Extreme Region Research Articles

Phase-matching of high-order harmonic generation in the extreme ultraviolet region with orbital angular momentum.

High-order harmonics can generate vortex beams with orbital angular momentum (OAM) in the extreme ultraviolet region. However, experimental research on their phase-matching (PM) characteristics is limited. In this study, vortex high-order harmonic generation (HHG) in the extreme ultraviolet region was generated with Ar gas. Phase-matched HHG with OAM was obtained by optimizing the focus position, laser energy, and gas pressure. The dependence of the PM characteristics on these parameters was analyzed. In addition, we conducted an experimental analysis of the dimensional properties of vortex harmonics under PM conditions. This study is a contribution towards the intense vortex high-order harmonic light sources and their applications.

Assessing Potential Evapotranspiration Methods in Future Drought Projections across Canada

abstract Recently, concerns have arisen as to whether temperature-based proxy methods used to estimate potential evapotranspiration (PET) are reliable when examining future drought severity, especially in the context of a warmer climate. The objective of this study was to assess the effect of different PET approaches, focusing on proxies for radiation and humidity, on future Standardized Precipitation Evapotranspiration Index (SPEI) calculations across Canada. Using output from 22 CMIP6 global climate models (GCMs), seasonal and annual SPEI comparisons were carried out between the physically-based Penman-Monteith (PM) method and two approaches that incorporate temperature proxies to calculate radiation and/or humidity. These included the temperature-based Hargreaves (HG) approach and a PM method with derived humidity (PM-m). Results revealed that although the general patterns of SPEI projections across Canada were consistent among the methods, notable spatial and temporal differences were apparent. Specifically, both median and extreme SPEI projections based on the two temperature proxy methods revealed less annual and summer drying in much of central, eastern, and northern regions of Canada when compared to the physically based SPEI-PM. In extreme western regions (British Columbia, Yukon) these two methods, particularly HG, projected drier conditions. Differences of using temperature derived radiation and humidity were also most apparent in spring (and to a lesser degree, autumn), where the HG approach overestimated spring drying (and autumn wetting) over large regions of the country. Overall, differences tended to be more pronounced for the fully temperature-based HG approach during all periods considered. Results from this study strongly suggest that when possible, a physically-based approach be used when estimating PET to assess future drought projections. If a temperature proxy is used, the differences to a physically-based method should be understood and resultant implications be evaluated.

The Atacama Rover Astrobiology Drilling Studies (ARADS) Project.

With advances in commercial space launch capabilities and reduced costs to orbit, humans may arrive on Mars within a decade. Both to preserve any signs of past (and extant) martian life and to protect the health of human crews (and Earth's biosphere), it will be necessary to assess the risk of cross-contamination on the surface, in blown dust, and into the near-subsurface (where exploration and resource-harvesting can be reasonably anticipated). Thus, evaluating for the presence of life and biosignatures may become a critical-path Mars exploration precursor in the not-so-far future, circa 2030. This Special Collection of papers from the Atacama Rover Astrobiology Drilling Studies (ARADS) project describes many of the scientific, technological, and operational issues associated with searching for and identifying biosignatures in an extreme hyperarid region in Chile's Atacama Desert, a well-studied terrestrial Mars analog environment. This paper provides an overview of the ARADS project and discusses in context the five other papers in the ARADS Special Collection, as well as prior ARADS project results.

XUE: Molecular Inventory in the Inner Region of an Extremely Irradiated Protoplanetary Disk

We present the first results of the eXtreme UV Environments (XUE) James Webb Space Telescope (JWST) program, which focuses on the characterization of planet-forming disks in massive star-forming regions. These regions are likely representative of the environment in which most planetary systems formed. Understanding the impact of environment on planet formation is critical in order to gain insights into the diversity of the observed exoplanet populations. XUE targets 15 disks in three areas of NGC 6357, which hosts numerous massive OB stars, including some of the most massive stars in our Galaxy. Thanks to JWST, we can, for the first time, study the effect of external irradiation on the inner (<10 au), terrestrial-planet-forming regions of protoplanetary disks. In this study, we report on the detection of abundant water, CO, 12CO2, HCN, and C2H2 in the inner few au of XUE 1, a highly irradiated disk in NGC 6357. In addition, small, partially crystalline silicate dust is present at the disk surface. The derived column densities, the oxygen-dominated gas-phase chemistry, and the presence of silicate dust are surprisingly similar to those found in inner disks located in nearby, relatively isolated low-mass star-forming regions. Our findings imply that the inner regions of highly irradiated disks can retain similar physical and chemical conditions to disks in low-mass star-forming regions, thus broadening the range of environments with similar conditions for inner disk rocky planet formation to the most extreme star-forming regions in our Galaxy.

