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Acute myeloid leukemia (AML) is a hematologic malignancy characterized by infiltration of the blood and bone marrow, exhibiting a low remission rate and high recurrence rate. Current research has demonstrated that class I HDAC inhibitors can downregulate anti-apoptotic proteins, leading to apoptosis of AML cells. In the present investigation, we conducted structural modifications of marine cytotoxin Santacruzamate A (SCA), a compound known for its inhibitory activity towards HDACs, resulting in the development of a novel series of potent class I HDACs hydrazide inhibitors. Representative hydrazide-based compound 25c exhibited concentration-dependent induction of apoptosis in AML cells as a single agent. Moreover, 25c exhibited a synergistic anti-AML effect when combined with Venetoclax, a clinical Bcl-2 inhibitor employed in AML therapy. This combination resulted in a more pronounced downregulation of anti-apoptotic proteins Mcl-1 and Bcl-xL, along with a significant upregulation of the pro-apoptotic protein cleaved-caspase3 and the DNA double-strand break biomarker γ-H2AX compared to monotherapy. These results highlighted the potential of 25c as a promising lead compound for AML treatment, particularly when used in combination with Venetoclax.

Highly accurate data on urban and rural settlement (URS) are essential for urban planning and decision-making in response to climate and environmental changes. This study developed an optimal random forest classification model for URSs based on spectral–topographic–radar polarization features using Landsat 8, NASA DEM, and Sentinel-1 SAR as the remote-sensing data sources. An optimal urban and rural settlement boundary (URSB) extraction technique based on morphological and pixel-level statistical methods was established to link discontinuous URSs and improve the accuracy of URSB extraction. An optimal random forest classification model for URSs was developed, as well as a technique to optimize URSB, using the Google Earth Engine (GEE) platform. The URSB of Xining, China, in 2020 was then extracted at a spatial resolution of 30 m, achieving an overall accuracy and Kappa coefficient of 96.21% and 0.92, respectively. Compared to using a single spectral feature, these corresponding metrics improved by 16.21% and 0.35, respectively. This research also demonstrated that the newly constructed Blue Roof Index (BRI), with enhanced blue roof features, is highly indicative of URSs and that the URSB was best extracted when the window size of the structural elements was 13 × 13. These results can be used to provide technical support for obtaining highly accurate information on URSs.

The calculation of a cockpit’s transient thermal load is important for determining the capacity of the cockpit environmental control system, ensuring the safety of electronic equipment and increasing the health and comfort of cockpit occupants. According to the structural parameters of the cockpit of a sightseeing airship, a physical model is established. The turbulence model and calculation method are selected and verified. The transient thermal load within full flight envelope, the cockpit thermal loads at different times of the day, and the cockpit thermal loads under different free-flow velocities are obtained based on the Computational Fluid Dynamics (CFD) method. The cockpit transient thermal loads during different seasons are also obtained. The results show that solar radiation has a great influence on the cockpit transient thermal load. As the flight altitude increases, the thermal load decreases from 8.8 kW (H = 0 m) to 4.7 kW (H = 3000 m). With the change in the solar radiation intensity and solar radiation angle, the thermal load increases considerably, from 2.2 kW (8:00 a.m.) to 5.4 kW (12:00 a.m.). The influence of the free-flow velocity is not very obvious at an altitude of 3000 m, as discussed in this study. The influence of seasons is significant. Finally, the influence of the solar absorptivity and infrared emissivity of the cockpit surface material are studied, and the temperature distribution on the cockpit’s surface is determined.

Bare permafrost refers to permafrost with almost no vegetation on the surface, which is an essential part of the ecosystem of the Tibetan Plateau. An accurate extraction of the boundaries of bare permafrost is vital for studying how it is being impacted by climate change. The accuracy of permafrost and bare land distribution maps is inadequate, and the spatial and temporal resolution is low. This is due to the challenges associated with obtaining significant amounts of data in high-altitude and alpine regions and the limitations of current mapping techniques in effectively integrating multiple factors. This study introduces a novel approach to extracting information about the distribution of bare permafrost. The approach introduced here involves amalgamating a sample extraction method, the fusion of multi-source remote sensing information, and a hierarchical classification strategy. Initially, the available multi-source permafrost data, expert knowledge, and refinement rules for training samples are integrated to produce extensive and consistent permafrost training samples. Using the random forest method, these samples are then utilized to create features and classify permafrost. Subsequently, a methodology utilizing a hierarchical classification approach in conjunction with machine learning techniques is implemented to identify an appropriate threshold for fractional vegetation cover, thereby facilitating the extraction of bare land. The bare permafrost boundary is ultimately derived through layer overlay analysis. The permafrost classification exhibits an overall accuracy of 90.79% and a Kappa coefficient of 0.806. The overall accuracies of the two stratified extractions in bare land were 97.47% and 96.99%, with Kappa coefficients of 0.954 and 0.911. The proposed approach exhibits superiority over the extant bare land and permafrost distribution maps. It is well-suited for retrieving vast bare permafrost regions and is valuable for acquiring bare permafrost distribution data across a vast expanse. It offers technical assistance in acquiring extended-term data on the distribution of exposed permafrost on the Tibetan Plateau. Furthermore, it facilitates the elucidation of the impact of climate change on exposed permafrost.

The CD13 inhibitor ubenimex is used as an adjuvant drug with chemotherapy for the treatment of cancer due to its function as an immunoenhancer, but it has limitations in its cytotoxic efficacy. The proteasome inhibitor ixazomib is a landmark drug in the treatment of multiple myeloma with a high anti-cancer activity. Herein, we conjugated the pharmacophore of ubenimex and the boric acid of ixazomib to obtain a dual CD13 and proteasome inhibitor 7 (BC-05). BC-05 exhibited potent inhibitory activity on both human CD13 (IC50 = 0.13 μM) and the 20S proteasome (IC50 = 1.39 μM). Although BC-05 displayed lower anti-proliferative activity than that of ixazomib in vitro, an advantage was established in the in vivo anti-cancer efficacy and prolongation of survival time, which may be due to its anti-metastatic and immune-stimulating activity. A pharmacokinetic study revealed that BC-05 is a potentially orally active agent with an F% value of 24.9%. Moreover, BC-05 showed more favorable safety profiles than those of ixazomib in preliminary toxicity studies. Overall, the results indicate that BC-05 is a promising drug candidate for the treatment of multiple myeloma.

