Explore the vast range of titles available.

An Ag-graphene layers-coated H-shaped photonic crystal fiber (PCF) surface plasmon resonance (SPR) sensor with a U-shaped grooves open structure for refractive index (RI) sensing is proposed and numerically simulated by the finite element method (FEM). The designed sensor could solve the problems of air-holes material coating and analyte filling in PCF. Two big air-holes in the x-axis produce a birefringence phenomenon leading to the confinement loss and sensitivity of x-polarized light being much stronger than y-polarized. Graphene is deposited on the layer of silver in the grooves; its high surface to volume ratio and rich π conjugation make it a suitable dielectric layer for sensing. The effect of structure parameters such as air-holes size, U-shaped grooves depth, thickness of the silver layer and number of graphene layers on the sensing performance of the proposed sensor are numerical simulated. A large analyte RI range from 1.33 to 1.41 is calculated and the highest wavelength sensitivity is 12,600 nm/RIU. In the linear RI sensing region of 1.33 to 1.36; the average wavelength sensitivity we obtained can reach 2770 nm/RIU with a resolution of 3.61 × 10−5 RIU. This work provides a reference for developing a high-sensitivity; multi-parameter measurement sensor potentially useful for water pollution monitoring and biosensing in the future.

To solve the problem of air hole coating and analyte filling in microstructured optical fiber-based surface plasmon resonance (SPR) sensors, we designed an exposed-core grapefruit fiber (EC-GFs)-based SPR sensor. The exposed section of the EC-GF is coated with a SPR, supporting thin silver film, which can sense the analyte in the external environment. The asymmetrically coated fiber can support two separate resonance peaks (x- and y-polarized peaks) with orthogonal polarizations and x-polarized peak, providing a much higher peak loss than y-polarized, also the x-polarized peak has higher wavelength and amplitude sensitivities. A large analyte refractive index (RI) range from 1.33 to 1.42 is calculated to investigate the sensing performance of the sensor, and an extremely high wavelength sensitivity of 13,500 nm/refractive index unit (RIU) is obtained. The silver layer thickness, which may affect the sensing performance, is also discussed. This work can provide a reference for developing a high sensitivity, real-time, fast-response, and distributed SPR RI sensor.

Existing ship emission inventories for coastal seas are typically gridded at 500 m to 1 km, a resolution too coarse to distinguish navigation channels from anchorage zones. Whether the hotspot patterns reported at such scales reflect true emission geography or are artifacts of spatial averaging remains an open question. We construct a 100 m resolution AIS-based emission inventory for two contrasting coastal environments in eastern China—the Yangtze River estuary and the Wenzhou coastal area—using the STEAM framework, and we quantify spatial concentration with Lorenz curve analyses. At this finer resolution, three emission archetypes become separable: discrete anchorage clusters, bankside berthing bands flanking navigation lanes, and sinuous riverbank traces in confined waterways. Emissions are extremely concentrated: the top 1% of grid cells capture over three-quarters of the total theoretical emission potential (Gini = 0.940), and this pattern persists across all months of 2023. Reaggregating the same data to 1 km reduces the top-1% share by roughly 10%, confirming that coarse gridding systematically understates anchorage contributions while overstating those of transit corridors. A dedicated sensitivity analysis on auxiliary engine load assumptions (±30% perturbation of canonical Jalkanen-style load brackets) shows that, while absolute emission totals carry approximately ±15% uncertainty, the spatial concentration of emissions is highly robust: Across all perturbation scenarios, the Gini coefficient varies by less than 0.01, the top-5% emission share varies by less than 2 percentage points, and the location of top-5% hotspot cells overlaps by ≥97.9% (Jaccard index). The results highlight stationary vessel hotspots—discrete anchorages and bankside berths—as a major and previously underemphasized contributor to the cumulative coastal ship emission budget, complementing rather than replacing the conventional navigation-lane focus, with direct implications for shore power siting, anchorage management, and emission control zone design.

We demonstrate a high-resolution temperature sensor based on optical heterodyne spectroscopy technology by virtue of the narrow linewidth characteristic of a single-frequency fiber laser. When the single-frequency ring fiber laser has a Lorentzian-linewidth <1 kHz and the temperature sensor operates in the range of 3−85 °C, an average sensitivity of 14.74 pm/°C is obtained by an optical spectrum analyzer. Furthermore, a resolution as high as ~5 × 10−3 °C is demonstrated through optical heterodyne spectroscopy technology by an electrical spectrum analyzer in the range of 18.26–18.71 °C with the figure of merit up to 3.1 × 105 in the experiment.

A relative humidity (RH) sensor based on single-mode–no-core–single-mode fiber (SNCS) structure is proposed and experimentally demonstrated. The agarose gel is coated on the no-core fiber (NCF) as the cladding, and multimode interference (MMI) occurs in the SNCS structure. The transmission spectrum of the sensor is modulated at different ambient relative humidities due to the tunable refractive index property of the agarose gel film. The relative humidity can be measured by the wavelength shift and intensity variation of the dip in the transmission spectra. The humidity response of the sensors, coated with different concentrations and coating numbers of the agarose solution, were experimentally investigated. The wavelength and intensity sensitivity is obtained as −149 pm/%RH and −0.075 dB/%RH in the range of 30% RH to 75% RH, respectively. The rise and fall time is tested to be 4.8 s and 7.1 s, respectively. The proposed sensor has a great potential in real-time RH monitoring.

