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Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the digestive tract usually characterized by diarrhea, rectal bleeding, and abdominal pain. IBD includes Crohn's disease and ulcerative colitis as the main entities. IBD is a debilitating condition that can lead to life-threatening complications, involving possible malignancy and surgery. The available therapies aim to achieve long-term remission and prevent disease progression. Biologics are bioengineered therapeutic drugs that mainly target proteins. Although they have revolutionized the treatment of IBD, their potential therapeutic benefits are limited due to large interindividual variability in clinical response in terms of efficacy and toxicity, resulting in high rates of long-term therapeutic failure. It is therefore important to find biomarkers that provide tailor-made treatment strategies that allow for patient stratification to maximize treatment benefits and minimize adverse events. Pharmacogenetics has the potential to optimize biologics selection in IBD by identifying genetic variants, specifically single nucleotide polymorphisms (SNPs), which are the underlying factors associated with an individual's drug response. This review analyzes the current knowledge of genetic variants associated with biological agent response (infliximab, adalimumab, ustekinumab, and vedolizumab) in IBD. An online literature search in various databases was conducted. After applying the inclusion and exclusion criteria, 28 reports from the 1685 results were employed for the review. The most significant SNPs potentially useful as predictive biomarkers of treatment response are linked to immunity, cytokine production, and immunorecognition. [ABSTRACT FROM AUTHOR]

Context. Forage cultivation plays a pivotal role in sustaining ruminants in resource-limited ecosystems such as the hot-summer Mediterranean agroforestry systems of the Iberian Peninsula comprising oak trees (Quercus spp.) and grassland species. Intercropping techniques can enhance forage yields and nutritional quality. Aims. This study assessed the yield and nutritional quality of organic forage mixtures in an Iberian oak grassland ecosystem. Methods. Six forage associations were assessed over two growing seasons: vetch (Vicia sativa L.)-triticale (Triticum x Secale), vetch-rye (Secale cereale), vetch-oat (Avena sativa), pea (Pisum sativum L.)-triticale, pea-rye, and pea-oat. The data were analysed using a multivariate procedure. Key results. Differences in forage composition influenced nutritional quality but not yield. Vetch-based associations exhibited higher crude protein, calcium, phosphorus, potassium, zinc, magnesium, manganese and sulfur contents, along with lower proportions of neutral-detergent fibre and ether extract. The vetch- oat mixture emerged as the highest quality association. Interannual variations in precipitation and temperature caused differences in forage yield, with greater values observed in 2020 than in 2019. Forage associations also had significantly higher nutritional quality in 2020 (increased protein and mineral content, and decreased neutral detergent fibre and lignin). Conclusions. All forages demonstrated satisfactory performance and quality, even in the drier conditions of 2019, suggesting high resilience and adaptability to hot-summer Mediterranean oak grassland ecosystems. Implications. The findings of this study are relevant for organic farmers in resource-limited ecosystems. This information empowers them to select the most suitable forage species based on the specific agroclimatic conditions of each growing season. [ABSTRACT FROM AUTHOR]

This study examined facilitators of and barriers to comprehensive and integrated services. A national sample of 267 full-time practicing school psychologists who were Regular Members or Early Career Members of the National Association of School Psychologists participated. We administered a survey to participants that measured the extent to which they engaged in comprehensive and integrated services and the top facilitators of and barriers to those services using web-based survey procedures. Results indicated that the majority of facilitators identified ("Personal Attributes," "Stakeholder Involvement," "Resources and Support," "School-Specific Variables," "Contextual Variables") predicted greater levels of comprehensive and integrated service delivery. Identified barriers--which often reflected the opposite condition of the facilitators--predicted lower levels of service delivery in some instances; however, they predicted services less often than did facilitators. Implications for research focused on better understanding factors that impact service delivery and on processes for facilitating increased comprehensive and integrated service delivery are discussed. Additionally, implications for practice include the assessment of facilitators and barriers, the use of data to plan for the delivery of comprehensive and integrated services, and advocacy for systems change.

