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Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.

Most extant ice caps mantling low-relief Arctic Canada landscapes remained cold based throughout the late Holocene, preserving in situ bryophytes killed as ice expanded across vegetated landscapes. After reaching peak late Holocene dimensions ∼1900 CE, ice caps receded as Arctic summers warmed, exposing entombed vegetation. The calibrated radiocarbon ages of entombed moss collected near ice cap margins (kill dates) define when ice advanced across the site, killing the moss, and remained over the site until the year of their collection. In an earlier study, we reported 94 last millennium radiocarbon dates on in situ dead moss collected at ice cap margins across Baffin Island, Arctic Canada. Tight clustering of those ages indicated an abrupt onset of the Little Ice Age at ∼1240 CE and further expansion at ∼1480 CE coincident with episodes of major explosive volcanism. Here we test the confidence in kill dates as reliable predictors of expanding ice caps by resampling two previously densely sampled ice complexes ∼15 years later after ∼250 m of ice recession. The probability density functions (PDFs) of the more recent series of ages match PDFs of the earlier series but with a larger fraction of early Common Era ages. Post 2005 CE ice recession has exposed relict ice caps that grew during earlier Common Era advances and were preserved beneath later ice cap growth. We compare the 106 kill dates from the two ice complexes with 80 kill dates from 62 other ice caps within 250 km of the two densely sampled ice complexes. The PDFs of kill dates from the 62 other ice caps cluster in the same time windows as those from the two ice complexes alone, with the PDF of all 186 kill dates documenting episodes of widespread ice expansion restricted almost exclusively to 250–450 CE, 850–1000 CE, and a dense early Little Ice Age cluster with peaks at ∼1240 and ∼1480 CE. Ice continued to expand after 1480 CE, reaching maximum dimensions at ∼1880 CE that are still visible as zones of sparse vegetation cover in remotely sensed imagery. Intervals of widespread ice cap expansion coincide with persistent decreases in mean summer surface air temperature for the region in a Community Earth System Model (CESM) fully coupled Common Era simulation, suggesting the primary forcings of the observed snowline lowering were both modest declines in summer insolation and cooling resulting from explosive volcanism, most likely intensified by positive feedbacks from increased snow cover and sea ice and reduced northward heat transport by the oceans. The clusters of ice cap expansion defined by moss kill dates are mirrored in an annually resolved Common Era record of ice cap dimensions in Iceland, suggesting this is a circum-North-Atlantic–Arctic climate signal for the Common Era. During the coldest century of the Common Era, 1780–1880 CE, ice caps mantled >11 000 km2 of north-central Baffin Island, whereas km2 is glaciated at present. The peak Little Ice Age state approached conditions expected during the inception phase of an ice age and was only reversed after 1880 CE by anthropogenic alterations of the planetary energy balance.

It has attracted much attention on the interplay between topology and criticality in topological quantum systems, i.e., gapless topological quantum phase transition (TQPT) on critical lines. It is demonstrated that the magnetocaloric effect (MCE) is able to signal the gapless TQPT in an extended Kitaev chain, which is witnessed by the unusual critical behavior of joint effect of Grüneison ration (GR) and isothermal entropy change. We uncover the conventional TQPT between gapped phases showing high symmetry (HS) and non-HS critical points with self-duality, which is signaled by the finite value of GR as T → 0 and the largest inverse MCE with T-linear relation of isothermal entropy change. Two critical lines intersect at a multicritical point, at which there is a T−1 critical divergence of GR as well as T1/2 critical behavior of isothermal entropy change. It manifests a “weak” gapless TQPT on one critical line, demonstrated by the shifting zero-crossing of GR without the largest inverse MCE. On the other critical line, it shows a “strong” gapless TQPT, which is evidenced by the negative dip of GR together with the largest inverse MCE. Our work provides a novel thermodynamic perspective on gapless TQPT.

Abstract Element isotopes are characterized by distinct atomic masses and nuclear spins, which can significantly influence material properties. Notably, however, isotopes in natural materials are homogenously distributed in space. Here, we propose a method to configure material properties by repositioning isotopes in engineered van der Waals (vdW) isotopic heterostructures. We showcase the properties of hexagonal boron nitride (hBN) isotopic heterostructures in engineering confined photon-lattice waves—hyperbolic phonon polaritons. By varying the composition, stacking order, and thicknesses of h10BN and h11BN building blocks, hyperbolic phonon polaritons can be engineered into a variety of energy-momentum dispersions. These confined and tailored polaritons are promising for various nanophotonic and thermal functionalities. Due to the universality and importance of isotopes, our vdW isotope heterostructuring method can be applied to engineer the properties of a broad range of materials.

