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In recent years, we have been observing the rapid growth and adoption of IoT-based systems, enhancing multiple areas of our lives. Concurrently, the utilization of machine learning techniques has surged, often for similar use cases as those seen in IoT systems. In this survey, we aim to focus on the combination of machine learning and the edge computing paradigm. The presented research commences with the topic of edge computing, its benefits, such as reduced data transmission, improved scalability, and reduced latency, as well as the challenges associated with this computing paradigm, like energy consumption, constrained devices, security, and device fleet management. It then presents the motivations behind the combination of machine learning and edge computing, such as the availability of more powerful edge devices, improving data privacy, reducing latency, or lowering reliance on centralized services. Then, it describes several edge computing platforms, with a focus on their capability to enable edge intelligence workflows. It also reviews the currently available edge intelligence frameworks and libraries, such as TensorFlow Lite or PyTorch Mobile. Afterward, the paper focuses on the existing use cases for edge intelligence in areas like industrial applications, healthcare applications, smart cities, environmental monitoring, or autonomous vehicles. [ABSTRACT FROM AUTHOR]

The request for faster and greener civil aviation is urging the worldwide scientific community and aerospace industry to develop a new generation of supersonic aircraft, which are expected to be environmentally sustainable and to guarantee a high-level protection of citizens. A key aspect to monitor the potential environmental impact of new configurations is the aerodynamic efficiency and its impact onto the real mission. To pursue this goal, this paper discloses increasing-fidelity aerodynamic modeling approaches to improve the conceptual design of high-speed vehicles. The disclosed methodology foresees the development of aerodynamic aerodatabases by means of incremental steps starting from simplified methods (panels methods and/or low-fidelity CFD simulations) up to very reliable data based on high-fidelity CFD simulations and experimental measurements with associated confidence levels. This multifidelity approach enables the possibility of supporting the aircraft design process at different stages of its design cycle, from the estimation of preliminary aerodynamic coefficients at the beginning of the conceptual design, up to the development of tailored aerodatabases at advanced design phases. For each design stage, a build-up approach is adopted, starting from the investigation of the clean external configuration up to the complete one, including control surfaces' effects and, if any, the effects of the integration of the propulsive effects. In addition, the applicability of the approach is guaranteed for a wide range of supersonic and hypersonic aircraft, and the developed methodology is here applied to the characterization of Mach 2 aircraft configuration, a relevant case study of the H2020 MORE&LESS project. [ABSTRACT FROM AUTHOR]

Maritime emissions contribute significantly to global pollution, necessitating accurate and efficient monitoring methods. Traditional methods for tracking ship emissions often face limitations in real-time data accuracy, with wind measurement being a critical yet challenging aspect. This paper introduces an innovative mission planner module for unmanned aerial vehicles (UAVs) that leverages onboard wind sensing capabilities to enhance maritime emission monitoring. The module's primary objective is to assist operators in making informed decisions by providing real-time wind data overlays, thus optimizing flight paths and data collection efficiency. Our experimental setup involves the testing of the module in simulated maritime environments, demonstrating its efficacy in varying wind conditions. The real-time wind data overlays provided by the module enable UAV operators to adjust their flight paths dynamically, reducing unnecessary power expenditure and mitigating the risks associated with low-battery scenarios, especially in challenging maritime conditions. This paper presents the implementation of real-time wind data overlays on an open-source state-of-the-art mission planner as a C# plugin that is seamlessly integrated into the user interface. The factors that affect performance, in terms of communication overheads and real-time operation, are identified and discussed. The operation of the module is evaluated in terms of functional integration and real-time visual representation of wind measurements, and the enhanced situational awareness that it can offer to mission controllers is demonstrated. Beyond presenting a novel application of UAV technology in environmental monitoring, we also provide an extensive discussion of how this work will be extended in the context of complete aerial environmental inspection missions and the future directions in research within the field that can potentially lead to the modernization of maritime emission monitoring practices. [ABSTRACT FROM AUTHOR]

The historic house of Sheikh Mohammed Bin Khalifa, Al Ain (Abu Dhabi, UAE) belongs to the World Heritage Cultural Sites. The courtyard and two rooms include mudbrick archaeological remains that are preserved in showcases for public display. The building lies in a hot desert climate and needs air conditioning to be comfortable. This paper is concerned with indoor showcases and their compatibility with the indoor climate. Sometimes, misting for condensation is generated on the glass panes for the temperature and humidity contrast between the room climate and the showcases that are embedded in the floor. When misting occurs, the remains cannot be viewed. This paper investigates indoor glass misting, the exchanges of heat and moisture between archaeological remains, showcases and rooms, and the results after a year of environmental monitoring. An aim is to assess the potential risks for the preservation of the remains, recognizing the frequency and severity of the conditions for condensation. Another aim is to discuss mitigation methodologies to avoid glass misting, e.g., (i) reducing the room cooling; (ii) raising the glass pane temperature with electrical devices; (iii) increasing air exchanges between showcases and rooms; (iv) adopting a combination of these methodologies. [ABSTRACT FROM AUTHOR]

Swarm robotics has emerged as a promising field with applications ranging from search and rescue operations to environmental monitoring. This paper introduces Anveshan, a 2-D swarm robot simulator developed to provide a user-friendly yet powerful platform for simulating and analyzing swarm behaviors. Focusing on the search use case, particularly in the context of Search and Rescue operations during disaster response, we showcase the capabilities of Anveshan in addressing the complexities associated with coordinated multi-robot systems. In this paper, we analyze the existing swarm simulators and define the architecture of Anveshan, along with details about all the included modules and configurable parameters offered by the simulator to demonstrate its flexibility. [ABSTRACT FROM AUTHOR]

Maritime transport, responsible for delivering over eighty percent of the world's goods, is the backbone of the global delivery industry. However, it also presents considerable environmental risks, particularly regarding aquatic contamination. Nearly ninety percent of marine oil spills near shores are attributed to human activities, highlighting the urgent need for continuous and effective surveillance. To address this pressing issue, this paper introduces a novel technique named OS-BREEZE. This method employs an Unmanned Surface Vehicle (USV) for assessing the extent of oil pollution on the sea surface. The OS-BREEZE algorithm directs the USV along the spill edge, facilitating rapid and accurate assessment of the contaminated area. The key contribution of this paper is the development of this novel approach for monitoring and managing marine pollution, which significantly reduces the path length required for mapping and estimating the size of the contaminated area. Furthermore, this paper presents a scale model experiment executed at the Coastal and Marine Engineering Research Institute (CAMERI). This experiment demonstrated the method's enhanced speed and efficiency compared to traditional monitoring techniques. The experiment was methodically conducted across four distinct scenarios: the initial and advanced stages of an oil spill at the outer anchoring, as well as scenarios at the inner docking on both the stern and port sides. [ABSTRACT FROM AUTHOR]