Epsilon toxin (ETX) is an exotoxin produced by type B and D Clostridium perfringens that causes enterotoxemia or necrotic enteritis in animals such as goats, sheep, and cattle. Vaccination is a key method in preventing such diseases. In this study, we developed a new type of dissolving microneedle patch (dMN) with a nanoparticle adjuvant for enhanced immune response to deliver the rETXY196E-C protein vaccine. We chose FDA-approved poly(lactic-co-glycolic acid) (PLGA) to prepare nanospheres as the vaccine adjuvant and introduced dimethyldioctadecylammonium bromide (DDAB) to make the surface of PLGA nanoparticles (PLGA NPs) positively charged for antigen adsorption. PLGA NPs with a diameter of 100~200 nm, a surface ZETA potential of approximately +40 mV, and good safety were successfully prepared and could effectively adsorb rETXY196E-C protein. Using non-toxic and antibacterial fish gelatin as the microneedle (MN) matrix, we prepared a PLGA-DDAB dMN vaccine with good mechanical properties that successfully penetrated the skin. After immunization of subcutaneous (SC) and dMN, antibody titers of the PLGA and Al adjuvant groups were similar in both two immune ways. However, in vivo neutralization experiments showed that the dMN vaccines had a better protective effect. When challenged with 100 × LD50 GST-ETX, the survival rate of the MN group was 100%, while that of the SC Al group was 80%. However, a 100% protective effect was achieved in both immunization methods using PLGA NPs. In vitro neutralization experiments showed that the serum antibodies from the dMN and SC PLGA NPs groups both protect naive mice from 10 × LD50 GST-ETX attack after being diluted 20 times and could also protect MDCK cells from 20 × CT50 GST-ETX attack. In conclusion, the PLGA-DDAB dMN vaccine we prepared has good mechanical properties, immunogenicity, and protection, and can effectively prevent ETX poisoning. This provides a better way of delivering protein vaccines.

Despite the fact that gross primary productivity (GPP) and water use efficiency (WUE) have been widely used as indicators to evaluate the water-carbon cycle, uncertainties exist in the patterns of GPP and WUE responses to climate variability along different aridity gradients. In this study, the aridity index was used to divide China into four arid-humid zones. The spatiotemporal variability of multiple vegetation types GPP and WUE in response to climate change under different arid-humid zones were investigated based on remote sensing data. The results indicated that the increasing trend of WUE in the four arid-humid zones of China was less pronounced than GPP from 2001 to 2021. The GPP value decreased gradually from the humid to the arid zone, and the WUE value in the arid zone was slightly higher than in the semi-arid zone. The GPP of all vegetation types in China showed a tendency to increase, while shrubland and wetland WUE tended to decrease. The major vegetation types (e.g., forest, cropland and grassland) in each aridity gradient contributed to the changes in local GPP and WUE. However, in individual arid-humid zones, wetland and shrubland also exhibited high GPP and WUE values that were not inferior to forest and cropland. Temperature and precipitation were the main climatic factors responsible for the increase in vegetation GPP in different aridity gradients, with a higher positive correlation for temperature than precipitation. WUE showed a distinct positive and negative correlation with the thermal factors (temperature and net radiation) and the moisture factors (precipitation and relative humidity); this pattern was more pronounced in the humid and semi-humid zones. Net radiation and precipitation may be the main climatic factors causing a slight upward trend in WUE across the arid-humid zones, while the decrease in shrubland and wetland WUE may be related to relative humidity and precipitation.

Background: As a Class A bioterrorism agent, botulinum neurotoxin serotype A (BoNT/A) carries the risk of being used by terrorists to cause mass poisoning. The microneedle (MN) patch has a great potential for application as a novel vaccine delivery method. The aim of this study is to develop a thermally stable, dissolving microneedle patch for the delivery of a recombinant protein vaccine using a recombinant C-terminal heavy chain of BoNT/A (Hc of BoNT/A, AHc) to prevent botulism. Methods: Fish gelatin, a natural non-toxic and bacteriostatic material, was selected as the microneedle matrix for the preparation of the dissolving microneedle vaccine. Subsequently, the mechanical performance, bacteriostatic properties, vaccination effect, and stability of the microneedle patches were evaluated using instruments such as the displacement-force test station and optical coherence tomography (OCT) scanner. Results: Fish gelatin matrix at high concentrations has good bacteriostatic properties, and excellent mechanical performance and vaccination effect, meeting the necessities of a vaccine. In both in vivo and in vitro neutralization experiments, MN vaccines containing different antigen doses achieved the same protective efficacy as subcutaneous vaccinations, protecting mice against 106 LD50 of BoNT/A injected intraperitoneally. Thermal stability analysis of the MN vaccines revealed that the fish gelatin matrix protected the AHc vaccine from protein denaturation even after 7 days of storage at 37 °C and enabled the vaccine patches to maintain good immunogenicity and protective efficacy even after 6 months of storage at room temperature. Conclusion: In this study, we successfully prepared a bacteriostatic MN patch using a fish gelatin matrix that not only has a good vaccination effect, but also obviates the need for a cold chain for the AHc vaccine, providing the possibility of rapid, painless, and large-scale vaccination.

