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ABSTRACT: Incubation behavior in chickens is closely associated with hypothalamus. Here, RNA sequencing of hypothalamus from Changshun green-shell laying hens, an indigenous chicken breed from China, in egg-laying period (LP) and incubation period (BP) was conducted to identify critical pathways and candidate genes involved in controlling the incubation behavior in hypothalamus. A total of 637 up-regulated and 305 down-regulated differently expressed genes (DEGs) were identified in chicken hypothalamus between LP and BP groups. Gene ontology term (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis further revealed that neuroactive ligand-receptor interaction, hippo signaling pathway, and focal adhesion were significantly enriched. Five candidate genes (POMC, IGF1R, CHAD, VCL, and MYL9) were suggested to play crucial roles in the regulation of chicken incubation behavior. Our results further indicated the complexity of reproductive behavior of different chicken breeds.

In the abnormal situation of an aluminum electrolysis cell, the setting of cell voltage is mainly based on manual experience. To obtain a smaller cell voltage and optimize the operating parameters, a multi-objective optimization method for cell voltage based on a comprehensive index evaluation model is proposed. Firstly, a comprehensive judgment model of the cell state based on the energy balance, material balance, and stability of the aluminum electrolysis process is established. Secondly, a fuzzy neural network (FNN) based on the autoregressive moving average (ARMA) model is designed to establish the cell-state prediction model in order to finish the real-time monitoring of the process. Thirdly, the optimization goal of the process is summarized as having been met when the difference between the average cell voltage and the target value reaches the minimum, and the condition of the cell is excellent. And then, the optimization setting model of cell voltage is established under the constraints of the production and operation requirements. Finally, a multi-objective antlion optimization algorithm (MOALO) is used to solve the above model and find a group of optimized values of the electrolysis cell, which is used to realize the optimization control of the cell state. By using actual production data, the above method is validated to be effective. Moreover, optimized operating parameters are used to verify the prediction model of cell voltage, and the cell state is just excellent. The method is also applied to realize the optimization control of the process. It is of guiding significance for stabilizing the electrolytic aluminum production and achieving energy saving and consumption reduction.

Abstract The fault diagnosis of building electrical systems are of great significance to the safe and stable operation of modern intelligent buildings. In this paper, it has many problems, such as various fault types, inconspicuous fault characteristics, uncertainty of fault type and mode, irregularity, unstable signal, large gap between fault data classes, small gap between classes and nonlinearity, etc. A method of building electrical system fault diagnosis based on the combination of variational mode decomposition and mutual dimensionless indictor (VMD-MDI) and quantum genetic algorithm-support vector machine (QGA-SVM) is proposed. Firstly, the method decomposes the original signal through variational modal decomposition to obtain the optimal number of Intrinsic Mode Function(IMF) containing fault feature information. Secondly, extracts the mutual dimensionless indicator for each IMF. Thirdly, the optimal penalty coefficient C of the support vector machine and the parameter gamma ( $$\gamma$$ γ ) in the radial basis kernel function are selected by the quantum genetic algorithm. Finally, SVM optimized by the QGA is used to identify and classify the faults. By applying the proposed method to the experimental platform data of building electrical system, and compared with the traditional feature extraction method Empirical Mode Decomposition (EMD), Singular Value Decomposition(SVD), Local Mean Decomposition(LMD). And compared with traditional SVM, Genetic Algorithm optimized Support Vector Machine (GA-SVM), One-Dimensional Convolutional Neural Network (1DCNN) for fault classification methods. The experimental results show that the method has better effect and higher accuracy in fault diagnosis and classification of building electrical system. Its average test accuracy can reach 91.67 $$\%$$ % .

Aiming at the problems of insufficient feature extraction, low prediction accuracy and sensitivity to noise in the cooling load prediction, a hybrid model named WTD–CNN–LSTM is proposed in this article, in order to accurately predict the cooling load. It is combined with wavelet threshold denoising (WTD), convolutional neural network (CNN) and long short-term memory network (LSTM). Firstly, WTD is selected to denoise the cooling load sequence. Then, a neural network combined with CNN and LSTM is built, which can extract complex temporal and spatial features. Finally, the performance of the model at different time granularities (TG) and time step lengths (TSL) is studied. Compared with SVR, CNN, RNN, LSTM, GRU, BiLSTM and CNN–LSTM models, the errors of proposed model at different TGs and TSLs are smaller than other models. The experimental results show that the prediction accuracy of the proposed model is higher, and the generalization ability is stronger.

