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As a crucial computer vision task, multi-objective semantic segmentation has attracted widespread attention and research in the field of remote sensing image analysis. This technology has important application value in fields such as land resource surveys, global change monitoring, urban planning, and environmental monitoring. However, multi-target semantic segmentation of remote sensing images faces challenges such as complex surface features, complex spectral features, and a wide spatial range, resulting in differences in spatial and spectral dimensions among target features. To fully exploit and utilize spectral feature information, focusing on the information contained in spatial and spectral dimensions of multi-spectral images, and integrating external information, this paper constructs the CD-MQANet network structure, where C represents the Channel Creator module and D represents the Dual-Path Encoder. The Channel Creator module (CCM) mainly includes two parts: a generator block and a spectral attention module. The generator block aims to generate spectral channels that can expand different ground target types, while the spectral attention module can enhance useful spectral information. Dual-Path Encoders include channel encoders and spatial encoders, intended to fully utilize spectrally enhanced images while maintaining the spatial information of the original feature map. The decoder of CD-MQANet is a multitasking decoder composed of four types of attention, enhancing decoding capabilities. The loss function used in the CD-MQANet consists of three parts, which are generated by the intermediate results of the CCM, the intermediate results of the decoder, and the final segmentation results and label calculation. We performed experiments on the Potsdam dataset and the Vaihingen dataset. Compared to the baseline MQANet model, the CD-MQANet network improved mean F1 and OA by 2.03% and 2.49%, respectively, on the Potsdam dataset, and improved mean F1 and OA by 1.42% and 1.25%, respectively, on the Vaihingen dataset. The effectiveness of CD-MQANet was also proven by comparative experiments with other studies. We also conducted a thermographic analysis of the attention mechanism used in CD-MQANet and analyzed the intermediate results generated by CCM and LAM. Both modules generated intermediate results that had a significant positive impact on segmentation. [ABSTRACT FROM AUTHOR]

In the wake of the wide promotion of 5G network, the era of super-high-speed networks and the Internet of Everything is approaching. Combining digital technologies led by 5G with landscape architecture has become an important way for the sustainable development of garden ecology. In order to achieve refined management of gardens and improve the accuracy and consistency of garden environmental data monitoring, this study constructs a new IoT sensor multi data fusion algorithm model. Considering the high redundancy and large error data collected by multiple sensors, this paper proposes a multi data fusion algorithm based on adaptive trust estimation and improved D-S evidence theory. The experimental data demonstrates that matched with IGA-BP, algorithm in this paper obtained the largest fitness value and the fastest convergence speed in three sensor application scenarios with different numbers of nodes. The lowest values were obtained in terms of unit energy consumption and network latency indicators. In the monitoring experiment for environmental data of landscape architecture, the algorithm obtained lower relative error and mean square error than IGA-BP in four environmental parameters of temperature, humidity, light intensity and carbon dioxide concentration. Therefore, the algorithm is effective in real-time monitoring of landscape garden environmental data, and can provide decision-making data for garden management as a reference. [ABSTRACT FROM AUTHOR]

The objective of this paper is to study smart home network systems and the application of LabVIEW to develop a human machine interface (HMI), so that traditional instrument panels can be replaced with virtual panels to reduce the consumption of hardware resources. For energy efficiency, MATLAB's fuzzy toolbox is used as the computing center, which is applied to the lighting system and air-conditioning system. This paper also uses LabVIEW's common gateway interface (CGI) tools to develop remote monitoring functions, as well as to embed the network-related syntax into the web pages. The user can not only use computer equipment, but can also use their mobile devices to connect to the networks and conduct remote monitoring, which enhances convenience and security. This paper is finally tested with actual cases, and the electricity consumption with and without fuzzy logic control is compared. The test results show that fuzzy logic control can reduce electricity consumption. As well as using computers to test the remote monitoring functions, cell phones and pads were used. [ABSTRACT FROM AUTHOR]

The site fidelity of mummichogs (Fundulus heteroclitus) within the upper Miramichi River estuary, New Brunswick, Canada, was investigated to assess the value of using this fish as a sentinel species for monitoring effects of point source anthropogenic effluents such as pulp and paper mill effluent. During the ice-free season (May to November) of 2002, 4123 adult mummichogs (>30 mm TL) were captured, by beach seine and minnow trap, biweekly from four sites within the estuary. Fish were marked intramuscularly using Visible Implant Elastomer (Northwest Marine Technologies, Inc., Shaw Island, Wash., U.S.). Recaptures were made at the marking sites and elsewhere during this period and again during the ice-free sea- son of April to November 2003. A total of 639 (15.5% of those marked) mummichogs were recaptured with 617 (96.6%) found within 200 m of the point of initial release. Twenty-nine of the 617 were recaptured 2 or 3 times at sites of original marking. The remaining 22 recaptured fish moved distances ranging from 600 to 3600 m up- and downstream of initial marking sites. Eighty-two percent of recaptures were made within 12 weeks of the start of marking with the remainder recovered up to 72 weeks later. These findings are consistent with results from studies of mummichog movement in smaller water bodies and other parts of the species' range. With regard to mobility, these results add to the growing body of literature supporting the usefulness of mummichogs as a sentinel species in environmental monitoring programs for point-source impacts in Atlantic Canadian estuaries. [ABSTRACT FROM AUTHOR]

Spatio-temporal models can be used to analyze data collected at various spatial locations throughout multiple time points. However, even with a finite number of spatial locations, there may be insufficient resources to collect data from every spatial location at every time point. We develop a spatio-temporal finite-population block kriging (ST-FPBK) method to predict a quantity of interest, such as a mean or total, across a finite number of spatial locations. This ST-FPBK predictor incorporates an appropriate variance reduction for sampling from a finite population. Through an application to moose surveys in the east-central region of Alaska, we show that the predictor has a substantially smaller standard error compared to a predictor from the purely spatial model that is currently used to analyze moose surveys in the region. We also show how the model can be used to forecast a prediction for abundance in a time point for which spatial locations have not yet been surveyed. A separate simulation study shows that the spatio-temporal predictor is unbiased and that prediction intervals from the ST-FPBK predictor attain appropriate coverage. For ecological monitoring surveys completed with some regularity through time, use of ST-FPBK could improve precision. We also give an R package that ecologists and resource managers could use to incorporate data from past surveys in predicting a quantity from a current survey. Supplementary materials accompanying this paper appear on-line. [ABSTRACT FROM AUTHOR]