In this paper, nano plugging agent AMPS/AM was prepared, and its plugging performance was evaluated by a microfracture simulation experiment and a shale pressure resistance experiment. The pressure loss decreased by 66.09% compared with the top pressure of 6.9 MPa, and the average core indentation hardness increased 58.12% with 3% AMPS/AM blocking mud. The experiments indicate that AMPS/AM can effectively seal the shale micropore and nanopore structure and greatly improve the stability of fractured shale well wall. The field application results of the YS129H well in the Zhaotong block show that the water-based drilling fluid with nano plugging agent AMPS/AM as the key agent has strong plugging performance. The well mud is of high temperature and pressure water loss < s4 mL, mud cake permeability reduction rate of 85.67%, which indicates that the drilling fluid system has good sealing properties and well wall stability.

To perform diagnosis and treatment, health systems, hospitals, and other patient care facilities require a wide range of supplies, from masks and gloves to catheters and implants. The “healthcare supply chain/healthcare operation management” refers to the stakeholders, systems, and processes required to move products from the manufacturer to the patient’s bedside. The ultimate goal of the healthcare supply chain is to ensure that the right products, in the right quantities, are available in the right places at the right time to support patient care. Hospitals and the concept of a healthcare delivery system are practically synonymous. Surgical services, emergency and disaster services, and inpatient care are the three main types of services they offer. Outpatient clinics and facilities are also available at some hospitals, where patients can receive specialty consultations and surgical services. There will always be a need for inpatient care, regardless of how care models develop. The focus of this monograph was on recent OM work that models the dynamic, interrelated effects of demand-supply matching in the ED, OR, and inpatient units. Decisions about staffing and scheduling in these areas are frequently made independently by healthcare managers and clinicians. Then, as demand changes in real-time, clinicians and managers retaliate as best as they can to reallocate staffing to the areas that require it most at a particular moment in time in order to relieve patient flow bottlenecks. We, as OM researchers, must create models that help healthcare administrators enhance OR scheduling policies, ED demand forecasting, and medium- and short-term staffing plans that consider the interdependence of how demand develops.

Deoxynivalenol (DON) contamination in germs and germ oil is posing a serious threat to food and feed security. However, the transformation pathway, the distribution of DON, and its degradation products in edible oil refining have not yet been reported in detail. In this work, we systematically explored the variation of DON in maize germ oil during refining and demonstrated that the DON in germ oil can be effectively removed by refining, during which a part of DON was transferred to the wastes, and another section of DON was degraded during degumming and alkali refining. Moreover, the DON degradation product was identified to be norDON B by using the ultraviolet absorption spectrum, high-performance liquid chromatography (HPLC), ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF MS), and nuclear magnetic resonance (NMR) methods, and the degradation product was found to be distributed in waste products during oil refining. This study provides a scientific basis and useful reference for the production of non-mycotoxins edible oil by traditional refining.

As next-generation large-aperture telescopes, synthetic aperture is a promising method for realizing high resolution observations. Co-phasing the misaligned segmented aperture is an important procedure for high-resolution observations with segmented telescopes. In this paper, a piston error detection method is proposed based on two interference patterns. Two interference patterns are generated by using a lens placed across two adjacent pupils in the exit pupil plane at two wavelengths and a method based on phase retrieval technique is proposed to extract the piston error from the two interference patterns. The introduction of dual-wavelength in the scheme overcomes the 2π ambiguities problem and expands the piston error detection range. Meanwhile, the proposed piston error extraction method based on phase retrieval technique allows high precision measurement of the piston error and is robust to offset lens. Various simulations are demonstrated and the feasibility of the proposed piston error detection method is validated.

Ubiquitination plays a major role in immune regulation after viral infection. An alternatively spliced porcine E3 ubiquitin ligase RNF122 promoted PRRSV infection and upregulated in PRRSV-infected PAM cells was identified. We characterized the core promoter of RNF122, located between −550 to −470 bp upstream of the transcription start site (TSS), which displayed significant differential transcriptional activities in regulating the transcription and expression of RNF122. The transcription factor HLTF was inhibited by nsp1α and nsp7 of PRRSV, and the transcription factor E2F complex regulated by nsp9. Together, they modulated the transcription and expression of RNF122. RNF122 could mediate K63-linked ubiquitination to raise stability of PRRSV nsp4 protein and thus promote virus replication. Moreover, RNF122 also performed K27-linked and K48-linked ubiquitination of MDA5 to degrade MDA5 and inhibit IFN production, ultimately promoted virus proliferation. In this study, we illustrate a new immune escape mechanism of PRRSV that enhances self-stability and function of viral nsp4, thus, regulating RNF122 expression to antagonize IFNα/β production. The present study broadens our knowledge of PRRSV-coding protein modulating transcription, expression and modification of host protein to counteract innate immune signaling, and may provide novel insights for the development of antiviral drugs.

A mild two-step method of black phosphorus (BP) flake thinning was demonstrated in this article. Slight ultraviolet–ozone (UVO) radiation followed by an argon plasma treatment was employed to oxidize mechanically exfoliated BP flakes and remove the surface remains of previous ozone treatment. The annealing process introduced aims to reduce impurities and defects. Low damage and efficient electronic devices were fabricated in terms of controlling the thickness of BP flakes through this method. These results lead to an important step toward the fabrication of high-performance devices based on two-dimensioned materials.

Distribution mismatch can be easily found in multi-sensor systems, which may be caused by different shoot angles, weather conditions and so on. Domain adaptation aims to build robust classifiers using the knowledge from a well-labeled source domain, while applied on a related but different target domain. Pseudo labeling is a prevalent technique for class-wise distribution alignment. Therefore, numerous efforts have been spent on alleviating the issue of mislabeling. In this paper, unlike existing selective hard labeling works, we propose a fuzzy labeling based graph learning framework for matching conditional distribution. Specifically, we construct the cross-domain affinity graph by considering the fuzzy label matrix of target samples. In order to solve the problem of representation shrinkage, the paradigm of sparse filtering is introduced. Finally, a unified optimization method based on gradient descent is proposed. Extensive experiments show that our method achieves comparable or superior performance when compared to state-of-the-art works.

