Miracle-dz/UniFAR-FTUs huggingface dataset:
2016). Graph Embedding Exploiting Subclasses. In 2015 IEEE Symposium Series on Computational Intelligence (SSCI 2015): Proceedings of a meeting held

—Recently, subspace learning methods for Dimensionality Reduction (DR), like Subclass Discriminant Analysis (SDA) and Clustering-based Discriminant Analysis (CDA), which use subclass information for the discrimination between the data classes, have attracted much attention. In parallel, important work has been accomplished on Graph Embedding (GE), which is a general framework unifying several sub-space learning techniques. In this paper, GE has been extended in order to integrate subclass discriminant information resulting to the novel Subclass Graph Embedding (SGE) framework. The kernelization of SGE is also presented. It is shown that SGE comprises a generalization of the typical GE including subclass DR methods. In this vein, the theoretical link of SDA and CDA methods with SGE is established. The efﬁcacy and power of the SGE has been substantiated by comparing subclass DR methods versus a diversity of unimodal methods all pertaining to the SGE framework via a series of experiments on various real-world data.
Leaching of concrete - experiments and modeling

Many concrete dams, and other concrete structures within the hydropower industry are old and in a more or less severe state of degradation. Leaching is, together with freeze-thaw, the most common degradation problem in Swedish hydraulic concrete structures. This report contains a literature survey of concrete leaching, and presents the results of an experimental determination of leaching.
Unethical Marketing Practices By Pharmaceutical Industry In Pakistan

The study aims to highlight the factors for unethical promotional practices by pharmaceutical industry in Pakistan. The study was intended to find who are responsible to initiate and continue these immoral services so that solutions may be suggested to minimize or end such practices. The population of the study contains doctors, pharma representatives, pharmacy stores, hospitals, health authorities and patients’ attendants in hospitals. A convenient based random sample (N=200) was selected to collect the data using a structured questionnaire, obtained from literature. The data was analysed and presented using statistical and mathematical tools including mean, standard deviation, averages, ANOVA, f-test and z-test. The study comes to conclusion that these practices were started by pharmaceuticals and doctors which are responsible to continue these practices to accomplish their benefits especially financial gains. Moreover few doctors and pharmaceutical companies are striving hard to stop such practices by enforcing the law and regulations. The study suggests new legislation to stop such unethical practices through the higher authorities/committees. Also actions may be taken to stop promotion to the non-qualified doctors. Further, the companies need to develop and follow strict code of conduct, which should be part of initial training programs for their representatives. At the same time government law enforcement agencies are expected to work with true spirit to ensure ethical boundaries to develop a healthy and ethical atmosphere for patients.
A STUDY OF BODE INDEX ON SEVERITY AND SYSTEMIC INVOLVEMENT IN PATIENTS WITH COPD

Background: To determine the correlation between BODE index and the level of systemic inflammation in patients with COPD. Material & Methods: A total of 120 patients who attended our outpatient clinic at the Tertiary Care Hospital, Telangana State were enrolled into the study. Of these, 90 patients with symptoms suggestive of COPD were selected as cases and 30 patients who came for Master health checkup were selected as controls. Results: Out in all the 120 subjects (including 90 COPD patients and 30 controls) after categorizing the variable. Baseline data was collected from patients without and with mild, moderate and severe COPD. Ages, body mass index, days of hospitalization, mean hemoglobin concentration, QRS, ejection fraction, pulmonary hypertension, serum albumin concentration, and CRP of all subjects were the parameters analyzed. Conclusion: Thus our study concludes that BODE index is reliable method to predict hopitalisation and the severity of systemic involvement in patients with COPD. Since the assessment of BODE index requires only a spirometer, which is relatively inexpensive and can easily be made available, this index could be of great practical value in primary health care set upto identify individuals who are at need for further evaluation in a higher centre. Thus the BODE index can be used for judicious referral of patients with COPD thereby preventing the wastage of the limited resources available.
Extratropical Cooling, Interhemispheric Thermal Gradients, and Tropical Climate Change