A novel Schiff base, N'-(4-(diethylamino)-2-hydroxybenzylidene) isonicotinohydrazide (S1) was synthesised by the condensation reaction between isonicotinohydrazide and 4-(diethylamino)-2-hydroxybenzaldehyde. The chemo-sensing behaviour of S1 towards Ni2+ was investigated and confirmed that the S1 exhibited fair selectivity and sensitivity. Furthermore, it was noted that there was no significant effect of co-existing cations and anions on the detection of Ni2+. The complexation ratio of S1 and Ni2+ was confirmed as 1:1 with Jobs continuous variation method and two isosbestic points on absorption titration further supported by DFT calculations. The binding constant (Kb) for S1+Ni2+ was calculated as 22,769 M−1 and 22,134 M−1. Moreover, the obtained limit of detection (LOD) for Ni2+ was found to be 375 nM. Finally, chemosensing applications of S1 as a paper test strip kit in the environmental monitoring assessment laboratory for the recovery of Ni2+ from real water samples were successfully designed and evaluated. [ABSTRACT FROM AUTHOR]

Recent advancements in particle dispersion modeling have significantly enhanced our understanding and capabilities in predicting and analyzing the behavior of particulate matter in various environments. However, this field still confronts several research gaps and challenges that span across scientific inquiry and technological applications. This paper reviews the current state of particle dispersion modeling, focusing on various models such as Lagrangian, Eulerian, Gaussian, and Box models, each with unique strengths and limitations. It highlights the importance of accurately simulating multi-phase interactions, addressing computational intensity for practical applications, and considering environmental and public health implications. Furthermore, the integration of emerging technologies like machine learning (ML) and artificial intelligence (AI) presents promising avenues for future advancements. These technologies could potentially enhance model accuracy, reduce computational demands, and enable handling complex, multi-variable scenarios. The paper also emphasizes the need for real-time monitoring and predictive capabilities in particle dispersion models, which are crucial for environmental monitoring, industrial safety, and public health preparedness. [ABSTRACT FROM AUTHOR]

Accurate measurement of pH-dependent analytes is crucial for a wide range of applications, including environmental monitoring, industrial processes, and healthcare diagnostics. In multi-sensor systems, combining data from multiple sensors offers the potential for more comprehensive analysis, yet it is important to be aware of the limitations of this approach. In this paper, we investigate the often-overlooked issue of response time mismatch among sensors, which can introduce significant errors in calculated sum parameters. We present a model and software application (SensinSilico) that allows predicting the error arising from a mismatch of sensor response times. The model was compared and validated using experimental results from calculations of total dissolved sulphide (TDS). These calculations were based on data from concurrent sensor measurements of hydrogen sulfide (H2S) and pH, which had different response times. We believe that SensinSilico has the potential to be a powerful tool for researchers, professionals, and end-users, enabling them to estimate and minimize errors arising from response time mismatches, enhancing the accuracy and reliability of their results. [ABSTRACT FROM AUTHOR]

For the first time, this paper reports a smart museum archive box that features a fully integrated wireless powered temperature and humidity sensor. The smart archive box has been specifically developed for microclimate environmental monitoring of stored museum artifacts in cultural heritage applications. The developed sensor does not require a battery and is wirelessly powered using Near Field Communications (NFC). The proposed solution enables a convenient means for wireless sensing with the operator by simply placing a standard smartphone in close proximity to the cardboard archive box. Wireless sensing capability has the advantage of enabling long-term environmental monitoring of the contents of the archive box without having to move and open the box for reading or battery replacement. This contributes to a sustainable preventive conservation strategy and avoids the risk of exposing the contents to the external environment, which may result in degradation of the stored artifacts. In this work, a low-cost and fully integrated NFC sensor has been successfully developed and demonstrated. The developed sensor is capable of wirelessly measuring temperature and relative humidity with a mean error of 0.37 °C and ±0.35%, respectively. The design has also been optimized for low power operation with a measured peak DC power consumption of 900 μW while yielding a 4.5 cm wireless communication range. The power consumption of the NFC sensor is one of the lowest found in the literature. To the author's knowledge, the NFC sensor proposed in this paper is the first reporting of a smart archive box that is wirelessly powered and uniquely integrated within a cardboard archive box. [ABSTRACT FROM AUTHOR]

Nucleic acid-based molecular detection of mitochondrial DNA (mtDNA) has been used for the detection of eukaryotic pathogens, genetic markers, and biomedical studies in humans and animals, forensic investigation, conservation biology, and environmental monitoring. Currently available molecular mtDNA testing is predominately dependent on relatively expensive laboratory equipment, which limits test applicability for on-site field testing. Recent technical advances in molecular testing of mtDNA using microfluidic chip technology and paper-based methods, and assays based on isothermal nucleic acid amplification show promise for field assay development. Microfluidic and paper-based components for mtDNA sample preparation and isothermal amplification establish the groundwork for targeted detection. The goal is on-site field detection mtDNA assays that combine sample processing, assay setup, and visual representation of the presence of mtDNA. This review examines advantages and disadvantages of alternative field-compatible tests for on-site detection of mtDNA. Despite challenges, developing an integrated "sample-through detection" field test platform is the future direction for mtDNA molecular detection in environmental samples. [ABSTRACT FROM AUTHOR]

Timely and accurate monitoring of pasture biomass and ground-cover is necessary in livestock production systems to ensure productive and sustainable management of forage for livestock. Interest in the use of proximal sensors for monitoring pasture status in grazing systems has increased, since such sensors can return data in near real-time, and have the potential to be deployed on large properties where remote sensing may not be suitable due to issues such as spatial scale or cloud cover. However, there are unresolved challenges in developing calibrations to convert raw sensor data to quantitative biophysical values, such as pasture biomass or vegetation ground-cover, to allow meaningful interpretation of sensor data by livestock producers. We assessed the use of multiple proximal sensors for monitoring tropical pastures with a pilot deployment of sensors at two sites on Lansdown Research Station near Townsville, Australia. Each site was monitored by a Skye SKR-four-band multi-spectral sensor (every 1 min), a digital camera (every 30 min), and a soil moisture sensor (every 1 min), each operated over 18 months. Raw data from each sensor were processed to calculate a number of multispectral vegetation indices. Visual observations of pasture characteristics, including above-ground standing biomass and ground cover, were made every 2 weeks. A methodology was developed to manage the sensor deployment and the quality control of the data collected. The data capture from the digital cameras was more reliable than the multi-spectral sensors, which had up to 63 % of data discarded after data cleaning and quality control. We found a strong relationship between sensor and pasture measurements during the wet season period of maximum pasture growth (January to April), especially when data from the multi-spectral sensors were combined with weather data. RatioNS34 (a simple band ratio between the near infrared (NIR) and lower shortwave infrared (SWIR) bands) and rainfall since 1 September explained 91 % of the variation in above-ground standing biomass (RSE = 593 kg DM ha-1, p < 0.01). RatioNS34 together with rainfall explained 95 % of the variation in the percentage of green vegetation observed in 2-dimensions (%Green2D) (RSE = 6 %, p < 0.01). The Green Leaf Algorithm index derived from the digital camera images and the rainfall accumulated since the 1 September explained 91 % of the variation in %Green2D (RSE = 9 %, p < 0.01, df = 20), but had a poor relationship with biomass. Although proximal sensors observe only a small area of the pasture, they deliver continual and timely pasture measurements to inform timely decision-making on-farm. [ABSTRACT FROM AUTHOR]

The article presents a study of the multidimensional approaches in integrate monitoring for Strategic Environment Assessment (SEA). It mentions the use of strategies and its implementation as required tools for decision support and monitoring to control the pressure on land. It suggests that monitoring should give an automatic alert when certain threshold of environmental pressure overpass by the dimension and velocity of change in land use.

