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Ultra-high-pressure tubular reactors are crucial pieces of equipment for polyethylene production. Long-term operation under high temperature, high pressure, and other extremely harsh conditions can lead to various defects, with circumferential cracks posing a major safety risk. Detecting cracks is challenging, particularly when they are under a protective layer of a certain thickness. This study designed a pulsed eddy current differential probe to detect circumferential cracks in ultra-high-pressure tubular reactors, with the lift-off distance acting as a protective layer. Detection models for traditional cylindrical and semi-circular excitation differential probes were established using finite element simulations. Corresponding experiments under different lift-off conditions were carried out, and the model’s accuracy was verified by the consistency between the simulation results and experimental data. The distribution of the eddy current field under different conditions and the disturbances caused by cracks at various positions to the detection signal were then calculated in the simulations. The simulation results showed that the cracks significantly disturbed the eddy current field of the semi-circular excitation differential probe compared with that of the traditional cylindrical probe. The designed differential probe effectively detected circumferential cracks of specific lengths and depths using the difference in the voltage signals. The experimental results were in agreement with the simulation results, showing that the designed probe could effectively detect 20 mm-long circumferential cracks at a lift-off of 60 mm. The experimental results also show that the probe’s detection coverage area in the axial direction varied with the lift-off height. The probe design and findings are valuable for detecting cracks in ultra-high-pressure tubular reactors with protective layers.

In recent years, extreme events have frequently occurred, and the extreme uncertainty of the source-demand side of high-ratio renewable energy systems poses a great challenge to the safe operation of power systems. Accurately generating extreme scenarios related to the source-demand side under a high percentage of new power systems is vital for the safe operation of power systems and the assessment of their reliability. However, at this stage, methods for extreme scenario generation that fully consider the correlation between wind power, solar power, and load are lacking. To address these problems, this paper proposes a method for extreme scenario generation based on information-maximizing generative adversarial networks (InfoGANs) for high-proportion renewable power systems. The example analysis shows that the method for extreme scenario generation proposed in this paper can fully explore the correlation between historical wind–solar–load data, greatly improve the accuracy with which extreme scenarios are generated, and provide effective theories and methodologies for the safe operation of a new type of power system.

Aiming at the problem of system controller performance failure caused by improperly setting the value of each weighting coefficient of the model predictive control (MPC), a fractional-order MPC strategy with Takagi–Sugeno fuzzy optimization (T–SFO MPC) is proposed for a vehicle active suspension system. Firstly, the fractional-order model predictive control framework for active suspension systems is designed based on a 1/4 vehicle model. Then, we analyze the influence of different weighting coefficients on the suspension performance and introduce the Takagi–Sugeno fuzzy optimization theory to adaptively adjust the weighting coefficients of the fractional-order MPC controller. Finally, the system responses of the T–SFO MPC, traditional MPC, linear quadratic regulator (LQR), and passive suspension control are numerically analyzed under various road conditions. Simulation results show that suspension response with the T–SFO MPC is significantly improved compared with passive suspension control, traditional MPC control, and LQR control, and the weight coefficients of the T–SFO MPC can be adaptively adjusted according to the dynamic changes of suspension response. Compared with passive suspension, the root mean square (RMS) value of the vertical acceleration of the T–SFO MPC under various roads decreased by a maximum of 37.97%, and the RMS value of suspension dynamic deflection and tire dynamic load decreased by a maximum of 32.94% and 37.8%, respectively. These results validate that the proposed control method can achieve coordinated optimization of vehicle comfort and handling stability.

Visual–Inertial SLAM (VI-SLAM) has a wide range of applications spanning robotics, autonomous driving, AR, and VR due to its low-cost and high-precision characteristics. VI-SLAM is divided into localization and mapping tasks. However, researchers focus more on the localization task while the robustness of the mapping task is often ignored. To address this, we propose a map-point convergence strategy which explicitly estimates the position, the uncertainty, and the stability of the map point (SoM). As a result, the proposed method can effectively improve the quality of the whole map while ensuring state-of-the-art localization accuracy. The convergence strategy mainly consists of a perpendicular-based triangulation and 3D Gaussian–uniform mixture filter, and we name it PGMF, perpendicular-based 3D Gaussian–uniform mixture filter. The algorithm is extensively tested on open-source datasets, which shows the RVM (Ratio of Valid Map points) of our algorithm exhibits an average increase of 0.1471 compared to VINS-mono, with a variance reduction of 68.8%.

Brevetoxins (PbTxs) are very potent marine neurotoxins that can cause an illness clinically described as neurologic shellfish poisoning (NSP). These toxins are cyclic polyether in chemistry and have increased their geographical distribution in the past 2 decades. However, the ethical problems as well as technical difficulties associated with currently employed analysis methods for marine toxins have spurred the quest for suitable alternatives to be applied in a regulatory monitoring regime. In this work, we reported the first instance of concurrent aptamer selection of Brevetoxin-1 (PbTx-1) and Brevetoxin-2 (PbTx-2) and constructed a biolayer interferometry (BLI) biosensor utilizing PbTx-1 aptamer as a specific recognition element. Through an in vitro selection process, we have, for the first time, successfully selected DNA aptamers with high affinity and specificity to PbTx-1 and PbTx-2 from a vast pool of random sequences. Among the selected aptamers, aptamer A5 exhibited the strongest binding affinity to PbTx-1, with an equilibrium dissociation constant (KD) of 2.56 μM. Subsequently, we optimized aptamer A5 by truncation to obtain the core sequence (A5-S3). Further refinement was achieved through mutations based on the predictions of a QGRS mapper, resulting in aptamer A5-S3G, which showed a significant increase in the KD value by approximately 100-fold. Utilizing aptamer A5-S3G, we fabricated a label-free, real-time optical BLI aptasensor for the detection of PbTx-1. This aptasensor displayed a broad detection range from 100 nM to 4000 nM PbTx-1, with a linear range between 100 nM and 2000 nM, and a limit of detection (LOD) as low as 4.5 nM. Importantly, the aptasensor showed no cross-reactivity to PbTx-2 or other marine toxins, indicating a high level of specificity for PbTx-1. Moreover, the aptasensor exhibited excellent reproducibility and stability when applied for the detection of PbTx-1 in spiked shellfish samples. We strongly believe that this innovative aptasensor offers a promising alternative to traditional immunological methods for the specific and reliable detection of PbTx-1.

Atherosclerosis is a chronic inflammatory disease characterized by lipid accumulation and foam cell formation in the arterial wall. Promoting macrophage autophagy has emerged as a promising therapeutic strategy against atherosclerosis. Dipsacoside B (DB) is an oleanane-type pentacyclic triterpenoid saponin extracted from Lonicerae flos with potential anti-atherosclerotic properties. In this study, we investigated the effects of DB on atherosclerosis progression in ApoE−/− mice fed a high-fat diet and explored the underlying mechanisms in oxidized low-density lipoprotein (ox-LDL)-induced foam cells. DB treatment significantly reduced atherosclerotic lesion size, improved plaque stability, and regulated lipid metabolism without impairing liver and kidney function in ApoE−/− mice. In vitro studies revealed that DB dose-dependently inhibited ox-LDL internalization and intracellular lipid accumulation in RAW264.7 macrophages. Mechanistically, DB induced autophagy, as evidenced by increased autophagosome formation and upregulated expression of autophagy markers LC3-II and p62 both in vivo and in vitro. Inhibition of autophagy by chloroquine abolished the antiatherosclerotic and pro-autophagic effects of DB. Furthermore, DB treatment increased LC3-II and p62 mRNA levels, suggesting transcriptional regulation of autophagy. Collectively, our findings demonstrate that DB exerts anti-atherosclerotic effects by inhibiting foam cell formation via autophagy induction, providing new insights into the pharmacological actions of DB and its potential as a therapeutic agent against atherosclerosis.