Recent studies suggest the existence of a global atmospheric teleconnection of extratropical cooling to the tropical rainfall climate, mediated through the development of a thermal contrast between the hemispheres—an interhemispheric thermal gradient. This teleconnection has been largely motivated by studies that show a global synchronization of rapid climate change during abrupt climate changes of the last glacial period, in addition to attribution studies of twentieth-century Sahel drought and studies that examined the climate impacts of anthropogenic aerosols. This research has led to interesting developments in atmospheric dynamics of the underlying mechanisms and in applications toward understanding past and present tropical climate change. The emerging teleconnection hypothesis promises to offer new insights into understanding future patterns of tropical rainfall changes due to interhemispheric thermal gradients from greenhouse warming, aerosols, and land-use change.
Chromosomal Instability in Pathogenesis and Prognosis of Colorectal Cancer

of genetic alterations: 17p loss and KRAS mutations, 8q and 13q gain, and 18q loss and 20q gain, respectively. This suggests that specific combinations of a few abnormalities, rather than a mere accumulation of events are important for progression. are with the existence of multiple independent chromosomal instability pathways of colorectal cancer progression, which may possibly disrupt similar biological such as proliferation, differentiation, apoptosis, and invasion, in different manners. and the of these chromosomal alterations with higher grades of dysplasia and a higher risk of progression to colorectal cancer . In addition, multiplex ligation-dependent probe amplification (MLPA) for identifying DNA copy number changes of multiple individual genes on chromosome 20 was performed on flat and polypoid tumors. Both flat and polypoid colorectal tumors with 20q gain showed similar patterns of copy number ratios for the individual genes tested. We concluded that flat colorectal tumors resemble one of the distinct patterns of chromosomal changes found in polypoid colorectal tumors, and that this pattern could convey a more aggressive clinical behavior.
A Lite-CNN for Landslides Recognition on Remote Sensing Images Via Structure Pruning

High-Efficient landslide recognition on remote sensing images is of great importance to hazard monitoring. In this paper, we introduce MobileL-K, a light Convolutional neural networks(CNNs) to achieve highly efficient landslide recognition. In this network, the depthwise separable convolutions with a large kernel is borrowed to exploit global features on an image. Moreover, an improved EC-based network pruning method was proposed based on continual masking. We prune the MobileL-K to apply to landslide recognition. The experiment results on a benchmark dataset show that proposed method outperforms other compared methods with smaller model size, less FLOPs and higher running speed on GPU.
Spectral Feature Learning for Anomaly Change Detection in Hyperspectral Image

Recently, anomaly change detection has attracted considerable interest in hyperspectral image (HSI) analysis. Hyperspectral anomaly change detection (HACD) focuses more on detecting anomalies between multi-temporal hyperspectral images. This paper presents a novel anomaly change detection algorithm that has the following innovations. First, using the variational autoencoder to reconstruct the background of input, forcing its latent features to obey the Gaussian distribution, which can help to reconstruct the background better. Second, in order to obtain a better quality reconstruction of the background map, the spectral angular distance is introduced as a model constraint. Third, impose constraints on the encoder to better learn the background information of the input and improve the reconstruction effect of the network. The proposed algorithm has superior performance over some other algorithms on "Viareggio 2013" datasets.
Iterative Annotation of Solar Panel Plants in Sentinel-2 Imagery

We explore an iterative annotation strategy adapted to recurrent multispectral imagery provided by constellations such as Sentinel-2 and applied to the monitoring of events that develop over time. Our key example is the tracking of the progress in the installation of solar power plants. This problem has four difficulties that seem hard to tackle with automatic methods: an unknown and variable spectrum for the solar panels, a variable background, a variable orientation of the panels causing variable cover of the ground, and a variability of lighting and atmosphere transparency due to cloud shadows and water vapor density. We found that each site is different and that only the interactive annotation of the time series can give an acceptable segmentation of the panels. At this point, we describe a weakly monitored interactive annotation tool that predicts the annotation of new paneled zones from an annotation at a previous date. In that way, the human operator intervention is aided and limited to a few corrections from date to date.
Distributed Optical Fiber Intrusion Identification Algorithm Based on Deep Learning

