Search

This manuscript offers a concise overview of paper microfluidics, emphasizing its sustainable sensing applications in healthcare, environmental monitoring, and food safety. Researchers have developed innovative sensing platforms for detecting pathogens, pollutants, and contaminants by leveraging the paper's unique properties, such as biodegradability and affordability. These portable, low-cost sensors facilitate rapid diagnostics and on-site analysis, making them invaluable tools for resource-limited settings. This review discusses the fabrication techniques, principles, and applications of paper microfluidics, showcasing its potential to address pressing challenges and enhance human health and environmental sustainability. [ABSTRACT FROM AUTHOR]

In this paper, the behavior of the Bi-Material Cantilever (B-MaC) response deflection upon fluidic loading was experimentally studied and modeled for bilayer strips. A B-MaC consists of a strip of paper adhered to a strip of tape. When fluid is introduced, the paper expands while the tape does not, which causes the structure to bend due to strain mismatch, similar to the thermal loading of bi-metal thermostats. The main novelty of the paper-based bilayer cantilevers is the mechanical properties of two different types of material layers, a top layer of sensing paper and a bottom layer of actuating tape, to create a structure that can respond to moisture changes. When the sensing layer absorbs moisture, it causes the bilayer cantilever to bend or curl due to the differential swelling between the two layers. The portion of the paper strip that gets wet forms an arc, and as the fluid advances and fully wets the B-MaC, the entire B-MaC assumes the shape of the initial arc. This study showed that paper with higher hygroscopic expansion forms an arc with a smaller radius of curvature, whereas thicker tape with a higher Young's modulus forms an arc with a larger radius of curvature. The results showed that the theoretical modeling could accurately predict the behavior of the bilayer strips. The significance of paper-based bilayer cantilevers lies in their potential applications in various fields, such as biomedicine, and environmental monitoring. In summary, the novelty and significance of paper-based bilayer cantilevers lie in their unique combination of sensing and actuating capabilities using a low-cost and environmentally friendly material. [ABSTRACT FROM AUTHOR]

Environmental toxic pollutants and pathogens that enter the ecosystem are major global issues. Detection of these toxic chemicals/pollutants and the diagnosis of a disease is a first step in efficiently controlling their contamination and spread, respectively. Various analytical techniques are available to detect and determine toxic chemicals/pathogens, including liquid chromatography, HPLC, mass spectroscopy, and enzyme-linked immunosorbent assays. However, these sensing strategies have some drawbacks such as tedious sample pretreatment and preparation, the requirement for skilled technicians, and dependence on large laboratory-based instruments. Alternatively, biosensors, especially paper-based sensors, could be used extensively and are a cost-effective alternative to conventional laboratory testing. They can improve accessibility to testing to identify chemicals and pollutants, especially in developing countries. Due to its low cost, abundance, easy disposal (by incineration, for example) and biocompatible nature, paper is considered a versatile material for the development of environmentally friendly electrochemical/optical (bio) sensor devices. This review presents an overview of sensing platforms constructed from paper, pointing out the main merits and demerits of paper-based sensing systems, their fabrication techniques, and the different optical/electrochemical detection techniques that they exploit. [ABSTRACT FROM AUTHOR]

Since the monumental work conducted by Whitesides et al. in 2007, research and development of paper-based microfluidics has been widely carried out, with its applications ranging from chemical and biological detection and analysis, to environmental monitoring and food-safety inspection. Paper-based microfluidics possesses several competitive advantages over other substrate materials, such as being simple, inexpensive, power-free for fluid transport, lightweight, biodegradable, biocompatible, good for colorimetric tests, flammable for easy disposal of used paper-based diagnostic devices by incineration, and being chemically modifiable. Myriad methods have been demonstrated to fabricate paper-based microfluidics, such as solid wax printing, cutting, photolithography, microembossing, etc. In this study, fabrication of paper-based microfluidics was demonstrated by spray on the printed paper. Different from the normally used filter papers, printing paper, which is much more accessible and cheaper, was utilized as the substrate material. The toner was intended to serve as the mask and the patterned hydrophobic barrier was formed after spray and heating. The processing parameters such as toner coverage on the printing paper, properties of the hydrophobic spray, surface properties of the paper, and curing temperature and time were systematically investigated. It was found that, after repetitive printing four times, the toner was able to prevent the hydrophobic spray (the mixture of PDMS and ethyl acetate) from wicking through the printing paper. The overall processing time for fabrication of paper-based microfluidic chips was less than 10 min and the technique is potentially scalable. Glucose detection was conducted using the microfluidic paper-based analytical devices (µPADs) as fabricated and a linear relationship was obtained between 1 and 10 mM. [ABSTRACT FROM AUTHOR]

