. In the Supreme Administrative Court’s opinion, point of the dispute in this case is based on the conclusion what date should have been taken as the date of acquisition of the property (interest in the property). Resolution of this issue required a detailed analysis of the provisions of the Civil Code. Although, not all relevant issues have been considered by the Voivodeship Administrative Court, the decision of the court of first instance is in fact lawful. The gloss is approving and declares that the position taken in the judgement of the Supreme Administrative Court is correct.&lt;/p&gt;