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[Display omitted] • MTZ-AgNPs based rapid, accurate Pb2+ detection. • Characterization of MTZ-AgNPs and mechanism for selective Pb2+ sensing. • Portable microfluidic paper-based devices for on-site Pb2+ detection. • High performance with low detection limits in different analyses. • Successful Pb2+ detection in environmental samples for real-time monitoring. This work presents the development of metronidazole-functionalized silver nanoparticles (MTZ-AgNPs) based LSPR sensing for rapidly detecting Pb2+ ions. Advanced characterization techniques provided insights into the morphological, structural, and surface properties of the MTZ-AgNPs. The sensing mechanism and selective interaction of MTZ-AgNPs with Pb2+ ions were explored, elucidating the binding process. The MTZ-AgNPs-based LSPR sensor showed a very low detection limit of 0.018 μM (3.73 ppb). MTZ-AgNPs-based sensing material was also integrated with smartphone-assisted dual read-out microfluidic paper-based analytical devices (µPADs) for rapid detection of Pb2+ ions. The fabricated devices were associated with color and height changes. The detection limits of developed sensors were 0.05 μM (10.36 ppb) for color change and 0.1 μM (20.72 ppb) height changes associated with dual read-out microfluidic paper-based analytical devices. The practicality of the MTZ-AgNPs-based sensing strategy was demonstrated by detecting Pb2+ in various samples, including soil, rock, air particulates, and water. [ABSTRACT FROM AUTHOR]

Introduction: Rewilding, the facilitation of self-sustaining and resilient ecosystems by restoring natural processes, is an increasingly popular conservation approach and potential solution to the biodiversity and climate crises. Outcomes of rewilding can be unpredictable, and monitoring is essential to determine whether ecosystems are recovering. Metabarcoding, particularly of environmental DNA (eDNA), is revolutionizing biodiversity monitoring and could play an important role in understanding the impacts of rewilding but has mostly been applied within aquatic systems. Methods: This systematic review focuses on the applications of eDNA metabarcoding in terrestrial monitoring, with additional insights from metabarcoding of bulk and ingested DNA. We examine publication trends, choice of sampling substrate and focal taxa, and investigate how well metabarcoding performs compared to other monitoring methods (e.g. camera trapping). Results: Terrestrial ecosystems represented a small proportion of total papers, with forests the most studied system, soil and water the most popular substrates, and vertebrates the most targeted taxa. Most studies focused on measuring species richness, and few included analyzes of functional diversity. Greater species richness was found when using multiple substrates, but few studies took this approach. Metabarcoding did not consistently outperform other methods in terms of the number of vertebrate taxa detected, and this was likely influenced by choice of marker, sampling substrate and habitat. Discussion: Our findings indicate that metabarcoding, particularly of eDNA, has the potential to play a key role in the monitoring of terrestrial rewilding, but that further ground- truthing is needed to establish the most appropriate sampling and experimental pipelines for the target taxa and terrestrial system of interest. Systematic Review Registration: https://osf.io/38w9q/?view%5fonly=47fdab224a7a43d298eccbe578f1fcf0 , identifier 38w9q. [ABSTRACT FROM AUTHOR]

Soil spectroscopy is a widely used method for estimating soil properties that are important to environmental and agricultural monitoring. However, a bottleneck to its more widespread adoption is the need for establishing large reference datasets for training machine learning (ML) models, which are called soil spectral libraries (SSLs). Similarly, the prediction capacity of new samples is also subject to the number and diversity of soil types and conditions represented in the SSLs. To help bridge this gap and enable hundreds of stakeholders to collect more affordable soil data by leveraging a centralized open resource, the Soil Spectroscopy for Global Good initiative has created the Open Soil Spectral Library (OSSL). In this paper, we describe the procedures for collecting and harmonizing several SSLs that are incorporated into the OSSL, followed by exploratory analysis and predictive modeling. The results of 10-fold cross-validation with refitting show that, in general, mid-infrared (MIR)-based models are significantly more accurate than visible and near-infrared (VisNIR) or near-infrared (NIR) models. From independent model evaluation, we found that Cubist comes out as the best-performing ML algorithm for the calibration and delivery of reliable outputs (prediction uncertainty and representation flag). Although many soil properties are well predicted, total sulfur, extractable sodium, and electrical conductivity performed poorly in all spectral regions, with some other extractable nutrients and physical soil properties also performing poorly in one or two spectral regions (VisNIR or NIR). Hence, the use of predictive models based solely on spectral variations has limitations. This study also presents and discusses several other open resources that were developed from the OSSL, aspects of opening data, current limitations, and future development. With this genuinely open science project, we hope that OSSL becomes a driver of the soil spectroscopy community to accelerate the pace of scientific discovery and innovation. [ABSTRACT FROM AUTHOR]

