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The launch dynamics theory for multibody systems emerges as an innovative and efficacious approach for the study of launch dynamics, capable of addressing the challenges of complex modeling, diminished computational efficiency, and imprecise analyses of system dynamic responses found in the dynamics research of intricate multi-rigid-flexible body systems, such as self-propelled artillery. This advancement aims to enhance the firing accuracy and launch safety of self-propelled artillery. Recognizing the shortfall of overlooking the band engraving process in existing theories, this study introduces a novel coupling calculation methodology for the launch dynamics of a self-propelled artillery multibody system. This method leverages the ABAQUS subroutine interface VUAMP to compute the dynamic response of the projectile and barrel during the launch process of large-caliber self-propelled artillery. Additionally, it examines the changes in projectile resistance and band deformation in relation to projectile motion throughout the band engraving process. Comparative analysis of the computational outcomes with experimental data evidences that the proposed method offers a more precise depiction of the launch process of self-propelled artillery, thereby enhancing the accuracy of launch dynamics calculations for self-propelled artillery.

Abstract The exploration of traumatic spinal fractures (TSFs) within the senior demographic has not been thoroughly scrutinized, particularly with respect to variations across genders, age groups, seasonal periods, and causative factors. This retrospective analysis aimed to dissect differences in the prevalence and characteristics of TSFs among the elderly, factoring in gender, age, seasonal timing, and causation. A retrospective analysis was conducted on the medical and imaging records of 1,415 patients, all aged 60 years or older, who were diagnosed with TSFs from 2013 to 2019. This study categorized the data by gender, age groups (60–70, 70–80, and 80 years or older), seasons, and the cause of injuries, including road traffic crashes (RTCs), falls from low heights (LHF), falls from high heights (HHF), and injuries incurred during everyday activities and agricultural labor (DFI). Male patients exhibited notably higher incidences of RTCs, high-height falls (HHFs), outdoor incidents, comas post-injury, fractures of the lower limbs (LLFs), pelvic fractures (PFs), rib fractures (RFs), intra-thoracic injuries (ITIs), intra-abdominal injuries (IAIs), cervical fractures, and spinal cord injuries (SCIs). With advancing age, there was a marked decline in occurrences of RTCs, HHFs, outdoor incidents, RFs, craniocerebral injuries (CCIs), ITIs, cervical fractures, and SCIs, while the incidences of DFIs, indoor incidents, and thoracic and lumbar (T + L) fractures notably increased. During autumn, LLF occurrences were significantly reduced, whereas the winter season saw an increase in thoracic fractures. Spring time was associated with a higher frequency of lumbar fractures and noncontiguous spinal fractures (NSFs). Significant distinctions were observed in the age distribution, injury circumstances, associated injuries, and SCIs between high-energy impacts (RTCs and HHFs) and low-energy traumas (LHFs and DFIs). In the elderly demographic, TSFs exhibited discernible distinctions based on gender, age, seasonal variations, and etiological factors, impacting the nature and circumstances of injuries, associated traumas, complications, fracture sites, and the occurrence of SCIs.

Objective To compare the clinical efficacy of anterior column reconstruction using single or double titanium mesh cage (TMC) after total en bloc spondylectomy (TES) of thoracic and lumbar spinal tumors. Methods A retrospective cohort study was performed involving 39 patients with thoracic or lumbar spinal tumors. All patients underwent TES, followed by anterior reconstruction and screw-rod instrumentation via a posterior-only procedure. Twenty-two patients in group A were treated with a single TMC to reconstruct the anterior column, whereas 17 patients in group B were reconstructed with double TMCs. Results The overall follow-up is 20.5 ± 4.6 months. There is no significant difference between the 2 groups regarding age, sex, body mass index, tumor location, operative time, and intraoperative blood loss. The time for TMC placement was significantly shortened in the double TMCs group (5.2 ± 1.3 minutes vs. 15.6 ± 3.3 minutes, p = 0.004). Additionally, postoperative neural complications were significantly reduced with double TMCs (5/22 vs. 0/17, p = 0.046). The kyphotic Cobb angle and mean intervertebral height were significantly corrected in both groups (p ≤ 0.001), without obvious loss of correction at the last follow-up in either group. The bone fusion rates for single TMC and double TMCs were 77.3% and 76.5%, respectively. Conclusion Using 2 smaller TMCs instead of a single large one eases the placement of TMC by shortening the time and avoiding nerve impingement. Anterior column reconstruction with double TMC is a clinically feasible, and safe alternative following TES for thoracic and lumbar tumors.

