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

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

The possibility of analyzing the general and functional diversity of the ecosystem is considered, as is unambiguous interpretation of the results obtained based on our work experience and previously obtained data. The prospects for studying small mammals without removal from the wild are discussed. We discuss distinguishing in a sample the resident component, which makes it possible to characterize local resource flows, and the nonresident component, dependent on the state of the territory as a whole. A description of the capture–mark–recapture protocol, methods for assessing spatial activity, calculating the density of the resident population per unit area, and the completeness of species richness estimate is provided. The estimates of animal numbers per unit of trapping effort in pooled sample, the population density of residents, and the index of the nonresident flow are compared. The possibility of analyzing the resource flows in "historical," "novel," and "hybrid" ecosystems, as well as using data to analyze the resilience of the ecosystem and detect the threshold point, is considered. [ABSTRACT FROM AUTHOR]

Sensor arrays, which draw inspiration from the mammalian olfactory system, are fundamental concepts in high-throughput analysis based on pattern recognition. Although numerous optical sensor arrays for various targets in aqueous media have demonstrated their diverse applications in a wide range of research fields, practical device platforms for on-site analysis have not been satisfactorily established. The significant limitations of these sensor arrays lie in their solution-based platforms, which require stationary spectrophotometers to record the optical responses in chemical sensing. To address this, this review focuses on paper substrates as device components for solid-state sensor arrays. Paper-based sensor arrays (PSADs) embedded with multiple detection sites having cross-reactivity allow rapid and simultaneous chemical sensing using portable recording apparatuses and powerful data-processing techniques. The applicability of office printing technologies has promoted the realization of PSADs in real-world scenarios, including environmental monitoring, healthcare diagnostics, food safety, and other relevant fields. In this review, we discuss the methodologies of device fabrication and imaging analysis technologies for pattern recognition-driven chemical sensing in aqueous media. [Display omitted] • Cross-reactive sensors are applied to paper-based arrays for simultaneous detection. • Portable digital recorders can be used for detecting various responses. • Imaging analysis techniques can accelerate accurate data processing. [ABSTRACT FROM AUTHOR]

Microfluidic paper-based point-of-care assays are a highly valuable technology with many promising applications in public health and environmental monitoring. High-sensitivity detection of certain trace substances is one of the critical important considerations for practical usage of microfluidic point-of-care techniques. Over the past decade, luminescence nanoprobes have been increasingly emerged as powerful toolboxes to achieve exceptional sensitivity and low detection limits for many chemical sensors. In this Review, we provide an overview of the fundamental principles underlying paper-based microfluidic point-of-care detection. We highlight various luminescence nanoprobes, including nano-diamonds, upconversion luminescent nanoparticles, inorganic quantum dots, and carbon dots, along with their corresponding sensing mechanisms. In subsequent sections, we emphasize the requisite utilization of specialized readout equipment for the effective implementation of fluorescence detection. Moreover, we summarize key microfluidic design strategies capable of achieving enhanced precision in the detection processes. We also discuss future research directions and the main challenges to be addressed for fluorescence nanoprobes-inspired paper-based microfluidic point-of-care technology. • Microfluidic point-of-care paper-based Luminescence assays. • Luminescence nanoprobes and corresponding mechanism. • Portable devices for signal readout and diagnosis. [ABSTRACT FROM AUTHOR]

Microfluidic devices have emerged as advantageous tools for detecting environmental contaminants due to their portability, ease of use, cost-effectiveness, and rapid response capabilities. These devices have wide-ranging applications in environmental monitoring of air, water, and soil matrices, and have also been applied to agricultural monitoring. Although several previous reviews have explored microfluidic devices' utility, this paper presents an up-to-date account of the latest advancements in this field for environmental monitoring, looking back at the past five years. In this review, we discuss devices for prominent contaminants such as heavy metals, pesticides, nutrients, microorganisms, per- and polyfluoroalkyl substances (PFAS), etc. We cover numerous detection methods (electrochemical, colorimetric, fluorescent, etc.) and critically assess the current state of microfluidic devices for environmental monitoring, highlighting both their successes and limitations. Moreover, we propose potential strategies to mitigate these limitations and offer valuable insights into future research and development directions. [ABSTRACT FROM AUTHOR]

