Search Results (37,220)

This study evaluated the use of visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning (ML) algorithms to predict soil fertility-related properties in two contrasting agroecological regions of Peru: the Highlands and the Rainforest. A total of 297 soil samples were analyzed using portable spectroradiometers covering a spectral range of 350–2500 nm, applying transformations such as Savitzky–Golay smoothing, first derivative, and band depth. Predictive models were developed using PLSR, Random Forest, Support Vector Machines, and neural networks. Results show variable predictive performance across soil properties and ecosystems. Organic matter in Highland soils and calcium in Rainforest soils achieved the strongest test-set accuracy (R² > 0.70), while pH and texture fractions showed moderate performance (R² = 0.42–0.67), and mobile nutrients including phosphorus, potassium, and sodium showed limited predictive accuracy due to their weak spectral expression. Spectral predictions were further integrated into a structured nutrient balance framework to assess agronomic reliability. Nitrogen fertilizer recommendations showed the strongest agreement between observed and predicted values across both ecosystems, whereas K₂O and CaO recommendations in Highland soils were substantially underestimated, demonstrating that property-level statistical performance does not guarantee agronomic reliability. These findings confirm that Vis-NIR spectroscopy combined with ML represents a fast, cost-effective, and sustainable alternative to conventional soil analysis, especially in rural areas with limited laboratory infrastructure. Expanding regional calibration datasets and exploring mid-infrared FTIR spectroscopy as a complementary technology are identified as priority directions for improving predictions of agronomically critical nutrients. Full article

Soil moisture critically governs terrestrial energy and water cycles. Precise monitoring of soil water content is essential for precision agriculture, drought early warning, and water resource management. Ground-based observations offer limited spatial coverage, and satellite remote sensing generally lacks high spatial resolution. Unmanned aerial vehicle (UAV) remote sensing, which provides centimeter-level spatial detail, can effectively address this gap and has therefore attracted considerable attention in soil moisture inversion research. Using CiteSpace, we performed a bibliometric analysis of 97 Chinese papers from the China National Knowledge Infrastructure (CNKI) and 321 English papers from the Web of Science Core Collection (2014–2025). The field has expanded rapidly since 2018, with China occupying a leading role. Domestically, Northwest A&F University represents a major research cluster, while the Chinese Academy of Sciences leads internationally. Key research topics include UAVs, soil moisture, machine learning, hyperspectral sensing, canopy temperature, and precision agriculture. Research themes have progressed from reliance on vegetation indices and temperature data toward the integration of hyperspectral and thermal infrared measurements, and toward the use of machine learning approaches to improve inversion models and achieve more accurate estimations. This study delineates the classification and developmental context of a knowledge system for soil moisture inversion using UAV remote sensing. Current work concentrates on integrating multi-sensor data with machine learning, while future efforts will emphasize coupling physical mechanisms with deep learning. These findings offer researchers a clear view of the field’s frontiers and a basis for planning future studies. Full article

Monitoring dissolved carbon dioxide (CO₂) in aquatic and sediment systems is critical for understanding carbon cycling and climate feedback. This study develops and characterizes a compact, low-cost microbolometer-based attenuated total reflectance (ATR) mid-infrared sensor for direct dissolved CO₂ measurement in liquid and soil-water environments. The system integrates a ZnSe ATR crystal with custom suspended SiN membrane microbolometers and uses evanescent-wave absorption at 4.26 μm with a broadband LED source and computational spectral reconstruction, eliminating the need for an interferometer. Calibration shows excellent linearity (R² ≈ 0.99) over 50–1000 ppm CO₂, with a practical limit of detection (LOD) of ~26–35 ppm at 5–25 °C. UV-induced CO₂ generation from soil-water mixtures was investigated across UV wavelengths, revealing UV-C (254 nm) as optimal, producing net ΔCO₂ ≈ 339 ppm above ambient levels in 30 min. Environmental factors (temperature 5–35 °C, pH 5–11, pressure 1–1.5 ATM, dissolved organic carbon) were systematically evaluated, confirming robust sensor performance (accuracy >90%, correlation r > 0.98 with reference instrument). This sensor represents the first integration of MEMS microbolometer detectors with ATR evanescent-wave spectroscopy for liquid-phase dissolved CO₂, enabling real-time monitoring and rapid sediment organic carbon assessment in a field-deployable platform. Full article