Aerogel is widely recognized as a superinsulating material with great potential for enhancing the thermal insulation performance of building walls. It can be applied in various forms such as aerogel plasters (AP), aerogel fibrous composites (AFC), and aerogel concrete (AC) in practical engineering applications. This study aims to investigate the most efficient application form for maximizing building insulation performance while minimizing the amount of aerogel used. To predict the thermal insulation performance of aerogel-insulated walls, a resistance–capacitance network model integrating the aerogels’ effective thermal conductivity model was developed and was validated by comparing it with Fluent simulation software results in terms of surface temperature. Using the validated models, the thermophysical parameters, transient thermal properties, and transmission load were predicted and compared among AP, AFC, and AC walls. The results indicate that using AFC can result in approximately 50% cost savings to achieve the same thermal resistance. After adding a 20 mm thickness of aerogel to the reference wall without aerogel, the AFC wall exhibited the highest improvement in thermal insulation performance, reaching 46.0–53.5%, followed by the AP wall, and then the AC wall, aligning with considerations of microstructural perspectives, thermal resistance distributions, and thermal non-uniformity factors. Therefore, giving priority to AFC use could reduce the required amount of silica aerogel and enhance economic efficiency. These results provide valuable insights for theoretical models and the application of aerogel-insulated walls in building engineering insulation.

Receiver Operating Characteristic (ROC) analysis is a prevalent tool for two-class problems in a wide range of science and engineering areas. The relevant scalar area under the ROC curve (AUC) has been proven to be a robust evaluation index for characterizing the performance of binary statistical models. However, tasks with more than two categories are frequently encountered in practice. As an extension of the ROC curve and AUC in parallel, the hyper ROC surface and the corresponding hyper-volume under the surface (HVUS) have attracted extensive interest. The current mainstream analysis tools of AUC and HVUS are based on the R program language. To provide researchers with an alternative tool for multi-class ROC analysis, in this work, we develop a python-based package and software tool, which allows the users to unbiasedly estimate HVUS, confidently compare HVUSs, and flexibly visualize 2D-ROC curve and 3D-ROC surface, etc.

Due to the advantage of automatically extracting features from raw data, deep learning (DL) has been increasingly favored in the field of machine fault diagnosis. However, DL exposes the problems of large sample size and long training time, and in actual working conditions, the amount of labeled fault data available is relatively small, so a DL model of good generalization and high accuracy is difficult to be trained. In order to solve these problems, a deep transfer convolutional neural network (DTCNN) is proposed in this research. ResNet-50 is selected as the pre-trained model of deep convolutional neural network, and is transferred to solve the problem of bearing fault classification based on the idea of transfer learning. Firstly, raw fault signals are converted into time-frequency images by using continuous wavelet transform (CWT). Then, the images are further converted into RGB formats, which are used as the input of DTCNN. Finally, an end-to-end fault diagnosis model based on DTCNN is designed. The proposed method is validated on two datasets collected from motor bearing and self-priming centrifugal pump, respectively. Most sub-datasets from motor bearing show the prediction accuracies near 100%, and in the self-priming centrifugal pump dataset, we achieve improvement in accuracy from 99.48%±0.1966 to 99.98%±0.0332. The experimental results demonstrate that the proposed method outperforms other DL methods and traditional machine-learning methods.

This paper focuses on the finite-time generalized synchronization problem of non-identical fractional order chaotic (or hyper-chaotic) systems by a designing adaptive sliding mode controller and its application to secure communication. The effects of both disturbances and model uncertainties are taken into account. A novel fractional order integral sliding mode surface is designed and its stability to the origin is proved in a given finite time. By the aid of the fractional Lyapunov stability theory, a robust controller with adaptive update laws is proposed and its finite-time stability for generalized synchronization between two non-identical fractional-order chaotic systems in the presence of model uncertainties and external disturbances is derived. Numerical simulations are provided to demonstrate the effectiveness and robustness of the presented approach. All simulation results obtained are in good agreement with the theoretical analysis. According to the proposed generalized finite-time synchronization criterion, a novel speech cryptosystem is proposed to send or share voice messages privately via secure channel. Security and performance analyses are given to show the practical effect of the proposed theories.

