Search Results (1,064)

Allium vegetables, including Welsh onion and onion, are important seasoning and processing crops worldwide. However, their production still relies heavily on manual labor because of their complex agronomic characteristics and the limited adaptability of existing machinery. Small seed size, fragile seedlings, narrow planting spacing, considerable variation in underground harvest organs, and high requirements for product quality present substantial challenges for mechanized production. With the continuous decline in agricultural labor availability and increasing production costs, the development of precision, intelligent, and full-process mechanization systems adapted to the biological characteristics of Allium vegetables has become essential for improving production efficiency and industrial sustainability. This review summarizes recent advances in mechanized production technologies and intelligent equipment for Allium vegetables, covering agronomic foundations and planting systems, precision seeding and nursery production, automatic transplanting, intelligent field management, mechanized harvesting, and digital technology applications. Advances in seed pelleting, pneumatic precision metering, and seed physical property-based parameter optimization have improved seeding accuracy and uniformity for small-seeded crops. Automated transplanting technologies, including oriented bulb planting, paper-pot seedling transplanting, and robotic seedling picking and placement, provide promising solutions for reducing labor requirements, although improvements are still needed in seedling recognition, pickup reliability, placement accuracy, and soil-covering coordination. Recent developments in field management have shifted from single mechanical or chemical operations toward integrated precision approaches involving intelligent mechanical weeding, mulch-based weed control, soil sensor-based monitoring, model-driven irrigation and fertilization regulation, biological control, and UAV- and satellite-assisted crop monitoring. Mechanized harvesting technologies have progressed through improvements in digging, soil separation, clamping and conveying, root and leaf cutting, windrowing, and collection systems. Among these processes, precise control of digging depth, soil disturbance, clamping force, and component synchronization remains critical for improving harvesting efficiency and reducing mechanical damage. Furthermore, emerging digital technologies, including machine vision, RTK positioning, LiDAR, hyperspectral sensing, multisource sensor fusion, and machine learning, are accelerating the transition of Allium vegetable machinery toward intelligent perception, autonomous navigation, and adaptive operation. However, challenges remain, including insufficient integration between agronomic requirements and machinery design, limited robustness of perception systems under complex field conditions, unclear mechanisms of harvest damage, inadequate equipment adaptability for small-scale and hilly production areas, and the lack of unified evaluation standards. Future research should focus on the coordinated development of varieties, cultivation practices, agricultural machinery, and digital technologies, with particular emphasis on high-speed precision seeding, flexible automatic transplanting, intelligent narrow-row crop management, low-damage harvesting, and closed-loop control based on multisource agricultural information. The establishment of standardized, lightweight, modular, and intelligent mechanized production systems will provide important support for the sustainable, high-quality, and large-scale development of the Allium vegetable industry. Full article

Tidal influence is a well-known phenomenon in the hydrogeology of coastal areas, as it is transmitted through aquifers connected to the sea, generating periodic fluctuations in borehole piezometric levels. The analysis of these fluctuations makes it possible to assess subsurface properties and derive characteristic parameters from the transmission of the pressure wave. Tidal influence results from a complex combination of the gravitational attraction exerted by the Sun and the Moon on the oceanic water mass, the tidal distribution conditioned by ocean morphology, and variable meteorological effects that may be highly localised. The combination of these factors generates oscillations ranging in magnitude from metres to centimetres. Under certain circumstances, such as spring tides coinciding with low atmospheric pressure, these oscillations may cause flooding and other undesirable effects in sensitive areas. The effect of earth tides in inland areas disconnected from the sea, sometimes at high elevations, is also well known and has received increasing scientific attention. Tidal oscillations, which are readily apparent in coastal areas, can also be recorded in certain geological materials, producing fluctuations that can be detected using high-precision sensors. These oscillations are caused by the same gravitational attraction responsible for ocean tides; however, in this case, the gravitational forcing acts on the geological formation itself, giving rise to what is known as an Earth tide or astronomical tide. Recent technological advances have facilitated the detection of these phenomena, which were previously difficult to identify. This study analyses the astronomical influence detected in areas clearly isolated from the sea, focusing on small, low-permeability aquifers where direct gravitational forcing of the groundwater mass cannot account for the observed response. Instead, astronomical forcing acts on the rock mass and indirectly affects piezometric levels. The selected experimental site comprises variable lithologies and different degrees of aquifer confinement and includes numerous research boreholes. It therefore provides an optimal setting for analysing the relationship between lithology and astronomical influence and may serve as a basis for future research in different hydrogeological settings. Full article

