Explore the vast range of titles available.

Mapping is a fundamental application of remote sensing images, and the accurate evaluation of remote sensing image information extraction using artificial intelligence is critical. However, the existing evaluation method, based on Intersection over Union (IoU), is limited in evaluating the extracted information’s boundary accuracy. It is insufficient for determining mapping accuracy. Furthermore, traditional remote sensing mapping methods struggle to match the inflection points encountered in artificial intelligence contour extraction. In order to address these issues, we propose the mean inflection point distance (MPD) as a new segmentation evaluation method. MPD can accurately calculate error values and solve the problem of multiple inflection points, which traditional remote sensing mapping cannot match. We tested three algorithms on the Vaihingen dataset: Mask R-CNN, Swin Transformer, and PointRend. The results show that MPD is highly sensitive to mapping accuracy, can calculate error values accurately, and is applicable for different scales of mapping accuracy while maintaining high visual consistency. This study helps to assess the accuracy of automatic mapping using remote sensing artificial intelligence.

The Microwave Radiation Imager (MWRI) onboard the FengYun satellite plays a crucial role in global change monitoring and numerical weather prediction. Estimating and correcting geolocation errors are important to retrieving accurate geophysical variables. However, the instantaneous field of view (IFOV) inter-channel deviation, which is mainly caused by the structure mounting error and measurement error of feedhorns, is less studied. In this present study, we constructed a general theoretical model to automatically estimate the IFOV inter-channel deviations suitable for conical-scanning instruments. The model can automatically detect the along-track and across-track vectors that pass through the land–sea boundary points and are perpendicular to the actual coastlines. Regarding the midpoints of the vectors as the brightness temperature (Tb) inflection points, the IFOV inter-channel deviation is the pixel offset or distance of the maximum gradients of the Tb near the inflection points for each channel relative to the 89-GHz V-pol channel. We tested the model’s operational performance using the FY-3G/MWRI-Rainfall Mission (MWRI-RM) observations. Considering that parameter uploading adjusted the IFOV inter-channel deviations, the model’s validity was verified by comparing the adjustments calculated by the model with the theoretical changes caused by parameter uploading. The result shows that the differences between them for all window channels are less than 100 m, indicating the model’s effectiveness in evaluating the IFOV inter-channel deviation for the MWRI-RM. Furthermore, the estimated on-orbit IFOV inter-channel deviations for the MWRI-RM show that all channel deviations are less than 1 km, meeting the instrument’s design requirement of 2 km. We believe this study will provide a foundation for IFOV inter-channel registration of passive microwave payloads and spatial matching of multiple payloads.

Abstract How to distinguish and quantify past human impacts on vegetation is a significant challenge in paleoecology. Here, we propose a novel method, the error inflection point-discriminant technique. It finds out the inflection points (IPs) of the regression errors of pollen–climate transfer functions using modern pollen spectra from vegetation with different values of the Human Influence Index (HII), which represent the HII threshold values of native/secondary and secondary/artificial vegetation systems. Our results show that the HII value at the native/secondary vegetation IPs is approximately 22 and globally uniform, whereas it varies regionally for the secondary/artificial vegetation IPs. In a case study of the Liangzhu archaeological site in the lower Yangtze River, discriminant functions for pollen spectra from three vegetation types and pollen–climate transfer functions of the native vegetation were established to reconstruct paleovegetation and paleoclimate over the past 6,600 years. Our study demonstrates this method's feasibility for quantitatively distinguishing human impacts on paleovegetation and assessing quantitative paleoclimate reconstructions using pollen data.

The kinetics of hydrogen oxidation reaction (HOR) declines with orders of magnitude when the electrolyte varies from acid to base. Therefore, unveiling the mechanism of pH-dependent HOR and narrowing the acid-base kinetic gap are indispensable and challenging. Here, the HOR behaviors of palladium phosphides and their counterpart (PdP 2 /C, Pd 5 P 2 /C, Pd 3 P/C, and Pd/C) in the whole pH region (from pH 1 to 13) are explored. Unexpectedly, there are non-monotonous relationships between their HOR kinetics and varied pHs, showing distinct inflection-point behaviors (inflection points and acid-base kinetic gaps). We find the inflection-point behaviors can be explained by the discrepant role of pH-dependent hydroxyl binding energy (OHBE) and hydrogen binding energy (HBE) induced HOR kinetics under the entire pH range. We further reveal that the strengthened OHBE is responsible for the earlier appearance of the inflection point and much narrower acid-base kinetic gap. These findings are conducive to understanding the mechanism of the pH-targeted HOR process, and provide a new strategy for rational designing advanced HOR electrocatalysts under alkaline electrolyte.

