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How much and what we eat and where it is produced can create huge differences in GHG emissions. On the basis of detailed household-expenditure data, we evaluate the unequal distribution of dietary emissions from 140 food products in 139 countries or areas and further model changes in emissions of global diet shifts. Within countries, consumer groups with higher expenditures generally cause more dietary emissions due to higher red meat and dairy intake. Such inequality is more pronounced in low-income countries. The present global annual dietary emissions would fall by 17% with the worldwide adoption of the EAT-Lancet planetary health diet, primarily attributed to shifts from red meat to legumes and nuts as principal protein sources. More than half (56.9%) of the global population, which is presently overconsuming, would save 32.4% of global emissions through diet shifts, offsetting the 15.4% increase in global emissions from presently underconsuming populations moving towards healthier diets.

Plant-based diets (PBDs) and planetary-health diets (PHDs) are recommended for their potential health and environmental benefits, but population-based evidence in diverse cultures is scarce.

Fringe projection technology is a commonly used technique in optical 3-D measurement. In high-speed motion scenarios, due to image noise and the effects of object motion, projecting more fringe patterns for high-precision phase unwrapping is a common method, which can significantly reduce the frame rate of 3-D reconstruction. Deep learning techniques have been employed for high-precision phase unwrapping, but typically, these models have a large parameter and computation, making them difficult to integrate into real-time 3-D reconstruction systems. In this article, we first employ the lookup table (LUT) technique for rapid computation of dual-frequency phases. Second, we design a deep learning model with a parameter size of only 276 kb for high-precision phase unwrapping and quickly embed it into a real-time 3-D reconstruction system through 8-bit quantization without compromising accuracy. Furthermore, we utilize the calibration parameters of a real fringe projection profilometry (FPP) system to establish a corresponding virtual FPP system for rapid generation of data required for model training. Finally, we optimize the generation of point clouds by avoiding the computationally slow inverse matrix operation process. Experiments show that our model can achieve high-precision real-time 3-D reconstruction at a rate of 130 frames/s.

In two-dimensional systems, perpendicular magnetic fields can induce a bulk band gap and chiral edge states, which gives rise to the quantum Hall effect. The quantum Hall effect is characterized by zero longitudinal resistance (Rxx) and Hall resistance (Rxy) plateaus quantized to h/(υe2) in the linear response regime, where υis the Landau level filling factor, eis the elementary charge and his Planck’s constant. Here we explore the nonlinear response of monolayer graphene when tuned to a quantum Hall state. We observe a third-order Hall effect that exhibits a nonzero voltage plateau scaling cubically with the probe current. By contrast, the third-order longitudinal voltage remains zero. The magnitude of the third-order response is insensitive to variations in magnetic field (down to ~5 T) and in temperature (up to ~60 K). Moreover, the third-order response emerges in graphene devices with a variety of geometries, different substrates and stacking configurations. We term the effect third-order nonlinear response of the quantum Hall state and propose that electron–electron interaction between the quantum Hall edge states is the origin of the nonlinear response of the quantum Hall state.

Phospholipase A2(PLA2) is a critical biomarker of many diseases. Among various fluorescent probes, the ratiometric ones have shown great promise because they can provide built-in calibration to avoid interference from analyte-independent factors by simultaneously monitoring two or more fluorescence peaks. Herein, we develop a biomimetic membrane-coated ratiometric fluorescent nanoprobe for the detection of PLA2. In this design, silica nanoparticles modified with fluorescein isothiocyanate (FITC) are functionalized with a layer of biomimetic membrane that is loaded with Dil dye. Therefore, when the nanocomposite is illuminated by 488 nm incident light, in addition to the fluorescence of FITC centered at 522 nm, that of Dil peaked at 568 nm originating from the energy transfer from FITC to Dil can also be monitored, forming a ratiometric fluorescent nanoprobe. When the target PLA2is added, FITC and Dil will no longer be in close proximity because it hydrolyzes the biomimetic membrane, cutting off the energy flow between the two dyes. Consequently, the ratiometric fluorescent signal of the nanoprobe is altered to quantitatively reflect the content of PLA2. This study provides an effective approach to PLA2detection, and the biomimetic membrane-mediated ratiometric design is promising in developing a new generation of biosensors.

