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In the large-scale farming, the environment of Pigsty has a direct impact on the health of pig and production capacity. Pigsty major environmental factors such as wind speed, temperature, humidity and ammonia concentration data were collected for one hundred and sixty-seven consecutive days, the acquisition interval was set to 60 seconds. Since the predicted ammonia concentration is 1 hour later, 3975 groups of hourly environmental data were obtained from the original data through calculation. Then, The LTSM neural network model was built to predict the Ammonia concentration. According to the time sequence, the data collected in the first 120 days were taken as training samples, and the rest of the data collected were test samples. It is shown in simulation experiment that network reaches the target error after 40 steps with each batch size of 72, the model has the characteristics of fast network convergence and high efficiency by using adam optimizer, the root mean square error between the actual environmental quality of 2880 groups of test data and the network prediction value is only 1.6%, the accuracy and timeliness of the ammonia concentration prediction of Pigsty is greatly improved. The ammonia concentration prediction model established in the paper can provide support for the Pigsty environment early warning and control.

Well cellar seedling transplanting can effectively avoid series of problems such as long recovery time and poor uniformity of seedlings caused by the server climate conditions outside. Well cellar making is quite difficult in the upland with low moisture content. An opener with large socket was newly designed for the well cellar making. The well cellar making process was simulated by means of discrete element method (DEM), and its verification was qualified by soil bin test, with a relative error 7.46%. Taking arc radius of the opener socket, rotation speed and penetration speed as control factors, and collapse rate of the cellar hole as evaluation index, the influence of control factors on the collapse rate and their significance were obtained by Box-Behnken Design (BBD) test and analysis of variance (ANOVA). A quadratic regression equation of collapse rate with control factors was established, and the optimal parameters of the control factors were obtained as arc radius 128 mm, rotation speed 380 r/min and penetration speed 0.15 m/s, with the minimum collapse rate 22.70%. The collapse rate of the newly designed well cellar opener decreased much compared to the traditional one.

Code review as an effective software quality assurance practice has been widely applied in many open-source software communities. However, finding a suitable reviewer for certain codes can be very challenging in open-source communities due to the difficulty of learning the characteristics of reviewers and the code-reviewer interaction sparsity in open-source software communities. To tackle this problem, most previous approaches focus on learning developers’ capabilities and experiences and recommending suitable developers based on their historical interactions. However, such approaches usually suffer from data-sparsity and noise problems, which may reduce the recommendation accuracy. In this paper, we propose an attentive neighbor embedding propagation enhanced code reviewer recommendation framework (termed ANEP). In ANEP, we first construct the reviewer–code interaction graph and learn the semantic representations of the reviewer and code based on the transformer model. Then, we explicitly explore the attentive high-order embedding propagation of reviewers and code and refine the representations along their neighbors. Finally, to evaluate the effectiveness of ANEP, we conduct extensive experiments on four real-world datasets. The experimental results show that ANEP outperforms other state-of-the-art approaches significantly.

Machine quality, mowing efficiency and work reliability of lawn mowers are main issues concerned in hilly and mountainous regions. Taking a rotary lawn mower of single-disc type as research object, structure and parameter design of the mower was defined according to empirical formula, performance of grass cutting and modal analysis of the cutting blades were conducted by means of EDEM and Creo, and field test was conducted to verify the overall performance of the machine. Results showed that symmetrically installed 2 cutting blades with revolution speed 3500 r/min and forward speed 1.4 m/s showed good performance of grass cutting and stability. The velocity of grass particles of cut grass was mostly in the range of 1.1-1.5 m/s, and the velocity of broken grass was stable, which proved good harvest and gathering of grass. The height of remained stubbles was about 50 mm, and the length of cut grass was distributed in the range of 30-60 mm, which met requirements of forage production.