Brown carbon (BrC) represents an important target for the “win-win” strategy of mitigating climate change and improving air quality. However, estimating co-benefits of BrC control remains difficult for China, partially because current measurement results are insufficient to represent the highly variable emission sources and meteorological conditions across different regions. In this study, we investigated, for the first time, the diurnal variations of BrC during two distinct seasons in a megacity in northeast China. The winter campaign conducted in January 2021 was characterized by low temperatures rarely seen in other Chinese megacities (down to about −20 ∘C). The mass absorption efficiencies of BrC at 365 nm (MAE365) were found to be ∼ 10 % higher at night. The variations of MAE365 could not be explained by the influence of residential biomass burning emissions or secondary aerosol formation but were strongly associated with the changes of a diagnostic ratio for the relative importance of coal combustion and vehicle emissions (RS/N). Given that most coal combustion activities were uninterruptible, the higher nighttime MAE365 in winter were attributed primarily to increased emissions from heavy-duty diesel trucks. The spring campaign conducted in April 2021 was characterized by frequent occurrences of agricultural fires, as supported by the intensive fire hotspots detected around Harbin and the more-than-doubled ratios of levoglucosan to organic carbon (LG/OC) compared to the winter campaign. In spring, the MAE365 depended little on RS/N but exhibited a strong positive correlation with LG/OC, suggesting open burning emissions as the dominant influencing factor for BrC's light absorption capacity. MAE365 were ∼ 70 % higher at night for the spring campaign, pointing to the prevalence of nighttime agricultural fires, which were presumably in response to local bans on open burning. It is noteworthy that the agricultural-fire emissions resulted in a distinct peak at ∼ 365 nm for the light absorption spectra of BrC, and candidates for the compounds at play were inferred to be aromatic species with nitro-functional groups. The presence of the ∼ 365 nm peak complicated the determination of absorption Ångström exponents for the agricultural-fire-impacted samples. In addition, the ∼ 365 nm peak became much less significant during the day, likely due to photobleaching of the relevant chromophores.

We study the ferrimagnetic quantum phase transitions (QPTs) and thermodynamics of a dimerized organic polymer, which is described as a periodic Anderson-like model using Green’s function theory. In the absence of magnetic field, it is shown that the Kondo coupling could destroy the Dirac semimetal and induce spin splitting, and the dimerization could open up an energy gap. In a magnetic field, the ferrimagnetic and ferromagnetic insulators as well as Weyl-Kondo semimetal are unveiled. The mechanism of Kondo-singlet competition with dimer-singlet is unveiled to account for the 1/3 magnetization plateau. The field induces second and first order QPTs in the small and large hybridization limit, which is demonstrated by the field scaling of Grüneisen ratio and temperature scaling of specific heat at low temperatures, providing novel thermodynamic means to diagnose the order of QPTs.

The g factors of 11/2− isomers in semimagic 109Sn and 111Sn (isomeric lifetimes τ=2.9(3) ns and τ=14.4(7) ns, respectively) were measured by an extension of the Time Differential Perturbed Angular Distribution technique, which uses LaBr3 detectors and the hyperfine fields of a gadolinium host to achieve precise measurements in a new regime of short-lived isomers. The results, g(11/2−;109Sn)=−0.186(8) and g(11/2−;111Sn)=−0.214(4), are significantly lower in magnitude than those of the 11/2− isomers in the heavier isotopes and depart from the value expected for a near pure neutron h11/2 configuration. Broken-symmetry density functional theory calculations applied to the sequence of 11/2− states reproduce the magnitude and location of this deviation. The g(11/2−) values are affected by shape core polarization; the odd 0h11/2 neutron couples to Jπ=2+,4+,6+... configurations in the weakly-deformed effective core, causing a decrease in the g-factor magnitudes.

Unmitigated disruptions pose a much more serious threat when large-scale tokamaks are operating in the high performance regime. Machine learning based disruption predictors can exhibit impressive performance. However, their effectiveness is based on a substantial amount of training data. In future reactors, obtaining a substantial amount of disruption data in high performance regimes without risking damage to the machine is highly improbable. Using machine learning to develop disruption predictors on data from the low performance regime and transfer them to the high performance regime is an effective solution for a large reactor-sized tokamak like ITER and beyond. In this study, a number of models are trained using different subsets of data from the HL-2A tokamak experiment. A SHapley Additive exPlanations (SHAP) analysis is executed on the models, revealing that there are different, even contradicting, patterns between different performance regimes. Thus, simply mixing data among different performance regimes will not yield optimal results. Based on this analysis, we propose an instance-based transfer learning technique which trains the model using a dataset generated with an optimized strategy. The strategy involves instance and feature selection based on the physics behind differences in high- and low-performance discharges, as revealed by SHAP model analysis. The TrAdaBoost technique significantly improved the model performance from 0.78 BA (balanced accuracy) to 0.86 BA with a few high-performance operation data.