Paper-based sensors are a new alternative technology for fabricating simple, low-cost, portable and disposable analytical devices for many application areas including clinical diagnosis, food quality control and environmental monitoring. The unique properties of paper which allow passive liquid transport and compatibility with chemicals/biochemicals are the main advantages of using paper as a sensing platform. Depending on the main goal to be achieved in paper-based sensors, the fabrication methods and the analysis techniques can be tuned to fulfill the needs of the end-user. Current paper-based sensors are focused on microfluidic delivery of solution to the detection site whereas more advanced designs involve complex 3-D geometries based on the same microfluidic principles. Although paper-based sensors are very promising, they still suffer from certain limitations such as accuracy and sensitivity. However, it is anticipated that in the future, with advances in fabrication and analytical techniques, that there will be more new and innovative developments in paper-based sensors. These sensors could better meet the current objectives of a viable low-cost and portable device in addition to offering high sensitivity and selectivity, and multiple analyte discrimination. This paper is a review of recent advances in paper-based sensors and covers the following topics: existing fabrication techniques, analytical methods and application areas. Finally, the present challenges and future outlooks are discussed. [ABSTRACT FROM AUTHOR]

Developing an inexpensive, sensitive, and point-of-use biosensor for pesticide detection is becoming an important area in sensing. Such sensors can be used in food packaging, agricultural fields, and environmental monitoring of pesticides. The present investigation has developed a zinc oxide (ZnO)-based biosensor on porous, flexible substrates such as carbon paper and carbon cloth to detect organophosphates such as paraoxon (OP). Here, the influence of morphology and underlying substrate on biosensor performance was studied. The biosensors were fabricated by immobilizing the acetylcholinesterase (AChE) enzyme on ZnO, which is directly grown on the flexible substrates. The ZnO biosensors fabricated on the carbon cloth demonstrated good performance with the detection limit of OP in the range of 0.5 nM–5 µM, higher sensitivity, and greater stability. [ABSTRACT FROM AUTHOR]

In today's world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become "smart" and "cognitive" and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants' data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy. [ABSTRACT FROM AUTHOR]

Paper-based analytical devices (PADs), including lateral flow assays (LFAs), dipstick assays and microfluidic PADs (μPADs), have a great impact on the healthcare realm and environmental monitoring. This is especially evident in developing countries because PADs-based point-of-care testing (POCT) enables to rapidly determine various (bio)chemical analytes in a miniaturized, cost-effective and user-friendly manner. Low sensitivity and poor specificity are the main bottlenecks associated with PADs, which limit the entry of PADs into the real-life applications. The application of nanomaterials in PADs is showing great improvement in their detection performance in terms of sensitivity, selectivity and accuracy since the nanomaterials have unique physicochemical properties. In this review, the research progress on the nanomaterial-based PADs is summarized by highlighting representative recent publications. We mainly focus on the detection principles, the sensing mechanisms of how they work and applications in disease diagnosis, environmental monitoring and food safety management. In addition, the limitations and challenges associated with the development of nanomaterial-based PADs are discussed, and further directions in this research field are proposed. [ABSTRACT FROM AUTHOR]

Lab-on-paper technologies, also known as paper-based analytical devices (PADs), have received increasing attention in the last years, and nowadays, their use has spread to virtually every application area, i.e., medical diagnostic, food safety, environmental monitoring, etc. Advantages inherent to on-field detection, which include avoiding sampling, sample preparation and conventional instrumentation in central labs, are undoubtedly driving many developments in this area. Heavy metals represent an important group of environmental pollutants that require strict controls due to the threat they pose to ecosystems and human health. In this overview, the development of PADs for Hg monitoring, which is considered the most toxic metal in the environment, is addressed. The main emphasis is placed on recognition elements (i.e., organic chromophores/fluorophores, plasmonic nanoparticles, inorganic quantum dots, carbon quantum dots, metal nanoclusters, etc.) employed to provide suitable selectivity and sensitivity. The performance of both microfluidic paper-based analytical devices and paper-based sensors using signal readout by colorimetry and luminescence will be discussed. [ABSTRACT FROM AUTHOR]

