Anthropogenic change is associated with population growth, land use change, and changing economies. However, internal migration patterns and armed conflicts are also key drivers behind anthropogenic and demographic processes. To better understand this sort of change, we explore the spatial relationship between forced migration due to armed conflict and changing demographic factors in Colombia, a country which has a recent history of 7 million internal migrants. In addition, we use remote sensing, Google Earth Engine, as well as spatial statistical analyses of demographic data in order to measure anthropogenic change between 1984 and 2008; and we look into spatiotemporal relationships between both demographic and anthropogenic changes, which are caused by forced migration. We find, thus, that the latter is significantly and positively related to an increasing rural-urban kind of migration which originates in armed conflict, and results show that it is also negatively associated with interregional expulsion. Indeed, anthropogenic prints (term hereafter used to denote changes in nighttime satellite imagery) pertaining to different regions have had different sensitivities towards forced migration, and across different time periods. Finally we discuss how social and political phenomena such as Colombia’s armed conflict can have significant effects on the dynamics and motions of humans and territories in countries of the Global South.

There are frequently interactions between active folds and major rivers (mean annual water discharges > 70 m³s^-1). The major river may incise across the fold, to produce a water gap across the fold, or a bevelling (or lateral planation) of the top of the fold. Alternatively, the major river may be defeated to produce a diversion of the river around the fold, with wind gaps forming across the fold in some cases, or ponding of the river behind the fold. Why a river incises or diverts is often unclear, though influential characteristics and processes have been identified. A new scheme for investigating fold-river interactions has been devised, involving a short description of the major river, climate, and structural geology, and 13 characteristics of river and fold geomorphology: 1) Channel width at location of fold axis, w, 2) Channel-belt width at location of fold axis, cbw, 3) Floodplain width at location of fold axis, fpw, 4) Channel sinuosity, Sc, 5) Braiding index, BI, 6) General river course direction, RCD, 7) Distance from fold core to location of river crossing, C-RC, 8) Distance from fold core to river basin margin, C-BM, 9) Width of geological structure at location of river crossing, Wgs, 10) Estimate of erosion resistance of surface sediments/rocks and deeper sediments/rocks in fold, ERs, ERd, 11) Channel water surface slope at location of fold axis, s, 12) Average channel migration rate, Rm, 13) Estimate of fold total uplift rate, TUR. The first 10 geomorphological characteristics should be readily determinable for nearly all major rivers using widely available satellite imagery and fine scale geological maps. The last 3 characteristics should be determinable for most major rivers where other data sources are available. This study demonstrates the methodology of this scheme, using the example of the major rivers Karun and Dez interacting with active folds in the foreland basin tectonic setting of lowland south-west Iran. For the rivers Karun and Dez (mean annual water discharges 575 m³s^-1 and 230 m³s^-1, respectively), it was found that geomorphological characteristics Nos. 2, 3 and 7 had statistically significant differences (p-value ≤ 0.05) between the categories of river incision across a fold and river diversion around a fold. For river incision, at the fold axis, channel-belt width was always < 2.7 km, and floodplain width was generally (80 % of cases) < 5.7 km; whereas for river diversion, at the projection of the fold axis, these two characteristics had a wide range of values. For river incision, the distance from the fold core to the location where the river channel crossed the fold axis, was generally (80 % of cases) ≤ 8.5 km; whereas for river diversion, this distance was always > 22 km. Since it is highly likely that different characteristics will be important for other major rivers interacting with other folds, it is recommended that this scheme is now used to investigate a variety of major rivers from across the globe. By comparing the same parameters for different major rivers, a better understanding of fold-river interactions should be achieved.

The scarcity of surface water resources in the dry season in the Kilinochchi district increases the demand for freshwater. Therefore, the existing groundwater resources should be managed to overcome the situation. Several authors worldwide have published studies on the delineation of potential groundwater zone. However, only a few studies addressed the delineation of potential groundwater zones in the Kilinochchi district. This study aims to delineate potential groundwater zones in Kilinochchi, Sri Lanka using integrated Remote Sensing, Geographic Information Systems, and Analytic Hierarchy Process techniques. Groundwater potential zones are demarcated for the Kilinochchi district by overlaying thematic layers: geology, geomorphology, land use/land cover, soil types, drainage density, slope, lineament, and rainfall. Saaty's scale was applied to the assigned weights of the chosen thematic layers and their features. The thematic layers were integrated into a Geographic Information System, and a weighted overlay analysis is carried out to delineate groundwater zones. Thus the resultant map is categorized into five different potential zones: very low, low, moderate, high, and very high. It was found that the very high groundwater potential zone is mainly found in the north-eastern part of the study area covering 111.26 km2. The upper north-western, middle, and eastern parts of the study area fall within the high groundwater potential zone covering about 507.74 km2. The moderate groundwater potential zones (309.89 km2) mainly occurred in the western part, and the extreme west part of the study area falls under low (207.78 km2) and very low (59.12 km2) zones. The groundwater potential map was validated with the existing seventy-nine wells, which indicated a good prediction accuracy of 81.8%. This research will help policymakers better manage the Kilinochchi district's groundwater resources and gives scope for further research into groundwater exploration in the area.

The research carried out concerned the laboratory assessment of the applicability of the Crop Water Stress Index (CWSI) as a practical tool for assessing the risk of desiccation for selected wetland habitats: transition mires and quaking bogs as well as alkaline fens. The analysis was carried out on 3 soil samples with a vegetation cover (with the dimensions of 40×40×30 cm) collected during the full vegetation season for each mentioned habitat, with a characteristic species composition. Experimental research was carried out between 17^th May 2018 and 19^th June 2018. Thermal, RGB and multispectral images, chlorophyll content, volumetric soil moisture, air temperature and relative humidity measurements were taken for each sample every two days. The obtained results clearly indicate the dependence between CWSI and plant condition parameters in the first phase of desiccation. At the same time, as a result of the observations taken, thresholds have been set, indicating different desiccation phases.

Chiroptical responses have been an essential tool over the last decades for chemical structural elucidation due to their exceptional sensitivity to geometry and intermolecular interactions. In recent times, there has been an increasing interest for the search of more efficient sensing by the rational design of tailored chiroptical systems. In this Review article, advances on chiroptical systems towards their implementation in sensing applications are summarized. Strategies to generate chiroptical responses are illustrated. Theoretical approaches to assist in the design of these systems are discussed. Development of efficient chiroptical reporters in different states of matter, essential for the implementation in sensing devises, is reviewed. In the last part, remarkable examples of chiroptical sensing applications are highlighted.

United Nations Framework Conventions on Climate Change (UNFCC) conventions in their conference of parties (COPs) has continuously considered and agreed reducing emission level in order to minimize the impact of global climate change. Reducing emission due to deforestation and degradation (REDD) ,was considered as one of the major activities in this regard during Kyoto protocol in 2009 which laid foundation for the participating countries to be compensated financially for reduced carbon emission. Mexico convention -2012 required the countries to develop and implement a transparent and consistent monitoring, reporting and verification (MRV) process. Later in Paris agreement-2015, the parties agreed to limit the global warming to 2 degree centigrade and with further efforts to 1.5-degree centigrade furthering entailing the parties to prepare and communicate nationally determined contributions (NDCs) every five years. Nepal aimed to decrease the average annual deforestation rate by 0.05 percent from existing 0.44 percent in the terai region and 0.1 percent in the Chure. Nepal decided to develop its forest reference level (FRL) in national level for the historical period 2000-2010 considering Carbon dioxide and carbon pools above and below ground. As per the Forestry Sector Strategy, Nepal aims to increase carbon stock growth by at least 5% by 2025 as compared to 2015 and decrease mean annual deforestation rate to 0.05. After major change in administrative division in Nepal, forest management responsibility has shifted down to the Sub-national level. But forest resource studies have not been conducted yet in these levels. Despite a small country, Nepal has at least four clear physiological regions. The amount of carbon stock stored by different forest type are different depending upon species distribution, carbon volume and density for each species, and their distribution along ecological and physiological regions. Sal (shorea Robusta), for example, having one of the highest carbon densities, is a major forest types in Nepal. The purpose of this study was to generate forest map of the country, calculate carbon stock, gain and loss, and their rate in each province due to deforestation/afforestation using remote sensing data. Further Sal forest map was generated and its contribution in carbon stock was calculated using averaged national carbon density as well as using regional density method. According to the study, around 5.1 million hectares of Nepali land was forest in 2015 increasing from 4.2 million hectares in 2005. However, Sal forest has decreased during the same period. Province 1 contributed the maximum (130 Tg) and Province 2 the minimum (40Tg) of Carbon stock in 2015. Using the conventional method of calculation with national average density (108.08 t/ha), a total of 36.7T CO2 yr-1 carbon sink was observed in the Country. Whereas, with the new approach of calculation, a total of 44.7 T CO2 e of carbon sink per year was estimated during the same period. This approach holds potential for qualifying as an MRV process of Nepal. The subnational level forest and carbon statistics produced during this study can be important assets for the better forest governance. This can also pave way for policy formation and preparation of action plan for sustainable forest management and intervention strategy and obtaining better financial incentives participating in the reduction of emission due to deforestation and forest degradation (REDD) plus programs.

Deep learning classification is the state-of-the-art of machine learning approach. Earlier work proves that the deep convolutional neural network has successfully and brilliantly in different applications such as images or video data. Recognizing and clarifying the remote sensing aspect of the earth's surface and exploit land cover and land use (LCLU). First, this article summarized the remote sensing emerging application and challenges for deep learning methods. Second, we propose four approaches to learn efficient and effective CNNs to transfer image representation on the ImageNet dataset to recognize LCLU datasets. We use VGG16, Inception-ResNet-V2, Inception-V3, and DenseNet201 models to extract features from the EACC dataset. We use pre-trained CNNs on ImageNet to extract features. For feature selection we proposed principal component analysis (PCA) to improve accuracy and speed up the model. We train our model by multi-layer perceptron (MLP) as a classifier. Lastly, we apply the multi-granularity encoding ensemble model. We achieve an overall accuracy of 92.3% for the nine-class classification problem. This work will help remote sensing scientists understand deep learning tools and apply them in large-scale remote sensing challenges

The present paper describes the development of a prosthetic hand based on the human hand anatomy. The hand phalanges are printed by using 3D printed with Polylactic Acid material. One of the main contributions is the investigation on the prosthetic hand joins; the proposed design enables to create personalized joins that allow the prosthetic hand a high level of movement by increasing the degrees of freedom of the fingers. Moreover, the driven wire tendons show a progressive grasping movement, being the friction of the tendons with the phalanges very low. Another important point is the use of force sensitive resistors for simulating the hand touch pressure. These are used for the grasping stop simulating touch pressure of the fingers. Surface Electromyogram (EMG) sensors allow the user to control the prosthetic hand grasping start. Their use may provide the prosthetic hand the possibility of classification of the hand movements. The practical results included in the paper prove the importance of the soft joins for the object manipulation and to get adapted to the object surface. Finally, the force sensitive sensors allow the prosthesis to actuate with more naturalness by adding conditions and classifications to the Electromyogram sensor.

Localizing the source of a signal is often as important as deciphering the signal’s message. Localization mechanisms must cope with the challenges of representing the spatial information of weak, noisy signals. Comparing these strategies across modalities and model systems allows a broader understanding of the general principles shaping spatial processing. In this review we focus on the electrosensory system of knifefish and provide an overview of our current understanding of spatial processing in this system, in particular, localization of conspecific signals. We argue that many mechanisms observed in other sensory systems, such as the visual or auditory systems, have comparable implementations in the electrosensory system. Our review therefore describes a field of research with unique opportunities to provide new insights into the principles underlying spatial processing.

Mediterranean coastal lagoons and their peripheral areas often provide a collection of habitats for many species, and they often face significant threats from anthropogenic activities. Diverse human activities in such areas directly affect the spatio-temporal dynamic of surface water and its ecological characteristics. Monitoring the surface water dynamic, and understanding the impact of human activities are of great significance for coastal lagoon conservation. The Regional Natural Park of Narbonne includes a typical Mediterranean lagoon complex where surface water dynamic and its potential link with local diverse human activities has not yet been studied. In this context, based on all the available Landsat images covering the study area during 2002-2016, this study identified the water and non-water classes for each satellite observation by comparing three widely used water indices (i.e., NDVI, NDWI and MNDWI) and using the Otsu method. The yearly water frequency index was then computed to present the spatio-temporal dynamic of surface water for each year, and three water dynamic scenarios were also identified for each year: permanent water (PW), non-permanent water (NPW) and non-water (NW). The spatial and inter-annual variation in the patterns of the three water scenarios were characterized by computing the landscape metrics at scenario-level quantifying area/edge, shape, aggregation and fragmentation. Finally, the quantitative link between different land use and land cover (LULC) types derived from the LULC maps of 2003, 2012 and 2015 and the surface water dynamic scenarios was established in each of the 300 m x 300 m grid cells covering the study area to determine the potential impact of human activities on the surface water dynamic. In terms of the inter-annual variation during 2002-2016, PW presented an overall stability, and NPW occupied only a small part of the water surface in each year and presented an inter-annual fluctuation. NPW had a smaller patch size, with lower connectivity degree and higher fragmentation degree. In terms of spatial variation during 2002-2016, NPW often occurred around PW, and its configurational features varied from place to place. Moreover, PW mostly corresponded to natural lagoon, and salt marsh (as a part of lagoons), and NPW had a strong link with arable land (agricultural irrigation) and salt marsh (salt production), sand beach/dune, coastal wetlands and lagoon for the LULC maps of 2003, 2012 and 2015. However, more in-depth analysis is required for understanding the impact of sand beach/dune, coastal wetlands and lagoon on surface water dynamics. This study covers the long-term variations of surface water patterns in a Mediterranean lagoon complex having intense and diverse human activities, and the potential link between LULC types and the water dynamic scenarios was investigated on different dates. The results of the study should be useful for environmental management and protection of coastal lagoons.