Progress monitoring data provide important information on student functioning in response to an intervention. Yet, there are several barriers to effective progress monitoring of internalizing symptoms among youth. To address these concerns, the current paper conceptualized the use of ecological momentary assessment (EMA) as a service delivery model for progress monitoring of internalizing symptoms. Discussed are the benefits of EMA and key considerations for its use in progress monitoring. Lastly, a case study is included to illustrate the practicality of EMA in a progress monitoring context. There is an influx of research using EMA to examine internalizing symptoms. Its growing utility suggests it may be beneficial as a novel way to monitor internalizing symptoms in schools. [ABSTRACT FROM AUTHOR]

Several coal-fired power plants (CFPPs) were built in South Africa, mainly in the central Mpumalanga Province, due to an increase in the demand for Eskom, the national power utility, to keep up with socio-economic growth. The CFPPs, of which 90% are owned by Eskom, generate a significant share of the country's electricity but contribute to the air pollution experienced in the country. The paper discusses sulphur dioxide (SO2), nitrogen dioxide (NO2) and particulate matter of size less than 10 micrometre (μm) in diameter (PM10), using data from 2014 to 2018. The statistics revealed higher PM10 concentrations during winter than in summer and spring at the Kriel and Komati sites; associated with the higher contribution of domestic burning. The study's results could influence legislation and policies and help to understand the source of poor ambient air quality by assessing the three pollutants within the area of the selected power plants. [ABSTRACT FROM AUTHOR]

The DIANA mission represents an underwater analog space mission designed to simulate and study the impact of long-duration spaceflight and extraterrestrial habitation on crew performance, psychosocial dynamics, and technological systems. The mission utilized an underwater habitat, the Hydronaut H03 DeepLab, to mimic the isolated, confined, and extreme (ICE) environment of space. Over eight days, a six-member crew lived and worked in underwater (3) and water surface (3) habitats, performing scientific experiments and operational tasks. The mission schedule encompassed a variety of activities such as drone exploration, extravehicular activities (EVAs), soil sampling, and media interactions, culminating in a simulated departure from the lunar surface. Data collection methods included continuous biomedical monitoring, cognitive task assessments, and sociomapping to analyze team communication and cooperation. This paper provides an overview of the mission architecture and outcomes, offering valuable insights into the challenges of future human space exploration and informing improvements in crew selection, training, and support systems. • Analog space mission DIANA simulated space conditions in underwater habitat. • Crew performed EVAs, drone exploration, soil sampling, and media interactions. • Advanced sensor systems monitored environmental and crew conditions continuously. • Experiment insights can aid future missions in crew selection and support systems. [ABSTRACT FROM AUTHOR]

Bird sound contains rich ecological information, and its related research results can be applied to animal behavior analysis, natural information collection and ecological environment monitoring. Since the early manual monitoring methods, many researchers have continuously innovated and improved the bird sounds recognition technology to overcome the long-standing drawbacks of long cycle time, high cost and poor effectiveness. These developments make bird sounds recognition a highly interesting, but also highly challenging research topic. Acoustic monitoring technology plays a vital role in the automatic recognition of bird sounds. With the popularization of acoustic monitoring technology, the technical routes based on traditional recognition models and neural networks have increased sharply, which has greatly promoted the development of bird sounds recognition. In view of these main technical routes, this paper summarized the research status of bird sounds recognition, provided a summary table of a variety of bird sound sample datasets, introduced the application evolution of various recognition technologies, and analyzed its open challenges. Meanwhile, this paper also cited the published experimental exploration on the improvement of deep learning networks. In general, this paper gives a comprehensive overview of the research process of bird sounds recognition based on acoustic monitoring technology, which has important theoretical and practical value to promote the development of bird sounds recognition technology, and provides a valuable reference for future related research. • Bird sounds recognition based on acoustic monitoring technology is summarized. • A valid summary table of various bird sound datasets is provided. • The evolution of traditional technology and neural network technology in bird sounds recognition is highlighted. • The application efficacy of various models and acoustic features are emphatically analyzed. • The prevailing challenges and prospects in bird sounds recognition are articulated. [ABSTRACT FROM AUTHOR]