The fast detection of trace amounts of hazardous contaminations can prevent serious damage to the environment. Paper-based sensors offer a new perspective on the world of analytical methods, overcoming previous limitations by fabricating a simple device with valuable benefits such as flexibility, biocompatibility, disposability, biodegradability, easy operation, large surface-to-volume ratio, and cost-effectiveness. Depending on the performance type, the device can be used to analyze the analyte in the liquid or vapor phase. For liquid samples, various structures (including a dipstick, as well as microfluidic and lateral flow) have been constructed. Paper-based 3D sensors are prepared by gluing and folding different layers of a piece of paper, being more user-friendly, due to the combination of several preparation methods, the integration of different sensor elements, and the connection between two methods of detection in a small set. Paper sensors can be used in chromatographic, electrochemical, and colorimetric processes, depending on the type of transducer. Additionally, in recent years, the applicability of these sensors has been investigated in various applications, such as food and water quality, environmental monitoring, disease diagnosis, and medical sciences. Here, we review the development (from 2010 to 2021) of paper methods in the field of the detection and determination of toxic substances. [ABSTRACT FROM AUTHOR]

This review paper provides an in-depth analysis of recent advancements in integrating two-dimensional (2D) materials with cellulose to enhance biosensing technology. The incorporation of 2D materials such as graphene and transition metal dichalcogenides, along with nanocellulose, improves the sensitivity, stability, and flexibility of biosensors. Practical applications of these advanced biosensors are explored in fields like medical diagnostics and environmental monitoring. This innovative approach is driving research opportunities and expanding the possibilities for diverse applications in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

Generalized target detection algorithms perform well for large- and medium-sized targets but struggle with small ones. However, with the growing importance of aerial images in urban transportation and environmental monitoring, detecting small targets in such imagery has been a promising research hotspot. The challenge in small object detection lies in the limited pixel proportion and the complexity of feature extraction. Moreover, current mainstream detection algorithms tend to be overly complex, leading to structural redundancy for small objects. To cope with these challenges, this paper recommends the PCSG model based on yolov5, which optimizes both the detection head and backbone networks. (1) An enhanced detection header is introduced, featuring a new structure that enhances the feature pyramid network and the path aggregation network. This enhancement bolsters the model's shallow feature reuse capability and introduces a dedicated detection layer for smaller objects. Additionally, redundant structures in the network are pruned, and the lightweight and versatile upsampling operator CARAFE is used to optimize the upsampling algorithm. (2) The paper proposes the module named SPD-Conv to replace the strided convolution operation and pooling structures in yolov5, thereby enhancing the backbone's feature extraction capability. Furthermore, Ghost convolution is utilized to optimize the parameter count, ensuring that the backbone meets the real-time needs of aerial image detection. The experimental results from the RSOD dataset show that the PCSG model exhibits superior detection performance. The value of mAP increases from 97.1% to 97.8%, while the number of model parameters decreases by 22.3%, from 1,761,871 to 1,368,823. These findings unequivocally highlight the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, display, and analysis of sensor data on a single platform. Recently, Artificial Intelligence (AI) has become very popular and widely used in various applications including IoT. To support this growth, the integration of AI into SEMAR is essential to enhance its capabilities after identifying the current trends of applicable AI technologies in IoT applications. In this paper, we first provide a comprehensive review of IoT applications using AI techniques in the literature. They cover predictive analytics, image classification, object detection, text spotting, auditory perception, Natural Language Processing (NLP), and collaborative AI. Next, we identify the characteristics of each technique by considering the key parameters, such as software requirements, input/output (I/O) data types, processing methods, and computations. Third, we design the integration of AI techniques into SEMAR based on the findings. Finally, we discuss use cases of SEMAR for IoT applications with AI techniques. The implementation of the proposed design in SEMAR and its use to IoT applications will be in future works. [ABSTRACT FROM AUTHOR]

