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An aquaculture automation system (AcAS) is a user-friendly single-window unit. This allows end users to monitor and control the entire system easily through a built-in, customizable graphical user interface. AcAS was designed for simplicity, making it easy to configure and use. This system was integrated with highly efficient industrial-grade environmental sensors (pH, conductivity, oxidation-reduction potential, and dissolved oxygen) to ensure precise and error-free results in harsh environments. It can also store user and system data in an built-in memory device. It is equipped with built-in Wi-Fi, LoRa/ZigBee, and 4G/5G modules for data transfer, making it compatible with modern communication technologies. The program was programmed to be farmer-friendly and helped farmers maintain optimal shrimp growth conditions by monitoring various parameters. AcAS takes corrective measures as required, and provides updates to farmers through a graphical display unit. Farmers can also configure devices to receive alerts for important field parameters or alarm conditions. Therefore, AcAS enhances the efficiency and sustainability of aquaculture farming by enabling precise control of farming conditions and proactive management of aquaculture. [ABSTRACT FROM AUTHOR]

The maximum permissible concentration (m.p.c.) of Cu2+ ions in drinking water, as set by the World Health Organization (WHO) is m.p.c. (Cu2+)WHO = 30 × 10−6 m, whereas the US Environmental Protection Agency (EPA) establishes a more restrictive value of m.p.c. (Cu2+)EPA = 20 × 10−6 m. Herein, for the first time ever, a family of m.p.c. (Cu2+) "visual" pass/fail sensors is developed based on water‐soluble lanthanide‐containing single‐chain nanoparticles (SCNPs) exhibiting an average hydrodynamic diameter less than 10 nm. Both europium (Eu)‐ and terbium (Tb)‐based SCNPs allow excessive Cu2+ to be readily detected in water, as indicated by the red‐to‐transparent and green‐to‐transparent changes, respectively, under UV light irradiation, occurring at 30 × 10−6 m Cu2+ in both cases. Complementary, dysprosium (Dy)‐based SCNPs show a yellow color‐to‐transparent transition under UV light irradiation at ≈15 × 10−6 m Cu2+. Eu‐, Tb‐, and Dy‐containing SCNPs prove to be selective for Cu2+ ions as they do not respond against other metal ions, such as Fe2+, Ag+, Co2+, Ba2+, Ni2+, Hg2+, Pb2+, Zn2+, Fe3+, Ca2+, Mn2+, Mg2+, or Cr3+. These new m.p.c. (Cu2+) "visual" pass/fail sensors are thoroughly characterized by a combination of techniques, including size exclusion chromatography, dynamic light scattering, inductively coupled plasma‐mass spectrometry, as well as infrared, UV, and fluorescence spectroscopy. [ABSTRACT FROM AUTHOR]

Extracellular vesicles (EVs) are promising tools for the early diagnosis of diseases, and bacterial membrane vesicles (MVs) are especially important in health and environment monitoring. However, detecting EVs or bacterial MVs presents significant challenges for the clinical translation of EV-based diagnostics. In this Review, we provide a comprehensive discussion on the basics of nanoplasmonic sensing and emphasize recent developments in nanoplasmonics-based optical sensors to effectively identify EVs or bacterial MVs. We explore various nanoplasmonic sensors tailored for EV or bacterial MV detection, emphasizing the application of localized surface plasmon resonance through gold nanoparticles and their multimers. Additionally, we highlight advanced EV detection techniques based on surface plasmon polaritons using plasmonic thin film and nanopatterned structures. Furthermore, we evaluate the improved detection capability of surface-enhanced Raman spectroscopy in identifying and classifying these vesicles, aided by plasmonic nanostructures. Nanoplasmonic sensing techniques have remarkable precision and sensitivity, making them a potential tool for accurate EV detection in clinical applications, facilitating point-of-care molecular diagnostics. Finally, we summarize the challenges associated with nanoplasmonic EV or bacterial MV sensors and offer insights into potential future directions for this evolving field. [ABSTRACT FROM AUTHOR]

The scope of this study was to apply advances in materials science, specifically the use of organosilicate nanoparticles as a high surface area platform for passive sampling of chemicals or pre-concentration for active sensing in multiple-phase complex environmental media. We have developed a novel nanoporous organosilicate (NPO) film as an extraction phase and proof of concept for application in adsorbing hydrophobic compounds in water and sediment. We characterized the NPO film properties and provided optimization for synthesis and coatings in order to apply the technology in environmental media. NPO films in this study had a very high surface area, up to 1325 m2/g due to the high level of mesoporosity in the film. The potential application of the NPO film as a sorbent phase for sensors or passive samplers was evaluated using a model hydrophobic chemical, polychlorinated biphenyls (PCB), in water and sediment. Sorption of PCB to this porous high surface area nanoparticle platform was highly correlated with the bioavailable fraction of PCB measured using whole sediment chemistry, porewater chemistry determined by solid-phase microextraction fiber methods, and the Lumbriculus variegatus bioaccumulation bioassay. The surface-modified NPO films in this study were found to highly sorb chemicals with a log octanol-water partition coefficient (Kow) greater than four; however, surface modification of these particles would be required for application to other chemicals. [ABSTRACT FROM AUTHOR]

Heavy metal contamination due to industrial and agricultural waste represents a growing threat to water supplies. Frequent and widespread monitoring for toxic metals in drinking and agricultural water sources is necessary to prevent their accumulation in humans, plants, and animals, which results in disease and environmental damage. Here, the metabolic stress response of bacteria is used to report the presence of heavy metal ions in water by transducing ions into chemical signals that can be fingerprinted using machine learning analysis of vibrational spectra. Surface-enhanced Raman scattering surfaces amplify chemical signals from bacterial lysate and rapidly generate large, reproducible datasets needed for machine learning algorithms to decode the complex spectral data. Classification and regression algorithms achieve limits of detection of 0.5 pM for As3+ and 6.8 pM for Cr6+, 100,000 times lower than the World Health Organization recommended limits, and accurately quantify concentrations of analytes across six orders of magnitude, enabling early warning of rising contaminant levels. Trained algorithms are generalizable across water samples with different impurities; water quality of tap water and wastewater was evaluated with 92% accuracy.

