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In recent years, the poultry breeding industry has been converted into a large-scale, intensive, and intelligent production mode. The Internet of Things (IoT) is under rapid development, which promotes the development of precision livestock farming. In this study, we developed an intelligent service platform for a facility environment based on the IoT structure, utilizing the capabilities of Platform as a Service (PaaS). The platform consists of four layers, including an information perception layer, network layer, management service layer, and application layer. By using the cloud service model with a distributed network architecture, asynchronous data transmission, and a distributed file system, the platform can centrally manage multiple farm's data. The intelligent service platform includes the following functions: displaying environmental data, water and electricity consumption, data analysis, and managing production data. Over a 500-day trial period in a live poultry house, the platform demonstrated high data integrity (>87%) and resilience against network disruptions and power outages. The data validity of each environmental element exceeded 94%, among which the validity of the temperature, humidity, and carbon dioxide concentration exceeded 99%. The overall accuracy of the dataset remained relatively high, providing a robust data foundation for further research. Key features included audio analysis, environmental monitoring, and production data management. The platform's operational status was efficiently communicated via data statistics and email alerts, facilitating timely system recovery. The demonstrated modules included sound recognition, psychrometric charts for visual alerts, and financial analysis tools, offering versatile solutions for integrating PLF models and advanced analytics. [ABSTRACT FROM AUTHOR]

The conformal antenna is a precision device installed on the wing of an aircraft, and its components are distributed on a curved surface. Quality detection is required after assembly. In solving the path planning problem for conformal antenna surface detection, the traditional genetic algorithm faces problems such as slow convergence and easily falling into a local optimal solution. To solve this problem, an improved genetic algorithm combining the historical optimal population (CHOP-IGA) is proposed. First, the algorithm uses the probability-based four-nearest-neighbor method to construct an initial population. Subsequently, the probabilities of the crossover and mutation operators are dynamically adjusted. Next, the algorithm applies the crossover and mutation operators to the population and performs mutation operations on each individual of the historical optimal population. Then, the fitness value is calculated and the next generation of individuals is selected from the parent, offspring, and historical optimal populations according to the elite selection strategy. Finally, the current best fitness is checked to determine whether updating the historical optimal population is necessary. When the termination condition is satisfied, the algorithm outputs the optimal result. Experiments showed that the algorithm satisfactorily solved the path planning problem for conformal antenna surface detection, with a 48.44% improvement in detection efficiency. [ABSTRACT FROM AUTHOR]

Aimed at the characteristics of multi-source heterogeneity and the rapid generation of data in digital twin workshops, as well as the current situation where communication protocols between equipment within the workshop vary greatly and are difficult to interconnect, a data integration method based on OPC UA is designed. Firstly, combining the process flow and data source characteristics of array antenna assembly, a data collection and transmission scheme based on OPC UA was designed. Secondly, a process information model of array antenna assembly was established to realize data perception and transmission and solve the difficulties of complex data structure, high real-time requirements, and heterogeneous data in digital twin workshop. Finally, the proposed method and model were applied to the performance prediction platform for an array antenna assembly process based on digital twins, achieving perception of process data during the assembly process of array antennas, and achieving performance prediction and visualization for various stages of array antennas based on assembly process data. [ABSTRACT FROM AUTHOR]

The processing of the three-dimensional circuit on the surface of conformal antennas is mainly performed via manual processing. At present, there is no automatic intelligent equipment for the processing of a similar small-sized circuit with variable curvature in China. Therefore, a high-precision, automated, full-process manufacturing method for three-dimensional circuits with flexible surfaces on conformal antennas of radar equipment has been proposed to improve processing quality and manufacturing efficiency. The processing relationship between solder paste spraying, resistor mounting, and laser welding in the flexible three-dimensional circuit manufacturing process was analyzed. The structure of the new conformal antenna three-dimensional circuit intelligent manufacturing equipment was determined, and simulation verification of the three-dimensional circuit processing was performed using Vericut. The optimal processing parameters were selected based on solid experiments. This method meets the electronic assembly requirements of radar equipment and fills the domestic gap. [ABSTRACT FROM AUTHOR]

