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Although noble metal-based catalysts are expensive, they are still the most promising oxygen evolution reaction (OER) electrocatalysts. Therefore, it is desirable to reduce the load of the precious metal without affecting the OER performance of the catalyst. In this work, we have innovatively proposed a strategy of interfacial anchoring of Ru atoms and successfully achieve the dispersion of precious metal ruthenium atoms in cross-growing NiS and NiSe multifaceted structures. The dispersion of Ru ensures Ru–NiS/NiSe/NF more active sites and more adequate contact with the electrolyte, which further improves the oxygen production property of the catalyst. As a direct outcome, the target catalyst (Ru–NiS/NiSe/NF) only requires low overpotential of 453 mV to excite a current density of 1000 mA cm−2. Meanwhile, it can remain stable to catalyze the OER for more than 50 h (200 mA cm−2), which gives it great application potential in future large-scale electrolytic water processes. This work provides a new perspective for the efficient utilization of precious metal for designing the efficient and stable OER electrocatalyst. • Staggered growth of NiS and NiSe builds a large number of highly active interfaces. • Highly reactive interface-anchored Ru enables high dispersion at the atomic level. • Ru–NiS/NiSe/NF only requires low overpotential of 453 mV at 1000 mA cm−2 to catalyze OER. • Ru–NiS/NiSe/NF operates stably for at least 50 h at 200 mA cm−2 for OER. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Sensors titled "Machine Learning Modeling for Spatial-Temporal Prediction of Geohazard." The special issue includes eleven research articles conducted by researchers from various countries, including Canada, China, Iran, Malaysia, Pakistan, and Sweden. The articles cover topics such as geohazard modeling, spatial-temporal prediction, machine learning, deep learning, and hybrid modeling. The research articles explore the application of advanced machine learning techniques in predicting geohazards such as landslides and debris flow in different regions. The special issue also includes a review article that provides a scientometric analysis of artificial intelligence for geohazard research. Overall, the special issue aims to contribute to the field of geohazard research and provide valuable insights for researchers and practitioners. [Extracted from the article]

In a typical GNSS receiver, pseudorange and pseudorange rate measurements are generated through the code and carrier tracking loops, respectively. These measurements are then employed to calculate the user's position and velocity (PV) solutions, which is typically achieved using a Kalman filter (KF) or the least squares (LS) algorithm. However, the LS method only uses the current observation without error analysis. The positioning result is greatly affected by the errors in the observed data. In KF, by using an iterative approach that combines predictions and measurements of PV information, more accurate estimates can be obtained because the PV information is time-correlated. Meanwhile, its optimal estimate requires that both the model and noise statistics are exactly known. Otherwise, achieving optimality cannot be guaranteed. To address this issue, this paper proposes and implements a novel GNSS solution method based on an unbiased finite impulse response (UFIR) filter. Two different field tests were conducted. The position results of UFIR are compared with those from the LS and KF methods, and the horizon positioning mean error is improved by 44% and 29%, respectively, which highlights its efficacy. The method offers two primary benefits: it is robust to noise uncertainty, and it leverages historical data within the UFIR framework to provide a more accurate estimate of the current state. [ABSTRACT FROM AUTHOR]

All-inorganic CsPbBr3 perovskite solar cells have received growing attention in the photovoltaic field due to their high stability, low cost, and simple preparation processes. However, the high-density defects in perovskite films and the large energy differences at interfaces have been the main challenges for achieving high power conversion efficiency and good stability. In this work, nickel oxide (NiOx) decorated graphene oxide (GO) is used as a hole collector at the perovskite/carbon interface for a carbon-based CsPbBr3 perovskite solar cell. The crystallinity of the CsPbBr3 perovskite layer and the hole extraction ability are markedly enhanced because of the p-type charge transfer doping of GO from oxygenic groups to NiOx. Finally, the all-inorganic CsPbBr3 perovskite solar cell achieves a power conversion efficiency of 8.59%. More importantly, the best solar cell free of encapsulation retains 94.2% of its initial efficiency in an air environment over 21 days. [ABSTRACT FROM AUTHOR]

A photovoltaic–hydrovoltaic-coupled carbon-based, all-inorganic Cs0.91Rb0.09PbBr3 perovskite solar cell achieved a peak power conversion efficiency of 9.40% under one sun illumination and a maximum voltage of 0.39 V and current of 1.40 μA in a flowing vapor atmosphere. [ABSTRACT FROM AUTHOR]

Carbon-based all-inorganic perovskite solar cells have attracted growing interest owing to their simple fabrication process, low cost, and high stability in air. On account of the large interfacial energy barriers and polycrystalline features of perovskite films, the carrier interface recombination and inherent defects in the perovskite layer are still great challenges in further increasing the power conversion efficiency and stability of carbon-based PSCs. We present here a trifunctional polyethylene oxide buffer layer at the perovskite/carbon interface to promote the PCE and stability of carbon-based all-inorganic CsPbBr3 PSCs: (i) the PEO layer increases the crystallinity of inorganic CsPbBr3 grains for low defect state density; (ii) the oxygenic groups in PEO chains passivate the defects on the perovskite surface; and (iii) the long hydrophobic alkyl chains improve the stability in moisture. The best encapsulated PSC achieves a PCE of 8.84% and maintains 84.8% of its initial efficiency in air with 80% RH over 30 days. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: Metal‐organic framework (MOF) materials have attracted intense scientific interest in the solar cell research community owing to their unique physiochemical properties and various preparation methods. The photovoltaic performance and stability of resultant optoelectronic devices can also be improved by adopting MOF materials. This review pays close attention to the summary and generalization of recent representative achievements in solar cells by utilizing MOFs, including dye‐sensitized solar cells, perovskite solar cells, organic solar cells, and so on. This mini‐review is expected to improve understanding of material properties and broaden the scope of future material applications. [ABSTRACT FROM AUTHOR]

Landslide disasters cause serious property losses and casualties every year. Landslide displacement prediction is fundamental for mitigating landslide disasters. Several approaches have been used to predict landslide displacement, yet a more accurate and reliable displacement prediction still has a poor understanding of landslide early warning systems for landslide mitigation, due to limited data and mutational displacements. To boost the robustness and accuracy of landslide displacement prediction, this paper assembled a new hybrid model containing the local mean decomposition (LMD), innovations state space models for exponential smoothing (ETS), and the temporal convolutional network (TCN). The proposed model, which is based on over 10 years of long-term time series monitoring GPS data, was tested on the selected case—stepwise Baijiabao landslide in the Three Gorges Reservoir area of China (TGRA) was tested by the proposed model. The results presented that the LMD–ETS–TCN model has the best performance in comparison with other benchmark models. Compared with autoregressive integrated moving average (ARIMA), support vector regression (SVR), and long short-term memory neural network (LSTM), the accuracy was noticeably improved by an average of 40.9%, 46.2%, and 22.1%, respectively. The robustness and effectiveness of the presented approach are attested, and it has discernible improvements for landslide displacement prediction. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) research is vital for averting and mitigating regional landslide disasters. Nevertheless, there has been a lack of systematic analysis regarding LSM's developmental drivers. Utilizing SciMAT, a scientometric tool, we analyzed 1661 papers on LSM from the Web of Science core collection database, spanning 1993 to 2022. We employed cluster and thematic evolution analysis in SciMAT to unveil trends. The results indicate a consistent upward trend over the past three decades. LSM modeling methods, geological data, and contributing factors are major focal points. Notably, the evolution of LSM models, with a rising adoption of machine learning and deep learning for risk assessment, emerges as a central knowledge pathway. This study offers valuable insights to scholars by identifying literature gaps and highlighting crucial research directions, facilitating informed decision-making in the realm of LSM. SciMAT tool initially employs cluster analysis and thematic evolution analysis to pinpoint the current popular themes and evolutionary trends of landslide susceptibility. The main trajectory of knowledge lies in the progression of landslide modeling methods. The latest trend in landslide susceptibility research is centered on the use of deep learning techniques, particularly convolutional neural networks, to achieve precise risk zonation. The study aims to highlight critical research directions, predict the future of LSM technology, and provide guidance for scholars. [ABSTRACT FROM AUTHOR]

