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(1) Background: The forest of Pinus sylvestris var. mongolica is an important semi-arid ecosystem in Hulunbuir sandy land that plays a key role in the carbon cycle and wind erosion control. It is crucial to explore the main factors affecting the radial growth of trees of P. sylvestris var. mongolica. (2) Methods: The study established the tree-ring chronology of P. sylvestris var. mongolica and analyzed the relationships among the radial growth, competition index, and climate variables using correlation analysis and a linear mixed effect model to explore the influence of competition and climate on radial growth of P. sylvestris var. mongolica. (3) Results: The results indicated that tree growth is mainly affected by the maximum average temperature (Tmax) and precipitation in June and July of the current year and that tree growth significantly decreased with increasing competition pressure. Analysis of the linear mixed effect model showed that tree age, competition intensity, self-calibrating Palmer drought severity index (scPDSI) from May to July, and vapor pressure deficit (VPD) have a significant impact on radial growth. (4) Conclusions: The competition plays a dominant role in radial growth of P.sylvestris var. mongolica compared to climate factors. This study helps to understand the growth mechanism of P. sylvestris var. mongolica forests under climate change and provides a scientific basis for effective management of semi-arid forests.

The Civilian Access Control Zone (CACZ), south of the Demilitarized Zone (DMZ) separating North and South Korea, has functioned as a unique bio-reserve owing to restrictions on human use. However, it is now increasingly threatened by damaged land and slope failures. In this study, a machine-learning-based method was used to assess slope stability by introducing the random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and logistic regression (LR) approaches. These classification models were trained and evaluated on 393 slope stability cases from 2009 to 2019 to assess slope stability in the northern area of the Civilian Control Line, South Korea. For comparison, the performance of these classification models was measured by considering the accuracy, Cohen’s kappa, F1-score, recall rate, precision, and area under the ROC curve (AUC). Furthermore, 14 influencing factors (slope, vegetation, structure conditions, etc.) were considered to explore feature importance. The evaluation and comparison of the results showed that the performance of all classifier models was satisfactory for assessing the stability of the slope, the ability of LR was validated (accuracy = 0.847; AUC = 0.838), and XGBoost proved to be the most efficient method for predicting slope stability (accuracy = 0.903; AUC = 0.900). Among the 14 influencing factors, the external condition was the most important. The proposed supervised learning method offers a promising method for assessing slope status, may be beneficial for government agencies in early-stage risk mitigation, and provides a database for efficient restoration management.

Background Late embryogenesis abundant (LEA) proteins were reported to be related to adversity stress and drought tolerance. Lea-3 from Arachis hypogaea L. (AhLea-3) was previously found to be related to salt tolerance according to the result of transcriptome profiling and digital gene expression analysis. So, AhLea-3 was cloned and the salt tolerance was validated by transgenic peanut plants. Results AhLea-3 was isolated from M34, a salt-resistant mutant of peanut, with its cDNA as the template. AhLea-3 contains one intron and two extrons, and the full-length cDNA sequence contains 303 bp. AhLea-3 was ligated to pCAMBIA1301 to obtain the overexpression vector pCAMBIA1301-AhLea-3, which was then transferred into peanut variety Huayu23. The expression level of AhLea-3, as determined by qRT-PCR analysis, was >10 times higher in transgenic than in non-transgenic plants. Five days after they were irrigated with 250 mM NaCl, the transgenic plants showed less severe leaf wilting, higher activities of antioxidant enzymes (superoxide dismutase, peroxidase, and catalase), and lower malonic dialdehyde content than non-transgenic plants. Relative to non-transgenic plants, the transgenic plants had a higher photosynthetic net rate, stomatal conductance, and transpiration rate, and a lower intercellular CO2 concentration after salt stress treatment (250 mM NaCl). Conclusions These results indicate that overexpression of AhLea-3 increased the salt tolerance of transgenic peanut plants. AhLea-3 might become a useful gene resource for the variety breeding of salinity tolerance in peanut. How to cite: Qiao L, Jiang P, Tang Y, et al. Characterization of AhLea-3 and its enhancement of salt tolerance in transgenic peanut plants. Electron J Biotechnol 2021;49. https://doi.org/10.1016/j.ejbt.2020.10.006