Abstract Objective This study aimed to develop and apply a nomogram with good accuracy to predict the risk of CRAB infections in neuro-critically ill patients. In addition, the difficulties and expectations of application such a tool in clinical practice was investigated. Methods A mixed methods sequential explanatory study design was utilized. We first conducted a retrospective study to identify the risk factors for the development of CRAB infections in neuro-critically ill patients; and further develop and validate a nomogram predictive model. Then, based on the developed predictive tool, medical staff in the neuro-ICU were received an in-depth interview to investigate their opinions and barriers in using the prediction tool during clinical practice. The model development and validation is carried out by R. The transcripts of the interviews were analyzed by Maxqda. Results In our cohort, the occurrence of CRAB infections was 8.63% (47/544). Multivariate regression analysis showed that the length of neuro-ICU stay, male, diabetes, low red blood cell (RBC) count, high levels of procalcitonin (PCT), and number of antibiotics ≥ 2 were independent risk factors for CRAB infections in neuro-ICU patients. Our nomogram model demonstrated a good calibration and discrimination in both training and validation sets, with AUC values of 0.816 and 0.875. Additionally, the model demonstrated good clinical utility. The significant barriers identified in the interview include “skepticism about the accuracy of the model”, “delay in early prediction by the indicator of length of neuro-ICU stay”, and “lack of a proper protocol for clinical application”. Conclusions We established and validated a nomogram incorporating six easily accessed indicators during clinical practice (the length of neuro-ICU stay, male, diabetes, RBC, PCT level, and the number of antibiotics used) to predict the risk of CRAB infections in neuro-ICU patients. Medical staff are generally interested in using the tool to predict the risk of CRAB, however delivering clinical prediction tools in routine clinical practice remains challenging.

Increasing numbers of public and private locations now have surveillance cameras installed to make those areas more secure. Even though many organizations still hire someone to monitor the cameras, the person hired is more likely to miss some unexpected events in the video feeds because of human error. Several researchers have worked on surveillance data and have presented a number of approaches for automatically detecting aberrant events. To keep track of all the video data that accumulate, a supervisor is often required. To analyze the video data automatically, we recommend using neural networks to identify the crimes happening in the real world. Through our approach, it will be easier for police agencies to discover and assess criminal activity more quickly using our method, which will reduce the burden on their staff. In this paper, we aim to provide anomaly detection using surveillance videos as input specifically for the crimes of arson, burglary, stealing, and vandalism. It will provide an efficient and adaptable crime-detection system if integrated across the smart city infrastructure. In our project, we trained multiple accurate deep learning models for object detection and crime classification for arson, burglary and vandalism. For arson, the videos were trained using YOLOv5. Similarly for burglary and vandalism, we trained using YOLOv7 and YOLOv6, respectively. When the models were compared, YOLOv7 performed better with the highest mAP of 87. In this, we could not compare the model’s performance based on crime type because all the datasets for each crime type varied. So, for arson YOLOv5 performed well with 80% mAP and for vandalism, YOLOv6 performed well with 86% mAP. This paper designed an automatic identification of crime types based on camera or surveillance video in the absence of a monitoring person, and alerts registered users about crimes such as arson, burglary, and vandalism through an SMS service. To detect the object of the crime in the video, we trained five different machine learning models: Improved YOLOv5 for arson, Faster RCNN and YOLOv7 for burglary, and SSD MobileNet and YOLOv6 for vandalism. Other than improved models, we innovated by building ensemble models of all three crime types. The main aim of the project is to provide security to the society without human involvement and make affordable surveillance cameras to detect and classify crimes. In addition, we implemented the Web system design using the built package in Python, which is Gradio. This helps the registered user of the Twilio communication tool to receive alert messages when any suspicious activity happens around their communities.

This paper investigates the relationship between unsteady flow and radiated noise in the near wake of a wind turbine tower due to the blade tower interaction (BTI) in Wind tunnel experiments. The two-dimensional hot-wire probe is used to collect the instantaneous velocity field in the BTI region, and the microphone sensor is used to collect sound field information. The effects of Reynolds stress and turbulent kinetic energy on BTI noise are further analyzed based on the instantaneous velocity field. The results show that the blade’s passing effect causes irregular velocity distribution and vortex migration and mixing in the near wake of the tower, resulting in the most significant difference in Reynolds shear stress at the 0.71R position of the blade during the blade’s transition from an azimuthal angle of 180°–210°(upward). Furthermore, a strong correlation is identified between the peak turbulent kinetic energy and the peak acoustic pressure value measured during the rotational cycle when the blade ran up to 210° azimuth angle. It is deduced that the aerodynamic noise at the rear of the tower is attributed to the increase in momentum exchange caused by fluid doping and bursting, which are driven by Reynolds shear stress. Momentum exchange induces an increase in turbulent kinetic energy, which results in fluid velocity pulsations, pressure pulsations, and, thus, noise. The reduction in fluid mixing and the reduction in pressure pulsation subsequently lead to a reduction in the noise generated by the tower. Therefore, a viable approach to reducing BTI noise involves minimizing momentum exchange.