Platinum nanoparticles (PtNPs) have been attracted worldwide attention due to their versatile application potentials, especially in the catalyst and sensing fields. Herein, a facile synthetic method of triethanolamine (TEOA)-capped PtNPs (TEOA@PtNP) for electrochemiluminescent (ECL) and colorimetric immunoassay of SARS-CoV spike proteins (SARS-CoV S-protein, a target detection model) is developed. Monodisperse PtNPs with an average diameter of 2.2 nm are prepared by a one-step hydrothermal synthesis method using TEOA as a green reductant and stabilizer. TEOA@PtNPs can be used as a nanocarrier to combine with antigen by the high-affinity antibody, which leads to a remarkable inhibition of electron transfer efficiency and mass transfer processes. On the basis of its peroxidase-like activity and easy-biolabeling property, the TEOA@PtNP can be used to establish a colorimetric immunosensor of SARS-CoV S-protein thought catalyzing the reaction of H

Ventilation rate is equally important as pollutant concentration in aerial emission quantification, but is technically challenging to determine for animal buildings. A simple and reliable method using 240-VAC to 6-VDC unregulated adapters to monitor 24 single-speed wall-mount ventilation fans was applied in a 12-room swine environmental research building. A portable Wall Fan Tester (WFT), consisting of nine equally-distanced impeller anemometers, was designed and developed to measure airflow rates of the wall fans of 508-mm and 356-mm diameters. Fan ventilation rates were tested using the WFT at 5 or 6 static pressures from −1.8 to −133.9 Pa and power voltages from 185 to 245 VAC at 15-VAC increments in five fan test sessions. Fan ventilation models were developed using a single independent variable (static pressure) and two independent variables (static pressure and fan power voltage). The innovative system and methodology considerably improved ventilation rate quantification for the 12 rooms. The study also found that the wall fans in the building delivered 16.9–40.1% less ventilation under different static pressures compared with the fan specifications. There were 17–29% ventilation rate variations among identical fans in different rooms. Daily power supply voltages to the fans often fluctuated for 3–6%, which could introduce errors in ventilation calculations if not taking them into account. Cross-sectional airflow profiles were not always symmetric and there were backflows in some sub-areas for the 356-mm fan inlet at conditions of low power voltages and high negative static pressures.

Ammonia (NH3) is considered one of the significant pollutions contributor to indoor air quality and odor gas emission from swine house because of the negative impact on the health of pigs, the workers and local environment. Prediction models could provide a reasonable way for pig industries and environment regulatory to determine environment control strategies and give an effective method to evaluate the air quality. The adaptive neuro fuzzy inference system (ANFIS) simulates human's vague thinking manner to solve the ambiguity and nonlinear problems which are difficult to be processed by conventional mathematics. Five kinds of membership functions were used to build a well fitted ANFIS prediction model. It was shown that the prediction model with "Gbell" membership function had the best capabilities among those five kinds of membership functions, and it had the best performances compared with backpropagation (BP) neuro network model and multiple linear regression model (MLRM) both in wintertime and summertime, the smallest value of mean square error (MSE), mean absolute percentage error (MAPE) and standard deviation (SD) are 0.002 and 0.0047, 31.1599 and 23.6816, 0.0564 and 0.0802, respectively, and the largest coefficients of determination (R2) are 0.6351 and 0.6483, repectively. The ANFIS prediction model could be served as a beneficial strategy for the environment control system that has input parameters with highly fluctuating, complexity, and non-linear relationship.

A novel wild-type α-amylase named wtAmy175 from Pseudoalteromonas sp. M175 strain was purified through ammonium sulphate precipitation, DEAE cellulose, and Sephadex G-75 sequentially (25.83-fold, 7.67%-yield) for biochemical characterization. SDS-PAGE and zymographic activity staining of purified enzyme showed a single band with a predicted molecular mass of about 61 kDa. The optimum temperature and pH for enzyme activity were 30 °C and 7.5, respectively. Additionally, the enzyme exhibited high activity and remarkable stability in 0–10 mM SDS. The values of Km and Vmax for soluble starch as substrate were 2.47 mg/ml and 0.103 mg/ml/min, respectively. Analysis of hydrolysis products of soluble starch and maltooligosaccharides showed that wtAmy175 cleaved the interior and the terminal α-1,4-glycosidic linkage in starch, and had transglycosylation activity. The result of fluorescence spectroscopy showed that wtAmy175 had strong binding affinity with soluble starch. In brief, this study discovered the first wild-type α-amylase so far with several distinctive properties of cold activity, SDS-resistance, and the mixed activity of α-amylase and α-glucosidase, suggesting that wtAmy175 possess high adaptive capability to endure harsh industrial conditions and would be an excellent candidate in detergent and textile industries.