R/BCDs ratiometric fluorescence probe were developed for sensitive quantitative and on-site visual detection of Pb2+ and L-cys. [Display omitted] • A ratio fluorescence probe was developed for selective detection of Pb2+ and L-cysteine using red and blue carbon dots. • The detection limits for Pb2+ and L-cysteine were 15 nM and 75 nM, respectively. • Application in real water samples and capsules revealed good recovery and reliability. • User-friendly fluorescence paper and hydrogel were established for on-site visual detection of Pb2+ and L-cysteine. The increasing presence of heavy metals and amino acids in environmental and pharmaceutical samples necessitates the development of effective and sensitive detection methods. Traditional analytical techniques often lack the specificity and sensitivity required for simultaneous detection of multiple analytes, leading to a need for novel approaches. In this study, we developed a ratiometric fluorescence probe based on a physical mixture of red and blue carbon dots (R/BCDs) for the selective detection of lead ions (Pb2+) and L-cysteine (L-Cys). The probe was synthesized through hydrothermal methods and characterized by various spectroscopic techniques. The R/BCDs probe exhibited a remarkable 11.6-fold fluorescence quenching in response to Pb2+, with a detection limit of 15 nM, while subsequent addition of L-Cys showed a 9.6-fold restored fluorescence signal, achieving a detection limit of 75 nM. Additionally, we demonstrated the feasibility of fabricating colorimetric test paper and hydrogels, enabling rapid on-site analysis. This dual-analyte detection capability was demonstrated through successful application in real water and pharmaceutical samples. The R/BCDs probe offers a novel, simple, and effective strategy for environmental monitoring and biological assessments, highlighting its potential for practical applications. [ABSTRACT FROM AUTHOR]

Aerosol radiative forcing (ARF) is an important parameter that describes the impact of atmospheric aerosols on the earth-atmosphere radiation balance. This parameter holds significant importance for environmental monitoring and understanding climate change. Aerosol optical depth (AOD) reflects the degree of atmospheric pollution and plays a key role in evaluating ARF. In this paper, the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) is utilized to calculate the ARF in different AOD in Beijing. The variation characteristics of ARF at different time scales are also studied. Meanwhile, the characteristics of ARF under different cloud types are analyzed, and the coupling relationship between cloud parameters and ARF is proposed. The results show the ARF of absorptive aerosol is comparable to that of fine-mode aerosol, while coarse-mode aerosol have the minimum ARF. The ARF varies significantly under different cloud types. The ARF varies from −59.18 ± 11.64 W/m2 to −104.52 ± 20.90 W/m2 at the bottom of atmosphere (BOA), from −9.94 ± 1.99 W/m2 to −27.41 ± 4.11 W/m2 at the top of the atmosphere (TOA), and from 49.24 ± 9.85 W/m2 to 77.11 ± 15.42 W/m2 at the atmosphere (ATM). The results also show a strong correlation between cloud optical thickness and ARF among cloud parameters. This paper contributes to a deeper understanding of aerosol-cloud interactions in the earth-atmosphere system and is important for predicting future climate change. • Radiative forcing of a long period of polluted environment in Beijing have been analyzed. • Calculations have been performed by a combination of satellite and ground data. • Cloud parameters coupled to aerosol radiative forcing have been established. [ABSTRACT FROM AUTHOR]

In recent years, surface plasmon resonance (SPR) technology has seen significant advancements and has been widely applied. Featuring a label-free detection approach with real-time monitoring, precise with cost-effective results, and low sample needs, this technology is widely used in sectors like bio-molecular sensing, environmental monitoring, food safety, clinical diagnostics, and biomedical applications. This paper attempts to review the fundamental principles of surface plasmon resonance, detailing its sensing mechanism through various configurations, and exploring its global market growth. It then delves into recent advancements in SPR technology, addressing some of the challenges faced, with a focus on innovative design approaches, multi-modal sensing platforms, engineered nanostructure surfaces, and the integration of smart materials and heterostructures. Finally concludes by discussing future directions in the field, highlighting the importance of ongoing innovation and interdisciplinary collaboration in SPR technology. With a strong belief, this could be a valuable resource for both beginners and industry professionals involved in plasmonic research. [ABSTRACT FROM AUTHOR]

Spectral filter array (SFA) imaging is a noval spectral image acquisition method that sets a filter array on one single sensor. It is widely used for environmental monitoring, agricultural assessment, geological exploration, and medical diagnosis. The spectral sensitivities on SFA highly influence the imaging and reconstruction quality. In low-light environments, the imaging quality of the narrowband filter array is significantly compromised, resulting in inadequate image brightness and loss of detail. To address the challenge that heuristic search methods often struggle to find the optimal solution, this paper presents a numerical design method for a global optimal broadband filter array with high light efficiency. The optimal broadband filter array is obtained by maximizing the minimal pairwise angles between filter vectors, and a light efficiency regularization term is used to ensure more light passes through. Further, a prototype spectral imaging system comprising a broadband filter array is built and demonstrated. The findings from the simulations conducted with publicly available multispectral datasets indicated that the broadband filter array demonstrated superior accuracy and enhanced noise resistance in the reconstruction of spectral information when compared to the narrowband filter array. Simultaneously, the simulation results demonstrated that excessive light efficiency will reduce both the anti-noise ability and the accuracy of the reconstruction. Thus, it is essential to choose a broadband filter array that demonstrates satisfactory light efficiency. This study will be beneficial to optimizing the filter array of broadband spectral imaging sensors. [ABSTRACT FROM AUTHOR]

