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The selective separation of MoS42–and WO42–using quaternary ammonium salt through solvent extraction or ion exchange methods has been well-established in the metallurgical industry. However, the conventional electrostatic adsorption theory falls short in explaining the separation mechanism. Through first-principles density functional theory (DFT) calculations and newly self-developed deep potential molecular dynamics (DPMD) simulation method, our work first reveals that the disparity in hydration structures of MoS42–and WO42–plays a crucial role in their selective separation. It is proposed that MoS42–and WO42–anions undergo hydration to form [MoS4(H2O)n]2–and [WO4(H2O)n]2–, respectively, facilitated by hydrogen bond (H-bond) interactions. Emphasis is placed on the discrepancy between MoS42–and WO42–in hydration structures by the hydration energy, Hirshfeld charge, evaluation of weak interactions, hydration radius, hydration coordination number, and H-bonds distribution. MoS42–presents a larger first hydration radius and a lower first hydration coordination number due to weaker interactions with H2O, while WO42–is subjected to enhanced hydration shielding, resulting in MoS42–anions being more susceptible to be selectively separated by a quaternary ammonium salt. This insight paves the way for the selective separation of MoS42–and WO42–, further bridging the gap between theory and industry applications.

Mercury nanoparticles are abundant in natural environments. Yet, understanding their contribution to global biogeochemical cycling of mercury remains elusive. Here, we show that microbial transformation of nanoparticulate divalent mercury can be an important source of elemental and methylmercury.Geobacter sulfurreducensPCA, a model bacterium predominant in anoxic environments (e.g., paddy soils), simultaneously reduces and methylates nanoparticulate Hg(II). Moreover, the relative prevalence of these two competing processes and the dominant transformation pathways differ markedly between nanoparticulate Hg(II) and its dissolved and bulk-sized counterparts. Notably, even when intracellular reduction of Hg(II) nanoparticles is constrained by cross-membrane transport (a rate-limiting step that also regulates methylation), the overall Hg(0) formation remains substantial due to extracellular electron transfer. With multiple lines of evidence based on microscopic and electrochemical analyses, gene knockout experiments, and theoretical calculations, we show that nanoparticulate Hg(II) is preferentially associated with c-type cytochromes on cell membranes and has a higher propensity for accepting electrons from the heme groups than adsorbed ionic Hg(II), which explains the surprisingly larger extent of reduction of nanoparticles than dissolved Hg(II) at relatively high mercury loadings. These findings have important implications for the assessment of global mercury budgets as well as the bioavailability of nanominerals and mineral nanoparticles.

Mn-N4sites on hierarchical porous N-doped carbon as an electrocatalyst for Li-O2battery can optimize the formation and decomposition pathway of Li2O2, and exhibits the excellent electrochemical performance.

The effect of initial grain size prior to cold rolling on the microstructure, texture evolution, and magnetic properties of Fe-3.5 wt% Si non-oriented electrical steel (NOES) was investigated through the introduction of normalization. There were massive intragranular shear bands in {111}<110> and {111}<112> deformed grains in the cold-rolled sheet with large initial grain size, providing nucleation sites for new recrystallized grains with Goss ({110}<001>), λ-fiber, γ-fiber, and {113}<631> components. Subsequent annealing resulted in weaker γ-fiber ({111}<110>−{111}<112>), stronger α*-fiber concentrated at {113}<631>, along with Goss and λ-fiber ({100}<001>−{100}<012>) textures. In addition, appropriate final annealing temperature effectively reduced iron loss. The simultaneous optimization of texture and grain size by normalization and annealing treatments led to improved magnetic properties, with the superior magnetic induction (B50 = 1.719) T, and the lowest core loss (P15/50 = 2.69 W/kg, P10/400 = 16.337 W/kg).

This paper provides a simulation model for the electronic paper display device, the model can quickly and efficiently simulate the initial steady state of the electronic ink system, thus improving the efficiency of device development. It mainly includes the following contents: Obtaining the parameters of the model, then the built‐in electric field strength is calculated through the model parameter information and the pre‐ constructed built‐in electric field model. The motion of each charged particle is simulated to obtain the simulation relationship between the display gray scale and the applied electric field force. Finally, it make various charged particles reach the force equilibrium state, so as to realize the simulation of the voltage‐gray scale relationship of electronic ink system.