Optical fiber intrusion presents new features of being more subtle, advanced and frequent, which puts forward higher requirements for optical fiber intrusion detection. Distributed optical fiber intrusion location detection technology has the advantages of long detection distance, anti-electromagnetic interference and easy maintenance, and has become one of the most promising technologies in the field of long-distance security detection. In this paper, the distributed optical fiber intrusion identification algorithm based on DL(Deep learning) is studied. With the help of DL technology, the spatial and temporal characteristics of traffic data are considered comprehensively. Firstly, CNN (Convolutional Neural Network) is used to extract the spatial features of traffic data, and then BPNN (BP neural network) is used to extract the temporal features of traffic data. By stacking CNN+BPNN modules and gradually increasing the learning granularity, the purpose of effectively extracting both spatial features and temporal features is achieved. The research results show that the distributed optical fiber intrusion identification algorithm based on CNN+BPNN can identify common intrusion events, and the recognition rate is as high as 93%. The algorithm in this paper is suitable for all three kinds of optical fiber artificial signals, and the feature extraction time is reduced by about 25% compared with the original algorithm. The validity and superiority of the distributed optical fiber intrusion identification algorithm based on CNN+BPNN proposed in this paper are verified.
Internal Wave Signature Extraction from SAR Imagery Based On Deep Learning

In this study, we proposed an automatic internal wave signature extraction method for SAR (Synthetic Aperture Radar) imagery based on deep convolutional neural networks (DCNNs). For model training, we collected 116 labeled ENVISAT (Environmental Satellite) ASAR (Advanced SAR) images with clear internal wave signatures in the northern South China Sea. The specially tailored deep-learning-based extraction network leverages three modifications of U-Net to improve the accuracy and robustness. As a result, the overall mean Precision, Recall, and F1-score of the model are 84.95%, 84.71%, and 84.83%, respectively. The statistical results imply that our method can significantly increase the internal wave signature extraction accuracy from SAR images, even under complicated imaging conditions.
Improvements of an Smart-and-Connected Low-Cost Sensor System for Measuring Canopy Properties in the Central U.S

This article describes the most recent updates on a smart-and-connected low-cost sensor system called I-Canopy Sensor that measures near-surface air(temperature, humidity, and pressure) and soil properties(temperature and moisture) in order to provide necessary data for irrigation systems. Designed and manufactured in Iowa, I-Canopy Sensor is a real-time solar-powered IoT device that can be used in urban or rural areas under a variety of conditions. I-Canopy supports both Wi-Fi and LoRa-WAN for short-distance and long-distance transmission. The updates improve the sensor performance by adding voltage control functions, Internet connection logic optimization, rechargeable NiMH battery research, and temporary data storage function. These improvements enable the sensor with an ability to run under extreme circumstances including but not limited to below-zero freezing weather, interruption of Wi-Fi and LoRa Wan signal, and poor battery energy capacity. I-Canopy sensor has been tested in farms in Nebraska and Iowa and urban areas in Iowa, Illinois, and New York. With the latest update, the frequency of unexpected shutdowns is greatly diminished. In the firmware part, this article will expand on the cause of different kinds of shutdowns and practical solutions implemented to mitigate the problems.
ASI's "Multi-Mission and Multi-Frequency SAR" Program for Algorithms Development and SAR Data Integration: Achievements and Future Perspectives

The "Multi-mission and multi-frequency SAR" program (2021-2023) of the Italian Space Agency supported the national community to develop algorithms combining SAR data collected in C, X and L-bands. Main achievements in terms of SAR data exploitation, integration and data fusion, and new products prototyping are presented with regard to agriculture, natural hazards, urban areas, cryosphere, sea and coast. Perspectives are finally outlined with regard to future SAR missions and towards downstream applications.
Deep Learning For Land Cover Mapping Using Sentinel-2 Imagery: A Case Study At Greater Cairo, Egypt