Online sensors, which monitor the ammonia oxidation and the dissimilatory nitrate reduction process, can optimize aerobic and anoxic phase duration. The purpose of this study was to comparatively evaluate the effectiveness of online sensors that were in situ–located in an intermittently aerated and fed membrane bioreactor (IAF-MBR) system. Ammonium and nitrate nitrogen sensors equipped with ion-selective electrodes as well as pH and oxidation–reduction potential (ORP) sensors were employed to online monitoring and optimizing of ammonia oxidation and nitrate reduction processes. The “ammonia valley” or pH bending point, which is indicative of ammonia depletion, was effectively and repeatedly detected by measuring the pH profile, while the “nitrate knee” point, which indicates the completion of the denitrification process, was online-detected by obtaining the ORP profile. The “ammonia valley” and “nitrate knee” were detected at pH and ORP values of 6.47 ± 0.02 and – 162 ± 39 mV, respectively. The ORP and pH first derivatives (dORP/dt and dpH/dt) were found to be more suitable than the untransformed ORP and pH values in detecting pH and ORP inflection points and controlling the shift from the anoxic to the aeration phase. Specifically, the ORP and pH bending points were detected at dORP/dt and dpH/dt values of 1.64 ± 0.82 mV min −1 and 0.005 ± 0.001 min −1 , respectively. Moreover, the ORP first derivative has appeared earlier than the ORP bending point.

To explore the clinical characteristics of noise-induced hearing loss (NIHL) caused by long-term exposure to steady-state noise and find a possible inflection point time leading to hearing deterioration. Subjects exposed to steady-state noise were selected as the noise-exposed group and matched with a control group of individuals not exposed to noise. Both groups underwent pure-tone audiometry (PTA) and distortion product otoacoustic emissions (DPOAE), and their hearing conditions were analyzed. The time inflection point with the most significant disparities in NIHL between early and late exposure was evaluated. The noise-exposed subjects were divided into 2 groups based on cumulative exposure time: the early exposure group (group A) and the late exposure group (group B). Retrospective analyses of clinical characteristics of hearing loss were conducted. The noise-exposed group exhibited significantly higher hearing thresholds and reduced otoacoustic emissions compared to the control group, with high-frequency hearing loss being the most prominent. The most significant disparity in high-frequency hearing loss in PTA was observed before and after 5 years of cumulative steady-state noise exposure. Among the 78 noise-exposed subjects, 37 were in group A (≤5 years) and 41 in group B (>5 years). In DPOAE, the most significant disparity occurred before and after 4 years of acexposure, with 33 subjects in group A (≤4 years) and 45 in group B (>4 years). Distortion product otoacoustic emissions identified the time inflection point of significant hearing deterioration 1 year earlier than PTA. Hearing loss caused by long-term exposure to steady-state noise showed evident deterioration after 4-5 years. The DPOAE can illustrate the inflection point of hearing deterioration 1 year earlier than PTA. Int J Occup Med Environ Health. 2025;38(1):57-69.

Indoor positioning technology is a key area of research in location-based services. Crowdsourced WiFi and mobile communication signal fingerprinting are critical for achieving large-scale indoor positioning for consumers. However, existing crowdsourced positioning solutions are not suitable for typical environments like shopping malls due to the need for additional equipment, and their learning methods often have low computational efficiency and generalization ability in complex environments. This paper proposes a system that introduces clustering concepts to repair remaining trajectories using representative trajectories. WiFi SSID and 5G NR SSB data collected along trajectories are used as features for clustering analysis. Reliable starting points are obtained through GNSS accuracy metrics to correct trajectories, and a Bi-LSTM model is utilized to extract trajectory inflection points. Unprocessed trajectories of the same category are corrected based on inflection point features, thereby constructing a WiFi-5G fingerprint database. In addition to providing positioning services, the proposed system iteratively infers the locations of shops, allowing for the construction of a semantic map. The experimental site is the first floor of a large shopping mall, with a dataset comprising 185 user-collected trajectories totaling 2 hours in duration. The trajectory clustering accuracy exceeds 80%, with an average localization error of 5.73 meters for static test points, and an average error of 4.38 meters for the semantic map. Compared to existing crowdsourced solutions, the proposed method shows significant improvements in feasibility, accuracy, and efficiency.