In the last decades, the increase in global industrialization and the consequent technological progress have damaged the quality of the environment. As a consequence, the high levels of hazardous compounds such as metals and gases released in the atmosphere and water, have raised several concerns about the health of living organisms. Today, many analytical techniques are available with the aim to detect pollutant chemical species. However, a lot of them are not affordable due to the expensive instrumentations, time-consuming processes and high reagents volumes. Last but not least, their use is exclusive to trained operators. Contrarily, colorimetric sensing devices, including paper-based devices, are easy to use, providing results in a short time, without particular specializations to interpret the results. In addition, the colorimetric response is suitable for fast detection, especially in resource-limited environments or underdeveloped countries. Among different chemical species, transition and heavy metals such as iron Fe(II) and copper Cu(II) as well as volatile compounds, such as ammonia (NH3) and acetaldehyde (C2H4O) are widespread mainly in industrialized geographical areas. In this work, we developed a colorimetric paper-based analytical device (PAD) to detect different contaminants, including Fe2+ and Cu2+ ions in water, and NH3 and C2H4O in air at low concentrations. This study is a "proof of concept" of a new paper sensor in which the intensity of the colorimetric response is proportional to the concentration of a detected pollutant species. The sensor model could be further implemented in other technologies, such as drones, individual protection devices or wearable apparatus to monitor the exposure to toxic species in both indoor and outdoor environments. [ABSTRACT FROM AUTHOR]

Microfluidic paper-based analytical devices (µPADs) have become promising tools offering various analytical applications for chemical and biological assays at the point-of-care (POC). Compared to traditional microfluidic devices, µPADs offer notable advantages; they are cost-effective, easily fabricated, disposable, and portable. Because of our better understanding and advanced engineering of µPADs, multistep assays, high detection sensitivity, and rapid result readout have become possible, and recently developed µPADs have gained extensive interest in parallel analyses to detect biomarkers of interest. In this review, we focus on recent developments in order to achieve µPADs with high-throughput capability. We discuss existing fabrication techniques and designs, and we introduce and discuss current detection methods and their applications to multiplexed detection assays in relation to clinical diagnosis, drug analysis and screening, environmental monitoring, and food and beverage quality control. A summary with future perspectives for µPADs is also presented. [ABSTRACT FROM AUTHOR]

Screen-printing technology has revolutionized many fields, including that of electrochemical biosensing. Due to their current relevance, this review, unlike other papers, discusses the relevant aspects of electrochemical biosensors manufactured using this technology in connection to both paper substrates and wearable formats. The main trends, advances, and opportunities provided by these types of devices, with particular attention to the environmental and biomedical fields, are addressed along with illustrative fundamentals and applications of selected representative approaches from the recent literature. The main challenges and future directions to tackle in this research area are also pointed out. [ABSTRACT FROM AUTHOR]

Tree species classification based on multi-source remote sensing data is essential for ecological evaluation, environmental monitoring, and forest management. The optimization of classification features and the performance of classification methods are crucial to tree species classification. This paper proposes Angle-weighted Standard Deviation Elliptic Cross-merge Rate (ASDER) as a separability metric for feature optimization. ASDER uses mutual information to represent the separability metric and avoids the difficulty of differentiation caused by multiple ellipse centers and coordinate origins forming straight lines by angle weighting. In classification method, Multi-head Self-attention Long Short-Term Memory—Full Convolution Network (MALSTM-FCN) is constructed in this paper. MALSTM-FCN enhances the global correlation in time series and improves classification accuracy through a multi-head self-attention mechanism. This paper takes Beijing Olympic Forest Park (after this, referred to as Aosen) as the research area, constructs a tree species classification dataset based on an actual ground survey, and obtains a classification accuracy of 95.20% using the above method. This paper demonstrates the effectiveness of ASDER and MALSTM-FCN by comparing temporal entropy and LSTM-FCN and shows that the method has some practicality for tree species classification. [ABSTRACT FROM AUTHOR]

With the growing integration of the Internet of Things in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of WSNs in a commercial multi-story building, specifically addressing the difficulty of dealing with the variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights into the potential to significantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system's performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. We provide an in-depth analysis for scrutinizing the disparities and commonalities between the datasets obtained from real-world deployments and simulation. The results obtained show the significance of accurate simulation models for reliable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. This research's findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental parameters. This includes insights into temperature, humidity, and light intensity, offering opportunities for enhanced comfort and efficiency in intelligent environments. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) swarms serve as a dynamic platform for diverse missions, including communication relays, search and rescue operations, and environmental monitoring. The success of these operations crucially depends on the resilience of their electrical support systems, especially in terms of battery management. This paper examines the reliability of electrical support for UAV swarms engaged in missions that require prioritization into high and low categories. The paper proposes a dynamic resource allocation strategy that permits the flexible reassignment of drones across different-priority tasks, ensuring continuous operation while optimizing resource use. By leveraging the Markov chain theory, an analytical model for the evaluation of the resilience of the battery management system under different operational scenarios was developed. The paper quantitatively assesses the impact of different operational strategies and battery management approaches on the overall system resilience and mission efficacy. This approach aims to ensure uninterrupted service delivery for critical tasks while optimizing the overall utilization of available electrical resources. Through modeling and analytical evaluations, the paper quantifies the impact of various parameters and operating strategies on overall system resilience and mission availability, considering the utilization strategies of batteries and their reliability and maintenance metrics. The developed models and strategies can inform the development of robust battery management protocols, resource allocation algorithms, and mission planning frameworks, ultimately enhancing the operational availability and effectiveness of UAV swarms in critical special missions. [ABSTRACT FROM AUTHOR]

