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To facilitate walking rehabilitation training for individuals with lower limb paralysis, a self-balancing exoskeleton robot with 12 degrees of freedom was conceived. The principal structural design was conducted in line with the biomechanics of the human lower limbs, and a kinematic model was formulated. The stipulated gait was resolved by reverse kinematics in MATLAB to derive the joint angle actuation curves. These curves served as the motive input in ADAMS kinematic simulation experiments, yielding a gait trajectory with an error margin of less than 2 mm compared to the prearranged gait, which is within a reasonable range of deviation. Experiments involving walking with the exoskeleton were also executed. The analysis of the six-axis force sensor data from the sole demonstrated that the ground reaction force curve consistently remained within the bounds of the foot’s support area, substantiating the exoskeleton’s capability for stable ambulation with a load. The simulations and walking experiments together verified the soundness of the exoskeleton’s structural design.

Soil organic matter (SOM) is an important carbon pool in terrestrial ecosystems and plays a key role in soil functions. Nevertheless, the effects of fertilization practices on the physical, chemical, biological, and comprehensive stability of SOM are still unclear. We carried out a long-term field experiment in the northeast black soil region in China with four different fertilization practices: no fertilizer (CK), single chemical fertilizer (NPK), chemical fertilizer + straw (NPKJ), and chemical fertilizer + organic manure (NPKM). The content of particulate organic matter (POM) and mineral-associated organic matter (MAOM), compound composition of SOM, carbon mineralization characteristics, active soil organic matter (ASOM), and inert soil organic matter (ISOM) were tested. The results showed that the application of fertilizers significantly increased the contents of POM and MAOM to 2.59–4.65 g kg−1 and 32.69–34.65 g kg−1 (p < 0.05), but decreased the MAOM/POM values by 37.8–42.4%, indicating reduced the physical stability of SOM. Fertilization practices increased the contents of aromatic, nitrogen-containing compounds and decreased the oxygen compounds of SOM, representing enhancement of the chemical stability. The contents of ASOM and ISOM increased in fertilization practices, while the biological stability index (BSI) under the NPKJ and NPKM treatments was lower than the CK treatment, suggesting that the biological stability decreased under the manure and straw application. In addition, the comprehensive stability of SOM increased by 26–116% through a reduction in the physical and biological stability, coupled with an increase in the chemical stability. Collectively, our study demonstrated that the application of manure and straw enhanced both the comprehensive stability and content of SOM and reduced the physical and biological stabilities while increasing the chemical stability, which made the largest contribution to the comprehensive stability.

The residues from selenium-enriched Cardamine violifolia after the extraction of protein were still rich in polysaccharides. Thus, the recovery of selenium polysaccharides (SePSs) was compared using hot water extraction and ultrasonic-assisted extraction techniques. The yield, extraction rate, purity, specific energy consumption, and content of total and organic selenium from different SePS extracts were determined. The results indicated that at conditions of 250 W (ultrasonic power), 30 °C, and a liquid-to-material ratio of 30:1 extracted for 60 min, the yield of SePSs was 3.97 ± 0.07%, the extraction rate was 22.76 ± 0.40%, and the purity was 65.56 ± 0.35%, while the total and organic selenium content was 749.16 ± 6.91 mg/kg and 628.37 ± 5.93 mg/kg, respectively. Compared to traditional hot water extraction, ultrasonic-assisted extraction significantly improves efficiency, reduces energy use, and boosts both total and organic selenium content in the extract. Measurements of particle size, molecular weight, and monosaccharide composition, along with infrared and ultraviolet spectroscopy, revealed that ultrasonic-assisted extraction breaks down long-chain structures, decreases particle size, and changes monosaccharide composition in SePSs, leading to lower molecular weight and reduced dispersity. The unique structure of SePSs, which integrates selenium with polysaccharide groups, results in markedly improved antioxidant activity and reducing power, even at low concentrations, due to the synergistic effects of selenium and polysaccharides. This study establishes a basis for using SePSs in functional foods.

The emergence of few-shot object detection provides a new approach to address the challenge of poor generalization ability due to data scarcity. Currently, extensive research has been conducted on few-shot object detection in natural scene datasets, and notable progress has been made. However, in the realm of remote sensing, this technology is still lagging behind. Furthermore, many established methods rely on two-stage detectors, prioritizing accuracy over speed, which hinders real-time applications. Considering both detection accuracy and speed, in this paper, we propose a simple few-shot object detection method based on the one-stage detector YOLOv5 with transfer learning. First, we propose a Segmentation Assistance (SA) module to guide the network’s attention toward foreground targets. This module assists in training and enhances detection accuracy without increasing inference time. Second, we design a novel detection head called the Triplet Head (Tri-Head), which employs a dual distillation mechanism to mitigate the issue of forgetting base-class knowledge. Finally, we optimize the classification loss function to emphasize challenging samples. Evaluations on the NWPUv2 and DIOR datasets showcase the method’s superiority.

Bacterial drug resistance is becoming an increasingly serious problem, and the development of antibacterial synergists is urgently needed. Combining existing antibiotics with promising nonantibiotic agents is one strategy that has been shown to be effective at overcoming the widespread emergence of antibiotic-resistant pathogens. In this study, we investigated the antibacterial activities and mechanism of naringenin (NG) combined with amikacin (AMK) against multidrug-resistant Escherichia coli (E. coli). We first measured the fractional inhibitory concentration (FIC) of NG combined with antibiotics via the checkerboard method. The results indicated that the combination of NG and AMK had a synergistic effect on E. coli ATCC 25922 and E. coli C7F3. In addition, this synergistic effect was verified by time-kill assays. Moreover, scanning electron microscopy (SEM) was used to observe cell morphology. The results showed that the cell wall of E. coli was destroyed. Furthermore, we assessed the leakage of alkaline phosphatase (AKP), K+, and protein. The extracellular AKP activity increased after the combinational group of 1/2MIC NG and 1/2MIC AMK, suggesting an impairment in cell wall permeability. An increase in the leakage of intracellular K+ and protein indicated an increase in cell inner membrane permeability. These results revealed that NG and AMK inhibited E. coli by damaging cell walls and membranes. In addition, PI uptake rapidly increased after treatment with NG and AMK. Confocal laser scanning microscopy (CLSM) revealed that NG caused cell wall and cell membrane damage in E. coli. In summary, our results provide a new strategy for responding to the development of E. coli drug resistance.

Shrews (Soricidae) are common small wild mammals. Some species of shrews, such as Asian house shrews (Suncus murinus), have a significant overlap in their habitats with humans and domestic animals. Currently, over 190 species of viruses in 32 families, including Adenoviridae, Arenaviridae, Arteriviridae, Astroviridae, Anelloviridae, Bornaviridae, Caliciviridae, Chuviridae, Coronaviridae, Filoviridae, Flaviviridae, Hantaviridae, Hepadnaviridae, Hepeviridae, Nairoviridae, Nodaviridae, Orthoherpesviridae, Orthomyxoviridae, Paramyxoviridae, Parvoviridae, Phenuiviridae, Picobirnaviridae, Picornaviridae, Polyomaviridae, Poxviridae, Rhabdoviridae, Sedoreoviridae, Spinareoviridae, and three unclassified families, have been identified in shrews. Diverse shrew viruses, such as Borna disease virus 1, Langya virus, and severe fever with thrombocytopenia syndrome virus, cause diseases in humans and/or domestic animals, posing significant threats to public health and animal health. This review compiled fundamental information about shrews and provided a comprehensive summary of the viruses that have been detected in shrews, with the aim of facilitating a deep understanding of shrews and the diversity, epidemiology, and risks of their viruses.