Global climate change and the increase in the frequency and intensity of drought have led to widespread forest decline and tree mortality. Studying the resilience components of tree growth to drought, including resistance (R

Peanut (Arachis hypogaea L.) is an allotetraploid oilseed crop worldwide due to its abundant high-quality oil production. Peanut oil stability and quality are determined by the relative proportions of saturated fatty acids (SFAs) and unsaturated fatty acids (UFAs). The principle approach to minimize the content of SFAs in peanut is to reduce the content of palmitic acid, which is linked to cardiovascular disease. Acyl-acyl carrier protein thioesterases (FATs) determine the types and levels of fatty acids that are exported them from the plastids. Two different classes of FAT have been classified into two families in plants, FatA and FatB. Among them, AhFatB has become the primary objective to genetically reduce the content of palmitic acid in peanut. Here, we identified 18 AhFatB genes in A. hypogaea genome and grouped into four major subfamilies through gene structures and phylogenetic relationships. Expression profiling of AhFatB genes was assessed using the publicly available RNA-seq data and qRT-PCR in 22 tissues. Using the CRISPR/Cas9 system, we designed two sgRNAs to edit the homologs AhFatB genes Arahy.4E7QKU and Arahy.L4EP3N, and identified different types of mutations. Additionally, we discovered mutations at Arahy.4E7QKU exhibited low palmitic acid and high oleic acid phenotypes. The obtained peanut mutants with altered SFAs content have great potential for improving peanut oil quality for human health.

As a medicinal plant, Artemisia annua L. is the main source of artemisinin in malaria drugs, but the lack of understanding of its distribution, environmental conditions and protection status limits the mass acquisition of artemisinin. Therefore, we used the ensemble forecast method to model the current and future global distribution areas of A. annua, evaluated the changes in suitable distribution areas on each continent under impacts of human activities and climate change, and its protection status on each continent in the corresponding period. The results showed that the main distribution areas of A. annua were concentrated in mid-latitudes in western and central Europe, southeastern Asia, southeastern North America and southeastern South America. Under the current climate scenario, human modifications have greatly reduced the suitable distribution area of A. annua, which was projected to expand inland with climate change and human socioeconomic impacts of CMIP6 in the future, but the effects of increasing temperature were different in different periods. Among all continents, the suitable distribution area in Europe was the most affected. However, at present and in the future, A. annua needs high priority protection on all continents. Asia and Europe have slightly better protection status scores than other continents, but the protection status scores of all continents are still very low. Our findings can be useful to guide development of protective measures for medicinal plants such as A. annua to further support drug production and disease treatment.

Due to the class-imbalance and redundancy of sample features, the network intrusion detection model based on classification algorithm has high false positive rate (FPR) for minority sample. A network intrusion detection model based on PCA + ADASYN and XGBoost is proposed. The principal component analysis (PCA) algorithm is used to reduce the redundancy features of the data. On this basis, the adaptive synthetic sampling (ADASYN) algorithm is used to oversample minority sample to solve the problem of class-imbalanced at the data level. Finally, XGBoost is used as a classifier to classify the detected data. In order to verify the validity of the model, several groups of comparative experiments were carried out on KDD CUP99 data set. The FPR of the proposed model for minority samples (r2l, u2r) were 17.3% and 19.7%, and the F1 were 90.1% and 84.5%. The experimental results show that by dealing with the problem of data redundancy and class-imbalanced, we can reduce the FPR of the detection model for minority sample and improve the F1.

The overall goal of this study is to assess the autonomic and behavioral effects of passive motion in rodents using the elevator vertical motion and Ferris-wheel rotation devices. These assays can help confirm the integrity and normal functioning of the autonomic nervous system. They are coupled to quantitative measures based on defecation counting, open-field examination, and balance beam crossing. The advantages of these assays are their simplicity, reproducibility, and quantitative behavioral measures. The limitations of these assays are that the autonomic reactions could be epiphenomena of non-vestibular disorders and that a functioning vestibular system is required. Examination of disorders such as motion sickness will be greatly aided by the detailed procedures of these assays.

