Search Results (4,671)

Staining-based assays are widely used for cell analysis but are invasive, alter physiology, and prevent longitudinal monitoring. Label-free, morphology-based approaches could enable real-time, non-invasive drug testing, yet detection of subtle and dynamic changes has remained difficult. We developed a deep learning framework for stain-free monitoring of leukemia cell cultures using automated bright-field microscopy in a semi-automated culture system (AICE3, LABMaiTE, Augsburg, Germany). YOLOv8 models were trained on images from K562, HL-60, and Kasumi-1 cells, using an NVIDIA DGX A100 GPU for training and tested on GPU and CPU environments for real-time performance. Comparative benchmarking with RT-DETR and interpretability analyses using Eigen-CAM and radiomics (RedTell) was performed. YOLOv8 achieved high accuracy (mAP@0.5 > 98%, precision/sensitivity > 97%), with reproducibility confirmed on an independent dataset from a second laboratory and an AICE3 setup. The model distinguished between morphologically similar leukemia lines and reliably classified untreated versus differentiated K562 cells (hemin-induced erythroid and PMA-induced megakaryocytic; >95% accuracy). Incorporation of decitabine-treated cells demonstrated applicability to drug testing, revealing treatment-specific and intermediate phenotypes. Longitudinal monitoring captured culture- and time-dependent drift, enabling separation of temporal from drug-induced changes. Radiomics highlighted interpretable features such as size, elongation, and texture, but with lower accuracy than the deep learning approach. To our knowledge, this is the first demonstration that deep learning resolves subtle, drug-induced, and time-dependent morphological changes in unstained leukemia cells in real time. This approach provides a robust, accessible framework for label-free longitudinal drug testing and establishes a foundation for future autonomous, feedback-driven platforms in precision oncology. Ultimately, this approach may also contribute to more precise and adaptive clinical decision-making, advancing the field of personalized medicine. Full article

Albendazole (ABZ) is a broad-spectrum anthelmintic drug whose residual presence in food and the environment raises public health concerns, requiring rapid and sensitive methods of detection. In this work, a sensitive Fabry–Pérot interferometer (FPI) probe was fabricated by realizing a cavity located at the tip of a single-mode optical fiber core with a molecularly imprinted polymer (MIP) for ABZ detection. The fabrication process involved the development of a photoresist-based micro-hole filled by the specific MIP via thermal polymerization. Interferometric measurements obtained using the proposed sensor system have demonstrated a limit of detection (LOD) of 27 nM, a dynamic concentration range spanning from 27 nM (LOD) to 250 nM, and a linear response at the nanomolar level (27 nM–100 nM). The selectivity test demonstrated no signal when interfering molecules were present, and the application of the sensor for ABZ quantification in a commercial pharmaceutical sample provided good recovery, in accordance with bioanalytical validation standard methods. These results demonstrate the capability of a MIP layer-based FPI probe to provide low-cost and selective optical-sensing strategies, proposing a competitive approach to traditional analytical techniques for ABZ monitoring. Full article

Fire hazards during cotton picker operations pose a significant safety concern, primarily caused by cotton blockages and friction-induced heat generation between the picking spindle and seed cotton under high-load conditions. Existing fire monitoring systems typically employ a uniform temperature threshold across multiple sensors. However, this approach overlooks the distinct characteristics of different cotton picker mechanisms and the influence of seed cotton moisture content, resulting in frequent false alarms and missed detections. To address these issues, this study pioneers and tests a dynamic, tiered temperature threshold warning strategy. This approach accounts for key cotton picker components and varying seed cotton moisture content (MC), specifically MC 9–12% and MC 12–15%. Additionally, based on the operational characteristics of the cotton conveying tube, this study proposes monitoring the wall surface temperature of the conveying tube and investigates the threshold for this temperature. Results indicate that during seed cotton open burning, the average temperature is 324 °C for MC < 9%, 261.9 °C for MC 9–12%, and 178.4 °C for MC 12–15%. After transitioning to smoldering, the temperatures were 226.6 °C, 191.5 °C, and 163.5 °C, respectively, with 163.5 °C being the lowest threshold for seed cotton open burning in the cotton bin. For smoldering seed cotton, the temperature thresholds were 240 °C for MC < 9% and MC 9–12%, and 280 °C for MC 12–15%. The temperature threshold for the cotton conveyor pipe wall surface was 49 °C. The friction-induced heat generation temperature threshold at the picking head, determined through combined testing and simulation, is set at 289 °C for MC < 9%, 306 °C for MC 9–12%, and 319 °C for MC 12–15%. The aforementioned tiered early warning strategy, developed through multi-source experiments and simulations, can be directly configured into controllers. It enables dynamic threshold alarms based on harvester location, seed cotton moisture content, and temperature zones, providing quantitative support for cotton harvester fire monitoring and risk management. Full article

