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Background Yaks living in the high-altitude hypoxic environment of Tibetan plateau (3600 m) have special gut microbes. However, it is still little research on yak probiotics until now. Therefore, the purpose of our study was to evaluate the growth promoting effect, antioxidant capability, immune effect, and anti-inflammatory ability of Bacillus subtilis and Bacillus velezensis isolated from Tibetan yaks in mice model. Results The results showed that the isolated strains supplementation not only improve the growth performance but also increased the length of villus in the small intestine and intestinal digestive enzyme activity. Importantly, we observed that the T-AOC, SOD, and GSH-PX levels were increased and the MDA content was reduced in probiotic-treated mice, which implied that probiotics supplementation can ameliorate the antioxidative activity of mice. The levels of AST and ALT correlated with the hepatic injury were reduced and the levels of AKP, TP, GLB, ALB, Ca, and P were markedly higher than those in the control group. Additionally, mice treated with probiotics exhibited higher serum IgG, IgM and IgA, which can reflect the immune status to some extent. At the same time, the major pro-inflammatory factor TNF-α, IL-6, and IL-8 were down-regulated and the anti-inflammatory factor IL-10 was up-regulated compared with the control groups. Conclusions In conclusion, these results demonstrated that Bacillus subtilis and Bacillus velezensis supplementation can increase overall growth performance and ameliorate the blood parameters related to inflammation and immunity of mice.

In the field of intelligent manufacturing, image anomaly detection plays a pivotal role in automated product quality inspection. Most existing anomaly detection methods are adept at capturing local features of images, achieving high detection accuracy for structural anomalies such as cracks and scratches. However, logical anomalies typically appear normal within local regions of an image and are difficult to represent well by the anomaly score map, requiring the model to possess the capability to extract global context features. To address this challenge while balancing the detection of both structural and logical anomalies, this paper proposes a lightweight anomaly detection framework built upon EfficientAD. This framework integrates the reconstruction difference constraint (RDC) and a logical anomaly detection module. Specifically, the original EfficientAD relies on the coarse-grained reconstruction difference between the student and the autoencoder to detect logical anomalies; but, false detection may be caused by the local fine-grained reconstruction difference between the two models. RDC can promote the consistency of the fine-grained reconstruction between the student and the autoencoder, thereby effectively alleviating this problem. Furthermore, in order to detect anomalies that are difficult to represent by feature maps more effectively, the proposed logical anomaly detection module extracts and aggregates the context features of the image, and combines the feature-based method to calculate the overall anomaly score. Extensive experiments demonstrate our method’s significant improvement in logical anomaly detection, achieving 94.2 AU-ROC on MVTec LOCO, while maintaining strong structural anomaly detection performance at 98.4 AU-ROC on MVTec AD. Compared to the baseline, like EfficientAD, our framework achieves a state-of-the-art balance between both anomaly types.

In quantum communication, the concept of dense coding traditionally relies on maximally entangled states as quantum channels. Recent advancements have expanded this framework to include non-maximally entangled states within the probabilistic dense coding paradigm. However, such schemes introduce a significant limitation: the receiver cannot always retrieve the complete dense coding information sent by the sender. Consequently, the receiver must inform the sender of the amount of information successfully received. Based on this feedback, the sender determines whether retransmission is necessary, leading to inefficient use of the quantum channel and reduced communication efficiency. To address these shortcomings, we propose an alternative deterministic quantum dense coding scheme that utilizes non-maximally entangled states as the quantum channel. This deterministic approach eliminates the need for retransmissions and significantly enhances communication efficiency while maintaining compatibility with non-maximally entangled states. Our scheme represents a substantial improvement over existing probabilistic methods and paves the way for more efficient quantum communication protocols.

With the improvement of technologies, people’s demand for intelligent devices of indoor and outdoor living environments keeps increasing. However, the traditional control system only adjusts living parameters mechanically, which cannot better meet the requirements of human comfort intelligently. This article proposes a building intelligent thermal comfort control system based on the Internet of Things and intelligent artificial intelligence. Through the literature review, various algorithms and prediction methods are analyzed and compared. The system can automatically complete a series of operations through IoT hardware devices which are located at multiple locations in the building with key modules. The code is developed and debugged by Python to establish a model for energy consumption prediction with environmental factors such as temperature, humidity, radiant temperature, and air velocity on thermal comfort indicators. By using the simulation experiments, 1700 data sets are used for training. Then, the output PMV predicted values are compared with the real figure. The results show that the performance of this system is superior to traditional control on energy-saving and comfort.

