Organisms hold an extraordinarily evolutionary advantage in forming complex, hierarchical structures across different length scales that exhibit superior mechanical properties. Mimicking these structures for synthesizing high-performance materials has long held a fascination and has seen rapid growth in the recent past thanks to high-resolution microscopy, design, synthesis, and testing methodologies. Among the class of natural materials, nacre, found in mollusk shells, exhibits remarkably high mechanical strength and toughness. The highly organized “brick and mortar” structure at different length scales is a basis for excellent mechanical properties and the capability to dissipate energy and propagation in nacre. Here, we employ large-scale atomistic coarse-grained molecular dynamics simulations to study the mechanical and viscoelastic behavior of nacre-like microstructures. Uniaxial tension and oscillatory shear simulations were performed to gain insight into the role of complex structure-property relationships. Specifically, the role played by the effect of microstructure  and polymer-crystal interactions on the mechanical and viscoelastic behavior is elucidated. The tensile property of the nanocomposite was seen to be sensitive to the microstructure, with a staggered arrangement of the crystalline tablets giving rise to a 20–30% higher modulus and lower tensile strength compared to a columnar arrangement. Importantly, the staggered microstructure is shown to have a highly tunable mechanical behavior with respect to the polymer-crystal interactions. The underlying reasons for the mechanical behavior are explained by showing the effect of polymer chain mobility and orientation and the load-carrying capacity for the constituents. Viscoelastic responses in terms of the storage and loss moduli and loss tangent are studied over three decades in frequency and again highlight the differences brought about by the microstructure. We show that our coarse-grained models offer promising insights into the design of novel biomimetic structures for structural applications. Our quest for a continual improvement in properties of high-performance materials has been kept alive thanks to novel structure-property relationships uncovered in natural materials. In recent years, the enhancement in the mechanical properties of materials has been bolstered by the structure and organization found in natural materials  holding excellent proficiency in creating complex configurations across different length scales. Nacre  is a classic example of hierarchical architecture that confers superior mechanical properties and has been the subject of biomimicry over the years. It forms the inner lining of mollusk shells and demonstrates a unique staggered composite architecture composed of about 95% aragonite, a crystallographic form of calcium carbonate, glued together with 5% of a biopolymer matrix . MillionThe foundation of the macromechanical properties of nacre was established as early as in the 1970s through extensive tensile deformation studies ,14,15,16arbonate .Over the past two decades, contrasting values of the tensile strength of the nacre have been reported, especially regarding loading along parallel and perpendicular directions with respect to the nacre tablets. The tensile strength parallels to tablet layers was estimated to be 150–200 MPa, whereas the strength along the perpendicular direction was reported to be 75–100 MPa, signifying the role played by the polymeric phase during deformation . At multThe remarkable toughness of nacre is also attributed to the frictional resistance offered by nanoasperities ,27 and tG″) for nacre can be as high as 40 GPa at a frequency of 100 Hz and under a load of 1000 μN, proving the efficient damping nature of layered structures .(8.(877].N) at an intermediate length/time scale where the Rouse modes The structural relaxations of polymer were investigated to consider the effect of polymer relaxation on the energy-dissipation characteristics of the nacre-like composites. The Rouse model describes the dynamics of polymer chains  atEquation ) is expeTo compute the Rouse modes, a homopolymer system of lgorithm .It is well known that in the harmonic approximation, the power spectrum of the velocity correlation is the vibrational density of states as per Equation  49,80].,80.49,80This section is divided into three subsections. The first presents results of characterization of the glass transition and polymer chain statistics of the composites. This is followed by two subsections on (a) tensile and (b) viscoelastic properties of the composites.V and To investigate the effect of the microstructure on the glass-transition temperature, 8*10−6τ−1. The volTo validate our results, terature . FurtherThe conformation of polymer chains, defined by their equilibrium end-to-end distance It is apparent that the composites exhibit an initial elastic regime followed by the yield point at strain ≈4 to of nacre . It is iInterestingly, the negligible effect of When subjected to extension, polymer chains exhibit morphological changes in response to the load. The conformational changes to polymer chains during extension are dependent on the conformational entropy to a large extent. To quantify this effect, the average radius of gyration A monotonic increment in Additionally, the