Explore the vast range of titles available.

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.

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 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.

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.

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.

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.

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.

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.

Using biomarkers to model disease course effectively and make early prediction is a challenging but critical path to improving diagnostic accuracy and designing preventive trials for neurological disorders. Leveraging the domain knowledge that certain neuroimaging biomarkers may reflect the disease pathology, we propose a model inspired by the neural mass model from cognitive neuroscience to jointly model nonlinear dynamic trajectories of the biomarkers. Under a nonlinear mixed-effects model framework, we introduce subject- and biomarker-specific random inflection points to characterize the critical time of underlying disease progression as reflected in the biomarkers. A latent liability score is shared across biomarkers to pool information. Our model allows assessing how the underlying disease progression will affect the trajectories of the biomarkers, and, thus, is potentially useful for individual disease management or preventive therapeutics. We propose an EM algorithm for maximum likelihood estimation, where in the E step, a normal approximation is used to facilitate numerical integration. We perform extensive simulation studies and apply the method to analyze data from a large multisite natural history study of Huntington's Disease (HD). The results show that some neuroimaging biomarker inflection points are early signs of the HD onset. Finally, we develop an online tool to provide the individual prediction of the biomarker trajectories given the medical history and baseline measurements.

Several theoretical and empirical models are available to correlate experimental breakthrough curves of water pollutants, one of which is the two-parameter Belter model. Although not as well known as the century-old Bohart–Adams model, the Belter model is being used with sufficient frequency to merit wider awareness of its strengths and weaknesses. Through a systematic analysis, it is shown that the two adjustable parameters of the Belter model are analogous to the equilibrium capacity and rate parameters of the Bohart–Adams model. Breakthrough curves predicted by the Belter model are perfectly symmetric because their inflection points are invariant and always correspond to the midpoint of the curves. As a consequence, the Belter model provides poor fits to asymmetric breakthrough curves. In this work, an improved version of the Belter model is introduced. The new model with a floating inflection point manifests excellent conformity with mildly and, more importantly, severely asymmetric breakthrough curves.