At present, with the rapid development of Internet of things technology, there are more and more intelligent devices connected to the Internet of things, which are faced with many problems such as unsafe data, easy disclosure of privacy and difficult sharing of data. In order to solve these problems, this paper proposes a flat and center-less building equipment Internet of things system based on blockchain technology, so as to better realize accurate perception, reliable transmission and intelligent processing of the Internet of things. The application of blockchain technology and Internet of things technology in intelligent buildings ensures data security, improves system operation efficiency and provides a new method to solve the problems of Internet of things.

Aiming at the problem that DGA domain name is difficult to detect effectively, a hybrid model based on bidirectional gated recurrent unity and multi-channel convolutional neural network is proposed for DGA domain name detection. The model consists of three parts: the character embedding layer, the feature extraction layer and the classification prediction layer. The character embedding layer completes the automatic encoding of the input characters; the feature extraction layer uses BiGRU to learn the dependency between data features and the MCNN uses different neural network channels to learn information from various aspects to obtain deep hidden information for automatic extraction. The characteristics of the input characters; the classification layer uses a three-layer fully connected neural network to achieve automatic prediction classification of DGA domain names. The experimental results show that the model achieves an accuracy of 92.27%, which improves the accuracy of detection compared with other deep learning methods.

Yuhua91 is a new peanut variety with high oleic acid content bred by Qingdao Agricultural University. The crossing was conducted with Luhua11 as female parent and with Kainong1715, an F435-type variety with high oleic acid content as male parent. The real F1 hybrids were screened by sequencing on PCR amplification products, and those homozygotes with bb genotype in F2 populations were screened by the same sequencing method as above. The content of oleic and linoleic acid was measured on the kernels harvested from F2 single plants by near infrared ray method, and those kernels whose content of oleic was above 80%, oleic and linoleic acid ratio was above 10.0 were obtained and planted into a row, with pedigree method for subsequent selection breeding. Yuhua91 has some characters of small pod, light and obvious pod texture, 148.06 g per 100 pods, 63.31 g per 100 kernels, 75.15% shelling percentage, long elliptic seed kernel, pink seed coat, without crack, white endotesta. Its content of protein, oil, oleic acid, linoleic acid and palmitic acid was 26.57%, 52.72%, 80.40%, 2.50% and 5.57% respectively. Yuhua91 has other characters of strong seedlings, compact pod areas, and moderate resistance to leaf spot disease and bacterial wilt. Average pod yield is 215.79 kg per Mu, 15.27% higher than the control variety Huayu20. Average seed kernels yield is 157.33 kg per Mu, 21.64% higher than the control variety Huayu20. Yuhua 91 has been registered on department of agriculture in 2018, and the registration No. is GPD peanut (2018) 370210, fit for growing in Shandong Province.宇花91 是青岛农业大学选育的高油酸花生新品种。以普通油酸含量品种鲁花11 号为母本，F435 型高油酸花生品种开农1715 为父本配置杂交组合。利用PCR 产物测序法筛选获得F1 代真杂种，对F2 代单株提取叶片基因组DNA，利用PCR 产物测序法筛选基因型纯合的单株个体。对当代收获的单株籽粒利用近红外法多粒模型测定油酸、亚油酸含量，筛选油酸含量在80%以上且油酸亚油酸比值在10.0 以上的单株种植成株行，随后利用系谱法进行选择育种。宇花91 荚果为普通型小果，网纹较细、较明显，百果重148.06 g，百仁重63.31 g，果皮薄，出米率75.15%。籽仁长椭圆形，种皮粉红色、无裂纹，内种皮白色。籽仁蛋白质含量26.57%，脂肪含量52.72%，油酸含量80.40%，亚油酸含量2.50%，棕榈酸含量5.57%，油酸亚油酸比值32.16。苗期生长旺盛，封垄早，结果集中，中抗叶斑病和青枯病。2017 年参加山东省夏播多点试验，平均荚果产量215.79 kg/667 m²，比对照花育20 号增产15.27%；平均籽仁产量157.33 kg/667 ² 号增产21.64%。2018 年通过国家花生品种登记，登记号：GPD 花生 (2018) 370210，适于在山东花生产区种植。.