This paper introduces the flexible control and trajectory planning medical two-arm surgical robots, and employs effective collision detection methods to ensure the safety and precision during tasks. Firstly, the DH method is employed to establish relative rotation matrices between coordinate systems, determining the relative relationships of each joint link. A neural network based on a multilayer perceptron is proposed to solve FKP problem in real time. Secondly, a universal interpolator based on Non-Uniform Rational B-Splines (NURBS) is developed, capable of handling any geometric shape to ensure smooth and flexible motion trajectories. Finally, we developed a generalized momentum observer to detect external collisions, eliminating the need for external sensors and thereby reducing mechanical complexity and cost. The experiments verify the effectiveness of the kinematics solution and trajectory planning, demonstrating that the improved momentum torque observer can significantly reduce system overshoot, enabling the two-arm surgical robot to perform precise and safe surgical tasks under algorithmic guidance.

In order to explore the impact of higher-order structures with smaller scales on key node group mining problems and with the goal of optimizing network propagation, a key node group recognition algorithm was proposed based on motif structure and degree information.Firstly, the influence of nodes was evaluated based on the motif structure, and the core nodes of the motif structure were excavated.Then, the VIKOR method was used to fuse it with degree information.Finally, the seed exclusion algorithm was used to exclude the neighbors of the seed nodes, effectively reducing the problem of influence overlap.Based on the SIR propagation model, six different undirected networks were selected for comparison with four benchmark algorithms.The experimental results show that the proposed algorithm performs better in terms of accuracy and stability.

Abstract Dual-parameter pressure-temperature sensors are widely employed in personal health monitoring and robots to detect external signals. Herein, we develop a flexible composite dual-parameter pressure-temperature sensor based on three-dimensional (3D) spiral thermoelectric Bi2Te3 films. The film has a (000l) texture and good flexibility, exhibiting a maximum Seebeck coefficient of −181 μV K–1 and piezoresistance gauge factor of approximately −9.2. The device demonstrates a record-high temperature-sensing performance with a high sensing sensitivity (−426.4 μV K−1) and rapid response time (~0.95 s), which are better than those observed in most previous studies. In addition, owing to the piezoresistive effect in the Bi2Te3 film, the 3D-spiral deviceexhibits significant pressure-response properties with a pressure-sensing sensitivity of 120 Pa–1. This innovative approach achieves high-performance dual-parameter sensing using one kind of material with high flexibility, providing insight into the design and fabrication of many applications, such as e-skin.

Abstract Consumer participation is critical to achieving successful food safety governance. However, in the field of food safety governance, consumer participation faces the dilemma of collective action. Based on social capital theory, this study introduces a total of 1229 questionnaires from online food consumers in China were collected by randomly distributing electronic questionnaires to online shoppers, and tests the effect and mechanism of social capital on consumer participation in food safety governance. By using ordered regression and multinomial logit models, the empirical results show that social capital can reduce the adverse effects of free-riding on consumers’ participation in food safety governance and in fact will ultimately encourage consumers to participate in food safety governance. Furthermore, social capital will enhance people’s participation in food safety governance through two paths: promoting consumers’ sharing of food safety information and promoting consumers’ direct supply of safe food. The conclusion of this paper provides inspiration for the promotion of consumer participation in the public affairs related to food safety governance in developing countries.

Aflatoxin B1 (AFB1) is a highly toxic carcinogen severely harmful to humans and animals. This study fabricated SDB-6-K-9@TiO2 composites via the hydrothermal synthesis method to reduce AFB1. The structural characterization results of the photocatalytic composites showed that TiO2 was successfully loaded onto SDB-6-K-9. The different photocatalytic degradation conditions, photocatalyst kinetics, recycling performance, and photocatalytic degradation mechanism were investigated. Photocatalysis with 6 mg of 4%SDB-6-K-9@TiO2 in a 100 μg/mL AFB1 solution presented a reduction of over 95%, exhibiting excellent performance, high stability, and reusability even after five cycles of photocatalytic experiments. Active species trapping experiments confirmed that holes (h+) played the most critical role. After structural analysis and identification of the photocatalytic degradation products, the photodegradation path and photocatalytic oxidation mechanism of 4%SDB-6-K-9@TiO2 were postulated. The results show a new way to improve TiO2’s photocatalytic performance, providing a certain theoretical basis for the effective AFB1 reduction.