Due to the constrained processing capabilities of real-time detection techniques in remote sensing applications, it is often difficult to obtain detection results with high accuracy in practice. To address this problem, we introduce a new real-time anomaly detection algorithm for hyperspectral images called cloud–edge RX (CE-RX). The algorithm combines the advantages of cloud and edge computing. During the data acquisition process, the edge performs real-time detection on the data just captured to obtain a coarse result and find the suspicious anomalies. At regular intervals, the suspicious anomalies are sent to the cloud for further detection with a highly accurate algorithm, then the cloud sends back the (high-accuracy) results to the edge for information updating. After receiving the results from the cloud, the edge updates the information of the detector in the real-time algorithm to improve the detection accuracy of the next acquired piece of data. Our experimental results demonstrate that the proposed cloud–edge collaborative algorithm can obtain more accurate results than existing real-time detection algorithms. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HIS) super-resolution aims to fuse a low-spatial-resolution HSI (LR-HSI) and a high-spatial-resolution multispectral image (HR-MSI) to obtain a high-resolution HSI (HR-HSI). Tensor-based methods have demonstrated their outstanding ability to construct the relationship between the LR-HSI and the HR-MSI. This letter introduces a nonlocal hierarchical Tucker decomposition (HTD) model for hyperspectral and multispectral image (HSI-MSI) fusion. First, similar nonlocal patch tensors are clustered according to their similarity in the HR-MSI. Next, the spatial/spectral relationship between the LR-HSI and the HR-MSI is extracted through HTD. The alternating direction method of multipliers (ADMMs) is employed to solve the proposed model. Furthermore, to overcome the high computational complexity of the model solver, we propose an efficient distributed and parallel method to accelerate the fusion process. Experimental results demonstrate that the proposed method not only substantially outperforms state-of-the-art HSI-MSI fusion methods, but also achieves a significant acceleration rate. [ABSTRACT FROM AUTHOR]

With the recent development of remote sensing technology, large image repositories have been collected. In order to retrieve the desired images of massive remote sensing data sets effectively and efficiently, we propose a novel central cohesion gradual hashing (CCGH) mechanism for remote sensing image retrieval. First, we design a deep hashing model based on ResNet-18 which has a shallow architecture and extracts features of remote sensing imagery effectively and efficiently. Then, we propose a new training model by minimizing a central cohesion loss which guarantees that remote-sensing hash codes are as close to their hash code centers as possible. We also adopt a quantization loss which promotes that outputs are binary values. The combination of both loss functions produces highly discriminative hash codes. Finally, a gradual sign-like function is used to reduce quantization errors. By means of the aforementioned developments, our CCGH achieves state-of-the-art accuracy in the task of remote sensing image retrieval. Extensive experiments are conducted on two public remote sensing image data sets. The obtained results support the fact that our newly developed CCGH is competitive with other existing deep hashing methods. [ABSTRACT FROM AUTHOR]

The characterization of quality brand meat products, such as "Chorizo Zamorano" dry fermented sausages, involves a wide range of data which makes it necessary to use alternative statistical methodologies. In this study, the feasibility of the Categorical Principal Components Analysis as a multivariate non-linear technique for the characterization of "Chorizo Zamorano" was assessed. The data analyzed were those of eight commercial brands covered by the quality mark over an eight-year period (2013–2020) and included parameters of the physicochemical composition and organoleptic properties of the product. The results showed that "Chorizo Zamorano" has an average moisture content (28.28%), high protein (38.38%) and fat (51.05%) contents, and a very low carbohydrate concentration (1.52%). Results showed that the fat and protein content and the sensory parameters related to external and internal odor appeared to be the studied variables with the greatest influence on the classification of the products according to their quality. [ABSTRACT FROM AUTHOR]