To obtain an efficient and low-cost adsorbent for the Sb(V) removal in Sb(V)-contaminated water, a novel composite manganese oxide/oxyhydroxide (CMO) was synthesized by a simple hydrothermal synthesis method. The synthesized adsorbent was characterized via scanning electron microscopy, X-ray diffraction, transmission electron microscopy, Brunauer-Emmett-Teller surface area, Fourier transform infrared, and X-ray photoelectron spectroscopy analyses. The results revealed that the as-prepared CMO adsorbent possessed a porous structure consisting of Mn3O4 nanoparticles and MnOOH nanorods. Batch experiments showed that the adsorption behaviours were well fitted by the Langmuir isotherm and the pseudo-second-order kinetic model, reaching the maximum adsorption capacity of 119.63 mg/g at 25 °C. The application of CMO adsorbent showed that the Sb(V) removal efficiency in 6.24 L Sb(V)-containing water with a concentration of 3.6 mg/L was more than 90%. The reusability of CMO adsorbent demonstrated that the Sb(V) removal efficiency was still more than 80% even after five times of regeneration. The adsorption mechanism for Sb(V) can be described as ligand exchange between hydroxyl groups on the adsorbent surface and hydroxyl groups in Sb(OH)6− molecules by forming inner-sphere complexes. Those results suggested that the CMO adsorbent can be considered as a potential adsorbent to remove Sb(V) from contaminated water. HIGHLIGHTS A novel composite manganese oxide/oxyhydroxide (CMO) adsorbent was synthesized.; The CMO adsorbent was composed of Mn3O4 nanoparticles and MnOOH nanorods.; It can be a potentially promising adsorbent to removal Sb(V).;

Topographic development via paraglacial slope failure (PSF) represents a complex interplay between geological structure, climate, and glacial denudation. Southeastern Tibet has experienced amongst the highest rates of ice mass loss in High Mountain Asia in recent decades, but few studies have focused on the implications of this mass loss on the stability of paraglacial slopes. We used repeat satellite- and unpiloted aerial vehicle (UAV)-derived imagery between 1990 and 2020 as the basis for mapping PSFs from slopes adjacent to Hailuogou Glacier (HLG), a 5 km long monsoon temperate valley glacier in the Mt. Gongga region. We observed recent lowering of the glacier tongue surface at rates of up to 0.88 m a−1 in the period 2000 to 2016, whilst overall paraglacial bare ground area (PBGA) on glacier-adjacent slopes increased from 0.31 ± 0.27 km2 in 1990 to 1.38 ± 0.06 km2 in 2020. Decadal PBGA expansion rates were ∼ 0.01 km2 a−1, 0.02 km2 a−1, and 0.08 km2 in the periods 1990–2000, 2000–2011, and 2011–2020 respectively, indicating an increasing rate of expansion of PBGA. Three types of PSFs, including rockfalls, sediment-mantled slope slides, and headward gully erosion, were mapped, with a total area of 0.75 ± 0.03 km2 in 2020. South-facing valley slopes (true left of the glacier) exhibited more destabilization (56 % of the total PSF area) than north-facing (true right) valley slopes (44 % of the total PSF area). Deformation of sediment-mantled moraine slopes (mean 1.65–2.63 ± 0.04 cm d−1) and an increase in erosion activity in ice-marginal tributary valleys caused by a drop in local base level (gully headward erosion rates are 0.76–3.39 cm d−1) have occurred in tandem with recent glacier downwasting. We also observe deformation of glacier ice, possibly driven by destabilization of lateral moraine, as has been reported in other deglaciating mountain glacier catchments. The formation, evolution, and future trajectory of PSFs at HLG (as well as other monsoon-dominated deglaciating mountain areas) are related to glacial history, including recent rapid downwasting leading to the exposure of steep, unstable bedrock and moraine slopes, and climatic conditions that promote slope instability, such as very high seasonal precipitation and seasonal temperature fluctuations that are conducive to freeze–thaw and ice segregation processes.

Symmetry breaking and topological quantum phase transitions (QPTs) are usually accompanied by a gap opening or closing. For the continuous QPT, it is marked by a quantum critical point. Herein, we study the topological feature of quantum phase boundary-quantum criticality of two coupled Su-Schrieffer-Heeger chains. Two topological nontrivial insulating phases and a trivial one are recapped with two different topological phase boundaries, which are termed as Dirac semimetals (DSMs) with one or two Dirac nodal points within considering spin degeneracy. Specially, a tricritical point holds the character of quadratic contact point semimetal (QCPSM) that is regarded as a parent phase of DSMs. Meanwhile, the QCPSM and DSMs are featured by the quantum critical scaling of thermal Drude weight and specific heat. Furthermore, the Grüneisen ratio serves as a superb tool for the diagnosis of topological semimetals, which is demonstrated with or without self-duality of quantum criticality explicitly.

Despite the growing interest in understanding haze formation in Chinese megacities, air pollution has been largely overlooked for the Harbin–Changchun (HC) metropolitan area, located in the severe cold climate region in northeast China. In this study, we unfolded significant variations of fine particulate matter (PM2.5) in HC's central city (Harbin) during two sequential heating seasons of 2018–2019 and 2019–2020, and we explored major drivers for the observed variations. The two campaigns showed comparable organic carbon (OC) levels but quite different OC sources. The biomass burning (BB) to OC contribution decreased substantially for 2019–2020, which was attributed primarily to the transition of local policies on agricultural fires, i.e., from the “legitimate burning” policy released in 2018 to the “strict prohibition” policy in 2019. Meanwhile, the contribution of secondary OC (OCsec) increased significantly, associated with the much more frequent occurrences of high relative humidity (RH) conditions during the 2019–2020 measurement period. Similar to OCsec, the major secondary inorganic ions, i.e., sulfate, nitrate and ammonium (SNA), also exhibited RH-dependent increases. Given the considerable aerosol water contents predicted for the high-RH conditions, heterogeneous reactions were likely at play in secondary aerosol formation even in the frigid atmosphere in Harbin (e.g., with daily average temperatures down to below −20 ∘C). In brief, compared to 2018–2019, the 2019–2020 measurement period was characterized by a policy-driven decrease of biomass burning OC, a RH-related increase of OCsec and a RH-related increase of SNA, with the first two factors generally offsetting each other. In addition, we found that open burning activities were actually not eliminated by the strict prohibition policy released in 2019, based on a synthesis of air quality data and fire count results. Although not occurring during the 2019–2020 measurement period, agricultural fires broke out within a short period before crop planting in spring of 2020, and this resulted in off-the-charts air pollution for Harbin, with 1 h and 24 h PM2.5 concentrations peaking at ∼ 2350 and 900 µg/m3, respectively. This study indicates that sustainable use of crop residues remains a difficult challenge for the massive agricultural sector in northeast China.

