Search Results (9,733)

Chemiresistive gas sensors are extensively employed in environmental monitoring, disease diagnostics, and industrial safety due to their high sensitivity, low cost, and miniaturization. However, the high cross-sensitivity and poor selectivity of gas sensors limit their practical applications in complex environmental detection. In particular, the mechanisms underlying the selective response of certain chemiresistive materials to specific gases are not yet fully understood. In this review, we systematically discuss material design strategies and system integration techniques for enhancing the selectivity and sensitivity of gas sensors. The focus of material design primarily on the modification and optimization of advanced functional materials, including semiconductor metal oxides (SMOs), metallic/alloy systems, conjugated polymers (CPs), and two-dimensional nanomaterials. This study offers a comprehensive investigation into the underlying mechanisms for enhancing the gas sensing performance through oxygen vacancy modulation, single-atom catalysis, and heterojunction engineering. Furthermore, we explore the potential of emerging technologies, such as bionics and artificial intelligence, to synergistically integrate with functional sensitive materials, thereby achieving a significant enhancement in the selectivity of gas sensors. This review concludes by offering recommendations aimed at improving the selectivity of gas sensors, along with suggesting potential avenues for future research and development. Full article

Xiahe County, in the northwestern Gannan Tibetan Autonomous Prefecture of Gansu Province, faces recurrent geological hazards—including landslides and debris flows. Geological hazards in highly vegetated regions pose severe threats to ecological balance, human settlements, and socio-economic sustainability, hindering the achievement of sustainable development goals (SDGs). Due to the significant topographic relief and high vegetation coverage in this region, traditional manual ground-based surveys face substantial challenges in the investigation and identification of geological hazards, necessitating the adoption of advanced monitoring and identification techniques. This study employs a comprehensive approach integrating optical remote sensing, interferometric synthetic aperture radar (InSAR), and unmanned aerial vehicle (UAV) photogrammetry to investigate and identify geological hazards in the eastern part of Xiahe County, exploring the application capabilities and effectiveness of multisource remote sensing techniques in hazard identification. The results indicate that this study has shortened the time required for on-site investigations by improving the efficiency of disaster identification while also providing comprehensive, multi-angle, and high-precision remote sensing outcomes. These achievements offer robust support for sustainable disaster management and land use planning in ecologically fragile regions. Optical remote sensing, InSAR, and UAV photogrammetry each possess unique advantages and application scopes, but single-technique approaches are insufficient to fully address potential hazard identification. Developing a comprehensive investigation and identification framework that integrates and complements the strengths of multisource technologies has proven to be an effective pathway for the rapid investigation, identification, and evaluation of geological hazards. These results contribute to regional sustainability by enabling targeted risk mitigation, minimizing disaster-induced ecological and economic losses, and enhancing the resilience of vulnerable communities. Full article

Raising awareness and understanding of climate change among younger generations is crucial for building a sustainable future. The Laboratory of Atmospheric Physics (LAP) within the School of Physics of the Aristotle University of Thessaloniki (AUTh) supports this goal by developing innovative educational activities centered on atmospheric processes and climate science. Drawing on its expertise in atmospheric monitoring and remote sensing, LAP makes complex scientific concepts accessible to school students through interactive workshops, hands-on experiments, and data-driven projects using real-time environmental measurements. By integrating research-grade tools and open-access satellite data from ESA, NASA, and EUMETSAT, LAP bridges academic research and public understanding. These activities foster critical thinking, environmental responsibility, and student engagement with real-world climate monitoring practices. Moreover, LAP contributes to the ACTRIS network, offering high-quality data and expertise at both national and European levels. Through these efforts, LAP serves as a hub for climate education, turning awareness into action and inspiring future climate-conscious citizens. Full article

This study demonstrates how mathematical ideas can be developed through genuine applications to problems that are attractive to the learners of mathematics due to consistency with their life experiences. To this end, the paper provides several examples of digital instruments both commonly available and designed by the authors with the goal to prepare schoolchildren of different ages to mathematize basic models of computer science and engineering. The mathematization includes construction and optimization of the models by using big ideas of mathematics at the level of common sense alone as a grade-appropriate prerequisite to their formal description. Also, the paper examines computer systems that can be depicted as the prototypes of artificial intelligence since, in the context of education, they can be used as tools enabling both motivation and support of one’s conceptual development rather than simply a means to carry out thinking for the learners of mathematics. Finally, by referring to a few notable contributors to mathematical, educational, and psychological knowledgebase, this study argues for the merit of intuition in the digital age as a support system in the advancement of computational problem-solving techniques. Full article