To date, ternary metal oxide semiconductor materials have attracted extensive attention worldwide due to their unique optoelectronic properties. As a classical ternary oxide, Zn2SnO4 is featured with the high electron mobility, wide band gap, negligible visible light absorption and tailorable electronic band structures. Thus, it is an ideal material for practical use in solar cells, lithium batteries, sensors, and photo‐catalysts. In this review, we summarize the recent research progress of this material with the focus on the synthesis methods, nanostructures, and the resulting effects on the crystal structure, optical properties, and photoelectrochemical properties. Moreover, their potential applications in different devices are highlighted and carefully discussed. [ABSTRACT FROM AUTHOR]

The interaction between rock and surrounding water is an important research topic in geotechnical engineering. In this study, the temporal variation in rock moisture content when immersed in water is investigated using the probability method based on the entropy theory. An analytical mathematical expression is derived by assuming the rock moisture content to be a random variable, maximizing the entropy function subject to a constraint condition and adopting a hypothesis regarding the rock moisture content cumulative distribution function. The proposed entropy-based expression models the temporal evolution of rock moisture content as immersion time progresses from null to infinity. Furthermore, the derived model is tested against eight experimental data sets from the literature, with good agreement, a high correlation coefficient (0.9689) and a low mean absolute relative error (0.0664). Based on previous experimental results, the impacts of three main factors—rock type, rock porosity and outer water pressure on the maximum capacity for moisture content growth, a key parameter of the proposed expression—are discussed. The entropy-based method is a useful tool for predicting the variation of rock moisture content with different immersion times during the water absorption process. [ABSTRACT FROM AUTHOR]

Heavy rainfall in mountainous terrain can trigger numerous landslides in hill slopes. These landslides can be deadly to the community living downslope with their fast pace, turning failures into catastrophic debris flows and avalanches. Active tectonics coupled with rugged topography in a complex geoenvironment multiplies this likelihood. The available hazard maps are usually helpful in mitigating disasters. However, fool-proof predicting landslide susceptibility identification remains a challenge in landslide discipline. Recently, ensemble machine learning (ML) techniques have proved the potential to provide a more accurate and efficient solution in spatial modeling. The main purposes of the current study are to examine and evaluate the predictive capability of support vector machine hybrid ensemble ML algorithms, i.e., the bagging, boosting, and stacking for modeling the catastrophic rainfall-induced landslide occurrences in the Northern parts of Kyushu Island, at the watershed scale in Japan. In this study, a landslide inventory map containing 265 landslide polygons was first interpreted from the aerial photographs and fieldwork after the September 2017 rainfall event. The raw data were randomly separated into two parts using a 70/30 sampling strategy for training and validating the landslide models. Then, 13 predisposing factors were prepared as predictors and dependent variable. The landslide susceptibility maps (LSM) were validated by the area under the receiver operating characteristic curve (AUC). The results of validation showed that the AUC values of the four models (SVM-Stacking, SVM, SVM-Bagging, and SVM-Boosting) varied from 0.74 to 0.91. The SVM-boosting model outperformed the other models, while SVM-stacking model has found to be the lowest performance. The outcome suggests that an ensemble ML model does not necessarily mean good performance. It is always preferable to select an appropriate model, such as the one proposed the hybrid novel ensemble SVM-boosting model, which could significantly improve the accuracies of LSM. Also, from Information Gain Ratio (IGR) we found that the rainfall factor mainly affects the results, that agrees with the analogy of present study. [ABSTRACT FROM AUTHOR]

An active absorption layer with a narrow band gap has been achieved through adjusting the halide ratio in a halide perovskite, and it had better energy alignment with the Zn2SnO4 electron transport layer. An efficiency of 19.37% can be obtained, and this has also been improved to 20.26% through the incorporation of cesium, giving a new triple cation perovskite. [ABSTRACT FROM AUTHOR]

Abstract Landslides represent a part of the cascade of geological hazards in a wide range of geo-environments. In this study, we aim to investigate and compare the performance of two state-of-the-art machine learning models, i.e., decision tree (DT) and random forest (RF) approaches to model the massive rainfall-triggered landslide occurrences in the Izu-Oshima Volcanic Island, Japan at a regional scale. At first, a landslide inventory map is prepared consisting of 44 landslide polygons (10,444 pixels) from aerial photo-interpretation and field surveys. To estimate the robustness of the models, we randomly adapted two different samples (S1 and S2), comprising of both positive and negative cells (70% of total landslides - 7293 pixels) for training and remaining (30%–3151 pixels) for validation. Twelve causative factors including altitude, slope angle, slope aspect, plan curvature, total curvature, compound topographic index, stream power index, distance to drainage network, drainage density, distance to geological boundaries, lithology and cumulative rainfall were selected as predictors to implement the landslide susceptibility model. The area under the receiver operating characteristics (ROC) curves (AUC) and other statistical signifiers were used to verify the model accuracies. The result shows that the DT and RF models achieved remarkable predictive performance (AUC > 0.9), producing near accurate susceptibility maps. The overall efficiency of RF (AUC = 0.956) is found significantly higher than the DT (AUC = 0.928) results. Additionally, we noticed that the performance of RF for modeling landslide susceptibility is very robust even though the training and validation samples are altered. Considering the performances, we suggest that both RF and DT models can be used in other similar non-eruption-related landslide studies in the tephra-deposited rich volcanoes, as they are capable of rapidly generating accurate and stable LSM maps for risk mitigation, management practices, and decision-making. Moreover, the RF-based model is promising and enough to be recommended as a method to map regional landslide susceptibility. Graphical abstract Unlabelled Image Highlights • Decision tree and random forest models applied to map landslide-prone areas in a volcanic Island. • Two sample set (S1, and S2) for computing the robustness of the model • LSM maps were compared using different assessment principles. • Random forest performs better on both samples with AUC > 0.9. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Zn 2 SnO 4 microspheres with controllable shell numbers are fabricated. • Double-shelled Zn 2 SnO 4 microspheres are synthesized for the first time. • A best efficiency is obtained by using double-shelled Zn 2 SnO 4 microspheres. Abstract In this report, Zn 2 SnO 4 microspheres with controllable shell numbers have been synthesized for the first time via a sol-gel route followed by a hydrothermal process, and have been used as electrodes for assembling dye-sensitized solar cells. It is found that the photovoltaic properties of cells is influenced by shell number and the best efficiency of 5.04% can be achieved for the cell composed of microsphere with a double-shell structure. This result might be contributed to the fact that the electrode composed of double-shell microspheres could increase dye adsorption amount, charge collection efficiency, and light scattering ability and suppress charge recombination. [ABSTRACT FROM AUTHOR]