Aiming at the problem of difficult crack detection in underground urban sewage pipelines, a lightweight sewage pipeline crack detection method based on sewage pipeline robots and improved YOLOv8n is proposed. The method uses pipeline robots as the equipment carrier to move rapidly and collect high-definition data of apparent diseases in sewage pipelines with both water and sludge media. The lightweight RGCSPELAN module is introduced to reduce the number of parameters while ensuring the detection performance. First, we replaced the lightweight detection head Detect_LADH to reduce the number of parameters and improve the feature extraction of modeled cracks. Finally, we added the LSKA module to the SPPF module to improve the robustness of YOLOv8n. Compared with YOLOv5n, YOLOv6n, YOLOv8n, RT-DETRr18, YOLOv9t, and YOLOv10n, the improved YOLOv8n has a smaller number of parameters of only 1.6 M. The FPS index reaches 261, which is good for real-time detection, and at the same time, the model also has a good detection accuracy. The validation of sewage pipe crack detection through real scenarios proves the feasibility of the proposed method, which has good results in targeting both small and long cracks. It shows potential in improving the safety maintenance, detection efficiency, and cost-effectiveness of urban sewage pipes.

It is important to improve the separation ability of photogenerated electrons and the adsorption capacity of carbon dioxide (CO2) for efficient photoreduction of CO2. Here, we synthesized ZnCdS quantum dots (ZCS-QDs) and cerium dioxide nanosheets (CeO2) using the solvothermal method and calcination method. We combined CeO2 and ZCS-QDs to effectively enhance the charge separation efficiency, and the lifetime of photogenerated electrons was increased 4.5 times. The CO evolution rate of the optimized composite (ZCS-QDs/CeO2) was up to 495.8 μmol g−1 h−1, and it had 100% product selectivity. In addition, the stability remained high after five cycles. The CO2 adsorption capacity of the catalyst surface was observed by in situ FTIR. The test results showed that improving CO2 capture ability and promoting photogenic electron separation had positive effects on enhancing photoreduction of CO2. This study provides a reference for constructing a zero-dimensional–two-dimensional (0D–2D) heterojunction and explores potential CO2 reduction reaction mechanisms.

Underwater image enhancement technology is crucial for the human exploration and exploitation of marine resources. The visibility of underwater images is affected by visible light attenuation. This paper proposes an image reconstruction method based on the decomposition–fusion of multi-channel luminance data to enhance the visibility of underwater images. The proposed method is a single-image approach to cope with the condition that underwater paired images are difficult to obtain. The original image is first divided into its three RGB channels. To reduce artifacts and inconsistencies in the fused images, a multi-resolution fusion process based on the Laplace–Gaussian pyramid guided by a weight map is employed. Image saliency analysis and mask sharpening methods are also introduced to color-correct the fused images. The results indicate that the method presented in this paper effectively enhances the visibility of dark regions in the original image and globally improves its color, contrast, and sharpness compared to current state-of-the-art methods. Our method can enhance underwater images in engineering practice, laying the foundation for in-depth research on underwater images.

In this work, the successful synthesis of a Cu@g-C3N4/CeO2-like Z-scheme heterojunction through hydrothermal and photo-deposition methods represents high CO2 reduction activity with remarkable CO selectivity, as evidenced by the impressive CO yield of 33.8 μmol/g for Cu@g-C3N4/CeO2, which is over 10 times higher than that of g-C3N4 and CeO2 individually. The characterization and control experimental results indicate that the formation of heterojunctions and the introduction of Cu sites promote charge separation and the transfer of hot electrons, as well as the photothermal effect, which are the essential reasons for the improved CO2 reduction activity. Remarkably, Cu@g-C3N4/CeO2 still exhibits about 92% performance even after multiple cycles. In situ FTIR was utilized to confirm the production of COOH* at 1472 cm−1 and to elucidate the mechanism behind the high selectivity for CO production. The study’s investigation into the wide-ranging applicability of the Cu@g-C3N4/CeO2-like Z-scheme heterojunction catalysts is noteworthy, and the exploration of potential reaction mechanisms for CO2 reduction adds valuable insights to the field of catalysis.

In order to address the challenges posed by the integration of regional electric vehicle (EV) clusters into the grid, it is crucial to fully utilize the scheduling capabilities of EVs. In this study, to investigate the energy storage characteristics of EVs, we first established a single EV virtual energy storage (EVVES) model based on the energy storage characteristics of EVs. We then further integrated four types of EVs within the region to form EV clusters (EVCs) and constructed an EVC virtual energy storage (VES) model to obtain the dynamic charging and discharging boundaries of the EVCs. Next, based on the dispatch framework for the participation of renewable energy sources (RESs) and loads in the distribution network, we established a dual-objective optimization dispatch model, with the objectives of minimizing system operating costs and load fluctuations. We solved this model with NSGA-II and TOPSIS, which guided and optimized the charging and discharging of EVCs. Finally, the simulation results show that the system operating cost was reduced by 7.81%, and the peak-to-valley difference of the load was reduced by 3.83% after optimization. The system effectively achieves load peak shaving and valley filling, improving economic efficiency.

(1) Background: The main factors and their interrelationships contributing to cardiac repolarization alternans (CRA) remain unclear. This study aimed to elucidate the calcium (Ca2+)-related mechanisms underlying myocardial ischemia (MI)-induced CRA. (2) Materials and Methods: CRA was induced using S1 stimuli for pacing in an in silico ventricular model with MI. The standard deviations of nine Ca2+-related subcellular parameters among heartbeats from 100 respective nodes with and without alternans were chosen as features, including the maximum systole and end-diastole and corresponding differences in the Ca2+ concentration in the intracellular region([Ca2+]i) and junctional sarcoplasmic reticulum ([Ca2+]jsr), as well as the maximum opening of the L-type Ca2+ current (ICaL) voltage-dependent activation gate (d-gate), maximum closing of the inactivation gate (ff-gate), and the gated channel opening time (GCOT). Feature selection was applied to determine the importance of these features. (3) Results: The major parameters affecting CRA were the differences in [Ca2+]i at end-diastole, followed by the extent of d-gate activation and GCOT among beats. (4) Conclusions: MI-induced CRA is primarily characterized by functional changes in Ca2+ re-uptake, leading to alternans of [Ca2+]i and subsequent alternans of ICaL-dependent properties. The combination of computational simulation and machine learning shows promise in researching the underlying mechanisms of cardiac electrophysiology.