Poyang Lake, China’s largest freshwater lake, is an ecologically significant but increasingly vulnerable system threatened by eutrophication and harmful algal blooms driven by human activities. Phytoplankton organisms, as primary producers and sensitive bioindicators, provide critical insights into these ecological changes; however, comprehensive seasonal assessments remain scarce. This study examined intra-annual phytoplankton dynamics at 15 representative sites, with the objectives of quantifying seasonal and spatial variations in community composition, density, biomass, and diversity, and identifying key environmental drivers. Surface water samples were collected during four seasons. Phytoplankton were identified microscopically, and diversity was quantified using Shannon–Wiener, Pielou’s evenness, and Margalef’s richness indices. Concurrent measurements included water temperature (WT), dissolved oxygen (DO), nutrients (TN, TP, NO₃⁻-N, NO₂⁻-N, NH₄⁺-N), chemical oxygen demand (COD), pH, and transparency. Pearson correlation and redundancy analysis (RDA) were applied to evaluate phytoplankton–environment relationships. A total of 118 phytoplankton species belonging to 7 phyla were identified. Chlorophyta, Cyanobacteria, and Bacillariophyta exhibited the highest species richness. The highest seasonal abundances were observed for Microcystis wesenbergii (0.998) in winter, Aulacoseira granulata var. angustissima (0.780) in spring, and Snowella lacustris (0.520) in autumn, indicating pronounced seasonal shifts in dominant taxa across Poyang Lake. Phytoplankton density and biomass peaked in summer, while diversity indices significantly declined with increasing WT. RDA revealed that WT, DO, TP, and transparency collectively explained 45.7% of the community variation, with DO emerging as the most influential factor. These findings demonstrate that physical drivers, particularly thermal conditions and oxygen availability, exert stronger influences on phytoplankton diversity than nutrients alone, challenging nutrient-centric paradigms. Management should integrate hydrological and oxygen regulation with nutrient control, while long-term monitoring, depth-stratified sampling, and trait-based approaches are recommended to improve predictive models under climate variability. Full article

This study proposes an Automatic Identification System (AIS)-based spatiotemporal allocation methodology to estimate catch distribution and fishing effort for large purse seine fisheries in Korean waters. AIS trajectory data from July 2019 to June 2022 were analyzed to identify fishing grounds, while carrier vessel port-entry records were used to estimate daily landings. These were allocated to specific fishing segments to derive spatially explicit catch quantities. Compared with periodic surveys or voluntary reports, the AIS-based approach significantly enhanced the accuracy of fishing ground identification and the reliability of catch estimation. The results showed that fishing activity peaked between November and February, with the highest catch densities observed south of Jeju Island and in adjacent East China Sea waters. Catch declined markedly from April to June due to the mackerel closed season. These findings demonstrate the method’s potential for evaluating the effectiveness of Total Allowable Catch (TAC) regulations, supporting dynamic and adaptive management frameworks, and strengthening IUU fishing monitoring. Although the current analysis is limited to TAC-regulated species, AIS-equipped vessels, and a three-year dataset, future studies could expand the timeframe, integrate environmental data, and apply this methodology to other fisheries to improve sustainable resource management. Full article