Background: Wearable sensors combined with machine learning (ML) offer new opportunities for movement analysis and performance monitoring in tennis and padel. However, the current evidence and potential applications remain unclear. This systematic review aimed to summarize the applications of ML to wearable sensor data in tennis and padel, focusing on movement recognition, performance monitoring, workload and fatigue assessment, injury-related applications, and rehabilitation. Methods: PubMed and Scopus were searched from inception to 1 August 2026. Studies were eligible if they investigated tennis or padel, used wearable sensors, and applied ML or deep learning methods. Study selection and data extraction were independently performed by two reviewers. Risk of bias was assessed across domains related to participants, predictors, outcomes, and analysis/validation. Due to substantial heterogeneity, a qualitative synthesis was performed. Results: Of 393 records identified, 16 studies were included. Fourteen investigated tennis, one padel, and one included tennis data within a multisport study. IMU-based systems were the most frequently used technology. Most studies focused on movement and stroke recognition, while fewer addressed performance, workload, fatigue, or injury-related applications. Reported model performance was generally high, but validation strategies were heterogeneous, and participant-independent or external validation was uncommon. Nine studies presented some concerns, and seven were classified as having high risk of bias. Evidence directly addressing rehabilitation and return-to-sport was particularly limited. Conclusions: ML applied to wearable sensor data shows promising potential for movement recognition and performance assessment in tennis and padel. However, methodological limitations, small and selected cohorts, and limited external validation restrict current generalizability and practical translation. Future research should prioritize larger and more diverse cohorts, subject-independent and external validation, and prospective studies addressing injury prevention, rehabilitation, and return-to-sport. Full article

Global food security faces escalating pressures from climate change, population growth, and resource limitations. Seed vigor, a complex physiological trait governing germination potential and stress resilience, remains critically underserved by conventional testing protocols that are destructive, time-intensive, and incompatible with industrial throughput. Surface plasmon resonance imaging (SPRi) offers a distinctive biosensing paradigm characterized by label-free detection, real-time kinetic analysis, and massively parallel multiplexing. Yet the agricultural SPR literature has been shaped by a single-analyte paradigm optimized for pesticide and pathogen detection, a framework that proves insufficient for the integrated interpretation of multiple biomarker panels that seed vigor demands. The biomarker panels considered here span macromolecular effectors that fall squarely within the direct detection range of SPRi and small-molecule osmoprotectants that are accessible through competitive, aptamer-based, or molecularly imprinted formats, a mixed-format multiplexing strategy for which experimental precedents on single sensor arrays exist. This review critically examines the operational boundaries of SPR/SPRi in agricultural matrices, surveys demonstrated performance across residue detection, soil monitoring, and breeding applications, and identifies why these achievements do not constitute a sufficient foundation for seed vigor assessment. We then analyze how SPRi technology must be strategically redesigned for non-destructive seed vigor evaluation, addressing biomarker selection criteria, non-invasive sampling hierarchies, surface functionalization for complex seed exudates, and kinetic phenotyping architectures for batch screening and stress-response fingerprinting. Performance enhancement through nanomaterial amplification, machine learning-based data interpretation, and microfluidic integration is evaluated alongside persistent gaps in matrix validation, biomarker standardization, and inter-laboratory reproducibility. The strategic significance of SPRi-enabled seed quality assurance for sustainable global agriculture is discussed in the context of these translational challenges. Full article