Indoor environment conditions in confined pig buildings are critical for pig health, welfare and reproductions. It is necessary to explore the correlations of indoor thermal environment and gas concentrations conditions and their characteristics in order to provide a basis for developing an optimal pig building environment control strategy. In this study, an innovative method integrating three correlation analyses was developed to make a comprehensive and quantitative description for the correlations and coupling abilities among multiple environment factors from different perspectives. A multiple regression model was developed for gas concentrations of NH3, CO2 and H2S, with second order polynomial expressions on temperatures of outdoor, pig living space (PLS) and pit, and the most correlated factors as input variables. The results showed that the PLS CO2 concentration and the outdoor temperature could be used as decisive factors for the established categories of GS and TH representing for gas concentration and thermal environment, respectively. It also found that the PLS air temperature varied greatly with the outdoor temperature, and the gas concentrations of NH3, CO2 and H2S were in the periodic and diel variation shapes of sine or cosine. The multiple regression model for the CO2 concentration achieved optimal performances determination coefficients R2 and root mean square error in the two rooms. Therefore, the model output of PLS CO2 concentration as well as the outdoor temperature could be used as a primary basis to improve the environmental control strategy, by means of reducing the types and number of sensors.

Chromium is one of main pollution in sea water, and the establishment of detecting kit is the research hot topic. In this study, immunized mice were obtained by immunogen Cr3+-EDTA-BSA, and hybridoma strains that secrete Cr3+-EDTA monoclonal antibody were selected. Cr3+-EDTA mAb were prepared with in vivo method. Then, Cr-kit for measurement of chromium was developed, and its application was executed in sea water and seafood. Two hybridoma strains of A321 and A424 were screened. The Cr-kit standard curve of the linear range was 1.0—512 μg/L, and limit of detection was 1.0 μg/L. The average recovery rates Cr spiked in samples of sea water, clam and shrimp were 102.62%, 94.69% and 96.29%, which were accordance with the results of ICP-AES. Totally, Cr-kit was successfully developed and behaved broad application potential in sea water and seafood.

A novel cold-adapted and salt-tolerantα-amylase gene (amy175) from Antarctic sea ice bacteriumPseudoalteromonassp. M175 was successfully cloned and expressed. The open reading frame (ORF) ofamy175 had 1722 bp encoding a protein of 573 amino acids residues. Multiple alignments indicated Amy175 had seven highly conserved sequences and the putative catalytic triad (Asp244, Glu286, and Asp372). It was the first identified member of GH13_36 subfamily which contained QPDLN in the CSR V. The recombinant enzyme (Amy175) was purified to homogeneity with a molecular mass of about 62 kDa on SDS-PAGE. It had a mixed enzyme specificity ofα-amylase andα-glucosidase. Amy175 displayed highest activity at pH 8.0 and 25°C and exhibited extreme salt-resistance with the maximum activity at 1 M NaCl. Amy175 was strongly stimulated by Mg2+, Ni2+, K+, 1 mM Ca2+, 1 mM Ba2+, 1 mM Pb2+, 1 mM sodium dodecyl sulphate (SDS), and 10% dimethyl sulfoxide (DMSO) but was significantly inhibited by Cu2+, Mn2+, Hg2+, 10 mMβ-mercaptoethanol (β-ME), and 10% Tween 80. Amy175 demonstrated excellent resistance towards all the tested commercial detergents, and wash performance analysis displayed that the addition of Amy175 improved the stain removal efficiency. This study demonstrated that Amy175 would be proposed as a novelα-amylase source for industrial application in the future.

In order to solve the data missing problem caused by sensor faults during the waste gas monitoring in animal building, a method for missing data recovery was presented based on support vector machine (SVM) combined with genetic algorithm (GA). Multiple factors that influence monitoring values of the waste gas in animal building such as temporal, spatial and environmental, were considered to established a SVM regression prediction model to estimate the missing data of the waste gas monitoring. Meanwhile, to obtain better prediction accuracy, model parameters were optimized by the GA. The data processing of the ammonia (NH3) concentration was taken as an example; monitoring data of 3 days were randomly selected in a farm to test the presented model in this paper. It is shown that there was a very little error between the estimated data and the monitoring data, the maximal relative error was 6.99 % (percent), and the average relative error was 2.15 % (percent). It is an effective method for missing data recovery and a practical way of data processing for waste gas monitoring in animal building.