Gastric glomus tumor mimicking neuroendocrine tumor on fine needle aspiration: a diagnostic pitfall

Abstract Introduction/Objective Glomus tumors (GT) are mesenchymal neoplasms of modified smooth muscle that most commonly affect subungual regions of distal extremities. Although they can occur everywhere in the body, gastric involvement is rare. Gastric GT is usually benign but given the overlapping morphologic and immunohistochemical features with other gastric mesenchymal tumors, GT is commonly misdiagnosed. Methods/Case Report We present two cases of gastric GT initially diagnosed as well-differentiated neuroendocrine tumor (WDNT), G1 on fine needle aspiration (FNA). A 57-year-old female presented with an incidental 1.5 cm gastric submucosal lesion discovered on endoscopy performed for esophageal food impaction. The second case is of a 73- year-old male with a 1.7 cm submucosal lesion found on endoscopy for reflux. Both lesions were located in the gastric antrum. FNA showed clusters of relatively uniform cells with moderate cytoplasm, round nuclei, and stippled chromatin. Tumor cells were focally positive for synaptophysin, while negative for CK7, CD117, DOG-1, and chromogranin. Ki-67 proliferative index was &amp;lt;3%. A diagnosis of WDNT, G1 was made. Subsequent partial gastrectomy showed a submucosal epithelioid neoplasm composed of cells with eosinophilic to clear cytoplasm, and distinct cellular membranes. Focally dilated, tumor-associated thin-walled vessels were noted. Immunohistochemically, the tumor was positive for SMA, calponin, and synaptophysin, and negative for CK AE1/AE3 and chromogranin, supporting the diagnosis of gastric GT. Results (if a Case Study enter NA) N/A Conclusion The rarity of gastric GT, combined with the morphological and immunohistochemical similarity with WDNT, led to diagnostic pitfalls in these cases. With the increased detection and sampling of incidental gastric lesions, cytopathologists should be aware of this uncommon tumor and its mimics, and as such, expand their differential diagnoses.

Milk Yield Prediction and Economic Analysis of Optimized Rearing Environment in a Cold Region Using Neural Network Model

The milk yield of dairy cows in a non-stressed state in the cold region in China is lower during cold seasons. In this study, the correlations between indoor environmental factors and milk production were analyzed. Temperature, relative humidity, and light intensity were found to be the main factors affecting milk yield. The warning values of these factors for lower milk production were 5 °C, 60%, and 300 lx, respectively. A neural network model predicting milk yield based on environmental factors was established, and the optimal model parameters were determined, resulting in a high accuracy of R2 = 0.802. This model was used to investigate the optimal measure for improving the indoor environment, which helps to increase milk production and economic benefits, including LED lights, heating radiators, and dehumidifiers. In conclusion, each type of device led to the growth of milk yield, reaching 2.341, 1.706, and 1.893 kg cow−1 Day−1. The combination of heating radiator and LED light resulted in the highest increased net benefit of 16.802 CNY cow−1 Day−1. This is the first time that a neural network model was successfully built to predict milk yield based on climatic features which was also applied to economic analysis of indoor environment improvement for dairy barns in extreme cold regions.

Deep learning enabled hemorrhage detection in retina with DPFE and splat segmentation in fundus images