Gallic acid (GA) is an organic compound with phenolic properties that occurs naturally and can be found in Guizhi Fuling capsules, showcasing a wide range of biological functionalities.

Pilots’ excessive mental workload could reduce their ability to perform concurrent tasks during emergency flights, which is one of the most critical aviation safety concerns. Several past efforts have attempted to investigate the underlying issues, but all had limited success owing to the challenge of collecting representative data under realistic operating conditions. This study aimed to address this challenge by conducting a flight simulator study involving a comparatively large number of participants, who were pilot cadets with flight experience, and using noninvasive functional near-infrared spectroscopy (fNIRS) to collect the pilots’ brain activity data. Pilots’ subjective ratings and brain activity records were collected over a total of 75 simulated flights under three subtask scenarios comprising different equipment failures. A statistical analysis was carried out on the subjective ratings and on the changes observed in the saturation of the oxyhemoglobin (ΔOxyHb) of individual fNIRS channels. The mental workload of the pilots was classified using a support vector machine hierarchical combination classifier, focusing on the question of whether it is feasible to classify pilots’ mental workload using brain activity signals (i.e., ΔOxyHb). The results suggested that the pilots’ mental workload levels were highly associated with the ΔOxyHb measures as well as with the activities of different brain regions, including the prefrontal-, motor-, and occipital cortex. The findings from this study could provide a reference for optimizing pilot training systems and improving pilot performance during emergency flight operations.

In recent years, generative artificial intelligence has been developing rapidly. In the image domain, image generation models based on deep learning have made remarkable achievements. Early frameworks for image generation models were dominated by generative adversarial networks (GANs) and variational autoencoders (VAEs). Nowadays, large-scale generative models based on diffusion models have become mainstream, and the quality of their generated images is significantly improved. We will review the research and development of image generation models and delve into the significant progress made in the field in recent years. Initially, we revisit the development of traditional image generation models like GANs and VAEs, emphasizing their contributions and challenges. We also introduce diffusion models, which have received much attention in the field of image generation due to their unique generative process and excellent generative performance. Subsequently, we emphasized the large vision models with SAM as the focal point. We also pay special attention to large-scale generative models like Stable Diffusion, which have demonstrated unprecedented capabilities in high-quality image generation tasks. Additionally, we explore target models and respective fine-tuning methods for domain-oriented image generation tasks, predicts future directions in image generation, and proposes potential research focuses and challenges.

Directly performing operations on compressed data has been proven to be a big success facing Big Data problems in modern data management systems. These systems have demonstrated significant compression benefits and performance improvement for data analytics applications. However, current systems only focus on data queries, while a complete Big Data system must support both data query and data manipulation. To solve this problem, we develop CompressDB, which is a new storage engine that can support data processing for databases without decompression. CompressDB has the following advantages. First, CompressDB utilizes context-free grammar to compress data, and supports both data query and data manipulation. Second, for adaptability, we integrate CompressDB to file systems so that a wide range of databases can directly use CompressDB without any change. Third, we enable operation pushdown to storage so that we can perform data query and manipulation in storage systems without bringing large data to memory for high efficiency. We validate the efficacy of CompressDB supporting various kinds of database systems, including SQLite, MySQL, LevelDB, MongoDB, ClickHouse, and Neo4j. We evaluate our method using seven real-world datasets with various lengths, structures, and content in both single node and cluster environments. Experiments show that CompressDB achieves 40% throughput improvement and 44% latency reduction, along with 1.75 compression ratio on average.

We propose and study numerically a new deep ridge InGaAlAs/InP multi-quantum well (MQW) transistor lasers (TL), in which an n-doped InAlAs layer is inserted below the MQW layer in the device. High current gain can be obtained at the same time of laser operation, in contrast to the notable gain decreasing after lasing threshold in previous TLs. Because of the n-InAlAs layer, a normal graded index separate confinement heterostructure (GRIN-SCH) which helps to enhance the device modulation performance can be adopted. Thus, high quality electrical drivers can be expected to be integrated with high speed lasers with this InGaAlAs/InP TL structure.