Remote sensing (RS) images play an indispensable role in many key fields such as environmental monitoring, precision agriculture, and urban resource management. Traditional deep convolutional neural networks have the problem of limited receptive fields. To address this problem, this paper introduces a hybrid network model that combines the advantages of CNN and Transformer, called MBT-UNet. First, a multi-branch encoder design based on the pyramid vision transformer (PVT) is proposed to effectively capture multi-scale feature information; second, an efficient feature fusion module (FFM) is proposed to optimize the collaboration and integration of features at different scales; finally, in the decoder stage, a multi-scale upsampling module (MSUM) is proposed to further refine the segmentation results and enhance segmentation accuracy. We conduct experiments on the ISPRS Vaihingen dataset, the Potsdam dataset, the LoveDA dataset, and the UAVid dataset. Experimental results show that MBT-UNet surpasses state-of-the-art algorithms in key performance indicators, confirming its superior performance in high-precision remote sensing image segmentation tasks. [ABSTRACT FROM AUTHOR]

In the framework of the space-air-ground-ocean integrated network, the underwater acoustic sensor network (UASN) plays a pivotal role. The design of media access control (MAC) protocols is essential for the UASN to ensure efficient and reliable data transmission. From the perspective of differentiated services in the UASN, a service-aware and scheduling-based hybrid MAC protocol, named the SSH-MAC protocol, is proposed in this paper. In the SSH-MAC protocol, the centralized scheduling strategy is adopted by sensor nodes with environmental monitoring service, and the distributed scheduling strategy is adopted by sensor nodes with target detection service. Considering the time-varying data generation rate of sensor nodes, the sink node will switch the scheduling mode of sensor nodes based on the specific control packet and adjust the resource allocation ratio between centralized scheduling and distributed scheduling. Simulation results show that the performance of the SSH-MAC protocol, in terms of utilization, end-to-end delay, packet delivery ratio, energy consumption, and payload efficiency, is good. [ABSTRACT FROM AUTHOR]

This review paper comprehensively analyzes recent advancements in optical fiber‐based biosensors, focusing on conventional fiber and photonic crystal structures. This paper overviews the significant applications of optical fiber biosensors, including bioimaging, quality analysis, food safety, and field environment monitoring, setting the stage for subsequent discussions. The primary objective of the review is to systematically evaluate recent literature concerning optical fiber‐based biosensors, emphasizing their sensitivities and resolutions. The second section explores integrating plasmonic materials such as graphene, TDMC, germanium, black phosphorus, and silicon within optical fiber biosensors, elucidating their roles in enhancing sensitivity and resolution in biosensing applications. A detailed examination of photonic crystal fibers (PCF) follows, categorizing them into internally and externally metal film‐coated biosensors, highlighting their distinct advantages and limitations. Comparative analyses in two tables delineate the performance and sensitivity of optical fiber‐based biosensors, mainly focusing on different coating strategies. The final section of the review discusses emerging trends and applications in optical fiber biosensing technologies, underscoring their potential to transform biomedical and environmental monitoring fields. By synthesizing recent developments and challenges, this review aims to offer researchers and practitioners a comprehensive understanding of optical fiber‐based biosensors, facilitating informed decision‐making and driving further advancements in the field. [ABSTRACT FROM AUTHOR]

Environmental monitoring is essential for safeguarding the health of our planet and protecting human health and well-being. Without trust, the effectiveness of environmental monitoring and the ability to address environmental challenges are significantly compromised. In this paper, we present a sensor platform capable of performing authenticated and trustworthy measurements, together with a lightweight security protocol for sending the data from the sensor to a central server anonymously. Besides presenting a new and very efficient symmetric-key-based protocol, we also demonstrate on real hardware how existing embedded security modules can be utilized for this purpose. We provide an in-depth evaluation of the performance and a detailed security analysis. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful technique for the detection and analysis of biomolecules due to its high sensitivity and selectivity. In recent years, SERS-based sensors have received significant attention for the detection of deoxyribonucleic acid (DNA) molecules, offering promising applications in fields such as medical diagnostics, forensic analysis, and environmental monitoring. This paper provides a concise overview of the principles, advancements, and potential of SERS-based sensors for DNA detection. First, the fundamental principles of SERS are introduced, highlighting its ability to enhance the Raman scattering signal by several orders of magnitude through the interaction between target molecules with metallic nanostructures. Then, the fabrication technologies of SERS substrates tailored for DNA detection are reviewed. The performances of SERS substrates previously reported for DNA detection are compared and analyzed in terms of the limit of detection (LOD) and enhancement factor (EF) in detail, with respect to the technical parameters of Raman spectroscopy (e.g., laser wavelength and power). Additionally, strategies for functionalizing the sensor surfaces with DNA-specific capture probes or aptamers are outlined. The collected data can be of help in selecting and optimizing the most suitable fabrication technology considering nucleotide sensing applications with Raman spectroscopy. [ABSTRACT FROM AUTHOR]

Climate change resulting in frequent flooding events have caused catastrophic effects to Small Island Developing States (SIDS) in recent times. Rainfall events have become less predictable in recent years. This paper presented the findings of a project that evaluated permeable pavement systems (PPS) using low-carbon materials, recycled aggregates, and carbon-negative aggregates within its structure as a sustainable drainage system (SuDS). It replicates typical drainage systems, reducing the surface runoff volumetric rates and retaining stormwater pollutants from downstream runoff. Low carbon permeable pavements and Carbon Negative pavement systems are novel structural pavements implementing materials which can withstand the same axial loading as the conventional pavements and can enhance stormwater quality by water treatment through filtration and infiltration between sub-base layers. Carbonnegative aggregates utilise patented technology that converts secondary waste products into high-quality aggregates based on a process that absorbs CO2 into the pavement materials. This paper evaluates two pilot-scaled Low-CarbonPorous Asphalt Pavement (LC-PAP) systems versus two carbon-negative interlocking concrete block permeable pavement systems (CN-ICB-PPS) on the overall environmental and structural performance. It was found that the pavement systems achieved similar permeability for stormwater remediation results using a combination of virgin aggregates, recycled aggregates, and carbon-negative aggregates for the CN-ICB-PPS and LC-PAP. The pavement systems utilising greater content of carbon-negative aggregates displayed a higher water infiltration rate when compared to the CN-ICB-PPS because of the sub-base design implemented. The LC-PAP systems could achieve the necessary strength at a lower cost, implementing low-carbon recycled materials and carbon-negative aggregates forming 70 % of sub-base layer of the pavements. For the LC-PAP system, ammonium, nitrates, colour, BOD and COD from the stormwater influent decreased significantly when compared to outflow water samples from the CN-ICB-PPS. Due to the variations in the top layers of the pavements with very small pore-spaces, this ensured a greater pollutant retention rate improving the overall stormwater quality being discharged from the pavement. The CN-ICB-PPS displaced a slight decrease in ammonium, nitrates, and colour over the period of study. Moreover, the LC-PAP contained higher content of low-carbon materials and recycled aggregates, placed above the saturation zone of the pavement, allowing some stormwater pollutants to filtrate easily through the pavement structure. [ABSTRACT FROM AUTHOR]