The range of diabetics, hypertension, occlusions in vascular are rapidly increasing in the modern era. Adversarial effects of these diseases are the organ damage which is increasing from child to old age people. One of the severe damages involves the causes of eye diseases like diabetic eye disease or Diabetic Retinopathy (DR). Late prediction of DR may lead to permanent loss of vision. Hence there is a need among ophthalmologists in detecting and treating the eye diseases at the earliest stage. Detection of abnormalities as early as possible is a crucial task in today world as the existing strategies possess some setbacks. In this research work, a deep learning framework has been developed for the betterment in the prediction of retinal hemorrhage with the fundus image. The Double Pierced Feature Extraction (DPFE) is planned by merging the Enhanced Long Short-Term Memory (ELSTM) CNN and Maximally Stable Extremal Regions (MSER) algorithm. Splat algorithm is focused for segmentation which partitions a retinal image into various segments called splats of similar color, similar intensity and the spatial position. Every splat carries several details from that diverse features can be extracted. This splat based segment establishes a boundary based on an appropriate set of detailed information—needed features such as different area, filter, texture, splat related features and color etc. At last trained ELSTM with CNN is applied to predict the redder lesions of the fundus image, which confirms the presence of hemorrhage in the retina. The developed framework was evaluated using the DIARETDB2 dataset, and its performance was measured using parameters such as Classification Accuracy, Negative Acceptance Rate (NAR), Specificity, and Sensitivity and the experimental results highlight that the proposed methodology achieved an impressive sensitivity of 98.67% and specificity of 98.91%, outperforming existing techniques such as “ANFIS Classification with Cuckoo,” “ANFIS Classification with PSO,” and “Motion Pattern Recognition.” This analysis confirms the effectiveness of the proposed framework in accurately identifying relevant instances while excluding non-relevant patterns, showcasing its potential for improved pattern recognition and classification.

Study of Electron Impact Excitation of Na-like Kr Ion for Impurity Seeding Experiment in Large Helical Device

In this work, a krypton gas impurity seeding experiment was conducted in a Large Helical Device. Emission lines from the Na-like Kr ion in the extreme ultraviolet wavelength region, such as 22.00 nm, 17.89 nm, 16.51 nm, 15.99 nm, and 14.08 nm, respective to 2p63p(2P1/2o)−2p63s(2S1/2), 2p63p(2P3/2o)−2p63s(2S1/2), 2p63d(2D3/2)−2p63p(2P3/2o), 2p63d(2D5/2)−2p63p(2P3/2o), and 2p63d(2D3/2)−2p63p(2P1/2o) transitions, are observed. In order to generate a theoretical synthetic spectrum, an extensive calculation concerning the excitation of the Kr25+ ion through electron impact was performed for the development of a suitable plasma model. For this, the relativistic multiconfiguration Dirac–Hartree–Fock method was employed along with its extension to the relativistic configuration interaction method to compute the relativistic bound-state wave functions and excitation energies of the fine structure levels using the General Relativistic Atomic Structure Package-2018. In addition, another set of calculations was carried out utilizing the relativistic many-body perturbation theory and relativistic configuration interaction methods integrated within the Flexible Atomic Code. To investigate the reliability of our findings, the results of excitation energies, transition probabilities, and weighted oscillator strengths of different dipole-allowed transitions obtained from these different methods are presented and compared with the available data. Further, the detailed electron impact excitation cross-sections and their respective rate coefficients are obtained for various fine structure resolved transitions using the fully relativistic distorted wave method. Rate coefficients, calculated using the Flexible Atomic Code for population and de-population kinetic processes, are integrated into the collisional-radiative plasma model to generate a theoretical spectrum. Further, the emission lines observed from the Kr25+ ion in the impurity seeding experiment were compared with the present plasma model spectrum, demonstrating a noteworthy overall agreement between the measurement and the theoretical synthetic spectrum.

A versatile wide energy range spectrometer for soft X-ray FELs

The design and main characteristics of the Wide Energy range Spectrometer at TIMEX (WEST) operating at the FERMI Free Electron Laser are described. WEST covers the entire photon energy range of the FERMI source including its fundamental emission (extreme ultraviolet region, EUV) and its highest harmonics (soft X-ray region) spanning about a thousand of electrovolts from 20 to 1000 eV. The instrument is specifically designed to measure FERMI FEL spectra with good resolving power (500<RP<7000) and provide remarkable signal to noise ratio adequate for single-shot experiments. Notably, WEST is aligned in transmission geometry downstream of the TIMEX experimental beamline thus offering a valid diagnostic tool for the characterization of both linear and nonlinear spectral effects driven by the interaction between FEL radiation and samples in gaseous, liquid and solid phase. Detailed theoretical analysis of the instrument performance complemented by experimental measurements is presented. Further possible upgrades of WEST are finally considered and discussed.

