Generative feature extraction from sentinel 1 and 2 data for prediction of forest aboveground biomass in the Italian Alps

—Aboveground biomass (AGB) is an important forest attribute directly linked to the forest carbon pool. The use of satellite remote sensing (RS) data has increased for AGB prediction due to their large footprint and low-cost availability. However, they have been limited due to saturation effect that leads to low prediction precision. In this article, we propose an innovative and dynamic architecture based on generative neural network that extracts target oriented generative features for predicting forest AGB using satellite RS data. These features are more resilient to mixed forest types and geographical conditions as compared to the traditional features and models. The effectiveness of the proposed features was assessed by experiments performed using multispectral, synthetic aperture radar, and combined dual-source datasets. The proposed model achieved best performance in terms of precision, model agreement, and overfitting as compared to the other conventional models for all analyzed datasets. The t-distributed stochastic neighbor embedding scatterplots of the generative features clearly show one dimension of the feature space associated with the target AGB. Feature importance analysis indicated that the produced generative features were more significant than the conventional analytical features. Also, the model provided a robust framework for homogeneous fusion of multisensor features from satellite RS data for predicting AG

Automated machine learning driven stacked ensemble modelling for forest aboveground biomass prediction using multitemporal sentinel-2 data

Modelling and large-scale mapping of forest aboveground biomass (AGB) is a complicated, challenging and expensive task. There are considerable variations in forest characteristics that creates functional disparity for different models and needs comprehensive evaluation. Moreover, the human-bias involved in the process of modelling and evaluation affects the generalization of models at larger scales. In this paper, we present an automated machine learning (AutoML) framework for modelling, evaluation and stacking of multiple base models for AGB prediction. We incorporate a hyperparameter optimization procedure for automatic extraction of targeted features from multitemporal Sentinel-2 data that minimizes human-bias in the proposed modelling pipeline. We integrate the two independent frameworks for automatic feature extraction and automatic model ensembling and evaluation. The results suggest that the extracted target-oriented features have excessive contribution of red-edge and short-wave infrared spectrum. The feature importance scale indicates a dominant role of summer based features as compared to other seasons. The automated ensembling and evaluation framework produced a stacked ensemble of base models that outperformed individual base models in accurately predicting forest AGB. The stacked ensemble model delivered the best scores of R2 cv = 0.71 and RMSE = 74.44 Mgha-1 . The other base models delivered R2 cv and RMSE ranging between 0.38–0.66 and 81.27– 109.44 Mg ha-1 respectively. The model evaluation metrics indicated that the stacked ensemble model was more resistant to outliers and achieved a better generalization. Thus, the proposed study demonstrated an effective automated modelling pipeline for predicting AGB by minimizing human-bias and deployable over large and diverse forest area

Effects of training parameter concept and sample size in possibilistic c-means classifier for pigeon pea specific crop mapping

4openInternationalInternational coauthor/editorThis research work aims to study the effect of training parameter concept and sample size in the process of classification by using a fuzzy Possibilistic c-Means (PCM) approach for Pigeon Pea specific crop mapping. For specific class extraction, the “mean” of the training data is considered as a training parameter of the classification algorithm. In this study, we proposed an “Individual Sample as Mean” (ISM) approach where the individual training sample is accounted as a mean parameter for the fuzzy PCM classifier. In order to avoid the spectral overlap of target Pigeon pea crop with other crops in the study area, a temporal indices database was generated from Sentinel 2A/2B satellite images acquired during the 2019–2020 Pigeon Pea crop cycle. The spectral dimensionality of temporal data was reduced to extract the required bands to achieve maximum enhancement of the target crop class in the temporal data. Further, the training sample size was increased to study the heterogeneity within the class in the classified output. The proposed ISM approach delivered a higher mean membership difference (MMD) between the Pigeon Pea crop and the co-cultivated Cotton crop as compared to the conventional mean method. This indicated that a better separation was achieved between the target crop and the spectrally similar crop grown, that were cultivated in the same study area. When the sample size was gradually increased from 5 to 60, the MMD values within the Pigeon Pea test fields remained in the range 0.013–0.02, thereby implying that the proposed algorithm works better even with a small number of training samples. The heterogeneity was better handled using the proposed ISM approach since the variance obtained within Pigeon Pea field was only 0.008, as compared to that of 0.02 achieved using the conventional mean approachopenSivaraj, Priyadarsini; Kumar, Anil; Koti, Shiva Reddy; Naik, ParthSivaraj, P.; Kumar, A.; Koti, S.R.; Naik, P