Simple Summary: Identifying bird species is crucial in various bird monitoring tasks. In this study, we implemented a bird species recognition method based on visual features by convolutional neural networks. This paper proposed an attention-guided data enhancement method and a decoupled knowledge distillation model compression method. Using the two methods, we created a novel and efficient lightweight fine-grained bird classification model. The model not only achieved high accuracy in bird classification but was also user-friendly for edge devices such as mobile. Our work may be useful for bird species recognition and bird biodiversity monitoring. Classifying birds accurately is essential for ecological monitoring. In recent years, bird image classification has become an emerging method for bird recognition. However, the bird image classification task needs to face the challenges of high intraclass variance and low inter-class variance among birds, as well as low model efficiency. In this paper, we propose a fine-grained bird classification method based on attention and decoupled knowledge distillation. First of all, we propose an attention-guided data augmentation method. Specifically, the method obtains images of the object's key part regions through attention. It enables the model to learn and distinguish fine features. At the same time, based on the localization–recognition method, the bird category is predicted using the object image with finer features, which reduces the influence of background noise. In addition, we propose a model compression method of decoupled knowledge distillation. We distill the target and nontarget class knowledge separately to eliminate the influence of the target class prediction results on the transfer of the nontarget class knowledge. This approach achieves efficient model compression. With 67% fewer parameters and only 1.2 G of computation, the model proposed in this paper still has a 87.6% success rate, while improving the model inference speed. [ABSTRACT FROM AUTHOR]

The primary uses of asbestos in Mongolia are in thermal power plants, construction and at railway companies. There is, however, limited data on both asbestos consumption and asbestos related disease (ARD) in Mongolia. The purpose of this paper is to report on the failure to completely ban asbestos in Mongolia. To write this paper, available asbestos related literature, published nationally and internationally, and legal regulations, national standards and guidelines on asbestos control were reviewed. Mongolia consumed a total of 44,421.9 metric tons of asbestos containing materials (AMCs) between 1996 and 2014. As a key indicator of ARD, 54 cases of mesothelioma were diagnosed at the National Cancer Center by pathological testing of tissue samples between 1994 and 2013. In 2010, The government made the decision to stop all types of asbestos use under the Law on Toxic and Hazardous Substances. However, there was no nationwide action plan to gradually reduce asbestos use, promote substitutes and raise awareness of health hazards and economic burdens in the future from asbestos use. There was also no planning for safe removal of asbestos currently in place. After the banning of asbestos, thermal power plants told the government that they could not produce electricity without insulation of AMCs and substitution materials were economically not feasible. Due to pressure from the energy sector and inadequate awareness of asbestos hazards, the government changed the legal status on asbestos in 2011 as a restricted chemical. Asbestos is still allowed to be used, and workers and the general community are still unnecessarily exposed to this carcinogen. [ABSTRACT FROM AUTHOR]

Data are vital for and creating knowledge-based solutions to development challenges facing Africa. As a result of gaps in government-funded data collection, and in the interest of promoting community engagement, there is a global movement towards consideration of nontraditional sources of data, including citizen science (CS) data. These data are particularly valuable when collected at a high resolution over large spatial extents and long time periods. CS projects and infrastructure are abundant and well documented in the Global North, while needs for participatory projects to fill environmental monitoring gaps may be greater in the Global South. The paper explores the contributions of citizen science projects originating in Africa for two Sustainable Development Goals (SDGs), namely SDG 6, and SDG 11 which are particularly important to the millions of lowincome residents of cities across Africa. Using a mixed methods approach that involves online surveys, interviews, expert conference panels, and a desk review, we analyze a total of 53 CS projects focusing on water, sanitation, and urban planning. The paper addresses CS in Africa and CS for SDGs, and documents evidence for participatory and CS data collection in Africa. It also describes the survey methods, including approaches to training of volunteers, sources of funding, data collection methods, and objectives of the tools and projects. Finally, it provides reflections on the challenges of integrating CS into official statistics in Africa, and some lessons learnt from CS projects in Africa. This paper recommends the establishment of an open-source database, creation of a network of CS projects for communication and collaboration, the uptake of citizen-generated data, and continuous funding for such projects in Africa. [ABSTRACT FROM AUTHOR]

Fires are used as a tool in the deforestation process. Yet, the relationship between fire and deforestation may vary temporally and spatially depending on the type of deforestation and climatic conditions. This study evaluates spatiotemporal dynamics of deforestation and fire represented by burned area over the 2002-2012 period in the Brazilian Legal Amazon. As a first step, we compared newly available Landsat-based maps of gross forest cover loss from the Global Forest Change (GFC) project with maps of deforestation extent from the Amazon Deforestation Monitoring Project (PRODES) produced by the Brazilian National Institute for Space Research (INPE). As a second step, we rescaled the Landsat-based data to the 500m resolution of the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area data (MCD64A1) and stratified this using MODIS land cover data to study the role of burned area in forest cover loss and deforestation. We found that while GFC forest cover loss and PRODES deforestation generally agreed on spatial and temporal dynamics, there were several key differences between the datasets. Both showed a decrease in the extent of forest cover loss or deforestation after 2004, but the drop was larger and more continuous in PRODES than in GFC. The observed decrease in forest cover loss or deforestation rates over our study period was mainly due to lower clearing rates in the evergreen broadleaf forests in the states of Mato Grosso, Pará and Rondônia. GFC indicated anomalous high forest cover loss in the years 2007 and 2010 not reported by PRODES. The burned area data showed that this was predominantly related to increased fire activity occurring outside of the tropical forest area during these dry years, mainly in Pará. This indicates that fire and forest loss dynamics in woodlands or secondary forests may be equally important as deforestation in regulating atmospheric CO2 concentrations. In addition to the decrease in forest cover loss rates, we also found that post-deforestation fire use declined; burned area within 5 years after forest cover loss decreased from 54 to 39% during our study period. [ABSTRACT FROM AUTHOR]

The vegetation indices normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI) provide indicators of pigmentation and photosynthetic activity that can be used to model photosynthesis from remote sensing with the light-use efficiency model. To help develop and validate this approach, reliable proximal NDVI and PRI sensors have been needed. We tested new NDVI and PRI sensors, "SRS" sensors recently developed by Decagon Devices, during spring activation of photosynthetic activity in evergreen and deciduous stands. We also evaluated two methods of sensor cross-calibration, one that considered sky conditions (cloud cover) at midday only, and the other that also considered diurnal sun angle effects. Cross-calibration clearly affected sensor agreement with independent measurements, with the best method dependent upon the study aim and time frame (seasonal vs. diurnal). The seasonal patterns of NDVI and PRI differed for evergreen and deciduous species, demonstrating the complementary nature of these two indices. Over the spring season, PRI was most strongly influenced by changing chlorophyll : carotenoid pool sizes, while over the diurnal time scale PRI was most affected by the xanthophyll cycle epoxidation state. This finding demonstrates that the SRS PRI sensors can resolve different processes affecting PRI over different time scales. The advent of small, inexpensive, automated PRI and NDVI sensors offers new ways to explore environmental and physiological constraints on photosynthesis, and may be particularly well-suited for use at flux tower sites. Wider application of automated sensors could lead to improved integration of flux and remote sensing approaches to studying photosynthetic carbon uptake, and could help define the concept of contrasting vegetation optical types. [ABSTRACT FROM AUTHOR]