Leaf water content is a critical metric during the growth and development of winter wheat. Rapid and efficient monitoring of leaf water content in winter wheat is essential for achieving precision irrigation and assessing crop quality. Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing technology has enormous application potential in the field of crop monitoring. In this study, UAV was used as the platform to conduct six canopy hyperspectral data samplings and field-measured leaf water content (LWC) across four growth stages of winter wheat. Then, six spectral transformations were performed on the original spectral data and combined with the correlation analysis with wheat leaf water content (LWC). Multiple scattering correction (MSC), standard normal variate (SNV), and first derivative (FD) were selected as the subsequent transformation methods. Additionally, competitive adaptive reweighted sampling (CARS) and the Hilbert–Schmidt independence criterion lasso (HSICLasso) were employed for feature selection to eliminate redundant information from the spectral data. Finally, three machine learning algorithms—partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF)—were combined with different data preprocessing methods, and 50 random partition datasets and model evaluation experiments were conducted to compare the accuracy of different combination models in assessing wheat LWC. The results showed that there are significant differences in the predictive performance of different combination models. By comparing the prediction accuracy on the test set, the optimal combinations of the three models are MSC + CARS + SVR (R² = 0.713, RMSE = 0.793, RPD = 2.097), SNV + CARS + PLSR (R² = 0.692, RMSE = 0.866, RPD = 2.053), and FD + CARS + RF (R² = 0.689, RMSE = 0.848, RPD = 2.002). All three models can accurately and stably predict winter wheat LWC, and the CARS feature extraction method can improve the prediction accuracy and enhance the stability of the model, among which the SVR algorithm has better robustness and generalization ability. Full article

Rapid building damage assessments are vital for an effective earthquake response. In Japan, traditional Earthquake Damage Certification (EDC) surveys—followed by the issuance of Disaster Victim Certificates (DVCs)—are often inefficient. With advancements in remote sensing technologies and deep learning algorithms, their combined application has been explored for large-scale automated damage assessment. However, the scarcity of remote sensing data on damaged buildings poses significant challenges to this task. In this study, we propose an Uncertainty-Guided Fusion Module (UGFM) integrated into a standard decoder architecture, with a Pyramid Vision Transformer v2 (PVTv2) employed as the encoder. This module leverages uncertainty outputs at each stage to guide the feature fusion process, enhancing the model’s sensitivity to collapsed buildings and increasing its effectiveness under diverse conditions. A training and in-domain testing dataset was constructed using post-earthquake aerial imagery of the severely affected areas in Noto Prefecture. The model approximately achieved a recall of 79% with a precision of 68% for collapsed building extraction on this dataset. We further evaluated the model on an out-of-domain dataset comprising aerial images of Mashiki Town in Kumamoto Prefecture, where it achieved an approximate recall of 66% and a precision of 77%. In a quantitative analysis combining field survey data from Mashiki, the model attained an accuracy exceeding 87% in identifying major damaged buildings, demonstrating that the proposed method offers a reliable solution for initial assessment of major damage and its potential to accelerate DVC issuance in real-world disaster response scenarios. Full article

Accurate classification of tree species is crucial for forest management and biodiversity conservation. Remote sensing technology offers a unique capability for classifying and mapping trees across large areas; however, the accuracy of extracting and identifying individual trees remains challenging due to the limitations of available imagery and phenological variations. This study presents a novel integrated machine learning (ML) and Geographic Object-Based Image Analysis (GEOBIA) framework to enhance tree species classification in a botanical garden using multi-temporal unmanned aerial vehicle (UAV) imagery. High-resolution UAV imagery (2.3 cm/pixel) was acquired across four different seasons (summer, autumn, winter, and early spring) to incorporate the phenological changes. Spectral, textural, geometrical, and canopy height features were extracted using GEOBIA and then evaluated with four ML models (Random Forest (RF), Extra Trees (ET), eXtreme gradient boost (XGBoost), and Support Vector Machine (SVM)). Multi-temporal data significantly outperformed single-date imagery, with RF achieving the highest overall accuracy (86%, F1-score 0.85, kappa 0.83) compared to 57–75% for single-date classifications. Canopy height and textural features were dominant for species identification, indicating the importance of structural variations. Despite the limitations of moderate sample size and a controlled botanical garden setting, this approach offers a robust framework for forest and urban landscape managers as well as remote sensing professionals, by optimizing UAV-based strategies for precise tree species identification and mapping to support urban and natural forest conservation. Full article