Deep neural networks have evolved significantly in the past decades and are now able to achieve better progression of sensor data. Nonetheless, most of the deep models verify the ruling maxim in deep learning—bigger is better—so they have very complex structures. As the models become more complex, the computational complexity and resource consumption of these deep models are increasing significantly, making them difficult to perform on resource-limited platforms, such as sensor platforms. In this paper, we observe that different layers often have different pruning requirements, and propose a differential evolutionary layer-wise weight pruning method. Firstly, the pruning sensitivity of each layer is analyzed, and then the network is compressed by iterating the weight pruning process. Unlike some other methods that deal with pruning ratio by greedy ways or statistical analysis, we establish an optimization model to find the optimal pruning sensitivity set for each layer. Differential evolution is an effective method based on population optimization which can be used to address this task. Furthermore, we adopt a strategy to recovery some of the removed connections to increase the capacity of the pruned model during the fine-tuning phase. The effectiveness of our method has been demonstrated in experimental studies. Our method compresses the number of weight parameters in LeNet-300-100, LeNet-5, AlexNet and VGG16 by 24×, 14×, 29× and 12×, respectively.

Domain adaptation aims to handle the distribution mismatch of training and testing data, which achieves dramatic progress in multi-sensor systems. Previous methods align the cross-domain distributions by some statistics, such as the means and variances. Despite their appeal, such methods often fail to model the discriminative structures existing within testing samples. In this paper, we present a sample-guided adaptive class prototype method, which consists of the no distribution matching strategy. Specifically, two adaptive measures are proposed. Firstly, the modified nearest class prototype is raised, which allows more diversity within same class, while keeping most of the class wise discrimination information. Secondly, we put forward an easy-to-hard testing scheme by taking into account the different difficulties in recognizing target samples. Easy samples are classified and selected to assist the prediction of hard samples. Extensive experiments verify the effectiveness of the proposed method.

With the advantages of uniform stress transfer and weight reduction, adhesive joints are widely used in engineering. The propagation of non-linear Lamb waves in an adhesive joint with micro-cracks distributing in a random way is systematically investigated by using the numerical simulation method in this paper. A finite element model of the tri-layer adhesive structure with micro-cracks distributing randomly is established, and the Lamb wave mode pair with a matching condition of the phase velocity is chosen to examine the interaction of the micro-cracks with Lamb waves. The results show that the micro-cracks within the adhesive layer will lead to the generation of second harmonics. We also find that the Acoustic Non-linearity Parameters (ANP) increase with the propagation distance in the micro-crack damage zone and the density of the micro-cracks. However, ANPs are less concerned with the friction coefficients of the surface of micro-cracks. This numerical research reveals that non-linear Lamb waves can be employed to effectively characterize the micro-cracks related damages within an adhesive joint.

The sensor-weapon−target assignment (S-WTA) problem is a crucial decision issue in C4ISR. The cooperative engagement capability (CEC) of sensors and weapons depends on the S-WTA schemes, which can greatly affect the operational effectiveness. In this paper, a mathematical model based on the synthetical framework of the S-WTA problem is established, combining the dependent and independent cooperative engagement modes of sensors and weapons. As this problem is a complex combinatorial optimization problem, a novel genetic algorithm is proposed to improve the solution of this formulated S-WTA model. Based on the prior knowledge of this problem, a problem-specific population initialization method and two novel repair operators are introduced. The performances of the proposed algorithm have been validated on the known benchmarks. Extensive experimental studies compared with three state-of-the-art approaches demonstrate that the proposed algorithm can generate better assignment schemes for the most of the benchmarks.

Time lapse microscopy is essential for quantifying the dynamics of cells, subcellular organelles and biomolecules. Biologists use different fluorescent tags to label and track the subcellular structures and biomolecules within cells. However, not all of them are compatible with time lapse imaging, and the labeling itself can perturb the cells in undesirable ways. We hypothesized that phase image has the requisite information to identify and track nuclei within cells. By utilizing both traditional blob detection to generate binary mask labels from the stained channel images and the deep learning Mask RCNN model to train a detection and segmentation model, we managed to segment nuclei based only on phase images. The detection average precision is 0.82 when the IoU threshold is to be set 0.5. And the mean IoU for masks generated from phase images and ground truth masks from experts is 0.735. Without any ground truth mask labels during the training time, this is good enough to prove our hypothesis. This result enables the ability to detect nuclei without the need for exogenous labeling.

Dempster-Shafer evidence theory (DST) has shown its great advantages to tackle uncertainty in a wide variety of applications. However, how to quantify the information-based uncertainty of basic probability assignment (BPA) with belief entropy in DST framework is still an open issue. The main work of this study is to define a new belief entropy for measuring uncertainty of BPA. The proposed belief entropy has two components. The first component is based on the summation of the probability mass function (PMF) of single events contained in each BPA, which are obtained using plausibility transformation. The second component is the same as the weighted Hartley entropy. The two components could effectively measure the discord uncertainty and non-specificity uncertainty found in DST framework, respectively. The proposed belief entropy is proved to satisfy the majority of the desired properties for an uncertainty measure in DST framework. In addition, when BPA is probability distribution, the proposed method could degrade to Shannon entropy. The feasibility and superiority of the new belief entropy is verified according to the results of numerical experiments.

Error factors in the encoded sun sensor (ESS) are analyzed and simulated. Based on the analysis results, an ESS error compensation model containing structural errors and fine-code algorithm errors is established, and the corresponding calibration method for model parameters is proposed. As external parameters, installation deviation between ESS and calibration equipment are introduced to the ESS calibration model, so that the model parameters can be calibrated accurately. The experimental results show that within plus/minus 60 degree of incident angle, the ESS measurement accuracy after compensation is three times higher on average than that before compensation.