Thin-walled concrete-filled steel tubes are efficient and economical with promising applications in civil and light industrial buildings. However, their local buckling resistance and deformation capacity are low, which adversely affects the seismic safety of structures. There are relatively few studies on thin-walled concrete-filled steel tubular columns with ultra-large diameter-to-thickness ratios, and there is also a lack of relevant experimental research on them. In this study, horizontal hysteresis tests were conducted on concrete columns with a large diameter-to-thickness ratio. The seismic performances of regular and straight-ribbed specimens were analyzed and compared, including the analyses of load-displacement hysteresis curves, strain distribution, skeleton curves, ductility, and energy dissipation capacity. Using these results, a restoring force model for concrete columns with a large diameter-to-thickness ratio was established. The findings indicate that under horizontal loading, the ductility of concrete columns with a regular thin-walled steel tube is 3.9, with an equivalent viscous damping coefficient of 1.65. Meanwhile, the ultimate bearing capacity is 201 kN. After adding stiffening ribs, the ultimate bearing capacity reaches 266 kN and the ductility coefficient reaches 4.4, resulting in the stiffeners increasing the ultimate bearing capacity and ductility by >30% and 12.8%, respectively. However, they have a less pronounced effect on deformation and energy dissipation. Building on these research outcomes, we propose a dimensionless three-line skeleton curve model and a restoring force model. The calculation results from these models align well with the test results, offering valuable insights for the seismic safety analysis of real-world engineering structures.

The porous media approach is regarded as an appropriate methodology for hydraulic simulations of complex pebble beds in fusion reactors. In order to determine the parameters (permeability α and inertial loss coefficient C) of the porous media accurately, two methods are proposed: the correction method and the fitting method. In this paper, a single-channel model with sequentially packed pebbles is constructed in order to obtain the pressure drop gradient against superficial velocities. Two methods, the correction method and fitting method, are employed to determine the permeability and inertial loss coefficient, and the results are evaluated with comparisons. Based on the results, both the correction method and fitting method are deemed feasible for the parameter determinations. In consideration of the consumption of resources and time for simulation, the fitting method is recommended during the preliminary design phase to shorten the duration of design, while the correction method is suggested to obtain precise results when the design is accomplished. Both of the methods would be evaluated with the data obtained from experiments in the future.

Since the 21st century, rice–crayfish fields have been widely distributed in the Yangtze River Basin in China. However, the spontaneous construction of these fields by farmers has given rise to the issue of rice–crayfish field fragmentation (RCFF) in certain areas. This study introduced a novel method for evaluating RCFF using township-level administrative regions as the evaluation units. Three key evaluation elements, including five landscape indices, were employed: area and edge metrics (rice–crayfish area ratio), shape metrics (perimeter–area ratio distribution), and aggregation metrics (rice–crayfish patch density, percentage of like adjacencies, and rice–crayfish contagion index). The RCFF was quantified and its spatial distribution pattern was analyzed through the entropy method and GIS spatial analysis. Empirical studies conducted in Qianjiang city yielded insightful results: (1) The contribution of evaluation elements to the RCFF was ranked in descending order as follows: aggregation metrics > shape metrics > area and edge metrics. (2) The RCFF of Yunlianghu farm was the lowest at 0.06, while the RCFF of Yangshi subdistrict 2 was the highest at 0.94. The spatial distribution of the RCFF exhibited a distinct trend, showing a gradual decrease from the northeast to the southwest in Qianjiang, and a low-RCFF area in the southwest. This evaluation system enables local government decisionmakers to comprehend the current status of rice–crayfish field management and construction. It facilitates the scientific planning of rice–crayfish field layouts and provides guidance for farmers in their expansion strategies. This method can be promoted in counties (cities) where rice–crayfish fields are primarily distributed in the Yangtze River Basin, promoting the transition of traditional agriculture to environmentally friendly agriculture in China.

Infrared small target detection technology plays a crucial role in various fields such as military reconnaissance, power patrol, medical diagnosis, and security. The advancement of deep learning has led to the success of convolutional neural networks in target segmentation. However, due to challenges like small target scales, weak signals, and strong background interference in infrared images, convolutional neural networks often face issues like leakage and misdetection in small target segmentation tasks. To address this, an enhanced U-Net method called MST-UNet is proposed, the method combines multi-scale feature decomposition and fusion and attention mechanisms. The method involves using Haar wavelet transform instead of maximum pooling for downsampling in the encoder to minimize feature loss and enhance feature utilization. Additionally, a multi-scale residual unit is introduced to extract contextual information at different scales, improving sensory field and feature expression. The inclusion of a triple attention mechanism in the encoder structure further enhances multidimensional information utilization and feature recovery by the decoder. Experimental analysis on the NUDT-SIRST dataset demonstrates that the proposed method significantly improves target contour accuracy and segmentation precision, achieving IoU and nIoU values of 80.09% and 80.19%, respectively.

High-altitude platform (HAP) drones and satellites collaborate to form a network that provides edge computing services to terrestrial internet of things (IoT) devices, which is considered a promising method. In this network, IoT devices’ tasks can be split into multiple parts and processed by servers at non-terrestrial nodes in different locations, thereby reducing task processing delays. However, splitting tasks and allocating communication and computing resources are important challenges. In this paper, we investigate the task offloading and resource allocation problem in multi-HAP drones and multi-satellite collaborative networks. In particular, we formulate a task splitting and communication and computing resource optimization problem to minimize the total delay of all IoT devices’ tasks. To solve this problem, we first transform and decompose the original problem into two subproblems. We design a task splitting optimization algorithm based on deep reinforcement learning, which can achieve online task offloading decision-making. This algorithm structurally designs the actor network to ensure that output actions are always valid. Furthermore, we utilize convex optimization methods to optimize the resource allocation subproblem. The simulation results show that our algorithm can effectively converge and significantly reduce the total task processing delay when compared with other baseline algorithms.

Clavatols exhibit a wide range of biological activities due to their diverse structures. A genome mining strategy identified an A5cla cluster from Penicillium sp. MYA5, derived from the Arctic plant Dryas octopetala, is responsible for clavatol biosynthesis. Seven clavatols, including one new clavatol derivate named penicophenone F (1) and six known clavatols (2–7), were isolated from Penicillium sp. MYA5 using a transcriptome mining strategy. These structures were elucidated by comprehensive spectroscopic analysis. Antibacterial, aldose reductase inhibition, and siderophore-producing ability assays were conducted on compounds 1–7. Compounds 1 and 2 demonstrated inhibitory effects on the ALR2 enzyme with inhibition rates of 75.3% and 71.6% at a concentration of 10 μM, respectively. Compound 6 exhibited antibacterial activity against Staphylococcus aureus and Escherichia coli with MIC values of 4.0 μg/mL and 4.0 μg/mL, respectively. Additionally, compounds 1, 5, and 6 also showed potential iron-binding ability.