Up to now, probing the quantum phase transition (QPT) and quantum critical (QC) phenomena at finite temperatures in one-dimensional (1D) spin systems still lacks an in-depth understanding. Herein, we study the QPT and thermodynamics of 1D spin-1/2 anisotropic Heisenberg antiferromagnetic chains by Green’s function theory. The quantum phase diagram is renormalized by the anisotropy (∆), which manifests a quantum critical point (QCP) hc = 1 + ∆ signaling the transition from gapless Tomonaga–Luttinger liquid (TLL) to gapped ferromagnetic (FM) state, demonstrated by the magnetic entropy and thermal Drude weight. At low temperatures, it is shown that two crossover temperatures fan out a QC regime and capture the QCP via the linear extrapolation to zero temperature. In addition, around QCP, the QC scaling is performed by analyzing the entropy and thermal Drude weight to extract the critical exponents (α, δ, and β) that fulfill the Essamm–Fisher scaling law, which provides a novel thermodynamic means to detect QPT for experiment. Furthermore, scaling hypothesis equations with two rescaled manners are proposed to testify the scaling analysis, for which all the data points fall on a universal curve or two independent branches for the plot against rescaled field or temperature, implying the self-consistency and reliability of the obtained critical exponents.

Aiming at the problems existing on the expression of atmospheric pollution dispersion, a data model of state-oriented and object-oriented hybrid modeling is designed. The model supports true three-dimensional grid representation and cellular automata deduction, and can combine the calculation and deduction of atmospheric pollution to improve the accuracy and efficiency of the model. The simulation experiments of single source and multi-source pollutants are carried out. The results show that: the model conforms to the diffusion law of atmospheric pollution. The pollutant concentration at any time, section or space in the whole diffusion process can be calculated and expressed dynamically, which improves the accuracy, rapidity, intuitiveness and analysation of the deduction of atmospheric pollutant diffusion process.

Inkjet technology is an essential tool for precise and quick delivery of liquids in micro-droplets. A key topic of the technology is to deliver the droplets efficiently by designing the nozzle that is related to the droplet speed and the droplet volume in a stable inkjet process. The ejected droplets are usually too small to determine their physical states through onsite measurement. Complex physical phenomena, such as the coupling effects of surface tension, viscous force and inertial force, make it difficult to optimize the nozzle design by experiments alone. In the paper, we adopt computational fluid dynamics to investigate the inkjet process with the orthogonal test method to arrange the studied cases. The computational results firstly have been verified through measuring a simulated case that could be observed in the experiment. Different nozzle structures then have been examined by numerical simulation. It is found that the Laval-shaped nozzle can improve the droplet speed significantly to deliver the droplets fast, and that the curvilinear-triangle-shaped nozzle can minimize the droplet volume to improve the printing accuracy. It is further revealed that a large ink viscosity and surface tension, as well as a low ink density can improve the process stability. Additionally, a parameter combined by the droplet speed, the droplet volume and the stability level is proposed to evaluate the comprehensive performance of the inkjet nozzle.

On the basis of the OMFIT integrated simulation platform and the parameters of the HL-2M device, a hybrid scenario with a typical safety factor q profile is simulated using the joint injection method of neutral beam and electron cyclotron wave with a normalized toroidal beta ( β _N ) of 2.39. At the same time, by analyzing the effect of impurity concentration ( Z _eff ) and pedestal density ( n _e,ped ) on confinement, a higher parameters hybrid scenario is obtained, with β _N reaching 3.118. The research results indicate that the turbulent stabilization effect caused by Z _eff gradually decreases from strong to weak with the increase of Z _eff , whereas the radiation power loss increases linearly as Z _eff increases. Consequently, there exists an optimal Z _eff value for optimal confinement. In addition, it is found that the optimal Z _eff value decreases with the increase of n _e,ped . The research results are crucial for the HL-2M device to accomplish the 1 MA advanced scenario discharge target, and they serve as a reference for future impurity seeding experiments.