With the prosperous development and successful application of deep learning technologies in the field of remote sensing, numerous deep-learning-based methods have emerged for synthetic aperture radar (SAR) automatic target recognition (ATR) tasks over the past few years. Generally, most deep-learning-based methods can achieve outstanding recognition performance on the condition that an abundance of labeled samples are available to train the model. However, in real application scenarios, it is difficult and costly to acquire and to annotate abundant SAR images due to the imaging mechanism of SAR, which poses a big challenge to existing SAR ATR methods. Therefore, SAR target recognition in the situation of few-shot, where only a scarce few labeled samples are available, is a fundamental problem that needs to be solved. In this paper, a new method named enhanced prototypical network with customized region-aware convolution (CRCEPN) is put forward to specially tackle the few-shot SAR ATR tasks. To be specific, a feature-extraction network based on a customized and region-aware convolution is first developed. This network can adaptively adjust convolutional kernels and their receptive fields according to each SAR image’s own characteristics as well as the semantical similarity among spatial regions, thus augmenting its capability to extract more informative and discriminative features. To achieve accurate and robust target identity prediction under the few-shot condition, an enhanced prototypical network is proposed. This network can improve the representation ability of the class prototype by properly making use of training and test samples together, thus effectively raising the classification accuracy. Meanwhile, a new hybrid loss is designed to learn a feature space with both inter-class separability and intra-class tightness as much as possible, which can further upgrade the recognition performance of the proposed method. Experiments performed on the moving and stationary target acquisition and recognition (MSTAR) dataset, the OpenSARShip dataset, and the SAMPLE+ dataset demonstrate that the proposed method is competitive with some state-of-the-art methods for few-shot SAR ATR tasks.

With the development of the power system, power users begin to use their own power supply in order to improve the power economy, but this also leads to the occurrence of the risk of self-provided power supply. The actual distribution network has few samples of power supply risk and it is difficult to identify the power supply risk by using conventional deep learning methods. In order to achieve high accuracy of self-provided power supply risk identification with small samples, this paper proposes a combination of transfer learning, convolutional block attention module (CBAM), and convolutional neural network (CNN) to identify the risk of self-provided power supply in an active distribution network. Firstly, in order to be able to further identify whether or not a risk will be caused based on completing the identification of the faulty line, we propose that it is necessary to identify whether or not the captive power supply on the faulty line is in operation. Second, in order to achieve high-precision identification and high-efficiency feature extraction, we propose to embed the CBAM into a CNN to form a CBAM-CNN model, so as to achieve high-efficiency feature extraction and high-precision risk identification. Finally, the use of transfer learning is proposed to solve the problem of low risk identification accuracy due to the small number of actual fault samples. Simulation experiments show that compared with other methods, the proposed method has the highest recognition accuracy and the best effect, and the risk recognition accuracy of active distribution network backup power is high in the case of fewer samples.

ABSTRACT This study aimed to develop and validate a simple-to-use nomogram model to assess the risk of infection caused by carbapenem-resistant Klebsiella pneumoniae (CRKP) in neurocritically ill patients. The clinical data of 544 patients with CRKP infection admitted to a neurointensive care unit (neuro-ICU) were retrospectively analyzed. The demographic data, laboratory test results, and clinical characteristics of patients in the neuro-ICU were collected. Subsequently, multivariate regression analysis was used to construct a nomogram to predict the risk of CRKP infection in these patients. The calibration ability, clinical effectiveness, and discriminative ability of the nomogram were evaluated. The incidence of CRKP infection was estimated to be 6.43%, and a majority of bacterial isolates causing the infection were found in sputum (74.3%). Multivariate regression analysis showed that the number of antibiotics of ≥2 [odds ratio (OR): 9.08, 95% confidence interval (CI): 2.78–29.71], undergoing surgery (OR: 3.84, 95% CI: 1.09–13.54), and long neuro-ICU stay (OR: 1.08, 95% CI: 1.01–1.14) were associated with CRKP infection in neurocritically ill patients. The nomogram model demonstrated good calibration and discrimination in both the training and validation sets, with area under the curve values of 0.860 and 0.907, respectively. This study developed and validated a nomogram that combines three easily accessed variables during clinical practice to predict the risk of nosocomial CRKP infection in neuro-ICU patients. The tool demonstrated a good predictive performance and discrimination, which might serve as a useful tool to facilitate early detection and reduction of the CRKP infection risk in neurocritically ill patients. IMPORTANCE Patients in neuro-ICU are at a high risk of developing nosocomial CRKP infection owing to complex conditions, critical illness, and frequent invasive procedures. However, studies focused on constructing prediction models for assessing the risk of CRKP infection in neurocritically ill patients are lacking at present. Therefore, this study aims to establish a simple-to-use nomogram for predicting the risk of CRKP infection in patients admitted to the neuro-ICU. Three easily accessed variables were included in the model, including the number of antibiotics used, surgery, and the length of neuro-ICU stay. This nomogram might serve as a useful tool to facilitate early detection and reduction of the CRKP infection risk of neurocritically ill patients.