In long term structural health monitoring (SHM), Operational modal analysis (OMA) techniques are commonly used to identify dynamic properties of structures. However, conventional OMA methods do not consider the effect of environment, which may cause bias in the identified dynamic properties. Some machine learning models (such as Gaussian process model) can quantitatively describe the effect of environment on structure, but the associated parameters do not have physical meaning. In order to address these problems, this paper proposes a novel fusion model that combines structural dynamics model and data-driven model to identify dynamic properties with the effect of environment considered. The proposed method adopts a probabilistic framework that can incorporate the uncertainties and correlations in the data. The proposed method is validated using synthetic data and applied to the monitoring data of two full-scale bridges. The results show that it can effectively identify the modal parameters while describing the correlation between the environmental variations and the structural responses. The proposed method can facilitate robust and reliable identification of dynamic properties with structures under environmental effect. [ABSTRACT FROM AUTHOR]

This study examines Moisture Buffer Value (M B V) of interior finishing layers, which impacts buildings internal relative humidity, thus indoor environmental quality. The M B V depends on material's moisture capacity and vapour diffusion resistance factor, both of which depends on relative humidity. The paper aims to (i) characterize a new recycled material plaster that includes construction and food industry waste through laboratory measurements, (ii) use dynamical building simulations to quantify the impact of the M B V of existing and the new interior plaster on relative humidity in real design scenarios, and (iii) evaluate how changing the definition of the M B V to consider its dependence on indoor air relative humidity can improve its accuracy. Results show that the plaster's moisture buffering properties significantly reduce the variations of the relative humidity of interior climates compared to a vapour-tight finishing layer. The performances of the new plaster made with recycled materials are comparable to those of the other plasters. The new M B V definitions ("Dynamical M B V ″ and "Summer/Winter M B V ") show a significantly improved correlation with the relative humidity variations of indoor climates of buildings, observed in dynamic simulations, with respect to the typically used practical M B V. The new definitions are therefore promising for practical applications. • A new MBV definition that accounts for relative humidity dependence is introduced. • A robust correlation between MBV and building interior climate is demonstrated. • The hygrothermal performances of a novel recycled-material plaster are analysed. • Recycled components do not adversely affect moisture buffering properties. [ABSTRACT FROM AUTHOR]

[Display omitted] • Flexible porous TPU/PPy films show excellent stretchability of up to 790 % strain. • TPU/PPy sensors exhibit multiple sensing performance for pressure, strain and gas. • TPU/PPy sensors present favorable breatheability and wearable comfort. With the rapid development of flexible wearable sensors in the fields of medical detection and environmental monitoring, it is urgent to develop multifunctional sensors with high sensitivity, fast response, wide sensing range, excellent comfort and multi-stimulus response. In this paper, a porous thermoplastic polyurethane (TPU) film with high stretchability and breathability was prepared by facial liquid phase separation, and a flexible wearable sensor was developed through vapor deposition of polypyrrole (PPy) within the porous TPU film matrix. The fabricated sensor can detect pressure, strain and gas, has a wide pressure detection range (up to 98 kPa), fast response speed (100 ms), sensitivity of up to 0.33 kPa−1 and working stability. Furthermore, it has an extremely high tensile strength of 790 % and can operate in the 0–400 % stretch range with a maximum sensitivity of 238.2. Importantly, the flexible porous TPU/PPy multifunctional sensor has excellent breathability and the capability to monitor NH 3 gas, and the gas detection limit can reach 10 ppm. This work provides a new route for achieving high-performance and wearing comfortable strain sensors with broad application prospects in human activity detection and NH 3 gas monitoring devices. [ABSTRACT FROM AUTHOR]