In the present study, the nanocrystalline Zn 2 SnO 4 dopen with Sr 2+ /Ba 2+ ions is synthesized via a hydrothermal method and their structure is analyzed in detail. Moreover, the photovoltaic properties of dye-sensitized solar cells composed of doped Zn 2 SnO 4 are investigated. The enlarged energy difference between the conduction band edge of Zn 2 SnO 4 and the lowest unoccupied molecular orbit (LUMO) of dye gives rise to the enhanced driving force for electron injection. The DSCs based on Sr 2+ /Ba 2+ doped Zn 2 SnO 4 improve the power efficiency through enhancing charge transport processes and reducing recombination rates. Compared to the photovoltaic conversion efficiency of pure Zn 2 SnO 4 cells, the DSCs based on ZTO-Sr (3.0) exhibit a greater power conversion efficiency of 4.91%. As a consequence, the device of ZTO-Ba (1.5) also demonstrates an improved efficiency of up to 4.94%. This corresponds to an enhancement in power conversion efficiency of 17%, respectively, relative to control devices. [ABSTRACT FROM AUTHOR]

Zinc stannate (ZTO) is a class of ternary oxides that are known for their stable properties and high electron mobility. Also, its chemical compositions and band structures are easy to be controlled, and which is thus ideal for applications in dye-sensitized solar cells (DSCs). However, the structures of most dyes are optimized for the widely used TiO 2 . Therefore, the efficient dyes that are especially suitable for ZTO are highly desired. In this study, the dyes containing phenylene-thiophenylene-phenylene bridge (PSP) were found to match well with the band structures of ZTO and could be used as efficient sensitizers for ZTO-based solar cells. Moreover, the pre-calcination temperatures have great influence on the band structures of ZTO. When ZTO nanoparticles were pre-sintered, although the adsorption amount of dyes was decreased, the charge collection efficiency was confirmed to be enhanced greatly. A cell sensitized by optimized PSP dyes exhibited a power conversion efficiency of 4.68%, which was much higher than that of widely used N719 and D131. [ABSTRACT FROM AUTHOR]

[Display omitted] • First analysis of topographic and vegetation changes after the Mw 6.6 2004 Chuetsu earthquake. • Topographic analysis shows that the earthquake steepens and roughens the terrain. • Good vegetation recovery (85.5%) by 2021 in affected areas indicates stable post-seismic topography. Ground shaking on steep slopes often triggers numerous landslides, which dramatically modify the landscape and have long-term effects on vegetation dynamics. However, we still have a limited understanding of the quantitative topographic evolution and the duration of post-seismic impacts on the landscapes due to the lack of long-term consistent observation, especially in low-elevation mountain regions. To address this issue, we used high-resolution pre-and post-earthquake DEMs to investigate topographic changes, as well as multi-period and multi-scale remote sensing images to analyze the post-seismic landslides and vegetation recovery in the sites affected by the Mw 6.6 2004 Chuetsu earthquake, Japan. Using a vegetation recovery rate (VRR) time series from 2004 to 2021, we examined the decaying tendency of post-seismic landslide activities and predicted the vegetation recovery rate. Our findings indicate that the Chuetsu earthquake mostly steepens and roughens the terrain in low-elevation areas. The changes to slope aspects are associated with coseismic lateral displacement. The vegetation damage area accounted for 87.98 % of the entire area after the earthquake, and the VRR reaches 85.55 % by June 2021. The number of active landslides decreased to 14.45 % after 15 years. Meanwhile, we predict that vegetation recovery areas will be restored to pre-earthquake levels in 2024, and the landslide activity rate will decrease by less than 1 % in 2026, indicating a stable hillslope devoid of debris flows in the future. [ABSTRACT FROM AUTHOR]

Due to its flexibility in calculating frequency spectrum, the chirp z‐transform (CZT) has been used in various applications where high‐frequency resolution is desirable, e.g. the acquisition of global positioning system (GPS) signals. However, the CZT is very time consuming when dealing with the very large size of input data. Thus, typically, only 1 ms data is used for GPS signal acquisition. In this Letter, the authors improve the acquisition precision and sensitivity of the CZT‐based acquisition by extending coherent integration time. To reduce computation load, they introduce a down‐sampling operation to the extended data without degrading acquisition performance. Results show that the CZT‐based acquisition with a coherent integration time of 10 ms obtains a frequency error of 5.9 Hz for the carrier‐to‐noise ratio (CNR) of 25 dB‐Hz and can acquire signals with a CNR down to 22 dB‐Hz. [ABSTRACT FROM AUTHOR]

Accurate prediction of landslide susceptibility relies on effectively handling absence samples in data-driven models. This study investigates the influence of different absence sampling methods, including buffer control sampling (BCS), controlled target space exteriorization sampling (CTSES), information value (IV), and mini-batch k-medoids (MBKM), on landslide susceptibility mapping in Songyang County, China, using support vector machines and random forest algorithms. Various evaluation metrics are employed to compare the efficacy of these sampling methods for susceptibility zoning. The results demonstrate that CTSES, IV, and MBKM methods exhibit an expansion of the high susceptibility region (maximum susceptibility mean value reaching 0.87) and divergence in the susceptibility index when extreme absence samples are present, with MBKM showing a comparative advantage (lower susceptibility mean value) compared to the IV model. Building on the strengths of different sampling methods, a novel integrative sampling approach that incorporates multiple existing methods is proposed. The integrative sampling can mitigate negative effects caused by extreme absence samples (susceptibility mean value is approximately 0.5 in the same extreme samples and presence-absence ratio) and obtain significantly better prediction results (AUC = 0.92, KC = 0.73, POA = 2.46 in the best model). Additionally, the mean level of susceptibility is heavily influenced by the proportion of absent samples. [ABSTRACT FROM AUTHOR]

This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner. [ABSTRACT FROM AUTHOR]