Background: A previous randomized controlled trial (RCT) demonstrated a trend toward a reduced risk of implant-related revision surgery following fixation with use of a Proximal Femoral Nail Antirotation (PFNA) with TRAUMACEM V+ Injectable Bone Cement augmentation versus no augmentation in patients with unstable trochanteric fractures. To determine whether this reduced risk may result in long-term cost savings, the present study assessed the cost-effectiveness of TRAUMACEM V+ cement augmentation versus no augmentation for the fixation of unstable trochanteric fractures from the German health-care payer's perspective.Methods: The cost-effectiveness model comprised 2 stages: a decision tree simulating clinical events, costs, and utilities during the first year after the index procedure and a Markov model extrapolating clinical events, costs, and utilities over the patient's lifetime. Sources of model parameters included the previous RCT, current literature, and administrative claims data. Outcome measures were incremental costs (in 2020 Euros), incremental quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs). Model uncertainty was assessed with deterministic and probabilistic sensitivity analyses.Results: The base-case analysis showed that fixation with cement augmentation was the dominant strategy as it was associated with cost savings (€50.3/patient) and QALY gains (0.01 QALY/patient). Major influential parameters for the ICER were the utility of revision, rates of revision surgery within the first year after fixation surgery, and the costs of augmentation and revision surgery. Probabilistic sensitivity analyses demonstrated that estimates of cost savings were more robust than those of increased QALYs (66.4% versus 52.7% of the simulations). For a range of willingness-to-pay thresholds from €0 to €50,000, the probability of fixation with cement augmentation being cost-effective versus no augmentation remained above 50%.Conclusions: Fixation with use of cement augmentation dominated fixation with no augmentation for unstable trochanteric fractures, resulting in cost savings and QALY gains. Given the input parameter uncertainties, future analyses are warranted when long-term costs and effectiveness data for cement augmentation are available.Level Of Evidence: Economic and Decision Analysis Level II . See Instructions for Authors for a complete description of levels of evidence. [ABSTRACT FROM AUTHOR]

Surgical site infection constitutes a serious complication in the healing process of bone fractures and has been associated with increases in medical resource use and healthcare costs. This study evaluates the economic impact of surgical site infection in tibial fractures in a Spanish cohort. It is a retrospective, single-centre, comparative cohort study of patients with tibial fractures with longitudinal follow-up for up to 18 months post-surgery. Included patients (n = 325) were adults, with tibial fracture, either isolated or polyfracture, or polytrauma with an Injury Severity Score >15. Patients had been surgically treated within 30 days of the tibial fracture by external or internal fixation, or external followed by internal fixation. Most patients (84.9%) had an American Society of Anaesthesiology score of 1–2. 20% of the patients had one open tibial fracture, 12.3% had polytrauma, and 20% had multiple fractures. Most patients were treated with a nail (41.8%) or a plate (33.8%). 56 patients (17.2%) developed surgical site infection. Patients with infection had significantly higher hospital length of stay (34.9 vs 12.0 days; p<0.001; +191%), readmissions (1.21 vs 0.25; p<0.001; +380%) and mean operating theatre time (499 vs 219 min; p<0.001; +128%) than patients without infection. Mean length of stay in intensive care did not significantly increase with infection (2.8 vs 1.7 days; p = 0.25). Total in-hospital costs for patients with infection increased from €7,607 to €17,538 (p<0.001; +131%). Overall, infections were associated with significantly increased healthcare resource use and costs. Preventive strategies to avoid infections could lead to substantial cost savings. [ABSTRACT FROM AUTHOR]

Subcutaneous bio-loggers were used to monitor temperature (T), heart rate (HR), and activity (ACT) of eight ewes managed under intensive housing conditions (permanent stabling and fed once per day). Animals were fed barley straw ad libitum and 0.45 kg concentrate per ewe per day at 08:00 h. Ewes received a surgically implanted bio-logger programmed to record data every 30 min for 12 d. Mean (±SE) T was lower (P < 0.001) in the day (37.88 ± 0.01°C) than at night (38.14 ± 0.01°C), and maximum T occurred at 23:00 h (38.31°C) and minimum T at 08:00 h (37.36°C), immediately after food distribution; however, mean HR (74.41 ± 0.49 bpm) and ACT (15.10 ± 0.30 mg) were higher (P < 0.001) in the day than at night (66.42 ± 0.38 bpm and 6.56 ± 0.22 mg, respectively); maximum HR (87.71 bpm) occurred at 09:30 h, and maximum ACT at 10:00 h (28.47 mg). The cosinor curves for T, HR, and ACT of the eight ewes fit a 24-h rhythm (P < 0.0001). In conclusion, ewes permanently housed presented a clear T, HR and ACT 24-h circadian rhythmicity, whose peaks were coincident with feeding time of the grain-based concentrate. [ABSTRACT FROM AUTHOR]