Flood events, intensified by climate change, pose significant threats to both human settlements and ecological systems. This study presents an integrated approach to evaluate flood impacts on ecosystem carbon dynamics using remote sensing and machine learning techniques. The case of the Emilia-Romagna region in Italy is presented, which experienced intense flooding in 2023. To understand flood-induced changes in the short term, we quantified the differences in net primary productivity (NPP) and above-ground biomass (AGB) before and after flood events. Short-term analysis of NPP and AGB revealed substantial localized losses within flood-affected areas. NPP showed a net deficit of 7.0 × 10³ g C yr⁻¹, and AGB a net deficit of 0.5 × 10³ Mg C. While the wider region gained NPP (6.7 × 10⁵ g C yr⁻¹), it suffered a major AGB loss (3.3 × 10⁵ Mg C), indicating widespread biomass decline beyond the flood zone. Long-term ecological assessment using the Remote Sensing Ecological Index (RSEI) showed accelerating degradation, with the “Fair” ecological class shrinking from 90% in 2014 to just over 50% in 2024, and the “Poor” class expanding. “Good” and “Very Good” classes nearly disappeared after 2019. High-hazard flood zones were found to contain 9.0 × 10⁶ Mg C in AGB and 1.1 × 10⁷ Mg C in soil organic carbon, highlighting the vulnerability of carbon stocks. This study underscores the importance of integrating flood modeling with ecosystem monitoring to inform climate-adaptive land management and carbon conservation strategies. It represents a clear, quantifiable carbon loss that should be factored into regional carbon budgets and post-flood ecosystem assessments. Full article

In complex process systems, accurate real-time anomaly detection is essential to ensure operational safety and reliability. This study proposes a novel detection method that combines information granulation with kernel principal component analysis (KPCA). Here, information granulation is introduced as a general framework, with the principle of justifiable granularity (PJG) adopted as the specific implementation. Time series data are first granulated using PJG to extract compact features that preserve local dynamics. The KPCA model, equipped with a radial basis function kernel, is then applied to capture nonlinear correlations and construct monitoring statistics including $T^2$ and SPE. Thresholds are derived from training data and used for online anomaly detection. The method is evaluated on the Tennessee Eastman process and Continuous Stirred Tank Reactor datasets, covering various types of faults. Experimental results demonstrate that the proposed method achieves a near-zero false alarm rate below 1% and maintains a missed detection rate under 6%, highlighting its effectiveness and robustness across different fault scenarios and industrial datasets. Full article

Enhanced Rock Weathering (ERW) is a promising carbon dioxide removal (CDR) strategy that accelerates mineral dissolution, sequestering atmospheric CO₂ while improving soil health. This study builds on prior applications of soil calcimetry by investigating its ability to resolve short-term carbonate fluxes and rainfall-modulated weathering dynamics in wollastonite-amended croplands. Conducted over a single growing season (May–October 2024) in temperate row-crop fields near Port Colborne, Ontario—characterized by fibric mesisol soils (Histosols, FAO-WRB)—this study tests whether calcimetry can distinguish between dissolution and precipitation phases and serve as a proxy for weathering flux within the upper soil horizon, under the assumption that rapid pedogenic carbonate cycling dominates alkalinity retention in this soil–mineral system. Monthly measurements of soil pH (Milli-Q and CaCl₂) and calcium carbonate equivalent (CCE) were conducted across 10 plots, totaling 180 composite samples. Results show significant alkalinization (p < 0.001), with average pH increases of ~+1.0 unit in both Milli-Q and CaCl₂ extracts over the timeline. In contrast, CCE values showed high spatiotemporal variability (−2.5 to +6.4%) without consistent seasonal trends. The calcimetry-derived weathering proxy, log (Σ ΔCCE/Δt), correlated positively with pH (r = 0.652), capturing net carbonate accumulation, while the kinetic dissolution rate model correlated strongly and negatively with pH (r ≈ −1), reflecting acid-promoted dissolution. This divergence confirms that the two metrics capture complementary stages of the weathering–precipitation continuum. Rainfall strongly modulated short-term carbonate formation, with cumulative precipitation over the previous 7–10 days enhancing formation rates up to a saturation point (~30 mm), beyond which additional rainfall yielded diminishing returns. In contrast, dissolution fluxes remained largely independent of rainfall. These results highlight calcimetry as a direct, scalable, and dynamic tool not only for monitoring solid-phase carbonate formation, but also for inferring carbonate migration and dissolution dynamics. In systems dominated by rapid pedogenic carbonate cycling, this approach captures the majority of alkalinity fluxes, offering a conservative yet comprehensive proxy for CO₂ sequestration. Full article