The ever‐increasing complexity of environmental pollutants urgently warrants the development of new detection technologies. Sensors based on the optical properties of hydrogels enabling fast and easy in situ detection are attracting increasing attention. In this paper, the data from 138 papers about different optical hydrogels (OHs) are extracted for statistical analysis. The detection performance and potential of various types of OHs in different environmental pollutant detection scenarios were evaluated and compared to those obtained using the standard detection method. Based on this analysis, the target recognition and sensing mechanisms of two main types of OHs are reviewed and discussed: photonic crystal hydrogels (PCHs) and fluorescent hydrogels (FHs). For PCHs, the environmental stimulus response, target receptors, inverse opal structures, and molecular imprinting techniques related to PCHs are reviewed and summarized. Furthermore, the different types of fluorophores (i.e., compound probes, biomacromolecules, quantum dots, and luminescent microbes) of FHs are discussed. Finally, the potential academic research directions to address the challenges of applying and developing OHs in environmental sensing are proposed, including the fusion of various OHs, introduction of the latest technologies in various fields to the construction of OHs, and development of multifunctional sensor arrays. [ABSTRACT FROM AUTHOR]

Accurate hydrological data with high spatial resolution is important for flood risk and water resource management, particularly under the context of climate change. The cost of monitoring networks, as well as the characteristics of the hydrological environment itself, can be a barrier to meeting these data requirements, however. This study covers the design and testing of a low-cost, "build-it-yourself", instream water depth sensor providing an assessment of its potential in future hydrological monitoring projects. The low-cost sensor was built using an Arduino microcontroller, a differential pressure sensor and a thermistor, a real-time clock, and an SD card module. The low-cost logger was deployed in tandem with a factory-calibrated Solinst®LevelLogger® 5 Junior for 6 months in the River Wissey, UK. We found the mean absolute error of the Arduino-based logger relative to the commercial setup to be ±0.69 cm for water depth and ±0.415 °C for water temperature. Economically, the Arduino-based logger offers an advantage, costing a total of £133.35 (USD 168.26 at time of publication) comparative to the industrial comparison's cost of £408 (USD 514.83 at time of publication). This study concludes that the low cost of the Arduino-based logger gives a strong advantage to its incorporation in hydrological data collection, if the trade-offs (i.e., time investment and accuracy) are considered acceptable and appropriate for a project. [ABSTRACT FROM AUTHOR]

The ubiquity of sensors in smart-homes facilitates the support of independent living for older adults and enables cognitive assessment. Notably, there has been a growing interest in utilizing movement traces for identifying signs of cognitive impairment in recent years. In this study, we introduce an innovative approach to identify abnormal indoor movement patterns that may signal cognitive decline. This is achieved through the non-intrusive integration of smart-home sensors, including passive infrared sensors and sensors embedded in everyday objects. The methodology involves visualizing user locomotion traces and discerning interactions with objects on a floor plan representation of the smart-home, and employing different image descriptor features designed for image analysis tasks and synthetic minority oversampling techniques to enhance the methodology. This approach distinguishes itself by its flexibility in effortlessly incorporating additional features through sensor data. A comprehensive analysis, conducted with a substantial dataset obtained from a real smart-home, involving 99 seniors, including those with cognitive diseases, reveals the effectiveness of the proposed functional prototype of the system architecture. The results validate the system's efficacy in accurately discerning the cognitive status of seniors, achieving a macro-averaged F1-score of 72.22% for the two targeted categories: cognitively healthy and people with dementia. Furthermore, through experimental comparison, our system demonstrates superior performance compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the continuous development of modern technology, the application of sensor technology in architectural landscape planning and design is becoming increasingly widespread. The combination of environmental sensors and 3D point cloud processing technology provides new possibilities for modern architectural landscape planning and design. Environmental sensors can provide accurate environmental data support for the design process by monitoring the climate conditions of the building site in real-time, better understanding the natural environmental characteristics of the building site, and making targeted planning and design. Environmental sensors can accurately measure the intensity and direction of light, helping designers arrange the layout and window design of buildings to maximize the use of natural lighting. By monitoring and analyzing temperature data, the design team can effectively optimize the energy consumption management of buildings and improve their energy efficiency performance. The combination of 3D point cloud processing technology and environmental sensor data accurately restores the real environment of the building site to a digital model, allowing designers to conduct comprehensive spatial analysis and design. This digital and intelligent design process improves the precision and efficiency of design, allowing the design team to better understand the environmental characteristics of the building site and achieve intelligent and personalized landscape design, Injecting new vitality and creativity into future architectural planning and design.