polymer-crystal coordination number Under non-equilibrium conditions, the viscoelastic behavior depends on various deformation conditions, most importantly the strain amplitude and the frequency of deformation, which are explored in the following sections. The nature of interactions between the crystalline and polymeric domains also naturally impact viscoelasticity and are studied in the following sections.GG″) are shown in Oscillatory shear simulations were performed by varying the shear strain amplitude from GG″ as a function of shear strain amplitude. A closer inspection of the transition regime indicated the delayed relaxation of polymer chains in the staggered structure over a wide range of shear strain amplitude (≈150% higher strain amplitude). The sharper decline of GG″ with strain, whereas the staggered structure shows a further increase in G″ for larger Beyond the Linear Viscoelastic Region (LVER), the polymer dynamics change from glassy to rubbery state due to the segmental relaxation of the composites, manifested by the transition in frequency-sweep test. The shear amplitude was fixed at The dependence of dynamic moduli on the oscillatory deformation frequency was studied by varying the frequency, G″ is negligible in comparison to GGGGFirst, at low frequencies, GG″ plots as seen in panels (a–d). The staggered composite showed 3 times higher G" value than the columnar composite exhibiting tunable viscoelastic properties with respect to the microstructure. Interestingly, the characteristic peak(s) in GG″ for the systems clearly propound the synergistic action among the crystallites and the polymer. The combined effect of crystallite vibration and stiffness enhancement in the polymer at high frequency led to an enhancement in GAt intermediate frequencies, namely between The variation of Finally, a significant rise in the storage modulus was observed at much higher frequencies in the range To characterize the stress-strain response during the oscillatory deformation further, the distribution of shear stress in each domain  is plotted as a function of frequency in (a)Polymer chain dynamicsOne of the dominant dissipative mechanisms in polymer nanocomposites is the frictional forces that arise from the relative sliding of polymer chains and the sliding between the polymer and filler. Although traditional nanocomposites contain filler fraction in the range 10–40%,83, our (b)Rouse mode analysisAs expected, the presence of crystallites dramatically slowed down the overall dynamics of the polymer chains. Typical solid-like behavior was observed for the polymeric domain at the temperature p = 1, 40, 80 and 99, were compared.The lack of polymer mobility was also corroborated by the computation of the Rouse relaxation times and its possible role in energy dissipation. It was thought that the segmental dynamics of polymers might be affected due to high crystallite loading, suggesting a possible correlation between the frequency of deformation  at maximum energy dissipation (which corresponds to the peak observed in the loss moduli) and the relaxation times of Rouse modes. Therefore, to verify the hypothesis, (c)Vibrational Density of StatesThe Rouse mode analysis was quite revealing in several ways. First, polymers at the melt condition showed a well-defined exponential decay characterizing the Rouse time for the individual modes. In contrast, at the glassy state, polymer dynamics were found to be substantially slower, with negligible segmental relaxation. This is a direct consequence of the highly confined polymeric domain and rules out the direct correlation of polymer relaxation to that of the peak dissipation time period, which is in the order of anharmonic coupling of vibrational modes as described in prior literature [The observation of peak damping in the high-frequency regime (as seen in ractions ,85. It wterature , rather The mechanical properties of biomimetic, nacre-like composites can be highly tunable for certain morphologies such as the staggered composite, especially as a function of the polymer-crystal interaction strength Conformational analysis of polymer chains during inelastic deformation exhibited the significant role played by The rough crystallite surface in the staggered composite dramatically arrests and delays the dynamics of polymer chains in the vicinity of the crystallites, exhibiting a notable increment in the glass-transition temperature. This, in turn, affects mechanical behavior.A detailed study of viscoelastic properties of the composites indicated a The dissipative behavior of the nacre-like composites is very sensitive to the deformation frequency and can be tuned by tuning the microstructure. The maximum loss tangent for the staggered composite was found to be 8, which was 40 times higher than that for the columnar structure, which in turn is 34 times lower with respect to the homogeneous polymer.At frequencies corresponding to maximum damping, about 15–30% of the overall stress was supported by the polymer domain.The dynamics of polymer chains were substantially restricted in the cage-like confined regions. The mean square displacement and Rouse mode analysis of polymeric chains essentially showed a solid-like behavior, corroborating its highly confined nature. Finally, the large damping effects, especially at high deformation