This paper presents the synthesis of a novel corn-stalk-like MnO₂/CoNi oxide composite using an in situ epitaxial attachment growth strategy, in which CoNi oxide nanosheets are anchored onto MnO₂ nanowires. The one-dimensional MnO₂ nanowires, with their large specific surface area, serve as a support to enhance the electronic conductivity of the CoNi oxides. Hexamethylenetetramine (HMTA) is employed as an alkaline linking agent, playing a key role in shaping the CoNi oxide nanosheets and ensuring their successful growth on the MnO₂ nanowires. The MnO₂/CoNi oxide composite-based electrochemical sensor exhibits excellent synergistic and interfacial effects, promoting electron transfer and charge migration. This composite material shows outstanding electrocatalytic performance for hydrazine detection, with a broad linear range (0.48−6106.58 μM), low detection limit (0.286 μM, S/N = 3), and high sensitivity (0.037 μA μM⁻1). Moreover, when tested for hydrazine detection in water samples, the sensor achieved a recovery rate of 95.7–105 %, highlighting its high sensitivity and rapid response in practical applications. Using an in-situ epitaxial growth strategy, CoNi oxide nanosheets were grown on MnO 2 nanowire scaffolds. The heterostructure formed between the large surface area of the MnO 2 nanowires and the highly conductive CoNi oxide nanosheets provides high sensitivity and rapid response for detecting hydrazine. [Display omitted] • A unique MnO 2 /CoNi oxide composite was synthesized using an in-situ epitaxial attachment growth strategy. • HMTA, acting as a linking agent, facilitated the effective growth of CoNi oxide on MnO 2. • The SSA of the MnO 2 nanowires effectively prevents the aggregation of CoNi oxide nanosheets. • The MnO 2 /CoNi oxide composite serves as a highly efficient sensor for detecting hydrazine. [ABSTRACT FROM AUTHOR]

[Display omitted] • The review addresses challenges, and future directions for PANI-based gas sensors. • PANI sensors aid public health, industrial safety, and environmental monitoring. • Key areas are highlighted to enhance sensitivity and selectivity for toxic gases. • IoT and AI integration enhances real-time environmental monitoring and data precision. • New PANI composites, hybrid materials, and portable sensors broaden application ranges. Polyaniline (PANI)-based sensors have emerged as promising tools for gas detection, owing to their remarkable electrical conductivity, environmental stability, and straightforward synthesis process. With applications spanning public health, industrial safety, and environmental monitoring, these sensors hold significant societal relevance. Recent progress has centered on advancing their design, modifying materials, and refining detection mechanisms. This study focuses on the critical challenges of improving the sensitivity, selectivity, and response time of PANI-based sensors, particularly under conditions involving interfering gases. Key strategies, such as nano-structuring and composite fabrication are discussed to overcome these limitations. Furthermore, the potential for interdisciplinary approaches to advance the development of these sensors is highlighted. The paper concludes by outlining critical research areas and proposing pathways to enhance the performance of PANI-based sensors for toxic gas detection. [ABSTRACT FROM AUTHOR]

The visualization and accurate on-site detection of tetracycline and Cu2+ residues in food are essential for maintaining food safety and public health. A novel poly (aspartic acid) Ratiometric fluorescent probe was designed for the sensitive visual detection of tetracycline (Tc) and Cu2+ using the EDTA-Eu3+ complex as the sensing unit and fluorescein dye as the internal standard. The fluorescence of Eu3+ at 617 nm was turned on significantly by Tc via the "antenna effect" and can be quenched by Cu2+ owing to the inner filter effect. The probes selectively detected Tc and Cu2+ over a wide linear range, low LODs of 10 nM and 333 nM, respectively, were obtained. A smartphone APP and probe-loaded test paper were also assembled into a biomass-based portable sensor for visual point-of-use analysis. The fabricated sensors successfully monitored the Tc and Cu2+ levels in milk, honey, and tap water samples, suggesting their broad applications in food safety monitoring and environmental analysis. • First design of a poly(aspartic acid) ratiometric fluorescent probe for the detection of tetracycline and Cu2+. • Low LODs of 10 nM and 333 nM for tetracycline and Cu2+, respectively. • Smartphone-based portable platform for the real-time and on-site detection of tetracycline and Cu2+ in food samples. [ABSTRACT FROM AUTHOR]

Traditional approaches to group decision-making (GDM) often assume that decision-makers (DMs) are perfectly rational, which neglects the psychological attitudes exhibited by DMs during the decision-making process. To address this issue, this paper designs a novel method for GDM with multiplicative distribution linguistic preference relations (MDLPRs), which incorporates twin multiplicative data envelopment analysis (TMDEA) regret-rejoice cross-efficiency models and a weighting model with individual tolerance. Firstly, we provide a clear definition of MDLPRs based on multiplicative linguistic terms that capture the asymmetrical qualitative recognition of DMs. Then, a weighting model is constructed to obtain DM's weights. This model assumes that DMs are boundedly rational and have a certain tolerance level for group consensus measurement. Subsequently, TMDEA cross-efficiency models are developed based on different perspectives. On this basis, we further propose TMDEA regret-rejoice cross-efficiency models that consider the psychological risks of DMs. Furthermore, the method for GDM based on TMDEA regret-rejoice cross-efficiency models and individual tolerance with MDLPRs is given. Finally, we present a case study of the selection of the Ya'an ecological monitoring station in China to test the validity and applicability of the proposed method. The merits and robustness of the constructed method are highlighted by sensitive analysis and comparative analysis. [ABSTRACT FROM AUTHOR]