Statistical models for environmental monitoring strongly rely on automatic data acquisition systems, using various physical sensors. Often, sensor readings are missing for extended periods of time while model outputs need to be continuously available in real time. With a case study in solar radiation nowcasting, we investigate how to deal with massively missing data (around 50% of the time some data are unavailable) in such situations. Our goal is to analyze the characteristics of missing data and recommend a strategy for deploying regression models, which would be robust to missing data in situations, where data are massively missing. We are after one model that performs well at all times, with and without data gaps. Due to the need to provide instantaneous outputs with minimum energy consumption for computing in the data streaming setting, we dismiss computationally demanding data imputation methods, and resort to a simple mean replacement. We use an established strategy for comparing different regression models, with the possibility of determining how many missing sensor readings can be tolerated before model outputs become obsolete. We experimentally analyze accuracies and robustness to missing data of seven linear regression models and recommend using regularized PCA regression. We recommend using our established guideline in training regression models, which themselves are robust to missing data. [ABSTRACT FROM AUTHOR]

Wireless sensor network technology is widely used in various modern scenarios, and various industries have higher and higher requirements for the performance indicators of wireless sensor networks. A reasonable and effective layout of wireless sensor networks is conducive to the monitoring of environmental quality, various transactions, and status and transmits a large number of sensing data to the data aggregation center for processing and analysis. However, the operation and development of traditional wireless sensor networks are extremely dependent on the energy supply of the network. When the corresponding supply energy is limited, the operation life of the corresponding wireless sensor network will be greatly reduced. Based on the above situation, this paper proposes a nonuniform clustering routing protocol optimization algorithm from the energy loss of cluster head and clustering form algorithm in wireless sensor networks. At the level of cluster head calculation in wireless sensor networks, firstly, based on the adaptive estimation clustering algorithm, the core density is used as the estimation element to calculate the cluster head radius of wireless sensor networks. At the same time, this paper creatively proposes a fuzzy logic algorithm to further solve the uncertainty of cluster head selection, integrate the residual energy of cluster head nodes, and finally complete the reasonable distribution of cluster heads and realize the balance of node energy consumption. In order to further reduce the algorithm overhead of transmission between cluster heads and realize energy optimization, an intercluster routing optimization algorithm based on the ant colony algorithm is proposed. The pheromone is updated and disturbed by introducing chaotic mapping to ensure the optimal solution of the algorithm, and the optimal path is selected from the perspective of energy dispersion coefficient and distance coefficient, so as to optimize the energy consumption between cluster heads. The experimental results show that compared with the traditional algorithm, the proposed nonuniform clustering routing protocol optimization algorithm prolongs the corresponding life cycle by 75% and reduces the total network energy consumption by about 20%. Therefore, the algorithm achieves the purpose of optimizing network energy consumption and prolonging network life to a certain extent and has certain practical value. [ABSTRACT FROM AUTHOR]

Foodborne illnesses can be infectious and dangerous, and most of them are caused by bacteria. Some common food-related bacteria species exist widely in nature and pose a serious threat to both humans and animals; they can cause poisoning, diseases, disabilities and even death. Rapid, reliable and cost-effective methods for bacterial detection are of paramount importance in food safety and environmental monitoring. Polymerase chain reaction (PCR), lateral flow immunochromatographic assay (LFIA) and electrochemical methods have been widely used in food safety and environmental monitoring. In this paper, the recent developments (2013–2023) covering PCR, LFIA and electrochemical methods for various bacterial species (Salmonella, Listeria, Campylobacter, Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli)), considering different food sample types, analytical performances and the reported limit of detection (LOD), are discussed. It was found that the bacteria species and food sample type contributed significantly to the analytical performance and LOD. Detection via LFIA has a higher average LOD (24 CFU/mL) than detection via electrochemical methods (12 CFU/mL) and PCR (6 CFU/mL). Salmonella and E. coli in the Pseudomonadota domain usually have low LODs. LODs are usually lower for detection in fish and eggs. Gold and iron nanoparticles were the most studied in the reported articles for LFIA, and average LODs were 26 CFU/mL and 12 CFU/mL, respectively. The electrochemical method revealed that the average LOD was highest for cyclic voltammetry (CV) at 18 CFU/mL, followed by electrochemical impedance spectroscopy (EIS) at 12 CFU/mL and differential pulse voltammetry (DPV) at 8 CFU/mL. LOD usually decreases when the sample number increases until it remains unchanged. Exponential relations (R2 > 0.95) between LODs of Listeria in milk via LFIA and via the electrochemical method with sample numbers have been obtained. Finally, the review discusses challenges and future perspectives (including the role of nanomaterials/advanced materials) to improve analytical performance for bacterial detection. [ABSTRACT FROM AUTHOR]

Infrared optoelectronic sensors have attracted considerable research interest over the past few decades due to their wide-ranging applications in military, healthcare, environmental monitoring, industrial inspection, and human–computer interaction systems. A comprehensive understanding of infrared optoelectronic sensors is of great importance for achieving their future optimization. This paper comprehensively reviews the recent advancements in infrared optoelectronic sensors. Firstly, their working mechanisms are elucidated. Then, the key metrics for evaluating an infrared optoelectronic sensor are introduced. Subsequently, an overview of promising materials and nanostructures for high-performance infrared optoelectronic sensors, along with the performances of state-of-the-art devices, is presented. Finally, the challenges facing infrared optoelectronic sensors are posed, and some perspectives for the optimization of infrared optoelectronic sensors are discussed, thereby paving the way for the development of future infrared optoelectronic sensors. [ABSTRACT FROM AUTHOR]

The rapid spread of Internet of Things technologies has enabled a continuous monitoring of indoor environmental quality in office environments by integrating monitoring devices equipped with low-cost sensors and cloud platforms for data storage and visualization. Critical aspects in the development of such monitoring systems are effective data acquisition, processing, and visualization strategies, which significantly influence the performance of the system both at monitoring device and at cloud platform level. This paper proposes novel strategies to address the challenges in the design of a complete monitoring system for indoor environmental quality. By adopting the proposed solution, one can reduce the data rate transfer between the monitoring devices and the server without loss of information, as well as achieve efficient data storage and aggregation on the server side to minimize retrieval times. Finally, enhanced flexibility in the dashboard for data visualization is obtained, thus enabling graph modifications without extensive coding efforts. The functionality of the developed system was assessed, with the collected data in good agreement with those from other instruments used as references. [ABSTRACT FROM AUTHOR]

The early detection of wildfires is a crucial challenge in environmental monitoring, pivotal for effective disaster management and ecological conservation. Traditional detection methods often fail to detect fires accurately and in a timely manner, resulting in significant adverse consequences. This paper presents FireXplainNet, a Convolutional Neural Network (CNN) base model, designed specifically to address these limitations through enhanced efficiency and precision in wildfire detection. We optimized data input via specialized preprocessing techniques, significantly improving detection accuracy on both the Wildfire Image and FLAME datasets. A distinctive feature of our approach is the integration of Local Interpretable Model-agnostic Explanations (LIME), which facilitates a deeper understanding of and trust in the model's predictive capabilities. Additionally, we have delved into optimizing pretrained models through transfer learning, enriching our analysis and offering insights into the comparative effectiveness of FireXplainNet. The model achieved an accuracy of 87.32% on the FLAME dataset and 98.70% on the Wildfire Image dataset, with inference times of 0.221 and 0.168 milliseconds, respectively. These performance metrics are critical for the application of real-time fire detection systems, underscoring the potential of FireXplainNet in environmental monitoring and disaster management strategies. [ABSTRACT FROM AUTHOR]