As a large number of flexible elements such as distributed power and flexible load are connected to microgrids, the economic improvement of microgrids has become an important topic through rational energy distribution. In this paper, an economic scheduling model considering the load demand for a microgrid system under the mechanism of a peak–valley tariff is proposed. A mathematical model of the microgrid components is proposed to determine the exchange power between the microgrid and main network. Meanwhile, an improved War Strategy Optimization (WSO) algorithm is proposed to investigate three scenarios: (i) without batteries, (ii) with batteries and (iii) with batteries and demand response. Additionally, demand response optimization is carried out with the Particle Swarm Optimization (PSO) algorithm and the improved WSO is compared with four other algorithms in scenario (iii). The comparison shows that the improved WSO algorithm has a better optimization performance in solving the proposed scheduling model. [ABSTRACT FROM AUTHOR]

Electronic equipment, including phased array radars, satellites, high-performance computers, etc., has been widely used in military and civilian fields. Its importance and significance are self-evident. Electronic equipment has many small components, various functions, and complex structures, making assembly an essential step in the manufacturing process of electronic equipment. In recent years, the traditional assembly methods have had difficulty meeting the increasingly complex assembly needs of military and civilian electronic equipment. With the rapid development of Industry 4.0, emerging intelligent assembly technology is replacing the original "semi-automatic" assembly technology. Aiming at the assembly requirements of small electronic equipment, we first evaluate the existing problems and technical difficulties. Then, we analyze the intelligent assembly technology of electronic equipment from three aspects: visual positioning, path and trajectory planning, and force–position coordination control technology. Further, we describe and summarize the research status and the application of the technology and discuss possible future research directions in the intelligent assembly technology of small electronic equipment. [ABSTRACT FROM AUTHOR]

The optimal economic power dispatching of a microgrid is an important part of the new power system optimization, which is of great significance to reduce energy consumption and environmental pollution. The microgrid should not only meet the basic demand of power supply but also improve the economic benefit. Considering the generation cost, the discharge cost, the power purchase cost, the electricity sales revenue, the battery charging and discharging power constraints, and the charging and discharging time constraints, a joint optimization model for a multi-scenario microgrid with wind–photovoltaic-load storage is proposed in our study. Additionally, the corresponding model solving algorithm based on particle swarm optimization is also given. In addition, taking the Wangjiazhai project in Baiyangdian region as a case study, the effectiveness of the proposed model and algorithm is verified. The joint optimization model for a microgrid with wind–photovoltaic-load storage in multiple scenarios is discussed and investigated, and the optimal economic power dispatching schemes in multiple scenarios are also provided. Our research shows that: (1) the battery can play a role in peak shaving and valley filling, which can make microgrids more economical; (2) when the power purchase price is lower than the cost of renewable energy power generation, if the wind turbine and the photovoltaics are allowed to be discarded the microgrid will produce higher economic benefits; and (3) restricting the exchange power between the microgrid and the main power network will lead to a negative impact on the economy for the microgrid. [ABSTRACT FROM AUTHOR]

An active distribution network is an important development trend of the power grid with widespread use of the distributed generation. The reliability of the active distribution network is not negligible due to the uninterruptible power supply. In the paper, the reliability evaluation method of the active distribution network is proposed in detail, based on combining the roulette wheel selection and the sequential Monte Carlo algorithm. The uncertainty of both the distribution generation and the load is taken into consideration based on the power probability distribution and the working state in the presented model. Furthermore, the IEEE-RBTS Bus 6 is used to verify the validity of the proposed method. The result shows that the new energy access improves the availability and the reliability of the active distribution network. [ABSTRACT FROM AUTHOR]

The multi-microgrid is gradually springing up with widespread use of the distributed generation. It is of great meaning to have research on the energy mutual optimization of the multi-microgrid to improve the new energy-consumption capacity. In this paper, a comprehensive economic model of the multi-microgrid is proposed for optimizing the power dispatching, and the source-network-load-storage is taken into account. Different from other studies, the special novelty of this paper is the improved cuckoo search (CS) algorithm which is adopted to optimize the power dispatching of the multi-microgrid. Comparing with the particle swarm optimization (PSO) algorithm, the improved CS algorithm has better performance in solving the proposed model. The optimal power supply strategy is determined by predicting the optimal state of charge of the battery in the model of the multi-microgrid. The model effectiveness of the multi-microgrid is confirmed in the case study of Wangjiazhai area. With this method, the optimal power dispatching is determined. [ABSTRACT FROM AUTHOR]