Interest in local warming for chronic wound care has re-emerged as flexible heaters, embedded sensors, and smart dressings make controlled heat delivery increasingly feasible. This narrative translational review treats thermotherapy as a dose-dependent wound-interface intervention rather than a single warming technology. It distinguishes normothermic radiant warming, water-filtered infrared A, resistive Joule heating, photothermal systems, and temperature-monitoring technologies by mechanism, thermal context, therapeutic purpose, and evidence level. The review integrates wound pathophysiology, biological responses to temperature, thermal-dose concepts, emerging heater platforms, sensing and closed-loop control, clinical evidence, safety, and regulatory translation. Direct human treatment evidence remains concentrated in small historical radiant-warming and water-filtered infrared A studies, with limited additional local-heat evidence, and cannot be transferred directly to contemporary flexible, wireless, or photothermal platforms. For lower-temperature warming, heater setpoint alone is an inadequate dose descriptor, and the biological meaning of cumulative equivalent minutes at 43 °C (CEM43) has not been validated; measured time–temperature profiles, peak temperature, spatial variation, sensor location, and uncertainty are more informative. Clinical translation therefore depends on reproducible wound-interface dosing under realistic moisture, pressure, perfusion, dressing, motion, and workflow conditions, together with wound-specific dose models, transparent reporting, realistic interface validation, and architecture-specific regulatory evidence. Full article

Carbon capture, utilization and storage (CCUS) is a critical technology for the fossil energy industry to achieve the dual carbon goals. Organic amine-based absorption methods, represented by mono-ethanolamine (MEA), methyl-diethanolamine (MDEA) and 2-amino-2-methyl-1-propanol (AMP), are currently the most widely adopted approaches for carbon dioxide capture. If the concentration of leaked organic amines exceeds the safety threshold, inhalation will cause severe respiratory irritation and even serious illnesses in humans. Accordingly, in situ monitoring of organic amine concentrations in waste gas is of great significance for process optimization, environmental protection, early health warning, energy conservation and emission reduction. In this study, a metal oxide semiconductor (MOS) sensor array was developed to identify categories and concentration variations in small-molecule organic amines. To realize effective gas classification and accurate concentration prediction, single-component gas data and mixed gas data with varying concentrations of the three amines collected in the laboratory were adopted to train a one-dimensional convolutional neural network (1D-CNN) and a bidirectional gated recurrent unit (Bi-GRU) via five-fold cross-validation. The proposed model achieved a classification accuracy of 0.9969 ± 0.0006. For MEA concentration prediction, the optimal determination coefficient (R²), root mean square error (RMSE), and mean absolute error (MAE) were 0.9969, 2.1306 and 1.5503, respectively. The experimental results demonstrate that the proposed method possesses promising practical application prospects in in situ gas monitoring and early hazard warning. Full article

Earthen levees are crucial flood defense systems but are susceptible to failure due to internal erosion and saturation-induced instability, potentially causing breaches and endangering lives, infrastructure, and ecosystems in protected areas. Understanding soil saturation dynamics is vital for improved monitoring and resilience. This study presents a novel method of levee health monitoring by deploying a wireless sensor network consisting of nine small sensing spike packages with long-term UAV-deployability potential. In-package pressure, temperature, humidity, and—most importantly—soil conductivity are all measured by the suite of environmental sensors integrated into each spike package. This integrated sensing capability gives spatiotemporal information about the embankment’s subsurface moisture conditions. A 2 m long, 1 m wide, and 0.45 m tall sand-filled embankment replica was built for a controlled flume experiment to verify the system, enabling close examination of moisture permeability and propagation. The deployment of cutting-edge analytical methods for data interpretation is one of this study’s main contributions. Discrete conductivity measurements were converted into continuous, two-dimensional maps of moisture propagation using interpolation techniques, namely radial basis function (RBF). This makes it possible to see the changing moisture front inside the embankment structure in detail. Important information about early warning signs of possible instability (piping failures) is provided by this combined analytical framework. Using a network of nine wireless sensing spike packages, the proposed framework monitored moisture propagation within a 2 m long earthen embankment over a 105 min lab experiment and identified resistance changes associated with piping-like seepage behavior at approximately 19 min and 34 min, which exited approximately 10 min after detection. However, only seven of the nine packages produced viable readings. Improving flood risk forecasting models, improving infrastructure health monitoring applications, and guiding the development of more resilient levee systems are all directly impacted by this. This study demonstrates the potential in autonomous levee monitoring and management given that it combines robust spatial analytics and an interpolation method with inexpensive, quickly deployable sensing technology. Full article