Milk is an ideal environment for the growth of microorganisms, especially psychrotrophic bacteria, which can survive under cold conditions and produce heat-resistant enzymes. Psychrotrophic bacteria create the great problem of spoiling milk quality and safety. Several ways that milk might get contaminated by psychrotrophic bacteria include animal health, cowshed hygiene, water quality, feeding strategy, as well as milk collection, processing, etc. Maintaining the quality of raw milk is critically essential in dairy processing, and the dairy sector is still affected by the premature milk deterioration of market-processed products. This review focused on the recent detection and control strategies of psychrotrophic bacteria and emphasizes the significance of advanced sensing methods for early detection. It highlights the ongoing challenges in the dairy industry caused by these microorganisms and discusses future perspectives in enhancing milk quality through innovative rapid detection methods and stringent processing controls. This review advocates for a shift towards more sophisticated on-farm detection technologies and improved control practices to prevent spoilage and economic losses in the dairy sector.

Multimode interferometers based on the single-mode-no-core-single-mode fiber (SNCS) structure have been widely investigated as functional devices and sensors. However, the theoretical support for the sensing mechanism is still imperfect, especially for the cladding refractive index response. In this paper, a modified model of no-core fiber (NCF) based on far from cut-off approximation is proposed to investigate the spectrum characteristic and sensing mechanism of the SNCS structure. Guided-mode propagation analysis (MPA) is used to analyze the self-image effect and spectrum response to the cladding refractive index and temperature. Verified by experiments, the performance of the SNCS structure can be estimated specifically and easily by the proposed method.

AbstractFor optical fiber sensor with single-mode-no-core-single-mode (SNCS) structure, the basic principle of SNCS structure sensing is to change the mode field distribution of SNCS structure by changing the refractive index distribution of the structure. The sensitivity can be improved by optimizing the structure or changing the material of the sensitive region. In this paper, the effects of structural parameters and assembly materials on the sensitivity of SNCS optical fiber sensor are studied by far from the cut-off condition. The correctness of these conclusions is verified by experiments. For the temperature sensor, the material with large thermal optical coefficient (TOC) can be used as the cladding or fiber core to improve the sensitivity of the sensor. These conclusions provide some reference for the design and assembly of temperature sensor with SNCS structure.

The permeability of coal reservoirs is predominantly controlled by the present tectonic stress field. This study systematically integrates macro‐ and micro‐scale analytical methods to investigate the controlling mechanisms of tectonic stress on pore‐fracture characteristics and permeability in the Longtan Formation coal reservoirs. Laboratory experiments were conducted to determine coal porosity, pore‐throat distribution, and microfracture development characteristics. Results indicate an mean porosity of approximately 3.5%, with a dominant pore diameter of ~8.38 nm. As burial depth increases, fractures and pores become progressively infilled with detrital materials, leading to a significant reduction in permeability. Dynamic mechanical parameters and present tectonic stress data were acquired using XMAC‐II logging, revealing maximum horizontal principal stresses ranging from 17.99 to 25.144 MPa and minimum horizontal principal stresses 11.63 to 15.88 MPa. Porosity and permeability exhibit synchronous covariation with horizontal stress variations, demonstrating that geostress governs the evolution of coal porosity, thereby dominating permeability. A quantitative model characterizing coal reservoir permeability‐horizontal stress relationships is established. Numerical simulations demonstrate that fault zones act as concentrated stress‐release areas, forming peripheral banded low‐stress zones where permeability peaks exhibit outward diffusion and gradual decline from fault cores. In contrast, synclinal structures exhibit reduced porosity and lower permeability due to compaction‐induced pore closure.

Heart disease is a leading cause of death and disability among middle-aged and elderly populations. Depression is a common comorbidity that impairs prognosis and quality of life. This study aimed to develop a machine learning (ML)-based depression risk prediction model based on China Health and Retirement Longitudinal Study (CHARLS) data. A total of 947 middle-aged and elderly heart disease patients from CHARLS 2015 were included after applying missing data criteria. Missing values were filled using random forest (RF), and data were split 7:3 into training and validation cohorts. Variables selection in the training cohort using univariate analysis, Lasso regression, recursive feature elimination (RFE), and feature importance evaluation using RF and decision tree (DT). Variables appearing in at least three of these five methods were selected. Eleven ML models were constructed and evaluated by area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve and decision curve analysis. Five-fold cross-validation enhanced stability and SHapley Additive exPlanation (SHAP) values interpreted feature importance. Fifty-eight variables were extracted. After multi-step variable selection within the training cohort, nine variables (address, grip-max, arthritis rheumatism, Hope, sleep time, pain, Retire, ADL, IADL) were initially identified. Among 11 ML models, the logistic regression (LR) algorithm demonstrated the best overall performance with an AUC of 0.792 in the validation cohort. A 4-variable LR model (pain, address, sleep time, and grip-max) was optimized, achieving a comparable AUC of 0.788. SHAP analysis confirmed pain as the most critical predictor (69.0% of depressed patients reported pain versus 26.9% of non-depressed patients). Rural residence (86.5% vs. 66.7%), shorter sleep time (median 5.25(4.00, 7.00) vs. 6.00(5.00, 8.00) hours), and lower grip-max (24.50(20.00, 30.00) vs. 27.00(22.50, 33.40) increased depression risk. A user-friendly web-based calculator was developed for clinical applications. The simplified LR model exhibits robust predictive performance and clinical applicability for assessing high depression risk in middle-aged and elderly patients with heart disease.