Land cover mapping is essential for various applications, and the integration of satellite imagery and deep learning techniques offers an accurate and efficient solution. This study focuses on mapping land cover and change detection in the new extensions of Greater Cairo, Egypt, using Sentinel-2 imagery and convolutional neural networks (CNNs). The CNN model was trained on the BigEarthNet dataset, and transfer learning was applied using a pre-trained U-Net model. The results reveal significant land cover changes in Greater Cairo, particularly in the eastern region due to the construction of the New Administrative Capital. The accuracy assessment metrics, including precision, recall, and F1-score, demonstrate high accuracy levels exceeding 90%. These findings contribute to the advancement of land cover mapping and its applications in urban development.
Strain Accumulation and Release in the Himalaya from ALOS-2 InSAR Analysis

We study the active Himalayan deformation via examining the interseismic, coseismic, and postseismic phases, exploring the strain on the Main Himalayan Thrust due to Indian and Eurasian Plate convergence. We examine postseismic deformations resulting from the 2015 M7.8 Gorkha Earthquake, employing InSAR data, ALOS-2 L-band, from the JAXA ALOS-2 SAR satellite. InSAR in the Himalayas is challenging due to the extreme topographic relief, steep slopes, vegetation and snow cover, and L-band is an advantage. We focus on the phase unwrapping, topographic, tropospheric, and ionospheric corrections to improve the InSAR time-series. The final interseismic results provide information on the evolution of the strain field. Initial results from the 2015-2019 Gorkha postseismic analysis are consistent with afterslip down-dip from the 2015 rupture and absence of afterslip on shallower megathrust. This approach sets the stage for future analyses of data from the forthcoming NISAR mission, with enhanced ionospheric and tropospheric corrections.
Mapping Forest Disturbance Types in China with Landsat Time Series

Forest is an important natural resource for the Earth. However, forest is frequently disturbance by different agents, which has different cascading impacts on surface energy balances, carbon dynamics et al. To better understand forest disturbance process in China, in this study, an object-based time series trajectory analysis technology was used to classify forest change data into different forest disturbance types, including forest logging and forest fire. The specific process includes: first, a hierarchical sample collection scheme was designed, and the visual interpretation of the samples is completed by different interpretation experts with the assistance of multi-source data; secondly, image segmentation of forest change data was performed, and the forest disturbance pixels that occur in the same year and are spatially adjacent are classified as the same object and used as the basic unit of subsequent processing; thirdly, the Landsat time series features of each image segmentation object were extracted, and the features representing their time series trajectory changes were further extracted; finally, the classifier was trained and optimized to complete the automatic classification of the national area. Experimental results show that the spatial and temporal distributions for different disturbance types vary greatly in China.
Dlafnet: A Direct Fusion Method of 2D Aerial Image and 3D Lidar Point Cloud for Semantic Segmentation

Semantic segmentation of high-resolution remote sensing images (RSIs) is developing rapidly. Multispectral images can provide rich spectral information for semantic segmentation, while 3D LiDAR point cloud data can provide depth information. Thus, semantic segmentation accuracy could be improved by fusing multispectral images and 3D LiDAR point cloud. In this paper, we propose a method titled Direct LiDAR-Aerial Fusion Network (DLAFNet) which directly uses RSIs and LiDAR point cloud for semantic segmentation tasks. In particular, owing to the fact that sparse features extracted from the KPConv branch are not as essential as features from RSIs, we design LiDAR Assisted Attention Module (L-AAM). Our experiments on the modified GRSS18 dataset prove that our method is proper and can obtain the best results by comparing with its components and other methods.
CTACL:Hyperspectral Image Change Detection Based on Adaptive Contrastive Learning