Artificial intelligence (AI) is increasingly embedded within organizational infrastructures, yet the foundational role of data in shaping AI outcomes remains underexplored. This study positions data at the center of complexity, uncertainty, and strategic decision-making in AI development, aligning with the emerging paradigm of data-centric AI (DCAI). Based on in-depth interviews with 74 senior AI and data professionals, the research examines how experts conceptualize and operationalize data throughout the AI lifecycle. A thematic analysis reveals five interconnected domains reflecting sociotechnical and organizational challenges—such as data quality, governance, contextualization, and alignment with business objectives. The study proposes a conceptual model depicting data as a dynamic infrastructure underpinning all AI phases, from collection to deployment and monitoring. Findings indicate that data-related issues, more than model sophistication, are the primary bottlenecks undermining system reliability, fairness, and accountability. Practically, this research advocates for increased investment in the development of intelligent systems designed to ensure high-quality data management. Theoretically, it reframes data as a site of labor and negotiation, challenging dominant model-centric narratives. By integrating empirical insights with normative concerns, this study contributes to the design of more trustworthy and ethically grounded AI systems within the DCAI framework. Full article

The rational placement of pollutant monitoring sensors has long been a prominent research focus in ocean environment science. Our method integrates an incremental Proper Orthogonal Decomposition with a mobility-constrained sensor selection strategy, enabling efficient monitoring and dynamic adaptation to spatio-temporal field changes. At each time step, the position of the sensors is updated based on the incoming measurements to minimize the reconstruction error while adhering to movement constraints. This online approach considers the need for mobility distance, making it suitable for long-term deployments in resource-limited scenarios. The proposed framework is validated in three scenarios: a linear advection–diffusion system with multiple moving pollution sources, the distribution of particulate matter with an aerodynamic diameter smaller than 2.5 $\mathsf{\mu }$m (PM2.5) across the United States, and scalar transport in flows past side-by-side cylinder arrays in the ocean. The results demonstrate that the method achieves high reconstruction accuracy with significantly fewer sensors. This study conducts a comparative analysis of three typical mobility constraints and their respective effects on reconstruction accuracy. In addition, the proposed localized sensor mobility strategy effectively tracks evolving plume structures and maintains a low approximation error, providing a generalizable solution for sparse monitoring of the marine environment. Full article

Inland lakes across the United States are increasingly impacted by nutrient pollution, sedimentation, and algal blooms, with significant ecological and economic consequences. While satellite-based monitoring has advanced our ability to assess water quality at scale, many lakes remain analytically underserved due to their spatial heterogeneity and the multivariate nature of pollution dynamics. This study presents an integrated framework for detecting spatiotemporal pollution patterns using satellite remote sensing, trend segmentation, hierarchical clustering and dimensionality reduction. Taking Horseshoe Lake (Illinois), a shallow eutrophic–turbid system, as a case study, we analyzed Sentinel-2 imagery from 2020–2024 to derive chlorophyll-a (NDCI), turbidity (NDTI), and total phosphorus (TP) across five hydrologically distinct zones. Breakpoint detection and modified Mann–Kendall tests revealed both abrupt and seasonal trend shifts, while correlation and hierarchical clustering uncovered inter-zone relationships. To identify lake-wide pollution windows, we applied Kernel PCA to generate a composite pollution index, aligned with the count of increasing trend segments. Two peak pollution periods, late 2022 and late 2023, were identified, with Regions 1 and 5 consistently showing high values across all indicators. Spatial maps linked these hotspots to urban runoff and legacy impacts. The framework captures both acute and chronic stress zones and enables targeted seasonal diagnostics. The approach demonstrates a scalable and transferable method for pollution monitoring in morphologically complex lakes and supports more targeted, region-specific water management strategies. Full article