MuA-SAR Fast Imaging Based on UCFFBP Algorithm with Multi-Level Regional Attention Strategy

Multistatic airborne SAR (MuA-SAR) benefits from the ability to flexibly adjust the positions of multiple transmitters and receivers in space, which can shorten the synthetic aperture time to achieve the required resolution. To ensure both imaging efficiency and quality of different system spatial configurations and trajectories, the fast factorized back projection (FFBP) algorithm is proposed. However, if the FFBP algorithm based on polar coordinates is directly applied to the MuA-SAR system, the interpolation in the recursive fusion process will bring the problem of redundant calculations and error accumulation, leading to a sharp decrease in imaging efficiency and quality. In this paper, a unified Cartesian fast factorized back projection (UCFFBP) algorithm with a multi-level regional attention strategy is proposed for MuA-SAR fast imaging. First, a global Cartesian coordinate system (GCCS) is established. Through designing the rotation mapping matrix and phase compensation factor, data from different bistatic radar pairs can be processed coherently and efficiently. In addition, a multi-level regional attention strategy based on maximally stable extremal regions (MSER) is proposed. In the recursive fusion process, only the suspected target regions are paid more attention and segmented for coherent fusion at each fusion level, which further improves efficiency. The proposed UCFFBP algorithm ensures both the quality and efficiency of MuA-SAR imaging. Simulation experiments verified the effectiveness of the proposed algorithm.

MSER-Vertical Sobel for Vehicle Logo Detection

Detecting a vehicle logo is the first step before realizing the identity of the logo. However, the detection of logos can pose difficulties due to various factors, including logo variations, differing scales and orientations, background interference, varying lighting conditions, and partial obstruction. This paper presents a vehicle logo detection method using hand-crafted features. We used a combination of Maximally Stable Extremal Region (MSER) and Vertical Sobel. We combine vertical Sobel with MSER to overcome MSER's limitation in recognizing objects of different sizes. These two features are merged using a closing morphology operation to form blobs selected as logo candidate areas. Moreover, a Support Vector Machine (SVM) is implemented to choose a logo area by analyzing each candidate's Histogram of Oriented Gradient (HOG). The proposed method was compared with other methods by implementing them on the same dataset. The significant advantage of using MSER-Vertical Sobel is its fast computation time. It is faster than other approaches that use non-handcrafted features. The test results show that the MSER-Vertical Sobel can achieve high accuracy and the fastest computation time.

A Pyramid Deep Feature Extraction Model for the Automatic Classification of Upper Extremity Fractures.

The musculoskeletal system plays a crucial role in our daily lives, and the accurate diagnosis of musculoskeletal issues is essential for providing effective healthcare. However, the classification of musculoskeletal system radiographs is a complex task, requiring both accuracy and efficiency. This study addresses this challenge by introducing and evaluating a pyramid deep feature extraction model for the automatic classification of musculoskeletal system radiographs. The primary goal of this research is to develop a reliable and efficient solution to classify different upper extremity regions in musculoskeletal radiographs. To achieve this goal, we conducted an end-to-end training process using a pre-trained EfficientNet B0 convolutional neural network (CNN) model. This model was trained on a dataset of radiographic images that were divided into patches of various sizes, including 224 × 224, 112 × 112, 56 × 56, and 28 × 28. From the trained CNN model, we extracted a total of 85,000 features. These features were subsequently subjected to selection using the neighborhood component analysis (NCA) feature selection algorithm and then classified using a support vector machine (SVM). The results of our experiments are highly promising. The proposed model successfully classified various upper extremity regions with high accuracy rates: 92.04% for the elbow region, 91.19% for the finger region, 92.11% for the forearm region, 91.34% for the hand region, 91.35% for the humerus region, 89.49% for the shoulder region, and 92.63% for the wrist region. These results demonstrate the effectiveness of our deep feature extraction model as a potential auxiliary tool in the automatic analysis of musculoskeletal system radiographs. By automating the classification of musculoskeletal radiographs, our model has the potential to significantly accelerate clinical diagnostic processes and provide more precise results. This advancement in medical imaging technology can ultimately lead to better healthcare services for patients. However, future studies are crucial to further refine and test the model for practical clinical applications, ensuring that it integrates seamlessly into medical diagnosis and treatment processes, thus improving the overall quality of healthcare services.