Nasopharyngeal carcinoma (NPC) is closely linked to Epstein–Barr virus (EBV) infection. Curcumae Rhizoma, a traditional Chinese herb, has shown antitumor effects, primarily through its component curcumol (Cur), which has been shown to reduce NPC cell invasion and migration by targeting nucleolin (NCL) and Epstein–Barr Virus Nuclear Antigen 1 (EBNA1). We constructed an EBV-positive NPC cell model using C666-1 cells and performed transcriptomics studies after treatment with curcumol, which revealed a significant enrichment of ubiquitin-mediated proteolysis, the PI3K-AKT and mTOR signaling pathways, cell cycle and apoptosis involved in tumor invasion and migration. To investigate the importance of NCL and EBNA1 in curcumol-resistant EBV-positive NPC, we performed a multi-omics study using short hairpin NCL (shNCL) and shEBNA1 EBV-positive NPC cells, and the proteomics results showed enrichment in complement and coagulation cascades and ubiquitin-mediated proteolysis signaling pathways. Here, we focused on ubiquitin-conjugating enzyme E2C (UBE2C), which plays an important role in the ubiquitin-mediated proteolysis signaling pathway. In addition, metabolomics revealed that UBE2C is highly associated with 4-Aminobutanoic acid (GABA). In vitro studies further validated the function of the key targets, suggesting that UBE2C plays an important role in NCL and EBNA1-mediated curcumol resistance to nasopharyngeal carcinoma invasion and metastasis.

Model-Based Systems Engineering (MBSE) supports the system-level design of complex products effectively. Currently, system design and optimization for complex products are two distinct processes that must be executed using different software or platforms, involving intricate data conversion processes. Applying multidisciplinary optimization to validate system optimization often necessitates remodeling the optimization objects, which is time-consuming, labor-intensive, and highly error-prone. A critical activity in systems engineering is identifying the optimal design solution for the entire system. Multidisciplinary Design Optimization (MDO) and reliability analysis are essential methods for achieving this. This paper proposes a SysML-based multidisciplinary reliability design optimization modeling method. First, by analyzing the definitions and mathematical models of multidisciplinary reliability design optimization, the SysML extension mechanism is employed to represent the optimization model based on SysML. Next, model transformation techniques are used to convert the SysML optimization model generated in the first stage into an XML description model readable by optimization solvers. Finally, the proposed method’s effectiveness is validated through an engineering case study of an in-vehicle environmental control integration system. The results demonstrate that this method fully utilizes SysML to express MDO problems, enhancing the efficiency of design optimization for complex systems. Engineers and system designers working on complex, multidisciplinary projects can particularly benefit from these advancements, as they simplify the integration of design and optimization processes, facilitating more reliable and efficient product development.

Accurately predicting wind speeds is of great significance in various engineering applications, such as the operation of high-speed trains. Machine learning models are effective in this field. However, existing studies generally provide deterministic predictions and utilize decomposition techniques in advance to enhance predictive performance, which may encounter data leakage and fail to capture the stochastic nature of wind data. This work proposes an advanced framework for the prediction and early warning of wind speeds by combining the optimized gated recurrent unit (GRU) and adaptive kernel density estimator (AKDE). Firstly, 12 samples (26,280 points each) were collected from an extensive open database. Three representative metaheuristic algorithms were then employed to optimize the parameters of diverse models, including extreme learning machines, a transformer model, and recurrent networks. The results yielded an optimal selection using the GRU and the crested porcupine optimizer. Afterwards, by using the AKDE, the joint probability density and cumulative distribution function of wind predictions and related predicting errors could be obtained. It was then applicable to calculate the conditional probability that actual wind speed exceeds the critical value, thereby providing probabilistic-based predictions in a multilevel manner. A comparison of the predictive performance of various methods and accuracy of subsequent decisions validated the proposed framework.

Reasonable dense planting is an important measure to increase crop yield per unit area and save resources. However, there is no unified view of the competition for photosynthetic radiation in different stratification structures of maize plants due to different planting densities, as well as the internal mechanism of yield increase. In order to investigate these issues, field experiments were conducted from 2021 to 2022 in Daxing, Beijing, China (39°37′ N, 116°25′ E, altitude 31.3 m a.s.l.). Field plots were arranged in a randomized block design, with the main plot factor representing plant density. In each replicate, four densities were set, i.e., 33,000 (D1), 42,000 (D2), 55,000 (D3), and 83,000 (D4) plants·ha−1. Canopy stratification structure characteristics, including leaf area index, biomass, and photosynthetically active radiation (PAR), were measured in each stratification structure, and transmitted PAR, radiation use efficiency (RUE), and light extinction coefficient (K) were calculated. We found that increasing plant density significantly increased biomass, leaf area index (LAI), and precipitation use efficiency (PUE), but the light extinction coefficient (K) and harvest index (HI) showed opposite trends. Compared to the D1 treatment, the grain yield, precipitation use efficiency (PUE), radiation use efficiency (RUE), and LAI increased by 22.6–88.2%, 9.2–50.5%, 26.7–116.9%, and 27.7 to 150.6% in the D2, D3, and D4 treatments, and K and HI decreased by 19.7–50.3% and 4.2–11.5%, respectively. These showed that a density of 83,000 plants·ha−1 was effective in promoting maize growth in the Daxing area of Beijing, China.

Characterizing the spatial distribution of particles smaller than 10 μm (PM10) is of great importance for air quality management yet is very challenging because of the sparseness of air quality monitoring stations. In this study, we use a model-agnostic meta-learning-trained artificial neural network (MAML-ANN) to estimate the concentrations of PM10 at 60 m × 60 m spatial resolution by combining satellite-derived aerosol optical depth (AOD) with meteorological data. The network is designed to regress from the predictors at a specific time to the ground-level PM10 concentration. We utilize the ANN model to capture the time-specific nonlinearity among aerosols, meteorological conditions, and PM10, and apply MAML to enable the model to learn the nonlinearity across time from only a small number of data samples. MAML is also employed to transfer the knowledge learned from coarse spatial resolution to high spatial resolution. The MAML-ANN model is shown to accurately estimate high-resolution PM10 in Beijing, with coefficient of determination of 0.75. MAML improves the PM10 estimation performance of the ANN model compared with the baseline using pre-trained initial weights. Thus, MAML-ANN has the potential to estimate particulate matter estimation at high spatial resolution over other data-sparse, heavily polluted, and small regions.