When optimizing an accelerated degradation testing (ADT) plan, the initial values of unknown model parameters must be pre-specified. However, it is usually difficult to obtain the exact values, since many uncertainties are embedded in these parameters. Bayesian ADT optimal design was presented to address this problem by using prior distributions to capture these uncertainties. Nevertheless, when the difference between a prior distribution and actual situation is large, the existing Bayesian optimal design might cause some over-testing or under-testing issues. For example, the implemented ADT following the optimal ADT plan consumes too much testing resources or few accelerated degradation data are obtained during the ADT. To overcome these obstacles, a Bayesian sequential step-down-stress ADT design is proposed in this article. During the sequential ADT, the test under the highest stress level is firstly conducted based on the initial prior information to quickly generate degradation data. Then, the data collected under higher stress levels are employed to construct the prior distributions for the test design under lower stress levels by using the Bayesian inference. In the process of optimization, the inverse Gaussian (IG) process is assumed to describe the degradation paths, and the Bayesian D-optimality is selected as the optimal objective. A case study on an electrical connector’s ADT plan is provided to illustrate the application of the proposed Bayesian sequential ADT design method. Compared with the results from a typical static Bayesian ADT plan, the proposed design could guarantee more stable and precise estimations of different reliability measures.

Accelerated degradation testing (ADT) is an efficient tool to conduct material service reliability and safety evaluations by analyzing performance degradation data. Traditional stochastic process models are mainly for linear or linearization degradation paths. However, those methods are not applicable for the situations where the degradation processes cannot be linearized. Hence, in this paper, a general ADT model based on the Wiener process is proposed to solve the problem for accelerated degradation data analysis. The general model can consider the unit-to-unit variation and temporal variation of the degradation process, and is suitable for both linear and nonlinear ADT analyses with single or multiple acceleration variables. The statistical inference is given to estimate the unknown parameters in both constant stress and step stress ADT. The simulation example and two real applications demonstrate that the proposed method can yield reliable lifetime evaluation results compared with the existing linear and time-scale transformation Wiener processes in both linear and nonlinear ADT analyses.

Accelerated degradation testing (ADT) is an efficient technique for evaluating the lifetime of a highly reliable product whose underlying failure process may be traced by the degradation of the product’s performance parameters with time. However, most research on ADT mainly focuses on a single performance parameter. In reality, the performance of a modern product is usually characterized by multiple parameters, and the degradation paths are usually nonlinear. To address such problems, this paper develops a new s-dependent nonlinear ADT model for products with multiple performance parameters using a general Wiener process and copulas. The general Wiener process models the nonlinear ADT data, and the dependency among different degradation measures is analyzed using the copula method. An engineering case study on a tuner’s ADT data is conducted to demonstrate the effectiveness of the proposed method. The results illustrate that the proposed method is quite effective in estimating the lifetime of a product with s-dependent performance parameters.

From the aspect of human circulation system structure, a complete hemodynamic model requires consideration of the influence of microcirculation load effect. This paper selected the seepage in porous media as the simulant of microcirculation load. On the basis of a bi-directional liquid-solid coupling tube model, we built a liquid-solid-porous media seepage coupling model. The simulation parameters accorded with the physiological reality. Inlet condition was set as transient single-pulse velocity, and outlet as free outlet. The pressure in the tube was kept at the state of dynamic stability in the range of 80–120 mmHg. The model was able to simulate the entire propagating process of pulse wave. The pulse wave velocity simulated was 6.25 m/s, which accorded with the physiological reality. The complex pressure wave shape produced by reflections of pressure wave was also observed. After the model changed the cardiac cycle length, the pressure change according with actual human physiology was simulated successfully. The model in this paper is well-developed and reliable. It demonstrates the importance of microcirculation load in hemodynamic model. Moreover the properties of the model provide a possibility for the simulation of dynamic adjustment process of human circulation system, which indicates a promising prospect in clinical application.

Plant–microbe interactions are key to nutrient-use efficiency. Root microbes are influenced by rhizosphere soil and plant cultivars. The impact of cultivar-by-nitrogen (N) interactions on the maize-root microbiome remains unclear, yet it is crucial for understanding N use efficiency in maize. This study evaluated the effects of maize cultivars and N forms, along with their interactions, on the diversity and composition of root bacteria and fungi. Additionally, we examined correlations between soil microbes and root metabolites. The maize cultivar Zhengdan 958 (ZD958) showed a positive response to the mixture of nitrate and ammonium N, resulting in increased in biomass, grain yield, shoot N content, grain N content, and root area. In contrast, the cultivar Denghai605 (DH605) did not exhibit a similar response. The diversity and composition of root bacteria and fungi differed between ZD958 and DH605. The N form primarily affected the community structure of rhizospheric fungi in ZD958 and rhizospheric bacteria in DH605, rather than endophytic microbes. A mixed N supply increased the relative abundance of Basidiomycota, which was positively correlated with ZD958 yield. For DH605, a mixed N treatment enhanced nitrification functions involving Bacteroidetes and Proteobacteria, while it reduced the effects of ammonium N supply. The dominant rhizospheric microbes in DH605 showed a stronger response to changes in root metabolites compared to those in ZD958. A mixed N supply increased the content of palmitoleic acid in ZD958 root exudates, facilitating the recruitment of beneficial rhizospheric microbes, which promotes maize growth. In DH605, a mixed N supply decreased the concentration of sphinganine, which is significantly correlated with Acidobacteria (negatively), Proteobacteria (negatively), Bacteroidetes (positively), and TM7 (positively). Our findings suggest that different maize cultivars respond differently to N forms, causing distinct rhizospheric microbial effects, and that root metabolites send metabolic signals to regulate and recruit key bacterial and fungal communities.

Artificial intelligence (AI) in complex networks has made revolutionary breakthroughs in this century, and AI-driven methods are being increasingly integrated into different scientific research [...]