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Remote sensing soil moisture (SM) has a low spatial resolution. By contrast, to validate remote SM maps, point measurements gathered from scattered in situ stations are typically used. Therefore, a single representative SM value for the entire domain is required. The simplest approach is the arithmetic mean. Here, eight upscaling methods for in situ SM based in geostatistical interpolations and physical characteristics were tested and used to validate the Soil Moisture and Ocean Salinity mission observations. Comparisons showed that the simple mean performs well and is similar to the proposed upscaling methods, while overcoming the need for ancillary data. [ABSTRACT FROM AUTHOR]

Simple Summary: Hormonal treatments used to synchronize estrus in sheep artificial insemination procedures can cause several physiological problems that can affect negatively fertility and animal health; however, alternative protocols based on the detection of natural estrus offer a more sustainable option while reaching successful fertility results. Therefore, in this study, an artificial insemination protocol at natural estrus in organic Churra sheep was performed. In the protocol design phase, 125 ewes were exocervically inseminated, and their fertility was assessed based on the number of inseminations, physiological state, body condition, estrus detection–insemination interval and vaginal fluids in cervix. That protocol was repeated in six consecutive years. In all ewes, fertilities based on the timing of insemination after estrus detection were very high. Lactating ewes produced better results than did dry ewes, which was probably because of the better feeding of the former. In addition, double insemination increased the fertility of ewes, whose estrus was detected within 16 h of onset. Body condition and amount of vaginal fluid were correlated with fertility. In conclusion, exocervical inseminations at natural estrus can produce acceptable fertility and prolificity in Churra ewes, avoiding physiological problems derived from the use of hormonal treatments. Hormonal treatments used in the artificial insemination (AI) of sheep can cause several physiological problems that can affect negatively fertility and animal health; however, AI protocols based on the detection of natural estrus offer a more sustainable option and can achieve high fertility. In this study, an AI protocol at natural estrus in organic Churra sheep was performed. In the first phase (AI protocol development), 125 ewes were exocervically inseminated, and their fertility was assessed based on the following factors: number of AI, physiological state, body condition, estrus detection–AI interval, and vaginal fluids in cervix. That protocol was repeated for six consecutive years. In all individuals, fertilities based on the timing of insemination after estrus detection were very high. Lactating ewes produced better results than did dry ewes, which was probably because of the better feeding of the former. In addition, double insemination increased the fertility of ewes whose estrus was detected within 16 h of onset. Body condition and amount of vaginal fluid were correlated with fertility. Exocervical inseminations at natural estrus can produce acceptable fertility and prolificity in Churra ewes. [ABSTRACT FROM AUTHOR]

Anomaly detection has become an important remote sensing application due to the abundant spectral and spatial information contained in hyperspectral images. Recently, hyperspectral anomaly detection methods based on the collaborative representation (CR) model have attracted significant attention. Nevertheless, these methods have to face two main challenges: 1) all features (spectral signatures) are constrained to share the same representation coefficient, which ignores the differences among features and 2) existing dictionaries for pixel-by-pixel detection models are usually not reliable. To address these issues, this article proposes a new relaxed CR detector for hyperspectral anomaly detection by using a novel nonglobal dictionary. The proposed detector conducts CR on each feature dimension of the pixel under test and simultaneously constrains the coding vectors of different features to be similar. To the best of our knowledge, this is the first time that a detection model is built from each feature dimension. To adjust the contributions of each feature, an adaptive feature weight constrained version of the method is also proposed. The nonglobal dictionary is constructed by combining the ${k}$ -nearest neighbor method and an existing global dictionary, which is more reliable and practical than the widely used dual-window dictionary. In addition, this article also designs a band selection strategy for the proposed method. Experiments on five real datasets indicate that the proposed method suppresses background well and outperforms other classical and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