Severe pressure pulsations caused by complex flow fields in pumped-storage power stations significantly threaten operational safety and stability. With advances in computational technology, fully three-dimensional simulations coupling pipelines and pump-turbine units have become feasible. In this study, a fully three-dimensional analysis model was developed, coupling the water conveyance system and a finely modeled prototype-scale pump-turbine with splitter blades, to numerically simulate the compressible flow field under the maximum head pump mode. The study reveals a strong bidirectional coupling between the flow in the long outlet pipe and the internal flow within the pump-turbine unit. Influenced by structural features such as bifurcations and flow impingement at the T-junction, complex three-dimensional vortices arise and cannot be neglected. Based on the flow field, the study further investigates the time-domain, frequency-domain, and spatial characteristics of pressure pulsations at various downstream hydraulic components, ranging from the vaneless space to the outlet of the long outlet pipe. The pressure pulsation frequencies are shown to be affected by both rotor–stator interactions and the complex vortical structures in the flow. These findings clearly demonstrate the necessity of fully three-dimensional simulations that incorporate both the water conveyance system and the pump-turbine unit.

Building energy waste has become one of the major challenges confronting the world today, so specifications and targets for building energy efficiency have been put forward in countries around the world in recent years. The schematic design stage matters a lot for building energy efficiency, while most architects nowadays are less likely to make energy efficiency design decisions in this stage due to the lack of necessary means and methods for analysis. An integrated multi-objective multivariate framework for optimization analysis is proposed for the schematic design stage in the paper. Here, the design parameters of the building morphology and the design parameters of the building envelope are integrated for analysis, and an integrated performance prediction model is established for low-rise and medium-rise residential buildings. Then, a comparison of the performance indicators of low-rise and medium-rise residential buildings under five typical urban climatic conditions is carried out, and the change patterns of the lighting environment, thermal environment, building energy demand, and life cycle cost of residential buildings in each city under different morphological parameters and design parameters of the building envelope are summarized. Specific analysis methods and practical tools are provided in the study for architectural design to ensure thermal comfort, lighting comfort, low energy consumption, and low life-cycle cost requirement, and this design method can inspire and guide the climate adaptation analysis and design process of low-rise and medium-rise residential buildings in China, improve architects’ perception of energy-saving design principles of low-rise and medium-rise residential buildings on the ontological level, as well as provide them with a method to follow and a case to follow in the actual design process.

In recent years, increasingly prominent energy and environmental problems have pushed for higher requirements for buildings’ energy saving. According to the conventional energy-saving design method, the cooperative operation between architects, structural and equipment engineers and other professionals cannot run smoothly, so the energy-saving and emission reduction efficiency of the whole building cannot be improved effectively. The integrated design process (IDP) is a systematic method, which is applied in the scheme design stage and according to which the multi-level design factors of cities and buildings are considered comprehensively. It provides a concrete path of multi-specialty collaborative operation for the building’s climate responsive design. In this article, the development, operation process, software platform, evaluation and decision-making methods of the IDP are reviewed in a comprehensive manner. Finally, the prospect of IDP applied to the climate responsive design of buildings is analyzed, and some suggestions for future development are put forward. The IDP framework proposed in the research can provide a reference method for architectural climate responsive design practice and help formulate the future policy of energy-saving design.