Hyperspectral image change detection (HSI-CD) can accurately identify changing regions by capturing subtle spectral differences and has become a research hotspot in the field of remote sensing (RS). Convolutional neural networks (CNNs) have excellent local context modeling capabilities and have been proven to be powerful feature extractors in HSI-CD. However, due to its inherent network structure limitation, CNN cannot well mine and represent the sequential properties of spectral features, especially the medium and long-term dependencies. In contrast, transformer-based network architecture shows a strong ability to model long-distance dependencies, which can fully mine and extract global features, but exhibits weak performance in extracting local information. To this end, we propose HSI-CD network based on adaptive contrastive learning (CTACL). Specifically, we first propose a parallel network of CNNs and transformers to mine local and global temporal-spatial-spectral features of HSI, respectively. Second, we propose adaptive contrastive learning to pre-train the network to learn the latent features of a large amount of unlabeled data and better mine and utilize local and global information. Experimental results on the farmland dataset show that the proposed method performs well.
EL COLAPSO DE LA PESQUERÍA DE LA MOJARRA RAYADA EUGERRES PLUMIERI (PISCES: GERREIDAE) EN LA CIÉNAGA GRANDE DE SANTA MARTA: ¿CAUSAS PESQUERAS, AMBIENTALES O BIOLÓGICAS?*

The collapse of the striped mojarra Eugerres plumieri (Pisces: Gerreidae) fishery in the Cienaga Grande de Santa Marta: Fishing, environmental or biological causes? The collapse of the striped mojarra (Eugerres plumieri) fishery, a traditional resource in Cienaga Grande de Santa Marta, from 1994-1996 to 2000-2007, is analyzed. The BACI (Before and After Control Impact) approach used 399-428
Pixel-Level Annotation of Specific Targets for Large-Scale Remote Sensing Images

Common remote sensing images usually contain targets such as airports, stations, stadiums, mountains, lakes and so on. The localization of these targets is of great research importance. However, a large-scale annotation of these images is difficult to be obtained, because these labels can only be annotated manually, and the annotation is undoubtedly very time-consuming and laborious. In the paper, we propose a framework to generate localization annotations via semi-supervised training. Firstly, optical remote sensing images, with or without target objects, are automatically collected, and a binary classification model is trained on these images. Then the feature map, as a preliminary candidate region for targets, can be obtained from the model. Finally, a saliency detection method based on transformer is employed to further refine the candidate regions and get more precise localization annotations. The proposed framework is a semi-supervised approach to automatically generate large-scale precise location annotation datasets, which can further help many related researches.
A Verification to Relationship between WAI and PAI While Estimating LAI with DHP Method

In the indirect measurement of Leaf Area Index (LAI), previous studies commonly assumed that the actual LAI can be obtained by subtracting the Woody Area Index (WAI) from the Plant Area Index (PAI), which includes the entire plant. This can be calculated using the equation LAI = PAI - WAI. However, this study, conducted using 3D model simulations and the DHP (Digital Hemispherical Photography) method to measure LAI, discovered that this conclusion may not be correct. The LAI value obtained by subtracting WAI directly from PAI showed significant discrepancies from the true LAI value in the simulated environment. Through the re-derivation and verification of the LAI calculation equation, this study proposes a more reasonable explanation for this discrepancy and presents the following conclusions based on the 3D simulation experiment and hemispherical photography method: using PAI-WAI to calculate LAI leads to overestimation of the values, while the calculation method described in this study obtained more accurate LAI values in the simulation experiments (with higher R2, lower RMSE and MAE).
Implementation of a Point Target Simulator for Better Estimating Parameter Offsets in Airborne SAR and InSAR Processing

Synthetic Aperture Radar (SAR) is a coherent radar tech system used in a wide range of applications. The UMass Point Target Simulator (UMass-PTS) can help identify such scenarios and provide simulated data, and evaluate the quality and performance of the simulations. The UMass-PTS under squinted geometries is evaluated, as well as evaluating the performance and quality of data. The results show that the UMass-PTS is capable of generating a large number of simulations under a variety of simulated scenarios.