The development of efficient and low-cost adsorbents for dye-contaminated wastewater remains an important challenge in environmental remediation. In this study, an interlayer-expanded polypyrrole/maghnite–Cu²⁺ nanocomposite (PPy/Mag–Cu²⁺) was successfully synthesized through purification of raw maghnite, sodium activation, Cu²⁺ ion exchange, and in situ oxidative polymerization of pyrrole. The obtained hybrid was characterized by X-ray fluorescence, X-ray diffraction, Fourier-transform infrared spectroscopy, UV–Vis spectroscopy, scanning electron microscopy, and cyclic voltammetry. The results confirmed the successful incorporation of Cu²⁺ and polypyrrole while preserving the layered aluminosilicate framework. XRD analysis revealed a progressive increase in basal spacing from 17.49 Å for raw maghnite to 25.95 Å for the final nanocomposite, indicating effective intercalation and formation of an expanded hybrid structure. The adsorption performance of PPy/Mag–Cu²⁺ was evaluated for methylene blue removal under batch conditions. Adsorption was strongly influenced by contact time, pH, and initial dye concentration, with equilibrium reached after approximately 80 min and optimum removal at pH 9. Equilibrium data were best fitted by the Langmuir model, with a maximum monolayer adsorption capacity of 43.66 mg g⁻¹, while kinetic data followed the pseudo-second-order model. These findings demonstrate that PPy/Mag–Cu²⁺ is a promising and cost-effective hybrid adsorbent for cationic dye removal from aqueous media. Full article

Garlic (Allium sativum) has antimicrobial and antioxidant properties. However, only the cloves are used from the bulb; the peels or husks are waste material with limited utility that nevertheless retain properties that can be exploited in other materials such as edible films or coatings. Chitosan is a widely used biopolymer, due its interesting properties. The same is true for alginate and pectin, which are polysaccharides that have interesting application areas; among the most common are film or coating materials in the food industry. Therefore, in this research, comprising the elaboration of films based on Chitosan-Pectin-Alginate (Q-P-A) reinforced with garlic husk (GH) particles, the films were characterized by Brookfield viscosity (the biopolymers solutions), Fourier Transform infrared Spectroscopy (FTIR), Dynamic mechanical analysis (DMA), and thermogravimetry (TGA). According to the results, the addition of GH caused a significant decrease in viscosity without altering the pseudoplasticity behavior and also generating physical interactions with the matrices; no chemical reaction byproducts were identified by FTIR. An increase in the reinforcing effect was identified in Q-GH films, whereas the opposite effect was observed in Q-P-A-GH films. In addition, no significant changes in the thermal stability were observed. Full article

Thermal hazards in industrial environments often remain undetected until critical failure or injury occurs. Conventional handheld infrared cameras require manual operation and limit continuous situational awareness. This study presents the design and field validation of a wearable helmet-mounted real-time thermal system based on the MLX90640 infrared array sensor and a Raspberry Pi 5 edge computing platform. Experimental validation was performed across multiple scenarios of 400 measurements based on industrial distances of 100 cm and 150 cm. The performance of the system was tested against a pre-calibrated hotspot infrared thermometer using linear regression analysis and standard error metrics to determine proportional agreement. The results indicate a strong proportional relationship between the two systems at both industrial distances, with R² values ranging from 0.9885 to 0.9973 at 100 cm and from 0.9586 to 0.9867 at 150 cm. A moderate increase in mean absolute error (MAE) was observed as the measurement distance increased. Statistically significant increases in error were identified in mechanically dynamic scenarios where statistically significant increases in measurement error were observed (p-value < 0.05), indicating distance-dependent sensitivity under moving mechanical conditions. The higher absolute errors at longer distances mainly result from field-of-view expansion, reduced target occupancy, and mixed-pixel hotspot effects rather than weakened proportional trend stability. An industrial distance-aware linear regression model was developed to describe behavior and support calibrations under different deployment conditions. Despite minor absolute deviations during dynamic operations, the system maintained strong trend-tracking performance, suggesting suitability for daily preliminary hazard monitoring in industrial safety maintenance. Full article

