Plant-parasitic nematodes pose a silent yet devastating threat to global agriculture, causing significant yield losses and economic damage. Traditional detection methods such as soil sampling, microscopy, and molecular diagnostics are slow, labor-intensive, and often ineffective in early-stage infestations. Nano biosensors: cuttingedge analytical tools that leverage nanomaterials like carbon nanotubes, graphene, and quantum dots to detect nematode-specific biochemical markers such as volatile organic compounds (VOCs) and oesophageal gland secretions, with unprecedented speed and accuracy. The real breakthrough lies in the fusion of artificial intelligence (AI) and nano-biosensor technology, forging a new frontier in precision agriculture. By integrating AI's powerful data analysis, pattern recognition, and predictive capabilities with the extraordinary sensitivity and specificity of nano-biosensors, it becomes possible to detect biomolecular changes in real-time, even at the earliest stages of disease progression. AI-driven nano biosensors can analyze real-time data, enhance detection precision, and provide actionable insights for farmers, enabling proactive and targeted pest management. This synergy revolutionizes nematode monitoring, paving the way for smarter, more sustainable agricultural practices. This review explores the transformative potential of AI-powered nano-biosensors in advancing precision agriculture. By integrating these technologies with smart farming systems, we move closer to real-time, costeffective, and field-deployable solutions, ushering in a new era of high-tech, eco-friendly crop protection.

Hypochlorite (ClO-) is a highly reactive chemical extensively used in households, public areas, and various industries due to its multiple functions of disinfection, bleaching, and sterilization. However, overuse of ClO- may contaminate the water, soil, air and food, leading to negative impacts on the environments, ecosystems and food safety. Meanwhile, excessive ClO- in human body can also cause severe damage to the immune system. Thus, the development of effective and precise detection tools for ClO- is of great significance to better understand its complicated roles in environments and biosystems. Herein, a new high-performance ratiometric fluorescent probe 2-amino-3-((10-propyl-10H-phenothiazin-3-yl)methylene)-amino)maleonitrile (PD) was developed for effective detection of ClO- in various bio/environmental and food samples. Probe PD exhibits highly-specific ratiometric fluorescent response to ClO- with rapid response (< 1 min), excellent sensitivity (detection limit, 47.4 nM), wide applicable pH range (4 -12), and excellent versatility in practical applications. In practical applications, PD enables the sensitive and quantitative detection of ClO- levels in various water samples, bio-fluids, dairy products, fruits and vegetables with high-precision (recoveries, 97.00 -104.40 %), as well as the successful application for visual tracking ClO- in fresh fruits and vegetables. Furthermore, test strips containing PD offer a visual and convenient tool for quick identification of ClO- in aqueous media by the naked eye. Importantly, the good biocompatibility of PD enables its practical applications in real-time bioimaging of endogenous/exogenous ClO- levels in living cells, bacteria, onion cells, Arabidopsis, as well as zebrafish. This study provided an effective method for visual monitoring and bioimaging of ClO- levels in various environments, foods and living biosystems.

The European rabbit (Oryctolagus cuniculus) is a keystone species in Mediterranean ecosystems but also considered a pest in some agricultural areas. Despite its threatened status due to diseases and habitat loss, rabbit populations thrive in motorway verges, causing conflicts with human activities. In this study we examine the factors affecting rabbit warren abundance in motorway verges in central Spain, with implications for conservation and management. The research aimed to assess the importance of infrastructure (e.g. motorway slopes) and landscape (e.g. land use, soil depth) factors on rabbit warren abundance along 1631 km of motorway verges and to develop an index for broader-scale abundance and risk assessment. Using generalized linear mixed models, the study revealed that both infrastructure (slope) and landscape factors (soil depth, vegetation structure and land cover gradients) significantly influenced warren abundance. Rabbit warrens were more abundant in agricultural landscapes with deep soils and in intermediate slope ranges. The findings suggest that rabbit abundance in motorway verges is driven by a combination of factors involving both infrastructure features but also land use in surrounding areas. The derived model predictions were able to correctly discriminate between crop damaged and non-damaged areas, highlighting its potential as a tool for conflict mitigation and conservation planning. The study underscores the need to integrate landscape and infrastructure features into wildlife management strategies to address human-wildlife conflicts effectively. Future work should include direct population monitoring and explore broader ecological impacts, such as predator dynamics and wildlife-vehicle collisions.