The rapid urbanization and industrialization have caused a significant increase in environmental pollution, especially in air quality. This paper introduces an IoT-based environmental monitoring system to address these issues. Using state-of-the-art IoT technology, the system deploys sensors in key locations to continuously collect data on air quality indicators like Particulate Matter (PM), Carbon Monoxide (CO), and Ozone (O3). The collected data is wirelessly transmitted to a central server for processing and analysis. The system provides real-time and historical data through a user-friendly interface, enabling users to monitor current conditions, identify trends, and identify potential pollution sources. The key features include scalability, allowing for the addition of new sensors, and proactive decision-making through alerts when environmental parameters exceed predefined thresholds. This paper aims to empower communities with the knowledge needed to address and mitigate air pollution, creating healthier and more liveable urban environments in the face of rapid global urbanization. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative IoT-enabled crop storage monitoring system designed to revolutionize the preservation and quality assurance of stored agricultural produce. Central to this system are IoT sensors strategically deployed within storage facilities. These sensors are tasked with the continuous tracking and monitoring of crucial environmental parameters, specifically temperature, humidity, and gas levels. Leveraging this real-time data, the system is engineered to promptly detect and respond to any deviations from the prescribed optimal storage conditions. The core strength of this system lies in its ability to generate instantaneous alerts upon detecting irregularities. These alerts serve as preemptive measures, effectively averting potential spoilage and curtailing post-harvest losses by enabling timely interventions and corrective actions. By harnessing IoT technology, this paper aims to create a proactive, automated, and responsive framework that ensures the integrity and safety of stored agricultural produce, ultimately contributing to enhanced food security and sustainability. [ABSTRACT FROM AUTHOR]

Preventive Conservation (PC) aims to minimize the risk of irreversible damage to cultural artifacts caused by environmental factors. Strategies for PC include implementing environmental recommendations and monitoring conditions. However, environmental monitoring alone is not enough, and predictive analysis is needed to assess the risk of damage. The collection "Preventive Conservation, Predictive Analysis and Environmental Monitoring" presents technological advances in studying material degradation and environmental monitoring. Papers cover topics such as paper degradation, wood degradation, canvas paintings, dyed textiles, sensor electronics, wireless communications, cloud computing, and machine learning. The collection provides valuable insights for the design of preventive conservation strategies. [Extracted from the article]

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

Per- and polyfiuoroalkyl substances (PFAS) on paper packaging resist water and oil to effectively contain food and beverages. However, previous studies have established correlations between PFAS and several diseases including COVID-19, cancers, and obesity. The goal of this collaborative research between the Philippines and Singapore is to set a baseline for PFAS levels in local packaging with the intended outcome of further increasing awareness on these contaminants in Southeast Asia, providing a starting point for migration experiments and risk assessments on PFAS in commercially-available food contact materials and articles, and initiating policy developments on these substances in the Philippines. In this study, 15 different types of PFAS were analyzed in selected paper packaging used by major quick service restaurants (QSRs) in Metro Manila. Using liquid chromatography-mass spectrometry (LC-MS), PFAS was detected in 100% of the samples with a total PFAS concentration range of 8.20-97.7 ng PFAS/100 cm². The highest amount of PFAS compound measured across all samples was PF-3,7-DMOA (89.8 ng/100 cm²). PFAS compounds regulated in European packaging such as PFHpA, PFOA, PFNA and PFDA were also detected in all samples but at trace levels. Paper wrappers for rice and small burgers were found to have the highest total PFAS of 97.7 ng PFAS/100 cm². This value translates to approximately 65.1 ng F/100 cm² which is way below 10,000 ng F/100 cm², the currently existing limit for PFAS in packaging set by the Danish Ministry of Environment and Food. While each packaging may be deemed safe compared to regulation, the compounded effects brought by use of multiple packaging, and high frequency and long-term exposures require further investigation. [ABSTRACT FROM AUTHOR]

This paper provides a thorough comparison of sophisticated biosensors and conventional sensors, explaining the underlying technologies, working theories, and performance measures of each in analytical detection applications. Through analyzing the essential distinctions and parallels between them, this paper brings to light the unique benefits and constraints of every technology with respect to sensitivity, specificity, reaction speed, and mobility. This paper also examines the various uses of sensors and biosensors in healthcare, environmental monitoring, and industrial processes, offering insights into the special qualities and possible collaborations of these platforms. Looking ahead, this paper explores new developments in data analytics, microfluidics, and nanotechnology that will lead to next-generation sensing technologies with improved analytical capabilities and wider practical uses. [ABSTRACT FROM AUTHOR]

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

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

This paper explores the impact of environmental protection tax on green technology innovation of Chinese resource-based enterprises and behavior of thirdparty environmental monitoring institutions. It uses game theory to analyze participants' strategy selections, and uses the triple difference method to empirically test the promotion effect. Our robust results find that (1) Chinese environmental tax policy effectively promotes the green innovation of resource-based enterprises and standardizes the behavior of third-party monitoring institutions; (2) local governments' actions such as increasing environmental protection tax, penalizing third-party monitoring agencies for violations, and promoting public preference for green consumption, all contribute to accelerating enterprises' green innovation; (3) the central government's increased regulation of local governments has a positive impact on enterprises and third-party monitoring institutions. [ABSTRACT FROM AUTHOR]