frequencies in nacre-like composites, are a direct consequence of the vibrational properties of the constituent atoms.This study set out to investigate the tensile and viscoelastic properties of nacre-like composites for two different microstructures, namely columnar and staggered. The role played by several design factors and deformation conditions such as the microstructure, the polymer-crystal interaction, deformation frequency, and amplitude, etc., were elucidated through non-equilibrium coarse-grained molecular dynamics simulations. Some of the significant findings are:We show ultimately that there are some molecular-level phenomena such as the vibrational density of states and polymer-crystal orientation effects that affect the viscoelastic and tensile properties, respectively, while other phenomena such as polymer diffusivity and relaxation do not play a role in the deformation characteristics, given that the polymeric domain is in a glassy state. Thus, macroscopic properties have direct molecular underpinnings. These results add to the rapidly expanding field of structure-property correlation of high-performance biomimetics. It is envisaged that these simulation techniques can be easily extended to model various biomimetic microstructures found in nature for optimization of mechanical properties such as strength, toughness, damping, and even high-strain rate deformations such as impact ,87 and s


In clinical practice, epilepsy is often comorbid with the autism spectrum disorders (ASDs). This warrants a search of animal models to uncover putative overlapping neuronal mechanisms. The Krushinsky-Molodkina (KM) rat strain is one of the oldest inbred animal models for human convulsive epilepsies. We analyzed the behavioral response of adult seizure-naive KM males in three-chambered tests for social preference. We found that a presence of social stimuli (encaged unfamiliar Wistar rats of the same age and sex) evoked a reduced or reversed exploratory response in freely moving KM individuals. The epilepsy-prone rats demonstrated remarkably shortened bouts of social contacts and displayed less locomotion around the stranger rat-containing boxes, together with a pronounced freezing response. The decrease in social preference was not due to a general decrease in activity, since relative measures of activity, the index of sociability, were decreased, too. The susceptibility to audiogenic seizures was verified in the KM cohort but not seen in the control Wistar group. We propose the KM rat strain as a new animal model for comorbid ASD and epilepsy. The prevalence of epilepsy among the general population is approximately 0.6%, and in 2015, it was the fourth most severe neurological disorder worldwide. Similarly, in the early 2010s, the prevalence of ASDs among the general population was estimated at about 0.8% .The study of rat lines genetically predisposed to epilepsy is promising for the emergence of new animal models with comorbidity of ASDs and epilepsy. Most of the main studies of the complex pathology of epilepsy and ASDs in laboratory rats were performed on pharmacological models of seizure provocations , which raises the question of the side effects of exposure to the chemical agent itself. It is preferable to use models with the hereditary nature of the disease, which makes it possible to investigate the mechanisms of pathology without externally introduced changes.Phenotyping epileptic rat strains for autistic-like traits can shed light on the mechanism that underlies the relation between autism and epilepsy. For example, the FAST rat strain, which has an increased proneness to pharmacologically induced seizures, demonstrates various behavioral features associated with the phenotype of ASDs with hyperactivity ,5. At thThe work was approved by the Institutional Ethical Committee and carried in accordance with the requirements of the ARRIVE guidelines.n = 12) and outbred Wistar strain (n = 16), aged 4–6 months and weighing 250–400 g were used in the study. The animals arrived at the animal chapter of IHNA at the age of 6–8 weeks. The cage contained 4-6 rats, with food and water ad libitum. All the animals were intact and drug-naive prior to the experiments.Adult male rats of the KM strain . TheNext, the empty box was changed for an identical container with a new rat, to test the response to a social novelty c. The neAll sessions were videorecorded with a digital camera linked to a computer in an adjacent room. The recordings were digitized and analyzed offline automatically  and offline by blind experts .A free software ToxTrac  was used for offline tracking the moving animals in all tests. Tracking was done using ToxTrac’s ID algorithm Toxld (Ssi.Rep.). The algorithm used allowed us to track the rostral part of the body of the freely moving animal . The following measurements were automatically taken: time spent in defined zones, total distance travelled in the defined zones, number and time of freezing episodes .The sociability index was calculated as the ratio:Upon completion of the behavioral testing, audiogenic seizures were provoked as described before ,14. As eThe main method for assessing the significance of differences between the experimental and control groups was the Mann–Whitney U-test; within group effects were estimated by the Wilcoxon test.KM rats