The hyperspectral image (HSI) distinguishes itself in material identification through its exceptional spectral resolution. However, its spatial resolution is constrained by hardware limitations, prompting the evolution of HSI super-resolution (SR) techniques. Single HSI SR endeavors to reconstruct high-spatial-resolution HSI from low-spatial-resolution inputs, and recent progress in deep learning-based algorithms has significantly advanced the quality of reconstructed images. However, convolutional methods struggle to extract comprehensive spatial and spectral features. Transformer-based models have yet to harness long-range dependencies across both dimensions fully, thus inadequately integrating spatial and spectral data. To solve the above problem, in this paper, we propose a new HSI SR method, SSAformer, which merges the strengths of CNNs and Transformers. It introduces specially designed attention mechanisms for HSI, including spatial and spectral attention modules, and overcomes the previous challenges in extracting and amalgamating spatial and spectral information. Evaluations on benchmark datasets show that SSAformer surpasses contemporary methods in enhancing spatial details and preserving spectral accuracy, underscoring its potential to expand HSI's utility in various domains, such as environmental monitoring and remote sensing. [ABSTRACT FROM AUTHOR]

Anthropogenic contamination of surface waters is a critical issue in urbanised catchments, especially in Mediterranean environments with scarce water resources and intermittent rivers. A combined hydro-geochemical and multivariate statistical methodology has been applied to evaluate the surface water quality of Wadi El Hatab catchment (Central Tunisia). More than half of the 28 samples collected in March 2021 exceeded WHO standards for sodium, chloride and sulphate contents. Most polluted sites are directly influenced by domestic and industrial effluents that contaminate the river for kilometers. Principal Component Analysis and Hierarchical Ascendant Classification distinguished four contamination sources: water-rock interaction, domestic wastewater, untreated industrial effluents mainly from paper mills and irrigation return. A Water Quality Index model indicated that 75% of the surface water was unsuitable for drinking purposes; while according to chemical indices such as saturation index, sodium absorption coefficient (SAR), magnesium ratio (MR) and percent sodium (%Na), 25 to 49% of samples were unsuitable for irrigation. Considering that water of Wadi El Hatab is used for vegetable irrigation, serious public health issues may arise. The investigation demonstrated that a single representative sampling campaign could be a useful way to identify contamination sources when interpreted with both statistical and geochemical tools. [ABSTRACT FROM AUTHOR]

Tapered optical fibers have continuously evolved in areas such as distributed sensing and laser generation in recent years. Their high sensitivity, ease of integration, and real-time monitoring capabilities have positioned them as a focal point in optical fiber sensing. This paper systematically introduces the structures and characteristics of various tapered optical fiber sensors, providing a comprehensive overview of their applications in biosensing, environmental monitoring, and industrial surveillance. Furthermore, it offers insights into the developmental trends of tapered optical fiber sensing, providing valuable references for future related research and suggesting potential directions for the further advancement of optical fiber sensing. [ABSTRACT FROM AUTHOR]

A number of sentinel species are regularly sampled from the environment near the Oil Sands Region (OSR) in Alberta, Canada. In particular, trout-perch are sampled as a proxy for the health of the aquatic ecosystem. As the development of the OSR began before the environmental monitoring program was in place, there is currently no established measure for the baseline health of the local ecosystem. A common solution is to calculate normal ranges for fish endpoints. Observations found to be outside the normal range are then flagged, alerting researchers to the potential presence of stressors in the local environment. The quality of the normal ranges is dependent on the accuracy of the estimates used to calculate them. This paper explores the use of neural networks and regularized regression for improving the prediction accuracy of fish endpoints. We also consider the trade-off between the prediction accuracy and interpretability of each model. We find that neural networks can provide increased prediction accuracy, but this improvement in accuracy may not be worth the loss in interpretability in some ecological studies. The elastic net offers both good prediction accuracy and interpretability, making it a safe choice for many ecological applications. A hybridized method combining both the neural network and elastic net offers high prediction accuracy as well as some interpretability, and therefore it is the recommended method for this application. [ABSTRACT FROM AUTHOR]

The utilization of triboelectric materials has gained considerable attention in recent years, offering a sustainable approach to energy harvesting and sensing technologies. Biomass-derived materials, owing to their abundance, renewability, and biocompatibility, offer promising avenues for enhancing the performance and versatility of triboelectric devices. This paper explores the synthesis and characterization of biomass-derived materials, their integration into triboelectric nanogenerators (TENGs), and their applications in energy harvesting, self-powered sensors, and environmental monitoring. This review presents an overview of the emerging field of advanced triboelectric applications that utilize the unique properties of biomass-derived materials. Additionally, it addresses the challenges and opportunities in employing biomass-derived materials for triboelectric applications, emphasizing the potential for sustainable and eco-friendly energy solutions. [ABSTRACT FROM AUTHOR]

Fluorescent probes are sensitive, selective, nontoxic in detection and thus provided a new solution in biomedical, environmental monitoring, and food safety. In order to expand the application of fluorescent probes in various fields, the paper discusses the design, synthesis, and characterization of fluorescent probes, explores new design and development trends of fluorescent probes in various fields, and improves the performance and applicability of fluorescent probes by using new materials and technologies to meet the evolving demands of molecular detection in various fields. [ABSTRACT FROM AUTHOR]

This paper explores the development of a Quadcopter-Rover System specifically designed for environmental survey applications. The system combines the capabilities of a quadcopter and a rover to provide a comprehensive and versatile solution for data collection and analysis. The paper presents a detailed overview of the system's modelling, design, and manufacturing of the two main components: the quadcopter and the differential wheel robot (the rover). The quadcopter’s main task is to carry the rover to/from the ground destination and collect aerial data while the rover`s main task is ground exploration and data collection. The paper discusses the development of a robust and efficient control algorithm that enables autonomous and coordinated operation between the quadcopter and the rover. Experimental results demonstrate the system's effectiveness in conducting environmental surveys, showcasing its ability to accurately navigate challenging terrains, and collect valuable data for environmental analysis. The Quadcopter-Rover System offers significant potential in applications such as ecological monitoring, disaster management, and precision agriculture, where comprehensive and efficient data collection is crucial for informed decision-making. [ABSTRACT FROM AUTHOR]