Disruption prediction has made rapid progress in recent years, especially in machine learning (ML)-based methods. If a disruption prediction model can be interpreted, it can tell why certain samples are classified as disruption precursors. This allows us to tell the types of incoming disruption for disruption avoidance and gives us insight into the mechanism of disruption. This paper presents a disruption predictor called interpretable disruption predictor based on physics-guided feature extraction (IDP-PGFE) and its results on J-TEXT experiment data. The prediction performance of IDP-PGFE with physics-guided features is effectively improved (true positive rate = 97.27%, false positive rate = 5.45%, area under the ROC curve = 0.98) compared to the models with raw signal input. The validity of the interpretation results is ensured by the high performance of the model. The interpretability study using an attribution technique provides an understanding of J-TEXT disruption and conforms to our prior comprehension of disruption. Furthermore, IDP-PGFE gives a possible mean on inferring the underlying cause of the disruption and how interventions affect the disruption process in J-TEXT. The interpretation results and the experimental phenomenon have a high degree of conformity. The interpretation results also gives a possible experimental analysis direction that the resonant magnetic perturbations delays the density limit disruption by affecting both the MHD instabilities and the radiation profile. PGFE could also reduce the data requirement of IDP-PGFE to 10% of the training data required to train a model on raw signals. This made it possible to be transferred to the next-generation tokamaks, which cannot provide large amounts of data. Therefore, IDP-PGFE is an effective approach to exploring disruption mechanisms and transferring disruption prediction models to future tokamaks.

The requirement of high growth temperature for high-quality epitaxial AlGaN, which is typically around 100 °C higher than the growth temperature of GaN, is unfavorable for p-type dopant (Mg) incorporation, representing a grand challenge for AlGaN deep ultraviolet (UV) light-emitting devices. In this context, we show high-quality AlGaN nanowires emitting in the deep UV band grown at merely the growth temperature of GaN nanowires by molecular beam epitaxy. This is enabled by the discovery of a narrow GaN nanowire template growth window. We have further compared the room-temperature internal quantum efficiency of the samples emitting around 255 nm grown in the low-temperature regime and high-temperature regime. It is found that the sample grown in the low-temperature regime can possess optical quality close to the sample grown in the high-temperature regime. This study, therefore, suggests that even with a low growth temperature, using nanowire structures can still lead to AlGaN alloys with a relatively high optical quality, and the use of low substrate temperature could be beneficial for p-type doping.

This study uses correlation analyses to explore relationships between the satellite-derived Evaporative Stress Index (ESI) – which depicts standardized anomalies in an actual to reference evapotranspiration (ET) fraction – and various land and atmospheric variables that impact ET. Correlations between the ESI and forcing variable anomalies calculated over sub-seasonal timescales were computed at weekly and monthly intervals during the growing season. Overall, the results revealed that the ESI is most strongly correlated to anomalies in soil moisture and 2 m dew point depression. Correlations between the ESI and precipitation were also large across most of the US; however, they were typically smaller than those associated with soil moisture and vapor pressure deficit. In contrast, correlations were much weaker for air temperature, wind speed, and radiation across most of the US, with the exception of the south-central US where correlations were large for all variables at some point during the growing season. Together, these results indicate that changes in soil moisture and near-surface atmospheric vapor pressure deficit are better predictors of the ESI than precipitation and air temperature anomalies are by themselves. Large regional and seasonal dependencies were also observed for each forcing variable. Each of the regional and seasonal correlation patterns were similar for ESI anomalies computed over 2-, 4-, and 8-week time periods; however, the maximum correlations increased as the ESI anomalies were computed over longer time periods and also shifted toward longer averaging periods for the forcing variables.

Numerous studies have been conducted to assess uncertainty in hydrological and non-point source pollution predictions, but few studies have considered both prediction and measurement uncertainty in the model evaluation process. In this study, the cumulative distribution function approach (CDFA) and the Monte Carlo approach (MCA) were developed as two new approaches for model evaluation within an uncertainty condition. For the CDFA, a new distance between the cumulative distribution functions of the predicted data and the measured data was established in the model evaluation process, whereas the MCA was proposed to address conditions with dispersed data points. These new approaches were then applied in combination with the Soil and Water Assessment Tool in the Three Gorges Region, China. Based on the results, these two new approaches provided more accurate goodness-of-fit indicators for model evaluation compared to traditional methods. The model performance worsened when the error range became larger, and the choice of probability density functions (PDFs) affected model performance, especially for non-point source (NPS) predictions. The case study showed that if the measured error is small and if the distribution can be specified, the CDFA and MCA could be extended to other model evaluations within an uncertainty framework and even be used to calibrate and validate hydrological and NPS pollution (H/NPS) models.