Among non-destructive testing (NDT) techniques, fast neutron radiography with a higher penetration capability has achieved rapid advancements. However, the application of the radiography detector in many fast neutron imaging systems is limited by unfavorable detection efficiency and imaging spatial resolution. In this paper, a fast neutron radiography detector was designed, which was composed of a pixelated EJ200 scintillator array, a 16 × 16 silicon photomultiplier (SiPM) array, and capacitive multiplexing network readout electronics. The main parameters of the detector were optimized using Monte Carlo simulations. In addition, the prototype of the detector was fabricated and tested under a 14 MeV D-T neutron source. The preliminary test results demonstrated that the spatial resolution of the prototype reached 1.2 mm. Moreover, the conflict between spatial resolution and detection efficiency could be mitigated by using a pixelated scintillator structure. Overall, SiPMs enabled the extensive application of the imaging system because of their excellent photon detection performance, relatively low price, and joint possibility for large areas.

DOX/TPOR4@CB[7]4 was synthesized via self-assembly, and its physicochemical properties and ability to generate reactive oxygen species (ROS) were evaluated. The impact of photodynamic therapy on SH-SY5Y cells was assessed using the MTT assay, while flow cytometry analysis was employed to detect cell apoptosis. Confocal laser scanning microscopy was utilized to observe the intracellular distribution of DOX/TPOR4@CB[7]4 in SH-SY5Y cells. Additionally, fluorescence imaging of DOX/TPOR4@CB[7]4 in nude mice bearing SH-SY5Y tumors and examination of the combined effects of photodynamic and chemical therapies were conducted. The incorporation of CB[7] significantly enhanced the optical properties of DOX/TPOR4@CB[7]4, resulting in increased ROS production and pronounced toxicity towards SH-SY5Y cells. Moreover, both the apoptotic and mortality rates exhibited significant elevation. In vivo experiments demonstrated that tumor growth inhibition was most prominent in the DOX/TPOR4@CB[7]4 group. π–π interactions facilitated the binding between DOX and photosensitizer TPOR, with TPOR’s naphthalene hydrophilic groups encapsulated within CB[7]’s cavity through host–guest interactions with CB[7]. Therefore, CB[7] can serve as a nanocarrier to enhance the combined application of chemical therapy and photodynamic therapy, thereby significantly improving treatment efficacy against neuroblastoma tumors.

Uncovering the trade-offs and synergy relationship of multiple ecosystem services (ESs) is important for scientific ecosystem management and the improvement of ecological service functions. In this study, we investigated the spatiotemporal changes of four typical ES types (i.e., water yield (WY), carbon storage (CS), soil conservation (SC), and habitat quality (HQ)) from 2001 to 2020 in the Han River Basin (HRB). Meanwhile, the trade-offs and synergies between paired ESs and the socioecological drivers of these ESs were further explored. The results showed that grassland, cropland, and bare land decreased by 12,141.3 km2, 624.09 km2, and 22.1 km2 during the study period, respectively, which can be attributed to their conversion to forests in the HRB. Temporally, the WY, CS, and SC all showed a continuously increasing trend. Spatially, WY and HQ exhibited bipolar clustering characteristics, with WY exhibiting low-value clustering in the upstream and high-value clustering in the downstream, while CS showed the clustering characteristics of a scattered distribution of cold and hot spots from 2001 to 2020. The spatial patterns of aggregation locations in CS and HQ were relatively similar, with clusters of higher ES values mainly distributed in the western and central regions and clusters of lower ES values mainly located in the eastern and southeastern regions, while the aggregation of WY was spatially concentrated. Overall, the CS showed a significant positive correlation with HQ, but a significant negative correlation with WY. Spatially, WY and HQ, CS, and SC showed a substantial trade-off relationship in the northwest and southeast parts of the study area, while HQ, CS, and SC mainly exhibited a synergistic relationship in most parts of the study area. Slope and temperature had high influencing factor coefficients on multiple ESs; the mixed effect of terrain and natural factors was significantly greater than the impact of a single factor on ESs, and terrain factors played an essential role in the changes in ESs. The findings can provide technical and theoretical support for integrated scientific ecosystem management and sustainable development at the local scale.

Enzymatically derived selenium-enriched peptides from Cardamine violifolia (CV) can serve as valuable selenium supplements. However, the industrial application of free enzyme is impeded by its limited stability and reusability. Herein, this study explores the application of co-immobilized enzymes (Alcalase and Dispase) on amino resin for hydrolyzing CV proteins to produce selenium-enriched peptides. The successful enzyme immobilization was confirmed through scanning electron microscopy (SEM), energy dispersive X-ray (EDX), and Fourier-transform infrared spectroscopy (FTIR). Co-immobilized enzyme at a mass ratio of 5:1 (Alcalase/Dispase) exhibited the smallest pore size (7.065 nm) and highest activity (41 U/mg), resulting in a high degree of hydrolysis of CV protein (27.2%), which was obviously higher than the case of using free enzymes (20.7%) or immobilized Alcalase (25.8%). In addition, after a month of storage, the co-immobilized enzyme still retained a viability level of 41.93%, showing fairly good stability. Encouragingly, the selenium-enriched peptides from co-immobilized enzyme hydrolysis exhibited uniform distribution of selenium forms, complete amino acid fractions and homogeneous distribution of molecular weight, confirming the practicality of using co-immobilized enzymes for CV protein hydrolysis.

While it is well known that plants interpret UV-B as an environmental cue and a potential stressor influencing their growth and development, the specific effects of UV-B-induced oxidative stress on the dynamics of membrane lipids and proteins remain underexplored. Here, we demonstrate that UV-B exposure notably increases the formation of ordered lipid domains on the plasma membrane (PM) and significantly alters the behavior of the Glycine max nodule autoregulation receptor kinase (GmNARK) protein in Arabidopsis leaves. The GmNARK protein was located on the PM and accumulated as small particles in the cytoplasm. We found that UV-B irradiation interrupted the lateral diffusion of GmNARK proteins on the PM. Furthermore, UV-B light decreases the efficiency of surface molecule internalization by clathrin-mediated endocytosis (CME). In brief, UV-B irradiation increased the proportion of the ordered lipid phase and disrupted clathrin-dependent endocytosis; thus, the endocytic trafficking and lateral mobility of GmNARK protein on the plasma membrane are crucial for nodule formation tuning. Our results revealed a novel role of low-intensity UV-B stress in altering the organization of the plasma membrane and the dynamics of membrane-associated proteins.

Autonomous vehicles (AV) hold great potential to increase road safety, reduce traffic congestion, and improve mobility systems. However, the deployment of AVs introduces new liability challenges when they are involved in car accidents. A new legal framework should be developed to tackle such a challenge. This paper proposes a legal framework, incorporating liability rules to rear-end crashes in mixed-traffic platoons with AVs and human-propelled vehicles (HV). We leverage a matrix game approach to understand interactions among players whose utility captures crash loss for drivers according to liability rules. We investigate how liability rules may impact the game equilibrium between vehicles and whether human drivers’ moral hazards arise if liability is not designed properly. We find that compared to the no-fault liability rule, contributory and comparative rules make road users have incentives to execute a smaller reaction time to improve road safety. There exists moral hazards for human drivers when risk-averse AV players are in the car platoon.