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

Impedance analysis is a powerful technique that has become increasingly important in various applications, it represents a leap forward in the field of electronic measurments and diagnostics. In this work, we present the development of miniaturized, multiplexed, and connected platform for impedance spectroscopy. Designed for online measurements and adapted to wireless network architectures, our platform has been tested and optimized to be used for multi-selective chemical organic sensor nodes. This compact and versatile circuit is built from low cost and low power consumption (250 mW) microelectronics components that achieve long duration operability (5 days and 16 h) without compromising on sensor measurement accuracy and precision. We used the well-known impedance network analyzer AD5933 (Analog Devices, Norwood, MA, USA) chip which can measure a spectrum of impedances in the range 5 kHz to 100 kHz. The proposed system is based on ESP32-C3 Microcontroller enabling the management of the AD5933 through its I2C interface. Our system benefits from two multiplexer components CD74HC4067 allowing calibration process and the interface of 15 conductimetric sensors with real time acquisition (less than 90 ms per acquisition). The system is capable of relaying information through the network for data analysis and storage. The paper describes the microelectronics design, the impedance response over time, the measurement’s sensitivity and accuracy and the testing of the platform with embedded chemical sensors for gas classification and recognition.Article Highlights: Single calibration process: The main contribution of this paper is to propose a portable multiplexed impedance measurement system with a unique self-calibration capability. This capability is facilitated through the use of a single calibration resistor, which significantly enhances the speed of data acquisition. Materials study: To validate our system, a materials study was conducted, focusing on the use of a conductive polymer poly(3hexylthiophene) P3HT doped with various triflate metals. This research aims to assess the system's effectiveness in gas recognition applications, demonstrating its potential for accuracy and speed crucial attributes for modern detection technologies. Gas recognition: Our developed system has been designed to be suitable to assess volatile samples by their quality, to systematically expose them to the miniaturized array under a static blow (the PCA of the partial dataset of resistance modulation published in our former studies quantifies on the systematicity of the exposures by the organization of the data clusters).These labels are indeed crucial to quantify the materials response with environmental physical variables which may greatly vary within an environment. [ABSTRACT FROM AUTHOR]

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

Due to the benefits of the high sensitivity, real-time response, no labeling requirement, and good selectivity, fiber optic sensors based on surface plasmon resonance (SPR) have gained popularity in biochemical sensing in recent years. The current research on such sensors is hot in enhancing sensitivity, improving detection accuracy, and achieving the detection of biochemical molecules. The goal of this work is to present a thorough overview of recent developments in the optical fiber SPR biosensor research. Firstly, it explores the basic principles and sensing structures of optical fiber SPR biosensors, focusing on four aspects. Subsequently, this paper introduces three fiber optic surface plasmon biosensors: SPR, localized surface plasmon resonance (LSPR), and long-range surface plasmon resonance (LRSPR). Each concept is explained from the perspective of the basic principles of fiber optic SPR biosensors. Furthermore, a classification of fiber optic SPR biosensors in health monitoring, food safety, environmental monitoring, marine detection, and other applications is introduced and analyzed. Eventually, this paper summarizes the current research directions of SPR biosensors. Meanwhile, it provides a prospective outlook on how fiber optic SPR sensors will develop in the future. [ABSTRACT FROM AUTHOR]

The paper introduces an innovative system that utilizes the Internet of Things (IoT) to provide an enhanced approach for reporting weather conditions at a specific location and making this information accessible globally. The IoT technology serves as the underlying mechanism for connecting various devices, such as electronic gadgets, sensors, and automotive electronic equipment, to the internet and establishing a network that encompasses a wide range of interconnected objects. In this system, sensors are employed to monitor and regulate environmental factors such as temperature, relative humidity, and pressure levels. [ABSTRACT FROM AUTHOR]

Intelligent Transport System (ITS) is a typical class of Cyber-Physical Systems (CPS), and due to the special characteristics of such systems, higher requirements are placed on system security. Runtime verification is a lightweight verification technique which is used to improve the security of such systems. However, current runtime verification methods often ignore the effects of the physical environment (e.g., the effects of rain, snow, and other weather changes on road conditions), which results in the inability of the monitor to effectively monitor the system according to the changes in the environment. To address this problem, this paper proposes a method for constructing a runtime monitor with environmental context-awareness capability. First, the physical environment factors affecting the system are formally described and constructed into an environment model, then the system statute is transformed into a Büchi automaton, and then a synthesis algorithm combining the environment model and the Büchi automaton is designed based on the network of automatons, and the corresponding monitor is generated. Finally, the proposed method is applied and verified on simulation and real objects. The experimental results show that the monitors generated based on the method of this paper can effectively monitor unsafe events in different environments, thus improving the safety of intelligent driving systems. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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