The monitoring technology used during drilling has become a crucial means of gathering information about the underground rock mass. However, the drilling response parameters are affected by the coupling of operating parameters and rock mass properties, which leads to the challenge of lithology inversion based on drilling parameters in complex strata. At present, the precise quantitative response mechanism between operating parameters and drilling parameters is still not clear in the common lithology of mining, which restricts the further improvement of the accuracy of lithology identification while drilling and the optimization of drilling technology. In order to improve the measurement of drilling technology, the relationship between rock parameters and drilling parameters in the process of mining drilling is explored. This paper carried out physical and mechanical experiments; built a small drilling platform (including magnetic suction drilling, a data monitoring system, and a rock confining pressure system); carried out three different specifications, 330 r/min, 360 r/min, and 390 r/min, of the initial speed of the drilling experiment; and added 330 r/min initial-speed-drilling different-strength rock-drilling experiments. The experimental results show that rock drilling is divided into three stages: the initial stage of drilling, the crack propagation stage, and the bit retreating stage. The rotation speed has a great influence on the drilling speed, torque, weight on bit, and drilling time. According to the Pearson fitting relationship of drilling parameters, the correlation between F and PR is −0.783, indicating a strong positive correlation, and the correlation between RPM and PR is 0.827, indicating a strong negative correlation. The power function y = ax^b is used to fit the drilling parameters and rock parameters. The fitting effect is good, and the torque and uniaxial tensile strength R2 is as high as 0.9966. The experimental conclusion provides a theoretical basis for lithology identification in intelligent mining drilling and discusses the feasibility of a dynamic monitoring scheme for the drilling rig. Full article

Ever-increasing demands to improve fuel burn efficiency of aero gas turbines lead to rises in fuel system pressures and temperatures, posing challenges for the structural integrity of the pump housing and creating internal deflections that can adversely affect volumetric efficiency. Non-invasive strain and vibration measurements could allow transient effects to be quantified and considered during the design process, leading to more robust fuel pumps. Fuel pumps used on a high bypass turbofan engine were instrumented with optical fibre Bragg grating (FBG) sensors, strain gauges and thermocouples. A hydraulic hand pump was used to facilitate measurements under static conditions, while dynamic measurements were performed on a dedicated fuel pump test rig. The experimental data were compared with the outputs from a finite element (FE) model and, in general, good agreement was observed. Where differences were observed, it was concluded that they arose from the sensitivity of the model to the selection of nodes that best matched the sensor location. Strain and vibration measurements were performed over the frequency range of 0 to 2.5 kHz and demonstrated the ability of surface-mounted FBGs to characterise vibrations originating within the internal sub-components of the pump, offering potential for condition monitoring. Full article

Indoor air pollution, including fine particulate matter (PM_2.5), poses a severe threat to human health. Due to the diverse sources of indoor PM_2.5 and its high spatial heterogeneity in distribution, traditional single-point fixed monitoring fails to accurately reflect the actual human exposure level. In recent years, the development of high spatiotemporal resolution monitoring technologies has provided a new perspective for revealing the dynamic distribution patterns of indoor PM_2.5. This study discusses two cutting-edge monitoring strategies: (1) mobile monitoring technology based on Indoor Positioning Systems (IPS) and portable sensors, which maps 2D exposure trajectories and concentration fields by having personnel carry sensors while moving; and (2) 3D dynamic monitoring technology based on in situ Lateral Scattering LiDAR (I-LiDAR), which non-intrusively reconstructs the 3D dynamic distribution of PM_2.5 concentrations using laser arrays. This study elaborates on the principles, calibration methods, application cases, advantages, and disadvantages of the two technologies, compares their applicable scenarios, and outlines future research directions in multi-technology integration, intelligent calibration, and public health applications. It aims to provide a theoretical basis and technical reference for the accurate assessment of indoor air quality and the prevention and control of health risks. Full article

The present study aims to compare the feasibility of using ultrasound techniques and image processing to obtain comprehensive experimental results on the dynamics of solid–liquid fraction changes during the melting and solidification of coconut oil as a phase change material (PCM). The discussion will focus on the advantages and limitations of various ultrasonic techniques and image data analysis for inspecting materials during phase transitions. Ultrasound enables the detection of phase changes in materials by analysing variations in their acoustic properties, such as wave velocity and amplitude, during transitions. This method is not only cost-effective compared to traditional non-destructive techniques, such as X-ray tomography, but also offers the potential for real-time monitoring in thermal energy storage systems. Furthermore, it can provide valuable information about internal mechanical parameters and the material’s structure. A detailed analysis of the melting and solidification dynamics has been conducted, confirming the feasibility of using ultrasound parameters to assess the reconstruction of material structures during phase changes. This study paves the way for more efficient and cost-effective monitoring of phase change materials in various applications. Full article