Enhanced cholinergic activity contributes to the production of complex autonomic manifestations of motion sickness (MS). However, whether anti-cholinergics exert their anti-MS effects through central or peripheral actions remained unclarified. In the present study, we investigated the effects of mecamylamine (MEC) and scopolamine (SCOP) on rotation-induced gastrointestinal symptoms (conditioned gaping and defecation), locomotion disturbances (hypoactivity and impaired balance performance), hypothermia as well as Fos expression in vestibulo-autonomic regions in rats. We also observed the effects of hexamethonium (HEX) and methyl scopolamine (MSCP) on those MS behavioral responses. The efficacy of all these drugs on rotation-induced emesis and other MS symptoms in cats was also examined. We found that intragastric administration of MEC and SCOP inhibited rotation-induced gaping and defecation in rats, but only MEC showed a dose-dependent manner. MEC aggravated rotation-induced balance disorder and failed to attenuate rotation-induced hypothermia as the SCOP did. MEC was more effective for inhibiting Fos expression in the caudal vestibular nucleus and nucleus of solitary tract than SCOP. Intraperitoneal injection of HEX and MSCP also significantly alleviated rotation-induced gastrointestinal symptoms, and showed benefit to balance performance in rats. In cats, MEC, SCOP and HEX had prophylactic effects against rotation-induced emesis and salivation, and deceased non-retching/vomiting symptoms, but MSCP only attenuated emesis. It suggested that MEC and SCOP might alleviate gastrointestinal symptoms of MS via inhibiting peripheral autonomic nervous system and central vestibulo-autonomic pathways. The nicotinic acetylcholine receptor inhibitors like MEC might be new candidates against gastrointestinal symptoms induced by MS or other vestibular disorders.

Due to the complexity and randomness of emergency demands, it is difficult to describe and analyze the Emergency Medical Service Systems (EMSS) just by certain modeling. Therefore, a stochastic modeling and simulating method for EMSS optimizing is proposed based on the Discrete Event System theory. With a fictive and stochastic medical emergency scenario, the relevant simulation model is constructed with the FLEXSIM software quickly and explicitly. And by simulating and analyzing, it is easily to discover the bottlenecks and to optimize the EMSS system.

Although fluoride is known to stimulate bone formation, the underlying mechanisms are not fully understood. Recent studies have implicated the Wnt/β-catenin pathway as a major signaling cascade in bone biology. Our earlier studies highlighted a probable role of canonical Wnt pathway in bone formation of chronic fluoride-exposed rats, but the mechanism remains unclear. The current study determined the involvement of Wnt/β-catenin signaling in fluoride-induced osteoblastic differentiation. Using primary rat osteoblasts, we demonstrated that fluoride significantly promoted osteoblasts proliferation and alkaline phosphate (ALP) expression as well as the mRNA expression levels of bone differentiation markers, including type I collagen (COL1A1), ALP and osteonectin. We further found fluoride induced phosphorylations at serine 473 of Akt and serine 9 of glycogen synthase kinase-3β (GSK3β), which resulted in GSK-3β inhibition and subsequently the nuclear accumulation of the β-catenin, as shown by Western blot and immunofluorescence analysis. Moreover, fluoride also induced the expression of Wnt-targeted gene runt-related transcription factor 2 (Runx2). Importantly, the positive effect of fluoride on ALP activity and mRNA expressions of COL1A1, ALP, osteonection and Runx2 was abolished by DKK-1, a blocker of the Wnt/β-catenin receptor. Taken together, these findings suggest that fluoride promotes osteoblastic differentiation through Akt- and GSK-3β-dependent activation of Wnt/β-catenin signaling pathway in primary rat osteoblasts. Our findings provide novel insights into the mechanisms of action of fluoride in osteoblastogenesis.

The influence of energy-transfer-upconversion effects and the divergence angle of pump beam on the laser performance of diode-end-pumped solid-state lasers are investigated. Based on it, the doping concentration and length of laser medium are optimized for diode-end-pumped Nd:YVO4 laser taking into account the thermal effect with ETU effects and the intrinsic loss of laser medium. The theoretical results are rather in good agreement with the published experimental data. The theoretical prediction of optimum neodymium doping concentration and the optimum length of Nd:YVO4 crystal are 0.08% and 13 mm, respectively.