Component temperature and emissivity are crucial for understanding plant physiology and urban thermal dynamics. However, existing thermal infrared unmixing methods face challenges in simultaneous retrieval and multicomponent analysis. We propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas. TRUST-DART utilizes the DART model and requires inputs including at-surface radiance imagery, downwelling sky irradiance, a 3D mock-up with component classification, and standard DART parameters (e.g., spatial resolution and skylight ratio). This method produces maps of component emissivity and temperature. The accuracy of TRUST-DART is evaluated using both vegetation and urban scenes, employing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images and DART-simulated pseudo-ASTER images. Results show a residual radiance error is approximately 0.05 W/(m2 & sdot;sr). In absence of the co-registration and sensor noise errors, the median residual error of emissivity is approximately 0.02, and the median residual error of temperature is within 1 K. This novel approach significantly advances our ability to analyze thermal properties of urban areas, offering potential breakthroughs in urban environmental monitoring and planning. The source code of TRUSTDART is distributed together with DART (https://dart.omp.eu).

The laboratory experiment is an effective tool for the rapid assessment of the unsaturated soil slopes instability induced by extreme weather events. However, traditional experimental methods for unsaturated soils, including the measurement of the soil-water characteristic curve (SWCC), soil hydraulic conductivity function (SHCF), shear strength envelope, etc., are time-consuming. To overcome this limitation, a rapid testing strategy is proposed. In the experimental design, the water saturation level is selected as the control variable instead of the suction level. In the suction measurement, the suction monitoring method is adopted instead of the suction control method, allowing for simultaneous testing of multiple soil samples. The proposed rapid testing strategy is applied to measure the soil hydro-mechanical properties over a wide suction/saturation range. The results demonstrate that: (1) only 3-4 samples and 2-5 days are in need in the measurement of SWCC; (2) 7 days is enough to determine a complete permeability function; (3) only 3 samples and 3-7 days are in need in the measurement of the shear strength envelope; (4) pore size/water distribution measurement technique is fast and recommended as a beneficial supplement to traditional test methods for unsaturated soils. Our findings suggest that by employing these proposed rapid testing methods, the measurement of pivotal properties for unsaturated soils can be accomplished within one week, thus significantly reducing the temporal and financial costs associated with experiments. The findings provide a reliable experimental approach for the rapid risk assessment of geological disasters induced by extreme climatic events.

Heavy metals (HMs) contamination poses a significant threat to environmental matrices, particularly soil, which is essential for food security, agricultural productivity, and key ecosystem services. Understanding how crops respond to HMs is crucial for developing biomonitoring strategies to assess soil contamination and inform remediation efforts. Plants, including crops, exhibit a range of functional traits (FT) that can indicate HMs stress and contamination levels. In this study, we investigated the response strategies of Zea mays L. var. Limagrain 31455, widely cultivated throughout the region of Land of Fires, a critically polluted area of southern Italy, to different concentrations of Zn, Pb, and Cr, corresponding to moderate to severe soil contamination. Functional traits related to the photosynthetic machinery, including gas exchange, chlorophyll fluorescence and reflectance indices, were examined. Root morpho-histochemical analysis were also conducted to correlate early root alterations with any observed changes in these photosynthetic traits. Results revealed distinct response patterns: tolerance to Zn, without adverse effects on photosynthetic traits; resistance to Pb, mediated by increased RD and photoprotection through change in reflectance indices; and sensitivity to Cr highlighted by severe functional impairments of all the studied photosynthetic traits and structural root damages. Functional traits, such as chlorophyll fluorescence parameters and the photochemical reflectance index or normalized difference vegetation index, demonstrated high potential for monitoring HMs stress responses; in addition, morpho-anatomical traits of the root system provided insights into biomass allocation and the capacity of var. Limagrain 31455 to tolerate and adapt to HMs stress. These findings underscore the importance of integrating physiological, anatomical, and spectral analyses to improve the biomonitoring and management of polluted soils and detecting spatial variability in contamination via remote sensing.