Most existing perception systems rely on sensory data acquired from cameras, which perform poorly in low light and adverse weather conditions. To resolve this limitation, we have witnessed advanced LiDAR sensors become popular in perception tasks in autonomous driving applications. Nevertheless, their usage in traffic monitoring systems is less ubiquitous. We identify two significant obstacles in cost-effectively and efficiently developing such a LiDAR-based traffic monitoring system: (i) public LiDAR datasets are insufficient for supporting perception tasks in infrastructure systems, and (ii) 3D annotations on LiDAR point clouds are time-consuming and expensive. To fill this gap, we present an efficient semi-automated annotation tool that automatically annotates LiDAR sequences with tracking algorithms while offering a fully annotated infrastructure LiDAR dataset—FLORIDA (Florida LiDAR-based Object Recognition and Intelligent Data Annotation)—which will be made publicly available. Our advanced annotation tool seamlessly integrates multi-object tracking (MOT), single-object tracking (SOT), and suitable trajectory post-processing techniques. Specifically, we introduce a human-in-the-loop schema in which annotators recursively fix and refine annotations imperfectly predicted by our tool and incrementally add them to the training dataset to obtain better SOT and MOT models. By repeating the process, we significantly increase the overall annotation speed by $3- 4$ times and obtain better qualitative annotations than a state-of-the-art annotation tool. The human annotation experiments verify the effectiveness of our annotation tool. In addition, we provide detailed statistics and object detection evaluation results for our dataset in serving as a benchmark for perception tasks at traffic intersections.

Hyperspectral images (HSIs) have spectral variability, which leads to spectral dependence in adjacent and nonadjacent regions, and this dependence is essential for the classification of regions with mixed pixels. Current neural architecture search (NAS) methods have achieved significant advantages in HSI classification, but these methods cannot capture spectral dependence in nonadjacent regions because only use feedforward connections. Meanwhile, the cost of the search process in NAS is proportional to the scale of the search space, which limits the expansion of the search space. To address these issues, we propose a sparse-coding-inspired feedback NAS (SCIF-NAS) method for HSI classification. First, we view HSI samples as sequences and introduce a feedback mechanism in NAS to model the spectral dependence of nonadjacent regions to mitigate the effects of spectral variation. Second, we design several feedforward operations according to the characteristics of HSI, to form the search space together with feedback operations. Meanwhile, a sparse-coding-inspired NAS accelerated strategy is introduced to alleviate the search time burden caused by the expansion of search space. Third, we integrate center loss with cross-entropy loss to construct a hybrid loss function that helps to obtain a better classification boundary. Finally, we conduct experiments on three popular HSI benchmarks, which show that SCIF-NAS outperforms the state-of-the-art methods in HSI classification.

Two-dimensional electron gas (2DEG) at oxide interfaces exhibits various exotic properties stemming from interfacial inversion and symmetry breaking. In this work, we report large nonlinear transverse conductivities in the LaAlO3/KTaO3interface 2DEG under zeromagnetic field. Skew scattering was identified as the dominant origin based on the cubic scaling of nonlinear transverse conductivity with linear longitudinal conductivity and 3-fold symmetry. Moreover, gate-tunable nonlinear transport with pronounced peak and dip was observed and reproduced by our theoretical calculation. These results indicate the presence of Berry curvature hotspots and thus a large Berry curvature triplet at the oxide interface. Our theoretical calculations confirm the existence of large Berry curvatures from the avoided crossing of multiple 5d-orbit bands, orders of magnitude larger than that in transition-metal dichalcogenides. Nonlinear transport offers a new pathway to probe the Berry curvature at oxide interfaces and facilitates new applications in oxide nonlinear electronics.