Occupancy information is essential for space management, energy efficiency, and in times of the COVID-19 pandemic, for crowd control. Obtaining labeled data is challenging due to hardware limitations, privacy considerations, and the required underlying costs. This study demonstrates the benefits of using Semi-Supervised Learning (SSL) for occupancy estimation in enclosed spaces; which requires less labeled data than other Machine Learning (ML) methods. It presents an empirical comparison between Supervised ML and SSL models in three real-life university classrooms (uncontrolled conditions). The data was collected for three weeks at each classroom using an in-house developed Internet of Things (IoT) device that measures air temperature, relative humidity, and atmospheric pressure. The ground truth records were gathered through manual logging of occupancy levels. Datasets’ sizes averaged 2350 entries with only 280 labeled instances per dataset. Support Vector Machine (SVM), Random Forest (RF), and Multi-Layer Perceptron (MLP) were used to define a performance baseline for supervised ML. Self-Training (ST) and Label Propagation (LP) were tested for SSL. ST achieved superior performance compared to baseline models (SVM, RF, MLP) with a highest average accuracy of 87.33% compared to SVM (86.66%). These results demonstrate the effectiveness of SSL for indirect occupancy estimation while reducing the need for extensive data collection and labeling.

SignificanceAgricultural sensor is the key technology for developing modern agriculture. Agricultural sensor is a kind of detection device that can sense and convert physical signal, which is related to the agricultural environment, plants and animals, into an electrical signal. Agricultural sensors could be applied to monitor crops and livestock in different agricultural environments, including weather, water, atmosphere and soil. It is also an important driving force to promote the iterative upgrading of agricultural technology and change agricultural production methods. ProgressThe different agricultural sensors are categorized, the cutting-edge research trends of agricultural sensors are analyzed, and summarizes the current research status of agricultural sensors are summarized in different application scenarios. Moreover, a deep analysis and discussion of four major categories is conducted, which include agricultural environment sensors, animal and plant life information sensors, agricultural product quality and safety sensors, and agricultural machinery sensors. The process of research, development, the universality and limitations of the application of the four types of agricultural sensors are summarized. Agricultural environment sensors are mainly used for real-time monitoring of key parameters in agricultural production environments, such as the quality of water, gas, and soil. The soil sensors provide data support for precision irrigation, rational fertilization, and soil management by monitoring indicators such as soil humidity, pH, temperature, nutrients, microorganisms, pests and diseases, heavy metals and agricultural pollution, etc. Monitoring of dissolved oxygen, pH, nitrate content, and organophosphorus pesticides in irrigation and aquaculture water through water sensors ensures the rational use of water resources and water quality safety. The gas sensor monitors the atmospheric CO2, NH3, C2H2, CH4 concentration, and other information, which provides the appropriate environmental conditions for the growth of crops in greenhouses. The animal life information sensor can obtain the animal's growth, movement, physiological and biochemical status, which include movement trajectory, food intake, heart rate, body temperature, blood pressure, blood glucose, etc. The plant life information sensors monitor the plant's health and growth, such as volatile organic compounds of the leaves, surface temperature and humidity, phytohormones, and other parameters. Especially, the flexible wearable plant sensors provide a new way to measure plant physiological characteristics accurately and monitor the water status and physiological activities of plants non-destructively and continuously. These sensors are mainly used to detect various indicators in agricultural products, such as temperature and humidity, freshness, nutrients, and potentially hazardous substances (e.g., bacteria, pesticide residues, heavy metals, etc. Agricultural machinery sensors can achieve real-time monitoring and controlling of agricultural machinery to achieve real-time cultivation, planting, management, and harvesting, automated operation of agricultural machinery, and accurate application of pesticide, fertilizer. [Conclusions and ProspectsIn the challenges and prospects of agricultural sensors, the core bottlenecks of large-scale application of agricultural sensors at the present stage are analyzed in detail. These include low-cost, specialization, high stability, and adaptive intelligence of agricultural sensors. Furthermore, the concept of "ubiquitous sensing in agriculture" is proposed, which provides ideas and references for the research and development of agricultural sensor technology.

The pivotal role of weather information in domains such as workplace safety, economic activities, and natural disaster prevention is undeniable. This study introduces an advanced weather monitoring system designed to observe weather conditions at specific locations and make the data accessible globally. Utilizing Internet of Things (IoT) technology, this system connects a network of devices, including electronic gadgets and sensors, to the internet, thereby creating an interconnected web of information. The system focuses on monitoring environmental parameters such as temperature, relative humidity, pressure, and rainfall. It employs sensors to gather data, which is then transmitted to a web page and graphically represented as statistical information. The data, accessible from any location via the internet, enhances the resolution and accuracy of weather monitoring. The implementation of high-precision sensors in this system ensures the delivery of detailed and accurate meteorological data, facilitating a comprehensive understanding of local weather conditions. Additionally, the system's design allows for the easy deployment of multiple weather stations, broadening the scope of weather monitoring and providing valuable insights into microclimates and localized weather patterns. A significant feature of this system is the incorporation of a robust security framework, safeguarding the integrity and confidentiality of meteorological data. This aspect is crucial for ensuring the reliability of the data for various applications, including disaster preparedness and response. Timely evacuation warnings, efficient resource allocation, and accurate damage assessment are facilitated by the real-time weather monitoring provided by this system. [ABSTRACT FROM AUTHOR]