Unmanned aerial systems (UASs) find diverse applications across military, civilian, and commercial sectors, including military reconnaissance, aerial photography, environmental monitoring, precision agriculture, logistics, and rescue operations, offering efficient, safe, and cost-effective solutions to various industries. To ensure the stable and reliable operation of UASs, fault diagnosis is essential, which can enhance safety, and minimize potential risks and losses. However, most existing fault diagnosis methods rely on a single physical quantity as the primary information source or solely consider fault data at a single moment, leading to challenges of low diagnostic accuracy and limited reliability. Aimed at this problem, this paper presents a fault diagnosis method based on time–space domain weighted information fusion for UASs. First, the Gaussian fault model is constructed for the data with different fault features in the space domain. Next, the weighted coefficient method is used to generate the basic probability assignment (BPA) by matching the fault data with the Gaussian fault model. Then, the Dempster's combination rule, which enables the Dempster–Shafer (D-S) evidence theory, is adopted to fuse the generated BPAs. Based on this, the pignistic probability transformation is performed to determine the fault type. Finally, numerical results demonstrate the effectiveness of the proposed fault diagnosis method in accurately identifying the fault types of UASs. [ABSTRACT FROM AUTHOR]

Geostatistical prediction methods are increasingly used in earth sciences and engineering to improve upon our knowledge of attributes in space and time. During mining activities, it is very important to have an estimate of any contamination of the soil and groundwater in the area for environmental reasons and to guide the reclamation once mining operations are finished. In this paper, we present the geostatistical analysis of the water content in certain pollutants (Cd and Mn) in a group of mines in Northern Greece. The monitoring points that were studied are 62. The aim of this work is to create a contamination prediction map that better represents the values of Cd and Mn, which is challenging based on the small sample size. The correlation between Cd and Mn concentration in the groundwater is investigated during the preliminary analysis of the data. The logarithm of the data values was used, and after removing a linear trend, the variogram parameters were estimated. In order to create the necessary maps of contamination, we employed the method of ordinary Kriging (OK) and inversed the transformations using bias correction to adjust the results for the inverse transform. Cross-validation shows promising results ( ρ = 65 % for Cd and ρ = 52 % for Mn, RMSE = 25.9 ppb for Cd and RMSE = 25.1 ppm for Mn). As part of this work, the Spartan Variogram model was compared with the other models and was found to perform better for the data of Mn. [ABSTRACT FROM AUTHOR]

Biosensors (biological sensors) are a promising alternative to standard analytical techniques for testing and monitoring in the environment, among others, heavy metals. Biological materials that detect heavy metals include enzymes, bacteria, fungi, and plant and animal cells. The paper aimed to evaluate the possibilities of biosensor usage in environmental analysis, considering an easy and environmentally friendly analysis of individual compounds (e.g., heavy metals) or their mixtures in various samples directly in the environment, as well as continuous monitoring of these compounds. The work considers biosensors' characteristics, classification, and operation principles. Attention is paid to the possibility of using bioluminescent bacteria as biological material in a biosensor. Examples of the study of heavy metals using various biological sensors were given. It also indicates the possibility of using biosensors to determine microplastics by applying specific hormones (estrogens) receptor use and peptides as a solution or incorporating them into living microbial cells. Additionally, support was given to the use of biosensors to monitor plastics' degradation in the water environment using recombinant whole-cell microbial biosensors as detectors for plastic monomers such as acrylic acid monomers. The application of the biosensors highlighted in the paper is to shorten the time of toxicity studies by using new indicators or acceptors. However, several challenges must be overcome, such as cell viability and activity. A continuous water-monitoring system that can handle a severely toxic sample has yet to be developed in toxicity monitoring [ABSTRACT FROM AUTHOR]

Low-light imaging capabilities are in urgent demand in many fields, such as security surveillance, night-time autonomous driving, wilderness rescue, and environmental monitoring. The excellent performance of SPAD devices gives them significant potential for applications in low-light imaging. This article presents a 64 (rows) × 128 (columns) SPAD image sensor designed for low-light imaging. The chip utilizes a three-dimensional stacking architecture and microlens technology, combined with compact gated pixel circuits designed with thick-gate MOS transistors, which further enhance the SPAD's photosensitivity. The configurable digital control circuit allows for the adjustment of exposure time, enabling the sensor to adapt to different lighting conditions. The chip exhibits very low dark noise levels, with an average DCR of 41.5 cps at 2.4 V excess bias voltage. Additionally, it employs a denoising algorithm specifically developed for the SPAD image sensor, achieving two-dimensional grayscale imaging under 6 × 10−4 lux illumination conditions, demonstrating excellent low-light imaging capabilities. The chip designed in this paper fully leverages the performance advantages of SPAD image sensors and holds promise for applications in various fields requiring low-light imaging capabilities. [ABSTRACT FROM AUTHOR]

The Daling River Basin is an important ecological functional area in the western region of Liaoning with outstanding environmental problems. The monitoring of ecological and environmental quality in the basin and the analysis of driving factors are of great importance for the protection of the ecological environment and the improvement of economic quality. In this paper, the three periods of Landsat remote sensing images in 1995, 2010 and 2020 are used as the basic data, and platforms and technical means such as RS and GIS are used to decipher and extract the three periods of land use information, and to construct the land use type transfer matrix. The remote sensing ecological index (RSEI) was improved, and the principal component analysis method was applied to construct the improved remote sensing ecological index (IRSEI) model based on the greenness (NDVI), moisture (WET), heat (LST) and new dryness (N-NDBSI), so as to realize the dynamic monitoring of ecological and environmental quality in the study area. Based on the land use change, combined with the trend of improved remote sensing ecological index (IRSEI) of Daling River Basin, thus achieving the purpose of rapid and efficient dynamic monitoring of ecological quality of Daling River Basin from 1995 to 2020. A geoprobe model was then used to systematically assess the drivers of ecological quality in the catchment. The results show that the improved remote sensing ecological index (IRSEI) can efficiently and accurately obtain the spatial distribution pattern and temporal variation trend of IRSEI in the study area, which is more in line with the characteristics of indicators in this study area. The IRSEI in the study area showed an increasing trend from 1995 to 2020, from 0.4794 to 0.5615, and the proportion of benign ecological classes increased year by year during the period. Among the evaluation indicators, NDVI and N-NDBSI are the main factors affecting the environmental and ecological quality of the Daling River Basin, and the increase of vegetation cover, climate regulation and human activities have obvious promoting effects on the improvement of the ecological environment of the Daling River Basin. This study provides a scientific theoretical basis for the implementation of further ecological environmental protection measures. [ABSTRACT FROM AUTHOR]

Insects are the most diverse group of animals on Earth, yet our knowledge of their diversity, ecology and population trends remains abysmally poor. Four major technological approaches are coming to fruition for use in insect monitoring and ecological research—molecular methods, computer vision, autonomous acoustic monitoring and radar-based remote sensing—each of which has seen major advances over the past years. Together, they have the potential to revolutionize insect ecology, and to make all-taxa, fine-grained insect monitoring feasible across the globe. So far, advances within and among technologies have largely taken place in isolation, and parallel efforts among projects have led to redundancy and a methodological sprawl; yet, given the commonalities in their goals and approaches, increased collaboration among projects and integration across technologies could provide unprecedented improvements in taxonomic and spatio-temporal resolution and coverage. This theme issue showcases recent developments and state-of-the-art applications of these technologies, and outlines the way forward regarding data processing, cost-effectiveness, meaningful trend analysis, technological integration and open data requirements. Together, these papers set the stage for the future of automated insect monitoring. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

