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With the increasing prevalence and social impacts of Alzheimer's disease (AD), innovative approaches to our understanding of its etiology, progression, and potential interventions are critical. Geospatial applications are a promising tool in addressing the complex spatial and temporal dynamics of AD. A substantial knowledge gap remains, however, regarding the comprehensive utilization and impact of geospatial applications in AD research. We comprehensively consider the current status of geospatial-information application in AD based on 159 research articles on geospatial applications published between 1991 and 2022 from the Web of Science and PubMed databases. Four main research themes are identified: geographic environmental factors, geographic disparities, activity behaviors, and care services. Geospatial information has been extensively applied using techniques such as spatial clustering and hotspot analysis. We investigate the influence of location and spatial factors on AD risk, the activity behavior of patients with AD using location data and spatial analysis, and medical-resource proximity via geospatial modeling and predictive analysis. Additionally, we identify three knowledge gaps: geographic environment measurement, spatiotemporal contrastive analysis of disease prevalence, and multidisciplinary integration. The review underscores the value of geospatial information in AD-related research and public health, offering researchers insights into prospective directions and collaborative opportunities with geographers. [ABSTRACT FROM AUTHOR]

Extracorporeal centrifugal blood pumps are used to treat cardiogenic shock. Owing to the imbalanced excitation or initial assembly configurations, the variation in the impeller axial position has the potential to affect the blood pump performance. This study compared the hydrodynamics and hemolysis outcomes at different impeller axial positions via numerical simulations. The result shows that pressure difference of the blood pump decreased with increasing impeller axial position, with decreasing by 4.5% at a flow rate of 2 L/min. Under axial impeller motion close to the top pump casing, average wall shear stress and scalar shear stress reached their maximum values (64.2 and 29.1 Pa, respectively). The residence time in the impeller center hole and bottom clearance were extended to 0.5 s by increasing impeller axial position. Compared to the baseline blood pump, hemolysis index increased by 12.3% and 24.3% when impeller axial position is 2.5 and 4.0 mm, respectively. As a novelty, the findings reveal that the impeller axial position adversely affects hemolysis performance when the impeller is close to the pump casing. Therefore, in the development process of centrifugal blood pumps, the optimal axial position of the impeller must be defined to ensure hemodynamic performance. [ABSTRACT FROM AUTHOR]

Background: Vascular complications after coronary angiography (CAG) or percutaneous coronary intervention (PCI) are a major concern for revascularization therapies. In this study, we investigated whether the application of a chitosan‐based hemostatic pad (HP) could ameliorate vascular complications after CAG or PCI procedures. Methods: We retrospectively analyzed patients undergoing CAG or PCI in our cardiovascular center from January 2019 to December 2021. The incidence of total vascular complications including errhysis, large hematoma, pseudoaneurysm, radial artery occlusion (RAO), blister, and pain was recorded. Subgroup analyses of patients with a history of hypertension, patients with uncontrolled hypertension, and patients with controlled hypertension were performed. Results: Our results demonstrated that the application of chitosan‐based HP had no effect on vascular complications after CAG or PCI procedures (odds ratio [OR]: 1.03 (0.84–1.26), p = 0.80). Nevertheless, chitosan‐based HP treatment markedly decreased the incidence of vascular complications in uncontrolled hypertensive patients (OR: 0.32 (0.11–0.95), p = 0.04), but not in hypertensive patients with controlled blood pressure (BP) (OR: 1.09 (0.88–1.35), p = 0.42). Conclusions: Chitosan‐based HP treatment could reduce the incidence of vascular complications in patients with uncontrolled hypertension. Nonetheless, applying chitosan‐based HP treatment after CAG or PCI procedures is not recommendable for controlled hypertensive patients. [ABSTRACT FROM AUTHOR]