With the development of artificial intelligence, the intelligence of agriculture has become a trend. Intelligent monitoring of agricultural activities is an important part of it. However, due to difficulties in achieving a balance between quality and cost, the goal of improving the economic benefits of agricultural activities has not reached the expected level. Farm supervision requires intensive human effort and may not produce satisfactory results. In order to achieve intelligent monitoring of agricultural activities and improve economic benefits, this paper proposes a solution that combines unmanned aerial vehicles (UAVs) with deep learning models. The proposed solution aims to detect and classify objects using UAVs in the agricultural industry, thereby achieving independent agriculture without human intervention. To achieve this, a highly reliable target detection and tracking system is developed using Unmanned Aerial Vehicles. The use of deep learning methods allows the system to effectively solve the target detection and tracking problem. The model utilizes data collected from DJI Mirage 4 unmanned aerial vehicles to detect, track, and classify different types of targets. The performance evaluation of the proposed method shows promising results. By combining UAV technology and deep learning models, this paper provides a cost-effective solution for intelligent monitoring of agricultural activities. The proposed method offers the potential to improve the economic benefits of farming while reducing the need for intensive hum.

Agronomic traits are key components in variety protection, cultivar development, and the formulation of DUS (distinct, uniform, and stable) test guidelines. P. giganteus is an increasingly popular and commercially promising edible macrofungi. In this study, both mycelial performance and fruiting body characters of 15 Pleurotus giganteus strains were investigated. The temperature gradient culture test indicated that, although most of the strains achieved optimal mycelial growth between 24 and 28 °C, a statistical difference in mycelial growth rates and temperature adaptability among strains were found, supporting that this trait has the potential to be adopted as an indicator in distinguishing strains. In the fruiting performance tests, the coefficient of variation (CV) of tested traits ranged from 5.30% (pileus diameter) to 18.70% (individual mushroom weight). The mushroom yields ranged from 103.37 g/bag (strain No. 15) to 275.76 g/bag (strain No. 9). The large divergence observed in individual mushroom weight tested strains, ranging from 40.88 g to 78.39 g (with median between 37.69 and 79.395 g), make it highly selective and a potential indicator in variety development. Strain No. 9 had the advantages of forming larger, heavier fruiting bodies and a more obvious funnel shape, which also exhibited the highest biological efficiency (15.61%). The results suggested some morphological traits showed high variety difference, such as pileus diameter (55.75 mm to 66.48 mm), stipe length (92.59 mm to 177.51 mm), stipe diameter (16.14 mm to 23.52 mm), and pileus thickness (13.38 mm to 19.75 mm). In the cluster analysis, the tested strains were grouped into four clusters based on agronomic traits: cluster Ⅰ comprised six strains (No. 6, No. 11, No. 8, No. 1, No. 14, and No. 9) with high mushroom yield; cluster Ⅱ included four strains (No. 3, No. 10, No. 7, and No. 4) with large pileus diameter and short stipe; cluster ⅡI consisted of four strains (No. 5, No. 12, No. 13, and No. 15) with relatively lower yields; and cluster Ⅳ included only strain No. 2 which was low in yield, individual mushroom weight, and biological efficiency, accompanied by smaller pileus size and shorter stipe. The results of the correlation analysis indicated three traits, including individual mushroom weight, stipe length, and pileus weight, were positively associated with high yield. This study suggested P. giganteus germplasm resources are of high abundance and their agronomic diversity is useful in distinguishing and developing different varieties. The findings of this work provide knowledge on the agronomic traits and cultivation performance of various P. giganteus strains, laying a foundation for the development of its DUS test guidelines and variety protection, as well as providing reference for the breeding and phenotype selection of high-quality cultivars.