BACKGROUND: The common vole (Microtus arvalis) is a very destructive agricultural pest. Particularly in Europe, its monitoring is essential not only for adequate management and outbreak forecasting, but also for accurately determining the vole's impact on affected fields. In this study, several alternatives for estimating the damage to alfalfa fields by voles through unmanned vehicle systems (UASs) and multispectral cameras are presented. Currently, both the farmers and agencies involved in the integrated pest management (IPM) programs of voles do not have sufficiently precise methods for accurate assessments of the real impact to crops. RESULTS: Overall, the four multispectral classification methods presented showed similar performances. However, the normalized difference vegetation index (NDVI)‐based segmentation exhibited the most accurate and reliable appraisal of the affected areas. Nevertheless, it must be noted that the simplest method, which was based on an automatic classification, provided results similar to those obtained by more complex methods. In addition, a significant direct relationship was found between the number of active burrows and damage to the alfalfa canopy. CONCLUSION: Unmanned vehicle systems, combined with multispectral imagery classification, are an effective and easily transferable methodology for the assessment and monitoring of common vole damage to agricultural plots. This combination of methods facilitates decision‐making processes for IPM control strategies against this pest. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

This paper describes sequences of extended morphological transformations for filtering and classification of high-dimensional remotely sensed hyperspectral datasets. The proposed approaches are based on the generalization of concepts from mathematical morphology theory to multichannel imagery. A new vector organization scheme is described, and fundamental morphological vector operations are defined by extension. Extended morphological transformations, characterized by simultaneously considering the spatial and spectral information contained in hyperspectral datasets, are applied to agricultural and urban classification problems where efficacy in discriminating between subtly different ground covers is required. The methods are tested using real hyperspectral imagery collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory Airborne Visible-Infrared Imaging Spectrometer and the German Aerospace Agency Digital Airborne Imaging Spectrometer (DAIS 7915). Experimental results reveal that, by designing morphological filtering methods that take into account the complementary nature of spatial and spectral information in a simultaneous manner, it is possible to alleviate the problems related to each of them when taken separately. Index Terms--Hyperspectral image analysis, morphological filtering, multichannel morphological transformations, neural network classifiers.

Linear spectral unmixing is a commonly accepted approach to mixed-pixel classification in hyperspectral imagery. This approach involves two steps. First, to find spectrally unique signatures of pure ground components, usually known as endmembers, and, second, to express mixed pixels as linear combinations of endmember materials. Over the past years, several algorithms have been developed for autonomous and supervised endmember extraction from hyperspectral data. Due to a lack of commonly accepted data and quantitative approaches to substantiate new algorithms, available methods have not been rigorously compared by using a unified scheme. In this paper, we present a comparative study of standard endmember extraction algorithms using a custom-designed quantitative and comparative framework that involves both the spectral and spatial information. The algorithms considered in this study represent substantially different design choices. A database formed by simulated and real hyperspectral data collected by the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) is used to investigate the impact of noise, mixture complexity, and use of radiance/reflectance data on algorithm performance. The results obtained indicate that endmember selection and subsequent mixed-pixel interpretation by a linear mixture model are more successful when methods combining spatial and spectral information are applied. Index Terms--Comparative and quantitative framework, endmember extraction, spatial/spectral analysis, spectral mixture analysis.

Insect larval meal is an increasingly common protein source in poultry systems. In this study, the effect of replacing soybean meal with Tenebrio molitor larval meal on the performance of slow-growing chickens was assessed. A total of 128 one-day-old chickens (Colorield) were randomly divided into a control group (C) (n = 64), fed with soybean meal, and an experimental group (TM) (n = 64), fed with T. molitor larvae meal. The chicks were slaughtered after 95 days. Three different isoenergetic and isoproteic diets (F1, F2 and F3) were used for each group. The F1 diet resulted in higher body weight gain and higher feed and water intakes in group C, but a lower feed conversion ratio. Contrarily, diets F2 and F3 did not produce differences in the studied parameters between the two groups, except for body weight gain in the case of diet F2, which was highest in group C. Therefore, weight gain and feed and water intakes were significantly higher in group C, but there were no differences in feed conversion ratio or live weight. In conclusion, the total replacement of soybean meal with T. molitor larvae meal resulted in a reduction in feed intake and a consequent reduction in weight. During this period, partial rather than total substitution may be recommended. [ABSTRACT FROM AUTHOR]

Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of the original image. Thus, the modeling is carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for the purpose of large hyperspectral dataset subpixel analysis, few methods are available in the literature for the extraction of appropriate endmembers in spectral unmixing. Most approaches have been designed from a spectroscopic viewpoint and, thus, tend to neglect the existing spatial correlation between pixels. This paper presents a new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. The method is based on mathematical morphology, a classic image processing technique that can be applied to the spectral domain while being able to keep its spatial characteristics. The proposed methodology is evaluated through a specifically designed framework that uses both simulated and real hyperspectral data. Index Terms--Automated endmember extraction, mathematical morphology, morphological eccentricity index, multidimensional analysis, spatial/spectral integration, spectral mixture model.

Hyperspectral imaging (HSI) is a competitive remote sensing technique in several fields, from Earth observation to health, robotic vision, and quality control. Each HSI scene contains hundreds of (narrow) contiguous spectral bands. The amount of data generated by HSI devices is often both a solution and a problem for a given application. Extracting information from HSI data cubes is a complex and computationally demanding problem. To tackle this challenge, convolutional neural networks (CNNs) have been widely applied to HSI classification. Despite their success, CNNs are computationally demanding algorithms with high memory requirements due to their large number of internal parameters. The recent interest in using HSI devices in mobile and embedded systems for air and spaceborne platforms turned the attention to computationally lightweight CNN architectures with good classification accuracy. In this article, we present a contribution in that direction. The proposed method combines the ghost-module architecture with a CNN-based HSI classifier to reduce the computational cost and, simultaneously, achieves an efficient classification method with high performance. Our new method is evaluated against nine standard HSI classifiers, and five improved deep-CNN architectures, over five commonly used HSI data sets for algorithm benchmarking. Conducted experiments show that the proposed method exhibits similar or better performance than the other classifiers, achieving top values in the considered performance metrics—even for very limited training sets—and, most importantly, with a fraction of the computational cost. Our novel approach for HSI classification is a strong candidate for implementation on systems with limited computational resources. [ABSTRACT FROM AUTHOR]

Six cows managed under extensive grazing conditions were used to study the effect of moving the animals to a higher grazing density on the circadian rhythms of temperature (T), heart rate (HR), and activity (ACT), which were recorded by implantable bio-loggers. Cows were maintained at a density of 1.5 livestock units per hectare (LSUs/ha; low density, LD) until they were moved to a grazing area at 128 LSUs/ha (high density, HD). Animals were implanted subcutaneously with a T, HR, and ACT bio-logger, which was programmed to record data at 5-min intervals. For each animal, cosinor rhythmometry (the study of circadian rhythms by fitting a sine wave to a time series) was applied to the data recorded over 5 days in LD and HD. Mean Midline Estimating Statistic of Rhythm (MESOR; the average value around which the variable oscillates), amplitude (difference between the peak and the mean value of a wave), and acrophase (timing of peak activity) were calculated and evaluated statistically. Differences between mean day and nighttime values, and mean LD and HD values were calculated. Cows presented cosinor curves that fit a 24-h rhythm (p < 0.001) in T, HR, and ACT at both densities. MESOR (T: 37.98 vs. 38.02°C; HR: 69.12 vs. 65.91 bpm; ACT: 49.39 vs. 40.41 mg, for LD and HD, respectively) and amplitude (T: 0.28 vs. 0.28°C; HR: 4.12 vs. 3.14 bpm; ACT: 18.14 vs. 11.28 mg, respectively) did not differ significantly between the two densities; however, significant (p < 0.05) differences between densities occurred in the acrophase of the three variables; specifically, the T acrophase was 2 h later at HD (22:45 h) than LD (20:45 h), and HR (LD: 19:51; HD: 16:49 h) and ACT acrophases 3 and 2 h earlier at HD than LD (LD: 14:47; HD: 12:49 h), respectively. T and ACT differed significantly (p < 0.01) between daytime (mean ± SE; 37.92 ± 0.19°C, 40.39 ± 4.74 mg) and nighttime (38.14 ± 0.17°C, 29.93 ± 5.66 mg). In conclusion, our study suggests that a high animal grazing density might exacerbate the social competence for valuable resources for animals, resulting in shifting the circadian rhythmicity of temperature, heart rate, and activity of the cows, advancing or delaying their acrophases. [ABSTRACT FROM AUTHOR]