[Significance]Agricultural sensor is the key technology for developing modern agriculture. Agricultural sensor is a kind of detection device that can sense and convert physical signal, which is related to the agricultural environment, plants and animals, into an electrical signal. Agricultural sensors could be applied to monitor crops and livestock in different agricultural environments, including weather, water, atmosphere and soil. It is also an important driving force to promote the iterative upgrading of agricultural technology and change agricultural production methods. [Progress]The different agricultural sensors are categorized, the cutting-edge research trends of agricultural sensors are analyzed, and summarizes the current research status of agricultural sensors are summarized in different application scenarios. Moreover, a deep analysis and discussion of four major categories is conducted, which include agricultural environment sensors, animal and plant life information sensors, agricultural product quality and safety sensors, and agricultural machinery sensors. The process of research, development, the universality and limitations of the application of the four types of agricultural sensors are summarized. Agricultural environment sensors are mainly used for real-time monitoring of key parameters in agricultural production environments, such as the quality of water, gas, and soil. The soil sensors provide data support for precision irrigation, rational fertilization, and soil management by monitoring indicators such as soil humidity, p H, temperature, nutrients, microorganisms, pests and diseases, heavy metals and agricultural pollution, etc. Monitoring of dissolved oxygen, p H, nitrate content, and organophosphorus pesticides in irrigation and aquaculture water through water sensors ensures the rational use of water resources and water quality safety. The gas sensor monitors the atmospheric CO2, NH3, C2H2, CH4 concentration, and other information, which provides the appropriate environmental conditions for the growth of crops in greenhouses. The animal life information sensor can obtain the animal’s growth, movement, physiological and biochemical status, which include movement trajectory, food intake, heart rate, body temperature, blood pressure, blood glucose, etc. The plant life information sensors monitor the plant’s health and growth, such as volatile organic compounds of the leaves, surface temperature and humidity, phytohormones, and other parameters. Especially, the flexible wearable plant sensors provide a new way to measure plant physiological characteristics accurately and monitor the water status and physiological activities of plants non-destructively and continuously. These sensors are mainly used to detect various indicators in agricultural products, such as temperature and humidity, freshness, nutrients, and potentially hazardous substances (e. g., bacteria, pesticide residues, heavy metals, etc. Agricultural machinery sensors can achieve real-time monitoring and controlling of agricultural machinery to achieve real-time cultivation, planting, management, and harvesting, automated operation of agricultural machinery, and accurate application of pesticide, fertilizer. [Conclusions and Prospects]In the challenges and prospects of agricultural sensors, the core bottlenecks of large-scale application of agricultural sensors at the present stage are analyzed in detail. These include low-cost, specialization, high stability, and adaptive intelligence of agricultural sensors. Furthermore, the concept of "ubiquitous sensing in agriculture" is proposed, which provides ideas and references for the research and development of agricultural sensor technology. [ABSTRACT FROM AUTHOR]

The scope of this study was to apply advances in materials science, specifically the use of organosilicate nanoparticles as a high surface area platform for passive sampling of chemicals or pre-concentration for active sensing in multiple-phase complex environmental media. We have developed a novel nanoporous organosilicate (NPO) film as an extraction phase and proof of concept for application in adsorbing hydrophobic compounds in water and sediment. We characterized the NPO film properties and provided optimization for synthesis and coatings in order to apply the technology in environmental media. NPO films in this study had a very high surface area, up to 1325 m2/g due to the high level of mesoporosity in the film. The potential application of the NPO film as a sorbent phase for sensors or passive samplers was evaluated using a model hydrophobic chemical, polychlorinated biphenyls (PCB), in water and sediment. Sorption of PCB to this porous high surface area nanoparticle platform was highly correlated with the bioavailable fraction of PCB measured using whole sediment chemistry, porewater chemistry determined by solid-phase microextraction fiber methods, and the Lumbriculus variegatus bioaccumulation bioassay. The surface-modified NPO films in this study were found to highly sorb chemicals with a log octanol-water partition coefficient (Kow) greater than four; however, surface modification of these particles would be required for application to other chemicals.

Smart cities represent a transformative vision of urban living, where technology seamlessly integrates with the urban landscape to enhance sustainability and quality of life. Central to this vision is the effective management of environmental factors, particularly air quality and temperature. This paper presents a comprehensive study on realtime environmental pollution detection within smart cities, utilizing Internet of Things (IoT) sensors. We explore the intricate relationships between air pollutant indicators (o3_AQI, no2_AQI, co_AQI, and pm25_AQI) and temperature, shedding light on the dynamic interactions that underlie urban atmospheric conditions. Our research employs a robust dataset and employs statistical analysis, including Ordinary Least Squares (OLS) regression, to uncover significant correlations between key environmental variables. These insights not only contribute to a deeper understanding of urban pollution dynamics but also enable the development of predictive models for temperature fluctuations based on pollutant levels. Such models hold promise for proactive environmental management and public health interventions. Furthermore, our study highlights the pivotal role of IoT sensors in revolutionizing smart city governance, offering real-time data-driven solutions for sustainable urban living. As cities worldwide strive to enhance their environmental resilience, this research provides valuable insights and tools for harnessing the potential of IoT technologies in the pursuit of cleaner and more livable urban environments. [ABSTRACT FROM AUTHOR]