The expressions for the spectral intensity of partially coherent Gaussian Schell-model (GSM) radial array beams for both the correlated and uncorrelated superpositions passing through ABCD optical systems have been derived by using the extended Huygens–Fresnel diffraction integral. The effects of the normalized radius R , the number of beamlets N , the spatial coherent parameter of array beamlets β and the optical system parameters on the on-axis and off-axis relative spectral shifts for the two types of superposition have been discussed in detail. The results show that for the correlated superposition, the on-axis spectral intensity in free space and the off-axis spectral intensity on the geometrical focal plane depends on the source spectral density S 0 ( ω ), the spatial coherent parameter of array beamlets β , the generalized Fresnel number of the system F , the normalized radius R and the number of beamlets N , whereas the spectral intensity for the uncorrelated superposition is independent of the number of beamlets N . Furthermore, as for on the actual focal plane, the off-axis spectral intensity for the two types of superposition is closely related to N .

According to the extended Huygens–Fresnel integral, the expressions for the on-axis spectrum of partially coherent Gaussian Schell-model (GSM) rectangular array beams passing through ABCD optical systems have been derived. The generalized Fresnel number of the system, the spatial coherent parameter of array beamlets and the array beam parameters including the number of beamlets and the separation distance between beamlets have been taken as the characteristic parameters to compare the spectral shifts of GSM array beams for the two types of the superposition, i.e. the correlated superposition and the uncorrelated superposition. In particular, the effect of characteristic parameters on the on-axis relative spectral shifts has been discussed in detail. The results show that the spectral intensity of GSM array beams for the two types of the superposition passing through ABCD systems depends on the source spectral density S 0(ω), the spatial coherent parameter of array beamlets β, the generalized Fresnel numbe...

An electronic nose (e-nose)-based network system is developed for monitoring odors in and around livestock farms remotely. This network is built from compact e-noses that are tailored to measure odor compounds and environmental conditions such as temperature, wind speed, and humidity. The e-noses are placed at various applicable locations in and around the farm, and the collected odor data are transmitted via wireless network to a computer server, where the data processing algorithms process and analyze the data. The developed e-nose network system enables more effective odor management capabilities for more efficient operation of odor control practice by providing consistent, comprehensive, real-time data about the environment and odor profile in and around the livestock farms. Experimental and simulation results demonstrate the effectiveness of the developed system.

There is a lack of knowledge about which risk factors are more important in West Nile virus (WNV) transmission and risk magnitude. A better understanding of the risk factors is of great help in developing effective new technologies and appropriate prevention strategies for WNV infection. A contribution analysis of all risk factors in WNV infection would identify those major risk factors. Based on the identified major risk factors, measures to control WNV proliferation could be directed toward those significant risk factors, thus improving the effectiveness and efficiency in developing WNV control and prevention strategies. Neural networks have many generally accepted advantages over conventional analytical techniques, for instance, ability to automatically learn the relationship between the inputs and outputs from training data, powerful generalization ability, and capability of handling nonlinear interactions. In this article, a neural network model was developed for analysis of risk factors in WNV infection. To reveal the relative contribution of the input variables, the neural network was trained using an algorithm called structural learning with forgetting. During the learning, weak neural connections are forced to fade away while a skeletal network with strong connections emerges. The significant risk factors can be identified by analyzing this skeletal network. The proposed approach is tested with the dead bird surveillance data in Ontario, Canada. The results demonstrate the effectiveness of the proposed approach.

A technique of intelligent computation of moisture content in shrinkable biomaterials from multiple predictors was developed. All measurable predictors were structured in three sets: biomaterial properties (volume, density, porosity, diffusivity); drying conditions (time, air temperature, humidity, velocity, pressure); and drying technologies. Two typical drying models were considered: time-dependent (thermodynamical) and time-independent (relational). The relationship between predictors and moisture content was established on the basis of multi-factorial linear regression (MLR) and neural networks (NN). Accuracy of statistical approximation was strongly dependent on drying model and chosen set of predictors. Time-independent models demonstrated better accuracy (MSE = 0.214) than time-dependent models (MSE = 0.254). Redundant predictors did not affect the accuracy and generalization ability of statistical models. Results of NN training and testing showed superior accuracy with respect to statistical model...