Scutellaria baicalensis Georgi, a Chinese herbal decoction, has been used for the treatment of the common cold, fever and influenza virus infections. In previous studies, we found that oral administration of baicalein resulted in the inhibition of influenza A virus replication in vivo, which was linked to baicalin in serum. However, the effective dose and underlying mechanisms of the efficacy of baicalin against influenza A virus have not been fully elucidated. In this study, the antiviral effects of baicalin in influenza-virus-infected MDCK cells and mice were examined. The neuraminidase inhibition assay was performed to investigate the mechanism of action of baicalin. In vitro results showed that baicalin exhibited a half-maximal effective concentration (EC) of 43.3 μg/ml against the influenza A/FM1/1/47 (H1N1) virus and 104.9 μg/ml against the influenza A/Beijing/32/92 (H3N2) virus. When added to MDCK cell cultures after inoculation with influenza virus, baicalin demonstrated obvious antiviral activity that increased in a dose-dependent manner, indicating that baicalin affected virus budding. Baicalin had clear inhibitory effects against neuraminidases, with half-maximal inhibitory concentration (IC) of 52.3 μg/ml against the influenza A/FM1/1/47 (H1N1) virus and 85.8 μg/ml against the influenza A/Beijing/32/92 (H3N2) virus. In vivo studies showed that an intravenous injection of baicalin effectively reduced the death rate, prolonged the mean day to death (MDD) and improved the lung parameters of mice infected with influenza A virus. These results demonstrate that baicalin acts as a neuraminidase inhibitor, with clear inhibitory activities that are effective against different strains of influenza A virus in both cell culture and a mouse model, and that baicalin has potential utility in the management of influenza virus infections. [ABSTRACT FROM AUTHOR]

Abstract: Shikimic acid (SA) is an important metabolic intermediate with diverse commercial applications. In this work, antisense RNA interference and gene deletion were carried out to inactivate the aroK gene in an SA-producing Escherichia coli strain, DHPYA-T7. In this strain, the aroL, ptsHIcrr and ydiB genes are deleted, and the tktA, glk, aroE and aroB genes are overexpressed. Flask cultivations of the DHPYA-T7 derivative strains showed that the accumulation of SA increased 2.69-fold after aroK gene deletion (DHPYAAS-T7) and 1.29-fold after antisense RNA interference (DHPYAS-T7). Furthermore, the activity of shikimate kinase in DHPYAAS-T7 was 0.21-fold of that in strain DHPYAS-T7. In a 10-L fermentation, SA accumulation increased to 1850mgL−1 in strain DHPYAAS-T7, which is a 1.5-fold increase over that in strain DHPYAS-T7. These results demonstrate that aroK gene inactivation in DHPYA-T7 leads to high SA accumulation, especially when this inactivation is caused by chromosomal deletion. [Copyright &y& Elsevier]

Abstract: Baicalein is a flavonoid derived from the root of Scutellaria baicalensis, a traditional Chinese medicine that has been used for hundreds of years; baicalein has also been demonstrated to have antiviral activity with low toxicity. The synergistic activity of baicalein with ribavirin against influenza virus infections in cell culture and in mice was investigated for the first time in our research. In vitro, maximal synergy at lower concentrations of baicalein (0.125μg/ml) and ribavirin (12.5μg/ml) was observed, and the reduced expression of the viral matrix protein (M) gene suggested that drug combinations caused greater inhibition than ribavirin alone, especially the combination of 0.5μg/ml baicalein and 5μg/ml ribavirin. In vivo, combinations of baicalein and ribavirin provided a higher survival rate and lower body weight loss. Moreover, fewer inflammatory responses in the lungs of mice infected with virus and treated with baicalein and ribavirin were observed; the mean scores were 1.0, 0.8, and 1.2 with the doses of ribavirin at 50mg/kg/d combined with baicalein at 100mg/kg/d, 200mg/kg/d, and 400mg/kg/d respectively, while the placebo group had a mean pathology score of 3.2. Thus, the data demonstrates that combinations of baicalein and ribavirin provide better protection against influenza infection than each compound used alone and could potentially be clinically useful. [Copyright &y& Elsevier]

Abstract: Cathelicidin-BF (BF-30) is found in the venom of the snake Bungarus fasciatus and exhibits broad antimicrobial activity against bacteria and fungi. Nevertheless, its antibacterial activity in vivo and antibacterial mechanism is unknown. In the present study, we examined the antibacterial activity of BF-30 in vitro against drug-resistant Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus, first identifying its protection against P. aeruginosa in infected burns and then delineating the antimicrobial mechanism of BF-30. The data showed that BF-30 had stronger antimicrobial activities against a broad spectrum of microorganisms than gentamicin, ampicillin or bacitracin. The killing curves of BF-30 against P. aeruginosa and S. aureus showed that CFU counts rapidly decreased by almost 2 logs within 6min, and it took just less than 2h to kill all the bacteria. In addition, we investigated whether BF-30 had antibacterial activity in a burn/acute infection rat model. Dose–response (0.75, 3, 12mg/kg/day) studies indicated that BF-30 significantly reduced the colonization of P. aeruginosa in the burn eschars, lungs and liver of burn injured rats and that it could prevent subsequent systemic infection and development of inflammation. The peptide induced chaotic membrane morphology and cell debris, as determined by electron microscopy, and caused the cytoplasmic membrane to crack, resulting in β-galactosidase leakage and EtBr accumulation. This suggests that the antimicrobial activity of BF-30 is based on cytoplasmic membrane permeability. Taken together, our data demonstrate that antibacterial activity of BF-30 has potential therapeutic value for the prevention and treatment of burn and wound infections. [Copyright &y& Elsevier]

Parainfluenza viruses are significant respiratory-tract pathogens that are notorious for infecting children. However, there are no clinical drugs to control the infection caused by these viruses. Sendai virus (SeV) belongs to the family Paramyxoviridae and causes fatal pneumonia in mice, its natural host. Baicalein is a flavonoid derived from the root of Scutellaria baicalensis, which is a traditional Chinese medicine that has been used for hundreds of years and has demonstrated a variety of biological activities. Our findings reveal that oral administration of baicalein to mice infected with Sendai virus results in a significant reduction in virus titers in the lungs and protection from death. The in vivo inhibitory effects of baicalein on Sendai virus are determined by baicalin in the serum. The mean IC of baicalin was 0.71 μg/ml in an HA inhibition assay and 3.22 μg/ml in an NA inhibition assay. The mean IC of baicalin in a CPE assay was measured to be 0.70 μg/ml, and significant inhibition was observed in a plaque assay at a concentration of 1.6 μg/ml baicalin in overlay medium, which suggests that baicalein is a potential anti-parainfluenzaviral agent in vivo. [ABSTRACT FROM AUTHOR]

In this study, an extended model for describing the temporal evolution of a characteristic floc size of cohesive sediment particles when the flocculation system is subject to a piecewise varied turbulent shear rate was derived by the probability methods based on the Shannon entropy theory following Zhu (2018). This model only contained three important parameters: initial and steady-state values of floc size, and a parameter characterizing the maximum capacity for floc size increase (or decay), and it can be adopted to capture well a monotonic pattern in which floc size increases (or decays) with flocculation time. Comparison with 13 literature experimental data sets regarding floc size variation to a varied shear rate showed the validity of the entropic model with a high correlation coefficient and few errors. Furthermore, for the case of tapered shear flocculation, it was found that there was a power decay of the capacity parameter with the shear rate, which is similar to the dependence of the steady-state floc size on the shear rate. The entropic model was further parameterized by introducing these two empirical relations into it, and the finally obtained model was found to be more sensitive to two empirical coefficients that have been incorporated into the capacity parameter than those in the steady-state floc size. The proposed entropic model could have the potential, as an addition to existing flocculation models, to be coupled into present mature hydrodynamic models to model the cohesive sediment transport in estuarine and coastal regions. [ABSTRACT FROM AUTHOR]