Optical water quality affects the quantity and spectral composition of underwater light available to photoautotrophs along with light reflected off the water surface. In reservoirs, prominent gradients for optical water quality and clarity measures occur between inlets and outlets, allowing rapid surveys of ecosystem structure across diverse conditions along with tests of relationships between in situ and remotely sensed water indices. We used a sensor‐equipped boat in a clay‐rich subtropical drinking water reservoir to examine continuous spatial patterns of water clarity and the relationship between in‐lake turbidity and normalized difference turbidity index (ndti) from Sentinel 2 imagery acquired on the same date. Over the 62.3 km boat path, surface turbidity varied between 14 and 85 NTU (mean = 30), with highest values in shallower water near the main inlet. Results indicated a strong linear relationship between sensor turbidity and lab‐determined turbidity. A positive relationship between the satellite turbidity index and in‐lake turbidity, combined with a negative exponential relationship between Secchi depth and turbidity, provided a basis for predicting water clarity metrics continuously over the entire lake surface. In this turbid water body, predicted Secchi depth and turbidity over the whole lake had means of 0.22 m and 54 NTU, with 75% of the lake area <0.29 m and >26 NTU. Pursuit of general relationships involving optical properties of water from high‐speed, data‐intensive spatial surveys and remotely sensed surface reflectance facilitates further development of spatially explicit models of aquatic systems. Plain Language Summary: In lakes and reservoirs, water clarity varies in space and over time, influencing the underwater light environment and light reflected off the water surface. Conditions near river inlets often differ from those of the main body of a lake or reservoir, where deeper water allows particles to settle out. By incorporating field surveys using sensor‐equipped boats and remote sensing using satellites, researchers can rapidly generate detailed maps of these differences in water properties. After establishing relationships between sensor‐based and satellite‐based measures of water clarity in a Texas, USA reservoir, we successfully generated detailed maps of these measures. In the future, this approach can be used to develop models that track and forecast water quality over entire surfaces of lakes and reservoirs. Key Points: Rapid spatial surveys provide a data‐intensive approach for understanding water clarity gradients in lakes and reservoirsA large turbidity gradient and relationships between in‐lake and satellite‐determined turbidity were observed in a Texas reservoirIn‐lake and satellite‐determined conditions together provide a basis to understand and model heterogeneity over entire lake surfaces [ABSTRACT FROM AUTHOR]

We report on the use of quartz-enhanced photoacoustic spectroscopy (QEPAS) for multi-gas detection. Photoacoustic (PA) spectra of mixtures of water (H 2 O), ammonia (NH 3 ), and methane (CH 4 ) were measured in the mid-infrared (MIR) wavelength range using a mid-infrared (MIR) optical parametric oscillator (OPO) light source. Highly overlapping absorption spectra are a common challenge for gas spectroscopy. To mitigate this, we used a partial least-squares regression (PLS) method to estimate the mixing ratio and concentrations of the individual gasses. The concentration range explored in the analysis varies from a few parts per million (ppm) to thousands of ppm. Spectra obtained from HITRAN and experimental single-molecule reference spectra of each of the molecular species were acquired and used as training data sets. These spectra were used to generate simulated spectra of the gas mixtures (linear combinations of the reference spectra). Here, in this proof-of-concept experiment, we demonstrate that after an absolute calibration of the QEPAS cell, the PLS analyses could be used to determine concentrations of single molecular species with a relative accuracy within a few % for mixtures of H 2 O, NH 3 , and CH 4 and with an absolute sensitivity of approximately 300 (±50) ppm/V, 50 (±5) ppm/V, and 5 (±2) ppm/V for water, ammonia, and methane, respectively. This demonstrates that QEPAS assisted by PLS is a powerful approach to estimate concentrations of individual gas components with considerable spectral overlap, which is a typical scenario for real-life adoptions and applications. [ABSTRACT FROM AUTHOR]

Abstract Mycelium-bound composites are potential alternatives to conventional materials for a variety of applications, including thermal and acoustic building panels and product packaging. If the reactions of live mycelium to environmental conditions and stimuli are taken into account, it is possible to create functioning fungal materials. Thus, active building components, sensory wearables, etc. might be created. This research describes the electrical sensitivity of fungus to changes in the moisture content of a mycelium-bound composite. Trains of electrical spikes initiate spontaneously in fresh mycelium-bound composites with a moisture content between $$\sim$$ ∼ 95% and $$\sim$$ ∼ 65%, and between $$\sim$$ ∼ 15% and $$\sim$$ ∼ 5% when partially dried. When the surfaces of mycelium-bound composites were partially or totally encased with an impermeable layer, increased electrical activity was observed. In fresh mycelium-bound composites, electrical spikes were seen both spontaneously and when induced by water droplets on the surface. Also explored is the link between electrical activity and electrode depth. Future designs of smart buildings, wearables, fungi-based sensors, and unconventional computer systems may benefit from fungi configurations and biofabrication flexibility.

Accurate hydrological data with high spatial resolution is important for flood risk and water resource management, particularly under the context of climate change. The cost of monitoring networks, as well as the characteristics of the hydrological environment itself, can be a barrier to meeting these data requirements, however. This study covers the design and testing of a low-cost, “build-it-yourself”, instream water depth sensor providing an assessment of its potential in future hydrological monitoring projects. The low-cost sensor was built using an Arduino microcontroller, a differential pressure sensor and a thermistor, a real-time clock, and an SD card module. The low-cost logger was deployed in tandem with a factory-calibrated Solinst®LevelLogger® 5 Junior for 6 months in the River Wissey, UK. We found the mean absolute error of the Arduino-based logger relative to the commercial setup to be ±0.69 cm for water depth and ±0.415 °C for water temperature. Economically, the Arduino-based logger offers an advantage, costing a total of £133.35 (USD 168.26 at time of publication) comparative to the industrial comparison’s cost of £408 (USD 514.83 at time of publication). This study concludes that the low cost of the Arduino-based logger gives a strong advantage to its incorporation in hydrological data collection, if the trade-offs (i.e., time investment and accuracy) are considered acceptable and appropriate for a project.