In this paper, Ti-5Mn alloy was subjected to different heat treatments to explore the possibility of preparing antimicrobial Ti-Mn alloys and to examine the effect of precipitate on the properties of the alloy. The microstructure, phase composition, hardness, biocorrosion properties and antimicrobial properties of Ti-5Mn alloys after different heat treatments was analyzed by metallurgical microscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), x-ray diffraction (XRD), microhardness tests, electrochemical tests and antimicrobial tests. The results have shown that the phase composition of the solid solution treated Ti-5Mn(T4) was mainly β -Ti phase, and the aged Ti-5Mn was composed of α -Ti phase and Ti _17 Mn _3 phase, while Ti _17 Mn _3 precipitate gradually increased with the extension of the aging time. Ti-5Mn(T4) showed the highest hardness and the best corrosion resistance and the aging process reduced the hardness of Ti-5Mn(T4) alloy. With the precipitation of Ti _17 Mn _3 , the corrosion resistance of the alloy became worse and the hardness was reduced, but the corrosion resistance of Ti-5Mn alloy was still better than that of cp-Ti. It was demonstrated that Ti-5Mn(T4) exhibited no antibacterial properties against Staphylococcus aureus , but the aging treatment improved the antibacterial property of Ti-5Mn(T4) alloy, and the antibacterial rate of Ti-5Mn alloy reached 69% after 50 h aging treatment.

The highly sensitive detection of heavy metal elements in aqueous solution has been achieved by the combination of circular grooves and nanoparticle-enhanced laser-induced breakdown spectroscopy. The effects of Ag nanoparticles (Ag NPs) and circular grooves on the spectral emission of Cu, Pb and Cr have been investigated. The results show that both Ag NPs and circular grooves can enhance the spectral emission of heavy metal elements, and the circular grooves can improve the spectral reproducibility. The prepared micro/nanostructures can suppress the coffee ring effect and improve the absorption efficiency of light. The average relative standard deviation of the spectral emission can be diminished. By establishing calibration curves for Cu, Pb, and Cr elements, the limits of detection (LODs) for Cu, Pb and Cr elements have been obtained. The results show that by employing Ag NPs and circular grooves the lowest LOD of 0.10 mg mL-1 can be achieved. This improved spectral performance (spectral intensity, stability and detection sensitivity) originates mainly from the collective oscillation of free electrons of Ag NPs excited by the incident laser pulse, which confines the electromagnetic energy with a strongly enhanced electric field. The enhancement in electric field intensity of Ag NPs is calculated and the seed electron is proved to be predominantly excited through a multiphoton photoemission process under the experimental conditions. The results provide an efficient pathway for improving the detection sensitivity of heavy metals in aqueous solution. [ABSTRACT FROM AUTHOR]

Abstract In order to augment the efficacy of the intelligent evaluation model for assessing the suitability of ice and snow tourism, this study refines the model by incorporating the Long Short-Term Memory (LSTM) network within the framework of the Internet of Things (IoT). The investigation commences with an elucidation of the application of IoT technology in environmental detection. After this, an analysis is conducted on the structure of LSTM and its merits in the realm of time series prediction. Ultimately, a novel model for appraising the suitability of ice and snow tourism is formulated. The efficacy of this model is substantiated through empirical experiments. The results of these experiments reveal that the refined model exhibits exceptional performance across diverse climatic conditions, encompassing mild, cold, humid, and arid climates. In regions characterized by mild climates, the predictive accuracy of the refined model progressively ascends from 88% in the initial quarter to 94% in the fourth quarter, surpassing the capabilities of conventional models. Consistently robust performance is demonstrated by the refined model throughout each quarter. In terms of operational efficiency, comparative analysis indicates that the refined model attains a moderate level, manifesting a 30–33 s runtime and maintaining a Central Processing Unit (CPU) usage rate between 40 and 43%. This observation implies that the refined model adeptly balances precision against resource consumption. Consequently, this study holds significance as a scholarly reference for the integration of IoT and LSTM networks in the domain of tourism evaluation.

Abstract Emerging evidence has demonstrated the profound impact of the gut microbiome on cardiovascular diseases through the production of diverse metabolites. Using an animal model of myocardial ischemia–reperfusion (I/R) injury, we found that the prophylactic administration of a well‐known probiotic, Bifidobacterium infantis (B. infantis), exhibited cardioprotective effects in terms of preserving cardiac contractile function and preventing adverse cardiac remodeling following I/R and that these cardioprotective effects were recapitulated by its metabolite inosine. Transcriptomic analysis further revealed that inosine mitigated I/R‐induced cardiac inflammation and cell death. Mechanistic investigations elucidated that inosine suppressed the production of pro‐inflammatory cytokines and reduced the numbers of dendritic cells and natural killer cells, achieved through the activation of the adenosine A2A receptor (A2AR) that when inhibited abrogated the cardioprotective effects of inosine. Additionally, in vitro studies using C2C12 myoblasts revealed that inosine attenuated cell death by serving as an alternative carbon source for adenosine triphosphate (ATP) generation through the purine salvage pathway when subjected to oxygen‐glucose deprivation/reoxygenation that simulated myocardial I/R injury. Likewise, inosine reversed the I/R‐induced decrease in ATP levels in mouse hearts. Taken together, our findings indicate that B. infantis or its metabolite inosine exerts cardioprotective effects against I/R by suppressing cardiac inflammation and attenuating cardiac cell death, suggesting prophylactic therapeutic options for acute ischemic cardiac injury.