Airborne particulate matter (PM) is of great concern in the modern-day atmosphere owing to its association with a variety of health impacts, such as respiratory and cardiovascular diseases. Of the various size fractions of PM, it is the finer fractions that are most harmful to health, in particular ultrafine particles (PM0.1; UFPs), with an aerodynamic diameter ≤ 100 nm. The smaller size fractions, of ≤2.5 µm (PM2.5; fine particles) and ≤0.1 µm (PM0.1; ultrafine particles), have been shown to have numerous linkages to negative health effects; however, their collection/sampling remains challenging. This review paper employed a comprehensive literature review methodology; 200 studies were evaluated based on the rigor of their methodologies, including the validity of experimental designs, data collection methods, and statistical analyses. Studies with robust methodologies were prioritised for inclusion. This review paper critically assesses the health risks associated with fine and ultrafine particles, highlighting vehicular emissions as the most significant source of particulate-related health effects. While coal combustion, diesel exhaust, household wood combustors' emissions, and Earth's crust dust also pose health risks, evidence suggests that exposure to particulates from vehicular emissions has the greatest impact on human health due to their widespread distribution and contribution to air pollution-related diseases. This article comprehensively examines current sampling technologies, specifically focusing on the collection and sampling of ultrafine particles (UFP) from ambient air to facilitate toxicological and physiochemical characterisation efforts. This article discusses diverse approaches to collect fine and ultrafine particulates, along with experimental endeavours to assess ultrafine particle concentrations across various microenvironments. Following meticulous evaluation of sampling techniques, high-volume air samplers such as the Chem Vol Model 2400 High Volume Cascade Impactor and low-volume samplers like the Personal Cascade Impactor Sampler (PCIS) emerge as effective methods. These techniques offer advantages in particle size fractionation, collection efficiency, and adaptability to different sampling environments, positioning them as valuable tools for precise characterisation of particulate matter in air quality research and environmental monitoring. [ABSTRACT FROM AUTHOR]

Community care encompasses inherent risks for both clients and healthcare providers. Maintaining a safe environment for the delivery of care services ensures that any risk of unintentional or intentional personal harm is minimised. The aim of this scoping review is to (a) provide an overview of existing knowledge by summarising the current literature and (b) identify gaps pertaining to understanding and managing environmental risk in community care settings. Guided by the Population/Concept/Context approach and PRISMA guidelines, this paper used two questions to answer how a score-based tool for assessing client suitability in community care is developed and how an environmental screening tool assists with reducing risk to community care workers. Literature searches of CINAHL, PubMed (Medline), Web of Science and PsychINFO databases were conducted between September 2023 and November 2023. We included full text articles published from 2018 to 2023. The following four broad areas were identified as key components in the structure of an environmental screening tool: environmental factors, health factors, socioeconomic factors and cultural factors. The results of this review provide valuable information which can be utilised by care organisations to develop and/or refine tools to ensure the safety and wellbeing of workers within the community care sector. [ABSTRACT FROM AUTHOR]

Fluid flow behaviour in paper is of increasing interest due to the advantages and expanding use of microfluidic paper-based analytical devices (known as μPADs). Applications are expanding from those which often have low sample fluid volumes, such as diagnostic testing, to those with an abundance of sample fluid, such as water quality testing. The rapid development of enhanced features in μPADs, along with a need for increased sensitivity and specificity in the embedded chemistry requires understanding the passively-driven fluid motion in paper to enable precise control and consistency of the devices. It is particularly important to understand the influence of parameters associated with larger fluid volumes and to quantify their impact. Here, we experimentally investigate the impacts of several properties during imbibition in paper, including geometry (larger width and length) and the surrounding conditions (humidity and temperature) using abundant fluid reservoirs. Fluid flow velocity in paper was found to vary with temperature and width, but not with length of the paper strip and humidity for the conditions we tested. We observed substantial post-wetting flow for paper strips in contact with a large fluid reservoir. [ABSTRACT FROM AUTHOR]