Portable Ocean Wave Energy Harvesters (POWEH) offer a potential approach for supplying autonomous low-power marine sensing systems; however, their long-term reliability, durability, and economic viability remain insufficiently demonstrated. But conventional wave energy converters are often big, expensive, and fixed to seabed structures, which makes it hard to use them in different places and makes it hard to sell them. People have been paying more attention to portable ocean wave energy harvesters in recent years. These are small, light systems that can be set up anywhere and do not need to be moored or anchored. These devices are meant to power autonomous ocean sensors, emergency buoys, and maritime applications that do not need to be connected to the grid. This review looks closely at the development trends, structural designs, and power take-off mechanisms of portable wave energy systems. We compare different types of harvesters, such as mechanical, electromagnetic, piezoelectric, and hybrid ones, based on their design principles, conversion efficiency, and scalability. Focus is directed towards the importance of symmetry in structural design, dynamic response, and energy conversion. The structure, whether symmetric or asymmetric, has a crucial role in determining mass distribution, stiffness traits, vibrational dynamics, hydrodynamic loading, and the capture of multi-directional wave energy, thereby influencing the overall performance, resilience, and stability of portable harvesters. The review goes on to talk about the main problems with dynamic stability, frequency tuning, environmental adaptability, and energy management when things are portable. Finally, new strategies like nonlinear dynamic designs, self-tuning mooring systems, and hybrid energy integration are suggested as ways to improve performance and reliability. The insights from this review are meant to help future innovations that will make it possible to use portable ocean wave energy technologies in a practical and long-lasting way. Full article

A two-dimensional large-strain quasi-distributed fiber optic sensor based on an integrated polymer flexible film with a fiber grating array has been proposed and developed to address the technical challenges of existing fiber optic sensing equipment, which struggles to simultaneously achieve two-dimensional vector strain identification, large deformation range detection, and multi-point quasi-distributed measurement. This sensor integrates the fiber grating array orthogonally onto the surface of a highly malleable polymer film substrate, combining the excellent mechanical tensile properties of the flexible film to overcome the limitations of traditional fiber optic sensors, such as small strain measurement range, single measurement dimension, and inability to identify the direction of strain vectors. The sensor mechanism has been studied, and sensor samples have been prepared. Test results show that the sensor can achieve multi-point distributed measurement of a two-dimensional large-strain range of at least 0–10% in both the X- and Y-directions, with excellent linearity and repeatability. The sensor has a simple structure and strong flexible conformability, providing new technical support for precise testing of two-dimensional global strain fields and engineering applications of flexible sensing technology. Full article

Accurate and efficient early pregnancy diagnosis (EPD) in livestock is crucial for optimizing breeding management and enhancing productivity in modern animal husbandry. Over the past century, EPD technology has evolved from empirical methods to sophisticated techniques, encompassing biochemical marker detection, ultrasonic imaging, and further extending to emerging non-invasive approaches such as infrared thermography (IRT) and spectroscopic analysis. These advancements have not only improved diagnostic accuracy but also broadened the research scope to include small livestock and multiple species. This review critically examines the historical evolution, current methodologies, and applications of EPD technology, with a focus on analyzing the advantages and limitations of both traditional and emerging techniques. Additionally, it explores the potential of multimodal fusion strategies and artificial intelligence (AI) in EPD. At present, machine vision, wearable monitoring, and several AI applications remain prospective approaches rather than validated tools for routine EPD. The conclusion highlights that, despite significant progress, current technologies still face limitations in achieving in situ, non-contact, and high-throughput detection. Looking ahead, the integration of cutting-edge technologies, such as AI, small wearable sensors, and physiological time-series data analysis, holds promise for overcoming these bottlenecks, enabling more intelligent and efficient pregnancy diagnosis, and providing scientific support for modern animal husbandry. Full article