Understanding the distribution of soil moisture is notoriously difficult in topographically complex regions that are subject to both large‐scale climate gradients and fine‐scale effects of terrain, vegetation, and soil structure. Remote sensing approaches capture large‐scale moisture patterns but are limited in spatial and temporal resolution, while commercial field sensors remain too expensive to deploy intensively over large spatial extents. Here, we demonstrate the use of low‐cost (<20 USD) custom sensors to create a large monitoring network of surficial (0–15 cm depth) volumetric soil moisture content (VMC) across Great Smoky Mountains National Park (GSMNP) (NC, TN, USA). In laboratory tests, temperature‐calibrated VMC values approached the accuracy of commercial probes. We deployed over 80 sensors across multiple watersheds, topographic positions, and a 1,800‐m elevation gradient, and created hierarchical models to understand associations of VMC with spatial (30‐m resolution) and temporal (daily) variables related to water supply and demand. Elevation had the strongest association with VMC, with a fivefold increase across the gradient reflecting 1.5‐fold changes in both (increased) precipitation and (decreased) evapotranspiration; slope angle was a strong mediating factor. Common proxies for moisture including topographic convergence index were not associated with VMC, likely due to limited contributions of surface drainage to local water balance. Our model predicted daily VMC of a set of validation sensors with a root mean square error of 4.8%, which may be improved by site‐specific field calibration. Our study indicates that spatially extensive, field‐based soil moisture networks are practical, accurate, and an important component of regional environmental monitoring. Plain Language Summary: Measuring hourly soil moisture across many locations simultaneously is expensive but required if we are to understand how local wetness is influenced by plants and soils rather than just weather. We built and tested low‐cost moisture sensors in the laboratory, and put them in over 80 locations across Great Smoky Mountains National Park in the southern Appalachians of Tennessee and North Carolina, USA. Surprisingly, we found that soil moisture responded to weather—and in particular how weather changes from low to high elevation—but did not vary substantially by landscape position (e.g., near streams vs. adjacent ridges). Our study suggests that increases in regional temperature are likely to reduce soil moisture in this unusually wet region. Key Points: Low‐cost, custom‐built soil moisture sensors allow high‐resolution monitoring of surficial moisture dynamics across large, topographically complex regionsPatterns of volumetric soil moisture content (VMC) across an 80± site sensor network in Great Smoky Mountains National Park (NC, TN, USA) indicate a fivefold increase in surficial (0–15 cm depth) VMC from low (250 m) to high (2,025 m) elevation, mediated by slope angleLow cost, low power requirements, and practicable accuracy (<5% vol vol−1 root mean square error) indicate field‐based soil moisture networks are feasible and may complement more precise, coarse‐grained regional monitoring of ecosystem processes [ABSTRACT FROM AUTHOR]

Every nation has its own approved parameters for water quality standards provided by the World Health Organization (WHO). These parameters undergo variations based on geography. The Sultanate of Oman has listed prime important parameters for unbottled drinking water according to Oman Standards. Currently, the utilization of advanced Internet of Things (IoT) technology involves numerous internal measurements through multiple sensors. These sensors continuously monitor water quality indicators such as pH levels, turbidity, and microbial content, ensuring that the drinking water meets the stringent standards set by the Sultanate of Oman. This integration of IoT technology enhances real-time data collection and analysis, facilitating prompt responses to any deviations from the prescribed water quality parameters. However, determining the specific purpose of water usage is not yet defined. Traditionally, water quality is typically examined only for physiochemical properties. However, given that water is a colorless liquid, its suitability for drinking cannot be solely determined based on these properties. By employing Time Series Neural Networks, we can identify the current parameters of water and display or transmit information wirelessly as a report. This report would include a comparison between the actual value and the measured value using recent sensors and instrumentation. Additionally, the Time Series Neural Networks allow for real-time monitoring, enabling swift detection of anomalies or fluctuations in water parameters, contributing to proactive decision-making in water management. The seamless integration of advanced technology not only enhances the accuracy of data analysis but also facilitates timely responses to potential environmental changes or emerging issues. [ABSTRACT FROM AUTHOR]

Flexible pavements are subjected to loading stresses and resulting strains dependent on the existing environmental conditions. Distresses in the form of rutting and cracking occur in pavements due to progressive damage accumulation caused by repeated traffic-induced stresses/strains. For the accurate design of pavements, a knowledge of in-place strain is necessary to calibrate the laboratory obtained models. In the form of traffic loading and response sensors and weather instruments, pavement instrumentation plays a critical role in bridging the gap between theory and practice in pavement design. The commonly used instruments consist of weigh in motion (WIM) sensors, multidepth deflectometer (MDD), asphalt strain gauges, soil compression gauges, and earth pressure cells. Weather instruments consist of anemometers, thermocouples, and pyranometer. This paper presents a review of state-of-the-art instruments, basic principles, and working mechanisms with examples. The needs for successful instrumentation programs, including the provision of rugged sensors and protections, triggering of sensors with the WIM, and collation of data for successful analysis, are discussed. Concepts of the Wheatstone bridge, pressure cells, and linear variable differential transformers (LVDT) for the various sensors are presented. The principles and working mechanisms of anemometer, pyranometer, and thermocouples are discussed for weather sensors. [ABSTRACT FROM AUTHOR]