Vegetation cover characteristics underpin the understanding of regional ecosystem status and guide sustainable development. While extensive research has documented long-term vegetation dynamics in Qinghai Province, critical gaps remain in identifying driving factors, quantifying their thresholds, and uncovering nonlinear relationships governing vegetation cover. In view of this, based on the MOD13Q1V6 dataset from the Google Earth Engine (GEE) platform, this study constructed a kernel normalized difference vegetation index (kNDVI) dataset for Qinghai Province spanning the period 2001–2023. Furthermore, the spatiotemporal characteristics and future evolution trends of vegetation cover were revealed by employing methods including the Theil–Sen–Mann–Kendall (Theil–Sen–MK) trend test, Hurst exponent, and centroid migration model. At a grid scale of 5 km × 5 km, based on the combined model of Extreme Gradient Boosting and SHapley Additive exPlanations (XGBoost-SHAP), this study integrated 10 multi-source remote sensing variables related to natural conditions, socioeconomic factors, and geographical accessibility to reveal the nonlinear effects between driving factors and kNDVI and identify the key threshold inflection points. The results showed the following: (1) From 2001 to 2023, the kNDVI of Qinghai Province exhibited a fluctuating growth trend with an annual growth rate of 0.0016 per year, presenting a spatial pattern of being higher in the southeast and lower in the northwest. Specifically, the kNDVI of unused land achieved the highest growth rate (65.96%), which was significantly higher than that of other land use types. (2) The kNDVI in Qinghai Province was dominated by stable areas, accounting for 52.75%. Future trend analysis indicated that the region was primarily characterized by sustainable improvement zones (39.91%), while areas with uncertain future trends accounted for 39.70%. (3) The XGBoost-SHAP model revealed that the annual mean precipitation (AMP) (47.26%) and Digital Elevation Model (DEM) (20.40%) exerted substantial impacts on the kNDVI. Marginal effect curves identified distinct threshold inflection points for the major characteristic factors: AMP = 363.2 mm (95%CI: 361.2–365.2 mm), DEM = 4463.9 m (95%CI: 4446.0–4481.1 m), grazing intensity = 1.8 SU (Stocking Unit)·ha−1 (95%CI: 1.8–1.9 SU·ha−1), and slope = 2.8° (95%CI: 2.7–3.0°) and 19.0° (95%CI: 18.8–19.3°). The interaction combinations of AMP × DEM and DEM × distance to construction land exerted a strong positive effect on the kNDVI in the study area, which was conducive to enhancing vegetation cover. These findings verified the effectiveness of ecological projects implemented in Qinghai Province to a certain extent and provided data support for subsequent differentiated restoration and management.

The presence of inflection points on data manifold or rapid variation makes it difficult for the second order derivative based graph Laplacian and Hessian regularization techniques to accurately approximate the marginal distribution parameters. Moreover, in general, function over-fitting on seen unlabeled instances due to lack of extrapolation power which makes graph Laplacian regularization based solution biased towards constant. Hessian solves this problem by opting a generic function based on the function’s divergence in more than one direction. However, due to the presence of inflection points in the dense region, the function remains unpenalized by Hessian manifold regularization. We propose a Jerk based manifold regularization (JR) for dense, oscillating and manifolds with inflection points. JR approximates the rate of change of curvature from the underlying manifold which appropriately identifies the unpenalized geodesic deviating functions and accurately penalizes them. It also helps in identifying the optimal function in the presence of inflection points. Extensive experiments on synthetic and real-world datasets show that the proposed JR technique approximates accurate and generic input space geometrical constraints to outperform existing state-of-the-art manifold regularization techniques by a significant margin.

PurposeThe emergence of a coronavirus disease 2019 (COVID-19) epidemic has had a tremendous impact on the world, and the characteristics of its evolution need to be better understood.Design/methodology/approachTo explore the changes of cases and control them effectively, this paper analyzes and models the fluctuation and dynamic characteristics of the daily growth rate based on the data of newly confirmed cases around the world. Based on the data, the authors identify the inflection points and analyze the causes of the new daily confirmed cases and deaths worldwide.FindingsThe study found that the growth sequence of the number of new confirmed COVID-19 cases per day has a significant cluster of fluctuations. The impact of previous fluctuations in the future is gradually attenuated and shows a relatively gentle long-term downward trend. There are four inflection points in the global time series of new confirmed cases and the number of deaths per day. And these inflection points show the state of an accelerated rise, a slowdown in the rate of decline, a slowdown in the rate of growth and an accelerated decline in turn.Originality/valueThis paper has a certain guiding and innovative significance for the dynamic research of COVID-19 cases in the world.