Relationship between caveman prehistoric life in terms of heat induced food processing and its geological ecosystems have received many attentions. Previous studies have investigated the sources of heat included using Fourier transform infrared spectroscopy and biomarker approaches. Here this study proposes the use of remote sensing to identify the relationship of 9500 year old (9.5 ka) prehistoric mongoloid occupancy with hydrothermal manifestations at Pawon cave of West Java. The hydrothermal manifestations around Pawon cave were identified using Landsat 8 band combinations, land surface temperature, and sedimentary lithology. The results showed the hydrothermal manifestations surrounding Pawon cave were within a distance of 0.5-2 km. The results also showed bones representing 12 animal taxon groups with high abundance of rodents. To conclude this study sheds the light of proximity and preferences of mongoloid prehistoric occupancy towards hydrothermal landscape due to its advantage as heat sources for food processing purposes.

The 2020 Elucidating the role of clouds-circulation coupling in climate - Ocean-Atmosphere (EUREC4A-OA) and Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) campaigns sought to improve the knowledge of the interaction between clouds, convection and circulation and their function in our changing climate. The campaign consisted of numerous research technologies, some of which are relatively novel to the scientific community. In this study we used a saildrone uncrewed surface vehicle to validate satellite and modelled sea surface salinity (SSS) products in the Western Tropical Atlantic. These products include the Soil Moisture Active Passive (SMAP) Jet Propulsion Laboratory (JPL), SMAP Remote Sensing Systems (RSS), and Hybrid Coordinate Ocean Model (HYCOM). In addition to the validation, we investigated a fresh tongue south east of Barbados. The saildrones accurately depicted the salinity conditions and all satellite and modelled products performed well in areas that lacked small-scale salinity variability. However, SMAP RSS 70 km outperformed its counterparts in areas with small submesoscale irregularities while RSS 40 km was better at identifying small irregularities in salinity such as a fresh tongue. These results will allow researchers to make informed decisions regarding the most ideal product for their application and aid in the improvement of mesoscale and submesoscale SSS products, which can lead to the refinement of numerical weather prediction (NWP) and climate models.

Cellular communications play pivotal roles in multi-cellular species, but they do so also in uni-cellular species. Moreover, cells communicate with each other not only within the same individual but also with cells in other individuals belonging to the same or other species. These communications occur between two unicellular species, two multicellular species, or between unicellular and multicellular species. The molecular mechanisms involved exhibit diversity and specificity, but they share common basic features which allow common pathways of communication between different, and sometimes very different species. These interactions have been made possible by the high degree of conservation of the basic molecular mechanisms of interaction of many ligand-receptor pairs in evolutionary remote species. These inter-species cellular communications played crucial roles during Evolution and must have been positively selected, particularly when collectively beneficial in hostile environments. We think that communications between cells did not arise after their emergence but was part of the very nature of first cells. Synchronization of populations of non-living protocells through chemical communications may have been a mandatory step towards their emergence as populations of living cells and explain the large commonality of cell communication mechanisms among microorganisms, plants, and animals.

Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies in homes and cities to enhance the quality of life for citizens. Sensing and human action recognition are crucial aspects of this concept. Smart living applications span various domains, such as energy consumption, healthcare, transportation, and education, which greatly benefit from effective human action recognition. This field, originating from computer vision, seeks to recognize human actions and activities using not only visual data but also many other sensor modalities. This paper comprehensively reviews the literature on human action recognition in smart living environments, synthesizing the main contributions, challenges, and future research directions. This review selects five key domains: Sensing Technology, Multimodality, Real-time Processing, Interoperability, and Resource-Constrained Processing, as they encompass the critical aspects required for successfully deploying human action recognition in smart living. These domains highlight the essential role that sensing and human action recognition play in successfully developing and implementing smart living solutions. This paper serves as a valuable resource for researchers and practitioners seeking to explore further and advance the field of human action recognition in smart living.

Automatic ice charting can not be achieved using only SAR modalities. It is fundamental to combine information from other remote sensors with different characteristics for more reliable sea ice characterization. In this paper, we employ principal feature analysis (PFA) to select significant information from multimodal remote sensing data. PFA is a simple yet very effective approach that can be applied to several types of data without loss of physical interpretability. Considering that different homogeneous regions require different types of information, we perform the selection patch-wise. Accordingly, by exploiting the spatial information, we increase the robustness and accuracy of PFA.

The investigation of the electromagnetic properties of biological particles in microfluidic platforms may enable wireless monitoring and interaction with functional activity of microorganisms. Of high relevance is the membrane potential as it is one of the most important parameters of living cells. In particular, the complex mechanisms of the cell’s membrane potential are comparable to the dynamics of bacteria membranes, providing a simplified platform for advancing the current techniques and knowledge of general bio-particle dynamics. In this work, we provide a theoretical analysis and experimental results on the microwave detection of bacteria on a microfluidic-based framework for sensing the membrane potential of bacteria. The results enable to further advance the state-of-the-art of electromagnetic bacteria sensing and microfluidic control, and their implication for measuring and interacting with the cell and its membrane potentials, which is of great importance for developing new biotechnological engineered systems and solutions.

Agricultural land fires have been linked to various and adverse impacts on ecosystems, food security and the agriculture sector. Understanding the patterns and drivers of agricultural land fires is essential for effective agricultural land fire management. The key objectives of this study were to (1) analyze the temporal and spatial patterns of agricultural land fires using satellite remote sensed data, (2) assess a range of environmental conditions that could drive the occurrence of agricultural land fires, (3) determine the best model for predicting agricultural land fires and (4) determine the relative contribution of each environmental condition variable on the best predictive model. We used both univariate and multivariate regressions for the fire prediction capability of four independent environmental conditions (fuel, weather, topographic and anthropogenic). Analysis of historical satellite data revealed that agricultural land fires were more frequent than forested land fires. Our analyses also revealed that fuel condition was the most important variable for predicting agricultural land fires followed by weather, topographic and anthropogenic conditions. This study provides a novel multivariate model for predicting agricultural land fires that harbors the potential to improve agricultural land fire management and reduce fire risk within the agricultural sector.

We demonstrate a multi-purpose plasmonic sensor based on nanovoid array fabricated via inexpensive and highly reproducible direct femtosecond laser patterning of thin glass-supported Au films. The proposed nanovoid array exhibits near-IR surface plasmon (SP) resonances, which can be excited under normal incidence and optimised for specific application by tailoring array periodicity as well as nanovoid geometric shape. Fabricated SP sensor offers competitive sensitivity of about 1600 nm/RIU at a figure of merit of 12 in bulk refractive index tests, as well as allows for identification of gases and ultra-thin analyte layers making the sensor particularly useful for common bioassay experiments. Moreover, isolated nanovoids support strong electromagnetic field enhancement at lattice SP resonance wavelength, allowing for label-free molecular identification via surface-enhanced vibration spectroscopy.

Sensing Technology (ST) plays a key role in Structural Health Monitoring (SHM) systems. ST focuses on developing sensors, sensory systems or smart materials that monitor a wide variety of materials properties aiming to create smart structures and smart materials, using Embedded Sensors (ESs), and allowing continuous and permanent measurements of the structural integrity. The integration of ESs is limited to the processing technology to embed the sensor due to its high-temperature sensitivity and the possibility of damage during its insertion into the structure. In addition, the technological process selection is dependent on the base material composition, either metallic or composite parts. The selection of smart sensors or the technology underlying them is fundamental to the monitoring mode. This paper presents a critical review of the fundaments and applications of sensing technologies for SHM employing ESs, focusing on the actual developments and innovation of these, as well as analysing the challenges that these technologies present, to build a path that allows a connected world through distributed measurement systems.

Improving air quality and reducing human exposure to unhealthy levels of airborne chemicals are important global missions, particularly in China. Satellite remote sensing offers a powerful tool to examine regional trends in NO2, thus providing a direct measure of key parameters that strongly affect surface air quality. To accurately resolve spatial gradients in NO2 concentration using satellite observations and thus understand local and regional aspects of air quality, a priori input data at sufficiently high spatial and temporal resolution to account for pixel-to-pixel variability in the characteristics of the land and atmosphere are required. In this paper, we adapt the Berkeley High Resolution product (BEHR v3.0A, v3.0B and v3.0C) and meteorological outputs from the Weather Research and Forecasting (WRF) model to describe column NO2 in southern China. The BEHR approach is particularly useful for places with large spatial variabilities and terrain height differences such as China. We retrieved tropospheric NO2 vertical column density (TVCD) within part of southern China, for four seasons of 2015, based upon satellite datasets from Ozone Monitoring Instrument (OMI). Retrieval results are validated by comparing with MAX-DOAS tropospheric column measurements conducted in Guangzhou. BEHR retrieval algorithms are more consistent with MAX-DOAS measurements than OMI-NASA retrieval, opening new windows into research questions that require high spatial resolution, for example retrieving NO2 vertical column and ground pollutant concentration in China and other countries.

Food preservatives are compound that are used for the treatment of food to improve the shelf life. In the food industry, is necessary to monitor all processes, for both safety and quality of the product. An electronic nose (or e-nose) is a biomimetic olfactory system that could find numerous industrial applications, including food quality control. Commercial electronic noses are based on sensor arrays composed by a combination of different sensors, which include conductometric metal oxide devices. Metal oxide nanowires are considered among the most promising materials for the fabrication of novel sensing devices, which can enhance the overall performances of e-noses in food applications. In the present work, is reported the fabrication of a novel sensor array based on SnO2, CuO and WO₃ nanowires deposited on top of commercial μHPs, provided by ams Sensor Solutions Germany GmbH. The array was tested for the discrimination of four typical compounds added to food products or used for their treatment to increase the shelf life: ethanol, acetone, nitrogen dioxide and ozone. Results are very promising: the sensors array was able to operate for long time consuming less than 50mW for each single sensor, and PCA analysis confirms that the device was able to discriminate between different compounds.

Agriculture in Morocco has been extensive until the middle of the 20th century due to the distribution of rainfall and the availability of water. In the middle of the last century hydraulic works were built that allowed the transition to intensive agriculture by the increase of irrigated areas, allowing that in the territories where there is water for irrigation and the climate allows it, the crops adapt to the demands of the market. The objective of the study is to assess by satellite images the land cover between 1985 and 2020, analyzing the changes in cultivation areas, as well as the changes in desert, sub-desert and forest areas of the Oum Er Rbia hydrological basin in Morocco. Landsat satellite images have been used since 1984 by the US government (Aerospace and Geological Agencies). A series of vegetation indices (NDVI, RVI, TNDVI and EVI) have been used; among which TNDVI (Transformed Normalized Vegetation Index) stands out for its better accuracy, which has allowed us to distinguish vegetation in cultivated and forest areas, as well as arid zones. In addition, the study has compared the use of two methodologies to calculate changes in the coverage of the Earth’s surface, has used local image processing from the Sentinel Application Platform tool and has also used the Google Earth Engine tool. The latter being the most optimal, although at the moment it has great limitations. In both methodologies and in the different indices it has been possible to observe during these 35 years as the cultivated area has increased (related to the availability of water by the construction of reservoirs and canals), how plant cover has improved in forest areas, and a range of variations in arid areas.

We analyze damage propagation modeling of turbo-engines in a data-driven approach. We investigate subspace tracking assuming a low dimensional manifold structure and a static behavior during the healthy state of the machines. Our damage propagation model is based on the deviation of the data from the static behavior and uses the notion of health index as a measure of the condition. Hence, we incorporate condition-based maintenance and estimate the remaining useful life based on the current and previous health indexes. This paper proposes an algorithm that adapts well to the dynamics of the data and underlying system, and reduces the computational complexity by utilizing the low dimensional manifold structure of the data. A significant performance improvement is demonstrated over existing methods by using the proposed algorithm on CMAPSS Turbo-engine datasets.