Multi-objective reservoir operation of reservoir flood control involves numerous factors and complex model solving, and exploring effective methods for solving the operation models has always been a hot topic in reservoir optimization operation research. The Multi-Objective Ant Lion Algorithm (MOALO) is an emerging heuristic intelligent optimization algorithm, but it has not yet been applied in reservoir optimization operation. Testing the effectiveness of this method on multi-objective reservoir scheduling and further improving the optimization performance of this method is of great significance for enhancing the overall benefits of reservoir operation. In this study, MOALO is applied to the optimal scheduling of reservoir flood control. To increase the search efficiency of MOLAO, the advanced MOALO method (AMOLAO) is proposed by reconstructing the search distribution in MOALO using a power function. Taking the Songshu Reservoir and Dongfeng Reservoir in the Fuzhou River Basin in Dalian City as an example, MOALO, AMOLAO, and other two traditional methods are applied for solving the multi-objective reservoir operation problem. Results show that the AMOALO method has high search efficiency, strong optimization ability, and good stability. AMOALO performs better than MOALO and the two traditional methods. The study provides an efficient method for solving the problems in multi-objective reservoir operation.

Transportation significantly influences economically underdeveloped arid regions, impacting economic growth and social progress. Analyzing Transport Superiority Degree (TSD) and its implications in such regions is crucial. A new arid region-specific evaluation framework addresses traditional limitations by considering indicators like route connectivity and desert interference. This article conducts an empirical study using Southern Xinjiang as a research case. It combines comprehensive evaluation methods, spatial autocorrelation methods, spatial Durbin models, and coupling coordination models to depict Transport Support Capability (TSC), Transport Access Capacity (TAC), and Transport Guarantee Capacity (TGC) at different scales in Southern Xinjiang from 2000 to 2020. The study reveals spatial patterns, evolutionary characteristics, economic impacts, and social effects of TSD at various scales. Key findings include: (1) Rapid expansion of transportation infrastructure in Southern Xinjiang. The levels of TSD at different scales have gradually increased, and spatial and temporal pattern differences are evident. At the county level, TSD forms a “core-periphery” spatial pattern centered around the Southern Xinjiang Railway, with “high-high” agglomeration areas centered around Kashgar city and “low-low” agglomeration areas centered around Qiemo County. (2) Prefectural-level TSD improvements have limited impacts on regional development, while county-level TSC, TAC, TGC, and TSD positively affect economic and social growth but also exhibit competitive effects. (3) TSD is transitioning from non-coordinated to coordinated development with economic and social progress at different scales. This research informs transportation facility evaluation in arid regions.

Highly sensitive surface acoustic wave (SAW) sensors have recently been recognized as a promising tool for various industrial and medical applications. However, existing SAW sensors generally suffer from a complex design, large size, and poor robustness. In this paper, we develop a simple and stable delay line ultra-high frequency (UHF) SAW sensor for highly sensitive detection of temperature. A Z-shaped delay line is specially designed on the piezoelectric substrate to improve the sensitivity and reduce the substrate size. Herein, the optimum design parameters of extremely short-pitch interdigital transducers (IDTs) are given by numerical simulations. The extremely short pitch gives the SAW sensor ultra-high operating frequency and consequently ultra-high sensitivity. Several experiments are conducted to demonstrate that the sensitivity of the Z-shaped SAW delay line sensor can reach up to 116.685°/°C for temperature detection. The results show that the sensor is an attractive alternative to current SAW sensing platforms in many applications.

GR24, a synthetic strigolactone analogue, plays a crucial role in a wide range of life processes. Studying the effects of the pre-harvest application of GR24 on strawberry ripening and fruit quality provides a scientific basis for the application of GR24 in horticultural crop production. GR24 solutions at concentrations of 0, 0.5, 1 and 2 µmol/L were applied to strawberries at the de-greening stage. The results showed that pre-harvest treatment with 1 µmol/L GR24 increased fruit weight and size; promoted fruit softening; increased the content of sugars, total flavonoids, total phenolics, and anthocyanins, and increased antioxidant activity. These results proved that GR24 effectively accelerated fruit ripening and improved fruit quality. In addition, to gain more insight into the biological mechanism of GR24 in fruit ripening, we conducted a combined transcriptomic and metabolomic analysis. It was found that the differences were related to sugar and flavonoid biosynthesis, particularly in the later stage of fruit ripening. qRT-PCR analysis revealed that GR24 increased gene expression activity and metabolite accumulation in the anthocyanin and sugar biosynthetic pathway, thereby promoting strawberry color and flavor.

In numerical analysis, the pile equivalent calculation method plays a key role in foundation pit calculation results. However, the effect and mechanism of different pile equivalent calculation methods in the foundation pit has remained unclear. To solve this question, based on FLAC3D, four pile equivalent calculation methods were introduced into a typical pile–anchor pit. This research was carried out from the perspective of soil displacement and stress, special points’ stress paths, and the plastic zone. The results revealed that there was a remarkable influence on the calculation results for different pile equivalent calculation methods. Specifically, compared with structural pile elements, displacements near the pile were smaller in solid pile element mode. Moreover, with the increase in excavation depth, stress concentration appeared in the solid pile element mode. The solid support pile with the interface, compared to that without the interface, had less displacement but more stress concentration around the pile, which led the special points’ stress paths around the solid pile to become irregular. Regarding the structural pile elements, it is suggested that both modeling approaches had a similar effect. These findings could help to provide a deeper insight into pile–anchor foundation pit numerical analysis.

Membrane computing is a natural computing branch inspired by the structure of biological cells. The mathematical abstract model of a membrane computing system is called a P System, which is one of the main topics in membrane computing research for the design and verification of a P System. Integer factorization is still a world-class problem and a very important research direction. If a fast method can be found to solve the integer factorization problem, several important cryptographic systems including the RSA public key algorithm will be broken. The aim of this paper is to design a P System capable of implementing integer decomposition, taking advantage of the characteristics of parallelism of P Systems. We construct a process with a main goal to study the modal exponential function f(x) = ax mod N and explore the possible periodic behavior for different values of a. We attempt to compute nontrivial prime factors by the period found and constrain the operation of the P System in polynomial time.