The traditional monitoring method of water resources environment mainly relies on manual sampling and laboratory analysis, which is limited in time and space and can not realize the comprehensive monitoring of water resources environment. With the continuous development of computer technology and network communication technology, real-time data monitoring method of water resources environment based on computer remote data acquisition and image analysis has gradually become a new solution. This paper aims to propose a real-time data monitoring method of water resources environment based on computer remote data acquisition and image analysis, in order to achieve accurate and efficient monitoring of water resources environment, timely acquisition and analysis of key data of water resources environment, and provide scientific basis for relevant decisions. In this paper, the key parameters of water quality, water quantity and water ecology are collected by using computer remote data acquisition and image analysis. Using computer image analysis technology, the collected image data are processed and analyzed, and the related characteristics and indicators of water resources environment are extracted. The results show that the real-time data monitoring method of water resources environment based on computer remote data acquisition and image analysis can monitor the data changes of water resources environment accurately and efficiently. Through the analysis and processing of the real-time data, the problems in the water resources environment can be found and warned in time, and the corresponding measures can be taken to regulate and manage. [ABSTRACT FROM AUTHOR]

Transformational advances in environmental sensing have been made possible by the convergence of Cloud Computing and the Internet of Things (IoT). The potential for these technologies is to work together and improve environmental monitoring data analytics. Integrating IoT in the cloud creates a robust environment for advanced analytics by overcoming previous hurdles in data granularity, real-time monitoring, and geographical coverage. These developments aren't without their own set of difficulties, however, including data security, interoperability, and ethical concerns. This paper explores integrating Cloud IoT with environmental sensing. The paper addresses these issues and emphasises the need to establish ethical data-gathering methods, implement standardised communication protocols, and address privacy concerns. The study concludes by examining the challenges, concerns, and potential of integrating Cloud IoT. The integration of Cloud Computing and IoT not only transforms environmental sensing but also provides a solid foundation for collaborative research, data-driven decision-making, and environmentally conscious management. [ABSTRACT FROM AUTHOR]

The efficient management of slurry, which is a by-product rich in nutrients derived from feces, urine, cleaning water, and animal waste that stands out for its high concentration of nutrients such as nitrogen, phosphorus, and potassium, is of vital importance, highlighting the importance of slurry management in storage ponds, which. The Murcia–Spain region has an important number of pig farms. Hence, infrastructures dedicated to managing by-products are necessary to prevent environmental pollution and eutrophication of groundwater. The aim of a recent study was to evaluate the relationship between electrical values and geochemical parameters of pig slurry stored in a pond using ERT and geochemical analysis. In addition, the study was designed to monitor the pond to determine the geochemical characteristics of the slurry and to assess the risk of lateral contamination. The study results indicate a noticeable decrease in electrical resistivity values at 0.4 and 1.6 m depth in surveys 1 and 2. The reduction ranges from 50 to 100 percent. This paper presents a new method for monitoring slurry ponds using electrical resistivity tomography. This non-invasive method provides detailed information on the distribution and characteristics of the fluids, as well as a clear picture of the electrical resistivity of the subsurface. [ABSTRACT FROM AUTHOR]

Snapshot spectral imaging technology enables the capture of complete spectral information of objects in an extremely short period of time, offering wide-ranging applications in fields requiring dynamic observations such as environmental monitoring, medical diagnostics, and industrial inspection. In the past decades, snapshot spectral imaging has made remarkable breakthroughs with the emergence of new computational theories and optical components. From the early days of using various spatial-spectral data mapping methods, they have evolved to later attempts to encode various dimensions of light, such as amplitude, phase, and wavelength, and then computationally reconstruct them. This review focuses on a systematic presentation of the system architecture and mathematical modeling of these snapshot spectral imaging techniques. In addition, the introduction of metasurfaces expands the modulation of spatial-spectral data and brings advantages such as system size reduction, which has become a research hotspot in recent years and is regarded as the key to the next-generation snapshot spectral imaging techniques. This paper provides a systematic overview of the applications of metasurfaces in snapshot spectral imaging and provides an outlook on future directions and research priorities. [ABSTRACT FROM AUTHOR]

This paper proposes a new kind of airship actuator configuration for surveillance and environmental monitoring missions. We present the design and application of a six-propeller electrical airship (Noamini) with independent tilting propellers, allowing improved and flexible maneuverability. The vehicle has different combinations of differential propulsion and can be used in a two-, four- or six-motor configuration. We developed a high-fidelity airship simulator for the Noamini airship, which was used to test and validate a control/guidance approach. Incremental Nonlinear Dynamic Inversion (INDI) is used for the velocity/attitude control to follow a high-level L1 guidance reference in a simulated waypoint-tracking mission with wind and turbulence. The proposed framework will be soon implemented in the onboard control system of the Noamini, an autonomous airship for environmental monitoring and surveillance applications. [ABSTRACT FROM AUTHOR]

The continuous monitoring of indoor environmental quality (IEQ) plays a crucial role in improving our understanding of the prominent parameters affecting building users' health and perception of their environment. In field studies, indoor environment monitoring often does not go beyond the assessment of air temperature, relative humidity, and CO2 concentration, lacking consideration of other important parameters due to budget constraints and the complexity of multi-dimensional signal analyses. In this paper, we introduce the Environmental Quality bOX (EQ-OX) system, which was designed for the simultaneous monitoring of quantities of some of the main IEQs with a low level of uncertainty and an affordable cost. Up to 15 parameters can be acquired at a time. The system embeds only low-cost sensors (LCSs) within a compact case, enabling vast-scale monitoring campaigns in residential and office buildings. The results of our laboratory and field tests show that most of the selected LCSs can match the accuracy required for indoor campaigns. A lightweight data processing algorithm has been used for the benchmark. Our intent is to estimate the correlation achievable between the detected quantities and reference measurements when a linear correction is applied. Such an approach allows for a preliminary assessment of which LCSs are the most suitable for a cost-effective IEQ monitoring system. [ABSTRACT FROM AUTHOR]

The environmental pollution caused by various sources has escalated the climate crisis making the need to establish reliable, intelligent, and persistent environmental monitoring solutions more crucial than ever. Mobile sensing systems are a popular platform due to their cost-effectiveness and adaptability. However, in practice, operation environments demand highly intelligent and robust systems that can cope with an environment's changing dynamics. To achieve this reinforcement learning has become a popular tool as it facilitates the training of intelligent and robust sensing agents that can handle unknown and extreme conditions. In this paper, a framework that formulates active sensing as a reinforcement learning problem is proposed. This framework allows unification with multiple essential environmental monitoring tasks and algorithms such as coverage, patrolling, source seeking, exploration and search and rescue. The unified framework represents a step towards bridging the divide between theoretical advancements in reinforcement learning and real-world applications in environmental monitoring. A critical review of the literature in this field is carried out and it is found that despite the potential of reinforcement learning for environmental active sensing applications there is still a lack of practical implementation and most work remains in the simulation phase. It is also noted that despite the consensus that, multi-agent systems are crucial to fully realize the potential of active sensing there is a lack of research in this area. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an innovative technology used in manufacturing, environmental monitoring, home automation, human health applications, and other fields. IoT devices, however, are susceptible to various security risks. Cyber-attacks on IoT systems can halt the entire system, resulting in data loss. Intrusion Detection Systems (IDS) can quickly identify malicious activity that could have compromised the network's performance. Effective IDS and Intrusion Prevention System (IPS) are necessary to protect IoT systems. Blockchain technology presents another innovative method to strengthen security systems against the latest attacks. This paper focuses on conducting a Systematic Literature Review (SLR) on Blockchain-based Intrusion Detection/Prevention Systems in IoT Networks. We reviewed several relevant blockchain-based IDS and IPS proposed for IoT networks and their mechanisms. The most recent research articles, published between 2017 and 2022, were selected from several databases. Consequently, a comprehensive review of these selected papers sheds light on their techniques, advantages, and drawbacks. [ABSTRACT FROM AUTHOR]