Most Australian vegetable growers apply fumigants or nematicides as a precautionary nematode control measure when crops susceptible to root-knot nematode (RKN, Meloidogyne spp.) are grown in soils and environmental conditions suitable for the nematode. The only way growers can make rational decisions on whether these expensive and environmentally disruptive chemicals are required is to regularly monitor RKN populations and decide whether numbers prior to planting are high enough to cause economic damage. However, such monitoring programs are difficult to implement because nematode quantification methods vary in efficiency and the damage threshold for RKN on highly susceptible vegetable crops is often < 10 root-knot nematodes /200 mL soil. Consequently, five nematode quantification methods were tested to see whether they could reliably detect these very low population densities of RKN. Two novel methods produced consistent results: 1) extracting nematodes from 2 L soil samples using Whitehead trays, quantifying the RKN DNA in the nematode suspension using molecular methods, and generating a standard curve so that the molecular results provided an estimate of the total number of RKN individuals in the sample, and 2) a bioassay in which two tomato seedlings were planted in pots containing 2 L soil and the number of galls produced on roots were counted after 21-25 days. Both methods could be used to quantify low populations of RKN, but bioassays are more practical because expensive equipment and facilities are not required and they can be done at a local level by people lacking nematological or molecular skills.

To enhance the safety and reliability of urban buried water supply networks, this study developed a monitoring and early warning system based on wireless transmission networks and Internet of Things (IoT) technology. Through numerical simulations, the structural tilt warning thresholds for ductile iron pipes were determined. Additionally, in conjunction with meteorological data, monitoring pore water pressure serves as a supplementary indicator for detecting potential pipeline leakage. This study further analyzed pipeline strength warning thresholds based on strength theory. In practical engineering applications, the proposed system enables real-time monitoring of the operational status, service environment, and structural integrity of buried water supply networks. Data analysis revealed the influence mechanisms of backfill soil conditions, daily operations, and third-party construction activities on the structural behavior and stress state of water supply pipelines. Results indicate that during the initial backfilling phase, uneven backfilling and soil settlement induce significant variations in pipeline tilt angle and stress distribution. Furthermore, longitudinal stress in the pipeline exhibits a strong correlation with ambient temperature fluctuations, with a pronounced increase observed during colder months. Notably, third-party construction activities were identified as a major contributor to pipeline anomalies, with all recorded early warnings in this study being attributed to such external interferences.

The dynamic response of historical masonry structures involves multiple sources of nonlinearity, arising from the materials used, the ageing, the complex geometries and boundary conditions involved. As a result, modelling the seismic response of these buildings requires detailed instrumentation beforehand. Crossed by active faults and frequently shaken by moderate earthquakes (Mw3-4), the Cusco region (Peru) has many stone and earth masonry buildings that turn out to be particularly vulnerable to the seismic hazard. We therefore conducted an ambient vibration-based survey in the 17th-century church of San Cristobal in Cusco, seriously damaged by the 1950 earthquake. By combining an Operational Modal Analysis, single-sensor monitoring for over a year and free-field microtremor measurements, our work highlights the existence of strong soil-structure interaction and topographic effects resulting in the excitation of a rigid-body-like mode. Continuous instrumentation also made it possible to study the structure's response to earthquakes, revealing an unexpected frequency drop during a Mw4.2 earthquake, followed by a slow recovery process that lasted more than two months. These results shed new light on the seismic vulnerability of the church, and call for further investigation into the processes behind the site effects and nonlinear dynamics that characterise the response of Andean built heritage.

Tensile cracks play a pivotal role in the formation and evolution of reservoir landslides. To investigate how tensile cracks affect the deformation and failure mechanism of reservoir landslides, a novel artificial tension cracking device based on magnetic suction was designed to establish a physical model of landslides and record the process of landslide deformation and damage by multifield monitoring. Two scenarios were analyzed: crack closure and crack development. The results indicate that under crack closure, secondary cracks still form, leading to retrogressive damage. In contrast, under crack development conditions, the failure mode changes to composite failure with overall displacement. The release of tensile stresses and compression of the rear soil are the main driving forces for this movement. Hydraulic erosion also plays a secondary role in changing landslide morphology. The results of multifield monitoring reveal the effects of tensile cracking on reservoir landslides from multiple perspectives and provide new insights into the mechanism of landslide tensile-shear coupled damage.