Lithium-sulfur batteries with high theoretical energy density and cheap cost can meet people’s need for efficient energy storage, and have become a focus of the research on lithium-ion batteries. However, owing to their poor conductivity and “shuttle effect”, lithium-sulfur batteries are difficult to commercialize. In order to solve this problem, herein a polyhedral hollow structure of cobalt selenide (CoSe2) was synthesized by a simple one-step carbonization and selenization method using metal-organic bone MOFs (ZIF-67) as template and precursor. CoSe2 is coated with conductive polymer polypyrrole (PPy) to settle the matter of poor electroconductibility of the composite and limit the outflow of polysulfide compounds. The prepared CoSe2@PPy-S composite cathode shows reversible capacities of 341 mAh g−1 at 3 C, and good cycle stability with a small capacity attenuation rate of 0.072% per cycle. The structure of CoSe2 can have certain adsorption and conversion effects on polysulfide compounds, increase the conductivity after coating PPy, and further enhance the electrochemical property of lithium-sulfur cathode material.

It is challenging to control and optimize the aluminum electrolysis process due to its non-linearity and high energy consumption. Reducing the cell voltage is crucial for energy consumption reduction. This paper presents an intelligent method of predicting and optimizing cell voltage based on the evaluation of modeling the comprehensive cell state. Firstly, the Savitzky–Golay filtering algorithm(SGFA) is adopted to denoise the sample data to improve the accuracy of the experimental model. Due to the influencing factors of the cell state, a comprehensive evaluation model of the cell state is established. Secondly, the model of the least squares supports vector machine (LSSVM) is proposed to predict the cell voltage. In order to improve the accuracy of the model, the state transition algorithm (STA) is employed to optimize the structure parameters of the model. Thirdly, the optimization and control model of the cell voltage is developed by an analysis of the technical conditions. Then, the STA is used to realize the optimization of the front model. Finally, the actual data were applied to the experiments of the above method, and the proposed STA was compared with other methods. The results of experiments show that this method is efficient and satisfactory. The optimization value of average cell voltage based on the STA-LSSVM is 3.8165v, and it can be used to guide process operation. The DC power consumption is 11,971 KW·h per tonne of aluminum, with a reduction in power consumption of 373 KW·h. This result guarantees the reduction of aluminum electrolysis energy consumption.

Fingerprint-based indoor localization has become one of the most attractive and promising techniques, and however, one primary concern for this technology to be fully practical is to maintain the fingerprint database to combat harsh indoor environmental dynamics, especially for the large-scale and long-term deployment. In this paper, focusing on room localization, we first analyze the effect of different factors on indoor location accuracy, then propose an integrity check algorithm and a fuzzy map mechanism in response to network changes accurately and timely, and then design an accuracy check algorithm and a regional adaptive periodic update approach to update the fingerprint database effectively when an environment changes. Meanwhile, we design the active static data collecting mechanism and the active pin data collecting mechanism to ensure the accuracy and reliability of fingerprints newly captured and reduce the overhead on mobile devices significantly. The experimental results demonstrate that the proposed solution improves the performance of updating the fingerprint database in real time and robustness and maintains the location accuracy over 95% no matter how the indoor environmental changes; meanwhile, it also reduces energy efficiency imposed on the mobile phones by over 50%.

In this paper, an image enhancement algorithm is presented for identification of corrosion areas and dealing with low contrast present in shadow areas of an image. This algorithm uses histogram equalization processing under the hue-saturation-intensity model. First of all, an etched image is transformed from red-green-blue color space to hue-saturation-intensity color space, and only the luminance component is enhanced. Then, part of the enhanced image is combined with the original tone component, followed by saturation and conversion to red-green-blue color space to obtain the enhanced corrosion image. Experimental results show that the proposed method significantly improves overall brightness, increases contrast details in shadow areas, and strengthens identification of corrosion areas in the image.