The widespread application of pesticides in Morocco's agriculture renders their monitoring in food and environmental samples very necessary. Recent years have witnessed a growing interest in reporting studies related to the monitoring of pesticide residues in food, water, groundwater, and soil as well as their quantitative health risk assessment. Most published studies have been done by university researchers. However, the lack of research reproducibility remains a problem that considerably limits the possibility of exploiting data from the literature. Our study involves an extensive literature review utilizing search engines with keywords like "pesticide residues," "monitoring," "vegetables and fruits," "water and soil," "risk assessment," and "Morocco" from 2009 to 2023. Analysis of pesticide residues in foodstuffs and environmental samples highlights concerns over compliance with EU regulations, the health risks associated with pesticide exposure, and the necessity for comprehensive monitoring and risk assessment strategies. This paper could help influence policies to develop a strategy and action plan for the sound management of pesticides, including measures to reduce their use, raise awareness, and monitor compliance. Also, this paper could be useful for scientists interested in understanding the current situation and challenges regarding pesticide residues in Morocco, as well as countries with which commercial links exist. [ABSTRACT FROM AUTHOR]

This paper presents an original wireless DYU Air Box of an environment-monitoring IoT (EMIoT) system on a campus to offer information on environmental conditions through the public ThingSpeak IoT platform for stakeholders including all the students and employees on the Da-Yeh University (DYU) campus in Taiwan. Firstly, the proposed wireless heterogeneous multi-sensor module aggregates BME680, SCD30, PMS7003, and BH1750 sensors with a TTGO ESP32 Wi-Fi device based on the I2C and UART interface standards of series communication. Through the DYU-802.1X Wi-Fi network with the WPA2 Enterprise security directly, the wireless multi-sensor monitoring module further forwards the observation data of environmental conditions on campus via the DYU-802.1X Wi-Fi network to the public ThingSpeak IoT platform, which is a cloud service platform to aggregate, visualize, and analyze live sensing data of air quality index (AQI), concentrations of PM1.0/2.5 and CO2, brightness, ambient temperature, and relative humidity (RH). The results illustrate the proposed DYU Air Box for monitoring the indoor environmental conditions on campus and validate them with sufficient accuracy and confidence with commercialized measurement instruments. In this work, the wireless smart environment-monitoring IoT system features monitoring and automatic alarm functions for monitoring AQI, CO2, and PM concentrations, as well as ambient illumination, temperature, and RH parameters and collaboration and interoperability through the Enterprise Intranet. All the organizational stakeholders interested in the environmental conditions of the DYU campus can openly access the information according to their interests. In the upcoming future, the information of the environmental conditions in the DYU campus will be developed to be simultaneously accessed by all the stakeholders through both the public ThingSpeak IoT platform and the private EMIoT system. [ABSTRACT FROM AUTHOR]

In environmental monitoring systems based on the Internet of Things (IoT), sensor nodes (SNs) typically send data to the server via a wireless gateway (GW) at regular intervals. However, when SNs are located far from the GW, substantial energy is expended in transmitting data. This paper introduces a novel unmanned aerial vehicle (UAV)-based environmental monitoring system. In the proposed system, the UAV conducts patrols in the designated area, and SNs periodically transmit the collected data to the GW or the UAV. This transmission decision is made while taking into account the respective distance between both the GW and the UAV. To ensure a high-quality environmental map, characterized by a consistent collection of a satisfactory amount of up-to-date data while preventing energy depletion in the SNs and the UAV, the UAV periodically decides on three types of UAV operations. These decisions involve deciding where to move, deciding whether to relay or aggregate the data from the SNs, and deciding whether to transfer energy to the SNs. For the optimal decisions, we introduce an algorithm, called DeepUAV, using deep reinforcement learning (DRL) to make decisions in UAV operations. In DeepUAV, the controller continually learns online and enhances the UAV's decisions through trial and error. The evaluation results indicate that DeepUAV successfully gathers a substantial amount of the current data consistently while mitigating the risk of energy depletion in SNs and the UAV. [ABSTRACT FROM AUTHOR]

Video surveillance is widely used in monitoring environmental pollution, particularly harmful dust. Currently, manual video monitoring remains the predominant method for analyzing potential pollution, which is inefficient and prone to errors. In this paper, we introduce a new unsupervised method based on latent diffusion models. Specifically, we propose a spatio-temporal network structure, which better integrates the spatial and temporal features of videos. Our conditional guidance mechanism samples frames of input videos to guide high-quality generation and obtains frame-level anomaly scores, comparing generated videos with original ones. We also propose an efficient compression strategy to reduce computational costs, allowing the model to perform in a latent space. The superiority of our method was demonstrated by numerical experiments in three public benchmarks and practical application analysis in coal mining over previous SOTA methods with better AUC, of at most over 3%. Our method accurately detects abnormal patterns in multiple challenging environmental monitoring scenarios, illustrating the potential application possibilities in the environmental protection domain and beyond. [ABSTRACT FROM AUTHOR]

The review paper provides a comprehensive analysis of nickel oxide (NiO) as an emerging material in environmental monitoring by surveying recent developments primarily within the last three years and reports the growth processing and strategies employed to enhance NiO sensing performance. It covers synthesis methods for pristine NiO, including vapor-phase, liquid-phase, and solution-processing techniques, highlighting advantages and limitations. The growth mechanisms of NiO nanostructures are explored, with a focus on the most recent research studies. Additionally, different strategies to improve the gas sensing performance of NiO are discussed (i.e., surface functionalization by metallic nanoparticles, heterostructure formation, carbon-based nanomaterials, and conducting polymers). The influence of these strategies on selectivity, sensitivity, response time, and stability of NiO-based sensors is thoroughly examined. Finally, the challenges and future directions that may lead to the successful development of highly efficient NiO-based gas sensors for environmental monitoring are introduced in this review. [ABSTRACT FROM AUTHOR]