Assessment of clinical impact of SARS-CoV-2 in people with type 1 diabetes: A cohort study

ObjectivesDuring the coronavirus 2019 pandemic, there had been more than 758 million COVID-19 cases as of February 13, 2023, and it is the main cause of death in many countries. Due to the variation in disease presentation, scientists determined that people living with type 2 diabetes mellitus were at higher risk of mortality. However, people living with type 1 diabetes have not been thoroughly studied, especially in extreme regions of developing countries. The objective of this study was to analyze the effects of SARS-CoV-2 pandemic restrictions on different variables in a cohort with type 1 diabetes. MethodsThis cohort-type study included pediatric and adult patients with type 1 diabetes at Regional Hospital Dr. Juan Noé Crevani in Arica, Chile. Biosocial and anthropometric factors, clinical history, self-care activities, and biochemical parameters were assessed and compared using analysis of variance and paired t tests between March 2020 and March 2021. ResultsA total of 150 patients were assessed during the SARS-CoV-2 pandemic in Arica, Chile. One year after the pandemic struck, the main causes for metabolic deterioration were a reduction of carbohydrate counting by an average of 8.67% (P = 0.000), a reduction of adherence to treatment by an average of 25% (P = 0.000), and a shift to telemedicine as a main health care service (P = 0.023); these factors raised hemoglobin A1c (HbA1c) levels by 1.81%, 1.78% and 0.075%, respectively. The participants’ average body mass index (BMI) increased by 1.26 kg/m2 and HbA1c levels increased by 0.16% during the first year of the pandemic. Also, hospitalizations increased about 2% (P = 0.984), and there was a significant increase in carbohydrate and snack intake (P = 0.330 and P = 0.811, respectively). Children's linear growth decreased by a standard deviation of 0.035 (P = 0.648), and their physical activity decreased by 12.67% (P = 0.383). ConclusionsThis study found that adherence to diabetes care was reduced during the pandemic owing to a variety of behavioral reasons and environmental changes (e.g., quarantines and food security). This affected this population's HbA1c levels, BMI, linear growth, and number of hospitalizations as main consequences. Telemedicine remains an important tool, but it must be reconsidered among all different age groups.

Automating Assessment and Providing Personalized Feedback in E-Learning: The Power of Template Matching

This research addressed the need to enhance template-matching performance in e-learning and automated assessments within Egypt’s evolving educational landscape, marked by the importance of e-learning during the COVID-19 pandemic. Despite the widespread adoption of e-learning, robust template-matching feedback mechanisms should still be developed for personalization, engagement, and learning outcomes. This study augmented the conventional best-buddies similarity (BBS) approach with four feature descriptors, Harris, scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and maximally stable extremal regions (MSER), to enhance template-matching performance in e-learning. We systematically selected algorithms, integrated them into enhanced BBS schemes, and assessed their effectiveness against a baseline BBS approach using challenging data samples. A systematic algorithm selection process involving multiple reviewers was employed. Chosen algorithms were integrated into enhanced BBS schemes and rigorously evaluated. The results showed that the proposed schemes exhibited enhanced template-matching performance, suggesting potential improvements in personalization, engagement, and learning outcomes. Further, the study highlights the importance of robust template-matching feedback in e-learning, offering insights into improving educational quality. The findings enrich e-learning experiences, suggesting avenues for refining e-learning platforms and positively impacting the Egyptian education sector.

Bilateral lower extremity injuries in pedestrian versus motor vehicle collisions

Background Lower extremity (LE) injuries following pedestrian versus motor vehicle collision (PMVC) are a common cause of morbidity and mortality in orthopedic trauma. There is currently no evidence illustrating the presentation of orthopedic bilateral LE injuries (BLEI). This study aimed to evaluate the patterns of BLEI on postoperative outcomes and hospital utilization following PMVC. Methods This retrospective cohort analysis included 532 consecutive patients presenting to a single level I trauma center with LE injuries following PMVC from January 1, 2017 to May 31, 2021. Injury location was categorized by extremity region (hip/femur, knee, leg, ankle, midfoot). Outcome measures included 30-day postoperative complications (infection, compartment syndrome, myocardial infarction, pneumonia, pulmonary embolism, sepsis, stroke) and hospital utilization (length of stay, ICU admission, prolonged ventilation [≥48 h], readmission). Mann–Whitney U, chi-square, and logistic regressions were performed calculating odds ratio (OR) with 95% confidence intervals. Results A total of 142 (26.7%) patients sustained BLEI. The most common BLEI complexes (212 total) were bilateral leg (25.0%), hip/femur and leg (17.0%), leg and knee (12.3%), and hip/femur and knee (8.0%). Bilateral and unilateral injuries showed no difference for postoperative complications (11.3% vs. 6.2%; p = 0.062). However, BLEI had higher odds of ICU admission (OR:2.97, CI:1.68–5.26), prolonged ventilation (OR:2.38, CI:1.24–4.59), and patients remained hospitalized 10.87 days longer (p &lt; 0.001). Conclusion Our findings indicate that more than a quarter of PMVCs with LE injuries present with BLEI and require significantly more hospital resources. This is the first study to characterize BLEI in PMVCs, highlighting associated patterns to encourage heightened awareness in trauma evaluation. Level of Evidence: III