High-quality-factor Fano resonances excited by metasurfaces have been playing an important role in the field of optical sensing. A high-performance sensor for refractive index and methane concentration measurement is designed and prepared in this paper. The symmetry of the structure is broken by altering the size of the rectangular holes in the tetramer, resulting fano resonance Q-factor of 34176 and modulation depth of 100%. The finite-difference time-domain (FDTD) method is used to simulate the structure. The results show that the refractive index and methane concentration sensitivity are 325 nm/RIU and −1.44 nm/%, respectively. The FOM can achieve 10156 RIU−1. Furthermore, experiments are carried out on the gas application of the structure and the sensitivity of methane concentration is −1.03 nm/%. Therefore, the proposed metasurface has more practical and feasible significance in background refractive index and gas environment monitoring. This work presents a periodic all-dielectric element structure for measuring refractive index and methane concentration, which consists of four rectangular rings arranged on a SiO 2 substrate. Theoretically, three Fano resonances with high Q-factors are excited in the near-infrared range by breaking the symmetry structure based on quasi-bound states in the continuum. The results show that the proposed meta-structure has the potential to significantly advance optical sensing, optical switch and laser technology. • The structure can be used as an optical sensor to measure refractive index and methane concentration. The methane sensor achieves a sensitivity of 1.44 nm%. For the refractive index sensor, the sensitivity (S) and figure of merit (FOM) are 325 nm/RIU and 10156 RIU−1, respectively. • The metasurface makes it easy to excite multiple Fano resonances, in which the maximum Q-factor is around 34176 and the modulation depth is close to 100%. • Comprehensive analysis of the resonance modes of the three distinct peaks and their sensitivity to refractive index. • The all-dielectric metastructure has the advantages of avoiding ohmic losses and compatibility with CMOS systems, which are more appropriate for the development of miniaturized and integrated modern optical systems. It has broad application potential in the fields of optical sensing and environmental monitoring. [ABSTRACT FROM AUTHOR]

Iron ion (Fe3+) is crucial for both biological organisms and the environment, and iron dyshomeostasis can lead to a variety of diseases and environmental issues. Therefore, the detection of Fe3+ is of significant importance across various fields. For this purpose, through the co-assembly of water-soluble tetralactam macrocycle H and 1,1,2,2-tetraphenylethylene (TPE), we prepared a new type of tetralactam macrocycle based aggregation-induced emission dots (AIE-dots), namely TPE@H. TPE@H exhibitted good water-solubility and allowed the water-insoluble TPE to aggregate and exhibit strong fluorescence emission in water. Furthermore, the fluorescence of TPE@H could be quenched by Fe3+ through the inner filter effect, making TPE@H a potential "turn-off" fluorescent probe for Fe3+ detection. TPE@H exhibited a limit of detection as low as 1 μM with high selectivity, and also retained good detection performance in domestic water, human serum, and even drugs, indicating its great potential for biological and environmental monitoring of Fe3+. This study not only provides a green and efficient method for detecting Fe3+, but also expands the approach for application of water-insoluble dyes in the aqueous environment. A macrocycle-based aggregation-induced emission dots (AIE-dots) was developed by the co-assembly of 1,1,2,2-tetraphenylethylene (TPE) and water-soluble tetralactam macrocycle (H). The TPE@H AIE-dots exhibited stable fluorescence emission, excellent water-solubility, and uniform diameter. TPE@H also displayed high selectivity and sensitivity in the detection of Fe3+ due to the inner filter effect. The Fe3+ concentrations detection was achieved in domestic water, human serum, and drugs, as well as test paper detection. [Display omitted] • A novel tetralactam macrocycle based aggregation-induced emission dots was synthesized. • The AIE-dots (TPE @ H) displayed uniform diameter, excellent water-solubility and stable fluorescence emission. • Efficient and rapid detection of Fe3+ in pure water. • New approach for application of water insoluble dyes in the aqueous environment. [ABSTRACT FROM AUTHOR]

Organic photoelectrochemical transistor (OPECT)-based sensors that use light-sensitive semiconductor materials as the gate have recently garnered increasing interest in various fields ranging from biological analysis to environmental monitoring. However, so far, the working principle and representative applications of OPECT sensors have not been discussed and reviewed systematically. In this review, we aim to present a comprehensive overview of the working principle and sensing mechanisms of OPECT-based sensors and various inorganic and organic photoactive gate materials used in OPECTs, with a focus on the representative applications and recent progress of these sensors in the fields of enzyme sensing, immunoassays, and nucleic acid-based sensing. Moreover, the challenges and outlooks that need to be addressed for future advancements in this field are summarized and discussed. This review will assist researchers in gaining a more comprehensive understanding and cognition of new OPECT-based sensing methods and devices. [ABSTRACT FROM AUTHOR]