This paper aims to provide a large coverage of recent developments regarding environmental monitoring using metal oxide-based sensors. Particular attention is given to the detection of gases such as H2, COx, SOx, NOx, and CH4. The developments and analyses of the design of sensors and types of metal oxide sensing materials are emphasized. The sensing mechanisms and peculiarities of metal oxides used in chemoresistive sensors are provided. The main parameters that affect the sensitivity and selectivity of metal oxide sensors are indicated and their significance to the sensor signal is analyzed. Modern data processing algorithms, employed to optimize the measurement process and processing of the sensor signal, are considered. The existing sensor arrays/e-nose systems for environmental monitoring are summarized, and future prospects and challenges encountered with metal oxide-based sensor arrays are highlighted. [ABSTRACT FROM AUTHOR]

Time series of vegetation indices (VI) derived from satellite imagery provide a consistent monitoring system for terrestrial plant systems. They enable detection and quantification of gradual changes within the time frame covered, which are of crucial importance in global change studies, for example. However, VI time series typically contain a strong seasonal signal which complicates change detection. Commonly, trends are quantified using linear regression methods, while the effect of serial autocorrelation is remediated by temporal aggregation over bins having a fixed width. Aggregating the data in this way produces temporal units which are modifiable. Analogous to the well-known Modifiable Area Unit Problem (MAUP), the way in which these temporal units are defined may influence the fitted model parameters and therefore the amount of change detected. This paper illustrates the effect of this Modifiable Temporal Unit Problem (MTUP) on a synthetic data set and a real VI data set. Large variation in detected changes was found for aggregation over bins that mismatched full lengths of vegetative cycles, which demonstrates that aperiodicity in the data may influence model results. Using 26 yr of VI data and aggregation over full-length periods, deviations in VI gains of less than 1% were found for annual periods, while deviations (with respect to seasonally adjusted data) increased up to 24% for aggregation windows of 5 yr. This demonstrates that temporal aggregation needs to be carried out with care in order to avoid spurious model results. [ABSTRACT FROM AUTHOR]

Nowadays, the monitoring of plant water stress is of high importance in smart agriculture. Instead of the traditional ground soil-moisture measurement, the leaf sensing is a new technology, which is used for the detection of plants needing water. In this paper, a novel, low-cost, and low-power system for leaf sensing using a new plant backscatter sensor node/tag is presented. The latter, can result in the prevention of water waste (water-use efficiency), when is connected to an irrigation system. Specifically, the sensor measures the temperature differential between the leaf and the air, which is directly related to the plant water stress. Next, the tag collects the information from the leaf sensor through an analog-to-digital converter, and then, communicates remotely with a low-cost software-defined radio reader using monostatic backscatter architecture. The tag consists of the sensor board, a microcontroller, an external timer, and an RF front-end for communication. The timer produces a subcarrier frequency for simultaneous access of multiple tags. The proposed work could be scaled and be a part of a large backscatter wireless sensor network. The communication protocol exploits the low-complexity Morse code modulation on a 868 MHz carrier signal. The presented novel proof-of-consent prototype is batteryless and was powered by a flexible solar panel consuming power around $20~\mu \text{W}$. The performance was validated in an indoors environment where wireless communication was successfully achieved up to 2 m distance. [ABSTRACT FROM AUTHOR]

Improved environmental monitoring of microorganisms is currently an important issue, since traditional detection methods are costly and time consuming (often requiring 2-3 days). In contrast, microfluidic chip-based technologies provide promising alternatives. The presence of environmental contaminants, especially bacterial, viral, and parasitic pathogens and their toxins, is creating a major public health problem. The detection of microbial communities in environmental samples is the first step to recognizing these organisms and insuring health and safety against their infection. This review addresses both the existing and innovative technologies for the detection of pathogens present in the environmental samples. First, impacts and challenges related to pathogens in the environmental samples are discussed, and standard techniques for their identification are addressed. Next, the microfluidic technologies are introduced as an alternative strategy with a particular focus on the detection of environmental contaminants. Challenges that still need to be overcome are also discussed. The review concludes with a section on paper-based microfluidic device and a discussion on future prospects. [ABSTRACT FROM AUTHOR]

The article focuses on several aspects involved for the preparation and implementation of an environmental monitoring program. It notes that the monitoring program aims at obtaining measurement result for parameters considering an evaluation as the ground for action. Furthermore, the evaluation criteria provide an effect that is critical on the performance of monitoring schemes which cover from the improvement of a strategy to the procedure utilized in measuring the gathered data.

This paper investigated the utility of drone-based environmental monitoring to assist with forest inventory in Queensland private native forests (PNF). The research aimed to build capabilities to carry out forest inventory more efficiently without the need to rely on laborious field assessments. The use of drone-derived images and the subsequent application of digital photogrammetry to obtain information about PNFs are underinvestigated in southeast Queensland vegetation types. In this study, we used image processing to separate individual trees and digital photogrammetry to derive a canopy height model (CHM). The study was supported with tree height data collected in the field for one site. The paper addressed the research question "How well do drone-derived point clouds estimate the height of trees in PNF ecosystems?" The study indicated that a drone with a basic RGB camera can estimate tree height with good confidence. The results can potentially be applied across multiple land tenures and similar forest types. This informs the development of drone-based and remote-sensing image-processing methods, which will lead to improved forest inventories, thereby providing forest managers with recent, accurate, and efficient information on forest resources. [ABSTRACT FROM AUTHOR]

With the continuous improvement of people's demand for ecological environment quality, the research on ecological environment monitoring, analysis and evaluation had been paid more and more attention by relevant departments and personnel. Because the images collected by remote sensing technology were many and multi-source, the features extracted from remote sensing images using traditional methods had been difficult to meet the needs of related industry applications. Therefore, this paper made use of the advantages of PIE remote sensing image processing software in data analysis and processing, and put forward the research on ecological environment monitoring, analysis and evaluation methods. Firstly, on the basis of summarizing the concepts and related problems of ecological environment, this paper analyzed the processing methods of remote sensing data sources of ecological environment, and explained the evaluation standards and common methods of ecological environment. Secondly, the composition of PIE remote sensing image processing technology system and its application advantages were described, the common indicators and analysis methods of ecological environment monitoring were given, and the index system and model of ecological environment comprehensive evaluation were established. Finally, through the analysis of experimental cases, the results showed that the ecological environment monitoring analysis and evaluation method proposed in this paper was feasible. Compared with the traditional methods, the method proposed in this paper could objectively evaluate the ecological environment. This paper can not only provide support for the analysis and processing of remote sensing image data, but also provide an important reference for the application of remote sensing technology in the field of ecological environment. [ABSTRACT FROM AUTHOR]