Human activity recognition using sensor data has become a research hotspot in the field of ubiquitous computing and has a wide range of application scenarios in real life. Effectively extracting and fusing the rich spatiotemporal information contained in the sensor data from various sensors and multiple wearable devices is a challenging task. Moreover, for practical deployment applications, the limited computational and storage resources of edge devices also impose requirements on the model size and inference speed. Hence, we propose STFNet, an enhanced and lightweight spatiotemporal fusion network for wearable human activity recognition (WHAR), in which we innovatively divide the extraction and fusion process of spatiotemporal features into three stages, including sensing feature extraction (SFE), temporal feature extraction (TFE), and channel fusion (CF), to learn incrementally and reduce information loss. We first extract shallow intrachannel features and learn local interchannel information using a lightweight local attention mechanism. Then, the learned features are fed into a hierarchical convolution structure with residual connections to extract multiscale and comprehensive temporal features. Finally, we use graph convolutions to further fuse the deep interchannel information. By replacing the conventional global attention mechanism with a local one and using hierarchical convolution to replace dilated or multikernel convolution, STFNet achieves the purpose of lightweight design. In the experiments, STFNet performs better on public datasets than the selected WHAR models published in recent years and demonstrates its lightweight characteristics on edge device deployment.

As a vitamin E isomer, δ-tocotrienol has attracted much attention owing to its rich biological activities for human health, especially anticancer activities. Microbial biosynthesis constitutes a promising strategy of δ-tocotrienol production owing to its economic and environmental advantages. In this study, we employed modular engineering strategies to reconstruct and optimize a de novo δ-tocotrienol biosynthesis pathway in Saccharomyces cerevisiae. Subsequently, the rate-limiting steps were identified and eliminated and the key enzymes were assembled in the δ-tocotrienol biosynthesis module to develop a substrate channeling and improve their catalytic efficiency. Furthermore, the shikimate and δ-tocotrienol biosynthesis modules were optimized via combination strategies to further increase δ-tocotrienol production, following which the δ-tocotrienol titer reached 1455.5 μg/L. Finally, overexpression of the endogenous transporter PDR11 and two-phase extraction fermentation were employed for δ-tocotrienol production, yielding up to 3262.2 μg/L δ-tocotrienol. Thus, the findings of this study demonstrate the possibility of efficient δ-tocotrienol biosynthesis in S. cerevisiae.

Flicker noise has become a critical issue in advanced FinFET technology with the rapid growth of analog/mixed-signal circuits. Due to the increasing variation of flicker noise, the applicability of conventional carrier and mobility fluctuation model faces challenges in the aspects of bias, frequency, and size dependences. In this article, we propose a method to separate the intertwined noise sources. Based on this method, the sources of flicker noise can be well clarified, enabling the modeling of different components independently. Considering the inelastic (de)trapping, quasi-ballistic transport, and access resistance (AR), the modeling method and feature are discussed. It is observed that ignoring noise sources other than oxide traps (OTs) can result in an underestimation of approximately 60% under operating conditions. In addition, the contribution of the OTs depends on the energy distribution of different types of traps. Their origins are also clarified by different characterization techniques and ab initio calculation. Therefore, this work is helpful to the variability-aware circuit design in advanced technology nodes.

Simulation-based Medical Education (SBME) has been developed as a cost-effective means of enhancing the diagnostic skills of novice physicians and interns, thereby mitigating the need for resource-intensive mentor-apprentice training. However, feedback provided in most SBME is often directed towards improving the operational proficiency of learners, rather than providing summative medical diagnoses that result from experience and time. Additionally, the multimodal nature of medical data during diagnosis poses significant challenges for interns and novice physicians, including the tendency to overlook or over-rely on data from certain modalities, and difficulties in comprehending potential associations between modalities. To address these challenges, we present DiagnosisAssistant, a visual analytics system that leverages historical medical records as a proxy for multimodal modeling and visualization to enhance the learning experience of interns and novice physicians. The system employs elaborately designed visualizations to explore different modality data, offer diagnostic interpretive hints based on the constructed model, and enable comparative analyses of specific patients. Our approach is validated through two case studies and expert interviews, demonstrating its effectiveness in enhancing medical training.