We present a new approach to unsupervised shape correspondence learning between pairs of point clouds. We make the first attempt to adapt the classical locally linear embedding algorithm (LLE)—originally designed for nonlinear dimensionality reduction—for shape correspondence. The key idea is to find dense correspondences between shapes by first obtaining high-dimensional neighborhood-preserving embeddings of low-dimensional point clouds and subsequently aligning the source and target embeddings using locally linear transformations. We demonstrate that learning the embedding using a new LLE-inspired point cloud reconstruction objective results in accurate shape correspondences. More specifically, the approach comprises an end-to-end learnable framework of extracting high-dimensional neighborhood-preserving embeddings, estimating locally linear transformations in the embedding space, and reconstructing shapes via divergence measure-based alignment of probability density functions built over reconstructed and target shapes. Our approach enforces embeddings of shapes in correspondence to lie in the same universal/canonical embedding space, which eventually helps regularize the learning process and leads to a simple nearest neighbors approach between shape embeddings for finding reliable correspondences. Comprehensive experiments show that the new method makes noticeable improvements over state-of-the-art approaches on standard shape correspondence benchmark datasets covering both human and nonhuman shapes.

Traditionally, analysis requires very large number of calculations; some engineering applications in particular require time-consuming numerical efforts. Classical reliability analysis methods with combinations of surrogate models can relieve the computational burden. An active learning reliability method that combines kriging and Monte Carlo simulation has drawn a great deal of attention in recent years. However, it is often challenging to find the most suitable sample point and speed up the convergence. In this paper, a method is proposed to deal with this problem, in which two strategies are employed: an active learning method is proposed to search the most suitable sample point in a specific region using the dichotomy method; an improved convergence criterion is developed based on standard deviation to improve the convergence speed. In addition, taking advantage of the excellent characteristics of Subset Simulation, a method for dealing with small failure probability problems is proposed based on the above strategies. The efficiency and accuracy of the proposed approaches are tested using three numerical examples and one finite element example. Results demonstrate its accuracy.

Reduction of soluble U(VI) to insoluble U(IV) based on semiconductor photocatalysts is a favored U(VI)-extraction method, because of its simplicity, environmental friendliness, and high efficiency. The key to implement this technology is the development of efficient photocatalysts with high activity and stability for sacrificial agents-free U(VI) photoreduction. Herein, we report a new type of CdS/covalent organic framework (COF) core-shell photocatalysts (CdS@COF-X, X= 5, 10, 15, and 20) with efficient chemisorption, enhanced carrier separation, and antiphotocorrosion ability for U(VI) photoreduction without additional sacrificial agents. The two-dimensional COF, formed by the polycondensation of 2,4,6-triformylphloroglucinol and 1,3,5-tris(4-aminophenyl)triazine, was selected to construct the hybrid materials due to its high chemical stability, matching band gaps and efficient chemisorption for U(VI). Remarkably, CdS@COF-10 realized a record high U(VI) extraction capacity of 1825.6 mg g−1after 90 min. Moreover, the reduction ratio of uranium was up to 82.5%, and the product was identified as uranium dioxide (UO2) after reaction. Further mechanistic studies indicated that the COF shell not only provided chemisorption sites for U(VI) to decrease the activation energy of U(VI) reduction, but also formed a strong built-in electric field at the interface with the CdS core to promote the carrier separation. More importantly, for all CdS@COF-X, CdS-COF-10 with appropriate COF shell content balanced the crystallinity, interfacial contact integrity, light absorption of CdS core, and number of U(VI) chemisorption sites, achieving the highest carrier separation efficiency and U(VI) photoreduction performance.

The efficient capture and immobilization of radioiodine (I–, IO3–) is of great importance in radioactive waste management. Here, a Cu2O-loaded three-dimensional bulk cationic hydrogel composite (Cu2O@CH) was successfully prepared by simple redox reactions and UV photopolymerization, which realized the rapid enrichment and efficient immobilization of I–and IO3–. The adsorption experiments showed that the maximum adsorption capacity of Cu2O@CH for I–in the solution at pH = 3 reached 416.5 mg/g, and the adsorption capacity of IO3–in the solution at pH = 6 could reach 313.4 mg/g. It exhibited extremely fast adsorption kinetics for I–and IO3–. In addition, Cu2O@CH also exhibited efficient I–and IO3–removal from simulated high-level liquid waste. The rapid capture and effective immobilization of radioiodine (I–, IO3–) were realized by the electrostatic interaction of −N+(CH3)3groups in Cu2O@CH with I–and IO3–, as well as the chemical reactions between Cu2O and I–. The bulk cationic hydrogel composite explored the multifunctional role toward fast, high adsorption capability and easy handling, highlighting its superiority compared to the powder adsorbent, which renders it a potential adsorbent for the removal of radioactive iodine (I–, IO3–) in nuclear wastewater treatment.