Highly dispersed nickel-platinum alloy with octahedral structure loaded on the surface of the carbon layer (NiPt/C) has been rationally designed and synthesized. The excellent electrical conductivity and small size of the alloy provides rapid electron transport and large electrolyte contact area for OER process. The pre-oxidation of Pt at OER potential contribute to the rapid production of nickel hydroxide, thus promoting the deposition of more iron active sites on its surface. [Display omitted] • An ultra-high dispersion NiPt/C alloy with octahedral structure is prepared. • The OER activity of NiPt/C catalyst is greatly enhanced through the modulation of Fe3+-containing electrolyte. • The oxidation of Pt can effectively contribute to production of more nickel hydroxide, and thus promotes the deposit of more Fe on the surface of NiPt/C. • The NiPt/C catalyst modified by Fe only requires low overpotential of 242 mV to output 1000 mA cm−2 oxygen evolution current. Constructing ultra-high activity, highly efficient and robust oxygen evolution reaction (OER) electrocatalysts, especially at large current density, plays a key role in the development of the electrolytic water industry but remains challenging. Highly dispersed alloy compound with large electrolyte contact area and excellent internal conductivity is considered as a kind of ideal candidate. Here, we have rationally designed and synthesized a nickel-platinum alloy with octahedral structure loaded on the surface of the carbon layer (NiPt/C). Under the in-situ modulation of the iron-containing electrolyte, the Fe modified NiPt/C catalyst displays an amazing OER activity in 1 M KOH, and only requires low overpotentials of 183 and 242 mV to reach 100 and 1000 mA cm−2, respectively. The comprehensive characterization analysis and property measurements reveal that the pre-oxidation of Pt at OER potential contribute to the rapid production of nickel hydroxide, thus promoting the deposition of more iron active sites on its surface. Moreover, the Pt/C/CP(−)//NiPt/C/CP(+) couple embedded in anion-exchange membrane water electrolyser also displays a low cell voltage of 2.03 V at 1000 mA cm−2 with significant electrochemical stability, which endows it a great potential in the field of hydrogen production and electrochemical energy storage. [ABSTRACT FROM AUTHOR]

The passivation of the defects derived from rapid‐crystallization with electron‐donating molecules is always a prerequisite to obtain desirable perovskite films for efficient and stable solar cells, thus, the in‐depth understanding on the correlations between molecular structure and passivation capacity is of great importance for screening passivators. Here, we introduce the double‐ended amide molecule into perovskite precursor solution to modulate crystallization process and passivate defects. By regulating the intermediate bridging skeletons with alkyl, alkenyl and benzene groups, the results show the passivation strength highly depends on the spin‐state electronic structure that serves as an intrinsic descriptor to determine the intramolecular charge distribution by controlling orbital electron transfer from the donor segment to acceptor segment. Upon careful optimization, the benzene‐bridged amide molecule demonstrates superior efficacy on improving perovskite film quality. As a physical proof‐of‐concept, the carbon‐based, all‐inorganic CsPbI2Br solar cell delivers a significantly increased efficiency of 15.51 % with a remarkably improved stability. Based on the same principle, a champion efficiency of 24.20 % is further obtained on the inverted (Cs0.05MA0.05FA0.9)Pb(I0.93Br0.07)3 solar cell. These findings provide new fundamental insights into the influence of spin‐state modulation on effective perovskite solar cells. [ABSTRACT FROM AUTHOR]

Alkylamines with different chain lengths including n-butylamine, n-hexylamine, and n-octylamine, are applied to regulate the CsPbIBr2 perovskite film quality by strain engineering. The status of residual strains is controllably modulated, resulting in improved efficiency and stability of carbon-based hole-transport-material free CsPbIBr2 perovskite solar cells. [ABSTRACT FROM AUTHOR]

Magnetic flux leakage (MFL) technology is remarkable for its capability to detect pipeline geometric deformation and general corrosion defects. However, it cannot characterize the MFL behavior in stress-concentrated areas, thereby greatly challenging the subsequent pipeline maintenance. This study suggests that the MFL characteristics of pipeline in stress-concentrated areas are caused by the combined effect of the face magnetic charge on the deformed end-face and the body magnetic charge of the dislocation stack. In addition, an improved force–magnetic coupling model of the pipeline in stress-concentrated areas is established based on the magnetic dipole model and Jiles–Atherton (J–A) theory. In the verification experiment, the Q235 steel plate is magnetized along the extension direction (axis of the pipeline) through the solenoid coil to obtain the distribution law of the MFL signal in the stress-concentrated area under different excitation intensities. The results show that with the increase in excitation intensity, the deformation of the MFL field signal caused by the end-face of the stress-concentrated area gradually increases to a stable state. Moreover, the internal stress of the MFL field signal generated by the pipe dislocation rapidly increases to a peak value and then decays exponentially to a certain base value. The overall change trend is in good agreement with the calculation results of the established force–magnetic coupling model. Meanwhile, the differentiation research between deformation and internal stress MFL field signals under different magnetic field intensities can provide a reliable theoretical basis for the subsequent accurate identification and quantification of pipeline stress-concentrated areas. [ABSTRACT FROM AUTHOR]

Special attention should be devoted to the thermal stability of hybrid perovskite solar cells (PSCs), because they are often operated at elevated temperatures. However, effective strategies are lacking for manipulation of heat flow in PSCs to improve their thermal stability. Here, a holistic solution is reported for the rapid removal of dissipated heat within the absorber by introducing hexagonal boron nitride (h‐BN) inside and radiator fin outside of the device. This strategy significantly improves the thermal conductivity of perovskite and speeds up the heat transfer of device, which effectively reduces the cell temperature under illumination of simulated AM 1.5G standard spectrum by ≈6.5 °C. Regardless of device configurations, the corresponding PSCs exhibit prolonged lifetimes aged at different temperatures, continuously operated under white light‐emitting diode (LED) lamp or full‐spectrum illumination. Of particular note, the optimized h‐BN/Cu device with n–i–p structure keeps 88% and 93% of its initial PCE after 1776 h of 85 °C thermal aging and 2451 h of maximum power point (MPP) tracking, respectively, and the device with p–i–n structure maintains 96% and 92% of its original PCE after 1704 h of 85 °C thermal aging and 2164 h of MPP tracking. [ABSTRACT FROM AUTHOR]

In this paper, we propose an intelligent joint filter (JF) for enhancing the performance of vector tracking loop (VTL) in the Global Navigation Satellite System (GNSS). The JF combines the advantages of extended Kalman filter (EKF) and unbiased finite-impulse response (UFIR) filter. To this end, a supervised machine learning algorithm, named Gaussian mixture model (GMM) clustering, was used for providing excellent joint strategy. Those three types of filter-based vector tracking loop were first implemented and then processed with a set of raw satellite signals based on the software-defined receiver (SDR). Finally, comparative analyses and results of the tracking performance of EKF/UFIR/JF were carried out. Results show that the EKF-VTL has optimal tracking performance but sensitive to the noise statistics, which means it's not robust. The UFIR-VTL is suboptimal but more robust compare to EKF-VTL. The proposed JF-VTL is both optimal and robust. [ABSTRACT FROM AUTHOR]