Soil-washing is a potential technology for the disposal of soil contaminated by e-waste; however, the produced soil-washing effluent will contain polybrominated diphenyl ethers (PBDEs) and a large number of surfactants, which are harmful to the environment, so the treatment of PBDEs and the recycling of surfactants are the key to the application of soil-washing technology. In this study, coconut shell granular-activated carbon (GAC) was applied to remove PBDEs from Triton X-100 (TX-100) surfactant which simulates soil-washing effluent. The adsorption results show that, GAC can simultaneously achieve effective removal of 4,4′-dibromodiphenyl ether (BDE-15) and efficient recovery of TX-100. Under optimal conditions, the maximum adsorption capacity of BDE-15 could reach 623.19 μmol/g, and the recovery rate of TX-100 was always higher than 83%. The adsorption process of 4,4′-dibromodiphenyl ether (BDE-15) by GAC could best be described using the pseudo-second-order kinetic model and Freundlich isothermal adsorption model. The coexistence ions had almost no effect on the removal of BDE-15 and the recovery rate of TX-100, and the solution pH had little effect on the recovery rate of TX-100; BDE-15 had the best removal effect under the condition of weak acid to weak base, indicating that GAC has good environmental adaptability. After adsorption, GAC could be regenerated with methanol and the adsorption effect of BDE-15 could still reach more than 81%. Density functional theory (DFT) calculation and characterization results showed that, Van der Waals interaction and π–π interaction are dominant between BDE-15 and GAC, and hydrogen bond interactions also exist. The existence of oxygen-containing functional groups is conducive to the adsorption of BDE-15, and the carboxyl group (-COOH) has the strongest promoting effect. The study proved the feasibility of GAC to effectively remove PBDEs and recover surfactants from the soil-washing effluent, and revealed the interaction mechanism between PBDEs and GAC, which can provide reference for the application of soil-washing technology. [ABSTRACT FROM AUTHOR]

In large-scale sow production, real-time detection and recognition of sows is a key step towards the application of precision livestock farming techniques. In the pig house, the overlap of railings, floors, and sows usually challenge the accuracy of sow target detection. In this paper, a non-contact machine vision method was used for sow targets perception in complex scenarios, and the number position of sows in the pen could be detected. Two multi-target sow detection and recognition models based on the deep learning algorithms of Mask-RCNN and UNet-Attention were developed, and the model parameters were tuned. A field experiment was carried out. The data-set obtained from the experiment was used for algorithm training and validation. It was found that the Mask-RCNN model showed a higher recognition rate than that of the UNet-Attention model, with a final recognition rate of 96.8% and complete object detection outlines. In the process of image segmentation, the area distribution of sows in the pens was analyzed. The position of the sow's head in the pen and the pixel area value of the sow segmentation were analyzed. The feeding, drinking, and lying behaviors of the sow have been identified on the basis of image recognition. The results showed that the average daily lying time, standing time, feeding and drinking time of sows were 12.67 h(MSE 1.08), 11.33 h(MSE 1.08), 3.25 h(MSE 0.27) and 0.391 h(MSE 0.10), respectively. The proposed method in this paper could solve the problem of target perception of sows in complex scenes and would be a powerful tool for the recognition of sows. [ABSTRACT FROM AUTHOR]