Chemosensing, an interdisciplinary scientific domain, plays a pivotal role ranging from environmental monitoring to healthcare diagnostics and (inter)national security. Metal‐organic gels (MOGs) are recognized for their stability, selectivity, and responsiveness, making them valuable for chemosensing applications. Researchers have explored the development of MOGs based on different metal ions and ligands, allowing for tailored properties and sensitivities, and have even demonstrated their applications as portable sensors such as paper‐based test strips for practical use. Herein, several studies related to MOGs development and their applications in the chemosensing field via UV‐visible or luminance along with electrochemical approach are presented. These papers explored MOGs as versatile materials with their use in sensing bio or environmental analytes. This review provides a foundational understanding of key concepts, methodologies, and recent advancements in this field, fostering the scientific community. [ABSTRACT FROM AUTHOR]

In underwater environments, the accurate estimation of state features for passive object is a critical aspect of various applications, including underwater robotics, surveillance, and environmental monitoring. This study presents an innovative neuro computing approach for instantaneous state features reckoning of passive marine object following dynamic Markov chains. This paper introduces the potential of intelligent Bayesian regularization backpropagation neuro computing (IBRBNC) for the precise estimation of state features of underwater passive object. The proposed paradigm combines the power of artificial neural network with Bayesian regularization technique to address the challenges associated with noisy and limited underwater sensor data. The IBRBNC paradigm leverages deep neural networks with a focus on backpropagation to model complex relationships in the underwater environment. Furthermore, Bayesian regularization is introduced to incorporate prior knowledge and mitigate overfitting, enhancing the model's robustness and generalization capabilities. This dual approach results in a highly adaptive and intelligent system capable of accurately estimating the state features of passive object in real-time. To evaluate the efficacy of this intelligent computing approach, a controlled supervised maneuvering trajectory for underwater passive object is constructed. Real-time estimations of location, velocity, and turn rate for dynamic target are scrutinized across five distinct scenarios by varying the Gaussian observed noise's standard deviation, aiming to minimize mean square errors (MSEs) between real and estimated values. The effectiveness of the proposed IBRBNC paradigm is demonstrated through extensive simulations and experimental trials. Results showcase its superiority over traditional nonlinear filtering methods like interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model unscented Kalman filter (IMMUKF), especially in the presence of noise, incomplete measurements and sparse data. [ABSTRACT FROM AUTHOR]

Infrared target detection is widely used in industrial fields, such as environmental monitoring, automatic driving, etc., and the detection of weak targets is one of the most challenging research topics in this field. Due to the small size of these targets, limited information and less surrounding contextual information, it increases the difficulty of target detection and recognition. To address these issues, this paper proposes YOLO-ISTD, an improved method for infrared small target detection based on the YOLOv5-S framework. Firstly, we propose a feature extraction module called SACSP, which incorporates the Shuffle Attention mechanism and makes certain adjustments to the CSP structure, enhancing the feature extraction capability and improving the performance of the detector. Secondly, we introduce a feature fusion module called NL-SPPF. By introducing an NL-Block, the network is able to capture richer long-range features, better capturing the correlation between background information and targets, thereby enhancing the detection capability for small targets. Lastly, we propose a modified K-means clustering algorithm based on Distance-IoU (DIoU), called K-means_DIOU, to improve the accuracy of clustering and generate anchors suitable for the task. Additionally, modifications are made to the detection heads in YOLOv5-S. The original 8, 16, and 32 times downsampling detection heads are replaced with 4, 8, and 16 times downsampling detection heads, capturing more informative coarse-grained features. This enables better understanding of the overall characteristics and structure of the targets, resulting in improved representation and localization of small targets. Experimental results demonstrate significant achievements of YOLO-ISTD on the NUST-SIRST dataset, with an improvement of 8.568% in mAP@0.5 and 8.618% in mAP@0.95. Compared to the comparative models, the proposed approach effectively addresses issues of missed detections and false alarms in the detection results, leading to substantial improvements in precision, recall, and model convergence speed. [ABSTRACT FROM AUTHOR]

Plantation mapping plays a vital role in agriculture, forestry, and land management. The integration of Artificial intelligence and Machine learning techniques with high-resolution satellite data has revolutionized the accuracy and efficiency of plantation mapping. Utilizing AI and machine learning tools for plantation mapping offers a transformative approach to efficient and accurate land management. These technologies enable automated analysis of satellite imagery and other geospatial data, facilitating rapid and precise identification of plantations, crop health assessment, and yield predictions. The integration of AI enhances the mapping process, providing valuable insights for sustainable agriculture, resource optimization, and environmental monitoring. The application of these advanced tools in plantation mapping represents a significant leap towards data-driven and environmentally conscious land management practices. It presents a promising advancement in agricultural practices. By leveraging these technologies for automated analysis of satellite imagery and geospatial data, accurate and timely mapping of plantations becomes feasible. The use of AI and ML tools in Plantation mapping, challenges in integration, the possible solutions and its future prospects are reviewed in this paper not only to enhance efficiency but also to offer insights into crop health, aiding in precision agriculture and resource optimization. [ABSTRACT FROM AUTHOR]

The aim of this survey is to limit the largest number of these techniques in one place in the form of tables in order for the researcher to distinguish between them and know the extent of their benefits and disadvantages, as well as in order for the researcher to avoid falling into these defects as much as possible when he makes his own cloned contract detection system. In this paper, we have conducted a comprehensive review of the collection of several techniques for detecting centralized and distributed replication attacks, where nodes can be static or mobile sensor nodes, and tables were made summarizing what was mentioned in these techniques, each according to the results reached by the researchers. A Wireless Sensor Network (WSN) is a system of self-contained sensor nodes that monitor environmental (or physical) parameters with limitations on battery life, memory capacity, and computational power. WSNs are open to several types of attacks due to their use in unmoderated and insecure contexts. Cloning attacks, or (replication attacks), are a type of physical attack. A network adversary can quickly control a single node and collect data from it. Then reprogram it to make a copy of the captured node. Identifying a duplicate node becomes difficult once these clones are spread throughout the network and are accepted as original nodes. A technology or (protocol) must be found that ideally prevents the node from being cloned, as researchers have not been able to create a 100% secure system to prevent the effects of node cloning, which include network traffic monitoring, sensor spoofing, mock data injection, sabotage of data collection, signal jamming, denial-of-service attacks, and disrupting network tasks. Creating a comparison table between techniques for preventing node cloning provides many benefits, including quickly finding the appropriate technique. It is considered a comprehensive and quick-access reference. It facilitates the decision-making process and prevents making mistakes that researchers made previously. It provides visual assistance for analyzing the strengths and weaknesses of each technique in an easier and faster way. The researcher was able to choose the most appropriate technology to develop and improve the quality of its performance to reach the ideal technology in future works. [ABSTRACT FROM AUTHOR]