The aim of this study was to optimize the formation of sodium caseinate (CS) and gum arabic (GA) complexes through the Maillard reaction and to evaluate their effectiveness in improving the emulsification properties and stability of docosahexaenoic acid (DHA) nanoemulsions. First, the best target polysaccharides were selected, and the best modification conditions were determined using orthogonal experiments. Secondly, the response surface experiments were used to optimize the preparation process of the emulsion. The stability, in vitro digestion characteristics, and rheological characteristics of the emulsion prepared by means of CS–GA were compared with the emulsion prepared using a whey protein isolate (WPI). After the orthogonal test, the optimal modification conditions were determined to be a reaction time of 96 h, a CS–GA mass ratio of 1:2, a reaction temperature of 60 °C, and a degree of grafting of 44.91%. Changes in the infrared (IR), Raman, ultraviolet (UV), and endogenous fluorescence spectra also indicated that the complex structure was modified. The response surface test identified the optimal preparation process as follows: an emulsifier concentration of 5 g/L, an oil-phase concentration of 5 g/L, and a homogenization frequency of five, and the emulsion showed good stability. Therefore, the use of a nanoemulsion as a nanoscale DHA algal oil delivery system is very promising for extending the shelf life and improving the stability of food.

The layered double hydroxide (LDH) has been practically applied in the field of tribology and materials science due to its unique physicochemical properties, weak bonding, flexible structural composition, and adjustable interlayer space. In this work, a series of ultrathin and flexible composition of Ni-Fe LDH samples were prepared via a cost-effective room-temperature co-precipitation process. Then, they were mechanically dispersed into GTL base oil and their lubricating performance were tested by a four-ball tribometer. It is found that the variation of Ni-Fe ratio of Ni-Fe LDH has a great influence on the improvement of lubricating performance of GTL base oil. At the same concentration (0.3 mg/mL), the Ni-Fe LDH with Ni/Fe ratio of 6 was demonstrated to exhibit the best lubricating performance and the AFC, WSD, the wear volume, surface roughness and average wear scar depth decreased 51.3%, 30.8%, 78.4%, 6.7% and 50.0%, respectively. SEM-EDS and X-ray photoelectron spectra illustrated that the tribo-chemical film consisting of iron oxides and NiO with better mechanical properties formed and slowly replaced the physical film, which resists scuffing and protect solid surface from severe collisions.

The uCVD (microchemical vapor deposition) graphene growth system is an improved CVD system that is suitable for scientific research and experimental needs, and it is characterized by its rapid, convenient, compact, and low-cost features. The micro-hotplate based on an SOI wafer is the core component of this system. To meet the requirements of the uCVD system for the micro-hotplate, we propose a suspended multi-cantilever heating platform composed of a heating chip, cantilevers, and bracket. In this article, using heat transfer theory and thermoelectric simulation, we demonstrate that the silicon resistivity, current input cross-sectional size, and the convective heat transfer coefficient have a huge impact on the performance of the micro-heating platform. Therefore, in the proposed solution, we adopt a selective doping process to achieve a differentiated configuration of silicon resistivity in the cantilevers and heating chip, ensuring that the heating chip meets the requirements for graphene synthesis while allowing the cantilevers to withstand high currents without damage. Additionally, by adding brackets, the surfaces of the micro-hotplate have the same convective heat transfer environment, reducing the surface temperature difference, and improving the cooling rate. The simulation results indicate that the temperature on the micro-hotplate surface can reach 1050.8 °C, and the maximum temperature difference at different points on the surface is less than 2 °C, which effectively meets the requirements for the CVD growth of graphene using Cu as the catalyst.

Due to its inherent high hardness, strength, and plasticity, tantalum–tungsten (Ta-W) alloy poses a considerable challenge in machining, resulting in pronounced tool wear, diminished tool lifespan, and suboptimal surface quality. This study undertook experiments utilizing uncoated carbide tools, TiAlN-coated carbide tools, and AlTiN-coated carbide tools for machining Ta-2.5W alloy. The investigation delved into the intricacies of surface temperature, tool longevity, and the distinctive wear characteristics under varying coating materials and cutting parameters. Concurrently, a comprehensive exploration of the wear mechanisms affecting the tools was conducted. Among the observed wear modes, flank wear emerged as the predominant issue for turning tools. Across all three tool types, adhesive wear and diffusion wear were identified as the principal wear mechanisms, with the TiAlN-coated tools displaying a reduced level of wear compared to their AlTiN-coated counterparts. The experimental findings conclusively revealed that TiAlN-coated carbide tools exhibited an extended tool lifespan in comparison to uncoated carbide tools and AlTiN-coated carbide tools, signifying superior cutting performance.

Microinjection is usually applied to the treatment of some retinal disorders, such as retinal vein cannulation and displaced submacular hemorrhage. Currently, the microinjection procedure is usually performed by using the viscous fluid control of a standard vitrectomy system, which applies a fixed air pressure through foot pedal activation. The injection process with the fixed pressure is uncontrollable and lacks feedback, the high flow rate of the injected drug may cause damage to the fundus tissue. In this paper, a liquid-driven microinjection system with a flow sensor is designed and developed specifically for fundus injection. In addition, a PID sliding mode control (SMC) method is proposed to achieve precise injection in the injection system. The experimental results of fundus simulation injection demonstrate that the microinjection system meets the requirements of fundus injection and reduces the impact of the injection process on the fundus tissue.

To investigate the effect of active immunisation with gonadotropin-releasing hormone (GnRH) on the reproductive function in male Sprague Dawley (SD) rats, 24 42-day-old rats were randomly assigned to treatment with GnRH6-MAP, GnRH-OVA, a surgical castration group, and a blank control group. Each rat in the treatment groups was intramuscularly injected at 6, 8, and 10 weeks of age. The serum concentrations of testosterone (T), follicle-stimulating hormone (FSH), luteinising hormone (LH), and anti-GnRH antibodies were determined using enzyme-linked immunosorbent assays. The results showed that active immunisation with recombinant GnRH6-MBP and GnRH-OVA significantly increased the serum levels of anti-GnRH antibodies and reduced the serum concentrations of testosterone compared to the black control. Eight weeks after immunisation, the rats’ testes were surgically removed for morphological evaluation, showing atrophy of the convoluted vasculature, relative emptying of the lumen, and insignificant differentiation of spermatogonial cells, which were increased in weight and volume compared with the blank control group. These findings indicated that active immunisation with GnRH can lead to testicular atrophy and reduce gonadal hormone concentrations, suggesting that GnRH is a highly effective immunogen.

Background and Objective: Intravenous therapy is widely used in clinics to deliver medications and fluids to patients, and overuse may cause intravenous-infusion-associated circulatory overload (IACO) and death. However, forensic data on deaths from the overuse of intravenous therapy are limited. We performed a retrospective study to identify whether causes of death in forensic practice were associated with IACO. Methods: A total of 572 medical-related cases with a history of intravenous infusion who suffered from injuries or illnesses and died after treatment in hospitals were recruited from two centers of forensic medicine between 2002 and 2018. Results: The results demonstrated that 6.47% of cases (37/572) were exposed to an infusion overdose that resulted in deaths related to IACO, and 43.24% of cases (16/37) had a net fluid retention ranging from 3.0 L/d to 13.8 L/d. The highest case was administered 1.4 L blood products and 13.6 L fluids within 25 h. We observed significant decreases in red blood cells, hematocrit, hemoglobin, and platelets from the time of “on admission” to “before death”, except for white blood cells. Autopsy findings of 16 cases revealed some organ or tissue pathological alterations related to IACO, including pale-yellow liquid overflow under the abdominal epidermis, much transudate in body cavities, and lung edema with a high tissue density and no obvious slurry deposition in the alveolar lumen. Another 21 cases died after a rapid infusion rate ranging from 111 to 293 drops/min, which resulted from viral myocarditis on autopsy. Conclusions: Our data on excessive or irrational use of intravenous therapy indicate a severe circulatory overload, which may eventually result in lethal outcomes. Therefore, the use of improper intravenous therapy should be reduced to ameliorate adverse health consequences during clinical treatment.