Cloud detection is essential for quantitative land-surface remote sensing and cloud-climate research. However, existing methods often prioritize spatial features over spectral features, which limits thin-cloud detection. To address this issue, this paper proposes a Thin-Cloud-Sensitive Network (TCSNet) for hyperspectral imagery. TCSNet employs an encoder–decoder architecture with a dual-branch design: a convolutional neural network (CNN) extracts multi-scale local features, while a PVTv2-B2 Transformer captures long-range spectral dependencies. To effectively integrate the complementary representations from both branches, a Cross-Modal Fusion (CMF) module with a lightweight single-channel gate is introduced at each stage, followed by a channel attention mechanism (SE) for feature recalibration. Subsequently, a Multi-Scale Fusion (MSF) module is used to integrate multi-level features through a top-down pathway, enabling deep semantic information to guide shallow feature expression. Furthermore, to enhance the decoder’s feature representation capability, a Combined Attention Mechanism (CAM) is incorporated at each decoder stage. This design enables the network to simultaneously focus on important channels, salient regions, and cloud boundaries, effectively alleviating spectral confusion between thin clouds and the underlying surface. Experimental results on Gaofen-5 01 hyperspectral data demonstrate that TCSNet achieves the highest recall (92.98%), ${\mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}}_{thin}$ (85.59%), and ${\mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}}_{thick}$ (99.75%), thereby validating its superiority for thin-cloud detection. Full article

Despite many efforts to curtail drunk driving, alcohol-related traffic fatalities and injuries continue to be a major public health problem in the United States (U.S.) and most of the world. Technologies exist that prevent an automobile from starting if the driver’s breath alcohol exceeds 20 milligrams per deciliter (mg/dL), but these devices are only fitted to vehicles of individuals who have been convicted of Driving Under the Influence (DUI). A new approach must be taken to reduce the incidence of drunk driving by integrating an alcohol sensor system in vehicles as part of the delivered hardware. The system must be fast, accurate, and contactless—meaning that a forced exhalation is not required to measure the concentration of alcohol on the breath. We report on a novel device, the Driver Alcohol Detection System for Safety (DADSS) Breath Alcohol Sensor System, which uses the mid-infrared region of the electromagnetic spectrum to concurrently monitor alcohol and expired carbon dioxide (CO₂) to accurately quantify the breath alcohol concentration in samples that have been diluted in the atmosphere before being measured. The system was validated in a research laboratory with 70 male and female volunteers in 187 individual study days. Participants were given various doses of alcohol to consume and then breath and blood samples were collected simultaneously. Pearson correlation coefficients between the DADSS Breath Alcohol Sensor system and blood samples indicate a strong correlation between the measures, with an overall Pearson correlation of 0.8875 over an alcohol concentration range of 0–220 mg/dL. These results indicate that incorporating the DADSS system into motor vehicles has the potential to reduce the incidence of drunk driving. Full article

Under low-light conditions, visible-spectrum images are prone to detail loss and contrast degradation, which substantially limits object detection performance. Although infrared imagery can provide complementary cues, direct fusion often introduces noise interference and thus undermines detection stability. To address this issue, this paper proposes a discriminative enhancement and selective fusion method for low-light cross-spectral object detection. Specifically, a task-oriented discriminative Retinex enhancement module is introduced at the front end to mitigate illumination interference while strengthening structural information. Meanwhile, a spectral-selective cross-scale fusion module is designed to suppress noise propagation through adaptive weighting and cross-scale interaction. In addition, mutual information loss and cross-scale consistency constraints are incorporated to enhance cross-spectral feature representation and prediction stability. Experimental results on multiple public datasets demonstrate that the proposed method can consistently improve the accuracy and robustness of object detection under low-light conditions. Full article