In livestock farming, odour measurement and reduction are necessary for a cleaner environment, lower health risks to humans, and higher quality of livestock production. There have been many studies on modelling of livestock farm odour by analysing the chemical components in odorous air. It is suggested that the component analysis approaches should be extended to factors such as temperature, relative humidity, and airflow speed. In this paper, a neuro-fuzzy-based method for analysing odour generation factors to the perception of livestock farm odour was proposed. The proposed approach incorporates neuro-adaptive learning techniques into fuzzy logic method. Rather than choosing the parameters associated with a given membership function by trail and error, these parameters could be tuned automatically in a systematic manner so as to adjust the membership functions of the input/output variables for optimal system performance. A multi-factor livestock farm odour model was developed, and both numeric factors and linguistic factors were considered. The proposed approach was tested with a livestock farm odour database. The results demonstrated the effectiveness of the proposed approach in comparison to a typical neural network model.

This paper introduces a new portable intelligent electronic nose system developed especially for measuring and analysing livestock and poultry farm odours. It can be used in both laboratory and field. The sensor array of the proposed electronic nose consists of 14 gas sensors, a humidity sensor, and a temperature sensor. The gas sensors were especially selected for the main compounds from the livestock farm odours. An expert system called "Odour Expert" was developed to support researchers' and farmers' decision making on odour control strategies for livestock and poultry operations. "Odour Expert" utilises several advanced artificial intelligence technologies tailored to livestock and poultry farm odours. It can provide more advanced odour analysis than existing commercially available products. In addition, a rank of odour generation factors is provided, which refines the focus of odour control research. Field experiments were conducted downwind from the barns on 14 livestock and poultry farms. Experimental results show that the predicted odour strengths by the electronic nose yield higher consistency in comparison to the perceived odour intensity by human panel. The "Odour Expert" is a useful tool for assisting farmers' odour management practises.

Odour emissions from livestock operations in Ontario have raised significant public concerns. In this study, downwind odour measurements were conducted during a 7-week period in July and August in 2004. The study involved taking measurements at two poultry farms, six dairy farms, and six pig farms in southern Ontario using both human assessor observed Nasal Ranger field olfactometers and an electronic nose developed in our research laboratory. The factors considered include animal species, distance to the odour source, and weather conditions (i.e. wind speed, temperature, cloud cover, and atmospheric stability). The collected data were studied and the relationships between individual factors and downwind odour intensity were analysed. In addition, the significance of the factors was investigated and ranked. The results show that downwind livestock farm odour should be considered as a complex system and it is suggested that multi-factor analysis be performed. The ranking of the significance of factors to odour intensity can improve the understanding of downwind odour systems and enhance the efficiency of odour evaluation and reduction techniques. Responses of the gas sensors in the electronic nose were compared with the perceived odour intensities, and good agreement was reported.

In pig farming, odour measurement and reduction are necessary for a cleaner environment, lower health risks to humans, and higher quality of pig production. There have been many studies on the modelling of pork farm odour by analysing the chemical components in odorous air. It is suggested that the component analysis approach should be extended to factors such as temperature, relative humidity, and airflow speed. However, previous component and factor analysis did not examine which components or factors contribute significantly to a complex odour. A relative contribution analysis of potential odour components and factors to the perception of odour would allow identification of significant odour components and major contributing factors. Odour reduction practice for pork farms could then be directed towards the significant components and factors, thus improving the efficiency in developing odour measurement and reduction technologies. It is generally accepted that neural networks have several advantages over conventional techniques, for instance, their ability to automatically learn the relationship between the inputs and outputs without any previous knowledge of the system being studied, their powerful generalisation ability, and their capability of handling non-linear interactions. However, typical neural network models suffer from the so-called ‘black box’ problem, i.e. it offers no information about the system other than the input/output relationship. In this paper, existing methods for odour strength prediction were reviewed, and a neural network based multi-component multi-factor odour model was developed. To reveal the relative contribution of the inputs, the neural network was trained using an algorithm called structural learning with forgetting. By applying the structural learning with forgetting based algorithm, unnecessary neural connections faded away and a skeletal network emerged. The resulted skeletal network enabled an analysis of the contribution of components and factors. The effectiveness of the proposed approach was demonstrated by simulation and comparison studies.