Growing agricultural demands for the global population are unlocking the path to developing innovative solutions for efficient water management. Herein, an intelligent variable rate irrigation system (fuzzy-VRI) is proposed for rapid decision-making to achieve optimized irrigation in various delimited zones. The proposed system automatically creates irrigation maps for a center pivot irrigation system for a variable-rate application of water. Primary inputs are spatial imagery on remotely sensed soil moisture (SSM), soil adjusted vegetation index (SAVI), canopy temperature (CT), and nitrogen content (NI). To eliminate localized issues with soil characteristics, we used the crop nitrogen content map to provide a focused insight on issues related to water shortage. The system relates these inputs to set reference values for the rotation speed controllers and individual openings of each central pivot sprinkler valve. The results showed that the system can detect and characterize the spatial variability of the crop and further, the fuzzy logic solved the uncertainties of an irrigation system and defined a control model for high-precision irrigation. The proposed approach is validated through the comparison between the recommended irrigation and actual irrigation at two field sites, and the results showed that the developed approach gives an accurate estimation of irrigation with a reduction in the volume of irrigated water of up to 27% in some cases. Future research should implement the fuzzy-VRI real-time during field trials in order to quantify its effect on irrigation use, yield, and water use efficiency.

This paper investigates the characteristics and impact of a major Saharan dust storm during June 14^th -19^th 2020 to atmospheric radiative and thermodynamics properties over the Atlantic Ocean. The event witnessed the highest ever aerosol optical depth (close to 2 during the peak of the storm) for June since 2002. The satellites and high-resolution model reanalysis products well captured the origin, spread and the effects of the dust storm. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) profiles, lower angstrom exponent values (~ 0.12) and higher aerosol index value (> 4) tracked the presence of elevated dust. It was found that the dust AOD was as much as 250-300% higher than their climatology resulting in an atmospheric radiative forcing ~200% larger. As a result, elevated warming ( 8-16 %) was observed, followed by a drop in relative humidity(2-4%) in the atmospheric column, as evidenced by both in-situ and satellite measurements. Quantifications such as these for extreme dust events provide significant insights that may help in understanding their climate effects, including improvements to dust simulations using chemistry-climate models

This study introduces a remote sensing application using satellite imagery to survey a network-scale aggregate stockpile inventory. First, a real scale aggregate quarry site was surveyed using a small Unmanned Aerial Vehicle (sUAV) to produce digital terrain models that enabled analysis of aggregate pile geometry. Second, a lab experiment was designed and performed to validate the applicability of close-range Structure from Motion (SfM) photogrammetry for measuring aggregate piles' physical properties such as volume and density. The other part of the lab experiment delved into direct measurement of aggregate density under varying compaction efforts. These experimental results, in conjunction with some simplifying assumptions, enabled the calculation of aggregate stockpile volumes and estimated weights from satellite imagery. We estimated that an inventory of 4.4 and 1.1 million metric tons of crushed aggregates and Reclaimed Asphalt Pavement (RAP), respectively, stockpiled in Washington State for asphalt production in 2017. The merit of producing such database was further showcased in an example on the economic and environmental impacts of material transportation. We approximated that hauling aggregates from quarry plants to construction sites within Washington State incurs a cost of about $50 thousand to over $4 million, consumes about 0.25 to 20 TJ of energy, and emits 20 to over 1,500 tons of CO₂-eq per asphalt plant annually.

CNN-based methods have dominated the field of aerial scene classification for the past few years. While achieving remarkable success, CNN-based methods suffer from excessive parameters and notoriously rely on large amounts of training data. In this work, we introduce few-shot learning to the aerial scene classification problem. Few-shot learning aims to learn a model on base-set that can quickly adapt to unseen categories in novel-set, using only a few labeled samples. To this end, we proposed a meta-learning method for few-shot classification of aerial scene images. First, we train a feature extractor on all base categories to learn a representation of inputs. Then in the meta-training stage, the classifier is optimized in the metric space by cosine distance with a learnable scale parameter. At last, in the meta-testing stage, the query sample in the unseen category is predicted by the adapted classifier given a few support samples. We conduct extensive experiments on two challenging datasets: NWPU-RESISC45 and RSD46-WHU. The experimental results show that our method yields state-of-the-art performance. Furthermore, several ablation experiments are conducted to investigate the effects of dataset scale, the impact of different metrics and the number of support shots; the experiment results confirm that our model is specifically effective in few-shot settings.

Electromagnetic sensing has been used for diverse applications of non-destructive testing, including the surface inspection, measurement of properties, object characterization. However, the measurement accuracy could be significantly influenced by the lift-off between sensors and samples. To address the issue caused by lift-offs, various strategies have been proposed for the permeability measurement of ferromagnetic steels, which mainly involves different sensor designs and signal features (e.g., the zero-crossing feature). In this paper, a single high-frequency scenario for the permeability retrieval is introduced. By combining the signal of two sensing pairs, the retrieval of magnetic permeability is less affected by the lift-off of sensors. Unlike the previous strategy on reducing the lift-off effect (directly taking the phase term out of the integration) using the Dodd-Deeds analytical method, the proposed method is based on a high-frequency linear feature of the phase term. Therefore, this method has the merit of high accuracy and fast processing for the permeability retrieval (a simplified version of Dodd-Deeds analytical formulas after the integration). Experimental measurement has been carried out on the impedance measurement of designed sensors interrogating ferromagnetic dual-phase steels. For sensor lift-offs of up to 10 mm, the error of the permeability retrieval is controlled within 4 % under the optimal frequency.

Forest damage has become more frequent in Europe in the last decades, and remote sensing offers a powerful tool for monitoring them rapidly and cost-effectively. A novel approach was developed to utilize high-resolution ESA Sentinel-2 satellite imagery and Google Earth Engine cloud computing. The processing, analysing, and visualization of vegetation and water index (NDVI, NDVIch, Z NDVI, EVI, NDWI) maps and charts derived from satellite images took place in the cloud to ensure the detection of forest disturbances in the Hungarian study site for the period 2017-2020. The index maps were classified to reveal forest disturbances, and the cloud-based method successfully showed drought and frost damage in the oak-dominated Nagyerdő forest of Debrecen. Differences in the reaction to damage between tree species were visible in the index values, therefore a Random Forest Machine Learning classifier was applied to show the spatial distribution of dominant species. Accuracy assessment was accomplished with confusion matrices that compared classified index maps to field-surveyed data, demonstrating 71% Total Accuracy for forest damage and 76% for tree species. Based on the results of this study and the resilience of Google Earth Engine, the presented method has the potential to be extended to monitor the entire Hungary.

In this study, a flexible strain sensor is devised using corrugated poly(3-hexylthiophene) (P3HT) thin film. In the previous studies, the P3HT-based photoactive thin film was shown to generate direct current (DC) under broadband light, and the generated DC voltage varied with applied tensile strain. Yet, the mechanical resiliency and strain sensing range of the P3HT-based thin film strain sensor were limited due to relatively more brittle thin film constituent—poly(3,4-ethylenedioxythiophene)-polystyrene(sulfonate) (PEDOT:PSS) conductive thin film as a bottom electrode. To address this issue, it is aimed to design mechanically resilient strain sensor using corrugated thin film constituents. Buckling is induced to form corrugation in the thin films by applying pre-strain to the substrate, where the thin films are deposited, and releasing the pre-strain afterwards. It is known that corrugated thin film constituents exhibit different optical and electronic properties from non-corrugated ones. Therefore, to optimize design of the flexible strain sensor, it was studied to understand how the applied pre-strain and thickness of the PEDOT:PSS thin film affect the optical and electrical properties. Also, pre-strain effect on light absorptivity of the corrugated P3HT-based thin films was studied. In addition, strain effect was investigated on the optical and electrical properties of the corrugated thin film constituents. Finally, flexible strain sensors are fabricated by following the design guideline, which is suggested from the studies on the corrugated thin film constituents, and DC voltage strain sensing capability was validated. As a result, flexible strain sensor exhibited tensile strain sensing range up to 5% at frequency up to 15 Hz with maximum gage factor ~7.

We have an unprecedented ability to map the Earth’s surface as deep learning technologies are applied to an abundance of high-frequency Earth observation data. Simple, free, and effective methods are needed to enable a variety of stakeholders to use these tools to improve scientific knowledge and decision making. Here we present a trained U-Net model that can map and delineate ground mounted solar arrays using publicly available Sentinel-2 imagery, and that requires minimal data pre-processing and no feature engineering. By using label overloading and image augmentation during training, the model is robust to temporal and spatial variation in imagery. The trained model achieved a precision and recall of 91.5% each and an intersection over union of 84.3% on independent validation data from two distinct geographies. This generalizability in space and time makes the model useful for repeatedly mapping solar arrays. We use this model to delineate all ground mounted solar arrays in North Carolina and the Chesapeake Bay watershed to illustrate how these methods can be used to quickly and easily produce accurate maps of solar infrastructure.

Environmental monitoring is an important topic and serves a vibrant scientific role by revealing long-term trends that can lead to new knowledge and understanding. Globally, there is active development of photonic sensors incorporating multidisciplinary research. The ultimate objective is to develop small, low-cost, sensitive, selective, quick, durable, remote-controllable sensors that are resistant to electromagnetic interference. Different photonic sensor designs and advances in photonic frameworks have shown the possibility to realize these capabilities. In this review paper, the latest developments in the field of optical waveguide and fiber-based sensors which can serve for environmental monitoring are discussed. Several important topics such as toxic gas, water quality, indoor environment, and natural disaster monitoring are reviewed.

Autonomous image recognition has numerous potential applications in the field of planetary science and geology. For instance, having the ability to classify images of rocks would allow geologists to have immediate feedback without having to bring back samples to the laboratory. Also, planetary rovers could classify rocks in remote places and even in other planets without needing human intervention. Shu et al. classified 9 different types of rock images using a Support Vector Machine (SVM) with the image features extracted autonomously. Through this method, the authors achieved a test accuracy of 96.71%. In this research, Convolutional Neural Networks(CNN) have been used to classify the same set of rock images. Results show that a 3-layer network obtains an average accuracy of 99.60% across 10 trials on the test set. A version of Self-taught Learning was also implemented to prove the generalizability of the features extracted by the CNN. Finally, one model has been chosen to be deployed on a mobile device to demonstrate practicality and portability. The deployed model achieves a perfect classification accuracy on the test set, while taking only 0.068 seconds to make a prediction, equivalent to about 14 frames per second.

: Mistrust, amplified by numerous artificial intelligence (AI) related incidents, has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle this challenge. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced to XAI. However, many deficiencies, particularly lack of explanation assessment methods and uncertainty quantification, plague this young field. In this paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as marker for anomalies expanding the prognostic explanation scope to include model’s confidence. Also, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of PHM-XAI publications. The quality of the explanation is finally examined employing local accuracy and consistency properties. The method is tested on real-world gas turbine anomalies and synthetic turbofan data failure prediction. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.

Although deep learning has received extensive attention and achieved excellent performances in various of scenarios, it suffers from adversarial examples to some extent. Especially, physical attack poses more threats than digital attack. However, existing researches pay less attention to physical attack of object detection in remote sensing images (RSIs). In this work, we systematically analyze the universal adversarial patch attack for multi-scale objects in the remote sensing field. There are two challenges for adversarial attack in RSIs. On one hand, the number of objects in remote sensing images is more than that of natural images. Therefore, it is difficult for adversarial patch to show adversarial effect on all objects when attacking a detector of RSIs. On the other hand, the wide range of height of photography platform causes that the size of objects diverse a lot, which brings challenges for generating universal adversarial perturbation for multi-scale objects. To this end, we propose an adversarial attack method on object detection for remote sensing data. One of the key ideas of the proposed method is the novel optimization of adversarial patch. We aim to attack as many objects as possible by formulating a joint optimization problem. Besides, we raise a scale factor to generate a universal adversarial patch that adapts to multi-scale objects, which ensures the adversarial patch is valid for multi-scale objects in the real world. Extensive experiments demonstrate the superiority of our method against state-of-the-art methods on YOLO-v3 and YOLO-v5. In addition, we also validate the effectiveness of our method in real-world applications.

Paddy field is an old agriculture practice that very common especially in Asia. The earliest paddy field found dated back to 4330 BC. Most paddy fields in the world are having rectangular shapes. Whereas, in Flores island, indigenous people have developed a spider web or circular paddy field instead of regular rectangular shape and this driven by culture and local wisdom. In here, the objectives of this study are to assess the characteristic, ecology and fertility of circular paddy field compared to common rectangular shape. Fertility values were assessed using Landsat 8 remote sensing with RGB combination of NIR, SWIR 1 and blue. The study site was paddy field within Flores island. The result shows that spider web paddy field appeared in many sizes, number, altitude, ecosystem and terrain. Remote sensing result confirms that the fertility of circular paddy field is similar to the rectangular shape. Likewise, circular field has higher NDVI than rectangular field. Considering semiarid environment, limited labor and resources in Flores island, circular paddy field shape can allow the use of pivot irrigation that more efficient.