Recently, metric-based few-shot learning (FSL) methods have achieved good performance in the hyperspectral image (HSI) classification. However, existing methods suffer from two problems: over-reliance on image modality information leads to inaccurate prototype representation, where a prototype refers to the centroid of each class in the dataset, and the impact of redundant and noisy pixels on model discriminability is rarely considered. These problems result in insufficient discriminability of the model for the target domain. To address the above issues, we propose a cross-domain few-shot HSI classification framework with cross-modal alignment and supervised contrastive learning (CDFS-CASCL). It is well known that human visual learning greatly benefits from the input of various modal information such as vision, language, and video. Inspired by the way humans abstract image class concepts in language form and understand the essence of classes, we perform cross-modal alignment (CA) between similar image and text prototypes and use abstract text semantics to guide the model to learn semantic-related features with good generalization ability in images, so as to improve the accuracy of image prototypes representation of the prototypes. In addition, through supervised contrastive learning (SCL) based on neighborhood pixel mask in the target domain, the enhanced sample features belonging to the same class are closer, while the enhanced sample features belonging to different classes are pulled further, enabling the model to learn mask-robust discriminative feature representations, suppressing the negative impact of redundant and noisy pixels, and improving the model’s discriminability. The experimental results demonstrate the superiority of the proposed CDFS-CASCL. The code is available at https://github.com/Li-ZK/CDFS-CASCL-2024.

Hopping cyclic codes (HCCs) are (non-linear) cyclic codes with the additional property that the $n$ cyclic shifts of every given codeword are all distinct, where $n$ is the code length. Optical orthogonal codes (OOCs) are constructed from constant weight binary HCCs by picking exactly one member from the $n$ cyclic shifts of every codeword. HCCs and OOCs have various practical applications and have been studied extensively over the years. In this paper, we present improved Gilbert-Varshamov type lower bounds on the size of both codes, when the minimum distance is bounded below by a linear factor of the code length. For HCCs, we improve the previously best known lower bound of Niu, Xing, and Yuan by a multiplicative linear factor of the code length. For OOCs, we improve the previously best known lower bound of Chung, Salehi, and Wei, and Yang and Fuja also by a multiplicative linear factor of the code length. Our proofs are based on tools from probability theory and graph theory, in particular the McDiarmid’s inequality on the concentration of Lipschitz functions and the independence number of locally sparse graphs.

Defect passivation is crucial to enhancing the performance of perovskite solar cells (PSCs). In this study, we successfully synthesized a novel organic compound named DPPO, which consists of a double phosphonate group. Subsequently, we incorporated DPPO into a perovskite solution. The presence of a P═O group interacting with undercoordinated Pb2+yielded a perovskite film of superior crystallinity, greater crystal orientation, and smoother surface. Additionally, the addition of DPPO can passivate defect states and enhance upper layer energy level alignment, which will improve carrier extraction and prevent nonradiative recombination. Consequently, an impressive champion efficiency of 24.24% was achieved with a minimized hysteresis. Furthermore, the DPPO-modified PSCs exhibit enhanced durability when exposed to ambient conditions, maintaining 95% of the initial efficiency for 1920 h at an average relative humidity (RH) of 30%.

Since the rapid development of additive manufacturing, it has been urgent to figure out the mechanism and performance of the welding between additively and traditionally prepared samples. In this paper, the plate samples of 420 stainless steel (420SS) with the thickness of 2 mm were fabricated by selective laser melting (SLM) and rolling methods, respectively. The two kinds of plates were joined by CO2laser welding with the power ranging from 1.0 to 1.3 kW, and the mechanical properties, microstructures, and cross-sectional morphologies of the welds were investigated. The average tensile strength of different laser welding powers were approximately 472.12 ± 16 MPa for 1.0 kW, 500.62 ± 7 MPa for 1.1 kW, 502.09 ± 5 MPa for 1.2 kW, and 501.87 ± 4 MPa for 1.3 kW, respectively. The grains in the heat-affected zone become coarser with increasing input laser power. Interestingly, such grain coarsening of the SLM prepared part is much more limited than that of the rolled part, suggesting the better welding performance given by the SLM technology. Different from the rolled samples, SLM processing leads to much narrower heat-affected zones. The weldability between SLM formed and rolled plates is acceptable.