In contrast to cumbersome benchtop spectrometers, integrated on-chip spectrometers are well-suited for portable applications in health monitoring and environmental sensing. In this paper, we have developed an on-chip spectrometer with a programmable silicon photonic filter by simply using parallel cascaded micro-ring resonators (MRs). By altering the transmission spectrum of the filter, multiple and diverse sampling of the input spectrum is achieved. Then, combined with an artificial neural network (ANN) model, the incident spectrum is reconstructed from the sampled signals. Each MR is coupled to adjacent ones, and the phase shifts within each MR can be independently tuned. Through dynamic programming of the phases of these MRs, sampling functions featuring diverse characteristics are obtained based on a single programmable filter with an adjustable number of sampling channels. This eliminates the need for a filter array, significantly reducing the area of the on-chip reconstructive spectrometer. The simulation results demonstrate that the proposed design can achieve the reconstruction of continuous and sparse spectra within the wavelength range of 1450 nm to 1650 nm, with a tunable resolution ranging from 2 nm to 0.2 nm, depending on the number of sampling states employed. This benefit arises from the programmable nature of the device. The device holds tremendous potential for applications in wearable optical sensing, portable spectrometry, and other related scenarios. [ABSTRACT FROM AUTHOR]

Carbon monoxide (CO) gas sensors are widely used, especially for environmental monitoring in confined spaces such as the landscape of mining cave ruins in mining parks, which is essential for ensuring the health and safety of tourists and staff. In this paper, a flexible CO gas sensor based on polyimide, interdigital electrodes, and reduced graphene oxide (RGO)/cuprous chloride (CuCl) composite film is designed and manufactured for reliable room temperature detection of high-concentration CO gas. The structure size of RGO/CuCl gas-sensitive film is 5 × 5 mm. The RGO with a 62.65% C-C bond is prepared by the thermal reduction method. The test results show that the sensor has a high response in the range of 400–2000 ppm CO gas concentration, and the maximum response is 1.56. The linear correlation coefficient of the sensor is 0.981, which indicates that the sensor has good output response characteristics. The response time of the sensor for 400 ppm CO gas is 332 s, which indicates that the sensor has a fast response rate. Furthermore, compared with other gases, the sensor shows higher gas selectivity for CO gas. This sensor has the characteristics of small size and easy attachment; therefore, it can be installed on the shoulder or helmet of tourists' safety suits, providing personalized real-time warning prompts for tourists' physical health status. [ABSTRACT FROM AUTHOR]

The refractive index of a liquid serves as a fundamental parameter reflecting its composition, thereby enabling the determination of component concentrations in various fields such as chemical research, the food industry, and environmental monitoring. Traditional methods for refractive index (RI) measurement rely on light deflection angles at interfaces between the liquid and a material with a known refractive index. In this paper, the authors present a new differential refractometer for the highly sensitive measurement of RI differences between two liquid samples. Using a configuration with two cells equipped with flat parallel plates as measuring elements, the instrument facilitates accurate analysis. Namely, the sensor signals from both the solution and the solvent cuvette are generated simultaneously with one laser pulse, reducing the possible fluctuations in the laser radiation intensity. Our evaluation shows the high sensitivity of RI measurements < 7 × 10 − 6 ), so this differential refractometer can be proposed not only as a high-sensitivity sensing tool that can be used for mobile detection of nanoparticles in solution samples but also to determine the level of environmental nano-pollution using water (including rain, snow) samples from various natural as well as industrial sources, thus helping to solve some important environmental problems. [ABSTRACT FROM AUTHOR]

This paper presents some preliminary results of a research project started in May 2002 on the German transportation planning system, commissioned by the Federal Environmental Agency. The main aim of this project is to find ways to integrate formal strategic environmental assessment (SEA) and to identify technical and methodological opportunities for implementing the SEA Directive. It reveals that whilst environmental aspects are already taken into account to a reasonable extent, certain amendments are needed in order to fully meet the requirements of the SEA Directive. Furthermore, the paper calls for a new type of ‘Federal Transport Development Plan’. This should focus on an assessment of strategic alternatives as the basis for the integration of individual projects. Copyright © 2004 John Wiley & Sons, Ltd and ERP Environment. [ABSTRACT FROM AUTHOR]

With the problem of nuclear leakage being concerned by more and more industries, the research of coastal ecological environment monitoring has become more and more important. Therefore, it is necessary to study the current unsystematic coastal ecological environment monitoring and protection system. Aiming at the accuracy of feature fusion and representation of single short environment information, this paper compares the classification effects of the three fusion methods on four classifiers: logistic regression, SVM, random forest, and naive Bayes, to verify the effectiveness of LDA and DS model fusion and determine the consistency vector representation method of short environment information data. This paper collects and analyzes the coastal data in recent years using multisource information fusion decision-making. In this paper, DS (Dempster Shafer) evidence algorithm is used to collect the data of coastal salinization degree and air relative humidity, and then, the DS feature matching model is introduced to fuse the whole index system. The method in the article completes the standardized and standardized processing of monitoring data digital conversion, quality control, and data classification, forms interrelated four-dimensional spatiotemporal data, and establishes a distributed, object-oriented, Internet-oriented dynamic management real-time and delayed database. Finally, this paper carries out tree decision processing on the coastal ecological environment monitoring data of multisource information fusion, to achieve the extraction and intuitive analysis of special data, and puts forward targeted protection strategies for the coastal ecological environment according to the data results of the DS algorithm. The research shows that the number of indicators in multisource information fusion in this paper is 16, a total of 3251 data, 2866 meaningful information, and 1869 data including ecological cycle. These data are the results of the collection of multi-information data. Based on the multilevel nature of the existing marine environment three-dimensional monitoring system, the study established a comprehensive resource-guaranteed framework and divided it into four levels according to the level of the marine monitoring system: country, sea area, locality, and data access point. In specific analysis, the guarantee resources involved in each level are introduced. On the basis of in-depth analysis of the requirements of the marine environment three-dimensional monitoring system operation guarantee and the guarantee resource structure, the marine environment three-dimensional monitoring operation comprehensive guarantee system is described from the internal structure and the external connection. The DS algorithm extracts the status information resources of various marine environment three-dimensional monitoring systems, through the interaction of various subsystems, realizes the operation and maintenance of the monitoring system, and provides various technical supports such as system evaluation and failure analysis. After multisource information fusion and decision-making, it is obtained that the index equilibrium module in the DS algorithm in this paper is 0.52, the sensitivity is 0.68, and the independence is 0.42. Among them, the range of sensitivity is the largest. In the simulation results, the eco-economic coefficient can be increased from 12% to 36%. Therefore, using the method of multisource information fusion for quantitative index analysis can provide data support for coastal ecological environment detection, to establish a more perfect protection system. [ABSTRACT FROM AUTHOR]

Detection of trace magnesium ion (Mg2+) is important in environmental and health monitoring. In this work, high‐performance Mg2+ sensor was developed from label‐free carbon dots. The novel carbon dots were prepared for the first time from natural rubber through the two‐step pyrolysis and hydrothermal method. They showed selectivity towards Mg2+ against a range of metal ions in solution and yielded a detection limit of 4.3 μM. The Mg2+ detection in real water samples showed an excellent recovery of 97.21 to 99.21 %. A paper‐based sensor was also fabricated as a portable device, showing a detection limit of 8.0 μM. UV‐vis and X‐ray absorption studies provided some evidences of the unique dynamic interactions between carbon dots and Mg2+ ions. In summary, the novelty of this work lies upon (i) developing a methodology of converting abundant, low‐cost natural rubber into carbon dots and (ii) fabricating sensitive and selective sensors from the label‐free carbon dots for the detection of Mg2+ both in solution and as paper‐based devices. The findings in this work will therefore have potential for environmental and health monitoring applications. [ABSTRACT FROM AUTHOR]