Activity Recognition in Residential Spaces with Internet of Things Devices and Thermal Imaging

In this paper, we design algorithms for indoor activity recognition and 3D thermal model generation using thermal images, RGB images, captured from external sensors, and the internet of things setup. Indoor activity recognition deals with two sub-problems: Human activity and household activity recognition. Household activity recognition includes the recognition of electrical appliances and their heat radiation with the help of thermal images. A FLIR ONE PRO camera is used to capture RGB-thermal image pairs for a scene. Duration and pattern of activities are also determined using an iterative algorithm, to explore kitchen safety situations. For more accurate monitoring of hazardous events such as stove gas leakage, a 3D reconstruction approach is proposed to determine the temperature of all points in the 3D space of a scene. The 3D thermal model is obtained using the stereo RGB and thermal images for a particular scene. Accurate results are observed for activity detection, and a significant improvement in the temperature estimation is recorded in the 3D thermal model compared to the 2D thermal image. Results from this research can find applications in home automation, heat automation in smart homes, and energy management in residential spaces

Optimal Placement of PMU for Power System Observability

ABSTRACT:Phasor Measurement Unit (PMU) is a relatively new technology that, when employed in power networks, offers real-time synchronised measurements of the voltages at buses and currents along the lines that connect them. This is accomplished by using a GPS based monitoring system which facilitates time synchronisation of measurements and unlike SCADA, makes the measured data available in Real-Time format. SCADA is not able to provide Real-time data due to the low speeds at which RTUs (Remote Terminal Units) provide data. Availability of time-stamped phasor measurements makes PMUs preferable for power system monitoring and control applications such as State Estimation, Instability Prediction Analysis, Real time monitoring of the system conditions, Islanding Detection, System Restoration and Bad Data Detection. Since PMUs are expensive, their procurement and installation needs to be planned both in terms of economy and utility. Usually utilities like to see that the power network becomes fully observable with minimum number of PMUs placed at strategic buses. Where full observability refers to all the buses in the network are actively monitored. Thus the problem of optimal placement of PMUs is formulated as an optimization problem where the number of PMUs is minimized subject to complete system observability. This paper solves the optimal placement of PMUs for power system observability using Integer Linear Programming (ILP) methodology. The method is tested on IEEE 14 Bus System

An acquired Bartter syndrome with secondary Sjögren syndrome

Renal tubular involvement in Sjögren's syndrome (SS) often described with renal tubular acidosis, nephrogenic diabetes insipidus, or rarely with Fanconi syndrome. SS presenting with clinical features of Bartter's syndrome or Gitelman's syndrome is rare. We report a case of a female patient who presented an acquired Bartter syndrome with a secondary SS. Our case highlights the fact that hypokalemia with metabolic alkalosis in an adult patient should prompt clinicians to look for common and uncommon conditions. While assessing for abnormal conditions, acquired Bartter syndrome should be considered if a patient has an underlying autoimmune, endocrine, or connective tissue disease

G12/13 Signaling Pathways Substitute for Integrin αIIbβ3-Signaling for Thromboxane Generation in Platelets

We have previously shown that ADP-induced TXA(2) generation requires signaling from αIIbβ3 integrin in platelets. Here we observed that, unlike ADP, protease-activated receptor (PAR)-mediated TXA(2) generation occurs independently of αIIbβ3. PAR agonists, but not ADP, activate G(12/13) signaling pathways. Hence, we evaluated the role of these pathways in TXA(2) generation.Inhibition of ADP-induced thromboxane generation by fibrinogen receptor antagonist SC57101 was rescued by co-stimulation of G(12/13) pathways with YFLLRNP. This observation suggested an existence of a common signaling effector downstream of integrins and G(12/13) pathways. Hence, we evaluated role of three potential tyrosine kinases; c-Src, Syk and FAK (Focal Adhesion Kinase) that are known to be activated by integrins. c-Src and Syk kinase did not play a role in ADP-induced functional responses in platelets. Selective activation of G(12/13) pathways resulted in the activation of FAK, in the absence of integrin signaling. Interestingly, αIIbβ3-mediated FAK activation occurred in a Src family kinase (SFK)-independent manner whereas G(12/13) pathway caused FAK activation in a SFK and RhoA-dependent manner. A FAK selective inhibitor TAE-226, blocked TXA(2) generation. However, in comparison to WT mice, Pf4-Cre/Fak-Floxed mice did not show any difference in platelet TXA(2) generation.Therefore, we conclude that differential activation of FAK occurs downstream of Integrins and G(12/13) pathways. However, the common effector molecule, possibly a tyrosine kinase downstream of integrins and G(12/13) pathways contributing to TXA(2) generation in platelets remains elusive