Automated Georectification, Mosaicking and 3D Point Cloud Generation Using UAV-Based Hyperspectral Imagery Observed by Line Scanner Imaging Sensors

Hyperspectral sensors mounted on unmanned aerial vehicles (UAV) offer the prospect of high-resolution multi-temporal spectral analysis for a range of remote-sensing applications. However, although accurate onboard navigation sensors track the moment-to-moment pose of the UAV in flight, geometric distortions are introduced into the scanned data sets. Consequently, considerable time-consuming (user/manual) post-processing rectification effort is generally required to retrieve geometrically accurate mosaics of the hyperspectral data cubes. Moreover, due to the line-scan nature of many hyperspectral sensors and their intrinsic inability to exploit structure from motion (SfM), only 2D mosaics are generally created. To address this, we propose a fast, automated and computationally robust georectification and mosaicking technique that generates 3D hyperspectral point clouds. The technique first morphologically and geometrically examines (and, if possible, repairs) poorly constructed individual hyperspectral cubes before aligning these cubes into swaths. The luminance of each individual cube is estimated and normalised, prior to being integrated into a swath of images. The hyperspectral swaths are co-registered to a targeted element of a luminance-normalised orthomosaic obtained using a standard red–green–blue (RGB) camera and SfM. To avoid computationally intensive image processing operations such as 2D convolutions, key elements of the orthomosaic are identified using pixel masks, pixel index manipulation and nearest neighbour searches. Maximally stable extremal regions (MSER) and speeded-up robust feature (SURF) extraction are then combined with maximum likelihood sample consensus (MLESAC) feature matching to generate the best geometric transformation model for each swath. This geometrically transforms and merges individual pushbroom scanlines into a single spatially continuous hyperspectral mosaic; and this georectified 2D hyperspectral mosaic is then converted into a 3D hyperspectral point cloud by aligning the hyperspectral mosaic with the RGB point cloud used to create the orthomosaic obtained using SfM. A high spatial accuracy is demonstrated. Hyperspectral mosaics with a 5 cm spatial resolution were mosaicked with root mean square positional accuracies of 0.42 m. The technique was tested on five scenes comprising two types of landscape. The entire process, which is coded in MATLAB, takes around twenty minutes to process data sets covering around 30 Ha at a 5 cm resolution on a laptop with 32 GB RAM and an Intel® Core i7-8850H CPU running at 2.60 GHz.

EmotionFusion: A unified ensemble R-CNN approach for advanced facial emotion analysis

To assess non-verbal reactions to commodities, services, or products, sentiment analysis is the technique of identifying exhibited human emotions utilizing artificial intelligence-based technology. The facial muscles flex and contract differently in response to each facial expression that a person makes, which facilitates the deep learning AI algorithms’ ability to identify an emotion. Facial emotion analysis has numerous applications across various industries and domains, leveraging the understanding of human emotions conveyed through facial expressions, so it is very much required in healthcare, security and survelliance, Forensics, Autism and cultural studies etc,.. In this study, facially expressed sentiments in real-time photographs as well as in an existing dataset are classified using object detection techniques based on deep learning. Fast Region-based Convolution Neural Network (R-CNN) is an object detection system that uses suggested areas to categorize facial expressions of emotion in real-time. Using a high-quality video collection made up of 24 actors who were photographed facially expressing eight distinct emotions (Happy, Sad, Disgust, Anger, Surprise, Fear, Contempt and Neutral). The Fast R-CNN and Mouth region-based feature extraction and Maximally Stable Extremal Regions (MSER) method used for classification and feature extraction respectively. In order to assess the deep network’s performance, the proposed work builds a confusion matrix. The network generalizes to new images rather well, as seen by the average recognition rate of 97.6% for eight emotions. The suggested deep network approach may deliver superior recognition performance when compared to CNN and SVM methods, and it can be applied to a variety of applications including online classrooms, video game testing, healthcare sectors, and automated industry.