Subwavelength hole array (HA) metasurfaces support the so-called extraordinary optical transmission (EOT) resonance that has already been exploited for sensing. In this work, we demonstrate the superior performance of a different resonant regime of HA metasurfaces called anomalous EOT, by doing a thorough numerical and experimental study of its ability as a thin-film label-free sensor in the terahertz (THz) band. A comprehensive analysis using both the regular and anomalous EOT resonances is done by depositing thin layers of a dielectric analyte of different thicknesses on the structures in different scenarios. We carry out a detailed comparison and demonstrate that the best sensing performance is achieved when the structure operates in the anomalous EOT resonance and the analyte is deposited on the non-patterned side, improving by a factor between 2 and 3 the results of the EOT resonance in any of the considered scenarios. This can be explained by the comparatively narrower linewidth of the anomalous EOT resonance. The results presented expand the reach of subwavelength hole arrays for sensing applications by considering the anomalous EOT regime that is usually overlooked in the literature.

Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air-sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we use chlorophyll-a concentration (Chla), sea surface temperature (SST), absorption due to dissolved and particulate detrital matter (Adg), diffuse attenuation coefficient of downwelling irradiance at 490nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results show that the root mean square error (RMSE) is 8.25μatm, the mean bias error (MAE) is 4.92μatm and the coefficient of determination (R2) can reach 0.946 in the validation set, which mean that the CatBoost model makes an improvement compared to other models in the published studies. In the further analysis of the spatial and temporal distribution of the sea surface pCO2 in the North Atlantic, it can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, and influenced by biological activities in some sub-regions.

This study has been monitored for five years by Sentinel-2 satellite images, at different seasons of the year, of the fluctuations in the water level of the Gallocanta Lake (between the provinces of Teruel and Zaragoza, Aragón, Spain) considered a hypersaline and endorheic wetland, which has characteristics that make it unique in the geographical area in which it is located, as well as for the operation of the system. Rainfall in the area has a wide variation giving the maximums in the months of May and June and the minimums in January and February. There are considerable fluctuations in the water level from the almost total drying of the lagoon to the filling with a depth of approximately 3 meters.

Magneto-optic (MO) imaging and sensing are at present the most developed practical applications of thin-film MO garnet materials. However, future component and system-level sensors and imagers technology improvements are still necessary in order to improve sensitivity for a range of established and also the forward-looking applications. These improvements are expected to originate from new material system development. We propose a set of technological modifications for the RF-magnetron sputtering deposition and crystallization annealing of magneto-optic bismuth-substituted iron-garnet films and investigate the improved material properties. Results show that standard crystallization annealing for the as-deposited ultrathin (sputtered 10 nm thick, amorphous phase) films resulted in more than a factor of two loss in the magneto-optical activity of the films in visible spectral region, compared to the liquid-phase grown epitaxial films. Results also show that an additional 10 nm-thick metal-oxide (Bi2O3) protective layer above the amorphous film results in ~2.7 times increase in the magneto-optical quality of crystallized iron-garnet films. On the other hand, the effects of post-deposition oxygen (O2) plasma treatment on the magneto-optical (MO) properties of Bismuth substituted iron garnet thin films material is investigated. Results show that, in the visible part of the electromagnetic spectrum (at 532 nm), the O2 treated (up to 3 minutes) garnet films retain higher specific Faraday rotation and figure of merit than those of non-treated garnet films.

The reliable online analysis of volatile compounds in exhaled breath remains a challenge as a plethora of molecules occur in different concentration ranges (i.e. ppt to %), and need to be detected against an extremely complex background matrix. While this complexity is commonly addressed by hyphenating a specific analytical technique with appropriate preconcentration and/or preseparation strategies prior to detection, we herein propose the combination of three analytical tools based on truly orthogonal measurement principles as an alternative solution: field-asymmetric ion mobility spectrometry (FAIMS), Fourier-transform infrared (FTIR) spectroscopy-based sensors utilizing substrate-integrated hollow waveguides (iHWG), and luminescence sensing (LS). These three tools have been integrated into a single compact analytical platform suitable for online exhaled breath analysis. The analytical performance of this prototype system was tested via artificial breath samples containing nitrogen (N2), oxygen (O2), carbon dioxide (CO2) and acetone as a model volatile organic compound (VOC) commonly present and detected in breath. Functionality of the combined system was demonstrated by detecting these analytes in their respectively breath-relevant concentration range and mutually independent of each other generating orthogonal yet correlated analytical signals. Finally, adaptation of the system towards the analysis of real breath samples during future studies is discussed.

Molecularly imprinted polymers (MIPs) can often bind target molecules with high selectivity and specificity. When used as MIPs, conductive polymers may have unique binding capabilities; they often contain aromatic rings, which have a great tendency to undergo covalent and hydrogen bonding interactions with similarly structured target (or template) molecules. In this work, an electrochemical method was used to optimize the synthetic self-assembly of poly(aniline-co-metanilic acid) and testosterone, forming testosterone-imprinted polymers (TIPs) on sensing electrodes. The linear sensing range for testosterone ranged from 0.1 to 100 pg/mL, and the limit of detection was as low as ~pM. Random urine samples were collected and diluted 1000 fold to measure testosterone concentration using the above TIP sensors in comparison with a commercial ARCHITECT ci 8200 system. The testosterone concentrations in the tested samples were in the range of 0.33± 0.09 to 9.13±1.33 ng/mL. The mean accuracy of the TIP-coated sensors was 90.3 ±7.0 %.

We apply deep convolutional neural networks (CNNs) to estimate wave breaking type from close-range monochrome infrared imagery of the surf zone. Image features are extracted using six popular CNN architectures developed for generic image feature extraction. Logistic regression on these features is then used to classify breaker type. The six CNN-based models are compared without and with augmentation, a process that creates larger training datasets using random image transformations. The simplest model performs optimally, achieving average classification accuracies of 89% and 93%, without and with image augmentation respectively. Without augmentation, average classification accuracies vary substantially with CNN model. With augmentation, sensitivity to model choice is minimized. A class activation analysis reveals the relative importance of image features to a given classification. During its passage, the front face and crest of a spilling breaker are more important than the back face. Whereas for a plunging breaker, the crest and back face of the wave are most important. This suggests that CNN-based models utilize the distinctive `streak' temperature patterns observed on the back face of plunging breakers for classification.

Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer melt ponds, and ice albedo. We have developed a method for mapping sea ice surface roughness using angular reflectance data from the Multi-angle Imaging SpectroRadiometer (MISR) and lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). Using an empirical data modeling approach, we derived estimates of Arctic sea ice roughness ranging from centimeters to decimeters meters within the MISR 275-m pixel size. Using independent ATM data for validation, we find that histograms of lidar and multi-angular roughness values are nearly identical for areas with roughness <20 cm but that for rougher regions, the MISR-derived roughness has a narrower range of values than the ATM data. The algorithm is able to accurately identify areas that transition between smooth and rough ice. Because of its coarser spatial scale, MISR-derived roughness data have a variance of about half that ATM roughness data.

Abstract: As the massive deployment of the heterogeneous IoT devices in the coexisting environment such as smart homes，Traditional channel-based spectrum sharing algorithms such as CSMA has great limitations to further optimize spectrum utilization. Therefore, exploring more efficient spectrum sensing algorithm becomes hot topic these years. This paper proposes Subcarrier-Sniffer, which utilizes Channel State Information (CSI) to sense the subcarrier-level detailed status of the spectrum. In order to evaluate the performance of Subcarrier-Sniffer, we implemented Subcarrier-Sniffer by USRP B200min, and the experimental results show that when the distance between Subcarrier-Sniffer and the monitored devices is not great than 7 m, the accuracy of subcarrier-level spectrum sensing could achieve 100% in our settings.

’Lockdown’ periods in response to COVID-19 have provided a unique opportunity to study the impacts of economic activity on environmental pollution (e.g. NO2, aerosols, noise, light). The effects on NO2 and aerosols have been very noticeable and readily demonstrated, but that on light pollution has proven challenging to determine. The main reason for this difficulty is that the primary source of nighttime satellite imagery of the earth is the SNPP-VIIRS/DNB instrument, which acquires data late at night after most human nocturnal activity has already occurred and much associated lighting has been turned off. Here, to analyze the effect of lockdown on urban light emissions, we use ground and satellite data for Granada, Spain, during the COVID-19 induced confinement of the city’s population from March 14 until May 31, 2020. We find a clear decrease in light pollution due both to a decrease in light emissions from the city and to a decrease in anthropogenic aerosol content in the atmosphere which resulted in less light being scattered. A clear correlation between the abundance of PM10 particles and sky brightness is observed, such that the more polluted the atmosphere the brighter the urban night sky. An empirical expression is determined that relates PM10 particle abundance and sky brightness at three different wavelength bands.

On January 8, 2005, a surface melt event began on the interior portion of Ross Ice Shelf. While many surface melt events on Ross Ice Shelf are caused by the advection of warm air onto the shelf from the Ross Sea, surface winds during this event were directed offshore and the spatial pattern of surface melt was inconsistent with the Southern Ocean serving as a heat source. Rather, due to the interior location of the surface melt coupled with prevailing wind direction and surface temperature data it is thought that adiabatic warming of Föhn winds is the driving cause of this melt event. Passive Microwave (SSM/I) imagery was used to determine surface melt occurrence and the event’s extent. Spatial patterns of surface melt were then compared to NCEP/NCAR reanalysis output for several synoptic weather variables including surface temperatures, sea level pressure and surface vector winds. Synoptic-scale weather conditions were consistent with those that would produce downsloping wind (föhn) conditions in the interior of the Ross Ice Shelf where the anomalous surface melt was located.

Oil spillage over a sea or ocean’s surface is a threat to marine and coastal ecosystems. Spaceborne synthetic aperture radar (SAR) data has been used efficiently for the detection of oil spills due to its operational capability in all-day all-weather conditions. The problem is often modeled as a semantic segmentation task. The images need to be segmented into multiple regions of interest such as sea surface, oil spill, look-alikes, ships and land. Training of a classifier for this task is particularly challenging since there is an inherent class imbalance. In this work, we train a convolutional neural network (CNN) with multiple feature extractors for pixel-wise classification; and introduce to use a new loss function, namely ‘gradient profile’ (GP) loss, which is in fact the constituent of the more generic Spatial Profile loss proposed for image translation problems. For the purpose of training, testing and performance evaluation, we use a publicly available dataset with selected oil spill events verified by the European Maritime Safety Agency (EMSA). The results obtained show that the proposed CNN trained with a combination of GP, Jaccard and focal loss functions can detect oil spills with an intersection over union (IoU) value of 63.95%. The IoU value for sea surface, look-alikes, ships and land class is 96.00%, 60.87%, 74.61% and 96.80%, respectively. The mean intersection over union (mIoU) value for all the classes is 78.45%, which accounts for a 13% improvement over the state of the art for this dataset. Moreover, we provide extensive ablation on different Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) based hybrid models to demonstrate the effectiveness of adding GP loss as an additional loss function for training. Results show that GP loss significantly improves the mIoU and F1 scores for CNNs as well as ViTs based hybrid models. GP loss turns out to be a promising loss function in the context of deep learning with SAR images.

Deep learning techniques are increasingly being recognized as effective image classifiers. Aside from their successful performance in past studies, the accuracies have varied in complex environments in comparison with the popularly applied machine learning classifiers. This study seeks to explore the feasibility for using a U-Net deep learning architecture to classify bi-temporal high resolution county scale aerial images to determine the spatial extent and changes of land cover classes that directly or indirectly impact tidal marsh. The image set used in the analysis is a collection of a 1-m resolution collection of National Agriculture Imagery Program (NAIP) tiles from 2009 and 2019 covering Beaufort County, South Carolina. The U-net CNN classification results were compared with two machine learning classifiers, the Random Trees (RT) and the Support Vector Machine (SVM). The results revealed a significant accuracy advantage in using the U-Net classifier (92.4%) as opposed to the SVM (81.6%) and RT (75.7%) classifiers for overall accuracy. From the perspective of a GIS analyst or coastal manager, the U-Net classifier is now an easily accessible nad powerful tool for mapping large areas. Change detection analysis indicated little areal change on marsh extent, though increased land development throughout the county has the potential to negatively impact the health of the marshes. Future work should explore applying the constructed U-Net classifier to coastal environments in large geographic areas, while also implementing other data sources (e.g., LIDAR, multispectral data) to enhance classification accuracy.