Records of Neoglacial glacier activity in the Arctic constructed from moraines are often incomplete due to a preservation bias toward the most extensive advance, often the Little Ice Age. Recent warming in the Arctic has caused extensive retreat of glaciers over the past several decades, exposing preserved landscapes complete with in situ tundra plants previously entombed by ice. The radiocarbon ages of these plants define the timing of snowline depression and glacier advance across the site, in response to local summer cooling. Erosion rapidly removes most dead plants that have been recently exposed by ice retreat, but where erosive processes are unusually weak, dead plants may remain preserved on the landscape for decades. In such settings, a transect of plant radiocarbon ages can be used to construct a near-continuous chronology of past ice margin advance. Here we present radiocarbon dates from the first such transect on Baffin Island, which directly dates the advance of a small ice cap over the past two millennia. The nature of ice expansion between 20 BCE and ∼ 1000 CE is still uncertain, but episodic advances at ∼ 1000 CE, ∼ 1200, and ∼ 1500 led to the maximum Neoglacial dimensions ~ 1900 CE. We employ a two-dimensional numerical glacier model calibrated using the plant radiocarbon ages ice margin chronology to assess the sensitivity of the ice cap to temperature change. Model experiments show that at least ∼ 0.44 °C of cooling over the past 2 kyr is required for the ice cap to reach its 1900 CE margin, and that the period from ∼ 1000 to 1900 CE must have been at least 0.25° C cooler than the previous millennium, results that agree with regional temperature reconstructions and climate model simulations. However, significant warming since 1900 CE is required to explain retreat to its present position, and, at the same rate of warming, the ice cap will disappear before 2100 CE.

Monitoring the Urban Land Cover/Land Use change detection is important as one of the main driving forces of environmental change because Urbanization is the biggest changes in form of Land, resulting in a decrease in cultivated areas. Using remote sensing ability to solve land resources problems. The purpose of this research is to map the urban areas at different times to monitor and predict possible urban changes, were studied the annual growth urban land during the last 29 years in Algiers City. Improving the productiveness of long-term training in land mapping, were have developed an approach by the following steps: 1) pre-processing for improvement of image characteristics; 2) extract training sample candidates based on the developed methods; and 3) Derive maps and analyzed of Algiers City on an annual basis from 1987 to 2016 using a Supervised Classifier Support Vector Machine (SVMs). Our result shows that the strategy of urban land followed in the region of Algiers City, developed areas mostly were extended to East, West, and South of Central Regions. The urban growth rate is linked with National Office of Statistics data. Future studies are required to understand the impact of urban rapid lands on social, economy and environmental sustainability, it will also close the gap in data of urbanism available, especially on the lack of reliable data, environmental and urban planning for each municipality in Algiers, develop experimental models to predict future land changes with statistically significant confidence.

Hong Lake is the largest lake in Hubei Province. With the increase of Hong Lake economic activity, the area, spatial location and shape of Hong Lake have changed greatly in the past. In this paper, we used the images, which is from the visible infrared imaging radiometer (VIIRS). First, we selected the images of Hong Lake waters on December 6, 2016 and December 26, 2015. Then we extracted the water bodies by the single-band method, spectral relationship method, normalized difference water index (NDWI) were used, and the effect-s were compared. Second, the images of Hong Lake waters in summer and winter were selected from 2012 to 2016, respectively. Last, The NDWI was used to extract the water body and compared with the MODIS image extraction effect in the same period. As a result of the vegetation around Hong Lake, the water is extracted by NDWI and normalized difference vegetation index (NDVI). It is found that for the VIIRS image, the NDWI is the best in the water extraction of Hong Lake. The NDVI + NDWI method is beneficial to the extraction of water covered with aquatic plants. VIIRS image extraction is better than MODIS image. In addition, from the study of VIIRS and MODIS to Hong Lake waters in the five years of water extraction and area calculation, 2012–2016 period, Hong Lake’s average area of 348.213 km2 in flood season, in dry season average area of 349.163 km2. The largest area for the 2012 flood season 389.751 km2, the smallest area of 2016 flood season 306.177 km2. Overall, Hong Lake’s area changes little.

Buildings change detection has the ability to quantify the temporal effect, on urban area, for urban evolution study or damage assessment in disaster cases. In this context, changes analysis might involve the utilization of the available satellite images with different resolutions for quick responses. In this paper, to avoid using traditional method with image resampling outcomes and salt-pepper effect, building change detection based on shape matching is proposed for multi-resolution remote sensing images. Since the object’s shape can be extracted from remote sensing imagery and the shapes of corresponding objects in multi-scale images are similar, it is practical for detecting buildings changes in multi-scale imagery using shape analysis. Therefore, the proposed methodology can deal with different pixel size for identifying new and demolished buildings in urban area using geometric properties of objects of interest. After rectifying the desired multi-dates and multi-resolutions images, by image to image registration with optimal RMS value, objects based image classification is performed to extract buildings shape from the images. Next, Centroid-Coincident Matching is conducted, on the extracted building shapes, based on the Euclidean distance measurement between shapes centroid (from shape T0 to shape T1 and vice versa), in order to define corresponding building objects. Then, New and Demolished buildings are identified based on the obtained distances those are greater than RMS value (No match in the same location).

Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE) with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE). Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI) datasets – Pavia University dataset and the Kennedy Space Centre (KSC) dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene classification. But it should be noticed that how the distinct features are fused to fully describe the challenging HSR images, which is a critical factor for scene classification. In this paper, a semantic-feature fusion fully sparse topic model (SFF-FSTM) is proposed for HSR imagery scene classification. In SFF-FSTM, three heterogeneous features – the mean and standard deviation based spectral feature, wavelet based texture feature, and dense scale-invariant feature transform (SIFT) based structural feature are effectively fused at the latent semantic level. The combination of multiple semantic-feature fusion strategy and sparse based FSTM is able to provide adequate feature representations, and can achieve comparable performance with limited training samples. Experimental results on the UC Merced dataset and Google dataset of SIRI-WHU demonstrate that the proposed method can improve the performance of scene classification compared with other scene classification methods for HSR imagery.