Background/Objectives: Change-of-direction (COD) movements represent a fundamental component of performance in multidirectional sports and constitute one of the primary mechanisms of knee injury. The biomechanical assessment of such movements has traditionally relied on optoelectronic systems and force platforms, tools characterized by high accuracy but limited applicability in field settings. Inertial measurement units (IMUs) represent a promising alternative; however, operational doubts and uncertainties frequently persist. Therefore, the purpose of this systematic review was to critically evaluate the concurrent validity and practical utility of IMU sensors for detecting the biomechanical characteristics of COD maneuvers in sport. Methods: The bibliographic search was conducted in March 2024 across the PubMed MEDLINE, Scopus, and Web of Science databases, using Boolean combinations of the following keywords: IMU, “inertial measurement unit”, CoD, “change of direction”, “cutting maneuvers”, sport, and soccer. Study selection and analysis were performed, and methodological quality was assessed using the QUADAS-2 tool. Results: In the original search (March 2024), ten studies were included, involving 208 participants; a formal update of the search (through August 2026), conducted with the same protocol, identified two additional candidate studies (14 and 30 participants), bringing the total to twelve studies and 252 participants; both supplementary studies underwent the same four-reviewer independent screening, full-text data extraction and QUADAS-2 appraisal applied to the original ten studies. The findings indicate that IMUs demonstrate good concurrent validity for kinematic variables in the sagittal plane, particularly at the hip and knee joints, with ICC values up to 0.99 and a mean RMSE below 2°. Performance decreases substantially in the frontal and transverse planes. Regarding ground reaction forces, IMUs provide acceptable estimates of mean values, but not of instantaneous forces. For temporal variables, IMUs proved comparable to timing gates in the assessment of explosive actions. Conclusions: Based on a limited and heterogeneous evidence base of twelve studies, IMUs appear to be a promising and accessible technology for specific field-based applications, particularly for the assessment of CODs at 0°, 45°, and 90° in the sagittal plane, for monitoring right-left asymmetries, and for counting explosive actions. Given the small number of included studies and their methodological heterogeneity, these findings should be interpreted with caution. IMUs cannot yet be recommended as a complete replacement for gold-standard systems in applications requiring high precision or detailed analysis of instantaneous forces. Full article

Livestock production occupies a paradoxical position with respect to climate change: farm animals are highly vulnerable to heat stress, feed and water scarcity, and climate-sensitive disease, while the sector contributes an estimated 14.5% of anthropogenic greenhouse gas emissions, most of which is biogenic methane from enteric fermentation. This review evaluates the evidence for precision livestock farming (PLF)—continuous, automated, real-time monitoring of individual animals’ health, welfare, production and environmental impact—across dairy and beef cattle, small ruminants, pigs, and poultry. For mitigation, precision feeding and additive-dosing strategies have been associated with enteric methane reductions of approximately 10–25%; for adaptation, wearable and non-invasive sensors have been reported to detect heat-stress-related behavioural changes before productivity losses become apparent, and smart climate-control systems have been associated with housing energy-use reductions of roughly 5–10%. Much of this evidence derives from single-farm, small-sample or short-duration studies and should be read as indicative rather than generalisable. Adoption remains constrained by high investment costs, limited interoperability, insufficient technical support, and uneven applicability to extensive and smallholder systems. We conclude that PLF is a valuable enabling technology that, combined with genetic, nutritional, and management strategies, can strengthen the resilience and environmental sustainability of livestock systems under a changing climate. Full article