This article presents an improved collaborative control to resist grid voltage unbalance for brushless doubly fed generator (BDFG)-based wind turbine (BDFGWT). The mathematical model of grid-connected BDFG including machine side converter (MSC) and grid side converter (GSC) in the αβ reference frame during unbalanced grid voltage condition is established. On this base, the improved collaborative control between MSC and GSC is presented. Under the control, the control objective of the whole BDFGWT system, including canceling the pulsations of electromagnetic torque and the unbalance of BDFGWT's total currents, pulsations of BDFGWT's total powers are capable of being realized; therefore, the control capability of BDFGWT to resist unbalanced grid voltage is greatly improved. Moreover, improved single-loop current controllers adopting PR regulators are proposed for both MSC and GSC where the sequence extractions for both MSC and GSC currents are not needed any more, and hence the proposed control is much simpler. In addition, the transient characteristics are also improved. Moreover, in order to achieve the decoupling control of current and average power, current controller also adopts the feedforward control approach. Case studies for a two MW BDFGWT system are implemented, and the results verify that the presented control is capable of effectively improving the control capability for BDFGWT to resist grid voltage unbalance and exhibit good stable and dynamic control performances. [ABSTRACT FROM AUTHOR]

The development of high active and stable Pt−M (M=transition metals) electrocatalysts for oxygen reduction reaction (ORR) is crucial for the commercialization of proton exchange membrane fuel cells (PEMFCs). The main challenge of Pt−M electrocatalysts is their instability, mainly due to the leaching of M. In this study, we report the design of P and O co‐doped octahedral PtCo alloy on Ketjen carbon black (Px‐Oct PtCo/C) through a seed‐mediated synthesis. In‐situ FTIR spectroscopy revealed that P0.61‐Oct PtCo/C has the lowest d‐band center, thereby enhancing the catalytic activity. The strong interaction formed by chemical bond between O and Co in Px‐Oct PtCo/C, observed by EXAFS spectra effectively inhibited the dissolution of Co and improved the ORR stability. The mass activity and specific activity of P0.61‐Oct PtCo/C can reach 0.61 A mgPt−1 and 2.4 mA cm−2, respectively, which is 4.1 times and 10.9 times higher than that of commercial Pt/C. After 50,000 potential cycles, the loss of mass activity is only 25 %, surpassing the DOE 2025 target of less than 40 % loss after 30,000 cycles. This study offers an alternative approach to enhancing the activity and durability of Pt−M electrocatalysts for the ORR. [ABSTRACT FROM AUTHOR]

The sublayer thickness of superlattices, as a key factor affecting lattice integrity, interface defects, and strain, deserves in-depth studies about its impact on improving ferroelectric properties. This study described and analyzed the performance of HfO2/ZrO2 superlattices with various sublayer thicknesses. It can be concluded that the structure of the thicker layers will guide the trend of the phase composition of the entire device: when ZrO2 layers are thicker, the superlattices will exhibit antiferroelectricity due to the higher content of the tetragonal phase (t-phase); when HfO2 layers become thicker, the fraction of the monoclinic phase (m-phase) will increase, leading to a decrease in ferroelectricity and an increase in leakage current. In this way, the device with a 1:1 HfO2/ZrO2 thickness ratio was optimized to have the largest remanent polarization and the lowest leakage current. Maintaining the same thickness ratio of the HfO2/ZrO2 superlattices, it was found that HfO2/ZrO2 superlattices with thinner sublayers exhibited a larger remanent polarization (Pr) value due to increased interlayer distortion. On the contrary, the thicker sublayers reduced leakage current, which was beneficial for improving the device lifespan. [ABSTRACT FROM AUTHOR]

Key Clinical Message: A comprehensive diagnostic approach, including immunohistochemistry, is crucial for confirming pulmonary Langerhans cell histiocytosis in adults. Individualized treatment with dynamically adjusted chemotherapy based on therapeutic response leads to significant absorption of lesions and symptom alleviation. Regular follow‐up and timely treatment adjustments according to the patient's condition are essential in managing this rare disease. [ABSTRACT FROM AUTHOR]