Predictive capability of landslide susceptibilities is assumed to be varied with different sampling techniques, such as (a) the landslide scarp centroid, (b) centroid of landslide body, (c) samples of the scrap region representing the scarp polygon, and (d) samples of the landslide body representing the entire landslide body. However, new advancements in statistical and machine learning algorithms continuously being updated the landslide susceptibility paradigm. This paper explores the predictive performance power of different sampling techniques in landslide susceptibility mapping in the wake of increased usage of artificial intelligence. We used logistic regression (LR), neural network (NNET), and deep learning neural network (DNN) model for testing and validation of the models. The tests were applied to the 2018 Hokkaido Earthquake affected areas using a set of 11 predictor variables (seismic, topographic, and hydrological). We found that the prediction rates are inconsequential with the DNN model irrespective of the sampling technique (AUC: 0.904 – 0.919). Whereas, testing with LR (AUC: 0.825 – 0.785) and NNET (AUC: 0.882 – 0.858) produces larger differences in the accuracies between the four datasets. Nonetheless, the highest success rates were obtained for samples within the landslide scarp area. The analogy was then validated with a published landslide inventory from the 2015 Gorkha earthquake. We, therefore, suggest that DNN models as an appropriate technique to increase the predictive performance of landslide susceptibilities if the landslide scarp and body are not characterized properly in an inventory. Unlabelled Image • Constructed a comprehensive inventory for 2018 Hokkaido coseismic landslides. • Four different sampling techniques were tested for the predictive performance of LSM. • DNN shows that different sampling strategies are less consequential in LSM. • Applicability of results is validated with the 2015 Gorkha landslide cases. [ABSTRACT FROM AUTHOR]

Heparin-like polymers have a good anticoagulant effect due to some groups similar to heparin. Herein, sulfonated keratin(SK) was prepared by chain transfer radical polymerization of 3-sulfopropyl methacrylate(SPMA) to improve blood coagulation nature of keratin. Poly(ε-caprolactone)(PCL)/SK nanofibrous mats with the ratio of 7 and 3 were then fabricated by electrospinning. In vitro cytotoxicity and blood compatibility tests were performed to assess the biocompatibility. Viability of NIH 3T3 cells on PCL/SK mats was higher than that on the pristine PCL mats, indicating their good cytocompatibility. These sulfonated keratin-containing mats enhanced endothelial cell growth, while inhibited smooth muscle cell proliferation and reduced platelet adhesion in the presence of GSH and GSNO, as a result of NO generation. Furthermore, the biocomposite mats prolonged the activated partial thromboplastin time(APTT) effectively without hemolysis. Taken together, PCL/SK mats are potential for applications in vascular tissue engineering. Image 1 • Sulfonated keratin(SK) mats are prepared and coelectrospun with PCL. • The mats show good both cell and blood compatibility. • The mats can generate NO in the presence of GSH and GSNO. • The mats have great potential as scaffold for vascular tissue engineering. [ABSTRACT FROM AUTHOR]

This paper focuses on two types of frequency lock loops (FLL) in Global Navigation Satellite System (GNSS) receivers, namely the conventional scalar frequency lock loop (SFLL) and the vector frequency lock loop (VFLL).The VFLL has been proven to have a better tracking performance than the SFLL in scenarios such as intermittent signal outages, high dynamics, etc. However, the FLL tracking performance under the equivalent noise bandwidth has not been explored in literature. To do this, we implemented three kinds of FLL, i.e., the SFLL, the weighted least square-based vector frequency loop (WLS-VFLL), and the extended Kalman filter-based vector tracking loop (EKF-VFLL). All these FLLs have the same noise bandwidth, based on which we made a fair comparison between them. The experimental static data and simulated high- dynamic data have been tested. Results show that the EKF-VFLL has a similar tracking performance with WLS-VFLL under static environment, both better than the SFLL. In high- dynamic environment, the advantages of EKF-VFLL are more prominent than the other two methods. Furthermore, EKF-VFLL takes the longest time in terms of computational efficiency. [ABSTRACT FROM AUTHOR]

Discriminative correlation filtering (DCF)-based trackers have demonstrated remarkable results in the field of visual tracking. Nevertheless, in most DCF-based trackers: (i) they apply correlation operations across the entire search area features without discriminative weights, rendering them highly susceptible to background interference; and (ii) the fixed aspect ratio scale search strategy is inadequate for accurate scale estimation under irregular scale variations. To overcome these challenges, this study proposes a new correlation filter through saliency-driven localization and cascaded scale estimation (SDCS-CF). Specifically, a U-like network is devised to generate a pixel-wise saliency map. This map is then multiplied element-wise with search area features to accentuate the target attribute, mitigating distractors and increasing localization confidence. Furthermore, a cascaded scale estimation model consisting of three one-dimensional filters is designed to refine the scale estimation process and improving the robustness. Extensive experimental results on three public datasets demonstrate that the proposed SDCS-CF outperforms most DCF-based trackers. • A new tracker via saliency-driven localization and cascade scale estimation. • A U-like network to construct target saliency features for accurate localization. • A two-stage cascaded scale estimation model to determine the scale variations. [ABSTRACT FROM AUTHOR]

Background: Asf1 is a well-conserved histone chaperone that regulates multiple cellular processes in different species. Two paralogous genes, Asf1a and Asf1b exist in mammals, but their role during fertilization and early embryogenesis remains to be investigated further. Methods: We analyzed the dynamics of histone chaperone Asf1a and Asf1b in oocytes and pre-implantation embryos in mice by immunofluorescence and real-time quantitative PCR, and further investigated the role of Asf1a and Asf1b during fertilization and pre-implantation development by specific Morpholino oligos-mediated knock down approach. Results: Immunofluorescence with specific antibodies revealed that both Asf1a and Asf1b were deposited in the nuclei of fully grown oocytes, accumulated abundantly in zygote and 2-cell embryonic nuclei, but turned low at 4-cell stage embryos. In contrast to the weak but definite nuclear deposition of Asf1a, Asf1b disappeared from embryonic nuclei at morula and blastocyst stages. The knockdown of Asf1a and Asf1b by specific Morpholino oligos revealed that Asf1a but not Asf1b was required for the histone H3.3 assembly in paternal pronucleus. However, knockdown of either Asf1a or Asf1b expression decreased developmental potential of pre-implantation embryos. Furthermore, while Asf1a KD severely reduced H3K56 acetylation level and the expression of Oct4 in blastocyst stage embryos, Asf1b KD almost eliminated nuclear accumulation of proliferating cell marker-PCNA in morula stage embryos. These results suggested that histone chaperone Asf1a and Asf1b play distinct roles during fertilization and pre-implantation development in mice. Conclusions: Our data suggested that both Asf1a and Asf1b are required for pre-implantation embryonic development. Asf1a regulates H3K56ac levels and Oct4 expression, while Asf1b safeguards pre-implantation embryo development by regulating cell proliferation. We also showed that Asf1a, but not Asf1b, was necessary for the assembly of histone H3.3 in paternal pronuclei after fertilization. [ABSTRACT FROM AUTHOR]