The spread of the FluA virus poses significant public health concerns worldwide. Fluorescent lateral flow immunoassay (LFIA) test strips have emerged as vital tools for the early detection and monitoring of influenza infections. However, existing quantitative virus-detection methods, particularly those utilizing smartphone-based sensing platforms, encounter accessibility challenges in resource-limited areas and among the elderly population. Despite their advantages in speed and portability, these platforms often lack user-friendliness for these demographics, impeding their widespread utilization. To address these challenges, this study proposes leveraging the optical pick-up unit (OPU) sourced from commercial optical drives as a readily available fluorescence excitation module for the quantitative detection of antibodies labeled with quantum-dot fluorescent microspheres. Additionally, we utilize miniaturized and high-performance optical components and 3D-printed parts, along with a customized control system, to develop an affordable point-of-care testing (POCT) device. Within the system, a stepping motor scans the test strip from the T-line to the C-line, enabling the calculation of the fluorescence-intensity ratio between the two lines. This simple yet effective design facilitates rapid and straightforward field or at-home testing for FluA. The proposed prototype platform demonstrates promising performance, achieving a limit of detection (LOD) of 2.91 ng/mL, a total detection time of no more than 15 min, and dimensions of 151 mm × 11.2 mm × 10.8 mm3. We believe that the proposed approach holds great potential for improving access to an accurate influenza diagnosis.

To better simulate the prices of underlying assets and improve the accuracy of pricing financial derivatives, an increasing number of new models are being proposed. Among them, the Lévy process with jumps has received increasing attention because of its capacity to model sudden movements in asset prices. This paper explores the Hamilton–Jacobi–Bellman (HJB) equation with a fractional derivative and an integro-differential operator, which arise in the valuation of American options and stock loans based on the Lévy-α-stable process with jumps model. We design a fast solution strategy that includes the policy iteration method, Krylov subspace method, and banded preconditioner, aiming to solve this equation rapidly. To solve the resulting HJB equation, a finite difference method including an upwind scheme, shifted Grünwald approximation, and trapezoidal method is developed with stability and convergence analysis. Then, an algorithmic framework involving the policy iteration method and the Krylov subspace method is employed. To improve the performance of the above solver, a banded preconditioner is proposed with condition number analysis. Finally, two examples, sugar option pricing and stock loan valuation, are provided to illustrate the effectiveness of the considered model and the efficiency of the proposed preconditioned policy–Krylov subspace method.

A chemical investigation of Anthriscus sylvestris roots led to the isolation and characterization of two new nitrogen-containing phenylpropanoids (1–2) and two new phenol glycosides (8–9), along with fifteen known analogues. Structure elucidation was based on HRESIMS, 1D and 2D NMR spectroscopy, and electronic circular dichroism (ECD). In addition, compounds 3, 6, 9–10, 12, and 17 exhibited inhibitory effects against the abnormal proliferation of pulmonary arterial smooth muscle cells with IC50 values ranging from 10.7 ± 0.6 to 57.1 ± 1.1 μM.

In the sixth generation (6G) era, intelligent machine network (IMN) applications, such as intelligent transportation, require collaborative machines with communication, sensing, and computation (CSC) capabilities. This article proposes an integrated communication, sensing, and computation (ICSAC) framework for 6G to achieve the reciprocity among CSC functions to enhance the reliability and latency of communication, accuracy and timeliness of sensing information acquisition, and privacy and security of computing to realize the IMN applications. Specifically, the sensing and communication functions can merge into unified platforms using the same transmit signals, and the acquired real-time sensing information can be exploited as prior information for intelligent algorithms to enhance the performance of communication networks. This is called the computing-empowered integrated sensing and communications (ISAC) reciprocity. Such reciprocity can further improve the performance of distributed computation with the assistance of networked sensing capability, which is named the sensing-empowered integrated communications and computation (ICAC) reciprocity. The above ISAC and ICAC reciprocities can enhance each other iteratively and finally lead to the ICSAC reciprocity. To achieve these reciprocities, we explore the potential enabling technologies for the ICSAC framework. Finally, we present the evaluation results of crucial enabling technologies to show the feasibility of the ICSAC framework.

The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy of the proposed anomaly detection method with wavelet decomposition and stacked denoising autoencoder methods. Anomaly detection based on UAV flight data is an important method of UAV condition monitoring and potential abnormal state mining, which is an important means to reduce the risk of UAV flight accidents. However, the diversity of UAV mission scenarios leads to a complex and harsh environment, so the acquired data are affected by noise, which brings challenges to accurate anomaly detection based on UAV data. Firstly, we use wavelet decomposition to denoise the original data; then, we used the stacked denoising autoencoder to achieve feature extraction. Finally, the softmax classifier is used to realize the anomaly detection of UAV. The experimental results demonstrate that the proposed method still has good performance in the case of noisy data. Specifically, the Accuracy reaches 97.53%, the Precision is 97.50%, the Recall is 91.81%, and the F1-score is 94.57%. Furthermore, the proposed method outperforms the four comparison models with more outstanding performance. Therefore, it has significant potential in reducing UAV flight accidents and enhancing operational safety.

Investigating the mathematical and geometric principles embedded in ancient classic architecture is a significant tradition in the history of architectural development. Drawing inspiration from the modular design and creative ideology based on the geometric proportions of squareness and roundness in ancient Chinese architecture, we propose a new mode of squareness and roundness composition based on scale proportion specifically for the design of multi-story buildings. Taking Yingxian Wooden Pagoda as the case study, we not only re-evaluate the modular system and proportional rules followed in the design of the entire pagoda, but also reveal the technical approaches and geometric rules for effectively controlling the form of multi-story buildings. In particular, the mode of squareness and roundness composition based on scale proportion, utilizing a modular grid combined with squareness and roundness drawings as decision-making tools, can control the scale and proportion of buildings across different design dimensions and organically coordinate the design of multi-story buildings’ plans and elevations. Thus, it can achieve an effective balance of multi-story architectural forms. This study has potential applications in the creation of traditional multi-story buildings and heritage restoration projects, and offers valuable insights for future research on ancient multi-story buildings.

A novel inserting pilot scheme to generate and distribute a frequency 16-tupling millimeter wave (MMW) radio over fiber (ROF) system without the bit walk-off effect via Mach–Zehnder modulators (MZMs) is proposed. The operation principle is analyzed and the feasibility of our proposed scheme is verified by simulation test. The main part of our scheme is a ±8th-order sidebands generator (SG), which is constructed by four MZMs connected in parallel. In the back-to-back (BTB) transmission case, by properly adjusting the voltage and initial phase of the radio frequency (RF) drive signals of the MZMs, ±8th-order sidebands are generated by the SG. In the data transmission case, the data signal is first split into two beams, one of which modulates the RF drive signal with an electrical phase modulator (PM) while the other is amplified by an electrical gainer (EG), and then the two beams are combined into one and used as the composite RF drive signal of the MZMs. By adjusting the modulation index of the PM and the gain of the EG, the data signal can only be modulated to the +8th-order sideband of the output of the SG. The optical carrier from the continuous wave (CW) laser is split into two paths: one is sent into the SG, and the other is used as a pilot signal. The output signal of SG is combined with the pilot signal and is transmitted to the base station (BS) via optical fiber. At the BS, the pilot signal is filtered out by a fiber Bragg grating (FBG) and used as the carrier for the uplink for carrier reuse. After filtering out the pilot, the signal from the FBG, which is composed of ±8th-order sidebands, is injected into a photodetector, and a frequency 16-tupling MMW with downlink data is generated. The key parameters’ influence on the bit error rate (BER) and Q factor in the system is also analyzed. Our scheme can not only effectively overcome the bit walk-off effect caused by optical fiber chromatic dispersion and greatly increase the fiber transmission distance but can also effectively improve the performance and the tunability of system. Therefore, it has important application prospects in ROF systems.