This paper focuses on the internal flow development of an S-shaped diffusing duct. Based on the experimental result of total pressure recovery coefficient, the Reynolds stress model (RSM) was selected as a suitable turbulence model for the present study. The numerical results show that 6.2 times the area ratio of the exit to inlet and the aggressive deflection at the first bend of the lower wall lead to strong adverse pressure gradient and very large flow separation, and create a pair of counter-rotating vortices along streamwise direction. The duct diffuser efficiency is 72.37 %. The area-averaged total pressure recovery coefficient at the exit of the duct is 0.9814, and the synthesis distortion index DC (60) is 0.7081.

In non-high-risk individuals, risk-category-based atherosclerotic cardiovascular disease (ASCVD) screening strategies may be more cost-effective than one-size-fits-all approaches. However, current decisions are constrained by a lack of research evidence. We aimed to explore appropriate risk-category-based screening interval strategies for non-high-risk individuals in ASCVD primary prevention in the Chinese population.

Dietary shifts from staples toward meats, fruits, and vegetables increase environmental impacts. Excessive red meat intake and micro-nutrient deficiencies also raise health concerns. Previous research examined environmental and health consequences of alternative diets but overlooked impacts on air pollution and land use change. Here we examine implications of four potential Chinese dietary shifts on ammonia and particulate matter (PM2.5) air pollution, greenhouse gas (GHG) emissions, carbon storage loss associated with land-use change, water use, and human health. We show that a diet that replaces red meat with soy benefits the environment and avoids 57,000 PM2.5-related premature deaths annually. Dietary health benefits, however, appear larger with adoption of the Chinese Dietary Guideline (CDG) and EAT-Lancet diets, which prevent over one million premature deaths annually. However, both diets increase water use and GHGs. CDG also increases land use change, but EAT-Lancet reduces it by cutting dairy and red meat. Complex benefits and trade-offs of dietary shifts emphasize the need for further improvements in agricultural management to enable larger health-environment co-benefits.

Not only a critical matter in the nuclear fuel cycle but uranium is also a global contaminant with both radioactive and chemical toxicity. Reducing soluble hexavalent uranium [U(VI)] to relatively nonimmigrated tetravalent uranium [U(IV)] by photocatalytic technologies is recognized as a highly promising strategy for avoiding environmental pollution and re-extracting uranium resources from nuclear wastewater. Herein, we have designed a heterojunction photocatalyst constructed from the carbon aerogels (CA) and the CdS nanoflowers with an S-vacancy (CA@CdS-SV). With the S-vacancy and heterojunction being synergized, the U(VI) removal rate exceeded 97% in 40 min without the addition of any sacrificial agents. As impacted by the synergistic effects of the S-vacancy and heterojunction, thermodynamics and kinetics revealed that photogenerated electrons were first captured via shallow traps generated by vacancies on CdS-SV and then transferred to the CA surfaces through the heterojunction to realize the spatial separation of carriers, thereby achieving a satisfactory performance. This work is considered to underpin the improvement of U(VI) immobilization by exploiting the synergistic effect of vacancy engineering and the Schottky heterojunction from the perspective of thermodynamics and kinetics.

Existing malware detectors on safety-critical devices have difficulties in runtime detection due to the performance overhead. In this article, we introduce Propedeutica, a framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) techniques. In Propedeutica, all software start executions are considered as benign and monitored by a conventional ML classifier for fast detection. If the software receives a borderline classification from the ML detector (e.g., the software is 50% likely to be benign and 50% likely to be malicious), the software will be transferred to a more accurate, yet performance demanding DL detector. To address spatial–temporal dynamics and software execution heterogeneity, we introduce a novel DL architecture (DeepMalware) for Propedeutica with multistream inputs. We evaluated Propedeutica with 9115 malware samples and 1338 benign software from various categories for the Windows OS. With a borderline interval of [30%, 70%], Propedeutica achieves an accuracy of 94.34% and a false-positive rate of 8.75%, with 41.45% of the samples moved for DeepMalware analysis. Even using only CPU, Propedeutica can detect malware within less than 0.1 s.