Final prediction accuracy is greatly influenced by the predictive error of individual RBF network output in the process of RBF Neural Network Ensemble Prediction. The predictive value of individual RBF network model with the help of SAS statistical analysis software is analyzed, and the prediction values that are unreliable are removed out, Therefore the final prediction accuracy is increased. The PMI is predicted based on the improved model and former model, respectively. The results show that the relative prediction error is reduced by about 0.2%.

Detecting and monitoring odour around livestock farms are difficult. In this paper, a dynamic neural network based model is proposed to locate odour dispersion around livestock facilities. The proposed dispersion model can dynamically represent complex or non-steady-state meteorological and topographical features in and around livestock farm areas. The proposed approach can also model odour dispersions from multiple odour sources, and from various types of sources such like point source, line source, and area source. In addition, the proposed model simulates the odour dispersion through the dynamic neural activity landscape, without explicitly additional models of the dynamic environment, odour sources, and farming activities.

Monitoring and analysis of livestock farm environment require a lot of data obtained from distributed farms. The whole process needs cooperation and collaboration of multiple partners with different backgrounds. A single person will have difficulty performing the full process and bringing those data records together, as the data measurement sites (farms) are far away from each other. This paper presents a dynamic and cooperative framework for monitoring and analysis of livestock farm odor. The key and novel contribution of this study is that the proposed framework utilizes a web-based portal application as a platform for distributed applications. This collaborative framework provides an efficient, robust, and user-friendly environment for distributed users to manage and process data records, share analysis results, and collaborate on distributed tasks.

There is a lack of knowledge about which risk factors are more important in West Nile virus (WNV) transmission and risk magnitude. A better understanding of the risk factors is of great help in developing effective new technologies and appropriate prevention strategies for WNV infection. A contribution analysis of all risk factors in WNV infection would identify those major risk factors. Based on the identified major risk factors, measures to control WNV proliferation could be directed toward those significant risk factors, thus improving the effectiveness and efficiency in developing WNV control and prevention strategies. Neural networks have many generally accepted advantages over conventional analytical techniques, for instance, ability to automatically learn the relationship between the inputs and outputs from training data, powerful generalization ability, and capability of handling nonlinear interactions. In this article, a neural network model was developed for analysis of risk factors in WNV infection. To reveal the relative contribution of the input variables, the neural network was trained using an algorithm called structural learning with forgetting. During the learning, weak neural connections are forced to fade away while a skeletal network with strong connections emerges. The significant risk factors can be identified by analyzing this skeletal network. The proposed approach is tested with the dead bird surveillance data in Ontario, Canada. The results demonstrate the effectiveness of the proposed approach.

An electronic nose-based network system is developed for monitoring odours around livestock farms remotely. This network is built from compact electronic noses which are tailored to detect odour compounds and environment conditions such as temperature, wind speed, and humidity. The electronic noses are placed at various locations of interest around the farm, and the collected odour data are dissemination via a wireless network to a computer server, where the sensor fusion algorithms process and analyze the data. The developed electronic nose network system can provide farmers and researchers with more accurate odour management capabilities for more efficient operation of odour control practice by providing consistent, comprehensive, detailed, real-time data about the environment and odour profile around livestock farms.

The development of an electronic nose for data dissemination via a wireless network is presented in this paper. The design is tailored to odour emissions, and meteorology around pork farms and utilizes four ad-hoc gas sensors as well as two extraneous temperature and humidity sensors to aid in the odour monitoring and analysis of pork odour. Empirical data was gathered and analyzed through experiments using a proven chemical recipe that closely mimics that of pork odour. Thus far the electronic nose has yielded highly qualitative results as compared to that expected of pork odour and has also exhibited the ability to transfer data across a network.

A novel neural network based approach for risk factor analysis of infection of West Nile virus (WNV) is proposed. A multi-factor risk analysis model is developed and learnt by an algorithm called structural learning with forgetting. Through the learning, unnecessary connections fade away and a skeletal network emerges. By analyzing the resulted skeletal networks, significant risk factors can be identified, and thus a more thorough understanding of WNV transmission mechanism can be obtained. The proposed approach is tested with a dead birds surveillance data. The results demonstrate the effectiveness of the proposed approach.