Effective in-season crop nitrogen (N) status diagnosis is important for precision crop N management, and remote sensing using an unmanned aerial vehicle (UAV) is one efficient means of conducting crop N nutrient diagnosis. Here, field experiments were conducted with six N levels and six maize hybrids to determine the nitrogen nutrition index (NNI) and yield, and to diagnose the N status of the hybrids combined with multi-spectral data. The NNI threshold values varied with hybrids and years, ranging from 0.99 to 1.17 in 2018 and 0.60 to 0.71 in 2019. A proper agronomic optimal N rate (AONR) was constructed and confirmed based on the measured NNI and yield. The NNI (R2 = 0.64–0.79) and grain yield (R2 = 0.70–0.73) were predicted well across hybrids using a random forest model with spectral, structural, and textural data (UAV). The AONRs calculated using the predicted NNI and yield were significantly correlated with the measured NNI (R2 = 0.70 and 0.71 in 2018 and 2019, respectively) and yield (R2 = 0.68 and 0.54 in 2018 and 2019, respectively). It is concluded that data fusion can improve in-season N status diagnosis for different maize hybrids compared to using only spectral data.

The accurate identification of forest tree species is important for forest resource management and investigation. Using single remote sensing data for tree species identification cannot quantify both vertical and horizontal structural characteristics of tree species, so the classification accuracy is limited. Therefore, this study explores the application value of combining airborne high-resolution multispectral imagery and LiDAR data to classify tree species in study areas of different altitudes. Three study areas with different altitudes in Muyu Town, Shennongjia Forest Area were selected. Based on the object-oriented method for image segmentation, multi-source remote sensing feature extraction was performed. The recursive feature elimination algorithm was used to filter out the feature variables that were optimal for classifying tree species in each altitude study area. Four machine learning algorithms, SVM, KNN, RF, and XGBoost, were combined to classify tree species at each altitude and evaluate the accuracy. The results show that the diversity of tree layers decreased with the altitude in the different study areas. The texture features and height features extracted from LiDAR data responded better to the forest community structure in the different study areas. Coniferous species showed better classification than broad-leaved species within the same study areas. The XGBoost classification algorithm showed the highest accuracy of 87.63% (kappa coefficient of 0.85), 88.24% (kappa coefficient of 0.86), and 84.03% (kappa coefficient of 0.81) for the three altitude study areas, respectively. The combination of multi-source remote sensing numbers with the feature filtering algorithm and the XGBoost algorithm enabled accurate forest tree species classification.

Objective: To define the characteristics of Mini LDH, develop new diagnostic references and examine the clinical efficacy of percutaneous endoscopic lumbar discectomy via a transforaminal approach (TF-PELD) for it. Methods: A total of 72 patients who underwent TF-PELD with Mini LDH from September 2019 to October 2022 were enrolled in this retrospective study. The patients’ basic information, symptoms, number of outpatient visits, duration of conservative treatment, physical examination findings and so on were obtained from the medical records. Clinical effects of TF-PELD for Mini LDH were assessed by means of the following: the Visual Analog Scale (VAS) for low back pain (LBP) and leg pain, Oswestry Disability Index (ODI) for functional status assessment and Modified Mac Nab criteria for patient satisfaction. Results: Mini LDH have specific clinical characteristics and imaging features. All included patients achieved obvious pain relief after TF-PELD surgery. Pain scores were repeated at postoperative day 1 and 1, 3, 6, 12 and 24 months later. Results were statistically analyzed. The average VAS-Back, VAS-Leg and ODI scores were all significantly reduced at the first postoperative day and gradually decreased with the follow-up time continuing. In total, 66 out of 72 patients received an excellent or good recovery and no poor result was reported according to the Modified Mac Nab criteria. Conclusions: Mini LDH is a type of LDH with special characteristics and in need of correct diagnosis and active treatment in clinical work. TF-PELD was also found to be an effective procedure for the treatment of Mini LDH.

Cembranoids produced by tobacco glandular trichomes have bioactivities in resistance to insect pests and pathogens. Cembratrien-ol synthase (CBTS) plays a key role in the biosynthesis of cembranoids and directly determines the cembranoid content in tobacco. This study examined the effect of changing CBTS1 expression on tobacco resistance to the insect pest Spodoptera frugiperda and oomycete pathogen Phytophthora nicotianae. The CDS sequence of CBTS1 was cloned into gene overexpression and silencing vectors and introduced into tobacco (Nicotiana tabacum L. cv. TN90) to obtain CBTS1-overexpression plants (CBTS1-OE) and CBTS1-silenced plants (CBTS1-RI). Compared with control plants, the content of cembratrien-ol (CBT-ol) was increased 4.48 times in the CBTS1-OE plants but decreased by 68% in the CBTS1-RI plants, while that of cembratrien-diol (CBT-diol) was increased 3.17 times in the CBTS1-OE plants but decreased by 76% in the CBTS1-RI plants. The S. frugiperda resistance of transgenic tobacco plants was evaluated by in vitro toxicity test, and the results showed that the resistance of CBTS1-OE plants to S. frugiperda was significantly improved but that of CBTS1-RI plants was reduced. The P. nicotianae resistance of transgenic tobacco plants was assessed by the detached leaf assay, and the results showed that the resistance of CBTS1-OE plants to P. nicotianae was enhanced, while that of CBTS1-RI plants was attenuated. Further gene expression analysis showed that overexpression of CBTS1 increased the expression of the pathogen-related gene PR-1a, while silencing of CBTS1 decreased its expression. This study demonstrated that manipulating the expression of CBTS1 could change the cembranoid content in tobacco plants and alter their resistance to both insect pests and oomycete pathogens.