Mixed‐halide perovskites are emerging as excellent photovoltaic candidates because of their tunable bandgaps for semitransparent and tandem perovskite solar cells. However, the notorious film quality originated from the rapidly downward crystallization process susceptibly propagates enormous detrimental defects, which deteriorate the photovoltaic performance and accelerate halide segregation. To address this issue, herein, a multilayer alkalis‐intercalated‐vermiculite is employed as pre‐buried interface modifier to regulate the perovskite lattice property. The matchable lattice structure between perovskite and vermiculite by forming Pb─O bond not only releases the interfacial strain during the film growth but also the embedded alkalis ions can gradually diffuse into perovskite lattice to form a favorable vertical gradient owing to the weak interlamellar van der Waals interaction, playing bis‐roles of atomical lubricant and ion‐reservoir to eliminate detrimental defects. As a result, the film quality and lattice stability is significantly improved with suppressed phase segregation for mixed‐halide perovskites, accompanying a champion efficiency of 11.42% for carbon‐based CsPbIBr2 device, 15.25% for carbon‐based CsPbI2Br device and 23.17% for p‐i‐n inverted (Cs0.05MA0.05FA0.9)Pb(I0.93Br0.07)3 cell. This work provides a new strategy on buried interface engineering for making high‐efficiency and stable perovskite platforms. [ABSTRACT FROM AUTHOR]

Plitidepsin (or dehydrodidemnin B), an approved anticancer drug, belongs to the didemnin family of cyclic depsipeptides, which are found in limited quantities in marine tunicate extracts. Herein, we introduce a new approach that integrates microbial and chemical synthesis to generate plitidepsin and its analogues. We screened a Tistrella strain library to identify a potent didemnin B producer, and then introduced a second copy of the didemnin biosynthetic gene cluster into its genome, resulting in a didemnin B titer of approximately 75 mg/L. Next, we developed two straightforward chemical strategies to convert didemnin B into plitidepsin, one of which involved a one‐step synthetic route giving over 90 % overall yield. Furthermore, we synthesized 13 new didemnin derivatives and three didemnin probes, enabling research into structure–activity relationships and interactions between didemnin and proteins. Our study highlights the synergistic potential of biosynthesis and chemical synthesis in overcoming the challenge of producing complex natural products sustainably and at scale. [ABSTRACT FROM AUTHOR]

Plitidepsin (or dehydrodidemnin B), an approved anticancer drug, belongs to the didemnin family of cyclic depsipeptides, which are found in limited quantities in marine tunicate extracts. Herein, we introduce a new approach that integrates microbial and chemical synthesis to generate plitidepsin and its analogues. We screened a Tistrella strain library to identify a potent didemnin B producer, and then introduced a second copy of the didemnin biosynthetic gene cluster into its genome, resulting in a didemnin B titer of approximately 75 mg/L. Next, we developed two straightforward chemical strategies to convert didemnin B into plitidepsin, one of which involved a one‐step synthetic route giving over 90 % overall yield. Furthermore, we synthesized 13 new didemnin derivatives and three didemnin probes, enabling research into structure–activity relationships and interactions between didemnin and proteins. Our study highlights the synergistic potential of biosynthesis and chemical synthesis in overcoming the challenge of producing complex natural products sustainably and at scale. [ABSTRACT FROM AUTHOR]

Landslide disasters pose a significant threat, with their highly destructive nature underscoring the critical importance of timely and accurate recognition for effective early warning systems and emergency response efforts. In recent years, substantial advancements have been made in the realm of landslide recognition (LR) based on remote sensing data, leveraging deep learning techniques. However, the intricate and varied environments in which landslides occur often present challenges in detecting subtle changes, especially when relying solely on optical remote sensing images. InSAR (Interferometric Synthetic Aperture Radar) technology emerges as a valuable tool for LR, providing more detailed ground deformation data and enhancing the theoretical foundation. To harness the slow deformation characteristics of landslides, we developed the FCADenseNet model. This model is designed to learn features and patterns within ground deformation data, with a specific focus on improving LR. A noteworthy aspect of our model is the integration of an attention mechanism, which considers various monitoring factors. This holistic approach enables the comprehensive detection of landslide disasters across entire watersheds, providing valuable information on landslide hazards. Our experimental results demonstrate the effectiveness of the FCADenseNet model, with an F1-score of 0.7611, which is 9.53% higher than that of FC_DenseNet. This study substantiates the feasibility and efficacy of combining InSAR with deep learning methods for LR. The insights gained from this research contribute to the advancement of regional landslide geological hazard monitoring, identification, and prevention strategies. [ABSTRACT FROM AUTHOR]

Rapid endothelialization and regulation of smooth muscle cell proliferation are crucial for small-diameter vascular grafts to address poor compliance, thromboembolism, and intimal hyperplasia, and achieve revascularization. As a gaseous signaling molecule, nitric oxide (NO) regulates cardiovascular homeostasis, inhibits blood clotting and intimal hyperplasia, and promotes the growth of endothelial cells. Due to the instability and burst release of small molecular NO donors, a novel biomacromolecular donor has generated increasing interest. In the study, a low toxic NO donor of S-nitrosated keratin (KSNO) was first synthesized and then coelectrospun with poly(ε-caprolactone) to afford NO-releasing small-diameter vascular graft. PCL/KSNO graft was capable to generate NO under the catalysis of ascorbic acid (Asc), so the graft selectively elevated adhesion and growth of human umbilical vein endothelial cells (HUVECs), while inhibited the proliferation of human aortic smooth muscle cells (HASMCs) in the presence of Asc. In addition, the graft displayed significant antibacterial properties and good blood compatibility. Animal experiments showed that the biocomposite graft could inhibit thrombus formation and preserve normal blood flow via single rabbit carotid artery replacement for 1 month. More importantly, a complete endothelium was observed on the lumen surface. Taken together, PCL/KSNO small-diameter vascular graft has potential applications in vascular tissue engineering with rapid endothelialization and vascular remolding. [Display omitted] [ABSTRACT FROM AUTHOR]