The European Ground Motion Service (EGMS) is an operational data service that uses interferometric analysis of Sentinel-1 radar images to provide high-resolution monitoring of ground deformation. The service is the first continental ground motion public dataset that is open and available for various applications and studies. This paper presents the validation workflow of the EGMS product in terms of spatial coverage and density of measurement points. The study employs open land cover data to evaluate the density per class and proposes statistical parameters associated with the data processing and timeseries estimation to ensure consistency across different selected sites.The validation process involves twelve selected sites across Europe, representing the four processing entities, covering both rural and urban areas. The paper highlights the importance of ensuring completeness and consistency of the EGMS product for its effective use, along with pointwise quality measures critical in assessing the quality of the EGMS point timeseries results. Additionally, the paper discusses the challenges faced during the pre-processing of the data from EGMS and presents a custom algorithm designed to eliminate biases in the data. As an open and freely available dataset, the EGMS will provide valuable resources for further research and applications, enabling better understanding and management of geohazards, environmental monitoring, and urban planning. [ABSTRACT FROM AUTHOR]

In many countries, water quality monitoring is limited due to the high cost of logistics and professional equipment such as multiparametric probes. However, low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks, providing valuable water quality information to the public. To facilitate the widespread adoption of these sensors, it is crucial to identify which sensors can accurately measure key water quality parameters, their manufacturers, and their reliability in different environments. Although there is an increasing body of work utilizing low-cost water quality sensors, many questions remain unanswered. To address this issue, a systematic literature review was conducted to determine which low-cost sensors are being used for remote water quality monitoring. The results show that there are three primary vendors for the sensors used in the selected papers. Most sensors range in price from US$6.9 to US$169.00 but can cost up to US$500.00. While many papers suggest that low-cost sensors are suitable for water quality monitoring, few compare low-cost sensors to reference devices. Therefore, further research is necessary to determine the reliability and accuracy of low-cost sensors compared to professional devices. [ABSTRACT FROM AUTHOR]

This study presents a novel method for high-altitude terrain following photogrammetric mapping using Unmanned Aerial Vehicles (UAVs) that adapt their height to the terrain's morphology. Traditional grid missions in UAV mapping surveys use nadir photographs to cover an area of interest which is taken from a fixed height from the datum.Many times this leads to difficulties in flight mission and processing when hilly/undulated terrain is under consideration. The proposed solution addresses this by adjusting the UAV's flying height based on real-time terrain information gathered by a range detection sensor. This paper tests the proposed method in a hilly area in the Himalayas, capturing high-resolution images and processing them using UAV-based photogrammetry software. The pre-acquired terrain information ensures safe separation from obstacles and enables trajectory generation at lower altitudes when planning initial flights. The experimental results validate the effectiveness of the proposed method and highlight its potential for generating high-resolution photogrammetric products applicable to various UAV mapping tasks and monitoring scenarios. Overall, this research contributes to advancing the field of UAV mapping by providing a practical and effective solution for UAV based surveys in hilly and undulated terrains. The findings of this study have significant implications for various applications, including land surveying, environmental monitoring, landslide mapping and other scenarios that require detailed and accurate data acquisition from UAVs. [ABSTRACT FROM AUTHOR]

In Canada, there is almost 30 years of experience in developing tiered and triggered adaptive monitoring programs focused on looking at whether environmental concerns remain when pulp and paper mills, or metal mines, are in compliance with their discharge limits. These environmental effects monitoring programs were based on nationally standardized designs. Many of the programs have been developed through multi-stakeholder working groups, and the evolution of the program faced repeated frictions and differing opinions on how to design environmental monitoring programs. This paper describes key guidance to work through the initial steps in program design, and includes scientific advice based on lessons learned from the development of the Canadian aquatic environmental effects monitoring program. [ABSTRACT FROM AUTHOR]

Information on mangrove stand development is rare because long-term monitoring data is often lacking. Such information is important in order to plan management measures effectively. Novel approaches are required to bridge this gap of knowledge based on existing data sets. This study uses a unique combination of field data analyses with simulation experiments in order to demonstrate how information on mangrove dynamics can be extracted if data are sparse. The paper provides a baseline characterization of stand development in a monospecific pioneer mangrove stand of Kandelia obovata. Point pattern analyses revealed that in the young stage, self-thinning has started but has not yet lead to a regularity of spatial tree distribution in the entire stand, and trees located in smaller clumps hinder each other in growth but do not lead to a significant size class differentiation. However, after ca. 2 decades the self-thinning and the size class differentiation start to become more visible. A mutual inhibition of growth was observed within 2 m circular distance (r) in the young stage and within 3 m distance after two decades of stand development as confirmed by the negative values of mark correlation function. As a stand grows older the spatial pattern of individuals become more regular from a clustered pattern. In order to understand and predict the future stand development, simulation experiments were carried out by means of the individual-based model KiWi. [ABSTRACT FROM AUTHOR]

One of the relevant processes driven by political discussion under the United Framework Convention on Climate Change is the monitoring of Essential Climate Variables. Land Cover is one of those variables and efforts are therefore to be made to develop land cover observation approaches which meet the climate modelling community expectations. This paper aims at contributing to this necessity. First, consultation mechanisms were established with the climate modelling community to identify its specific requirements in terms of satellite-based global land cover products. This assessment highlighted specific needs in terms of land cover characterization and products accuracy, stability and consistency that are currently not met. Based on this outcome, the paper calls into question the current land cover representation and for revisiting global land cover mapping approaches. Increasing the flexibility of current classification systems and making the mapping techniques less sensitive to the period of observation are proposed as two key aspects to enhance the usability of global land cover dataset. [ABSTRACT FROM AUTHOR]

Anionic surfactants are a class of compounds with important applications in many industrial processes, in agriculture, research and also in household and personal care. They are largely used but they are released in the environment where they act as pollutants. The analysis of surfactant concentration is required in the surfactants industry, in the quality control and also in the environment monitoring, especially in surface water. Potentiometric titration of anionic surfactants is an attractive alternative method of anionic surfactants determination to the titration with chemical indicators or to the extractive spectrophotometric based on methylene blue complex. [ABSTRACT FROM AUTHOR]

Environmental monitoring activities in Chile are relatively new and have traditionally relied on physicochemical measurements. The pulp mill industry in Chile is highly competitive in the global market and several new large mills have recently opened. Early studies on fish in the receiving environments revealed lower species richness and an increase in the abundance of introduced species relative to native ones near pulp mill discharges. Even though changes were observed, their relationship with the discharges was unclear. Several difficulties related to small body sizes and the unavailability of basic biological data for native Chilean fish species were found during initial field studies. One of the main challenges is the standardization of monitoring methods (including fish species selection, sampling sizes, indicators, reference sites, etc.) and consensus about the responses that should be considered in a river monitoring program in the Chilean context. This paper summarizes major findings from a series of studies looking at impacts on fish at different levels of biological organization and the current approach used in Chile for monitoring impacts of pulp mill effluents on wild fish populations. [ABSTRACT FROM AUTHOR]