This article discusses the intelligent analysis of multisensory biogas data obtained from an experimental dataset generated by a Lab-on-Chip platform. The relevance of this work stems from the need for real-time monitoring of biogas quality and biomass condition under anaerobic digestion conditions, where changes in the concentrations of methane, carbon dioxide, hydrogen sulfide, oxygen, and temperature directly affect the stability of the technological process and the energy efficiency of the plant. This study utilizes a multisensor Lab-on-Chip/biosensor platform designed for rapid analysis of small samples of biogas, biomass, and biomix. The platform integrates gas, liquid, and optical sensor channels, as well as a module for transmitting data to the cloud. The experimental data obtained are processed using intelligent data analysis methods, including statistical analysis, correlation analysis, anomaly detection, and assessment of the relationships between monitored parameters. The scientific significance of this work lies in the application of an integrated approach to the analysis of multichannel experimental data obtained from the ESP32 microcontroller, which enables a more accurate and timely assessment of the state of the biogas process. The practical significance lies in the ability to use the proposed approach for remote monitoring, early detection of anomalies, and improving the efficiency of biogas plant management. Full article

Fetal magnetocardiography (fMCG) provides direct non-invasive assessment of fetal cardiac electrophysiology, enabling detailed evaluation of cardiac rhythm, conduction, and repolarization. However, the clinical adoption of conventional fMCG has been limited by its reliance on superconducting quantum interference device (SQUID) systems, which require cryogenic cooling and specialized infrastructure. Optically pumped magnetometers (OPMs) have emerged as a promising cryogen-free alternative with the potential to broaden access to fetal electrophysiological assessment. This review summarizes the technological evolution and early clinical evaluation of OPM-based fMCG through an analysis of original in vivo human studies published up to June 2026. Twelve eligible studies were identified and synthesized narratively. Advances in sensor design, magnetic shielding, acquisition strategies, and signal-processing algorithms have enabled SQUID-comparable signal quality and cardiac interval measurements while substantially reducing cryogenic and infrastructure requirements. OPM-fMCG has demonstrated the potential to assess fetal cardiac time intervals, heart rate variability, fetal movement, and clinically important arrhythmias, including congenital long QT syndrome, atrioventricular block, and supraventricular and ventricular tachyarrhythmias. However, the available evidence remains dominated by small, single-centre studies, with relatively few fetuses affected by clinically significant arrhythmias. Prospective multicenter clinical validation, protocol standardization, independent replication, and regulatory evaluation are therefore required before OPM-fMCG can be integrated into routine diagnostic pathways for pregnancies requiring advanced fetal electrophysiological assessment. Full article

Microalgae biomanufacturing is a promising technology that converts carbon dioxide into valuable products. However, it faces limitations in scaling, online monitoring, and controlling biological variables. In photobioreactors, light intensity influences growth and operational efficiency. This study presents Alg-flex, a low-cost, open-source monitoring and control platform based on a Raspberry Pi 4. Alg-flex integrates sensors for light intensity regulation and real-time monitoring of pH, temperature, and dissolved oxygen, enabling data visualization and logging, while providing full portability without hardware modifications. These features offer broader applicability than conventional single-variable loggers. The hardware was obtained for under $1834 USD, approximately 4% of the average cost of similar commercial units. Its performance was evaluated during the cultivation of Haematococcus lacustris in a bubble column photobioreactor. The pH and temperature sensors were validated against certified benchtop probes over the operating range used in this study, yielding median errors below 1.5%. Under the tested conditions, bioreactor cultures showed approximately two-fold higher final optical density that was significantly different than flask cultures (p = 0.0333). These results confirm that growth dynamics in small-volume microalgal cultures may differ substantially from controlled photobioreactor systems. The affordability, reliability, and multi-reactor compatibility of Alg-flex support broader biomanufacturing applications beyond microalgae cultivation. Full article