Inversion tillage and organic fertilizer application can break the plow pan and improve soil quality. However, the effects of combining these practices on the soil microbial resource limitation and maize yield in Haplic Chernozem are unclear. In this research, a field experiment was established in 2018, and soil samples were collected in 2021 in Longjiang County in Northeast China, which is a Haplic Chernozem region. Four treatments comprising conventional tillage (T15), conventional tillage with organic fertilizer (T15+M), inversion tillage (T35), and inversion tillage with organic fertilizer (T35+M) were randomly arranged with four replications. Compared with T15 and T15+M treatments, soil bulk density significantly decreased by 11.1–16.3% in the 15–35 cm layer under T35 and T35+M treatments, accompanied by the improvement in soil pore structure (e.g., soil porosity, circularity, and Euler number). T15+M treatment significantly increased soil organic carbon and soil nutrient contents by 11.1–16.3% and 3.9–24.5% in the 0–15 cm layer compared with other treatments. However, soil organic carbon, total nitrogen, available phosphorus content, microbial biomass, and enzymatic activities reached the maximum values in the 0–35 cm layer under T35+M treatment. In addition, T35+M treatment had the highest maize yield and sustainable yield index. Extracellular enzymatic stoichiometry suggested that soil microorganisms are generally co-limited by carbon and phosphorus in Haplic Chernozem. However, T35+M treatment significantly reduced soil microbial resource limitation, which was one important factor impacting maize yield and sustainability. Random-forest and partial least-squares path modeling showed that T35+M treatment could reduce soil microbial resource limitation and increase the stability and sustainability of the maize yield by improving soil available nutrients, microbial biomass, and pore structure. Therefore, the incorporation of inversion tillage and organic fertilizer is a suitable soil management practice in view of increasing soil quality and crop yields in a Haplic Chernozem region.

Mean-field games (MFGs) are developed to model the decision-making processes of a large number of interacting agents in multi-agent systems. This paper studies mean-field games on graphs (G-MFGs). The equilibria of G-MFGs, namely, mean-field equilibria (MFE), are challenging to solve for their high-dimensional action space because each agent has to make decisions when they are at junction nodes or on edges. Furthermore, when the initial population state varies on graphs, we have to recompute MFE, which could be computationally challenging and memory-demanding. To improve the scalability and avoid repeatedly solving G-MFGs every time their initial state changes, this paper proposes physics-informed graph neural operators (PIGNO). The PIGNO utilizes a graph neural operator to generate population dynamics, given initial population distributions. To better train the neural operator, it leverages physics knowledge to propagate population state transitions on graphs. A learning algorithm is developed, and its performance is evaluated on autonomous driving games on road networks. Our results demonstrate that the PIGNO is scalable and generalizable when tested under unseen initial conditions.

Plate position is an important parameter of a New Type Concrete Expanded-Plate (NT-CEP) pile. In this study, two small-scale test models and two, four, six, and nine finite element models were established using a visual small-scale model of half-section piles, an undisturbed soil pull-out test, and the ANSYS finite element software R19.0. The NT-CEP pile groups were studied with the plates set at staggered positions. This study mainly analyzes the displacement contours, stress curves, and load-displacement curves under the action of vertical tension and determines the influence of staggered plate positions on the performance and bearing capacity of the NT-CEP pile group, which provides theoretical support for its application in practical engineering. The bearing plate positions affect the performance of the pile group. The stress distribution in each pile in the pile group is uneven when plate positions are staggered, and the pile with the lower plate position bears a greater force. This has a great influence on the bearing capacity of the NT-CEP pile group. If allowed, the plates can be first set at the same position. If appropriate, the plates can be set in staggered positions; however, a reasonable distance between the upper and lower plates should be considered.

This paper proposes a scalable learning framework to solve a system of coupled forward–backward partial differential equations (PDEs) arising from mean field games (MFGs). The MFG system incorporates a forward PDE to model the propagation of population dynamics and a backward PDE for a representative agent’s optimal control. Existing work mainly focus on solving the mean field game equilibrium (MFE) of the MFG system when given fixed boundary conditions, including the initial population state and terminal cost. To obtain MFE efficiently, particularly when the initial population density and terminal cost vary, we utilize a physics-informed neural operator (PINO) to tackle the forward–backward PDEs. A learning algorithm is devised and its performance is evaluated on one application domain, which is the autonomous driving velocity control. Numerical experiments show that our method can obtain the MFE accurately when given different initial distributions of vehicles. The PINO exhibits both memory efficiency and generalization capabilities compared to physics-informed neural networks (PINNs).

Aiming at the traditional single sensor vibration signal cannot fully express the bearing running state, and in the high noise background, the traditional algorithm is insufficient for fault feature extraction. This paper proposes a fault diagnosis algorithm based on multi-sensor and hybrid multimodal feature fusion to achieve high-precision fault diagnosis by leveraging the operating state information of bearings in a high-noise environment to the fullest extent possible. First, the horizontal and vertical vibration signals from two sensors are fused using principal component analysis, aiming to provide a more comprehensive description of the bearing’s operating condition, followed by data set segmentation. Following fusion, time-frequency feature maps are generated using a continuous wavelet transform for global time-frequency feature extraction. A first diagnostic model is then developed utilizing a residual neural network. Meanwhile, the feature data is normalized, and 28 time-frequency feature indexes are extracted. Subsequently, a second diagnostic model is constructed using a support vector machine. Lastly, the two diagnosis models are integrated to derive the final model through an ensemble learning algorithm fused at the decision level and complemented by a genetic algorithm solution to improve the diagnosis accuracy. Experimental results demonstrate the effectiveness of the proposed algorithm in achieving superior diagnostic performance with a 97.54% accuracy rate.

The digital thread, as a crucial technology for industrial digitization and the realization of smart manufacturing, has garnered extensive attention and research in recent years. Furthermore, there is a growing interest in the key technologies supporting the implementation of the digital thread. Given the diversity of product lifecycle models, various definitions, reference architectures, and implementation methods have been proposed to study the digital thread. Thus, this study systematically investigates the current definition, key technologies, and applications of the digital thread. A comprehensive analysis of 94 articles spanning from 2015 to 2023 was conducted, clarifying the definition of the digital thread and its relationship with related terms. Building upon this foundation, this study delves into the research methodologies concerning pivotal technologies in implementing the digital thread (such as authoritative sources of truth, data linkage, and model integration) and scrutinizes various application scenarios of the digital thread, providing a comprehensive summary. Finally, this study presents the research findings along with recommendations for future research endeavors.