In confined swine buildings, temperature, humidity, and air quality are all important for animal health and productivity. However, the current swine building environmental control is only based on temperature; and evaluation and control methods based on multiple environmental factors are needed. In this paper, fuzzy comprehensive evaluation (FCE) theory was adopted for multi-factor assessment of environmental quality in two commercial swine buildings using real measurement data. An assessment index system and membership functions were established; and predetermined weights were given using analytic hierarchy process (AHP) combined with knowledge of experts. The results show that multi-factors such as temperature, humidity, and concentrations of ammonia (NH3), carbon dioxide (CO2), and hydrogen sulfide (H2S) can be successfully integrated in FCE for swine building environment assessment. The FCE method has a high correlation coefficient of 0.737 compared with the method of single-factor evaluation (SFE). The FCE method can significantly increase the sensitivity and perform an effective and integrative assessment. It can be used as part of environmental controlling and warning systems for swine building environment management to improve swine production and welfare.

In intensive and large-scale farming, piggery environmental monitoring is an important method for improving productivity and preventing disease in pig herds. The piggery environment mainly includes air temperature, air humidity, and ammonia concentration. Piggery environmental monitoring mostly concentrates on a single environmental factor and data processing. Few studies focus on multiple environmental factors and information fusion from a variety of monitoring sensors. Therefore, in this paper, the main environmental factors that affect herd health, such as temperature, humidity, and ammonia concentration, were monitored. According to the analysis of piggery environmental adaptability indicators, a multi-sensor data fusion model with five layers based on the adaptive fuzzy neural network of T-S inference was established. The structure of the model is 3-8-18-18-1. To check the performance of the model, 432 groups of data were selected by monitoring a piggery environment for three consecutive days. A total of 288 sets of data were obtained for training, and 144 sets of data were obtained for checking. The result shows that the model effectively fuses multi-sensor data. After 600 training cycles, a Root Mean Squared Error (RMSE) of 0:14 10 −3 between the actual output of the system and the desired output can be achieved. The system output has high accuracy. The RMSE of the checking data and fuzzy system output is 0:18 10 −2 . Moreover, the operation efficiency of the system is high. Thus, the system is suitable for analyzing data in real time and for the processing of piggery environments. This system provides a good data processing method for monitoring and controlling piggery environments.

Indoor thermal environment is a critical factor for animal health and production in confined livestock facilities. In order to improve indoor thermal environment control and save energy, a novel dynamic thermal exchange model was developed using the energy balance equation (EBE) and 87 days of data collected in three different seasons in a pig building to simulate the heat transfer and energy consumption in the building. To evaluate the performances of the EBE model, a comparison was made using adaptive neuro fuzzy inferring system (ANFIS) for indoor air temperature prediction. Also, the EBE model was evaluated comparing its outputs of indoor temperature with the dataset of six days, under three different ventilation modes (Min-vent, Low-vent, and High-vent) that represent for the cold, warm and hot weather, obtained through a monitoring period in pig buildings during the production. The results showed that, under three different ventilations modes, the maximum errors between the EBE model simulated and measured data were 1.5 °C compared with 2.6 °C of the ANFIS model; and the averaged coefficients of determination R2 were 0.945 and 0.743, respectively, for the EBE and ANFIS models. Compared with the present ventilation operation, there was 358.301 kW h power saved with the EBE model in a pig room during the whole research period of 87 days. Therefore, this research has several practical applications: the model can be used in developing strategies of indoor thermal environmental control, it can also increase the knowledge about energy consumption in the livestock house.

In order to solve the data missing problem caused by sensor faults during the air pollutants monitoring in henhouse, a method for missing data recovery was presented based on support vector machine. To obtain better recovery accruacy, SVM regression model parameters were optimized by by a novel hybrid optimization algorithm which was combined standard genetic algorithm with quantum genetic strategy and simulated annealing tactics. The estimation results of missing data shown that the maximal relative error was 5.87%; the average relative error was 1.77%. It is verified that this method of missing data recovery based on QGSA-SVM is feasible and valid.