Simple Summary: This study proposes a method and device for the intelligent mobile monitoring of oestrus on a sow farm; this type of monitoring is applied in the field of sow production. A boar model that imitates the sounds, smells, and touch of real boars was built to detect the oestrus in sows after weaning. The models resulted in recognition accuracy rates of 96.12%, 98.25%, and 90.00%. The interaction times and frequencies between the sow and the bionic boar and the static behaviours of both ears during heat were further analysed. The results show that there is a strong correlation between the duration of contact between the oestrus sow and the bionic boar and the static behaviours of both ears. The average contact duration between the sows in oestrus and the bionic boars was 29.7 s/3 min, and the average duration in which the ears of the oestrus sows remained static was 41.3 s/3 min. The interactions between the sow and the bionic boar were used as the basis for judging the sow's oestrus states. This approach can more accurately obtain the oestrus duration of a sow and provide a scientific reference for the sow's conception time. This study proposes a method and device for the intelligent mobile monitoring of oestrus on a sow farm, applied in the field of sow production. A bionic boar model that imitates the sounds, smells, and touch of real boars was built to detect the oestrus of sows after weaning. Machine vision technology was used to identify the interactive behaviour between empty sows and bionic boars and to establish deep belief network (DBN), sparse autoencoder (SAE), and support vector machine (SVM) models, and the resulting recognition accuracy rates were 96.12%, 98.25%, and 90.00%, respectively. The interaction times and frequencies between the sow and the bionic boar and the static behaviours of both ears during heat were further analysed. The results show that there is a strong correlation between the duration of contact between the oestrus sow and the bionic boar and the static behaviours of both ears. The average contact duration between the sows in oestrus and the bionic boars was 29.7 s/3 min, and the average duration in which the ears of the oestrus sows remained static was 41.3 s/3 min. The interactions between the sow and the bionic boar were used as the basis for judging the sow's oestrus states. In contrast with the methods of other studies, the proposed innovative design for recyclable bionic boars can be used to check emotions, and machine vision technology can be used to quickly identify oestrus behaviours. This approach can more accurately obtain the oestrus duration of a sow and provide a scientific reference for a sow's conception time. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the possible protective effect of N-acetylserotonin (NAS) against acute hepatic ischemia-reperfusion (I/R) injury in mice. Adult male mice were randomly divided into three groups: sham, I/R, and I/R + NAS. The hepatic I/R injury model was generated by clamping the hepatic artery, portal vein, and common bile duct with a microvascular bulldog clamp for 30 min, and then removing the clamp and allowing reperfusion for 6 h. Morphologic changes and hepatocyte apoptosis were evaluated by hematoxylin-eosin (HE) and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining, respectively. Activated caspase-3 expression was evaluated by immunohistochemistry and Western blot. The activation of aspartate aminotransferase (AST), malondialdehyde (MDA), and superoxide dismutase (SOD) was evaluated by enzyme-linked immunosorbent assay (ELISA). The data show that NAS rescued hepatocyte morphological damage and dysfunction, decreased the number of apoptotic hepatocytes, and reduced caspase-3 activation. Our work demonstrates that NAS ameliorates hepatic IR injury.

Heat stress is one of the most important environmental stressors facing poultry production and welfare worldwide. The detrimental effects of heat stress on poultry range from reduced growth and egg production to impaired health. Animal vocalisations are associated with different animal responses and can be used as useful indicators of the state of animal welfare. It is already known that specific chicken vocalisations such as alarm, squawk, and gakel calls are correlated with stressful events, and therefore, could be used as stress indicators in poultry monitoring systems. In this study, we focused on developing a hen vocalisation detection method based on machine learning to assess their thermal comfort condition. For extraction of the vocalisations, nine source-filter theory related temporal and spectral features were chosen, and a support vector machine (SVM) based classifier was developed. As a result, the classification performance of the optimal SVM model was 95.1 ± 4.3% (the sensitivity parameter) and 97.6 ± 1.9% (the precision parameter). Based on the developed algorithm, the study illustrated that a significant correlation existed between specific vocalisations (alarm and squawk call) and thermal comfort indices (temperature-humidity index, THI) (alarm-THI, R = -0.414, P = 0.01; squawk-THI, R = 0.594, P = 0.01). This work represents the first step towards the further development of technology to monitor flock vocalisations with the intent of providing producers an additional tool to help them actively manage the welfare of their flock.

Due to the increasing scale of farms, it is increasingly difficult for farmers to monitor their animals in an automated way. Because of this problem, we focused on a sound technique to monitor laying hens. Sound analysis has become an important tool for studying the behaviour, health and welfare of animals in recent years. A surveillance system using microphone arrays of Kinects was developed for automatically monitoring birds' abnormal vocalisations during the night. Based on the principle of time-difference of arrival (TDOA) of sound source localisation (SSL) method, Kinect sensor direction estimations were very accurate. The system had an accuracy of 74.7% in laboratory tests and 73.6% in small poultry group tests for different area sound recognition. Additionally, flocks produced an average of 40 sounds per bird during feeding time in small group tests. It was found that, on average, each normal chicken produced more than 53 sounds during the daytime (noon to 6:00 p.m.) and less than one sound at night (11:00 p.m.⁻3:00 a.m.). This system can be used to detect anomalous poultry status at night by monitoring the number of vocalisations and area distributions, which provides a practical and feasible method for the study of animal behaviour and welfare.