Ground-object classification using remote-sensing images of high resolution is widely used in land planning, ecological monitoring, and resource protection. Traditional image segmentation technology has poor effect on complex scenes in high-resolution remote-sensing images. In the field of deep learning, some deep neural networks are being applied to high-resolution remote-sensing image segmentation. The DeeplabV3+ network is a deep neural network based on encoder-decoder architecture, which is commonly used to segment images with high precision. However, the segmentation accuracy of high-resolution remote-sensing images is poor, the number of network parameters is large, and the cost of training network is high. Therefore, this paper improves the DeeplabV3+ network. Firstly, MobileNetV2 network was used as the backbone feature-extraction network, and an attention-mechanism module was added after the feature-extraction module and the ASPP module to introduce focal loss balance. Our design has the following advantages: it enhances the ability of network to extract image features; it reduces network training costs; and it achieves better semantic segmentation accuracy. Experiments on high-resolution remote-sensing image datasets show that the mIou of the proposed method on WHDLD datasets is 64.76%, 4.24% higher than traditional DeeplabV3+ network mIou, and the mIou on CCF BDCI datasets is 64.58%. This is 5.35% higher than traditional DeeplabV3+ network mIou and outperforms traditional DeeplabV3+, U-NET, PSP-NET and MACU-net networks. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is one of the efflorescing technologies of recent years with splendid real-time applications in the fields of healthcare, agriculture, transportation, industry, and environmental monitoring. In addition to the dominant applications and services of IoT, many challenges exist. As there is a lack of standardization for IoT technologies, the architecture emerged as the foremost challenge. The salient issues in designing an IoT architecture encompass connectivity, data handling, heterogeneity, privacy, scalability, and security. The standard IoT architectures are the ETSI IoT Standard, the ITU-T IoT Reference Model, IoT-A Reference Model, Intel's IoT Architecture, the Three-Layer Architecture, Middle-Based Architecture, Service-Oriented Architecture, Five-Layer Architecture, and IWF Architecture. In this paper, we have reviewed these architectures and concluded that IWF Architecture is most suitable for the effortless development of IoT applications because of its immediacy and depth of insight in dealing with IoT data. We carried out this review concerning smart healthcare as it is among the major industries that have been leaders and forerunners in IoT technologies. Motivated by this, we designed the novel Smart Healthcare Reference Architecture (SHRA) based on IWF Architecture. Finally, present the significance of smart healthcare during the COVID-19 pandemic. We have synthesized our findings in a systematic way for addressing the research questions on IoT challenges. To the best of our knowledge, our paper is the first to provide an exhaustive investigation on IoT architectural challenges with a use case in a smart healthcare system. [ABSTRACT FROM AUTHOR]

Accurately identifying and locating water-crossing objects is of utmost importance for environmental protection. However, traditional detection algorithms often exhibit poor anti-interference performance and low detection accuracy in complex environments. To address these issues, this paper proposes a YOLOW algorithm based on a one-stage object detection algorithm for the automatic identification of water objects. The proposed algorithm incorporates two new modules, namely the SPDCS module and the SPPAUG module, to improve the model's performance. Specifically, the SPDCS module retains all information in the channel dimension, thereby enhancing the model's detection accuracy and recognition ability for water-crossing objects. The SPPAUG module performs multiscale feature fusion, which further improves the model's detection accuracy and recognition ability. Moreover, the C2f module is introduced from YOLOv8 to increase the detection speed. Experimental results on a water-floating object dataset demonstrate that the improved YOLOW model outperforms the standard YOLOv5s algorithm, especially in water-crossing object detection. This research has significant implications for environmental monitoring and protection. [ABSTRACT FROM AUTHOR]

Sample collection and field studies of sea ice take place under harsh conditions which, combined with the logistical difficulties and high cost of voyages to the polar regions, limits the abilities of researchers to determine its properties. Observations of laboratory-grown sea ice can help quantify important sea-ice properties and incorporate them into numerical models. The growth of laboratory sea ice requires experimental set-ups that consider the complexity of sea-ice growth. Regulation and monitoring of environmental variables allow for growth and melt conditions to be controlled, manipulated and reproduced. Facilities thus vary widely because of differing research objectives. This paper presents a summary of some of the published sea-ice laboratories that study the physical properties of sea ice and an overview of their major design considerations, such as tank size, freezing method and instrumentation. It also discusses how these design considerations were implemented in the set-up of the new sea-ice growth laboratory at the Marine and Antarctic Research for Innovation and Sustainability. This paper should guide others in designing their facilities as well as in their understanding of other facilities for results comparison. [ABSTRACT FROM AUTHOR]

Context: The Ramsar Convention on Wetlands is critical to conserving wetlands. However, it has received much criticism, particularly about the inadequacy of reporting by Contracting Parties. Aim: We previously developed a rapid assessment tool called the Rapid Ramsar Management Effectiveness Evaluation Tool (Rapid R-MEET) to alleviate the reporting burden and emphasise the Convention's priorities. Methods: In this paper, we pilot this tool in the Caroni Swamp Ramsar Site in Trinidad. We also conduct a policy-gap analysis to understand the context in which management decisions are being made. Key results: We found serious gaps in management capacity and a complex institutional context in which wetland decision-making takes place that undermine the successful implementation of the Ramsar Convention in the twin island nation. Conclusions: These limitations have created a system of Ramsar Sites with minimal monitoring, research, and enforcement to achieve the Convention's goals, particularly crippling reporting on ecological character. Implications: Better coordination among agencies involved in wetland management, as well as more robust resourcing for management, is needed to achieve wetland conservation goals at the national and site levels. We also found that the Rapid R-MEET can be completed efficiently and effectively by external researchers, providing an avenue to increase collaboration, support under-resourced management teams, and improve reporting to the Convention. The Ramsar Convention on Wetlands is a critical tool in global efforts to conserve wetlands and their myriad values. In this paper, we pilot the Rapid Ramsar Management Effectiveness Evaluation Tool in the Caroni Swamp Ramsar Site in Trinidad. We found serious gaps in capacity and institutional context that undermine implementation of the Ramsar Convention in the twin island nation. [ABSTRACT FROM AUTHOR]

Sensor networks, as a special subtype of wireless networks, consist of sets of wirelessly connected sensor nodes often placed in hard-to-reach environments. Therefore, it is expected that sensor nodes will not be powered from the power grid. Instead, sensor nodes have their own power sources, the replacement of which is often impractical and requires additional costs, so it is necessary to ensure minimum energy consumption. For that reason, the energy efficiency of wireless sensor networks used for monitoring environmental parameters is essential, especially in remote networking scenarios. In this paper, an overview of the latest research progress on wireless sensor networks based on LoRa was provided. Furthermore, analyses of energy consumption of sensor nodes used in agriculture to observe environmental parameters were conducted using the results of real measurements in the field, as well as simulations carried out based on collected data about real equipment. Optimization methods of energy consumption, in terms of choosing the appropriate data collection processes from the conducted field measurements, as well as the settings of network radio parameters imitating real conditions used in conducted simulations were highlighted. In the analyses, special emphasis was placed on choosing the optimal packet size. Unlike in other papers analyzing energy efficiency of LoRa communication, in this paper, it was proven that the adjustment of the transmission speed to the actual size of the packet is important for better energy efficiency of communication and that it can reduce energy consumption considerably. Moreover, in the paper, the contents of a packet that can be used in precision agriculture is suggested in order to prove that the 6-bit packet is sufficient for energy-efficient collection of parameters from the environment, in contrast to the 11-bit packets used in standard commercially available equipment. [ABSTRACT FROM AUTHOR]