Storage conditions play a vital role in maintaining biomass quality as a suitable bioenergy feedstock. Research has shown that biomass undergoes significant changes under different storage conditions and that these may influence its suitability for various biorefining and bioenergy opportunities. This study explores the effects of different tarp covers on the properties of stored-comminuted forest harvest residue from the Great Lakes St. Lawrence Forest. Characteristics of the biomass were evaluated upon harvesting and after one year in storage. The physical state of the different tarps used for pile coverage was monitored onsite. Results indicated that tarp material considerably affects micro-climatic conditions inside piles, yielding variation in the characteristics of stored biomass over the storage period. While plastic based tarps were easier to work with and lasted longer than paper-based tarps, the paper-based tarps were more breathable and resulted in less degradation of biomass. However, the paper-based tarps did not maintain their structural integrity for the full duration of the storage period. Moisture content of original biomass (48.99%) increased to a maximum of 65.25% under plastic cover after 1 year of storage. This negatively influenced the net heating value of the biomass, causing it to decrease from 8.58 MJ/kg to 4.06 MJ/kg. Overall, the use of covers was not considered successful in preserving the original quality of biomass but may enhance its quality for other biorefinery opportunities. [ABSTRACT FROM AUTHOR]

Airflow sensor is a crucial component for monitoring environmental airflow conditions in many engineering fields, especially in the field of aerospace engineering. However, conventional airflow sensors have been suffering from issues such as complexity and bulk in structures, high cost in fabrication and maintenance, and low stability and durability. In this work, we developed a facile direct-writing method for fabricating a low-cost piezoresistive element aiming at high-performance airflow sensing, in which a commercial pen was utilized to drop solutions of single-walled carbon nanotubes onto tissue paper to form a piezoresistive sensing element. The encapsulated piezoresistive element was tested for electromechanical properties under two loading modes: one loading mode is the so-called pressure mode in which the piezoresistive element is pressed by a normal pressure, and another mode is the so-called bending mode in which the piezoresistive element is bended as a cantilever beam. Unlike many other developed airflow sensors among which the sensing elements are normally employed as cantilever beams for facing winds, we designed a fin structure to be incorporated with the piezoresistive element for airflow sensing; the main function of the fin is to face winds instead of the piezoresistive element, and subsequently transfer and enlarge the airflow pressure to the piezoresistive element for the normal pressure loading mode. With this design, the piezoresistive element can also be protected by avoiding experiencing large strains and direct contact with external airflows so that the stability and durability of the sensor can be maintained. Moreover, we experimentally found that the performance parameters of the airflow sensor could be effectively tuned by varying the size of the fin structure. When the fin sizes of the airflow sensors were 20 mm, 30 mm, and 40 mm, the detection limits and sensitivities of the fabricated airflow sensors were measured as 8.2 m/s, 6.2 m/s, 3.2 m/s, 0.0121 (m/s)−2, 0.01657 (m/s)−2, and 0.02264 (m/s)−2, respectively. Therefore, the design of the fin structure could pave an easy way for adjusting the sensor performance without changing the sensor itself toward different application scenarios. [ABSTRACT FROM AUTHOR]

Many studies in recent years have been devoted to estimating the per-pixel proportions of three broad classes of materials (e.g., photosynthetic vegetation, non-photosynthetic vegetation and bare soil) using data from multispectral sensors. Oftentimes, the estimated proportions are used to monitor environmental change in both urban and non-urban environments. Many of these papers use proportion estimation methods based on the linear mixture model. Very few of these papers assess the accuracy of their estimators. This paper shows how to produce confidence intervals (CIs) and joint confidence regions (JCRs) for the proportions associated with various linear mixture models. There are two main models, both of which assume that the coefficients in the model are non-negative. The first model assumes that the coefficients sum to 1. The second does not, but uses rescaling of the estimated coefficients to produce estimated proportions. Three variants of these two models are also analysed. JCRs are shown to be particularly informative, because they are typically better at localising the information than CIs are. The methodology is illustrated using examples from Landsat Thematic Mapper data at 1169 locations across Australia, each of which has associated field observations. There is also a discussion about the extent to which the methodology can be extended to hyperspectral data. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]