Ultraviolet radiation (UV) has strong chemical and biological effects on human health and ecosystems, and it plays an important role in the atmospheric environment by affecting photochemical processes, etc. Clouds and aerosols are the main factors affecting UV radiation and analyzing the quantitative impact of them on UV radiation is of great significance. Using the observation data of UV radiation in Beijing from 2005 to 2020, as well as the data of aerosol optical depth (AOD), single scattering albedo (SSA), and other related parameters, this paper simulated the surface UV radiation in two scenarios of cloudless without aerosol and cloudless with aerosol based on the TUV (Tropospheric Ultraviolet-Visible model), and quantitatively evaluated the attenuation of UV radiation by aerosol and cloud in the Beijing area. The results show that UV radiation is more sensitive to changes in AOD. Fixing the SSA value to 0.9, when the AOD increases from 0.2 to 1.0, the UV radiation decreases from 21.16 W/m2 to 12.64 W/m2 at 12:00; when AOD is maintained at 0.64, the SSA increases from 0.7 to 0.95, and the UV radiation increases from 14.55 W/m2 to 19.91 W/m2. The average annual attenuation rates of ultraviolet radiation by aerosols and clouds from 2005 to 2020 are 30.64% and 40.22%, respectively; the monthly averaged attenuation rates are 30.48% and 42.04%, respectively; and the daily averaged attenuation rates are 31.02% and 50.45%, respectively.

The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences among money laundering groups. In this paper, we tackle the challenges presented by the organized, heterogeneous, and noisy nature of Bitcoin money laundering. We propose a novel subgraph-based contrastive learning algorithm for heterogeneous graphs, named Bit-CHetG, to perform money laundering group detection. Specifically, we employ predefined metapaths to construct the homogeneous subgraphs of wallet addresses and transaction records from the address–transaction heterogeneous graph, enhancing our ability to capture heterogeneity. Subsequently, we utilize graph neural networks to separately extract the topological embedding representations of transaction subgraphs and associated address representations of transaction nodes. Lastly, supervised contrastive learning is introduced to reduce the effect of noise, which pulls together the transaction subgraphs with the same class while pushing apart the subgraphs with different classes. By conducting experiments on two real-world datasets with homogeneous and heterogeneous graphs, the Micro F1 Score of our proposed Bit-CHetG is improved by at least 5% compared to others.

Omnidirectional images have gained significant popularity and drawn great attention nowadays, which poses challenges to omnidirectional image processing in solving the bottleneck of storage and transmission. Projecting onto a two-dimensional image plane is generally used to compress an omnidirectional image. However, the most commonly used projection format, the equirectangular projection (ERP), results in a significant amount of redundant samples in the polar areas, thus incurring extra bitrate and geometric distortion. We derive the optimal latitude-adaptive bit allocation for each image tile. Subsequently, we propose a greedy algorithm for non-negative integer bit allocation (NNIBA) for non-uniform quantization under an omnidirectional image quality metric WMSE. In our experiment, we design quantization tables based on JPEG and compare our approach with other sampling-related methods. Our method achieves an average bit saving of 7.9% compared with JPEG while outperforming other sampling-related methods. Besides, we compare our non-uniform quantization approach with two proposed bit allocation methods, achieving an average improvement of 0.35 dB and 2.66 dB under WS-PSNR, respectively. The visual quality assessment also confirms the superiority of our method.

Children with autism exhibit more pronounced symptoms of both problem behaviors and motor coordination difficulties. Yoga, recognized as an effective intervention modality, can be valuable after assessing its efficacy in addressing problem behaviors and motor coordination challenges, ultimately contributing to symptom alleviation in autism. The randomized controlled trial (RCT) was used to divide 17 children with autism into an intervention group (n = 9) and a control group (n = 8). The intervention group participated in an 8-week yoga intervention training (three sessions/week, 45–50 min/session), and the control group did not participate in yoga training but only in daily program activities. Pre-test, mid-test, post-test, and after delayed test, teachers assessed the effect of yoga intervention on problem behaviors of children with autism through the Aberrant Behavior Checklist (ABC) and the effect of yoga intervention on motor coordination through the Movement Assessment Battery for Children—Second Edition (MABC2). Results show that the yoga intervention is effective in reducing problem behaviors and improving motor coordination in children with autism. Yoga intervention significantly reduces irritability and social withdrawal in children with autism. Yoga intervention had the most significant improvement in ball skills and static and dynamic balance.

Paper-based test strips with on-site visual detection have become a hot spot in the field of target detection. Yet, low specific surface area and uneven deposition limit the further application of test strips. Herein, a novel “turn-on” ratio of molecularly imprinted membranes (Eu@CDs-MIMs) was successfully prepared based on a Eu complex-doped polyvinylidene fluoride membrane for the selective, rapid and on-site visual detection of norfloxacin (NOR). The formation of surface-imprinted polymer-containing carbon dots (CDs) improves the roughness and hydrophilicity of Eu@CDs-MIMs. Fluorescence lifetimes and UV absorption spectra verified that the fluorescence enhancement of CDs is based on the synergistic effect of charge transfer and hydrogen bonding between CDs and NOR. The fluorescent test strip showed a linear fluorescent response within the concentration range of 5–50 nM with a limit of detection of 1.35 nM and a short response time of 1 min. In comparison with filter paper-based test strips, Eu@CDs-MIMs exhibit a brighter and more uniform fluorescent color change from red to blue that is visible to the naked eye. Additionally, the applied ratio fluorescent test strip was combined with a smartphone to translate RGB values into concentrations for the visual and quantitative detection of NOR and verified the detection results using high-performance liquid chromatography. The portable fluorescent test strip provides a reliable approach for the rapid, visual, and on-site detection of NOR and quinolones.

Water-jet-scouring technology finds extensive applications in various fields, including marine engineering. In this study, the pulse characteristics are introduced on the basis of jet-scouring research, and the sand-scouring characteristics of a pulsed jet under different Reynolds numbers and the impact distances are deeply investigated using Flow-3D v11.2. The primary emphasis is on the comprehensive analysis of the unsteady flow structure within the scouring process, the impulse characteristics, and the geometric properties of the resulting scouring pit. The results show that both the radius and depth of the scour pit show a good linear correlation with the jet-flow rate. The concentration of suspended sediment showed an increasing and then decreasing trend with impinging distance. The study not only helps to enrich the traditional theory of jet scouring, but also provides useful guidance for engineering applications, which have certain theoretical and practical significance.