Graphene-based samples have shown a plethora of exotic characteristics and these properties may help the realization of a new generation of fast electronic devices. However, graphene’s centrosymmetry prohibits second-order electronic transport. Here, we show giant second-order nonlinear transports in graphene moiré superlattices at zero magnetic field, both longitudinal and transverse to the applied current direction. High carrier mobility and inversion symmetry breaking by hexagonal boron nitride lead to nonlinear conductivities five orders of magnitude larger than those in WTe2. The nonlinear conductivity strongly depends on the gate voltage as well as on the stacking configuration, with a giant enhancement originating from the moiré bands. Longitudinal nonlinear conductivity cannot originate from Berry curvature dipoles. Our theoretical modelling highlights skew scattering of chiral Bloch electrons as the physical origin. With these results, we demonstrate nonlinear charge transport due to valley-contrasting chirality, which constitutes an alternative means to induce second-order transports in van der Waals heterostructures. Our approach is promising for applications in frequency-doubling and energy harvesting via rectification.

Hospitals in China are classified into tiers (1, 2 or 3), with the largest (tier 3) having more equipment and specialist staff. Differential health insurance cost-sharing by hospital tier (lower deductibles and higher reimbursement rates in lower tiers) was introduced to reduce overcrowding in higher tier hospitals, promote use of lower tier hospitals, and limit escalating healthcare costs. However, little is known about the effects of differential cost-sharing in health insurance schemes on choice of hospital tiers.

TiO2has been intensively investigated as an anode material for lithium-ion batteries (LIBs) in 1.0–3.0 V (vsLi+/Li). However, it is a challenge to realize its theoretical capacity (336 mAh g–1) in this limited potential range. Extending the potential range below 1.0 V would increase its capacity but usually at the expense of its cyclic stability owing to the sluggish ionic diffusion and unsatisfactory structural stability. Here, three-dimensional (3D) macroporous TiO2microspheres with interconnected pores and nanocrystalline thin walls have been constructed through a scalable template-assisted spray drying method to overcome these obstacles. When applied to LIBs, high and stable discharge capacity (300 mAh g–1at 0.1 A g–1) as well as superior cyclic stability (242 mAh g–1after 1000 cycles at 1.0 A g–1) can be achieved under deep discharging/charging conditions (0.01–3.0 V vsLi+/Li). Furthermore, the 3D macroporous structure is well preserved under deep discharging/charging and the in situX-ray diffraction (XRD) patterns and Raman spectra reveal the dominant pseudocapacitive contribution at low potentials (0.01–1.0 V). This work not only develops a facile method to synthesize macroporous metal oxides but also provides insight into the lithium storage mechanism of TiO2under deep discharging/charging conditions.

Improvements in the thermoelectric performance of n-type Bi2Te3materials to more closely match their p-type counterparts are critical to promote the continued development of bismuth telluride thermoelectric devices. Here the unconventional heteroatom dopant, niobium, has been employed as a donor in Bi2Te3. Nb substitutes for Bi in the rhombohedral Bi2Te3structure and exhibits multiple roles in its modulation of electrical transport and defect-induced phonon scattering. The carrier concentration is significantly increased as electrons are afforded by aliovalent doping and formation of vacancies on the Te sites. In addition, incorporation of Nb in the pseudoternary Bi2–xNbxTe3−δsystem increases the effective mass, m*, which is consistent with cases of “conventional” elemental doping in Bi2Te3. Lastly, inclusion of Nb induces both point and extended defects (tellurium vacancies and dislocations, respectively), enhancing phonon scattering and reducing the thermal conductivity. As a result, an optimum zTof 0.94 was achieved in n-type Bi0.92Nb0.08Te3at 505 K, which is dramatically higher than an equivalent undoped Bi2Te3sample. This study suggests not only that is Nb an exciting and novel electron dopant for the Bi2Te3system but also that unconventional dopants might be utilized with similar effects in other chalcogenide thermoelectrics.