The infield measurement of nutrients, heavy metals, and other contaminants in water is still a needed tool in environmental sciences. The Lab-on-a-chip approach can develop deployable instruments that use the standardized analytical assay in a miniaturized manner in the field. This paper presents a Lab-on-a-chip platform for colorimetric measurements that can be deployed for nutrient monitoring in open water (oceans, rivers, lakes, etc.). Nitrite was selected as an analyte. Change to other analytes is possible by changing the reagents and the detection wavelength. In this paper, the principle of the sensor, technical realization, setup of the sensor, and test deployment are described. The sensor prototype was deployed at the Jade Bay (German Bight) for 9 h, measuring the nitrite value every 20 min. Reference samples were taken and processed in the lab. The work presented here shows that an infield measurement using a colorimetric assay is possible by applying Lab-on-a-chip principles. [ABSTRACT FROM AUTHOR]

The promising field of organic electronics has ushered in a new era of biosensing technology, thus offering a promising frontier for applications in both medical diagnostics and environmental monitoring. This review paper provides a comprehensive overview of organic electronics' remarkable progress and potential in biosensing applications. It explores the multifaceted aspects of organic materials and devices, thereby highlighting their unique advantages, such as flexibility, biocompatibility, and low-cost fabrication. The paper delves into the diverse range of biosensors enabled by organic electronics, including electrochemical, optical, piezoelectric, and thermal sensors, thus showcasing their versatility in detecting biomolecules, pathogens, and environmental pollutants. Furthermore, integrating organic biosensors into wearable devices and the Internet of Things (IoT) ecosystem is discussed, wherein they offer real-time, remote, and personalized monitoring solutions. The review also addresses the current challenges and future prospects of organic biosensing, thus emphasizing the potential for breakthroughs in personalized medicine, environmental sustainability, and the advancement of human health and well-being. [ABSTRACT FROM AUTHOR]

Within the scope of the ongoing efforts to fight climate change, the application of multi-robot systems to environmental mapping and monitoring missions is a prominent approach aimed at increasing exploration efficiency. However, the application of such systems to gas sensing missions has yet to be extensively explored and presents some unique challenges, mainly due to the hard-to-sense and expensive-to-model nature of gas dispersion. For this paper, we explored the application of a multi-robot system composed of rotary-winged nano aerial vehicles to a gas sensing mission. We qualitatively and quantitatively analyzed the interference between different robots and the effect on their sensing performance. We then assessed this effect, by deploying several algorithms for 3D gas sensing with increasing levels of coordination in a state-of-the-art wind tunnel facility. The results show that multi-robot gas sensing missions can be robust against documented interference and degradation in their sensing performance. We additionally highlight the competitiveness of multi-robot strategies in gas source location performance with tight mission time constraints. [ABSTRACT FROM AUTHOR]

LoRaWAN is a communication protocol designed especially for Internet of Things (IoT) applications that offers benefits such as long-distance connection and low power consumption. Due to the characteristics of LoRaWAN, this technology has gained great popularity in various IoT applications, such as environmental monitoring, smart agriculture, and applications in the areas of health and mobility, among others. Given this situation, the objective of this work is to provide an in-depth overview of LoRaWAN technology in terms of its applications, as well as the devices that have been used for the development of such applications. Additionally, this work reviews what other areas of LoRaWAN have been covered in different scientific articles, i.e., performance improvement and security. Among the main results of this study though analyzing previous works, we can say that most of them have been developed in the area of environmental monitoring and have used low-cost devices such as Arduinos, Raspberry Pis, and relatively low-cost commercial products such as those of the Semtech and STMicroelectronics brands. The analysis of the present work shows objectively and formally that LoRaWAN technology can be applied in various applications and that there are many studies that try to optimize its performance and security. This paper seeks to identify and describe the most relevant applications of LoRaWAN in different sectors, such as agriculture, health, and environmental monitoring, among others, and the challenges and solutions found in each area. This literature review will provide a valuable reference to understand the potential and opportunities offered by LoRaWAN technology. [ABSTRACT FROM AUTHOR]

As rainfall exhibits high spatiotemporal variability, accurate and real-time rainfall monitoring is vitally important in fields such as hydrometeorological research, agriculture and disaster prevention and control. Nevertheless, the current dedicated rain sensors cannot fulfill the requirement for comprehensive precipitation observation, owing to their respective limitations. Within the last two decades, the utilization of commercial microwave links (CMLs) for rainfall estimation, as an opportunistic sensing method, has generated considerable attention. Relying on CML networks deployed and maintained by mobile network operators can provide near-surface precipitation information over large areas at a low cost. Although scholars have developed several algorithms for obtaining rainfall estimates from CML data, the rainfall estimation technique based on CMLs remains challenging due to the complex effect in the microwave radiation transmission process. In this paper, we provide a comprehensive review of the technical principles, developments and workflows for this technology, alongside its application in environmental monitoring and hydrological modeling. Furthermore, this paper outlines the current challenges and future research directions, which will hopefully draw the attention of researchers and provide valuable guidance. [ABSTRACT FROM AUTHOR]

Simple Summary: The behavior and pose of ducks during the breeding process are directly related to their welfare and health status. The real-time and accurate monitoring of their different behaviors and poses is significant. In this study, we proposed a duck pose estimation method using computer vision techniques, achieving accurate detection of eight keypoints, including the head, beak, chest, left leg, right leg, left foot, right foot, and tail, under six behaviors: standing, drinking, preening, spreading wings, walking, and resting, and forming correct body expressions. Furthermore, the model's generalization capability was tested with different lighting intensity levels. Ducks of various ages, breeds, and farming methods were used to validate the model's comprehensive pose estimation capability. The model's comprehensive detection capability was compared with mainstream pose estimation methods, proving its superiority. The results of this study are considered instrumental in smart poultry farming. In breeding ducks, obtaining the pose information is vital for perceiving their physiological health, ensuring welfare in breeding, and monitoring environmental comfort. This paper proposes a pose estimation method by combining HRNet and CBAM to achieve automatic and accurate detection of duck's multi-poses. Through comparison, HRNet-32 is identified as the optimal option for duck pose estimation. Based on this, multiple CBAM modules are densely embedded into the HRNet-32 network to obtain the pose estimation model based on HRNet-32-CBAM, realizing accurate detection and correlation of eight keypoints across six different behaviors. Furthermore, the model's generalization ability is tested under different illumination conditions, and the model's comprehensive detection abilities are evaluated on Cherry Valley ducklings of 12 and 24 days of age. Moreover, this model is compared with mainstream pose estimation methods to reveal its advantages and disadvantages, and its real-time performance is tested using images of 256 × 256, 512 × 512, and 728 × 728 pixel sizes. The experimental results indicate that for the duck pose estimation dataset, the proposed method achieves an average precision (AP) of 0.943, which has a strong generalization ability and can achieve real-time estimation of the duck's multi-poses under different ages, breeds, and farming modes. This study can provide a technical reference and a basis for the intelligent farming of poultry animals. [ABSTRACT FROM AUTHOR]