A field study was conducted to investigate the variations in the carbon:nitrogen:phosphorus (C:N:P) stoichiometry of winter wheat (Triticum aestivum L.) and its relationships with wheat growth rate (r) and water use efficiency (WUE) under different rates of N fertilization. The results indicated that the growth rate and WUE of wheat significantly increased with N fertilization and were strongly correlated with the N and P contents and C:N:P ratio. The C content in wheat was relatively stable, regardless of N fertilization, and the N contents were higher in the N fertilization treatments. In addition, the P contents were higher in the treatments without N fertilization, and changes in the C:P and N:P ratios mainly depended on changes in P. The response ratio of the N:P ratio in straw was higher than that of the other investigated traits and largely depended on changes in the plant P content due to N addition.

The present work constructs the fruit fly optimization algorithm integrated with multi-strategies modified weights support vector regression model (FOAAMWP-SVR). It is based on fruit fly optimization algorithm (FOA) by introducing multi-strategies modified searching weights and linear modified optimal fruit fly weight, and then combining it with support vector regression (SVR). To construct FOAAMWP-SVR model, the support vector regression model based on fruit fly optimization algorithm (FOA-SVR) is firstly formed to obtain the optimal parameter of support vector regression, and proceeds to integrate with multi-strategies to modify searching weight and the optimal fruit fly weight. In addition, a simulation experiment of activity product regression of solid solution in KCl-NH4Cl-H2O ternary system is applied to test FOA and FOAAMWP-SVR algorithms, and the calculation shows that the regression error of two models is small, while FOAAMWP-SVR is more accurate than FOA model.

A common challenge for pump-probe studies of structural dynamics at X-ray free-electron lasers (XFELs) is the determination of time zero (T0)—the time an optical pulse (e.g., an optical laser) arrives coincidently with the probe pulse (e.g., a XFEL pulse) at the sample position. In some cases, T0 might be extracted from the structural dynamics of the sample's observed response itself, but generally, an independent robust method is required or would be superior to the inferred determination of T0. In this paper, we present how the structural dynamics in ultrafast melting of bismuth can be exploited for a quickly performed, reliable and accurate determination of T0 with a precision below 20 fs and an overall experimental accuracy of 50 fs to 150 fs (estimated). Our approach is potentially useful and applicable for fixed-target XFEL experiments, such as serial femtosecond crystallography, utilizing an optical pump pulse in the ultraviolet to near infrared spectral range and a pixelated 2D photon detector for recording crystallographic diffraction patterns in transmission geometry. In comparison to many other suitable approaches, our method is fairly independent of the pumping wavelength (UV–IR) as well as of the X-ray energy and offers a favorable signal contrast. The technique is exploitable not only for the determination of temporal characteristics of the experiment at the interaction point but also for investigating important conditions affecting experimental control such as spatial overlap and beam spot sizes.

Intrauterine growth retardation (IUGR) is associated with the development of adult-onset diseases, including pulmonary hypertension. However, the underlying mechanism of the early nutritional insult that results in pulmonary vascular dysfunction later in life is not fully understood. Here, we investigated the role of tyrosine phosphorylation of voltage-gated potassium channel 1.5 (Kv1.5) in this prenatal event that results in exaggerated adult vascular dysfunction. A rat model of chronic hypoxia (2 weeks of hypoxia at 12 weeks old) following IUGR was used to investigate the physiological and structural effect of intrauterine malnutrition on the pulmonary artery by evaluating pulmonary artery systolic pressure and vascular diameter in male rats. Kv1.5 expression and tyrosine phosphorylation in pulmonary artery smooth muscle cells (PASMCs) were determined. We found that IUGR increased mean pulmonary artery pressure and resulted in thicker pulmonary artery smooth muscle layer in 14-week-old rats after 2 weeks of hypoxia, while no difference was observed in normoxia groups. In the PASMCs of IUGR-hypoxia rats, Kv1.5 mRNA and protein expression decreased while that of tyrosine-phosphorylated Kv1.5 significantly increased. These results demonstrate that IUGR leads to exaggerated chronic hypoxia pulmonary arterial hypertension (CH-PAH) in association with decreased Kv1.5 expression in PASMCs. This phenomenon may be mediated by increased tyrosine phosphorylation of Kv1.5 in PASMCs and it provides new insight into the prevention and treatment of IUGR-related CH-PAH.

Web service can bring together applications running on diverse platforms, users can access and share various data, information and models more effectively and conveniently from certain web service platform. Cloud computing emerges as a paradigm of Internet computing in which dynamical, scalable and often virtualized resources are provided as services. With the rampant growth of massive data and restriction of net, traditional web services platforms have some prominent problems existing in development such as calculation efficiency, maintenance cost and data security. In this paper, we offer a spatial statistics service based on Microsoft cloud. An experiment was carried out to evaluate the availability and efficiency of this service. The results show that this spatial statistics service is accessible for the public conveniently with high processing efficiency.