Micro-tools comprising difficult-to-machine materials have seen widespread application in micro-manufacturing to satisfy the demands of micro-part processing and micro-device development. Taking micro-shafts as an example, the related developmental technology, based on wire electric discharge grinding (WEDG) as the core method, is one of the key technologies used to prepare high-precision micro-shafts. To enable efficient and high-precision machining of micro-shafts with target diameters, instead of performing multiple repeated on-machine measurements and reprocessing, a geometric constraint strategy is proposed based on the previously introduced twin-mirroring-wire tangential feed electrical discharge grinding (TMTF-WEDG). This strategy encompasses the tool setting method, tangential feed distance compensation, and an equation that establishes the relationship between tangential distance and diameter variation. These components are derived from a key points analysis of the geometric constraints. The micro-shafts with diameters of 50 µm and consistencies of ±1.5 µm are repeatedly processed. A series of micro-shafts with diameters ranging from 30 µm to 120 µm achieve geometric constraints with a diameter accuracy of ±2 µm, accompanied by the complete continuous automation of the entire process. Accordingly, it can be concluded that the geometric constraint strategy is flexible and stable and can be controlled with high precision in the TMTF-WEDG process.

To achieve rapid and precise target counting, the quality of target detection serves as a pivotal factor. This study introduces the Sheep’s Head-Single Shot MultiBox Detector (SH-SSD) as a solution. Within the network’s backbone, the Triple Attention mechanism is incorporated to enhance the MobileNetV3 backbone, resulting in a significant reduction in network parameters and an improvement in detection speed. The network’s neck is constructed using a combination of the Spatial Pyramid Pooling module and the Triple Attention Bottleneck module. This combination enhances the extraction of semantic information and the preservation of detailed feature map information, with a slight increase in network parameters. The network’s head is established through the Decoupled Head module, optimizing the network’s prediction capabilities. Experimental findings demonstrate that the SH-SSD model attains an impressive average detection accuracy of 96.11%, effectively detecting sheep’s heads within the sample. Notably, SH-SSD exhibits enhancements across various detection metrics, accompanied by a significant reduction in model parameters. Furthermore, when combined with the DeepSort tracking algorithm, it achieves high-precision quantitative statistics. The SH-SSD model, introduced in this paper, showcases commendable performance in sheep’s head detection and offers deployment simplicity, thereby furnishing essential technical support for the advancement of intelligent animal husbandry practices.

In recent years, the realm of deep learning has witnessed significant advancements, particularly in object detection algorithms. However, the unique challenges posed by remote sensing images, such as complex backgrounds, diverse target sizes, dense target distribution, and overlapping or obscuring targets, demand specialized solutions. Addressing these challenges, we introduce a novel lightweight object detection algorithm based on Yolov5s to enhance detection performance while ensuring rapid processing and broad applicability. Our primary contributions include: firstly, we implemented a new Lightweight Asymmetric Detection Head (LADH-Head), replacing the original detection head in the Yolov5s model. Secondly, we introduce a new C3CA module, incorporating the Coordinate Attention mechanism, strengthening the network’s capability to extract precise location information. Thirdly, we proposed a new backbone network, replacing the C3 module in the Yolov5s backbone with a FasterConv module, enhancing the network’s feature extraction capabilities. Additionally, we introduced a Content-aware Feature Reassembly (content-aware reassembly of features) (CARAFE) module to reassemble semantic similar feature points effectively, enhancing the network’s detection capabilities and reducing the model parameters. Finally, we introduced a novel XIoU loss function, aiming to improve the model’s convergence speed and robustness during training. Experimental results on widely used remote sensing image datasets such as DIOR, DOTA, and SIMD demonstrate the effectiveness of our proposed model. Compared to the original Yolov5s algorithm, we achieved a mean average precision (mAP) increase of 3.3%, 6.7%, and 3.2%, respectively. These findings underscore the superior performance of our proposed model in remote sensing image object detection, offering an efficient, lightweight solution for remote sensing applications.

Adulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5–50% (vol/vol) cow milk or water were used. Mid-infrared spectroscopy (MIRS) combined with modern statistical machine learning was used for the discrimination and quantification of cow milk or water adulteration in BM, GM, and CM. Compared to partial least squares (PLS), modern statistical machine learning—especially support vector machines (SVM), projection pursuit regression (PPR), and Bayesian regularized neural networks (BRNN)—exhibited superior performance for the detection of adulteration. The best prediction models for the different predictive traits are as follows: The binary classification models developed by SVM resulted in differentiation of CM-cow milk, and GM/CM-water mixtures. PLS resulted in differentiation of BM/GM-cow milk and BM-water mixtures. All of the above models have 100% classification accuracy. SVM was used to develop multi-classification models for identifying the high and low proportions of cow milk in BM, GM, and CM, as well as the high and low proportions of water adulteration in BM and GM, with correct classification rates of 94%, 100%, 100%, 99%, and 100%, respectively. In addition, a PLS-based model was developed for identifying the high and low proportions of water adulteration in CM, with correct classification rates of 100%. A regression model for quantifying cow milk in BM was developed using PCA + BRNN, with RMSEV = 5.42%, and RV2 = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and RV2 = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS.

Hydrogels are particularly suitable materials for loading drug delivery agents; their high water content provides a biocompatible environment for most biomolecules, and their cross-linked nature protects the loaded agents from damage. During delivery, the delivered substance usually needs to be released gradually over time, which can be achieved by degradable cross-linked chains. In recent years, biodegradable hydrogels have become a promising technology in new methods of disease treatment and drug delivery methods due to their many advantageous properties. This review briefly discusses the degradation mechanisms of different types of biodegradable hydrogel systems and introduces the specific applications of degradable hydrogels in several new methods of disease treatment and drug delivery methods.

Eight new scalarane sesterterpenes, phyllofenones F–M (1–8), together with two known analogues, carteriofenones B and A (9–10), were isolated from the marine sponge Phyllospongia foliascens collected from the South China Sea. The structures of these compounds were determined based on extensive spectroscopic and quantum chemical calculation analysis. The antibacterial and cytotoxic activity of these compounds was evaluated. Among them, only compounds 4 and 6 displayed weak inhibitory activity against Staphylococcus aureus and Escherichia coli, with MIC values of 16 μg/mL and 8 μg/mL, respectively. Compounds 1–10 exhibited cytotoxic activity against the HeLa, HCT-116, H460, and SW1990 cancer cell lines, with IC50 values ranging from 3.4 to 19.8 μM.

In the field of shield tunnels, the occurrence of uneven longitudinal settlement in segment linings has presented persistent challenges, including heightened risks of localized damage and water leakage. While the adoption of a secondary lining has been proposed as a viable solution to these issues, the question of how to select an appropriate construction time for the secondary lining, one that enables it to fully harness its load-bearing capacity while optimizing the tunnel’s overall stress and deformation characteristics, continues to be a pressing concern. To address this issue, this study established a three-dimensional longitudinal refined numerical model of double-layer-lined shield tunnel. In addition, the deformation degree of the segment lining was used as a time indicator to define the construction time for the secondary lining. Subsequently, an analysis of the impact of the construction time of the secondary lining on the longitudinal mechanical properties of the double-layer-lined shield tunnel is conducted through an assessment of tunnel longitudinal deformation and structural stress. The research findings indicated that the construction of the secondary lining improved the longitudinal deformation resistance of shield tunnels. Simultaneously, it led to a significant increase in the longitudinal shear forces within the segment lining and a notable reduction in longitudinal bending moments. Moreover, the construction time of the secondary lining played a pivotal role in these alterations. Considering the longitudinal force situations and load-bearing characteristics of the double-layer lining structure, it was determined that the optimal construction time for the secondary lining fell within the range of 20% to 40% of the total construction duration. In this scenario, the deformation and internal forces within the segment lining remained within permissible limits. Additionally, both the segment lining and the secondary lining were able to fully utilize their load-bearing capacities, ensuring the economic and safety aspects of the tunnel.