Melatonin, insulin, and Δ9-tetrahydrocannabinol (THC) have been shown to reverse cognitive deficits and attenuate neuropathologies in transgenic mouse models of Alzheimer’s disease (AD) when used individually. Here, we evaluated the therapeutic properties of long-term intranasal treatment with a novel nanoformulation containing melatonin, insulin, and THC in aged APPswe/PS1ΔE9 (APP/PS1) mice, a transgenic model of AD. Transgenic mice at the age of 12 months were intranasally administered with a new nanoformulation containing melatonin, insulin, and THC at doses of 0.04, 0.008, and 0.02 mg/kg, respectively, once daily for 3 months. The spatial memory of the mice was assessed using the radial arm water maze (RAWM) test before and after drug treatment. Brain tissues were collected at the end of the treatment period for the assessment of Aβ load, tauopathy state, and markers of mitochondrial function. The RAWM test revealed that the treatment with the melatonin–insulin–THC (MIT) nasal spray improved the spatial learning memory of APP/PS1 mice significantly. Results of protein analyses of brain homogenates indicated that MIT treatment significantly decreased the tau phosphorylation implicated in tau toxicity (p < 0.05) and the expression of CKMT1 associated with mitochondrial dysfunction. Moreover, MIT significantly decreased the expression of two mitochondrial fusion-related proteins, Mfn2 and Opa1 (p < 0.01 for both), while increasing the expression of a mitophagy regulator, Parkin, suggesting a compensatory enhancement of mitophagy due to MIT-promoted mitochondrial fusion. In conclusion, this study was the first to demonstrate the ability of an MIT nanoformulation to improve spatial memory in AD mice through its multi-targeting effects on Aβ production, tau phosphorylation, and mitochondrial dynamics. Thus, MIT may be a safe and effective therapeutic for AD.

Infrared point-target detection is one of the key technologies in infrared guidance systems. Due to the long observation distance, the point target is often submerged in the background clutter and large noise in the process of atmospheric transmission and scattering, and the signal-to-noise ratio is low. On the other hand, the target in the image appears in the form of fuzzy points, so that the target has no obvious features and texture information. Therefore, scholars have proposed many object detection methods for dimming infrared images, which has become a hot research topic on account of the flow-rank model based on the image patch. However, the result has a high false alarm rate because the most low-rank models based on the image patch do not consider the spatial-temporal characteristics of the infrared sequences. Therefore, we introduce 3D total variation (3D-TV) to regularize the foreground on account of the non-convex rank approximation minimization method, so as to consider the spatial-temporal continuity of the target and effectively suppress the interference caused by dynamic background and target movement on the foreground extraction. Finally, this paper proposes the minimization of the non-convex spatial-temporal tensor low-rank approximation algorithm (MNSTLA) by studying the related algorithms of the point infrared target detection, and the experimental results show strong robustness and a low false alarm rate for the proposed method compared with other advanced algorithms, such as NARM, RIPT, and WSNMSTIPT.

Due to their strong bacterial binding and bacterial toxicity, cationic liposomes have been utilized as effective antibacterial materials in many studies. However, few researchers have systematically compared their antibacterial activity with their mammalian cell cytotoxicity or have deeply explored their antibacterial and cytotoxicity mechanisms. Here, we prepared a series of cationic liposomes (termed CLs) using dimethyldioctadecylammonium chloride (DODAC) and lecithin at different molar ratios. CLs have the ability to effectively bind with Gram-positive and Gram-negative bacteria through electrostatic and hydrophobic interactions. Further, the CLs with high molar ratios of DODAC (30 and 40 mol%) can disrupt the bacterial wall/membrane, efficiently inducing the production of reactive oxygen species (ROS). More importantly, we carefully compared the antibacterial activity and the mammalian cell cytotoxicity of various CLs differing in DODAC contents and liposomal concentrations and revealed that, whether they are bacterial or mammalian cells, an increasing DODAC content in CLs can lead to an elevated cytotoxicity level. Further, there exists a critical DODAC contents (>20 mol%) in CLs to endow them with effective antibacterial ability. However, the variation in the DODAC content and liposomal concentration of CLs has different degrees of influence on the antibacterial activity or cytotoxicity. For example, CLs at high DODAC content (i.e., CL0.3 and CL0.4) could effectively kill both types of bacterial cells but only cause negligible toxicity to mammalian cells. We believe that a systematic comparison between the antibacterial activity and the cytotoxicity of CLs with different DODAC contents will provide an important reference for the potential clinical applications of cationic liposomes.

Phenolipids such as alkyl gallates (A-GAs) have been approved by the food industry as non-toxic antioxidant additives, which are also regarded as an emerging source of functional food ingredients. However, comprehensive understanding of their digestive absorption is needed. Thus, the models of live mice and anaerobic fermentation were used to clarify the distribution and microbial hydrolysis characteristics of A-GAs in the gastrointestinal tract. HPLC-UV results demonstrated that A-GAs could be hydrolyzed by intestinal lipases and gut microorganisms including Lactobacillus to produce free gallic acid (GA). Through regulating the chain length of the lipid part in A-GAs, the sustained and controllable release of the GA can be easily achieved. Furthermore, A-GAs were also able to reach the colon and the cecum, which would lead to potential gastrointestinal protective effects. Therefore, A-GAs may be applied as possible ingredient for functional foods.

Traditional collaborative filtering recommendation algorithms only consider the interaction between users and items leading to low recommendation accuracy. Aiming to solve this problem, a graph convolution collaborative filtering recommendation method integrating social relations is proposed. Firstly, a social recommendation model based on graph convolution representation learning and general collaborative filtering (SRGCF) is constructed; then, based on this model, a social relationship recommendation algorithm (SRRA) is proposed; secondly, the algorithm learns the representations of users and items by linear propagation on the user–item bipartite graph; then the user representations are updated by learning the representations with social information through the neighbor aggregation operation in the social network to form the final user representations. Finally, the prediction scores are calculated, and the recommendation list is generated. The comparative experimental results on four real-world datasets show that: the proposed SRRA algorithm performs the best over existing baselines on Recall@10 and NDCG@10; specifically, SRRA improved by an average of 4.40% and 9.62% compared to DICER and GraphRec, respectively, which validates that the proposed SRGCF model and SRRA algorithm are reasonable and effective.