The identification of priority management areas (PMAs) is essential for the control of non-point-source (NPS) pollution, especially for a large-scale watershed. However, previous studies have typically focused on small-scale catchments adjacent to specific assessment points; thus, the interactions between multiple river points remain poorly understood. In this study, a multiple-assessment-point PMA (MAP-PMA) framework was proposed by integrating the upstream sources and the downstream transport aspects of NPS pollution. Daning River watershed was taken as a case study in this paper, which has demonstrated that the integration of the upstream input changes was vital for the final PMAs map, especially for downstream areas. Contrary to conventional wisdom, this research recommended that the NPS pollutants could be best controlled among the upstream high-level PMAs when protecting the water quality of the entire watershed. The MAP-PMA framework provided a more cost-effective tool for the establishment of conservation practices, especially for a large-scale watershed.

Sub-pixel mapping technique can specify the location of each class within the pixels based on the assumption of spatial dependence. Traditional sub-pixel mapping algorithms only consider the spatial dependence at the pixel level. The spatial dependence of each sub-pixel is ignored and sub-pixel spatial relation is lost. In this paper, a novel multi-objective sub-pixel mapping framework based on memetic algorithm, namely MSMF, is proposed. In MSMF, the sub-pixel mapping is transformed to a multi-objective optimization problem, which maximizing the spatial dependence index (SDI) and Moran's I, synchronously. Memetic algorithm is utilized to solve the multi-objective problem, which combines global search strategies with local search heuristics. In this framework, the sub-pixel mapping problem can be solved using different evolutionary algorithms and local algorithms. In this paper, memetic algorithm based on clonal selection algorithm (CSA) and random swapping as an example is designed and applied simultaneously in the proposed MSMF. In MSMF, CSA inherits the biologic properties of human immune systems, i.e. clone, mutation, memory, to search the possible sub-pixel mapping solution in the global space. After the exploration based on CSA, the local search based on random swapping is employed to dynamically decide which neighbourhood should be selected to stress exploitation in each generation. In addition, a solution set is used in MSMF to hold and update the obtained non-dominated solutions for multi-objective problem. Experimental results demonstrate that the proposed approach outperform traditional sub-pixel mapping algorithms, and hence provide an effective option for sub-pixel mapping of hyperspectral remote sensing imagery.

This study investigated the effects of in ovo administration of equol (Eq) on post-hatch growth and hepatic lipid metabolism in broiler chickens. Fertilized eggs (146 eggs/group) were injected with 0 μg (control, Con), 20 μg (low dose, L) and 100 μg (high dose, H) Eq in the albumen on the 7th day of incubation. Except a trend increase in the weight of total fat (P = 0.09), Eq had no effect on growth or liver weight in broilers at 49 days of age. Males presented higher liver and BWs and lower total fat and relative liver weights than females (P < 0.01). However, there were no significant effects of Eq or Eq–gender interactions on growth performance or tissues weight (P > 0.05). With respect to lipid parameters in the serum, the results showed that female broilers presented higher triacyglycerol (TG) and low-density lipoprotein cholesterol concentrations than males, whereas there was no gender difference in serum total cholesterol (TC) or high-density lipoprotein cholesterol (HDLC) concentration (P > 0.05). Eq administration significantly decreased serum TG and TC but increased HDLC concentrations in serum of broilers at 49 days of age (P < 0.05), whereas there were no interactions between gender and Eq (P > 0.05). To elucidate the mechanism behind the significant changes of serum TG and TC levels, the expression of genes involved in lipid metabolism in the liver was investigated in female chickens using reverse transcription-PCR. Carnitine palmitoyl transferase I (CPTI) messenger RNA (mRNA) was significantly upregulated by 20 and 100 μg Eq (P < 0.05). High-dose Eq significantly decreased fatty acid synthase (FAS) and enhanced cholesterol-7alpha-hydroxylase (CYP7A1) mRNA levels in the liver (P < 0.05). Eq had no significant effects on acetyl-CoA carboxylase, sterol regulatory element binding protein-1c, malic enzyme, low-density lipoprotein receptor or 3-hydroxy-3-methylglutaryl coenzyme A reductase mRNA in the liver (P > 0.05). These results in female broilers suggest that Eq decreased blood TG by upregulating CPTI and downregulating FAS mRNA expression in the liver, and that high serum cholesterol levels stimulated CYP7A1 gene transcription in the liver.

We present a crystallography chip enabling in situ room temperature crystallography at microfocus synchrotron beamlines and X-ray free-electron laser (X-FEL) sources. Compared to other in situ approaches, we observe extremely low background and high diffraction data quality. The chip design is robust and allows fast and efficient loading of thousands of small crystals. The ability to load a large number of protein crystals, at room temperature and with high efficiency, into prescribed positions enables high throughput automated serial crystallography with microfocus synchrotron beamlines. In addition, we demonstrate the application of this chip for femtosecond time-resolved serial crystallography at the Linac Coherent Light Source (LCLS, Menlo Park, California, USA). The chip concept enables multiple images to be acquired from each crystal, allowing differential detection of changes in diffraction intensities in order to obtain high signal-to-noise and fully exploit the time resolution capabilities of XFELs.