Well integrity is paramount for the safe, environmentally responsible, and economically viable operation of wells throughout their lifecycle, encompassing conventional oil and gas production, unconventional resource extraction (e.g., shale gas and tight oil), and geological storage applications (CO₂, H₂, and natural gas). This review presents a comprehensive synthesis of well integrity challenges, failure mechanisms, monitoring technologies, and management strategies across these operational domains. Key integrity threats—including cement sheath degradation (chemical attack, debonding, cracking, microannuli), casing failures (corrosion, collapse, burst, buckling, fatigue, wear, and connection damage), sustained casing pressure (SCP), and wellhead leaks—are examined in detail. Unique challenges posed by hydraulic fracturing in unconventional wells and emerging risks in CO₂ and hydrogen storage, such as corrosion, carbonation, embrittlement, hydrogen-induced cracking (HIC), and microbial degradation, are also highlighted. The review further explores the evolution of integrity standards (NORSOK, API, ISO), the implementation of Well Integrity Management Systems (WIMS), and the integration of advanced monitoring technologies such as fiber optics, logging tools, and real-time pressure sensing. Particular emphasis is placed on the role of digital technologies—including artificial intelligence, machine learning, and digital twin systems—in enabling predictive maintenance, early failure detection, and lifecycle risk management. The novelty of this review lies in its integrated, cross-domain perspective and its emphasis on digital twin applications for continuous, adaptive well integrity surveillance. It identifies critical knowledge gaps in modeling, materials qualification, and data integration—especially in the context of long-term CO₂ and H₂ storage—and advocates for a proactive, digitally enabled approach to lifecycle well integrity. Full article

Multi-threshold image segmentation plays an irreplaceable role in extracting discriminative structural information from complex images. It is one of the core technologies for achieving accurate target detection and regional analysis, and its segmentation accuracy directly affects the analysis quality and decision reliability in key fields such as medical imaging, remote sensing interpretation, and industrial inspection. However, most existing image segmentation algorithms suffer from slow convergence speeds and low solution accuracy. Therefore, this paper proposes an Adaptive Cooperative Pelican Optimization Algorithm (ACPOA), an improved version of the Pelican Optimization Algorithm (POA), and applies it to global optimization and multilevel threshold image segmentation tasks. ACPOA integrates three innovative strategies: the elite pool mutation strategy guides the population toward high-quality regions by constructing an elite pool composed of the three individuals with the best fitness, effectively preventing the premature loss of population diversity; the adaptive cooperative mechanism enhances search efficiency in high-dimensional spaces by dynamically allocating subgroups and dimensions and performing specialized updates to achieve division of labor and global information sharing; and the hybrid boundary handling technique adopts a probabilistic hybrid approach to deal with boundary violations, balancing exploitation, exploration, and diversity while retaining more useful search information. Comparative experiments with eight advanced algorithms on the CEC2017 and CEC2022 benchmark test suites validate the superior optimization performance of ACPOA. Moreover, when applied to multilevel threshold image segmentation tasks, ACPOA demonstrates better accuracy, stability, and efficiency in solving practical problems, providing an effective solution for complex optimization challenges. Full article

Cannabis is a strictly regulated plant in China, and its illegal cultivation presents significant challenges for social governance. Traditional manual patrol methods suffer from low coverage efficiency, while satellite imagery struggles to identify illicit plantations due to its limited spatial resolution, particularly for sparsely distributed and concealed cultivation. UAV remote sensing technology, with its high resolution and mobility, provides a promising solution for cannabis monitoring. However, existing detection methods still face challenges in terms of accuracy and robustness, particularly due to varying target scales, severe occlusion, and background interference. In this paper, we propose AMS-YOLO, a cannabis detection model tailored for UAV imagery. The model incorporates an asymmetric backbone network to improve texture perception by directing the model’s focus towards directional information. Additionally, it features a multi-scale fusion neck structure, incorporating partial convolution mechanisms to effectively improve cannabis detection in small target and complex background scenarios. To evaluate the model’s performance, we constructed a cannabis remote sensing dataset consisting of 1972 images. Experimental results show that AMS-YOLO achieves an mAP of 90.7% while maintaining efficient inference speed, outperforming existing state-of-the-art detection algorithms. This method demonstrates strong adaptability and practicality in complex environments, offering robust technical support for monitoring illegal cannabis cultivation. Full article