Chronic graft-versus-host-disease (cGVHD) is the leading cause of late non-relapse mortality after allogeneic hematopoietic stem cell transplantation(HSCT). There is no standard therapy for patients refractory or dependent to corticosteroid treatment. We hypothesized that hydrogen may exert therapeutic effects on cGVHD patients with few side effects. A prospective open-label phase 2 study of hydrogen was conducted. Patients received hydrogen-rich water 4ml/kg orally three times a day. Responses were graded in the skin, mouth, Gastrointestinal(GI), liver, eyes, lungs and joints and fascia every 3 months. A total of 24 patients (median age 27) were enrolled. Of the 24 patients, 18 (75%; 95% CI, 55.1% to 88%) had an objective response. No significant toxicity was observed. The estimated 4-year overall survival rate was 74.7%(95% CI, 54.9%–94.5%). The survival time was significantly prolonged in the response group. The survival rate at 4 years in the response group is significantly higher than the nonresponse group (86.6% vs 0%; p= 0.000132). Hydrogen showed great efficacy on cGVHD patients and long-term administration of hydrogen was not associated with significant toxic effects. The trial was registered at www.ClinicalTrials.Gov, NCT02918188.

Hydrogen evolution reaction (HER) is receiving a lot of attention because it produces clean energy hydrogen. Catalyst is the key to the promotion and application of HER. However, the precious metal catalysts with good catalytic performance are expensive, and the preparation process of non-precious metal catalysts is extremely complicated. The simple preparation process is the most important problem to be solved in HER catalyst development. We synthetized cobalt oxide (CoOx) catalyst for HER through a simple hydrothermal process. The CoOx catalyst shows excellent HER catalytic activity. Characterization results reveal that there are a great deal of surface hydroxyl groups or oxygen vacancy on the surface of CoOx catalyst. In alkaline media the CoOx catalyst shows an over-potential of 112 mV at 20 mA cm−2 and a small Tafel slope of 94 mV dec−1. This paper provides a simple and easy method for HER catalyst preparation.

Fingerprint-based indoor localization has become one of the most attractive and promising techniques; however, one primary concern for this technology to be fully practical is to maintain the fingerprint database to combat harsh indoor environmental dynamics, especially in the large-scale and long-term deployment. In this article, focusing on three key problems now existing in fingerprint database updating approaches such as the mechanism for triggering updates, the collection of new fingerprints and determination of fingerprints’ location information, we propose a fuzzy map mechanism and decision methods of neighbours’ fingerprints in response to all kinds of changes in indoor environments. Meanwhile, we design a static data collecting mechanism to filter reliable information from numerous users’ inputs and propose a neighbours’ fingerprint-assisted technique to calculate the location of fingerprints. Experimental results demonstrate that the proposed solution not only improves the performance of updating the fingerprint database in real time and robustness by 40% and 50%, respectively, but also reduces the update frequency and improves mean location accuracy by over 40%.

Background: The diagnosis of myelodysplastic syndrome (MDS), especially hypoplastic MDS, and MDS with low blast counts or normal karyotype may be problematic. This study characterized ID4 gene methylation in patients with MDS and aplastic anemia (AA). Methods: The methylation status of ID4 was analyzed by bisulfite sequencing polymerase chain reaction (PCR) and quantitative real-time methylation-specific PCR (MethyLight PCR) in 100 patients with MDS and 31 patients with AA. Results: The MDS group had a higher ID4 gene methylation positivity rate (22.22%) and higher methylation levels (0.21 [0-3.79]) than the AA group (P < 0.05). Furthermore, there were significant differences between the hypoplastic MDS and AA groups, the MDS with low blast count and the AA groups, and the MDS with normal karyotype and the AA groups. The combination of genetic and epigenetic markers was used in much more patients with MDS (62.5% [35/56]) than the use of genetic markers only (51.79% [29/56]). Conclusions: These results showed that the detection of ID4 methylation positivity rates and levels could be a useful biomarker for MDS diagnosis.