The objective of this investigation was to appraise the mitigative effects of Lonicera caerulea pomace (LCP)-supplemented diets on Dextran Sulfate Sodium (DSS)-induced colitis, and to discuss the potential mechanisms. LCP, a by-product of fruit juice processing, harbors a higher content of polyphenols and dietary fiber compared to the L. caerulea pulp. In a murine model of colitis, the LCP diet attenuated the symptoms of colitis, as evidenced by the reduction in the disease activity index (DAI), extension of colon length, and amelioration of histopathological damage. The anti-inflammatory attributes of LCP were substantiated by a decrease in myeloperoxidase (MPO) activity and suppression of inflammatory cytokine expressions within the colon. Meanwhile, LCP mediated the repair of the intestinal barrier, characterized by the upregulation of gene expressions of tight junction (TJ) proteins and Muc2. Furthermore, LCP altered the composition of the gut microbiota, manifested in increased alpha diversity, enhancement of the abundance of beneficial bacteria (Akkermansia, Coprococcus and Bifidobacterium), and diminishment in the abundance of pathogenic bacteria (Escherichia, Enterococcus, Mucispirillum and Clostridium). Dietary LCP also increased the concentrations of SCFAs within the intestinal luminal contents of colitis mice. Given the affirmative impact of LCP on colitis, LCP may possess great potential in promoting intestinal health.

The protein phosphatase 2C (PP2C) constitutes a large gene family that plays crucial roles in regulating stress responses and plant development. A recent study has shown the involvement of an AtPP2C family member in long-distance nitrogen signaling in Arabidopsis. However, it remains unclear whether maize adopts a similar mechanism. In this study, we conducted a genome-wide survey and expression analysis of the PP2C family in maize. We identified 103 ZmPP2C genes distributed across 10 chromosomes, which were further classified into 11 subgroups based on an evolutionary tree. Notably, cis-acting element analysis revealed the presence of abundant hormone and stress-related, as well as nitrogen-related, cis-elements in the promoter regions of ZmPP2Cs. Expression analysis demonstrated the distinct expression patterns of nine genes under two nitrogen treatments. Notably, the expression of ZmPP2C54 and ZmPP2C85 in the roots was found to be regulated by long-distance signals from the shoots. These findings provide valuable insights into understanding the roles of ZmPP2Cs in long-distance nitrogen signaling in maize.

Pennycress, as an annual cover crop in North America, has around 30–36% of oil and 20–25% of crude protein. Pennycress oil can be converted into biodiesel, while pennycress meal (PM) has limited use in animal nutrition, mainly due to the high content of glucosinolates and indigestible fiber. The nutrition of PM can be improved by processing with edible fungi. This study used Pleurotus ostreatus (PO), Rhizopus oryzae (RO), Aspergillus oryzae (AO), and Mucor circinelloides (MC) to ferment PM (60% moisture content) at 28 °C for 6 to 12 days. Compared to non-fermented PM, essential amino acids such as threonine (Thr) in PO and AO and tryptophan (Trp) and lysine (Lys) in all fungal treatments were enriched. PM fermented by all fungi resulted in concentrated digestible fiber (cellulose) at 12–46%. RO, AO, and MC-fermented PM had degraded sinigrin by 81, 33, and 12% and phytate by 47%, 37%, and 33%, with a corresponding increase in free P by 44%, 1.17-fold, and 89%, respectively. In addition, zearalenone was reduced by 97%, 50%, 39.3%, and 32% in PO, RO, AO, and MC-fermented PM, respectively. This study demonstrated the feasibility of fungi to improve the feeding value of PM, potentially promoting the economic return of pennycress plantations.

This paper introduces the concept of symmetric difference operators in terms of overlap and grouping functions, for which the associativity property is not strongly required. These symmetric difference operators are weaker than symmetric difference operators in terms of positive and continuous t-norms and t-conorms. Therefore, in the sense of the characters of mathematics, these operators do not necessarily satisfy certain properties, such as associativity and the neutrality principle. We analyze several related important properties based on two models of symmetric differences.

Dynamic objects bring about a large number of error accumulations in pose estimation of mobile robots in dynamic scenes, and result in the failure to build a map that is consistent with the surrounding environment. Along these lines, this paper presents a robust semi-direct 3D simultaneous localization and mapping (SLAM) algorithm for mobile robots based on dense optical flow. First, a preliminary estimation of the robot’s pose is conducted using the sparse direct method and the homography matrix is utilized to compensate for the current frame image to reduce the image deformation caused by rotation during the robot’s motion. Then, by calculating the dense optical flow field of two adjacent frames and segmenting the dynamic region in the scene based on the dynamic threshold, the local map points projected within the dynamic regions are eliminated. On this basis, the robot’s pose is optimized by minimizing the reprojection error. Moreover, a high-performance keyframe selection strategy is developed, and keyframes are inserted when the robot’s pose is successfully tracked. Meanwhile, feature points are extracted and matched to the keyframes for subsequent optimization and mapping. Considering that the direct method is subject to tracking failure in practical application scenarios, the feature points and map points of keyframes are employed in robot relocation. Finally, all keyframes and map points are used as optimization variables for global bundle adjustment (BA) optimization, so as to construct a globally consistent 3D dense octree map. A series of simulations and experiments demonstrate the superior performance of the proposed algorithm.

This paper investigates the throughput optimization strategy in an unmanned aerial vehicle (UAV)-enabled emergency outdoor–indoor fairness communication scenario, with the UAV as a mobile relay station in the air, to provide outdoor–indoor communication services for users inside buildings. The occurrence of severe signal fading caused by outdoor transmission loss through wall loss as well as indoor transmission loss when the UAV forwards the information to the indoor users reduces the channel gain and degrades the system downlink throughput. To improve the downlink throughput of the system and ensure communication fairness for indoor users, we designed a joint UAV location deployment and resource allocation (JLRO) algorithm that optimized UAV three-dimensional (3D) deployment location, power and bandwidth resource allocation. The simulation results demonstrate the convergence and validity of the proposed JLRO algorithm, as well as its superiority compared to benchmark algorithms.