While exploring the use and visibility of fluorescent tracers for estimating flow velocities under varying water turbidity, this study introduces a technique for estimating suspended sediment concentration (SSC) in water. Laboratory and field experiments were conducted using a dual above/underwater optical camera monitoring system to measure and compare the visibility (brightness intensity) of fluorescent tracers applied into water under varying SSC loads. Based on the fluorescent properties of quinine under low luminosity conditions, a quinine solution, in both liquid and solid (ice cube) states, was applied to the water surface. Its visibility was recorded using the monitoring system, and brightness intensity was quantified using image processing techniques such as grayscale conversion, object segmentation, and pixel intensity extraction. Results indicate that the tracer brightness intensity decreases as SSC loads increase, with the underwater camera recording lower visibility than the above-water camera, particularly at higher SSC loads. Empirical regression equations were developed to describe the relationship between the tracer brightness intensity recorded by the underwater camera and the SSC loads. These findings suggest the ability of the presented monitoring system, using quinine solution in both liquid and solid states, along with an underwater camera, to estimate a relevant range of SSC loads under low luminosity conditions. [ABSTRACT FROM AUTHOR]

Rapid urban growth is a subject of worldwide interest due to environmental problems. Population growth, especially migration from rural to urban areas, leads to land use and land cover (LULCC) changes in urban centres. Therefore, LULCC and urban growth analyses are among the studies that will help decision-makers achieve better sustainable management and planning. The objective of this study was to ascertain the impact of urbanization, which resulted from migration, on the alterations in LULCC, with a particular focus on the changes in forest areas surrounding the Bartın city centre between 2000 and 2020. Spatial databases for two periods were used to determine changes in urban growth. The spatial and temporal LULCC patterns of land use were quantified by interpreting spatial data. Remote sensing (RS) and geographical information systems (GIS) have been used for data collection, analysis, and presentation. The LULCC was assessed under nine classes using optical remote sensing methods on stand-type maps created from aerial photos. To determine how urban growth affects LULCC, land use status and transition matrices were created for each of the five sprawl zones around Bartın city. The annual change in forest areas is determined by the "annual forest rate". The results indicate that the urbanization of Bartın city from 2000 to 2020 increased by approximately 19% (2510645.82 m2). However, this did not harm the forests; cover increased by 10.32% (174729.65 m²) over the same period. The process of urbanization was particularly evident in open areas and agricultural zones. During this period, there was a 37% reduction in agricultural areas (2943229.85 m²) and a 59% reduction in open areas (1265457.76 m²). The sprawl of Bartın city can be attributed to changes in its demographic structure, which mainly includes the migration of the rural population to urban areas and the emergence of new job opportunities. Factors such as challenging urban living conditions, insecure environments because of the increase in temporary foreign asylum seekers, and retirees returning to their hometowns are believed to have contributed to this population growth. [ABSTRACT FROM AUTHOR]

This study presents an energy-efficient Internet of Things (IoT)-based wireless sensor network (WSN) framework for autonomous data validation in remote environmental monitoring. We address two critical challenges in WSNs: ensuring data reliability and optimizing energy consumption. Our novel approach integrates an artificial neural network (ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture. The proposed ANN model is designed to simultaneously detect multiple fault types, including spike faults, stuck-at faults, outliers, and out-of-range faults. We collected sensor data at 5-minute intervals over three months, using temperature and humidity sensors. The ANN was trained on 70% of the 26,280 data points per sensor, with 15% each for validation and testing. Our framework demonstrated a 97.1% improvement in fault detection accuracy (measured by F1 score) compared to existing methods, including rule-based, moving average, and statistical outlier detection approaches. The energy efficiency of the system was evaluated through 24-h power consumption tests, showing significant savings over traditional WSN architectures. Key contributions include a multi-fault detection ANN model balancing accuracy and computational efficiency, an energy-optimized IoT-WSN architecture for remote deployments, and a comprehensive performance evaluation framework. While our approach offers improvements in both data validation and energy efficiency, we acknowledge limitations such as potential scalability issues and the need for further real-world testing. This research advances the field of remote environmental monitoring by providing a robust, energy-efficient solution for ensuring data reliability in challenging deployment scenarios. Future work will explore more advanced machine learning techniques and extended field testing to further validate and improve the system's performance. [ABSTRACT FROM AUTHOR]