Smart Living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor for Smart Living services and applications, from energy management to healthcare and transportation, is the efficacy of human action recognition (HAR). HAR, rooted in computer vision, seeks to identify human actions and activities using visual data and various sensor modalities. This paper extensively reviews the literature on HAR in Smart Living services and applications, amalgamating key contributions and challenges while providing insights into future research directions. The review delves into the essential aspects of Smart Living, the state of the art in HAR, and the potential societal implications of this technology. Moreover, the paper meticulously examines the primary application sectors in Smart Living that stand to gain from HAR, such as smart homes, smart healthcare, and smart cities. By underscoring the significance of the four dimensions of Context Awareness, Data Availability, Personalization, and Privacy in HAR, this paper serves as a valuable resource for researchers and practitioners striving to advance Smart Living services and applications.

This paper focuses on processing low oversampling echo data of staggered synthetic aperture radar (SAR). In staggered mode, the non-uniformly sampling and irregular loss of echo data cause azimuth ambiguity which severely degrades the imaging quality. To solve this problem, we propose a compressed sensing (CS) method in which the non-uniform fast Fourier transform (NUFFT) technique is adopted to obtain uniform azimuth spectrum, and the fast iterative shrinkage thresholding algorithm (FISTA) is utilized to efficiently reconstruct the ambiguity-free image from in-complete echo data. Simulation results demonstrate the proposed method can effectively suppress the azimuth ambiguity in the vicinity of targets.

An increasing global aridification due to climate change has made the health monitoring of vegetation indispensable to maintaining the food supply chain. Cost-effective and smart irrigation systems are required not only to ensure the efficient distribution of water, but also to track the moisture of plant leaves, which is an important marker of the overall health of the plant. This paper presents a novel electromagnetic method to monitor the water content (WC) and characterization in plant leaves utilizing the absorption spectra of water molecules in the terahertz (THz) frequency for four consecutive days. We extracted the material properties of leaves of eight types of pot herbs from the scattering parameters, measured using a material characterization kit in the frequency range of 0.75 to 1.1 THz. From the computed permittivity, it is deduced that the leaf specimens increasingly become transparent to the THz waves as they dry out with the passage of days. Moreover, the loss in weight and thickness of leaves were observed due to the natural evaporation of leaf moisture cells and change occurred in the morphology of fresh and water-stressed leaves. It is also illustrated that loss observed in WC on day 1 was in the range of 5\% to 22\%, and increased from 83.12\% to 99.33\% on day 4. Furthermore, we observed an exponential decaying trend in the peaks of the real part of the permittivity from day 1 to 4, which was reminiscent of the trend observed in the weight of all leaves. Thus, results in paper demonstrated that timely detection of water stress in leaves can help to take proactive action in relation to plants health monitoring, and for precision agriculture applications, which is of high importance to improve the overall productivity.

According to the risk investigations of being involved in an accident, alcohol-impaired driving is one of the major causes of motor-vehicles accidents. Preventing highly intoxicated persons from driving would potentially save many lives. This paper proposes a lightweight in-vehicle alcohol detection that processes the data generated from 6-alcohol sensors (MQ-3 Alcohol Sensors) using an optimizable shallow neural network (O-SNN). The experimental evaluation results exhibit a high-performance detection system scoring a 99.8% of detection accuracy with a very short inferencing delay of 2.22 µ seconds. Hence, the proposed model can be efficiently deployed and used to discover in-vehicle alcohol with high accuracy and low inference overhead as a part of the driver alcohol detection system for safety (DADSS) system aiming at massive deployment of alcohol sensing systems that could potentially save thousands of lives annually.

Oxide semiconductors are conventionally being used as sensing materials in gas sensors, limiting the detection of gases at room temperature (RT). In this work, a hybrid of copper oxide (CuO) with functionalized graphene (rGO) is proposed to achieve gas sensing at RT. The combination of high surface area and presence of many functional groups in CuO/rGO hybrid material makes it highly sensitive for gas absorption and desorption. To prepare the hybrid material, a copper oxide suspension synthesized using copper acetate precursor is added to the graphene oxide solution during its reduction using ascorbic acid. Material properties of CuO/rGO hybrid and its drop-casted thin films are investigated using Raman, FTIR, SEM, TEM, and four-point probe measurement systems. We find that the hybrid material is enriched with oxygen functional groups (OFGs) and defective sites along with electrical conductivity (~1.5 kΩ/□). The fabricated QCM (quartz crystal microbalance) sensor with a thin layer of CuO/rGO hybrid, demonstrates a high sensing response which is twice the response of the rGO-based sensor for CO₂ gas at RT. We believe that the CuO/rGO hybrid can be highly suitable for existing and future gas sensors used for domestic and industrial safety.

Sparse recovery of fluid flows using data-driven proper orthogonal decomposition (POD) basis is systematically explored in this work. Fluid flows are manifestations of nonlinear multiscale PDE dynamical systems with inherent scale separation that impact the system dimensionality. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches require the knowledge of the underlying basis space spanning the manifold in which the system resides. In this study, we employ an approach that learns basis from singular value decomposition (SVD) of training data to reconstruct sparsely sensed information. This results in a set of four control parameters for sparse recovery, namely, the choice of basis, system dimension required for sufficiently accurate reconstruction, sensor budget and their placement. The choice of control parameters implicitly determines the choice of algorithm as either $l_2$ minimization reconstruction or sparsity promoting $l_1$ norm minimization reconstruction. In this work, we systematically explore the implications of these control parameters on reconstruction accuracy so that practical recommendations can be identified. We observe that greedy-smart sensor placement provides the best balance of computational complexity and robust reconstruction for marginally oversampled cases which happens to be the most challenging regime in the explored parameter design space.

Visual Evoked Potentials (VEPs) are used in clinical applications in ophthalmology, neurology and extensively in brain computer interface (BCI) research. BCI literature covers steady state VEP (SSVEP) and code modulated VEP (c-VEP) BCIs along with sophisticated methods to improve information transfer rates (ITR). There is a gap of knowledge regarding the VEP adaptation dynamics, physiological generation mechanisms and relation with BCI performance. A simple dual display VEP switch was developed to test signatures elicited by non-isochronic, non-singular, low jitter stimuli at the rates of 10, 32, 50 and 70 reversals per second (rps). Non-isochronic, low-jitter stimulation elicits Quasi-Steady-State VEPs (QSS-VEPs) that are utilized for simultaneous generation of transient VEP and QSS-VEP. QSS-VEP is a special case of c-VEPs and it is assumed that it shares the similar generators of the SSVEPs. Eight subjects were recorded and the performance of the overall system was analyzed by means of Receiver Operating Characteristic (ROC) curves, accuracy plots and ITRs. In summary QSS-VEPs performed better than transient VEPs. It was found that in general 32rps stimulation had the highest ROC area, accuracy and ITRs in general. To investigate the reasons behind this, adaptation dynamics of transient VEPs and QSS-VEPs at all four rates were analyzed and speculated. Moreover, QSS-VEPs were found to lead to higher accuracy by the template matching compared to SSVEPs at 10rps and 32rps.

Vegetation in drylands is sensitive to climatic changes and human activities. Remote sensing and spatial analyses provide us useful tools for monitoring long-term vegetation dynamics over large regional scale. In this study, we analyzed the oasis vegetation cover change of the Tarim Basin using Landsat data sets from six epochs, 1975s, 1990s, 2000s, 2005s, 2010s and 2014. The results show that vegetation cover of oases increases from 34600 km² in 1975s to 101000 km² in 2014, though there was a vegetation coverage decrease from 77600 km² in 2000s to 42680 km² in 2010s. The percentage of annul water consumption has increased from 34% in 1970s to 52% in 2010s in the upper Tarim River, and decreased from 15% in 1970s to 9% in 2010s in the lower Tarim River. The decrease of oases area from 2000s to 2010s probably resulted from the rapid urbanization and large scale land reclamation. Although there is an increasing trend for oases coverage, local degradation of oases especially in the northern part occurred. This may be caused by inadequate water supply of the Tarim River. The results of multiple regression show that human activities contribute 70% of oases area change. Human induced water resources reallocation and heat energy balance is the primary cause of total oasis change.

Imbalance of homeostasis between the microbial communities and the host system leads to dysbiosis in oral micro flora. DMTU (1,3-di-m-tolyl-urea), is a biocompatible compound that was shown to inhibit Streptococcus mutansbiofilms by inhibiting its communication system (quorum sensing). Here, we hypothesized that DMTU is able to inhibit multispecies biofilms. We developed a multispecies oral biofilm model comprising an early colonizer Streptococcus gordonii, a bridge colonizer Fusobacterium nucleatum, and late colonizers Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans. We performed comprehensive investigations to demonstrate the effect of DMTU on planktonic cells and biofilms. Our findings showed that DMTU inhibits and disrupts multispecies biofilms without bactericidal effects. Mechanistic studies revealed significant down regulation of biofilm and virulence related genes in P. gingivalis. Taken together, our study highlights the potential of DMTU to inhibit polymicrobial biofilm communities and their virulence.

The emergence of cloud computing, big data analytics, and machine learning has catalysed the use of remote sensing technologies to enable more timely land management of sustainability indicators such as ground cover and grassland biomass, given the uncertainty of future climate and drought conditions. Here, we examine the potential of “regenerative agriculture”, as an adaptive grazing management strategy to minimise bare ground exposure while maximising pasture biomass productivity. High-intensity sheep grazing treatments were conducted in small fields (less than 1 hectare) for short durations (typically less than 1 day). Paddocks were subsequently spelled to allow pasture biomass recovery (treatments comprising 3, 6, 9, 12, and 15 months) with each compared with control treatments with lighter stocking rates for longer periods (2,000 DSE). Pastures were composed of wallaby grass (Austrodanthonia species), kangaroo grass (Themeda triandra), Phalaris (Phalaris aquatica, and cocksfoot (Dactylis glomerata) were destructively sampled to estimate total standing dry matter (TSDM), standing green biomass, standing dry biomass and trampled biomass. We then invoked a machine learning model using Sentinel-2 imagery to quantify TSDM, standing green biomass and standing dry biomass. Faced with La Nina conditions, regenerative grazing did not significantly impact pasture productivity, with all treatments showing similar TSDM and green biomass. However, regenerative treatments significantly impacted litter fall and trampled material, with the high intensity grazing treatments causing more dry matter trampling, increasing litter, enhancing decomposition rates and surface organic matter. Pasture digestibility was greatest for treatments with minimal spelling (3 months), whereas both standing senescent and trampled material were significantly greater for the treatment with 15-month spell periods. Estimates of TSDM using machine learning with Sentinel-2 imagery underestimated TSDM in treatment plots but explained spatiotemporal variability associated within and across treatments. The root mean square error between the measured and modelled TSDM was 903 kg DM/ha, which was less than the variability measured in the field. We conclude that regenerative grazing with short recovery periods (3-6 months) are most conducive to increasing pasture production under high rainfall conditions, and we speculate that high intensity grazing is likely to positively impact on soil organic carbon through increased litterfall and trampling. Our study paves the way forward for using machine learning with satellite imagery to quantify pasture biomass at small scales, enabling management of pastures from afar.

This study focuses on the ability of the global land data assimilation system LDAS-Monde to improve the representation of land surface variables (LSVs) over Burkina Faso through the joint assimilation of satellite derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) from January 2001 to June 2018. The LDAS-Monde offline system is forced by the latest European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis ERA5, leading to a 0.25° x 0.25° spatial resolution reanalysis of the LSVs. Within LDAS-Monde, SSM and LAI observations from the Copernicus Global Land Service (CGLS) are assimilated using the CO2-responsive version of the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model (LSM). First, it is shown that ERA5 better represents precipitation and incoming solar radiation than ERA-Interim former reanalysis from ECMWF. Results of two experiments are compared: open-loop simulation (i.e. no assimilation) and analysis (i.e. joint assimilation of SSM and LAI). After jointly assimilating SSM and LAI, it is noticed that the assimilation is able to impact soil moisture in the first top soil layers (the first 20 cm), and also in deeper soil layers (from 20 cm to 60 cm and below). The assimilation is able to improve the simulation of both SSM and LAI. For LAI in particular, the southern region of the domain (dominated by a Sudan-Guinean climate) highlights a strong impact of the assimilation compared to the other two sub-regions of Burkina Faso (dominated by Sahelian and Sudan-Sahelian climates). In the southern part of the domain, differences between the model and the observations are the largest, prior to any assimilation. These differences are linked to the model failing to represent the behavior of some specific vegetation species, which are known to put on leaves before the first rains of the season. The LDAS-Monde analysis is very efficient at compensating for this model weakness. Evapotranspiration estimates from the Global Land Evaporation Amsterdam Model (GLEAM) project as well as upscaled carbon uptake from the FLUXCOM project are used in the evaluation process, again demonstrating improvements in the representation of evapotranspiration and gross primary production after assimilation.