Three crosslinked polyurethane copolymers were successfully synthesized as polymeric solid–solid phase change materials (SSPCMs) for thermal energy storage. These materials were fabricated utilizing trihydroxy compounds (glycerol, triethanolamine, and trimethylolethane) as chain extenders and polyethylene glycol (PEG) as the phase change functional segment. A comprehensive suite of characterization techniques was employed to investigate the chemical structures, thermal properties, and crystalline behaviors of the resulting SSPCMs. Fourier transform infrared (FTIR) spectroscopy confirmed the successful synthesis of the crosslinked polyurethane network. Polarizing optical microscopy (POM) and wide-angle X-ray diffraction (WAXD) analyses revealed that all three SSPCMs exhibit regular spherulitic morphologies with sharp diffraction peaks resembling those of pure PEG, although variations in spherulite size and diffraction intensity were observed among the samples. Differential scanning calorimetry (DSC) demonstrated the reversible latent heat storage and release capabilities of the synthesized SSPCMs, with a maximum endothermic enthalpy (ΔH_endo) of 115.7 J/g. Furthermore, thermal cycling tests and thermogravimetric (TG) analysis verified their exhibit excellent reusability, thermal reliability, and high thermal stability. Full article

Traditional leaf area index (LAI) measurement methods are destructive, time-consuming, and labor-intensive. In this study, 282 four-year-old central-leader apple trees were used as research subjects. Canopy reflectance spectra in the range of 4000−10,000 cm⁻¹ were collected, and the corresponding true LAI values were measured destructively by harvesting all leaves from a representative branch of each tree using a leaf area meter. The dataset was randomly divided into training (70%) and testing (30%) sets. Eight spectral pretreatment methods were compared. The Competitive Adaptive Reweighted Sampling (CARS) algorithm was employed to extract characteristic wavelengths. Subsequently, both a BP neural network and a Support Vector Machine (SVM) model for LAI prediction were constructed. The optimal model was selected based on evaluation metrics including the coefficient of determination (R²), mean absolute error (MAE), mean bias error (MBE), and mean absolute percentage error (MAPE). The combined preprocessing of MSC and SD yielded the optimal results, screening out 26 characteristic wavelengths. The SVM linear kernel model (c = 5, g = 0.3) constructed based on MSC + SD preprocessing performed best, achieving a validation set R² of 0.90, MAE of 0.2117, MBE of −0.1214, and MAPE of 16.09%. The performance on the training set and validation set was comparable, with no overfitting observed. The MSC + SD preprocessing combined with CARS feature screening and SVM linear kernel modeling enables rapid, non-destructive estimation of apple canopy LAI, providing an effective technical tool for precision orchard management. Full article

Laparoscopic cholecystectomy is now established as the worldwide gold standard for the treatment of benign gallbladder disease. Despite technical advancements, bile duct injury (BDI) remains a major concern, especially in patients with obesity. It is well known that in patients with a Body Mass Index (BMI) ≥ 30 kg/m², identification of Calot’s triangle and the achievement of the Critical View of Safety (CVS) during laparoscopic cholecystectomy (LC) are made more challenging due to excessive visceral adiposity and concomitant hepatic steatosis reducing the workspace. Near-Infrared Fluorescence Cholangiography (NIRF-C) with Indocyanine Green (ICG) has emerged as an innovative, safe and effective technique to visualize the biliary anatomy and minimize the risk of iatrogenic BDI. However, its specific benefit in patients with obesity remains under-discussed compared to the general population. The main aim of this narrative review is to evaluate whether the intraoperative use of ICG in patients with obesity may reduce operative times and the risk of BDI. A focused review of the literature is performed on articles from 2010 to 2025 published on PubMed, Scopus and Web of Science. The application of ICG fluorescence in LC for patients with obesity represents a tangible clinical advantage, not only for anatomical identification and significant improvement of procedural efficiency, but also for the reduction in operative time. Full article