The bathymetry of nearshore coastal environments and lakes is constantly reworking because of the change in the patterns of energy dispersal and related sediment transport pathways. Therefore, updated and accurate bathymetric models are a crucial component in providing necessary information for scientific, managerial, and geographical studies. Recent advances in satellite technology revolutionized the acquisition of bathymetric profiles, offering new vistas in mapping. This contribution analyzed the suitability of Sentinel-2 and Landsat-8 images for bathymetric mapping of coastal and lake environments. The bathymetric algorithm was developed using an empirical approach and a random forest (RF) model based on the available high-resolution LiDAR bathymetric data for Mobile Bay, Tampa Bay, and Lake Huron regions obtained from the National Oceanic and Atmospheric Administration (NOAA) National Geophysical Data Center (NGDC). Our results demonstrate that the satellite-derived bathymetry is efficient for retrieving depths up to 10 m for coastal regions and up to 30 m for the lake environment. While using the empirical approach, the root-mean-square error (RMSE) varied between 1.99 m and 4.74 m for the three regions. The RF model, on the other hand, provided an improved bathymetric model with RMSE between 1.13 m and 1.95 m. The comparative assessment suggests that Sentinel-2 has a slight edge over Landsat-8 images while employing the empirical approach. On the other hand, the RF model shows that Landsat-8 retrieves a better bathymetric model than Sentinel-2. Our work demonstrated that the freely available Sentinel-2 and Landsat-8 imageries proved to be reliable data for acquiring updated bathymetric information for large areas in a short period. [ABSTRACT FROM AUTHOR]

Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been largely focused at the single event-susceptibility model. Such type of modeling is found insufficient in places where the triggering mechanism involves both factors such as one found in the Chuetsu region, Japan. Generally, a single event model provides only limited enlightenment of landslide spatial distribution and thus understate the potential combination-effect interrelation of earthquakes- and rainfall-triggered landslides. This study explores the both-effect of landslides triggered by Chuetsu-Niigata earthquake followed by a heavy rainfall event through examining multiple traditional statistical models and data mining for understanding the coupling effects. This paper aims to compare the abilities of the statistical probabilistic likelihood-frequency ratio (PLFR) model, information value (InV) method, certainty factors (CF), artificial neural network (ANN) and ensemble support vector machine (SVM) for the landslide susceptibility mapping (LSM) using high-resolution-light detection and ranging digital elevation model (LiDAR DEM). Firstly, the landslide inventory map including 8459 landslide polygons was compiled from multiple aerial photographs and satellite imageries. These datasets were then randomly split into two parts: 70% landslide polygons (5921) for training model and the remaining polygons for validation (2538). Next, seven causative factors were classified into three categories namely topographic factors, hydrological factors and geological factors. We then identified the associations between landslide occurrence and causative factors to produce LSM. Finally, the accuracies of five models were validated by the area under curves (AUC) method. The AUC values of five models vary from 0.77 to 0.87. Regarding the capability of performance, the proposed SVM is promising for constructing the regional landslide-prone potential areas using both types of landslides. Additionally, the result of our LSM can be applied for similar areas which have been experiencing both rainfall-earthquake landslides. [ABSTRACT FROM AUTHOR]

In the present study, Rotation Forest ensemble was integrated with different base classifiers to develop different hybrid models namely Rotation Forest based Support Vector Machines (RFSVM), Rotation Forest based Artificial Neural Networks (RFANN), Rotation Forest based Decision Trees (RFDT), and Rotation Forest based Naïve Bayes (RFNB) for landslide susceptibility modelling. The validity of these models was evaluated using statistical methods such as Root Mean Square Error (RMSE), Kappa index, accuracy, and the area under the success rate and predictive rate curves (AUC). Part of the landslide prone area of Pithoragarh district, Uttarakhand, Himalaya, India was selected as the study area. Results indicate that the RFDT is the best model showing the highest predictive capability (AUC = 0.741) in comparison to RFANN (AUC = 0.710), RFSVM (AUC = 0.701), and RFNB (AUC = 0.640) models. The present study would be helpful in the selection of best model for landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

Disasters caused by landslides pose a considerable threat to people's lives and property, resulting in substantial losses each year. Landslide displacement rate prediction (LDRP) provides a useful fundamental tool for mitigating landslide disasters. However, more accurately predicting LDRP remains a challenge in the study of landslides. Lately, ensemble deep learning algorithms have shown promise in delivering a more precise and effective spatial modeling solution. The core aims of this research are to explore and evaluate the prediction capability of three progressive evolutionary deep learning (DL) techniques, i.e., a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU) ensemble AdaBoost algorithm for modeling rainfall-induced and reservoir-induced landslides in the Baihetan reservoir area in China. The outcomes show that the ensemble DL model could predict the Wangjiashan landslide in the Baihetan reservoir area with improved accuracy. The highest accuracy was achieved in the testing set when the window length equaled 30. However, assembling two predictors outperformed the accuracy of assembling three predictors, with the mean absolute error and root mean square error reaching 1.019 and 1.300, respectively. These findings suggest that the combination of strong learners and DL can yield satisfactory prediction results. [ABSTRACT FROM AUTHOR]

Karstic terrains are usually dominated by aquifer systems and/or underground cavities. Overexploitation of groundwater in such areas often induces land subsidence and sometimes causes sinkholes. The Cheria basin in Algeria suffers from severe land subsidence issues, and this phenomenon has been increasing in recent years due to population expansion and uncontrolled groundwater exploitation. This work uses GPS data and persistent scatterer interferometry synthetic aperture radar (PS-InSAR) techniques to monitor the land subsidence rate by employing Sentinel-1 satellite data for the period from 2016 to 2022. Our results demonstrate that the Cheria basin experiences both uplift and subsidence in places, with an overall substantial change in the land surface. The total cumulative subsidence over 6 years reached a maximum of 500 mm. Comparison of land deformation between PSI and GPS showed root mean square error (RMSE) values of about 2.83 mm/year, indicating that our analyzed results are satisfactorily reproducing the actual changes. Nonetheless, these results can be used to extract the susceptible zones for vertical ground displacement and evaluate the surface deformation inventory map of the region for reducing damages (e.g., human losses, economic impact, and environmental degradation) that may occur in the future (e.g., sinkholes) and can be further utilized in perspective for a sinkhole early warning system. [ABSTRACT FROM AUTHOR]

SnO 2 coated urchin-like TiO 2 hollow microspheres are prepared via a facile one-step hydrothermal method by using titanium tetrabutoxide (TBOT) as titanium source. The synthesized products are characterized by XRD, SEM and TEM measurements. It's found that the as-prepared microspheres with a diameter of 500–800 nm are consisted of densely interconnected nanowires and possessed a high specific surface area of 134.92 m 2 g −1 . Moreover, HRTEM and element mapping results show that the surface of urchin-like microsphere is coated by lots of SnO 2 nanoparticles. When used as scattering layer for dye-sensitized solar cells, the microspheres show good dye adsorption capability, superior light scattering and electron diffusibility, leading to a higher photovoltaic conversion efficiency of 8.33%, which is a 28.4% enhancement comparable to that of bare nanocrystalline TiO 2 (Dyesol 18NR-T, 6.49%). [ABSTRACT FROM AUTHOR]

Metal-organic frameworks, ZIF-8, derived carbon materials are firstly applied as a counter electrode of dye-sensitized solar cells due to their easy fabrication, large specific surface area and high catalytic activities towards the reduction of I 3 − ions. An efficiency of 7.32% is achieved under the illumination of 1 sun (AM 1.5, 100 mW/cm 2 ), which is comparable to that of the solar cell based on Pt electrode. [ABSTRACT FROM AUTHOR]