Small Region, Big Impact: Highly Anisotropic Lyman-continuum Escape from a Compact Starburst Region with Extreme Physical Properties

Extreme, young stellar populations are considered to be the primary contributor to cosmic reionization. How the Lyman continuum (LyC) escapes these galaxies remains highly elusive, and it is challenging to observe this process in actual LyC emitters without resolving the relevant physical scales. We investigate the Sunburst Arc, a strongly lensed LyC emitter at z = 2.37 that reveals an exceptionally small-scale (tens of parsecs) region of high LyC escape. The small (<100 pc) LyC-leaking region has extreme properties: a very blue UV slope (β = −2.9 ± 0.1), a high ionization state ([O iii] λ5007/[O ii] λ3727 = 11 ± 3 and [O iii] λ5007/Hβ = 6.8 ± 0.4), strong oxygen emission (EW([O iii]) = 1095 ± 40 Å), and a high Lyα escape fraction (0.3 ± 0.03), none of which are found in nonleaking regions of the galaxy. The leaking region’s UV slope is consistent with approximately “pure” stellar light that is minimally contaminated by the surrounding nebular continuum emission or extinguished by dust. These results suggest a highly anisotropic LyC escape process such that LyC is produced and escapes from a small, extreme starburst region where the stellar feedback from an ionizing star cluster creates one or more “pencil-beam” channels in the surrounding gas through which LyC can directly escape. Such anisotropic escape processes imply that random sight-line effects drive the significant scatters between measurements of galaxy properties and LyC escape fraction, and that strong lensing is a critical tool for resolving the processes that regulate the ionizing budget of galaxies for reionization.

Risk Attitude of Farmers in the Climate Extreme Region of Andhra Pradesh, India

Aim: The objective of the current study was to assess the risk attitude of both insured and non-insured farmers and identify the factors influencing the risk attitude of insured and non-insured farmers.&#x0D; Study Area and Design: An ex-post-facto study was conducted, and the stratified random sampling method was used to collect the data at selected villages in Prakasam district, Andhra Pradesh.&#x0D; Methodology: A sample of 150 farmers of chilli and cotton was collected (90 insured and 60 non-insured). Data regarding the agriculture year 2021-22 was analysed using Moscardi and de Janvry approach. The socioeconomic, institutional and farm characteristics were analysed by using discriminant analysis to identify the factors influencing farmers’ risk attitudes.&#x0D; Results: The present study results showed that the majority of the farmers are risk neutral. In the case of risk preferers insured farmers are more compared to non-insured farmers. Insured chilli farmers more than non-insured chilli farmers in case of risk-averse but, it is the reverse in the case of cotton farmers. Occupation, constraints in getting credit facility, membership of the association, the proportion of crop income to the total farm income and family size of insured chilli farmers and family size, education level membership of the association, constraints in getting credit facility, the proportion of crop income to total farm income of non-insured chilli farmers are significant. Variables like age, occupation, farming experience, constraints in getting credit facility and age, occupation, education level, and farming experience of insured and non-insured cotton farmers were found to be significant. &#x0D; Conclusion: The majority of the farmers (both insured and non-insured) are risk neutral.

Molecular basis of the glycosomal targeting of PEX11 and its mislocalization to mitochondrion in trypanosomes.

PEX19 binding sites are essential parts of the targeting signals of peroxisomal membrane proteins (mPTS). In this study, we characterized PEX19 binding sites of PEX11, the most abundant peroxisomal and glycosomal membrane protein from Trypanosoma brucei and Saccharomyces cerevisiae. TbPEX11 contains two PEX19 binding sites, one close to the N-terminus (BS1) and a second in proximity to the first transmembrane domain (BS2). The N-terminal BS1 is highly conserved across different organisms and is required for maintenance of the steady-state concentration and efficient targeting to peroxisomes and glycosomes in both baker's yeast and Trypanosoma brucei. The second PEX19 binding site in TbPEX11 is essential for its glycosomal localization. Deletion or mutations of the PEX19 binding sites in TbPEX11 or ScPEX11 results in mislocalization of the proteins to mitochondria. Bioinformatic analysis indicates that the N-terminal region of TbPEX11 contains an amphiphilic helix and several putative TOM20 recognition motifs. We show that the extreme N-terminal region of TbPEX11 contains a cryptic N-terminal signal that directs PEX11 to the mitochondrion if its glycosomal transport is blocked.