Abstract Background We investigated the value of metagenomic next-generation sequencing (mNGS) in diagnosing infectious diseases in patients receiving allogeneic hematopoietic stem cell transplantation (allo-HSCT). Methods Fifty-four patients who had fever following allo-HSCT from October 2019 to February 2022 were enrolled. Conventional microbiological tests (CMTs) and mNGS, along with imaging and clinical manifestations, were used to diagnose infection following allo-HSCT. The clinical diagnostic value of mNGS was evaluated. Results A total of 61 mNGS tests were performed, resulting in the diagnosis of 46 cases of infectious diseases. Among these cases, there were 22 cases of viral infection, 13 cases of fungal infection, and 11 cases of bacterial infection. Moreover, 27 cases (58.7%) were classified as bloodstream infections, 15 (32.6%) as respiratory infections, 2 (4.3%) as digestive system infections, and 2 (4.3%) as central nervous system infections. Additionally, there were 8 cases with non-infectious diseases (8/54, 14.81%), including 2 cases of interstitial pneumonia, 2 cases of bronchiolitis obliterans, 2 cases of engraftment syndrome, and 2 cases of acute graft-versus-host disease. The positive detection rates of mNGS and CMT were 88.9% and 33.3%, respectively, with significant differences (P

The vegetation pattern generated by aeolian sand movements is a typical type of vegetation patterns in arid and semi-arid areas. This paper presents a vegetation-sand model with nonlocal interaction characterized by an integral term with a kernel function. The instability of the Turing pattern was analyzed and the conditions of stable pattern occurrence were obtained. At the same time, the multiple scales method was applied to obtain the amplitude equations at the critical value of Turing bifurcation. The spatial distributions of vegetation under different delays were obtained by numerical simulation. The results revealed that the vegetation biomass increased as the interaction intensity decreased or as the nonlocal interaction distance increased. We demonstrated that the nonlocal interaction between vegetation and sand is a crucial mechanism for forming vegetation patterns, which provides a theoretical basis for preserving and restoring vegetation.

The purpose of this study was to design novel drug nanocrystals (NCs) stabilized by glycyrrhizic acid (GL) for achieving liver targeted drug delivery due to the presence of GL receptor in the hepatocytes. Quercetin (QT) exhibits good pharmacological activities for the treatment of liver diseases, including liver steatosis, fatty hepatitis, liver fibrosis, and liver cancer. It was selected as a model drug owing to its poor water solubility. QT NCs stabilized by GL (QT-NCs/GL) were fabricated by wet media milling technique and systemically evaluated. QT-NCs stabilized by poloxamer 188 (QT-NCs/P188) were prepared as a reference for comparison of in vitro and in vivo performance with QT-NCs/GL. QT-NCs/GL and QT-NCs/P188 with similar particle size around 130 nm were successfully fabricated by wet media milling technique. Both of QT-NCs/GL and QT-NCs/P188 showed irregular particles and short rods under SEM. XRPD revealed that QT-NCs/GL and QT-NCs/P188 remained in crystalline state with reduced crystallinity. QT-NCs/GL and QT-NCs/P188 exhibited significant solubility increase and drug release improvement of QT as compared to raw QT. No significant difference for the plasma concentration–time curves and pharmacokinetic parameters of QT were found following intravenous administration of QT-NCs/GL and QT-NCs/P188. However, a significantly higher liver distribution of QT following intravenous administration of QT-NCs/GL was observed in comparison to QT-NCs/P188, indicating QT-NCs stabilized by GL could achieve liver targeted delivery of QT. It could be concluded that GL used as stabilizer of QT NCs have a great potential for liver targeted drug delivery.