Background/Objectives: Goupi plaster (GP) is a traditional black plaster composed of a biphasic fibrous–oil matrix containing multiple bioactive compounds, and it has been widely used for the treatment of musculoskeletal disorders. Representative active compounds include sinomenine, osthole, cinnamaldehyde, and imperatorin, which exhibit anti-inflammatory and analgesic effects. However, due to its heterogeneous matrix structure and multi-component nature, the pharmaceutical delivery behavior of GP remains difficult to evaluate using conventional methods. Therefore, this study aimed to establish an integrated structure–release–permeation–pharmacokinetic evaluation framework to systematically characterize the transdermal delivery behavior of GP. Methods: GP was evaluated using multi-level analysis, including microstructural imaging (FESEM), in vitro release, ex vivo skin permeation, and in vivo dual-site microdialysis. Four representative bioactive compounds (sinomenine, osthole, cinnamaldehyde, and imperatorin) were selected as marker compounds. Release data were fitted to kinetic models, and structure–release relationships were examined using the Higuchi release constant (k_h). Skin-barrier alterations were assessed by attenuated total reflectance–Fourier transform infrared spectroscopy (ATR–FTIR) and differential scanning calorimetry (DSC). Local concentrations in subcutaneous (SC) and intra-articular (IA) compartments were measured by ultra-performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) to explore potential in vitro–in vivo correlation (IVIVC). Results: FESEM revealed a fibrous–oil network structure. GP exhibited sustained, diffusion-dominated release, with k_h = 0.9908–0.9977 and Korsmeyer–Peppas (K–P) release exponents (n) = 0.61–0.66, differing from active pharmaceutical ingredient (API) controls. Fiber area fraction and fiber length density showed negative correlations with k_h (r = −0.91 to −0.99); ex vivo permeation profiles varied among compounds, and ATR–FTIR and DSC analyses showed moderate changes in skin-barrier properties. Dual-site microdialysis demonstrated sustained local exposure, and a positive relationship was observed between in vitro release and in vivo concentrations. Conclusions: This study establishes an integrated structure–release–permeation–pharmacokinetic evaluation framework for traditional black plaster systems. The observed IVIVC is descriptive rather than predictive, reflecting a trend-level association under the current experimental conditions. These findings highlight the importance of integrating in vitro release, skin permeation, and local pharmacokinetics for understanding drug delivery behavior in complex transdermal matrix systems, and provide a methodological basis for quality consistency evaluation of traditional black plaster formulations. Full article

Native cellulose I can be converted into crystalline polymorphs II and III_I, while cellulose II can be further converted into III_II through chemical treatments that induce significant structural, physical, and chemical changes. Accurate identification and differentiation of these polymorphs is essential for predicting fiber reactivity and processing behavior, but current methods are time-consuming. This study demonstrates the potential of near-infrared hyperspectral imaging (HSI-NIR) combined with linear discriminant analysis as a rapid, non-destructive tool for polymorph discrimination. Cellulose I, II, III_I, and III_II were produced from bleached kraft pulps of eucalyptus and pine and from cotton linters using NaOH (20% w/v) and ethylenediamine treatments. HSI-NIR successfully differentiated polymorphs based on spectral signatures in the 1480–1600 nm range, regardless of botanical source. Complementary characterization by XRD confirmed polymorph conversions, showing crystallinity reductions of 10–15% for cellulose I→II and I→III_I conversions, with crystallite size decreasing from 7.2 nm (cotton CI) to 3.2–3.4 nm in all CIII_II samples. XPS analysis revealed increased C-O surface accessibility in cellulose II and III, with complete disappearance of COOH groups in cellulose III samples. These results establish HSI as a promising screening tool for cellulose polymorph identification (>95% classification accuracy) and provide comprehensive baseline data on structural and chemical transformations that govern fiber reactivity in chemical and enzymatic processes. Full article

This study presents an application-oriented CFD–FEM integrated workflow for analyzing chamber-side field non-uniformity and kelp-side thermal response during infrared drying. A three-dimensional steady-state CFD model was first established to reconstruct the chamber temperature, airflow, and incident radiation fields under certain operating conditions. Numerical consistency was checked through residual convergence; monitored variables; and global mass balance, for which the net mass imbalance was 0.004077 kg s⁻¹. The reconstructed mid-plane fields were then processed in MATLAB to extract the mean values, extrema, and coefficients of variation, and a composite objective function was used to screen the tested operating conditions in terms of field uniformity, temperature band compliance, and overheating risk. The thermal loads obtained via CFD were subsequently mapped onto a kelp finite element model to simulate the transient surface temperature evolution. Among the tested cases, case01 yielded the lowest composite objective value (J = 0.4535); its mapped kelp response showed a mean surface temperature of 62.23 °C and a maximum temperature of 63.57 °C at the exported time step. The proposed framework is therefore suitable for thermal response assessment and operating condition screening, although determining the full drying behavior still requires coupling of moisture transfer and improved experimental validation. Full article