Marine sponges, a well documented prolific source of natural products, harbors numerous microbial communities believed to possess N-acyl homoserine lactones (AHLs) mediated Quorum sensing (QS) as one of the mechanisms of interaction. Bacteria and eukaryotic organisms are known to produce molecules that can interfere with QS signaling, thus affecting microbial genetic regulation and function. In the present study, we established the potential for production of both QS signal molecules as well as QS interfering molecules (QSI) in the same sponge species Sarcotragus spinosulus. A total of eighteen saturated acyl chain AHLs were identified along with six putative unsaturated acyl chain AHLs. Bioassay guided purification led to the isolation of two brominated metabolites with QS-interfering activity. The structures of these compounds were elucidated by comparative spectral analysis of 1HNMR and HR-MS data and was identified as 3-Br-N-methyltyramine (1) and 5,6-dibromo-N,N-dimethyltryptamine (2). The QSI activity of compounds 1 and 2 were evaluated using reporter gene assays for long- and short-chain signals (E. coli pSB1075 and E. coli pSB401) and was confirmed by measuring dose dependent inhibition of proteolytic activity and pyocyanin production in P. aeruginosa PAO1. The obtained results showed the co-existence of QS and QSI in S. spinosulus, a complex network which may mediate the orchestrated function of the microbiome within the sponge holobiont.

In the present study, a five-year follow-up was performed by remote sensing of the calcium carbonate precipitation in La Gitana karstic lake (located on the province of Cuenca, Spain). The important role that calcium carbonate precipitation plays in the ecology of the lake is well known for its influence on the vertical migrations of phytoplankton, the concentration of bioavailable phosphorus and, therefore, the eutrophication and quality of the waters. Whiting take place between the months of July and August, and it can be studied at this time through its optical properties, with the main objective of offering updated data on a phenomenon traditionally studied and establishing possible relationships between abiotic factors such as temperature and/or rainfall. The atmospheric temperature data collected by the meteorological station suggest a possible relationship between the appearance of the white phenomenon and a pulse of previous maximum temperatures. On the other hand, no apparent relationship was found between rainfall and water bleaching.

Surface temperature influences human health directly and alters the biodiversity and productivity of the environment. While previous research has identified that the composition of urban landscapes influences the physical properties of the environment such as surface temperature, a generalizable and flexible framework is needed that can be used to compare cities across time and space. This study employs the Structure of Urban Landscapes (STURLA) classification combined with remote sensing of New York City’s (NYC) surface temperature. These are then linked using machine learning and statistical modeling to identify how greenspace and the built environment influence urban surface temperature. It was observed that areas with urban units composed of largely the built environment hosted the hottest temperatures while those with vegetation and water were coolest. Likewise, this is reinforced by borough-level spatial differences in both urban structure and heat. Comparison of these relationships over the period between2008 and 2017 identified changes in surface temperature that are likely due to the changes in prevalence in water, lowrise buildings, and pavement across the city. This research reinforces how human alteration of the environment changes ecosystem function and offers units of analysis that can be used for research and urban planning.

The retrieval of sun-induced chlorophyll fluorescence is greatly beneficial to studies of marine phytoplankton biomass, physiology, and composition and is required for user applications and services. Customarily phytoplankton chlorophyll fluorescence is determined from satellite measurements through a fluorescence line-height algorithm using three bands around 680 nm. We propose here a modified retrieval, making use of all available bands in the relevant wavelength range with the goal to improve the effectiveness of the algorithm in optically complex waters. For the Ocean and Land Colour Instrument (OLCI) we quantify a Fluorescence Peak Height from fitting a Gaussian function and related terms into the top-of-atmosphere reflectance bands between 650 and 750 nm. This algorithm retrieves, what we call Fluorescence Peak Height from fitting a Gaussian function upon other terms to top-of-atmosphere reflectance bands between 650 and 750 nm. This approach is applicable to Level-1 and Level-2 data. We find a good correlation of the retrieved fluorescence product to global in-situ chlorophyll measurements, as well as a consistent relation between chlorophyll concentration and fluorescence from radiative transfer modelling and OLCI/in-situ comparison. The algorithm is applicable to complex waters without needing an atmospheric correction and vicarious calibration and features an inherent correction of small spectral shifts, as required for OLCI measurements.

The evaluation of geometric defects is necessary in order to maintain the integrity of structures over time. These assessments are designed to detect damages of structures and ideally help inspectors to estimate the remaining life of structures. Current methodologies for monitoring structural systems, while providing useful information about the current state of a structure, are limited in the monitoring of defects over time and in linking them to predictive simulation. This paper presents a new approach to the predictive modeling of geometric defects. A combination of segmented from point clouds are parametrized using the convex hull algorithm to extract features from detected defects, and a stochastic dynamic model is then adapted to these features to model the evolution of the hull over time. Describing a defect in terms of its parameterized hull enables consistent temporal tracking for predictive purposes, while implicitly reducing data dimensionality and complexity as well. In this study, 2D point clouds analogous to information derived from point clouds were first generated over simulated life-cycles. The evolutions of point cloud hull parameterizations were modeled as stochastic dynamical processes via autoregressive integrated moving average (ARIMA) and vectorized autoregression (VAR) and compared against ground truth. The results indicate that this convex hull approach provides consistent and accurate representations of defect evolution across a range of defect topologies and is reasonably robust to noisy measurements, however assumptions regarding the underlying dynamical process play a significant the role in predictive accuracy. The results were then validated on experimental data from fatigue testing with high accuracy. Longer term, the results of this work will support finite element model updating for predictive analysis of structural capacity.

Terrestrial laser scanning (TLS) provides detailed three-dimensional representation of the surrounding forest structure. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees and especially the upper parts of forest canopy is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point cloud is complemented with a photogrammetric point cloud acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data was considered feasible especially in characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (H_g) and mean stem volume (V_mean). Most notably the root mean square error (RMSE) in H_g improved from 0.88 m to 0.58 m and the bias improved from -0.75 m to -0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands the mere TLS captured also the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, V_mean, H_g and basal area-weighted mean diameter with a relative RMSE less than 5.5% for all of the sample plots. Although the multisensorial close-range sensing approach mainly enhanced characterization of forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.

Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in vegetated regions. The article begins by introducing the sources and processing methods of RS data, which serve as the foundation for subsequent analysis. Moreover, it highlights the techniques and methodologies employed for lithological classification in vegetated areas. Notably, hyperspectral RS and Synthetic Aperture Radar (SAR) have emerged as prominent tools in lithological identification. In addition, this paper addresses the limitations inherent in RS technology, including issues related to vegetation cover and terrain effects, which significantly impact accurate lithological mapping. To propel further advancements in the field, the paper proposes promising avenues for future research and development. These include the integration of multi-source data to enhance classification accuracy and the exploration of novel RS technologies and algorithms. In summary, this paper presents valuable insights and recommendations for advancing the study of RS-based lithological identification in vegetated areas.

Intertidal seagrass plays a vital role in estimating the overall health and dynamics of coastal environments due to its interaction with tidal changes. However, most seagrass habitats around the globe have been in steady decline due to human impacts, disturbing the already delicate balance in environmental conditions that sustain seagrass. Miniaturization of multi-spectral sensors has facilitated very high resolution mapping of seagrass meadows, which significantly improve the potential for ecologists to monitor changes. In this study, two analytical approaches used for classifying intertidal seagrass habitats are compared: Object-based Image Analysis (OBIA) and Fully Convolutional Neural Networks (FCNNs). Both methods produce pixel-wise classifications in order to create segmented maps, however FCNNs are an emerging set of algorithms within Deep Learning with sparse application towards seagrass mapping. Conversely, OBIA has been a prominent solution within this field, with many studies leveraging in-situ data and multiscale segmentation to create habitat maps. This work demonstrates the utility of FCNNs in a semi-supervised setting to map seagrass and other coastal features from an optical drone survey conducted at Budle Bay, Northumberland, England. Semi-supervision is also an emerging field within Deep Learning that has practical benefits of achieving state of the art results using only subsets of labelled data. This is especially beneficial for remote sensing applications where in-situ data is an expensive commodity. For our results, we show that FCNNs have comparable performance with standard OBIA method used by ecologists, while also noting an increase in performance for mapping ecological features that are sparsely labelled across the study site.

Miscanthus holds a great potential in the frame of the bioeconomy and yield prediction can help improving Miscanthus logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. Random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass and standing biomass) using VIs time series and predicted yield using peak descriptor derived from VIs time series with 2.3 Mg DM ha-1 of RMSE. The study demonstrates the potential of UAVs multispectral in HTP applications and in yield prediction for providing important information needed to increase sustainable biomass production.

Multispectral remote sensing data may contain component images which are heavily corrupted by noise and pre-filtering (denoising) procedure is often applied to enhance these component images. To do this, one can use reference images – component images having relatively high quality and which are similar to the image subject to pre-filtering. Here we study the following problems: how to select component images that can be used as references (e.g., for the Sentinel multispectral remote sensing data) and how to perform the actual denoising. We demonstrate that component images of the same resolution as well as component images of a better resolution can be used as references. Examples of denoising of real-life images demonstrate high efficiency of the proposed approach.

The detection of waste plastics in the marine and terrestrial environment using satellite Earth Observation data offers the possibility of large-scale mapping, and reducing on-the-ground manual investigation. In addition, costs are kept to a minimum by utilizing free-to-access Copernicus data. A Machine Learning based classifier was developed to run on Sentinel-1 and -2 data. In support of the training and validation, a dataset was created with terrestrial and aquatic cases by manually digitizing varying landcover classes alongside plastics under the sub-categories of greenhouses, plastic, tyres and waste sites. The trained classifier, including an Artificial Neural Network and post-processing decision tree, was verified using five locations encompassing these different forms of plastic. Although exact matchups are challenging to digitize, the performance has generated high accuracy statistics, and the resulting land cover classifications have been used to map the occurrence of plastic waste in aquatic and terrestrial environments.

We discuss a topological method of storing and retrieving information from flash memory. We first present a sensing method where the threshold voltage level, as represented by a sensing time, is extracted in one sensing cycle. The sense time distribution from one set of flash cells, e.g. one physical row, is then processed in software to decode the digital state of each cell. The decoding method uses topological constraints but no rigid or predetermined voltage thresholds to digitize the distribution. The software defined nature of the topologically defined flash (TDF) allows greater flexibility for allocating cells to arbitrary number of digital states.

Inland lake variations are considered sensitive indicators of global climate change. However, human activity is playing as a more and more important role in inland lake area variations. Therefore, it is critical to identify whether anthropogenic activity or natural event is playing as the dominant factor in inland lake surface area change. In this study, we proposed a Douglas-Peucker simplification algorithm and bend simplification algorithm combined method to locate major lake surface area disturbances; these disturbances were then characterized to extract the time series change features according to documented records; and the disturbances were finally classified into anthropogenic or natural. We took the nine lakes in Yunnan Province as test sites, a 31 years long (from 1987 to 2017) time series Landsat TM/OLI images and HJ-1A/1B used as data sources, the official records was used as references to aid the feature extraction and disturbance identification accuracy. Results of our method for both disturbance location and the disturbance identification could be concluded as follows: 1) The method can accurately locate the main lake changing events based on the time series lake surface area curve. The accuracy of this model for segmenting the lake area time series curves in our study area was 95.24%. 2) Our proposed method achieved an overall accuracy of 91.67%, with F-score of 94.67 for anthropogenic disturbances and F-score of 85.71 for natural disturbances. 3) According to our results, lakes in Yunnan Provence, China, have undergone extensive disturbances, and the human-induced disturbances occurred almost twice as often as natural disturbances, indicating intensified disturbances caused by human activities. This inland lake area disturbance identification method is expected to uncover whether a disturbance to inland lake area is human activity-induced or natural event.

Consistent data is seldom available for whole-catchment flood modelling in many developing regions, thus this study demonstrates how the complementary strengths of open and readily available geospatial datasets and tools can be leverage to map flood risk within acceptable levels of uncertainty for flood risk management. Available fragmented remotely-sensed and in situ datasets (including hydrological data, altimetry, digital elevation model, bathymetry, aerial photos, optical and radar imageries) are systematically integrated using 2-dimensional CAESAR-LISFLOOD model to quantify and recreate the extent and impact of the historic 2012 flood in Nigeria. Experimental modelling, calibration and validation is undertaken for the whole Niger-South hydrological catchment area of Nigeria, then segmented into sub-domains for re-validation to understand how data variability and uncertainties impact on the accuracy of model outcomes. Furthermore, aerial photos are applied for the first time in the study area for flood model validation and to understand how different physio-environmental properties influence synthetic aperture radar flood delineation capacity in the Niger Delta region of Nigeria.