Many animal species produce protective foams, the majority of which exhibit evolutionary adaptability. Although the function and composition of foams have been widely studied, the genetic basis of foam secretion remains unknown. Unlike most species that produce foam under specific situations, spittlebugs continuously secrete foams throughout all nymphal stages. Here, we capitalize on the rice spittlebug (Callitettix versicolor) to explore the genetic basis of foam secretion through genomic and transcriptomic approaches. Our comparative genomic analysis for C. versicolor and eight other insect species reveals 606 species-specific gene families and 66 expanded gene families, associated with carbohydrate and lipid metabolism. These functions are in accordance with the composition of foams secreted by spittlebugs. Transcriptomic analyses of malpighian tubules across developmental stages detected 3192 differentially expressed genes. Enrichment analysis of these genes highlights functions also revealed by our comparative genomic analysis and aligns with previous histochemical and morphological observations of foam secretion. This consistency suggests the important roles of these candidate genes in foam production. Our study not only provides novel insights into the genetic basis of foam secretion in rice spittlebugs but also contributes valuable knowledge for future evolutionary studies of spittlebugs and the development of pest control strategies for C. versicolor.

This paper aims to use location-based social media data to infer the impact of the Russia–Ukraine war on human mobility. We examine the impact of the Russia–Ukraine war on changes in human mobility in terms of the spatial range of check-in locations using social media location data. Specifically, we collect users’ check-in location data from Twitter and analyze the average gyration of check-ins from a region across the timeline of major events associated with the war. Change-point detection is performed on these time-series check-ins to identify the timeline of abrupt changes, which are shown to be consistent with the timing of a series of sanctions and policies. We find that war-related events may contribute secondary impacts (e.g., the surge in gas prices) to users’ travel patterns. The impact of the Russia–Ukraine war on users’ travel patterns can differ based on their own scope. Our case study demonstrates that users’ gyration in Warsaw, Paris, and Berlin experienced a decrease of over 50% during periods of gas price surges. These changes in users’ gyration patterns were particularly noticeable in neighboring countries like Poland compared to the other three countries. The findings of this study can assist policymakers, regulators, and urban planners to evaluate the impact of the war and to be adaptable to city planning after the war.

The effects of vanadium addition on the solidification microstructure and mechanical properties of Al–4Ni alloy were investigated via thermodynamic computation, thermal analysis, microstructural observations, and mechanical properties testing. The results show that the nucleation temperature of primary α-Al increased with increased vanadium addition. A transition from columnar to equiaxed growth took place when adding vanadium to Al–4Ni alloys, and the average grain size of primary α-Al was reduced from 1105 μm to 252 μm. When the vanadium addition was 0.2 wt%, the eutectic nucleation temperature increased from 636.2 °C for the Al–4Ni alloy to 640.5 °C, and the eutectic solidification time decreased from 310 s to 282 s. The average diameter of the eutectic Al3Ni phases in the Al–4Ni–0.2V alloy reduced to 0.14 μm from 0.26 μm for the Al–4Ni alloy. As the vanadium additions exceeded 0.2 wt%, the eutectic nucleation temperature had no obvious change and the eutectic solidification time increased. The eutectic Al3Ni phases began to coarsen, and the number of lamellar eutectic boundaries increased. The mechanical properties of Al–4Ni alloys gradually increased with vanadium addition (0–0.4 wt%). The Al–4Ni–0.4V alloy obtained the maximum tensile strength and elongation values, which were 136.4 MPa and 23.5%, respectively. As the vanadium addition exceeded 0.4 wt%, the strength and elongation decreased, while the hardness continued to increase. Fracture in the Al–4Ni–0.4V alloy exhibited ductile fracture, while fracture in the Al–4Ni–0.6V alloy was composed of dimples, tear edges, and cleavage planes, demonstrating mixed ductile–brittle fracture. The cleavage planes were caused by the primary Al10V and coarse Al3Ni phases at the boundary of eutectic cells.

Additive manufacturing of titanium alloys usually ends up with large columnar grains due to the steep thermal gradients within melt pools during solidification. In this study, ZrN particles were added into a beta titanium alloy, Ti-10V-2Fe-3Al, with the aim of promoting columnar-to-equiaxed grain transition during laser bed powder fusion (L-PBF). It was found that the addition of ZrN leads to the development of alternate layers of equiaxed grains and refined columnar grains, which is in sharp contrast to the dominant large columnar grains formed in the pure L-PBF-processed titanium alloy. An investigation on single laser melted tracks revealed that the sample with added ZrN showed fine equiaxed grains in the upper regions of solidified melt pools and columnar grains in the lower regions, whereas the solidified melt pools of the pure titanium alloy were dominated by large columnar grains due to epitaxial growth from the previous layer. The formation of equiaxed grains in the former sample is attributed to multiple factors including an increased gradient of liquidus temperature due to the solution of N and a reduced actual melt temperature gradient due to the melting of high-melting-point ZrN particles, which would have expanded constitutional undercooling, a grain growth restriction effect induced by the segregation of N along grain boundaries and the accumulation of unmelted ZrN particles in the upper regions of melt pools. The addition of ZrN also resulted in significant α precipitation, which showed strong variant selection and was found to be driven by laser reheating and the N solution in the matrix.

Evapotranspiration (E), a pivotal phenomenon inherent to hydrological and thermal dynamics, assumes a position of utmost importance within the intricate framework of the water–energy nexus. However, the quantitative study of E on a large scale for the “Grain for Green” projects under the backdrop of climate change is still lacking. Consequently, this study examined the interannual variations and spatial distribution patterns of E, transpiration (Et), and soil evaporation (Eb) in the Northern Foot of Yinshan Mountain (NFYM) between 2000 and 2020 and quantified the contributions of climate change and vegetation greening to the changes in E, Et, and Eb. Results showed that E (2.47 mm/a, p < 0.01), Et (1.30 mm/a, p < 0.01), and Eb (1.06 mm/a, p < 0.01) all exhibited a significant increasing trend during 2000–2020. Notably, vegetation greening emerged as the predominant impetus underpinning the augmentation of both E and Eb, augmenting their rates by 0.49 mm/a and 0.57 mm/a, respectively. In terms of Et, meteorological factors emerged as the primary catalysts, with temperature (Temp) assuming a predominant role by augmenting Et at a rate of 0.35 mm/a. Temp, Precipitation (Pre), and leaf area index (LAI) collectively dominated the proportional distribution of E, accounting for shares of 32.75%, 28.43%, and 25.01%, respectively. Within the spectrum of predominant drivers influencing Et, Temp exerted the most substantial influence, commanding the largest proportion at 33.83%. For Eb, the preeminent determinants were recognized as LAI and Temp, collectively constituting a substantial portion of the study area, accounting for 32.10% and 29.50%, respectively. The LAI exerted a pronounced direct influence on the Et, with no significant effects on E and bare Eb. Wind speed (WS) had a substantial direct impact on both E and Et. Pre exhibited a strong direct influence on E, Et, and Eb. Relative humidity (RH) significantly affected E directly. Temp primarily influenced Eb indirectly through radiation (Rad). Rad exerted a significant direct inhibitory effect on Eb. These findings significantly advanced our mechanistic understanding of how E and its components in the NFYM respond to climate change and vegetation greening, thus providing a robust basis for formulating strategies related to regional ecological conservation and water resources management, as well as supplying theoretical underpinnings for constructing sustainable vegetation restoration strategies involving water resources in the region.