Background: SnapECG is a new handheld single-lead electrocardiograph (ECG) device used for arrhythmia screening, it is widely used in clinical practice but not in primary care. Aims: To evaluate the arrhythmia screening value of SnapECG among a community-based population. Methods: A cross-sectional community-based study of multistage stratified cluster sampling was conducted from March 1st to April 30th 2019. The sensitivities, specificities and the area under the receiver operating characteristic (AUCROC) curves of the SnapECG and reference 12-lead ECG on arrhythmia were calculated in three age-groups [50–64 years, 65–74 years, and over-75 years]. Results: A total of 2263 participants took part in the arrhythmia screening, these included 1479 aged 50–64 years, 602 aged 65–74 years and 182 aged over-75 years. The SnapECG categorized 1828 (80.8%) as sinus rhythm, 161 (7.1%) as premature atrial/ventricular contractions (PAVs/PCVs), 32 (1.4%) as possible atrial fibrillation (AF), 56 (2.5%) as supraventricular tachycardias or sinus bradycardia (SVT/SB) and 186 (8.2%) as unreadable. SnapECG had 89% sensitivity (95% CI 0.52–1.00) and 99% specificity (95% CI 0.97–0.99) of detecting AF in the 65–74 years age-group. The AUCROCto detect AF was 0.94 for the 65–74 years age-group, 0.77 for over-75 years, 0.62 for the 50–64 years. Discussion: This study is the first community screening application of SnapECG. Main limitation is the SnapECG and the 12-lead ECG were not done simultaneously. Conclusions: In the people aged 65–74 years, AF can be detected accurately by the SnapECG with high sensitivity, specificity and large area under the ROC curve, which might have the highest screening predictive accuracy.

An intrinsic high‐barrier polyimide (2,7‐CPAPPI) containing rigid planar carbazole moiety and amide group in main chain was prepared. The 2,7‐CPAPPI shows very attractive barrier performances, possessing water vapor transmission rate (WVTR) and oxygen transmission rate (OTR) low to 0.04 g m−2day−1and 0.11 cm3m−2day−1, respectively. Meanwhile, 2,7‐CPAPPI also displays exceptional thermal stability with a glass transition temperature (Tg) of 552°C and coefficient of thermal expansion (CTE) of 15.48 ppm/K. The barrier performances of 2,7‐CPAPPI are compared with those of a structural analog (2,7‐CPPI, containing only carbazole moiety in the main chain) and a typical polyimide (Kapton). The structure–barrier performances relationship was investigated by molecular simulations, wide angle X‐ray diffraction (WAXD), and positron annihilation lifetime spectroscopy (PALS). The results show that 2,7‐CPAPPI has more number of intermolecular hydrogen bonds among the three PIs, which leads to close chain packing and thereby high crystallinity, low free volume, and poor chains mobility. That is, the high crystallinity and low free volume of 2,7‐CPAPPI decrease the diffusion and solubility of gases. Meanwhile, the poor chains mobility further reduces the gases diffusion. The decreased diffusion and solubility of gases consequently promote the improvement of barrier properties for 2,7‐CPAPPI. An intrinsic high‐barrier polyimide (2,7‐CPAPPI) has been prepared through the polycondensation of pyromellitic dianhydride (PMDA) with a novel diamine (2,7‐CPAPDA), which contains rigid planar carbazole moiety and amide group. With the introduction of carbazole moiety and amide group into the main chain of 2,7‐CPAPPI, the crystallinity increases, and the free volume and chains mobility reduce. Those factors lead to the decrease of gases diffusion and solubility in the PI matrix and consequently improve the barrier properties.

Graphitic carbon nitride (g-C3N4) nanotubes were produced by using salicylic acid-mediated melamine successfully. The obtained long g-C3N4nanotube possesses a large specific surface area and a parallel channel structure. Salicylic acid and its decomposition products present in g-C3N4influenced its formation process and inhibited the crystal growth of g-C3N4. A possible mechanism is proposed: Salicylic acid and its decomposition products facilitated the formation of g-C3N4nanosheet, and then the nanosheet coiled to form a nanotube. The tube-like structure facilitates the increase in photocatalytic activities, which are 6.7 and 2.4 times that of pristine g-C3N4in 2-propanol decomposition and CO2photoreduction, respectively. The enhanced photocatalytic performance was contributed by the large specific surface area, better photogenerated charge carrier transmission, and the porous nanotube structure. This research provides an easy synthetic method for the large-scale production of g-C3N4nanotubes applied in the photocatalytic field.