Over six million Americans rely on solid fuels for residential heating, which can result in high concentrations of fine and ultrafine particulate matter (PM). Little is known on the characteristics of indoor PM in rural homes, including lung deposited surface area (LDSA) concentration and metals composition, and whether these characteristics may vary by fuel type.Homes using oil, cordwood, or pellet were recruited into the study. Indoor air quality assessment over 48 h included PM2.5 and PM10 mass concentration (Purple Air monitors) and ultrafine (LDSA) PM < 1 μm, number concentration, and diameter (Partector). Teflon© or polytetrafluoroethylene (PTFE) filters were used to collect PM mass for metals characterization by inductively coupled plasma mass spectrometry (ICP-MS). Participants completed a time activity diary to link household behaviors to changes in LDSA, particle number, and size distribution.Ten homes completed the study- three used primary oil-fueled furnaces/boilers, four used primarily cordwood, and three used pellet fuel. Activity logs and real time measurements highlighted elevated indoor pollution events such as loading wood stoves and cooking. Maximum indoor air particle number concentrations measured were similar across fuel types, though total average particle concentrations, maximum mass, and LDSA concentrations were highly variable. For individual elements Ca, K, Cu, Cd, and Pb, there was a trend of higher concentrations in cordwood heated home, followed by pellet, and then oil heat. Significance: Exposure characteristics and elemental composition of fine and ultrafine particles in rural homes vary by heating fuel type. Due to the contributions from other indoor activities to ultrafine PM, we suggest trace elements might be a better marker of woodsmoke exposure compared to other particle metrics. [ABSTRACT FROM AUTHOR]

Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors. These vehicles are crucial in various fields, including environmental science research, ecological and environmental monitoring projects, disaster response, and emergency management. A key method employed in these vehicles for achieving high-precision positioning is LiDAR (lightlaser detection and ranging)-Visual Simultaneous Localization and Mapping (SLAM). However, maintaining high-precision localization in complex scenarios, such as degraded environments or when dynamic objects are present, remains a significant challenge. To address this issue, we integrate both semantic and texture information from LiDAR and cameras to enhance the robustness and efficiency of data registration. Specifically, semantic information simplifies the modeling of scene elements, reducing the reliance on dense point clouds, which can be less efficient. Meanwhile, visual texture information complements LiDAR-Visual localization by providing additional contextual details. By incorporating semantic and texture details from paired images and point clouds, we significantly improve the quality of data association, thereby increasing the success rate of localization. This approach not only enhances the operational capabilities of ecological monitoring vehicles in complex environments but also contributes to improving the overall efficiency and effectiveness of ecological monitoring and environmental protection efforts. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (HSI) has gained significant attention in recent years due to its broad applications across agriculture, environmental monitoring, urban planning, infrastructure management, and defense and security for object detection and classification. Despite its potential, current methodologies face challenges such as insufficient feature extraction, noise interference, and inadequate spatial-spectral fusion, limiting classification accuracy and robustness. This study reviews advancements in HSI object detection and classification methodologies, emphasizing the role of machine-learning (ML) and deep-learning (DL) techniques. Hence, this work proposes a novel framework to address these challenges, prioritizing robust feature extraction, effective spatial-spectral fusion, and comprehensive noise removal mechanisms. By integrating DL techniques and training with HSI noisy data, this framework aims to enhance classification accuracy and robustness. The findings suggest that the proposed approach significantly improves the reliability and performance of HSI-based object classification systems. This research provides a pathway for future development in the domain, promising to elevate the effectiveness of HSI applications in real-world scenarios. [ABSTRACT FROM AUTHOR]

An advanced sensing platform for the on-site and real-time monitoring of cyanide (CN−) pollution was developed, addressing the key challenges of enhancing the selectivity and sensitivity, whilst simplifying the sensing process. This platform features a novel colorimetric sensor (PDA-BMN) created by incorporating a receptor for CN− recognition within a polydiacetylene (PDA) conjugated polymer system. The interaction of CN− with the receptor distorted the π-conjugated backbone, causing a distinct color change from blue to orange, with a limit of detection (LOD) of 0.55 µM, thereby highlighting its high sensitivity. The PDA-BMN system was integrated into a lateral flow assay (LFA) strip to facilitate on-site monitoring. The LFA strip was strategically designed with a partially pressed zone to reduce the fluid flow rate, enhance the reaction time, and increase the probability of interaction with the PDA-BMN sensor. This innovation resulted in a significant colorimetric transition, enabling the rapid and sensitive detection of CN− without the requirement for analytical instruments. This study demonstrates the effectiveness of combining PDA and LFA systems for real-time environmental monitoring, offering a robust, efficient, and user-friendly solution for detecting CN− pollution. [Display omitted] • A PDA-BMN colorimetric sensor was developed for the detection of CN–. • Interactions between CN− and the receptor distorted the π-conjugated backbone. • This sensor was sensitive (LOD = 0.66 μM), giving a blue-to-orange color change. • The PDA-BMN system was integrated into an LFA strip to facilitate on-site monitoring. • Pressing this strip reduced the fluid flow rate, enhancing the detection performance. [ABSTRACT FROM AUTHOR]