Digital Twin (DT) technology has emerged as a transformative tool in urban flood risk management (UFRM), enabling real-time data integration, predictive modeling, and decision support. This systematic review synthesizes existing literature to evaluate the scientific impact, technological advancements, and practical applications of DTs in UFRM. Using the PRISMA 2020 framework, we retrieved 1085 records (Scopus = 85; Web of Science = 1000), merged and deduplicated them using DOI and fuzzy-matched titles, screened titles/abstracts, and assessed full texts. This process yielded 85 unique peer-reviewed studies published between 2018 and 2025. Key findings highlight the role of remote sensing (e.g., satellite imagery, IoT sensors) in enhancing DT accuracy, the integration of machine learning for predictive analytics, and case studies demonstrating reduced flood response times by up to 40%. Challenges such as data interoperability and computational demands are discussed, alongside future directions for scalable, AI-driven DT frameworks. This review identifies key technical and governance challenges while recommending the development of modular, AI-driven DT frameworks, particularly tailored for resource-constrained regions. Full article

The complexity of global food supply chains challenges public health, requiring advanced detection technologies beyond traditional lab methods. Fluorescent sensing, known for its sensitivity and quick response, is promising for food safety but hindered by inefficient probe design and difficulties in analyzing complex signals in food. Deep Learning (DL) offers solutions with its nonlinear modeling and pattern recognition capabilities. This review explores recent advancements in DL applications for fluorescent sensing. We explore deep learning methods for predicting fluorescent probe properties and generating fluorescent molecule structures, highlighting their role in accelerating high-performance probe development. We then offer a detailed discussion on the pivotal technologies of deep learning in the intelligent analysis of complex fluorescent signals. On this basis, we engage in a thorough reflection on the core challenges presently confronting the field and propose a forward-looking perspective on the future developmental trajectories of fluorescent sensing technology, offering a comprehensive and insightful roadmap for future research in this interdisciplinary domain. Full article

The increasing integration of automation and intelligent sensing technologies in daily-use ceramic manufacturing poses new challenges for efficient scheduling under hybrid flow-shop and shared-kiln constraints. To address these challenges, this study proposes a Mixed-Integer Linear Programming (MILP) model and an Improved Discrete Hippopotamus Optimization (IDHO) algorithm designed for smart, network-aware production environments. The MILP formulation captures key practical features such as batch processing, no-idle kiln constraints, and machine re-entry dynamics. The IDHO algorithm enhances global search performance via segment-based encoding, nonlinear population reduction, and operation-specific mutation strategies, while a parallel evaluation framework accelerates computational efficiency, making the solution viable for industrial-scale, time-sensitive scenarios. The experimental results from 12 benchmark cases demonstrate that IDHO achieves superior performance over six representative metaheuristics (e.g., PSO, GWO, Jaya, DBO), with an average ARPD of 1.04%, statistically significant improvements (p < 0.05), and large effect sizes (Cohen’s d > 0.8). Compared to the commercial solver CPLEX, IDHO provides near-optimal results with substantially lower runtime. The proposed approach contributes to the development of intelligent networked scheduling systems for cyber-physical manufacturing environments, enabling responsive, scalable, and data-driven optimization in smart sensing-enabled production settings. Full article

The article examines the role of Geographic Information Systems (GIS) as a tool for environmental management and for the planning and development of renewable energy sources (RES). Based on a review of the literature, it is demonstrated that GIS support key managerial functions, including planning, monitoring, decision-making, and communication, by enabling comprehensive spatial analysis and the integration of environmental data. The study emphasizes the importance of GIS in facilitating a systemic and interdisciplinary approach to environmental governance. The paper examines how GIS can help with environmental management, specifically in locating high-risk areas and strategically placing energy investments. Examining GIS’s organizational, technological, and legal facets, it emphasizes how it is increasingly collaborating with cutting-edge decision-support technologies like artificial intelligence (AI), the Internet of Things (IoT), remote sensing, and big data. The analysis emphasizes how GIS help achieve sustainable development’s objectives and tasks. Full article

Offshore drilling platforms leak hundreds of thousands of tons of oil every year causing immeasurable damage to the marine environment, therefore it is important to be able to monitor for oil leakage. A hyperspectral camera, as an advanced device integrating spectral technology and imaging technology, can keenly capture the differences in spectral reflectance of different types of oil and seawater. This study presents the design of a hyperspectral camera covering the 400 nm–900 nm spectral band (90 bands total) and establishes a monitoring system comprising a high-precision inertial navigation system, a stabilization system, and a data acquisition system. Furthermore, this study conducted a field flight experiment using a Cessna aircraft, acquiring hyperspectral data with a one m spatial resolution of a drilling platform around the South China sea at 3000 m altitude, which effectively delineated the spectral characteristics of the oil spill area. The detection system developed in this study provides a robust means for oil spill monitoring on drilling platforms in remote sensing of the marine environment. Full article