Vision-based tactile sensors (VBTS) have become the de facto method of giving robots the ability to obtain tactile feedback from their environment. Unlike other solutions to tactile sensing, VBTS offers high spatial resolution feedback without compromising on instrumentation costs or incurring additional maintenance expenses. However, conventional cameras used in VBTS have a fixed update rate and output redundant data, leading to computational overhead downstream. In this work, we present a neuromorphic vision-based tactile sensor (N-VBTS) that employs observations from an event-based camera for contact angle prediction. Particularly, we design and develop a novel graph neural network, dubbed TactiGraph, that asynchronously operates on graphs constructed from raw N-VBTS streams exploiting their spatiotemporal correlations to perform predictions. Although conventional VBTS uses an internal illumination source, TactiGraph is reported to perform efficiently in both scenarios, with and without an internal illumination source. Rigorous experimental results revealed that TactiGraph achieved a mean absolute error of 0.62∘ in predicting the contact angle and was faster and more efficient than both conventional VBTS and other N-VBTS, with lower instrumentation costs. Specifically, N-VBTS requires only 5.5% of the compute-time needed by VBTS when both are tested on the same scenario.

Variable nitrogen(N) rate fertilization based on remote sensing is challenging for cotton production fields, but active crop canopy sensors (ACS) appear as an alternative to make this practical on farm since they can be used at night as well. The crop spatial variability in inherent in crop production in general, and not on-the-go solutions can be used with this type of active sensing technologies. Thus, the purpose of this study was to investigate the potential of two vegetation indices to identify the N requirement variability for cotton plants and to develop prototype algorithms for topdressing nitrogen variable rate on commercial and experimental areas, using the N-sufficiency methodology based on virtual reference. The concept of virtual reference is to use a histogram to characterize the vegetation index of properly fertilized plants without establishing an N-rich plot as a reference strip. The experiment was conducted in strips with four different N rates (0, 45, 90 and 180 kgN ha^-1) during the 2015, 2016, 2017 and 2018 crop seasons in partnership with large cotton producers in Mato Grosso and also in experimental area of Embrapa Agrosilvopastoral. Two algorithms for variable rate nitrogen fertilization for cotton were developed, namely: 1) N recommendation algorithm for cotton in commercial production system: N rate (kg.N ha^-1) = -234.79 ISN² + 49,879 ISN + 195.15; R² = 0.97; and 2) for cotton grown in experimental area: N dose (kgN ha^-1) = -174.73 ISN² - 107.21 ISN + 306.78; R² = 0.94.

The article describes the results of comparison of occurence of buildings (and address points) in Poland with delimitations of land use belonging to particular classes in the CORINE Land Cover (CLC) 2018 dataset. Large discrepancies have been identified, which reach on average approx. 34% of addresses and 35% of buildings located outside class 1 (artificial surfaces), mainly on terrains of class 2 (agricultural areas). Among single-family buildings it was 37% and among new addresses (forecasted or ‘under construction’ buildings) – as much as 50%. This puts a question mark over the possibility of using CLC data with resolution of 25 ha for monitoring of spatial planning and development in Poland for purposes of the diagnosis and assessment of the scale of dispersion of built-up areas. It is worth carrying out similar analyses in other countries, known for the deconcentration processes and a relatively large share of dispersed settlement e.g. other CEE countries, Spain, Portugal, Italy.

With the widespread of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose to augment the time series signals from inertia sensors with signals from ambient sensing to train deep convolutional neural networks (DCNN) models. DCNN provides the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertia and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors such as environment noise level and illumination, with an overall improvement of 5.3% accuracy.

As climate changes, our daily life has been much influenced by abnormal meteorological phenomena such as heavy rainfall, heat wave, heavy snowfall, and fine dust. Atmospheric air quality is worsening day by day and indoor air quality is also affected by interconnected daily activities throughout the inside and outside of buildings and houses. Nowadays, pollutants from various sources are emitted, transformed by sunlight, vapor, and ozone and transported into the city from country to country. Due to these reasons, there have been high demands to monitor the transportation of particulate matters and improve air quality. Monitoring of pollutants and identification of type and its concentration enables us to track and control its generation and consequently find out the solution. However, monitoring of pollutants, especially, particulate matter generation and its transportation is still not fully operated in atmospheric air due to its open nature and meteorological factors. Even though indoor air is relatively easy to monitor and control than outdoor in the aspect of specific volume and contaminant source, but it still needs to consider the meteorological parameters because indoor air is not fully separated from the outdoor air flow and contaminants transportation. In this study, optical approach using spectral sensor was attempted to reveal the feasibility of wavelength and chromaticity values of reflected light from specific particles. From the analysis of reflected light of various particulate matters according to different liquid additives, parameter studies were performed to investigate which experimental conditions can contribute to the enhanced selective sensing of particulate matters. Five different particulate matters such as household dust, soil, talc powder, gypsum powder and yellow pine tree pollen were utilized and observed to elucidate the relationship between property of particulate matter and detected light spectrum. Applicable approaches to assist current particle matter sensors and improve the selective sensing were suggested.

In the present work, cost-effective strain gauges were fabricated by using inkjet printing and photonic curing on flexible and recyclable PET substrates. Ohmic resistance (a.k.a. DC resistance) (R0) and complex electrical impedance (Z) as a function of test frequency were characterized, respectively, with the state-of-the-art electronic testing equipments. For the fabrication process, commercially available silver nanoparticle (AgNP) inks and printing substrates were used in order to eliminate any apriori ink processing. In order to validate the in-house cantilever beam measurement setup and devices, first, commercially available metallic foil strain gauges (with the provided gauge factor GF=2 by the manufacturer) were tested at different locations. Thereafter, the printed strain gauges were investigated with several repetitions at different measurement locations. The measurement results demonstrated an affordable, rapid and tailorable design and repeatable fabrication approach for strain gauges with GFavg~6.6, which has potential applications in remote sensing and structural monitoring applications.

Data from environmental satellites are underutilized in many branches of operational oceanography. Several challenges exist for potential users of satellite data that may impede or preclude employing satellite products in their work. Users outside of the satellite community often encounter difficulty in discovering the types of satellite measurements that are available, and determining which satellite products are best for operational activities. In addition, the large choice of satellite data providers, each with their own data access protocols and formats, can make data access confusing and inefficient. NOAA’s CoastWatch Program is designed to be a Value Added Provider, whose mission is to help make satellite data easier for users to access. With this objective the West Coast Node of CoastWatch developed the NOAA Ocean Satellite Course, which introduces scientists and resource managers to ocean satellite products, and provides them tools to facilitate data access when using ArcGIS or R software. These tools leverage upon the data services provided by ERDDAP, a data distribution system designed to make data access easier for both humans and machines. The course has been offered annually since 2006, and over 350 participants have taken it. Results of post-course surveys are analyzed to measure course effectiveness. Lessons learned from conducting these courses are summarized.

The development of unmanned satellite space technology is increasingly willing, the emergence of medium resolution satellites with sensitivity and spectral variants such as Landsat is very effective in observing environmental changes, while the purpose of this study is to monitor the development of built-in land using image transformation techniques, estimating built-in land changes. The research method uses the NDVI image transformation technique, NDBI and Built Up Index, with Landsat satellite image data obtained from USGS. Accuracy sampling is done by purposive sampling with confusion matrix accuracy test technique. The research results were found. developed land for the period 2004 - 2010 with a percentage of 19.25%, for stages 2010 - 2018 with a percentage of 30.25%. The land development was built based on the area of ​​the highest sub-district in the Kubung area in the early period with a percentage of 7.20% then in the second period with a percentage of 32.23%. The quality of the accuracy of the results of image analysis using confusion matrix technique with an image accuracy level in a field sample of 185 with an image accuracy of 86.04%.

Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process and enhancing the interpretability of the feature layer. The research work in this paper reveals the necessity and effectiveness of a priori information on image blurring levels in image deblurring tasks. By studying and exploring the image blurring levels, the network model focuses more on the basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree description model, which can effectively represent the blurring degree of aerial remote sensing images. The comparison experiments show that the algorithm in this paper can effectively recover images with different degrees of blur, obtain high-quality images with clear texture details, outperform the comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve the object detection performance of aerial remote sensing blurred images. Moreover, the average PSNR of this paper's algorithm tested on the publicly available dataset RealBlur-R reached 41.02dB, surpassing the latest SOTA algorithm.

Characterization of flying insects in-situ measurement using remote sensing spectroscopy is an emerging research field. Also, most analysis techniques in remote sensing spectroscopy are based on the use of an intensity threshold which introduces indeterminacies in the number of detected specimens. In this manuscript, we investigated the possibility of analysing passive remote sensing spectroscopy measurement data using the maximum noise fraction method. The results obtained show that this analysis technique can help to overcome the measurement of background noise in spectroscopic measurements.

This work focuses on the analysis of the performance of satellite-based precipitation products for monitoring extreme rainfall events. Five precipitation products are inter-compared and evaluated in capturing indices of extreme rainfall events during 1998-2019 considering four indices of extreme rainfall. Satellite products show a variable performance, which in general indicates that the occurrence and amount of rainfall of extreme events can be both underestimated or overestimated by the datasets in a systematic way throughout the country. Also, products that consider the use of ground truth data have the best performance.

Wireless sensor networks are attractive largely because they need no wired infrastructure. But precisely this feature makes them energy constrained. Recent studies find that sensing behaviors that are otherwise deemed efficient consume comparable energy with communication. The duty cycle scheduling is perceived as contributing to achieving energy efficiency of sensing. Because of different research assumptions and objectives, various scheduling schemes have various emphases. This paper designed an adaptive sensing scheduling strategy. The objective function of the scheduling strategy includes minimizing average energy expenditure and maximizing sensing coverage (reducing event miss-rate), and it requires relatively loose assumptions. We determine the functional relationship between the variables of the objective function and the step-size parameters of the proposed strategy through the numerical fitting. We found that the objective function aggregated by the fitting functions is a bivariate multi-peak function that favors the Fibonacci tree optimization algorithm. Once the optimization of parameters is done, the strategy can be easily deployed and behaves consistently in the coming hours. We name the proposed strategy as “FTOS”. The experimental results show that the Fibonacci tree optimization algorithm gets a better optimistic effect than the comprehensive learning particle swarm optimization (CLPSO) algorithm and differential evolution (DE) algorithm. The FTOS strategy is superior to the fixed time scheduling strategy in achieving the scheduling objectives. It also outperforms other strategies with the same scheduling objectives such as LDAS, BS, DSS and PECAS.

Evaluating the performance of water indices and mapping the spatial distribution of water-related ecosystems are important for monitoring surface water resources. This is particularly the case for Ethiopia since there is limited information available on water resources development over time despite its relevance for the people and ecosystems. To address this problem, this paper evaluates the performance of seven water indices for country-scale surface water detection based on high spatial and multi-temporal resolution Sentinel-2 data, processed using the Google Earth Engine cloud computing system. Results show that the water index (WI) and automatic water extraction index with shadow (AWEIsh) are the most accurate ones to extract surface water. Comparisons are based on qualitative visual inspections and quantitative accuracy indicators. For the latter, WI and AWEIsh obtained kappa coefficients of 0.96 and 0.95, respectively, and an overall accuracy of 0.98 each. Both indices accounted for similar spatial coverages of surface waters with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia.

This work presents the dynamics of forest clearing in the Brazilian Amazon during the period 2006–2019 in which includes the approval of the new Brazilian Forest Code in 2012. The study was carried out in the Brazilian Amazon, Pará State and in the municipality of Novo Progresso (Pará State). The analysis was based on deforestation and fire hotspot datasets issued by the Brazilian Institute for Space Research (INPE), produced based on optical and thermal sensors onboard different satellites. Deforestation data was also used to assess greenhouse gas (GHG) emissions from the slash-and-burn practices. The work showed a good correlation between the occurrence of fires in the newly deforested area in the municipality of Novo Progresso and the slash-and-burn practices. The same trend was also observed in the Pará State, suggesting a common practice along the deforestation arch. The study indicated positive coefficients of determination of 0.72 and 0.66 between deforestation and fire occurrences for the municipality of Novo Progresso and Pará State, respectively. The increased number of fire occurrences in the primary forest suggests possible ecosystem degradation. Deforestation reported for 2019 surpassed 10,000 km2, a significant intensification comparatively higher than the previous ten years which was on an average of 6,760 km2. The steady increase of deforestation in the Amazon after 2012 has been a worldwide concern because of the forest loss itself as well as the massive GHG emitted in the Brazilian Amazon (295 million tons of net CO2 equivalent in the year 2019). Better correlation of deforestation and fires occurrences reported from satellite images confirmed the slash-and-burn practice and the secondary effect of deforestation, which degrades primary forest surrounding the deforested areas.

pH is considered one of the paramount factors in bodily functions, because most of the cellular tasks exclusively rely on precise pH values. The regulation of pH is a necessary feature of the intracellular atmosphere and can be established as a strong indicator to judge a physiological abnormality in most of the cases. In this context, the current techniques for pH sensing provide us with the futuristic insight to further design therapeutic and diagnostic tools. Thus, pH-sensing (electrochemically and optically) is rapidly evolving toward exciting new applications and expanding researchers’ interests in many chemical contexts, especially in biomedical applications. The adaptation of cutting-edge technology is subsequently producing the modest form of these biosensors as wearable devices, which are providing us the opportunity to target the real-time collection of vital parameters, including pH for improved healthcare systems. The motif of this review is to provide an insight of trending tech-based systems employed in real time or in-vivo pH responsive monitoring. Herein, we briefly go through the pH regulation in the human body to help the beginners and scientific community with quick background knowledge, recent advances in the field, and pH detection in cancerous environments. In the end, we summarize our review by providing an outlook; challenges that need to be addressed and prospective integration of various pH in vivo platforms with modern electronics that can open new avenues of cutting-edge techniques for disease diagnostics and prevention.