Different turbulent entrainment‐mixing mechanisms between clouds and environment are essential to cloud‐related processes; however, accurate representation of entrainment‐mixing in weather/climate models still poses a challenge. This study exploits the use of machine learning (ML) to address this challenge. Four ML (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting, Random Forest, and Support Vector Regression) are examined and compared. It is found that LGB performs best, and thus is selected to understand the impact of entrainment‐mixing on microphysics using simulation data from Explicit Mixing Parcel Model. Compared with traditional parameterizations, the trained LGB provides more accurate microphysical properties (number concentration and cloud droplet spectral dispersion). The partial dependences of predicted microphysics on features exhibit a strong alignment with physical mechanisms and expectations, as determined by the interpreting method, thus overcoming the limitations of the "black box" scheme. The underlying mechanisms are that the smaller number concentration and larger spectral dispersion correspond to more inhomogeneous entrainment‐mixing. Specifically, number concentration after entrainment‐mixing is positively correlated with adiabatic number concentration and liquid water content affected by entrainment‐mixing, and inversely correlated with adiabatic volume mean radius. Spectral dispersion after entrainment‐mixing is negatively correlated with liquid water content affected by entrainment‐mixing, turbulent dissipation rate and relative humidity of entrained air. Sensitivity analysis further suggests that number concentration is mainly determined by cloud microphysical properties whereas spectral dispersion is influenced by both cloud microphysical properties and environmental variables. The results indicate that the LGB scheme has the potential to enhance the representation of entrainment‐mixing in weather/climate models. Plain Language Summary: Entrainment‐mixing processes occurring between clouds and environmental air have significant effects on cloud‐climate feedback, precipitation, and radiative transfer. Accurately representing these processes has been challenging with previously proposed parameterizations. Machine learning (ML) excels at identifying complex nonlinear relationships and avoids the inherent limitations of conventional parameterizations. Thus, we explore the use of ML to acquire the number concentration and cloud droplet spectral dispersion affected by entrainment‐mixing processes. Simulation data from Explicit Mixing Parcel Model are employed to train, validate, and test the four MLs, including Light Gradient Boosting Machine (LGB), eXtreme Gradient Boosting, Random Forest and Support Vector Regression. After evaluation, the LGB is shown to obtain the most accurate microphysics among the four ML schemes and traditional parameterizations. Additionally, the interpreting method for peeking inside the "black box" reveals that a smaller number concentration and a larger spectral dispersion are indicative of the more inhomogeneous entrainment‐mixing. The relative correlations between predicted microphysics and various features align with our expectations and the underlying physical principles. Sensitivity tests further confirm that incorporating more features produces a more robust and efficient prediction. Overall, this study affirms the reliability and applicability of ML to develop/replace subgrid parameterizations in actual weather/climate models. Key Points: Machine learning schemes are trained to predict cloud microphysical properties affected by turbulent entrainment‐mixing processesThe proposed Light Gradient Boosting Machine provides more accurate microphysical properties compared with traditional parameterization schemesThe partial dependencies of microphysics on features prove a robust alignment with physical mechanisms through the interpreting method [ABSTRACT FROM AUTHOR]

We have studied the variations in the temperature-dependent absorption edge of a bulk InSe-layered semiconductor using photoconductivity (PC) measurements. From both the X-ray diffraction (XRD) and Raman experimental results, the structural phase of the as-prepared InSe sample was confirmed to be γ-polytype. Upon heating from 15 K to 300 K, the absorption edge of PC spectra was found to shift significantly toward lower energy, and the absorption edge as a function of temperature was further analyzed by the Varshni's relationship and Bose–Einstein empirical equation. The Urbach energy as a function of temperature was obtained by fitting the absorption tail below the absorption coefficient of the PC spectrum, and the effective phonon energy can be derived from the temperature-dependent steepness parameter associated with Urbach energy. Our study indicates that the broadening of the absorption edge in the as-synthesized bulk γ-InSe is caused by a combination of electron/exciton–phonon interactions and thermal/structural disorder. [ABSTRACT FROM AUTHOR]