Medicine waste is a global issue, with economic, environmental, and social consequences that are only predicted to worsen. A structured review of the literature on medicine reuse revealed that there is a lack of technological applications addressing the key concerns raised by pharmaceutical stakeholders on the safety and feasibility of redispensing medication. A basis and guidelines for solutions aiming at enabling medicine reuse were devised by exploring a conceptual model of a Circular Pharmaceutical Supply Chain (CPSC), discussing concerns raised within the literature and identifying methods to influence the public and pharmaceutical companies. SPaRAS, a novel system to validate the storage conditions and streamline the assessment of returned medicines, is proposed. The Smart Packaging System (SPS) will record the storage conditions of medication while in patient care. The companion Returns Assessment System (RAS) will efficiently communicate with the SPS through RFID, configure the sensors within the SPS to the needs of its assigned medicine and assess the returns against tailored eligibility criteria. The increased safety and efficiency provided by SPaRAS addresses the concerns of large pharmaceutical companies and the public, offering a method to reuse previously owned medication and reduce the effects of unnecessary medicine waste. [ABSTRACT FROM AUTHOR]

Over the years, there has been a gradual increase in the emission of pollutants, and it is imperative to establish mechanisms to monitor air quality. In addition to carbon dioxide (CO2), particulate matter (PM) is considered one of the main types of air pollution. However, there is a wide variety of pollutants, and high investment is required to carry out detailed air quality monitoring. We present the third version of a previously proposed air quality monitoring platform based on CO2 concentration measurements. In this new version, a specific sensor for PM measurements and an artificial intelligence algorithm were added. The added algorithm traced associations between measurements of CO2 and PM concentrations. Thus, the measurement of a pollutant can be used for estimating the concentration of another. This can contribute to the development of a simpler and cheaper monitoring system. The acquisition of CO2 and PM concentrations was carried out daily over a period of one month. Pollutant measurements were taken in three strategic locations in a Brazilian city. It was possible to determine a correlation between pollutant concentrations for the monitored locations. Thus, it would be possible to efficiently estimate the PM concentration based on the measured CO2 concentration. [ABSTRACT FROM AUTHOR]

The ubiquity of sensors in smart-homes facilitates the support of independent living for older adults and enables cognitive assessment. Notably, there has been a growing interest in utilizing movement traces for identifying signs of cognitive impairment in recent years. In this study, we introduce an innovative approach to identify abnormal indoor movement patterns that may signal cognitive decline. This is achieved through the non-intrusive integration of smart-home sensors, including passive infrared sensors and sensors embedded in everyday objects. The methodology involves visualizing user locomotion traces and discerning interactions with objects on a floor plan representation of the smart-home, and employing different image descriptor features designed for image analysis tasks and synthetic minority oversampling techniques to enhance the methodology. This approach distinguishes itself by its flexibility in effortlessly incorporating additional features through sensor data. A comprehensive analysis, conducted with a substantial dataset obtained from a real smart-home, involving 99 seniors, including those with cognitive diseases, reveals the effectiveness of the proposed functional prototype of the system architecture. The results validate the system’s efficacy in accurately discerning the cognitive status of seniors, achieving a macro-averaged F1-score of 72.22% for the two targeted categories: cognitively healthy and people with dementia. Furthermore, through experimental comparison, our system demonstrates superior performance compared with state-of-the-art methods.

The smart city concept has been popularized in the urbanization of major metropolitan areas through the implementation of intelligent systems and technology to serve the increasing human population. This work developed an automatic light adjustment system at Thammasat University, Rangsit Campus, Thailand, with a primary objective of optimizing energy efficiency, while providing sufficient illumination for the campus. The development consists of two sections: the device control and the prediction model. The device control functionalities were developed with the user interface to enable control of the smart street light devices and the application programming interface (API) to send the light-adjusting command. The prediction model was created using an AI-assisted data analytic platform to obtain the predicted illuminance values so as to, subsequently, suggest light-dimming values according to the current environment. Four machine-learning models were performed on a nine-month environmental dataset to acquire predictions. The result demonstrated that the three-day window size setting with the XGBoost model yielded the best performance, attaining the correlation coefficient value of 0.922, showing a linear relationship between actual and predicted illuminance values using the test dataset. The prediction retrieval API was established and connected to the device control API, which later created an automated system that operated at a 20-min interval. This allowed real-time feedback to automatically adjust the smart street lighting devices through the purpose-designed data analytics features. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We report on the use of quartz-enhanced photoacoustic spectroscopy (QEPAS) for multi-gas detection. Photoacoustic (PA) spectra of mixtures of water (H2O), ammonia (NH3), and methane (CH4) were measured in the mid-infrared (MIR) wavelength range using a mid-infrared (MIR) optical parametric oscillator (OPO) light source. Highly overlapping absorption spectra are a common challenge for gas spectroscopy. To mitigate this, we used a partial least-squares regression (PLS) method to estimate the mixing ratio and concentrations of the individual gasses. The concentration range explored in the analysis varies from a few parts per million (ppm) to thousands of ppm. Spectra obtained from HITRAN and experimental single-molecule reference spectra of each of the molecular species were acquired and used as training data sets. These spectra were used to generate simulated spectra of the gas mixtures (linear combinations of the reference spectra). Here, in this proof-of-concept experiment, we demonstrate that after an absolute calibration of the QEPAS cell, the PLS analyses could be used to determine concentrations of single molecular species with a relative accuracy within a few % for mixtures of H2O, NH3, and CH4 and with an absolute sensitivity of approximately 300 (±50) ppm/V, 50 (±5) ppm/V, and 5 (±2) ppm/V for water, ammonia, and methane, respectively. This demonstrates that QEPAS assisted by PLS is a powerful approach to estimate concentrations of individual gas components with considerable spectral overlap, which is a typical scenario for real-life adoptions and applications.