As a newly emerging technology, deep learning (DL) is a very promising field in big data applications. Remote sensing often involves huge data volumes obtained daily by numerous in-orbit satellites. This makes it a perfect target area for data-driven applications. Nowadays, technological advances in terms of software and hardware have a noticeable impact on Earth observation applications, more specifically in remote sensing techniques and procedures, allowing for the acquisition of data sets with greater quality at higher acquisition ratios. This results in the collection of huge amounts of remotely sensed data, characterized by their large spatial resolution (in terms of the number of pixels per scene), and very high spectral dimensionality, with hundreds or even thousands of spectral bands. As a result, remote sensing instruments on spaceborne and airborne platforms are now generating data cubes with extremely high dimensionality, imposing several restrictions in terms of both processing runtimes and storage capacity. In this article, we provide a comprehensive review of the state of the art in DL for remote sensing data interpretation, analyzing the strengths and weaknesses of the most widely used techniques in the literature, as well as an exhaustive description of their parallel and distributed implementations (with a particular focus on those conducted using cloud computing systems). We also provide quantitative results, offering an assessment of a DL technique in a specific case study (source code available: https://github.com/mhaut/cloud-dnn-HSI). This article concludes with some remarks and hints about future challenges in the application of DL techniques to distributed remote sensing data interpretation problems. We emphasize the role of the cloud in providing a powerful architecture that is now able to manage vast amounts of remotely sensed data due to its implementation simplicity, low cost, and high efficiency compared to other parallel and distributed architectures, such as grid computing or dedicated clusters. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have proven to be a powerful tool for the classification of hyperspectral images (HSIs). The CNN kernels are able to naturally include spatial information to smooth out the spectral variability and the noise present in HSI data. However, these kernels are composed of a large number of learning parameters that must be correctly adjusted to achieve good performance. This forces the model to consume a large amount of training data, being prone to overfitting when limited labeled samples are available. In addition, the execution of kernels is computationally very expensive, increasing quadratically with respect to the size of the convolution filter. This significantly reduces the performance of the model. To overcome the aforementioned limitations, this work presents a new few-parameter CNN (based on shift operations) for HSI classification that dramatically reduces both the number of parameters and the computational complexity of the model in terms of floating-point operations (FLOPs). The operational module combines a shift kernel (which adjusts the input data in particular directions without involving any parameters nor FLOPs) with pointwise convolutions that perform the feature extraction stage. The newly developed shift-based CNN has been employed to conduct HSI classification over five widely used and challenging data sets, achieving very promising results in terms of computational performance and classification accuracy. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) exhibit great performance in the task of hyperspectral image (HSI) classification. However, these models are usually overparameterized and require large amounts of training data in order to properly avoid the curse of dimensionality and the variability of spectral signatures, thus suffering from overfitting problems when very few training samples are available, due to poor generalization ability in this particular case. The traditional regularization dropout (DO) strategy has been shown to be effective in fully connected DNNs but not in convolutional-based ones. This is mainly due to the way these architectures manage the spatial information. In this letter, we introduce a new approach to improve the generalization of convolutional-based models for HSI classification. Specifically, we develop a neighboring region DO technique that selectively cuts off certain neighboring outputs, creating spatial dropped regions. Our experimental results with two well-known HSIs reveal that the newly proposed method helps to achieve better classification accuracy than the traditional DO strategy, with a low computational cost. [ABSTRACT FROM AUTHOR]