BACKGROUND: Hot compressed water (HCW), also known as subcritical water (SCW), refers to high‐temperature compressed water in a special physical and chemical state. It is an emerging technology for natural product extraction. The volatile organic compounds (VOCs) generated from the Maillard reaction between l‐ascorbic acid (ASA) and l‐cysteine (Cys) have attracted significant interest in the flavor and fragrance industry. This study aimed to explore the formation mechanism of VOCs from ASA and Cys and examine the effects of reaction parameters such as temperature, time, and pH in HCW. RESULTS: The identified VOCs were predominantly thiophene derivatives, polysulfides, and pyrazine derivatives in HCW. The findings indicated that thiophene derivatives were formed under various pH conditions, with polysulfide formation favored under acidic conditions and pyrazine derivative formation preferred under weak alkaline conditions, specifically at pH 8.0. CONCLUSION: The Maillard reaction between ASA and Cys mainly produced thiophene derivatives, polysulfides, and pyrazine derivatives in HCW. The generation mechanism was significantly dependent on the surrounding pH conditions. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

White matter (WM) functional activity has been reliably detected through functional magnetic resonance imaging (fMRI). Previous studies have primarily examined WM bundles as unified entities, thereby obscuring the functional heterogeneity inherent within these bundles. Here, for the first time, we investigate the function of sub‐bundles of a prototypical visual WM tract—the optic radiation (OR). We use the 7T retinotopy dataset from the Human Connectome Project (HCP) to reconstruct OR and further subdivide the OR into sub‐bundles based on the fiber's termination in the primary visual cortex (V1). The population receptive field (pRF) model is then applied to evaluate the retinotopic properties of these sub‐bundles, and the consistency of the pRF properties of sub‐bundles with those of V1 subfields is evaluated. Furthermore, we utilize the HCP working memory dataset to evaluate the activations of the foveal and peripheral OR sub‐bundles, along with LGN and V1 subfields, during 0‐back and 2‐back tasks. We then evaluate differences in 2bk‐0bk contrast between foveal and peripheral sub‐bundles (or subfields), and further examine potential relationships between 2bk‐0bk contrast and 2‐back task d‐prime. The results show that the pRF properties of OR sub‐bundles exhibit standard retinotopic properties and are typically similar to the properties of V1 subfields. Notably, activations during the 2‐back task consistently surpass those under the 0‐back task across foveal and peripheral OR sub‐bundles, as well as LGN and V1 subfields. The foveal V1 displays significantly higher 2bk‐0bk contrast than peripheral V1. The 2‐back task d‐prime shows strong correlations with 2bk‐0bk contrast for foveal and peripheral OR fibers. These findings demonstrate that the blood oxygen level‐dependent (BOLD) signals of OR sub‐bundles encode high‐fidelity visual information, underscoring the feasibility of assessing WM functional activity at the sub‐bundle level. Additionally, the study highlights the role of OR in the top‐down processes of visual working memory beyond the bottom‐up processes for visual information transmission. Conclusively, this study innovatively proposes a novel paradigm for analyzing WM fiber tracts at the individual sub‐bundle level and expands understanding of OR function. [ABSTRACT FROM AUTHOR]

Extracting both global and local features from pedestrian images has become the mainstream inperson re-identification.While among most of current deep learning based person re-identification models,the relations between adjacent body parts are seldom taken into consideration during extracting local features.This may decay the capability of distinguishing different persons when they share similar attributes of local regions.To address this problem,a novel method is proposed to learn region correlated deep features for person re-identification.In our model,the output feature map of backbone network is partitioned with multiple granularities first.And then the structure information preserved local features are learned via a new designed Region Correlated Network (RCNet) module.The RCNet makes full use of the structure maintenance of average pooling and the performance advantage of max pooling,endowing local features with rich structural information.By jointly processing current feature and local features from other regions,they are strongly related to each other due to the spatial correlation.As a result,the discrimination of them is significantly enhanced.For better optimization of the whole network,the shortcut connection in deep residual networks is also employed in the architecture of RCNet.Finally,the re-identification is conducted with both global features and the local features with structural information incorporated.Experimental results show that the proposed method achieves higher matching accuracies in comparison with existing approaches on the public Market-1501,CUHK03 and DukeMTMC-reID datasets,demonstrating favorable re-identification performance.