miR-29a is a conserved miRNA that participates in bone formation and immune response in vertebrates. miR-29a of Pinctada martensii (Pm-miR-29a) was identified in the previous research though deep sequencing. In this report, the precise sequence of mature Pm-miR-29a was validated using miRNA rapid amplification of cDNA ends (miR-RACE) technology. The precursor sequence of Pm-miR-29a was predicted to have 87 bp. Stem loop qRT-PCR analysis showed that Pm-miR-29a was easily detected in all the tissues, although expressions in the mantle and gill were low. The microstructure showed the disrupted growth of the nacre after Pm-miR-29a over-expression, which was induced by mimic injection into P. martensii. Results of the target analysis indicated that neuropeptide Y receptor type 2 (Y2R) was the potential target of Pm-miR-29a. Meanwhile, Pm-miR-29a mimics could obviously inhibit the relative luciferase activity of the reporter containing 3' UTR (Untranslated Regions) of the Y2R gene. Furthermore, the expression of Y2R was downregulated whereas expressions of interleukin 17 (IL-17) and nuclear factor κB (NF-κB) were upregulated after Pm-miR-29a over-expression in the mantle and gill, thereby suggesting that Pm-miR-29a could activate the immune response of the pearl oyster. Results showed that Pm-miR-29a was involved in nacre formation and immune response by regulating Y2R in pearl oyster P. martensii.

MicroRNAs (miRNAs) are noncoding RNA molecules that function as negative regulators of target genes. In our previous research, 258 pm-miRNAs were identified in Pinctada martensii by Solexa deep sequencing. Pm-miR-2305 was one of the identified pm-miRNAs with a potential function in biomineralization. In the present study, the precursor of pm-miR-2305 was predicted with 96 bp, containing a characteristic hairpin structure. Stem-loop qRT-PCR analysis indicated that pm-miR-2305 was constitutively expressed in all the tissues (adductor muscle, gill, mantle, hepatopancreas, foot, and gonad) of P. martensii and was highly expressed in the foot. After the over-expression of pm-miR-2305 in the mantle by mimics injection into the muscle of P. martensii, nacre demonstrated disorderly growth, as detected by scanning electron microscopy. Dual luciferase reporter gene assay indicated that pm-miR-2305 mimics could significantly inhibit the luciferase activity of the reporter containing the 3'UTR of the pearlin gene. Western blot analysis demonstrated that the protein expression of pearlin was down-regulated in the mantle tissue after the over-expression of pm-miR-2305. Therefore, our data showed that pm-miR-2305 participated in nacre formation by targeting pearlin in P. martensii.

Bone morphogenetic protein 7 (BMP7), also called osteogenetic protein-1, can induce bone formation. In this study, the obtained full-length cDNA of BMP7 from Pinctada martensii (Pm-BMP7) was 2972 bp, including a 5'-untranslated region (UTR) of 294 bp, an open reading fragment of 1290 bp encoding a 429 amino acid polypeptide and a 3'-UTR of 1388 bp. The deduced protein sequence of Pm-BMP7 contained a signal peptide, a pro-domain and a mature peptide. The mature peptide consisted of 135 amino acids and included a transforming growth factor β family domain with six shared cysteine residues. The protein sequence of Pm-BMP7 showed 66% identity with that from Crassostrea gigas. Two unigenes encoding Pm-BMPRI (Pm-BMP receptor I) and Pm-BMPRII were obtained from the transcriptome database of P. martensii. Tissue expression analysis demonstrated Pm-BMP7 and Pm-BMPRI were highly expressed in the mantle (shell formation related-tissue), while Pm-BMPRII was highly expressed in the foot. After inhibiting Pm-BMP7 expression using RNA interference (RNAi) technology, Pm-BMP7 mRNA was significantly down-regulated (p < 0.05) in the mantle pallium (nacre formation related-tissue) and the mantle edge (prismatic layer formation related-tissue). The microstructure, observed using a scanning electron microscope, indicated a disordered growth status in the nacre and obvious holes in the prismatic layer in the dsRNA-Pm-BMP7 injected-group. These results suggest that Pm-BMP7 plays a crucial role in the nacre and prismatic layer formation process of the shell.