Soybean peptides (SPs) have bioactivities of enzyme inhibition that are beneficial to human health, but their mechanism is not clear. This study aimed to identify peptide fragments in SPs that simultaneously inhibit α-amylase and α-glucosidase and to explore their enzyme inhibition mechanism. Firstly, the inhibitory activity of SPs against the enzymes was determined. And two octapeptides, LDQTPRVF and SRNPIYSN, were identified for the first time by using HPLC-QTOF-MS/MS and virtual screening. Molecular simulation results showed that hydrogen bonds and π–π bonds were the key factors, and the N-terminal (Leu and Ser) and C-terminal (Phe) of peptide were important inhibiting sites. Both octapeptides were synthesized, and their IC50 values were 3.08 and 5.58 mmol/L for α-amylase, and 2.52 and 4.57 mmol/L for α-glucosidase, respectively. This study provided evidence for SPs as a potential inhibitor of α-amylase and α-glucosidase in special dietary foods.

This paper investigates the evolution of global eddies and various types of eddies originating from baroclinic instability (BCI) by utilizing satellite altimetry, Argo profiles, and climatology datasets. The structure of global eddies with regard to potential temperature anomalies experiences downward propagation and spreading from the periods of eddy growth to stabilization. However, from the eddy’s stabilization to the decay period, the process of spreading primarily occurs horizontally, and this process is usually accompanied by weakening. By comparing the evolution of eddies in three typical regions dominated by distinct types of BCI, we found that the basic properties of eddies related to different BCI types evolve similarly; however, there are notable differences in their vertical structures and evolution. Eddies associated with Phillips + Charney_s-type, Charney_s-type, and Eady-type BCIs exhibit dual-core, single-core, and dual-core structures, respectively. In particular, the intrusion of the Okhotsk cold water mass into the Northwest Pacific region forms cold-core anticyclonic eddies, resulting in AEs that are significantly distinct from the rest of the ocean. The evolution of surface-layer cores closely resembles that of the global eddies, while the decay of subsurface and bottom-layer cores is comparably sluggish. Additionally, we examine the impact of local oceanic stratification conditions on eddy decay and determine that stronger vertical gradients result in more vigorous eddy decay, accounting for the concentration of eddies at depths where vertical gradients are weaker during their evolution.

Water environment governance of large rivers is an essential measurement of ecological protection in China. The effectiveness of governance depends highly on the fulfillment of strategic CSR (corporate social responsibility) of Chinese leading enterprises. However, the fulfillment of strategic CSR often brings economic burden to the leading enterprises and needs government compensation support. Therefore, this paper explores the impact of government compensation on the fulfillment of strategic CSR of Chinese leading enterprises by constructing a multi-stage portfolio model. The main results show that (1) the prerequisite for leading enterprises to fulfill strategic CSR is sufficient profits; (2) the increase in government compensation can significantly affect the proportion of strategic CSR fulfillment of leading enterprises; (3) only when the portfolio considers both strategic CSR and project benefits, can the leading enterprises obtain the highest economic returns and better fulfill strategic CSR. The results can provide policy reference for the government to stimulate the fulfillment of leading enterprises’ strategic CSR and also give suggestions for the leading enterprises to alleviate the economic pressure brought by fulfilling strategic CSR.

Natural montane peatlands are generally not a significant source of nitrous oxide (N2O) due to environment limitations, including phosphorus (P) scarcity and temperature lowness. Phosphorus enrichment and warming caused by global change are altering these limitations, and are likely to increase the source function of N2O. However, the combined effects of P addition and warming on N2O fluxes and biotic/abiotic factors in peatlands are still uncertain. To address this, we investigated the long-term (12 yrs) effects of P addition (5 and 10 kg ha−1 yr−1) and its interaction with warming on N2O fluxes in a peatland. The results showed that although long-term P addition did not significantly affect the source/sink function of N2O in the peatland, it stimulated enzyme activities and promoted peat decomposition. However, warming amplified the effect of P addition to increase N2O emissions by stimulating enzyme activities and changing soil stoichiometry, so even turned the peatland into a significant source of N2O with an emission of approximate 100 g m−2 during the growing season. Our study suggests that P enrichment against the current background of global warming will enhance the possibility of strong N2O emissions in montane peatlands, which may increase the risk that global warming will be further aggravated.

Internal Solitary Waves (ISWs) play a pivotal role in transporting energy and matter within the ocean and also pose substantial risks to ocean engineering, navigation, and underwater communication systems. Consequently, measures need to be adopted to alleviate their negative effects and minimize linked risks. An effective method entails extracting ISW positions from Synthetic Aperture Radar (SAR) data for precise trajectory prediction and efficient avoidance strategies. However, manual extraction of ISWs from SAR data is time-consuming and prone to inaccuracies. Hence, it is imperative to develop a high-precision, rapid, and automated ISW-extraction algorithm. In this paper, we introduce Middle Transformer U2-net (MTU2-net), an innovative model that integrates a distinctive loss function and Transformer to improve the accuracy of ISWs’ extraction. The novel loss function enhances the model’s capacity to extract bow waves, whereas the Transformer ensures coherence in ISW’s patterns. By conducting experiments involving 762 image scenes, incorporating ISWs, from the South China Sea, we established a standardized dataset. The Mean Intersection over Union (MIoU) achieved on this dataset was 71.57%, surpassing the performance of other compared methods. The experimental outcomes showcase the remarkable performance of our proposed model in precisely extracting bow wave attributes from SAR data.

As the intensive development of aquaculture persists, the demand for fishmeal continues to grow; however, since fishery resources are limited, the price of fishmeal remains high. Therefore, there is an urgent need to develop new sources of protein. They are rich in proteins, fatty acids, amino acids, chitin, vitamins, minerals, and antibacterial substances. Maggot meal-based diet is an ideal source of high-quality animal protein and a new type of protein-based immune enhancer with good application prospects in animal husbandry and aquaculture. In the present study, we investigated the effects of three different diets containing maggot protein on the growth and intestinal microflora of Litopenaeus vannamei. The shrimp were fed either a control feed (no fly maggot protein added), FM feed (compound feed with 30% fresh fly maggot protein added), FF feed (fermented fly maggot protein), or HT feed (high-temperature pelleted fly maggot protein) for eight weeks. The results showed that fresh fly maggot protein in the feed was detrimental to shrimp growth, whereas fermented and high-temperature-pelleted fly maggot protein improved shrimp growth and survival. The effects of different fly maggot protein treatments on the intestinal microbiota of L. vannamei also varied. Fermented fly maggot protein feed and high-temperature-pelleted fly maggot protein feed increased the relative abundance of Ruegeria and Pseudomonas, which increased the abundance of beneficial bacteria and thus inhibited the growth of harmful bacteria. In contrast, fresh fly maggot proteins alter the intestinal microbiome, disrupting symbiotic relationships between bacteria, and causing invasion by Vibrio and antibiotic-resistant bacteria. These results suggest that fresh fly maggot proteins affect the composition of intestinal microorganisms, which is detrimental to the intestinal tract of L. vannamei, whereas fermented fly maggot protein feed affected the growth of L. vannamei positively by improving the composition of intestinal microorganisms.