Antarctic ice microalga can survive and thrive in channels or pores containing high salinity in Antarctic ice layer. In this study, it was found that cell membrane permeability of green microalga Chlamydomonas sp. L4 from Antarctic sea ice was high in cells treated with hypersalinity due to the induction of active oxygen and radicals. However, increased super oxide dismutase (SOD) scavenged harmful free radicals effectively to keep cell membrane integrity. Also, the analysis of membrane fatty acids demonstrated the content of saturated fatty acids and monounsaturated fatty acids increased and polyunsaturated fatty acids decreased under the high-salt treatment for 14 d, which effectively reduced the membrane fluidity and minimized the injury to cell membrane. The morphological changes showed that hypersalinity induced the increase of cell volume and the consumption of starch granules. However, because of the increase in detoxification of vacuoles, electron-dense deposits and SOD activity under high-salt stress, the complete noninterference thylakoids, mitochondria and cell nucleus maintained cellular fundamental metabolism. Global-expression profiling of proteins showed eight protein spots disappeared, 18 protein spots decreased and 18 protein spots were enhanced after the high-salt shock obviously (P

A bacterium hydrolyzing carboxymethylcellulose, isolated from Antarctic sea ice, was identified as Pseudoalteromonas sp. based on 16S rDNA gene sequences and named as Pseudoalteromonas sp. AN545. The extracellular endo-1,4-β-glucanase AN-1 was purified successively by ammonium sulfate precipitation, DEAE-Sepharose ion exchange chromatography and Sephadex G-75 gel filtration chromatography. The molecular mass of AN-1 was estimated to be 47.5 kDa utilizing SDS-PAGE and gel chromatography analysis. AN-1 could hydrolyze caboxymethylcellulose, avicel and β-glucan, but not cellobiose, xylan and p-Nitrophenyl-β-D-glucopyranoside. The optimal temperature and pH for the β-glucanase activity of AN-1 were determined to be at 30°C and pH 6.0, respectively. AN-1 was stable at acidic solutions of pH 5.0–6.5 and temperatures below 30°C for 1 h. Moreover, the specific activity was enhanced by Ca2+ and Mg2+, and inhibited by Cu2+. The kinetic parameters Michaelis constant (K m) and maximum velocity (V max) of AN-1 were 3.96 mg/mL and 6.06×10−2 mg/(min·mL), respectively.

Image recognition and feature extraction play an important role in precision agriculture. In this paper, a manifold learning algorithm was used for dimension reduction of gray and RGB color images. To clarify the boundaries of disease spots and leaf background, three clustering algorithms were applied in experiments to realize clearer maize leaf disease images. Locally, linear embedding (LLE) and Gustafson–Kessel (GK) algorithms were selected to realize image feature extraction. It was shown that the recognition rate of feature extraction for gray and color images were 95% and 99%, respectively.

In order to solve the data missing problem caused by sensor faults during the waste gas monitoring in animal building, a method for missing data recovery was presented based on support vector machine (SVM) combined with genetic algorithm (GA). Taking the data processing of ammonia concentration as an example, the proposed method was tested with the monitoring data of 3 days in a farm. The estimation results of missing data shown that the maximal relative error was 6.99%; the average relative error was 2.15%. It is verified that this method of missing data recovery based on GSA-SVM is feasible and valid.

Under the heavy metal polluted circumstances, microorganisms certainly have some changes in terms of species, quantity, community structure and diversity to adapt the environments. Now, many heavy metal tolerant microbe groups have been studied. In the study, a heavy metal tolerant and psychrophilic bacterium strain from Antarctica was screened. Based on 16S rDNA sequence analysis, this strain belongs to Planococcus, named as Planococcus sp. O5. The capacity of antimetal of Planococcus sp. O5 is Pb2+ > Cu2+ > Hg2+ > Cd2+ > Zn2+, and the MICs is 320 mg/L, 130 mg/L, 80 mg/L, 80 mg/L and 40 mg/L, respectively. Lipid peroxidation (indicated by malonydialdehyde content) happened in strain O5 induced with Cu2+. At the same time, the antioxidation enzyme activity (such as SOD, POD and CAT) had stimulus-controlled improvement, which is a certain protection against heavy metals. Therefore, as an important feature adapting the stress environments, the activity of antimetal, can reflect the adaptive strategy of microorganism to some extent. This paper studied the activity of antimetal and antioxidation of a bacterial strain, which can help us better understand the bacteria how to adapt the extreme environments.

At present, although the shape of soybean leaves can be simulated, but the method of establishing leaves outline geometric features that provided in these studies was complex, the data volumes of geometric modeling was large. This paper presents a method of obtaining leaves feature points based on an edge detection technique, using the three parameters spline curve to generate contour of soybean leaves. Loop technology was used to subdivide leaf surfaces to get a smooth surface, and finally texture-mapping technology was used to achieve real model of soybean leaves. The method of 3D accurate modeling for soybean leaves in this paper provides the necessary means and lay the foundation for the study on virtual plant leaves geometric modeling, which has certain practical value.