Translational autoimmunity in pemphigus and the role of novel Bruton tyrosine kinase inhibitors

Bruton tyrosine kinase (BTK) is involved in a multifarious inflammatory and autoimmune process. As a result, BTK has emerged as a promising novel remedial target for amalgamated autoimmune diseases. Medicament corporations have recently devoted considerable attention to the evolution of BTK inhibitors. Pemphigus is an uncommon and often fatal autoimmune illness. Blisters and erosions on cutaneous surfaces and mucous membranes are crippling symptoms of pemphigus vulgaris, which are caused by immunoglobulin G autoantibodies binding to keratinocyte proteins, resulting in keratinocyte adhesion defects. Although systemic corticosteroids and adjuvant medications are used to treat pemphigus, some patients are resistant to these. BTK inhibitors inhibit B-cell signaling, which is clinically useful in treating pemphigus. Assorted clinical trials are underway to assess the safety, tolerability, and pharmacokinetics of distinct BTK inhibitors, including PRN473 and remibrutinib. The current review evaluates translational autoimmunity in pemphigus and discusses BTK inhibitors in the treatment of pemphigus

Recent insights into the management of treatment-resistant pediatric atopic dermatitis

Atopic dermatitis (AD) is a prevalent protracted inflammatory skin condition that affects approximately 12% of children globally. Topical remedies, such as pharmacologic and nonpharmacologic management, and off-label systemic medicines, have traditionally been used to treat pediatric AD patients. To minimize comorbidities, sleep disturbances, pruritus, and signs of inflammation and improve the patient’s quality of life, it is vital to optimize severe AD management in pediatric patients. Treatment resistance can be caused by a variety of circumstances, including deficient obedience or inappropriate medicine usage, a shortage of adequate pharmaceuticals, hypersensitivity reciprocation to local application of therapeutics, cutaneous infections, and other infuriating ecological provoking factors. If these elements are eliminated, a skin biopsy is required to exclude other AD-like cutaneous disorders. New regimens that target peculiar avenues with improved proficiency and promise minimal adverse events have resulted from recent developments and understanding of the etiology of AD. Although the condition of most patients improves quickly with this treatment, some do not respond well. In this review, the author discusses the management of treatment-resistant atopic dermatitis, with an emphasis on the pediatric population

Medical-Grade Honey Outperforms Conventional Treatments for Healing Cold Sores—A Clinical Study

Cold sores are nasolabial blisters caused by herpes simplex virus (HSV) infections. Novel therapies demonstrating simultaneously antiviral activity and improved wound healing are warranted. The aim of this study was to investigate the efficacy of medical-grade honey (MGH) for treating HSV-induced cold sores. A crossover trial was performed in patients with recurrent cold sores (n = 29). The majority (65.6%) of these patients experience four or more episodes per year, thus forming a valid self-control group. In this study, patients applied an MGH-based formulation (L-Mesitran Soft) on their cold sore at the onset of symptoms (62.1%) or appearing of blister (37.9%) and compared it to their conventional treatments. After complete healing, patients filled in a questionnaire evaluating healing, pain, and itching. The average absolute healing time was 72.4% slower with conventional treatment (10.0 days) compared to MGH (5.8 days). After MGH treatment, 86.2% of all patients experienced faster objective healing (6.9% similar and 6.9% slower) and the subjective healing score was higher in 79.3% of the patients (20.7% similar). If the patients normally experience pain and itching during their cold sores, these levels were lower with MGH therapy compared to conventional treatment in 72.7% and 71.4% of the patients, respectively. Moreover, 100% of the patients prefer MGH treatment over conventional treatment and will use it again on future cold sores. MGH is a promising alternative treatment for cold sores, likely by combining both increased antiviral and wound healing activities while alleviating pain and itching