Biomimetic delivery systems (BDSs), inspired by the intricate designs of biological systems, have emerged as a groundbreaking paradigm in nanomedicine, offering unparalleled advantages in therapeutic delivery. These systems, encompassing platforms such as liposomes, protein-based nanoparticles, extracellular vesicles, and polysaccharides, are lauded for their targeted delivery, minimized side effects, and enhanced therapeutic outcomes. However, the translation of BDSs from research settings to clinical applications is fraught with challenges, including reproducibility concerns, physiological stability, and rigorous efficacy and safety evaluations. Furthermore, the innovative nature of BDSs demands the reevaluation and evolution of existing regulatory and ethical frameworks. This review provides an overview of BDSs and delves into the multifaceted translational challenges and present emerging solutions, underscored by real-world case studies. Emphasizing the potential of BDSs to redefine healthcare, we advocate for sustained interdisciplinary collaboration and research. As our understanding of biological systems deepens, the future of BDSs in clinical translation appears promising, with a focus on personalized medicine and refined patient-specific delivery systems.

Vegetation net primary productivity (NPP) serves as a crucial and intuitive indicator for assessing ecosystem health. However, the nonlinear dynamics and influencing factors operating at various time scales are not yet fully understood. Here, the ensemble empirical mode decomposition (EEMD) method was used to analyze the spatiotemporal patterns of NPP and its association with hydrothermal factors and anthropogenic activities across different temporal scales for the Yellow River Basin (YRB) from 2000 to 2020. The results indicate that: (1) the annual average NPP was 236.37 g C/m2 in the YRB and increased at rates of 4.64 g C/m2/a1 (R2 = 0.86, p < 0.01) during 2000 to 2020. Spatially, nonlinear analysis indicates that 72.77% of the study area exhibits a predominantly increasing trend in NPP, while 25.17% exhibits a reversing trend. (2) On a 3-year time scale, warming has resulted in an increase in NPP in the majority of areas of the study area (69.49%). As the time scale widens, the response of vegetation to climate change becomes more prominent; especially under the long-term trend, the percentage areas of the correlation between vegetation and precipitation and temperature increased with significance, reaching 48.21% and 11.57%, respectively. (3) Through comprehensive time analysis and multivariate regression analysis, it was confirmed that both human activities and climate factors had comparable impacts on vegetation growth. Among different vegetation types, climate was still the main factor affecting grassland NPP, and only 15.74% of grassland was affected by human activities. For shrubland, forest, and farmland, human activity was a dominating factor for vegetation NPP change. There are still few studies on vegetation change using nonlinear methods in the Yellow River Basin, and most studies have not considered the effect of time scale on vegetation evolution. The findings highlight the significance of multi-time scale analysis in understanding the vegetation dynamics and providing scientific guidance for future vegetation restoration and conservation efforts.

Machine learning techniques have garnered significant attention in various engineering disciplines due to their potential and benefits. Specifically, in reservoir numerical simulations, the core process revolves around solving the partial differential equations delineating oil, gas, and water flow dynamics in porous media. Discretizing these partial differential equations via numerical methods is one cornerstone of this simulation process. The synergy between traditional numerical methods and machine learning can enhance the precision of partial differential equation discretization. Moreover, machine learning algorithms can be employed to solve partial differential equations directly, yielding rapid convergence, heightened computational efficiency, and accuracies surpassing 95%. This manuscript offers an overview of the predominant numerical methods in reservoir simulations, focusing on integrating machine learning methodologies. The innovations in fusing deep learning techniques to solve reservoir partial differential equations are illuminated, coupled with a concise discussion of their inherent advantages and constraints. As machine learning continues to evolve, its conjunction with numerical methods is poised to be pivotal in addressing complex reservoir engineering challenges.

Grasslands, a vital ecosystem and component of the global carbon cycle, play a significant role in evaluating ecosystem health and monitoring the global carbon balance. In this study, based on the Carnegie–Ames–Stanford Approach (CASA) model, we estimated the Net Primary Productivity (NPP) of grasslands in northern Shaanxi from 2000 to 2020. Employing trend analysis, stability analysis, multiple regression analysis, and residual analysis, the research examined the dynamic changes of grassland NPP and its response to climatic and human factors. Key findings include: (1) Grassland NPP showed a significant increasing trend during 2000–2020, with high-coverage grasslands showing a higher rate of increase than medium and low-coverage grasslands. (2) Most grasslands (>90%) exhibited unstable growth and high NPP fluctuation. (3) While temperature, precipitation, and radiation undulate, the trends were not significant. Rainfall and radiation emerged as dominant factors affecting NPP, with temperature suppressing NPP increase to some extent. (4) Policies like returning farmland to grassland had a positive impact on grassland recovery, vegetation productivity, and regional ecosystem health.

Mapping potential landslides is crucial to mitigating and preventing landslide disasters and understanding mountain landscape evolution. However, the existing methods to map and demonstrate potential landslides in mountainous regions are challenging to use and inefficient. Therefore, herein, we propose a method using hot spot analysis and convolutional neural networks to map potential landslides in mountainous areas at a regional scale based on ground deformation detection using multitemporal interferometry synthetic aperture radar. Ground deformations were detected by processing 76 images acquired from the descending and ascending orbits of the Sentinel-1A satellite. In total, 606 slopes with large ground deformations were automatically detected using hot spot analysis in the study area, and the extraction accuracy rate and the missing rate are 71.02% and 7.89%, respectively. Subsequently, based on the high-deformation areas and potential landslide conditioning factors, we compared the performance of convolutional neural networks with the random forest algorithm and constructed a classification model with the area under the curve (AUC), accuracy, recall, and precision for testing being 0.75, 0.75, 0.82, and 0.75, respectively. Our approach underpins the ability of interferometric synthetic aperture radar (InSAR) to map potential landslides regionally and provide a scientific foundation for landslide risk management. It also enables an accurate and efficient identification of potential landslides within a short period and under extremely hazardous conditions.

Background: Post-hepatectomy liver failure (PHLF) remains a complication with the potential risk of mortality for hepatocellular carcinoma (HCC) patients. The systemic inflammatory response (SIR) has been demonstrated to be associated with a bad prognosis of liver cirrhosis and tumors. This study aims to evaluate the incremental prognostic value of inflammatory markers in predicting PHLF in patients with HCC. Methods: Clinical characteristics and variables were retrospectively collected in 2824 patients diagnosed with HCC who underwent radical hepatectomy from the First Medical Center of the General Hospital of the People’s Liberation Army. A recently published prognostic model for PHLF was used as the reference model. The increase in AUC (ΔAUC), integrated discrimination improvement (IDI), and the continuous version of the net reclassification improvement (NRI) were applied for quantifying the incremental value of adding the inflammatory markers to the reference model. A p value < 0.05 was considered statistically significant. Results: The reference PHLF model showed acceptable prediction performance in the current cohort, with an AUC of 0.7492 (95%CI, 0.7191–0.7794). The calculated ΔAUC associated with procalcitonin (PCT) was the only one that was statistically significant (p < 0.05), with a value of 0.0044, and demonstrated the largest magnitude of the increase in AUC. The continuous NRI value associated with the systemic immune-inflammation index (SII) was 35.79%, second only to GPS (46.07%). However, the inflammatory markers of the new models with statistically significant IDI only included WBC count, lymphocyte count, and SII. IDI associated with SII, meanwhile, was the maximum (0.0076), which was consistent with the performance of using the ΔAUC (0.0044) to assess the incremental value of each inflammatory variable. Conclusions: Among a wide range of inflammatory markers, only PCT and SII have potential incremental prognostic value for predicting PHLF in patients with radical resectable HCC.