With the increase in elevator usage, more and more elevator real-time monitoring equipment is being applied to the operation of elevators. Traditional elevator monitoring equipment adopts a multi-sensor decentralized installation and layout, and the monitoring accuracy is low, which directly affects the effective alarm of the monitoring system; however, existing online monitoring systems cannot quickly alarm for faults. Aiming to solve the above problems, an elevator online monitoring system based on narrow-band Internet of Things (NB-IoT) is designed. The system is highly integrated with an STM32 main control chip, a six-axis acceleration gyroscope sensor, and an air pressure sensor to realize the edge calculation of the monitoring system. At the same time, this paper eliminates the temperature drift of the pressure sensor by using a temperature compensation algorithm and inputs the extracted characteristic parameters into the BP neural network for training to eliminate the zero drift so as to obtain the real-time height data of the elevator. The six-axis acceleration gyroscope sensor is used to calculate the posture so as to avoid the problem that a three-axis acceleration sensor or a three-axis gyroscope sensor alone cannot obtain accurate posture data. In order to further improve the monitoring accuracy, the peak-to-peak value of the signal is calculated by using a 95% confidence interval algorithm to reduce the suppression of the high-frequency components of the signal by noise and ensure that the signal has a large signal-to-noise ratio so that the obtained elevator car posture and vibration operation data are more accurate. Finally, the effectiveness of the proposed method is verified by experiments.

Experimental investigations on dynamic in-plane compressive behavior of a plain weave composite were performed using the split Hopkinson pressure bar. A quantitative criterion for calculating the constant strain rate of composites was established. Then the upper limit of strain rate, restricted by stress equilibrium and constant loading rate, was rationally estimated and confirmed by tests. Within the achievable range of 0.001/s–895/s, it was found that the strength increased first and subsequently decreased as the strain rate increased. This feature was also reflected by the turning point (579/s) of the bilinear model for strength prediction. The transition in failure mechanism, from local opening damage to completely splitting destruction, was mainly responsible for such strain rate effects. And three major failure modes were summarized under microscopic observations: fiber fracture, inter-fiber fracture, and interface delamination. Finally, by introducing a nonlinear damage variable, a simplified ZWT model was developed to characterize the dynamic mechanical response. Excellent agreement was shown between the experimental and simulated results. [Display omitted] •A criterion for calculating the constant strain rate of composites is established.•The upper limit of strain rate in SHPB tests was rationally estimated.•A turning point of the strain rate effects is found.•The transition of failure mechanism explains the change in strength.•A viscoelastic model with damage is developed to characterize mechanical response.

Urbanization has led to homogenizing heritage site landscapes, and the protective measures have become disconnected from public needs. Additionally, the complex and diverse overall characteristics of heritage sites and the uneven distribution of values across different areas are related to the lack of landscape experience assessment. The “subjective + objective” cognitive evaluation and visual perception framework that adopts the Scenic Beauty Estimation Procedure-Semantic Differential (SBE–SD) method and eye-tracking analysis can compensate for the limitations of a single evaluation method by integrating quantitative and qualitative analysis. This research takes the Yi’an Fortress in Zhangpu County, Fujian Province, as the object and examines the visual experiences of different areas and types of landscapes within the Yi’an Fortress. The findings reveal several key insights: (1) Significant differences were found in the landscape experiences of different areas within the heritage site. The visual experience score of the core building area of Yi’an Fortress is (1.01) > the heritage entrance area (0.897) > the residential area (0.841) > the natural ecological area (0.784), indicating that the natural ecological area should be the focus of future protection and development efforts, with a particular emphasis on enhancing the ‘landscape aesthetic’ and ‘landscape cultural’ aspects. (2) The landscape experience scores can be used to understand the reasons for the differences in participants’ experiences of different landscapes. The architectural heritage landscape of the Yi’an Fortress scored highest in the experience evaluation due to its superior performance in terms of ‘landscape form’ factors. The cultural decorative landscape scored next, while the garden greening landscape scored the lowest, due to their poorer performance in terms of ‘landscape form’ and ‘landscape aesthetics’. (3) The eye-tracking data was consistent with the results of the subjective evaluation, validating the “subjective + objective” cognitive evaluation and visual perception framework, employing the SBE–SD method and eye-tracking analysis is a scientific and effective method for assessing the visual experience of heritage landscapes. These results provide a scientific basis for the heritage planners and managers of the Yi’an fortress to improve the landscape environment, better meet public needs, and preserve the unique character of this vital cultural resource. Furthermore, this study offers a new research method and approach for the protection of other heritage landscapes.