Pyrite, as a disturbing gangue mineral in the beneficiation of valuable sulfide minerals and coal resources, is usually required to be depressed for floating in flotation practice. Specifically, the depression of pyrite is achieved by causing its surface to be hydrophilic with the assistance of depressants, normally with inexpensive lime used. Accordingly, the progressive hydrophilic processes of the pyrite surface in high-alkaline lime systems were studied in detail using density functional theory (DFT) calculations in this work. The calculation results suggested that the pyrite surface is prone to hydroxylation in the high-alkaline lime system, and the hydroxylation behavior of the pyrite surface is beneficial to the adsorption of monohydroxy calcium species in thermodynamics. Adsorbed monohydroxy calcium on the hydroxylated pyrite surface can further adsorb water molecules. Meanwhile, the adsorbed water molecules form a complex hydrogen-bonding network structure with each other and with the hydroxylated pyrite surface, which makes the pyrite surface further hydrophilic. Eventually, with the adsorption of water molecules, the adsorbed calcium (Ca) cation on the hydroxylated pyrite surface will complete its coordination shell surrounded by six ligand oxygens, which leads to the formation of a hydrophilic hydrated calcium film on the pyrite surface, thus achieving the hydrophilization of pyrite.

Single nucleotide polymorphism (SNP) genotyping arrays provide an optimal high-throughput platform for genetic research and molecular breeding programs in both animals and plants. In this study, a high-quality and custom-designed Rice3K56 SNP array was developed with the resequencing data of 3024 rice accessions worldwide, which was then tested extensively in 192 representative rice samples. Printed on the GeneTitan chips of Affymetrix Axiom each containing 56,606 SNP markers, the Rice3K56 array has a high genotyping reliability (99.6%), high and uniform genome coverage (an average of 6.7-kb between adjacent SNPs), abundant polymorphic information and easy automation, compared with previously developed rice SNP arrays. When applied in rice varietal differentiation, population diversity analysis, gene mapping of 13 complex traits by a genome-wide association study analysis (GWAS), and genome selection experiments in a recombinant inbred line and a multi-parent advanced generation inter-cross populations, these properties of the Rice3K56 array were well demonstrated for its power and great potential to be a highly efficient tool for rice genetic research and genomic breeding.

Due to the increasing demand for microbially manufactured products in various industries, it has become important to find optimal designs for microbial cell factories by changing the direction of metabolic flow and its flux size by means of metabolic engineering such as knocking out competing pathways and introducing exogenous pathways to increase the yield of desired products. Recently, with the gradual cross-fertilization between computer science and bioinformatics fields, machine learning and intelligent optimization-based approaches have received much attention in Genome-scale metabolic network models (GSMMs) based on constrained optimization methods, and many high-quality related works have been published. Therefore, this paper focuses on the advances and applications of machine learning and intelligent optimization algorithms in metabolic engineering, with special emphasis on GSMMs. Specifically, the development history of GSMMs is first reviewed. Then, the analysis methods of GSMMs based on constraint optimization are presented. Next, this paper mainly reviews the development and application of machine learning and intelligent optimization algorithms in genome-scale metabolic models. In addition, the research gaps and future research potential in machine learning and intelligent optimization methods applied in GSMMs are discussed.

Purpose: Endogenous hyperinsulinemic hypoglycemia (EHH) is an uncommon disease characterized by inappropriately high plasma insulin levels despite low plasma glucose levels. Some rare etiologies can lead to EHH. Correct diagnosis is a prerequisite for treatment. Hence, although challenging, it is crucial for patients with EHH to identify the different causes. Methods: We describe a case series of three patients, all of whom had obvious hypoglycemic symptoms and extraordinary hyperinsulinemia. Their plasma glucose, insulin, and C-peptide levels were tested simultaneously when hypoglycemia occurred. Moreover, other biochemical indices and relevant antibody levels were measured and imaging examinations were conducted. Results: According to their medical history, physical examination, laboratory results, and imaging findings, the three patients were diagnosed with insulinoma, type B insulin resistance syndrome, and insulin autoimmune syndrome. After precise treatments, hypoglycemia was ultimately eliminated. Conclusion: Although these diseases have similar symptoms and biochemical abnormalities, the treatment and prognosis are different. The case series presented here highlights the challenges in the differential diagnosis of EHH. An accurate diagnosis is necessary for hypoglycemia treatment.