The space–air–ground collaborative network can provide computing service for ground users in remote areas by deploying edge servers on satellites and high-altitude platform (HAP) drones. However, with the growing number of ground devices required to be severed, it becomes imperative to address the issue of spectrum demand for the HAP drone to meet the access of a large number of users. In addition, the long propagation distance between devices and the HAP drone, and between the HAP drone and LEO satellites, will lead to high data transmission energy consumption. Motivated by these factors, we introduce a space–air–ground collaborative network that employs the non-orthogonal multiple access (NOMA) technique, enabling all ground devices to access the HAP drone. Therefore, all devices can share the same communication spectrum. Furthermore, the HAP drone can process part of the ground devices’ tasks locally, and offload the rest to satellites within the visible range for processing. Based on this system, we formulate a weighted energy consumption minimization problem considering power control, computing frequency allocation, and task-offloading decision. The problem is solved by the proposed low-complexity iterative algorithm. Specifically, the original problem is decomposed into interconnected coupled subproblems using the block coordinate descent (BCD) method. The first subproblem is to optimize power control and computing frequency allocation, which is solved by a convex algorithm after a series of transformations. The second subproblem is to make an optimal task-offloading strategy, and we solve it using the concave–convex procedure (CCP)-based algorithm after penalty-based transformation on binary variables. Simulation results verify the convergence and performance of the proposed iterative algorithm compared with the two benchmark algorithms.

A chemical investigation of the Arctic-derived fungus Eutypella sp. D-1 based on the OSMAC (one strain many compounds) approach resulted in the isolation of five cytosporin polyketides (compounds 1–3 and 11–12) from rice medium and eight cytosporins (compounds 2 and 4–11) from solid defined medium. The structures of the seven new compounds, eutypelleudesmane A (1), cytosporin Y (2), cytosporin Z (3), cytosporin Y1 (4), cytosporin Y2 (5), cytosporin Y3 (6), and cytosporin E1 (7), were elucidated by analyzing their detailed spectroscopic data. Structurally, cytosporin Y1 (4) may be a key intermediate in the biosynthesis of the isolated cytosporins, rather than an end product. Compound 1 contained a unique skeleton formed by the ester linkage of two moieties, cytosporin F (12) and the eudesmane-type sesquiterpene dihydroalanto glycol. Additionally, the occurrence of cyclic carbonate moieties in compounds 6 and 7 was found to be rare in nature. The antibacterial, immunosuppressive, and cytotoxic activities of all compounds derived from Eutypella sp. D-1 were evaluated. Unfortunately, only compounds 3, 6, 8, and 10–11 displayed immunosuppressive activity, with inhibitory rates of 62.9%, 59.5%, 67.8%, 55.8%, and 68.7%, respectively, at a concentration of 5 μg/mL.

Monitoring grassland growth in large areas usually needs multiple images from different sensors or on different dates to cover the study area completely. Images from different sensors or on different dates need consistency correction to eliminate the sharp differences between images. The main contribution of this study is to promote a method for consistency correction of images on different days by constructing a linear regression equation of land cover types and the classification pixel mean. Taking a prefecture-level area in China as a test area, the consistency corrected images were applied for monitoring grassland growth. The results showed the following. First, compared with the normal correction equation constructed for two images, taking all features into account, the coefficient of determination of the equation corrected by the land cover types was improved, and the root mean square error was also significantly reduced. Secondly, the areas of consistency in the corrected image were improved compared with the original image, with an improvement rate of 21% for images from the same sensor and 25% for images from different sensors. The pixel average was much closer to the benchmark images, indicating that the corrected image was more consistent than the original image. Thirdly, when applied for monitoring grassland growth, consistency correction can solve the problem of misjudging grassland degradation. Grassland that was judged to be degraded using direct imagery, in fact, showed stable growth after consistency correction, and this type accounted for 7.33% of the regional grassland area. The seasonal characteristics of grass growth in the region were also obtained by monitoring the growth of grass in the region throughout the year. The application test showed that an effective image consistency correction method can improve the accuracy of grassland growth monitoring across a large area.

The new-generation FengYun geostationary meteorological satellite has a high spatial and temporal resolution, which is advantageous in environmental assessments and air pollution monitoring. This study researched the ground-level particulate matter concentration estimation, based on satellite-observed radiations. The radiation of ground-level particulate matter is separate from the apparent radiation observed by satellites. The positive correlation between PM2.5 and PM10 is also considered to improve the accuracy of inversion results and the interpretability of the estimation model. Then, PM2.5 and PM10 concentrations were estimated synchronously every 5 min in mainland China based on FY-4A satellite directly observed radiations. The validation results showed that the improved model estimated results were close to the ground site measured results, with a high determination coefficient (R2) (0.89 for PM2.5, and 0.90 for PM10), and a small Root Mean Squared Error (RMSE) (4.69 μg/m3 for PM2.5 concentrations, and 13.77 μg/m3 for PM10 concentrations). The estimation model presented a good performance in PM2.5 and PM10 concentrations during typical haze and dust storm cases, indicating that it is applicable in different weather conditions and regions.

The development of a neoantigen-based personalized vaccine has promise in the hunt for cancer immunotherapy. The challenge in neoantigen vaccine design is the need to rapidly and accurately identify, in patients, those neoantigens with vaccine potential. Evidence shows that neoantigens can be derived from noncoding sequences, but there are few specific tools for identifying neoantigens in noncoding regions. In this work, we describe a proteogenomics-based pipeline, namely PGNneo, for use in discovering neoantigens derived from the noncoding region of the human genome with reliability. In PGNneo, four modules are included: (1) noncoding somatic variant calling and HLA typing; (2) peptide extraction and customized database construction; (3) variant peptide identification; (4) neoantigen prediction and selection. We have demonstrated the effectiveness of PGNneo and applied and validated our methodology in two real-world hepatocellular carcinoma (HCC) cohorts. TP53, WWP1, ATM, KMT2C, and NFE2L2, which are frequently mutating genes associated with HCC, were identified in two cohorts and corresponded to 107 neoantigens from non-coding regions. In addition, we applied PGNneo to a colorectal cancer (CRC) cohort, demonstrating that the tool can be extended and verified in other tumor types. In summary, PGNneo can specifically detect neoantigens generated by noncoding regions in tumors, providing additional immune targets for cancer types with a low tumor mutational burden (TMB) in coding regions. PGNneo, together with our previous tool, can identify coding and noncoding region-derived neoantigens and, thus, will contribute to a complete understanding of the tumor immune target landscape. PGNneo source code and documentation are available at Github. To facilitate the installation and use of PGNneo, we provide a Docker container and a GUI.

Blind Image Quality Assessment (BIQA) for synthetic and authentic distortions has attracted much attention in the community, and it is still a great challenge. The existing quality metrics are mildly consistent with subjective perception. Traditional handcrafted quality metrics can easily and directly extract low-level features, which mainly account for the outline, edge, color, texture, and shape features, while ignoring the important deep semantics of the distorted image. In the field of popular deep learning, multilevel features can be acquired easily. However, most of them either use only high-level features, ignoring the shallow features, or they simply combine features at different levels, resulting in limited prediction performance. Motivated by these, this paper presents a novel BIQA for synthetic and authentic distortions with hierarchical feature fusion in a flexible vision-Transformer framework. First, multiscale features are extracted from a strong vision-Transformer backbone. Second, an effective hierarchical feature fusion module is proposed to incorporate the features at different levels progressively. To eliminate redundant information, a simple but effective attention mechanism is employed after each fusion. Third, inspired by the human visual system, local and global features from the fusion features are extracted to represent different granularity distortions. Finally, these local and global features are mapped to the final quality score. Extensive experiments on three authentic image databases and two synthetic image datasets show that the proposed method is superior to the state-of-the-art quality metrics for both single-database testing and cross-database testing.