Abstract The gut microbiome has emerged as a potential target for the treatment of cardiovascular disease. Ischemia/reperfusion (I/R) after myocardial infarction is a serious complication and whether certain gut bacteria can serve as a treatment option remains unclear. Lactobacillus reuteri (L. reuteri) is a well‐studied probiotic that can colonize mammals including humans with known cholesterol‐lowering properties and anti‐inflammatory effects. Here, the prophylactic cardioprotective effects of L. reuteri or its metabolite γ‐aminobutyric acid (GABA) against acute ischemic cardiac injury caused by I/R surgery are demonstrated. The prophylactic gavage of L. reuteri or GABA confers cardioprotection mainly by suppressing cardiac inflammation upon I/R. Mechanistically, GABA gavage results in a decreased number of proinflammatory macrophages in I/R hearts and GABA gavage no longer confers any cardioprotection in I/R hearts upon the clearance of macrophages. In vitro studies with LPS‐stimulated bone marrow‐derived macrophages (BMDM) further reveal that GABA inhibits the polarization of macrophages toward the proinflammatory M1 phenotype by inhibiting lysosomal leakage and NLRP3 inflammasome activation. Together, this study demonstrates that the prophylactic oral administration of L. reuteri or its metabolite GABA attenuates macrophage‐mediated cardiac inflammation and therefore alleviates cardiac dysfunction after I/R, thus providing a new prophylactic strategy to mitigate acute ischemic cardiac injury.

The improvement of the capability of poly(N-isopropylacrylamide) (PNIPAAm) hydrogel coating in cell adhesion and detachment is critical to efficiently prepare cell sheets applied in cellular therapies and tissue engineering. To enhance cell response on the surface, the amine group-modified PNIPAAm (PNIPAAm-APTES) nanohydrogels were synthesized and deposited spontaneously on tannic acid (TA)-modified polyethylene (PE) plates. Subsequently, TA was introduced onto PNIPAAm-APTES nanohydrogels to fabricate coatings composed of TA-modified PNIPAAm-APTES (PNIPAAm-APTES-TA). Characterization techniques, including TEM, SEM, XPS, and UV-Vis spectroscopy, confirmed the effective deposition of hydrogels of PNIPAAm as well as the morphologies, content of chemical bonding-TA, and stability of various coatings. Importantly, the porous hydrogel coatings exhibited superhydrophilicity at 20 °C and thermo-responsive behavior. The fluorescence measurement demonstrated that the coating’s stability effectively regulated protein behavior, influencing cell response. Notably, cell response tests revealed that even without precise control over the chain length/thickness of PNIPAAm during synthesis, the coatings enhanced cell adhesion and detachment, facilitating efficient cell culture. This work represented a novel and facile approach to preparing bioactive PNIPAAm for cell culture.

We have established and released a new stellar index library of the Ca ii triplet, which serves as an indicator for characterizing the chromospheric activity of stars. The library is based on data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS) Data Release 9. To better reflect the chromospheric activity of stars, we have defined new indices R and R ^+ . The library includes measurements of R and R ^+ for each Ca ii infrared triplet (IRT) from 699,348 spectra of 562,863 F, G, and K-type solar-like stars with a signal-to-noise ratio higher than 100, as well as the stellar atmospheric parameters and basic information inherited from the LAMOST LRS catalog. We compared the differences between the three individual indices of the Ca ii triplet and also conducted a comparative analysis of ${R}_{\lambda 8542}^{+}$ to the Ca ii H and K S and ${R}_{\mathrm{HK}}^{+}$ index databases. We observe the fraction of less active stars decreases with T _eff and the fraction of more active stars first decreases with decreasing temperature and turns to increase with decreasing temperature at 5800 K. We also find that a significant fraction of stars that show a high activity index in both Ca ii H and K and IRT are binaries with low activity; some of them could be discriminated in the Ca ii H and K S index and ${R}_{\lambda 8542}^{+}$ space. This new stellar library serves as a valuable resource for studying chromospheric activity in stars and can be used to improve our comprehension of stellar magnetic activity and other astrophysical phenomena.

The Gaia Data Release 3 (DR3) mission has identified and provided about 440,000 binary systems with orbital solutions, offering a valuable resource for searching binaries including a compact component. By combining the Gaia DR3 data with radial velocities from the LAMOST spectroscopic survey, we identify three wide binaries possibly containing a compact object. For two of these sources with a main-sequence companion, no obvious excess is observed in the blue/red band of the Gaia DR3 XP spectra, and the LAMOST medium-resolution spectra exhibit clear single-lined features. The absence of an additional component from spectral disentangling analysis further suggests the presence of compact objects within these systems. On the other hand, the visible star of the third source is a stripped giant star. In contrast to most binaries including stripped stars, no emission line is detected in the optical spectra. The unseen star could potentially be a massive white dwarf or neutron star, but the possibility of an F-type dwarf star scenario cannot be ruled out. An examination of about 10 binaries containing white dwarfs or neutron stars using both kinematic and chemical methods suggests most of these systems are located in the thin disk of the Milky Way.