Quartz, a common inorganic nonmetallic mineral, is usually removed or purified by beneficiation, normally flotation. Given the strong polarity of the quartz surface, it is easy to hydrate to form a hydroxylation layer, which makes it impossible to float quartz with sodium oleate (OL) used alone. An ideal flotation method for quartz is preactivation with Ca2+, followed by collection with OL. Herein, the effects of surface hydroxylation on the adsorption of the anionic collector OL on the quartz surface before and after Ca2+activation are systematically investigated by density functional theory (DFT) calculations. The results show that the displacement adsorption of surface hydroxyl substituted by OL–is not feasible in thermodynamics, and the OL–can only bind to the H atoms of the hydroxylated quartz surface via hydrogen bonds, namely, hydrogen binding adsorption. Due to the electrostatic repulsion and steric hindrance effect induced by the surface hydroxylation structure, the adsorption ability of OL–on the quartz surface mediated by hydroxyl bridges is very weak, which is insufficient to realize quartz floating. However, Ca2+ions are easily adsorbed on the hydroxylated quartz surface, providing favorable active sites for subsequent adsorption of OL–, thus becoming a credible solution for the industrial flotation of the strong hydrophilic mineral quartz. These findings shed some new insights for accurately understanding the flotation mechanism of strongly hydrophilic oxide minerals and are beneficial to promoting the development of mineral flotation fundamentals.

3D printed silicones have demonstrated great potential in diverse areas by combining the advantageous physiochemical properties of silicones with the unparalleled design freedom of additive manufacturing. However, their low-temperature performance, which is of particular importance for polar and space applications, has not been addressed. Herein, a 3D printed silicone foam with unprecedented low-temperature elasticity is presented, which is featured with extraordinary fatigue resistance, excellent shape recovery, and energy-absorbing capability down to a low temperature of −60 °C after extreme compression (an intensive load of over 66000 times its own weight). The foam is achieved by direct writing of a phenyl silicone-based pseudoplastic ink embedded with sodium chloride as sacrificial template. During the water immersion process to create pores in the printed filaments, a unique osmotic pressure-driven shape morphing strategy is also reported, which offers an attractive alternative to traditional 4D printed hydrogels in virtue of the favorable mechanical robustness of the silicone material. The underlying mechanisms for shape morphing and low-temperature elasticity are discussed in detail.

Promotions are commonly used by e-commerce merchants to boost sales. The efficacy of different promotion strategies can help sellers adapt their offering to customer demand in order to survive and thrive. Current approaches to designing promotion strategies are either based on econometrics, which may not scale to large amounts of sales data, or are spontaneous and provide little explanation of sales volume. Moreover, accurately measuring the effects of promotion designs and making bootstrappable adjustments accordingly remains a challenge due to the incompleteness and complexity of the information describing promotion strategies and their market environments. We present PromotionLens, a visual analytics system for exploring, comparing, and modeling the impact of various promotion strategies. Our approach combines representative multivariant time-series forecasting models and well-designed visualizations to demonstrate and explain the impact of sales and promotional factors, and to support “what-if” analysis of promotions. Two case studies, expert feedback, and a qualitative user study demonstrate the efficacy of PromotionLens.

Topological supramolecular polymers are responsible for design of innovative materials with unique physical properties but remain a challenging task to prepare by means of supramolecular polymerization. In this contribution, we present a novel method of region-confined amphiphilic supramolecular polymerization (RASP) in a controllable two-step self-organization pathway, which was certified by a new type of pyridine-oxadiazole alternating 48-membered macrocycles with structurally regional distribution of distinct self-assembling groups that can self-organize into circular supramolecular architectures. Meanwhile, water molecule plays a crucial role in RASP, and the water content in nonpolar solvent chloroform is sensitive to trigger controllable amphiphilic self-organization. Moreover, differing from the traditional rodlike micelles formed by self-assembly of linearly amphiphilic molecules, this approach of RASP exclusively gives rise to the formation of circularly assembled supramolecular polymers.