Proteins containing PER-ARNT-SIM (PAS) domains are commonly associated with environmental adaptation in a variety of organisms. The PAS domain is found in proteins throughout Archaea, Bacteria, and Eukarya and often binds small-molecules, supports protein-protein interactions, and transduces input signals to mediate an adaptive physiological response. Signaling events mediated by PAS sensors can occur through induced phosphorelays or genomic events that are often dependent upon PAS domain interactions. In this perspective, we briefly discuss the diversity of PAS domain containing proteins, with particular emphasis on the prototype member, the aryl hydrocarbon receptor (AHR). This ligand-activated transcription factor acts as a sensor of the chemical environment in humans and many chordates. We conclude with the idea that since mammalian PAS proteins often act through PAS-PAS dimers, undocumented interactions of this type may link biological processes that we currently think of as independent. To support this idea, we present a framework to guide future experiments aimed at fully elucidating the spectrum of PAS-PAS interactions with an eye towards understanding how they might influence environmental sensing in human and wildlife populations.

The present work introduces the use of environmental sensors to assess indoor air quality (IAQ) in combination with human biometrics. The sensor array included temperature, relative humidity, carbon dioxide, and noise monitors. The array was used in a classroom as well as in a vehicle cabin to assess the carbon dioxide production rate of individuals in a closed ventilation environment. Analysis of carbon dioxide production allowed for the quantification of the average metabolic rate of the group of individuals in the classroom, and for one individual in the vehicle cabin. These results yielded a mere 5% difference from the values assessed using commercial metabolic rate instruments, and averaged values from epidemiological studies. The results presented in this work verify the feasibility of determining an individual's metabolic rate using passive environmental sensors; these same sensors are able to provide a metric of IAQ that helps characterize the safety of the environment in which the individual is present.

Medicine waste is a global issue, with economic, environmental, and social consequences that are only predicted to worsen. A structured review of the literature on medicine reuse revealed that there is a lack of technological applications addressing the key concerns raised by pharmaceutical stakeholders on the safety and feasibility of redispensing medication. A basis and guidelines for solutions aiming at enabling medicine reuse were devised by exploring a conceptual model of a Circular Pharmaceutical Supply Chain (CPSC), discussing concerns raised within the literature and identifying methods to influence the public and pharmaceutical companies. SPaRAS, a novel system to validate the storage conditions and streamline the assessment of returned medicines, is proposed. The Smart Packaging System (SPS) will record the storage conditions of medication while in patient care. The companion Returns Assessment System (RAS) will efficiently communicate with the SPS through RFID, configure the sensors within the SPS to the needs of its assigned medicine and assess the returns against tailored eligibility criteria. The increased safety and efficiency provided by SPaRAS addresses the concerns of large pharmaceutical companies and the public, offering a method to reuse previously owned medication and reduce the effects of unnecessary medicine waste.

Over the years, there has been a gradual increase in the emission of pollutants, and it is imperative to establish mechanisms to monitor air quality. In addition to carbon dioxide (CO2), particulate matter (PM) is considered one of the main types of air pollution. However, there is a wide variety of pollutants, and high investment is required to carry out detailed air quality monitoring. We present the third version of a previously proposed air quality monitoring platform based on CO2 concentration measurements. In this new version, a specific sensor for PM measurements and an artificial intelligence algorithm were added. The added algorithm traced associations between measurements of CO2 and PM concentrations. Thus, the measurement of a pollutant can be used for estimating the concentration of another. This can contribute to the development of a simpler and cheaper monitoring system. The acquisition of CO2 and PM concentrations was carried out daily over a period of one month. Pollutant measurements were taken in three strategic locations in a Brazilian city. It was possible to determine a correlation between pollutant concentrations for the monitored locations. Thus, it would be possible to efficiently estimate the PM concentration based on the measured CO2 concentration.

The COVID-19 pandemic has changed our common habits and lifestyle. Occupancy information is valued more now due to the restrictions put in place to reduce the spread of the virus. Over the years, several authors have developed methods and algorithms to detect/estimate occupancy in enclosed spaces. Similarly, different types of sensors have been installed in the places to allow this measurement. However, new researchers and practitioners often find it difficult to estimate the number of sensors to collect the data, the time needed to sense, and technical information related to sensor deployment. Therefore, this systematic review provides an overview of the type of environmental sensors used to detect/estimate occupancy, the places that have been selected to carry out experiments, details about the placement of the sensors, characteristics of datasets, and models/algorithms developed. Furthermore, with the information extracted from three selected studies, a technique to calculate the number of environmental sensors to be deployed is proposed. [ABSTRACT FROM AUTHOR]

This article presents a platform for environmental data named "Environmental Cloud for the Benefit of Agriculture" (CEBA). The CEBA should fill the gap of a regional institutional platform to share, search, store and visualize heterogeneous scientific data related to the environment and agricultural researches. One of the main features of this tool is its ease of use and the accessibility of all types of data. To answer the question of data description, a scientific consensus has been established around the qualification of data with at least the information "when" (time), "where" (geographical coordinates) and "what" (metadata). The development of an on-premise solution using the data lake concept to provide a cloud service for end-users with institutional authentication and for open data access has been completed. Compared to other platforms, CEBA fully supports the management of geographic coordinates at every stage of data management. A comprehensive JavaScript Objet Notation (JSON) architecture has been designed, among other things, to facilitate multi-stage data enrichment. Data from the wireless network are queried and accessed in near real-time, using a distributed JSON-based search engine. [ABSTRACT FROM AUTHOR]