Due to the energetic transition at course, new geological exploration technologies are needed to discover mineral deposits containing critical materials such as lithium (Li). The vast majority of European Li deposits are related to Li–Cs–Ta (LCT) pegmatites. Literature review indicates that conventional exploration campaigns are dominated by geochemical surveys and related exploration tools. However, other exploration techniques must be evaluated namely remote sensing (RS) and geophysics. This work presents the results of the INOVMINERAL4.0 project obtained through alternative approaches to traditional geochemistry that were gathered and integrated into a webGIS application. The specific objectives were to: (i) assess the potential of high-resolution elevation data; (ii) evaluate geophysical methods, particularly radiometry; (iii) establish a methodology for spectral data acquisition and build a spectral library; (iv) compare obtained spectra with Landsat 9 data for pegmatite identification; and (v) implement a user-friendly webGIS for data integration and visualization. Radiometric data acquisition using geophysical techniques effectively discriminated pegmatites from host rocks. The developed spectral library provided valuable insights for space-based exploration. Landsat 9 data accurately identified known LCT pegmatite targets, compared to Landsat 8. The user-friendly webGIS facilitated data integration, visualization, and sharing, supporting potential users in similar exploration approaches.

The increasingly common occurrence of antibiotic-resistant bacteria has become an urgent public health issue. There are currently some infections without any effective treatment, which require new therapeutic strategies. An attractive alternative is the design of compounds capable of disrupting bacterial communication known as quorum sensing (QS). In gram-negative bacteria, such communication is regulated by acyl-homoserine lactones (AHLs). QS allows bacteria to proliferate before expressing virulence factors. Our group previously reported that hexyloxy phenyl imidazoline (9) demonstrated 71% inhibitory activity of QS at 100 µM (IC50=90.9 µM) in Chomobacterium violaceum, a gram-negative bacterium. The aim of the present study was to take 9 as a lead compound to design and synthesize three 2-imidazolines (13-15) and three 2-oxazolines (16-18), to be evaluated as quorum sensing inhibitors on C. violaceum CV026. We were looking for compounds with a higher affinity towards the Cvi receptor of this bacterium and the ability to inhibit QS. The binding mode of the test compounds on the Cvi receptor was explored with docking studies and molecular dynamics. It was found that 8-pentyloxyphenyl-2-imidazoline 13 reduced the production of violacein (IC50=56.38 µM) without affecting bacterial growth, suggesting inhibition of quorum sensing. Indeed, compound 13 is apparently one of the best QS inhibitors known to date. Molecular docking revealed the affinity of compound 13 for the orthosteric site of the N-hexanoyl homoserine lactone (C₆-AHL) on the CviR protein. Ten aminoacid residues in the active site of C₆-AHL interacted with 13, and 7 of these are the same as those interacting with AHL. Contrarily, 8-octyloxyphenyl-2-imidazoline 14, 8-decyloxyphenyl-2-imidazoline 15 and 9-decyloxyphenyl-2-oxazoline 18 bound only to an allosteric site and thus did not compete with C₆-AHL for the orthosteric site.

This work mainly discusses an innovative teaching platform on Unmanned Aerial Vehicle digital mapping for Remote Sensing (RS) education at Wuhan University, underlining the fast development of RS technology. Firstly, we introduce and discuss the future development of the Virtual Simulation Experiment Teaching Platform for Unmanned Aerial Vehicle (VSETP-UAV). It includes specific topics such as the Systems and function Design, teaching and learning strategies, and experimental methods. This study shows that VSETP-UAV expands the usual content and training methods related to RS education, and creates a good synergy between teaching and research. The results also show that the VSETP-UAV platform is of high teaching quality producing excellent engineers, with high international standards and innovative skills in the RS field. In particular, it develops students' practical skills with technical manipulations of dedicated hardware and software equipment (e.g., UAV) in order to assimilate quickly this particular topic. Therefore, students report that this platform is more accessible from an educational point-of-view than theoretical programs, with a quick way of learning basic concepts of RS. Finally, the proposed VSETP-UAV platform achieves a high social influence, expanding the practical content and training methods of UAV based experiments, and providing a platform for producing high-quality national talents with internationally recognized topics related to emerging engineering education.

Acinetobacter baumannii is a nosocomial pathogen capable of causing serious infections associated with high rates of morbidity and mortality. Due to its antimicrobial drug resistance profile, A. baumannii is categorized as an urgent priority pathogen by the Centers for Disease Control and Prevention in the United States and priority group 1 critical microorganism by the World Health Organization. Understanding how A. baumannii adapts to different host environments may provide critical insights into strategically targeting this pathogen with novel antimicrobial and biological therapeutics. Exposure to human fluids was previously shown to alter the gene expression profile of a highly drug susceptible A. baumannii strain A118 leading to persistence and survival of this pathogen. Herein, we explore the impact of human pleural fluid (HPF) and human serum albumin (HSA) on the gene expression profile of a highly multi-drug resistant strain of A. baumannii AB5075. Differential expression was observed for ~30 genes, whose products are involved in quorum sensing, quorum quenching, iron acquisition, fatty acid metabolism, biofilm formation, secretion systems and type IV pilus formation. Phenotypic and further transcriptomic analysis using quantitative RT-PCR confirmed RNA-seq data and pointed out a distinctive role of HSA as the molecule involved in A. baumannii response.

We propose a novel data hiding method in an audio host with a compressive sampling technique. An over-complete dictionary represents a group of the watermark. Each row of the dictionary is a Hadamard sequence representing multiple bits of the watermark. Then, the singular values of segment-based host audio in a diagonal matrix multiply by the over-complete dictionary producing a lower size matrix. At the same time, we embed the watermark into the compressed audio. In the detector, we detect the watermark and reconstruct the audio. This proposed method offers not only hiding the information but also compressing the audio host. The application of the proposed method is a broadcast monitoring and biomedical signal recording. We can mark and secure the signal content by hiding the watermark inside the signal while we compress the signal for memory efficiency. We evaluate the performance in terms of payload, compression ratio, audio quality, and watermark quality. The proposed method can hide the data imperceptibly, in range 729-5292 bps with compression ratio 1.47-4.84 and perfect detected watermark.

Safe administration of highly cytotoxic chemotherapeutic drugs is a challenging problem in cancer treatment due to the adverse side effects and collateral damage to non-tumorigenic cells. To mitigate these problems, new promising approaches, based on the paradigm of controlled targeted drug delivery (TDD), utilizing drug nanocarriers with biorecognition ability to selectively target neoplastic cells, are being considered in cancer therapy. Herein, we report on the design and testing of a nanoparticle-grid based biosensing platform to aid in the development of new targeted drug nanocarriers. The proposed sensor grid consists of superparamagnetic gold-coated core-shell Fe₂Ni@Au nanoparticles, further functionalized with folic acid targeting ligand, model thiolated chemotherapeutic drug doxorubicin (DOX), and a biocompatibility agent, 3,6,-dioxa-octanethiol (DOOT). The employed dual transduction based on electrochemical and enhanced Raman scattering detection have enabled efficient monitoring of the drug loading onto the nanocarriers, attached to the sensor surface, as well as the drug release under simulated intracellular conditions. The grid’s nanoparticles serve here as the model nanocarriers for new TDD systems under design and optimization. The superparamagnetic properties of the Fe₂Ni@Au NPs aid in nanoparticles’ handling and constructing a dense sensor grid with high plasmonic enhancement of the Raman signals due to the minimal interparticle distance.

Globally, seagrass meadows provide critical ecosystem services. However, seagrasses are globally degraded at an accelerated rate. The lack of information on seagrass spatial distribution and seagrass health status seriously hinders seagrass conservation and management. Therefore, this study proposes to combine remote sensing big data with a new hybrid machine learning model (RF-SWOA) to predict potential seagrass habitats. The multivariate remote sensing data is used to train the machine learning model, which can improve the prediction accuracy of the model. This study shows that a hybrid machine learning model (RF-SWOA) can predict potential seagrass habitats more accurately and effectively than traditional models. At the same time, it has been shown that the most important factors influencing the potential habitat of seagrass in the Hainan region were the distance from land (38.2%) and the depth of the ocean (25.9%). This paper provides a more accurate machine learning model approach for predicting the distribution of marine species, which can help develop seagrass conservation strategies to restore healthy seagrass ecosystems.

An accurate radiative transfer model (RTM) is essential for the retrieval of soil moisture (SM) from microwave remote sensing data, such as the passive microwave measurements from the Soil Moisture Active Passive (SMAP) mission. This mission delivers soil moisture products based upon L-band brightness temperature data, via retrieval algorithms for surface and root-zone soil moisture, the latter is retrieved using data assimilation and model support. We found that the RTM based on the tau-omega (?-ω) model, can suffer from significant errors over croplands (in average between -9.4K and + 12.0K for Single Channel Algorithm SCA; -8K and + 9.7K for Dual-Channel Algorithm DCA) if the vegetation scattering albedo (omega) is treated as a constant and the temporal variations are not accounted. In order to reduce this uncertainty, we propose a time-varying parameterization of omega for the widely established zeroth order radiative transfer ?-ω model. The main assumption is that omega can be expressed by a functional relationship between vegetation optical depth (tau) and the Green Vegetation Fraction (GVF). The validation was performed from 14 May to 13 December 2015 over 61 Climate Reference Network sites (SCRN) classified as croplands. The application of the proposed time-varying vegetation scattering albedo results in a consistent improvement for the unbiased root mean square error of 16% for SCA and 15% for DCA. The reduction for positive and negative biases was 45% and 5% for SCA and 26% and 12% for DCA, respectively. This indicates that vegetation dynamics on croplands are better represented by a time-dynamic single scattering albedo.

In the new antibiotic era, the exponential increase of multiresistant bacterial strains become the main global health problem. Many researchers focused their efforts to explore novel or combined strategies for combating bacterial resistance. The good knowledge of molecular mechanisms of resistance and bacterial virulence factors as key targets gives us a good scenario to resolve the problem. One particularly attractive and promising way is to attack the main regulatory “network” of bacterial virulence determinants known as Quorum sensing (QS). The inhibition of QS signals will be a novel way for screening more effective Quorum sensing inhibitors (QSIs) and will put a key role in next-generation antimicrobials in the resistance battle. This determined the aim of the present review: comprehensive clarification of the regulatory mechanisms of quorum-sensing signaling pathways in Chromobacterium violaceum and discovery of potential plant quorum sensing inhibitors.

Specific climate adaptation and resilience measures can be efficiently designed and implemented at the regional and local level. Climate and environmental databases are of critical importance to achieving sustainability goals (SDGs) and for the efficient planning and implementation of suitable mitigation measures: Available databases can serve municipalities as a vital starting points to determine requirements, prioritize resources and allocate investments under consideration of commonly tight budget restrictions. High-quality geo, climate and environmental data are now available – data from remote sensing, i.e. Copernicus services will be of crucial importance. Forward-looking approaches exist to using such data to derive forecasts for urban planning process optimization for municipal administrations. On municipal level, however, the existing data have so far only been used to a limited extent, since there are no practical tools for urban planning that can be used to merge and meaningfully combine remote sensing data with local data and to further process and apply in municipal planning processes. Therefore, our project CoKLIMAx aims at the development of new digital products, advanced urban services and procedures, such as the development of practice-oriented technical tools that acquire various remote sensing and in-situ data sets for validation and further processing.

Since smart cities aim at becoming self-monitoring and self-response systems, their deployment relies on close resource monitoring through large-scale urban sensing. The subsequent gathering of massive amounts of data makes essential the development of event filtering mechanisms that enable the selection of what is relevant and trustworthy. Due to the rise of mobile event producers, location information has become a valuable filtering criterion as it not only offers extra information on the event described but also enhances trust on the producer. Implementing mechanisms that validate the quality of location information becomes then imperative. The lack of such strategies in cloud architectures compels the adoption of new communication schemes for IoT-based urban services. To serve the demand for location verification in urban event-based systems (DEBS), we have designed three different fog architectures that combine proximity and cloud communication. Moreover, we have successfully assessed their performance using network simulations with realistic urban traces.