displayed a decrease in motor activity and increased anxiety. Both horizontal and vertical elements of the exploratory behavior were reduced, and the time spent in the open arms was shorter compared to the control group a–c. The p < 0.01) in the group of KM rats . In contrast to this, the Wistar rats increased the time spent in the new “habituated” zone  compared to its previous “empty” state . Nam. Nam15])The results presented strongly support a hypothesis on consistent social deficits in KM rats. Time spent in social interaction, as well as the number of approaches to it, were remarkably reduced in the KM group a. SomewhWe confirmed several previously discovered behavioral features of KM rats, such as increased anxiety and reduced locomotor activity ,16, and Given the heterogeneity of idiopathic ASDs, the determination of the mechanisms leading to ASD–epilepsy comorbidity can help in the stratification of autism. Since the causes leading to the idiopathic ASDs are poorly understood, progress on this path is largely based on the study of genetic syndromes associated with autistic traits , butHere, we show the social deficits in inbred KM rats, genetically susceptible to audiogenic seizures. Thus, this rat strain can be considered as a potential animal model of ASD–epilepsy comorbidity, these rats also showed hypoactivity. Future experiments are planned for more extensive phenotyping of this model and the exploration of the putative effects of seizure experience on the behavioral traits of KM animals.


In greenhouses, sensors are needed to measure the variables of interest. They help farmers and allow automatic controllers to determine control actions to regulate the environmental conditions that favor crop growth. This paper focuses on the problem of the lack of monitoring and control systems in traditional Mediterranean greenhouses. In such greenhouses, most farmers manually operate the opening of the vents to regulate the temperature during the daytime. Therefore, the state of vent opening is not recorded because control systems are not usually installed due to economic reasons. The solution presented in this paper consists of developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) as a soft sensor to estimate vent opening using the measurements of different inside and outside greenhouse climate variables as input data. A dataset from a traditional greenhouse located in Almería (Spain) was used. The data were processed and analyzed to study the relationships between the measured climate variables and the state of vent opening, both statistically (using correlation coefficients) and graphically . The dataset (with 81 recorded days) was then used to train, validate, and test a set of candidate LSTM-based networks for the soft sensor. The results show that the developed soft sensor can estimate the actual opening of the vents with a mean absolute error of 4.45%, which encourages integrating the soft sensor as part of decision support systems for farmers and using it to calculate other essential variables, such as greenhouse ventilation rate. These actuators are necessary to regulate essential variables like air temperature, humidity, or CO2 concentration to improve the growth of plants and fruits [Nowadays, agriculture faces numerous challenges, mainly, the growth of the world population, the effects of climate change, strict market regulations, and energy inefficiency . Overcomd fruits . This reTraditionally, sensors and automatic controllers are not installed in greenhouses due to the associated costs. Farmers are used to manually activating the actuators based on their own experience and continuous supervision of the crop and the weather. In recent years, with the availability of low-cost devices, monitoring and control systems are more common in commercial greenhouses, mainly due to the proliferation of emerging technologies such as wireless sensor networks (WSN)  and the In this context, this paper focuses on the problem of the lack of monitoring and control systems in traditional Mediterranean greenhouses. These greenhouses operate in warmer climatic conditions and with higher values of solar radiation. Therefore, not many actuators are typically used, as the control actions are mainly focused on air temperature regulation, which directly affects crop growth . In greeThe importance of knowing the state of vent opening  lies in the possibility of performing a more detailed analysis of the climatic information measured in a greenhouse, such as for modeling tasks or climate prediction. It is essential for IoT platforms as decision support systems that provide recommendations to farmers after analyzing the data measured by the stations installed in greenhouses . Such reThe solution proposed in this paper consists of developing a soft sensor to estimate the state of vent opening when it is not measured in a greenhouse. Soft sensors, also known as virtual sensors, are useful tools for estimating a variable from the measurements provided by other physical sensors installed in a given system . There aTo the best of our knowledge, no previous work has been published on the estimation of the vent opening of greenhouses because it is assumed to be a measurable variable. This work is the first attempt to implement a tool for this purpose. In this sense, there are no mathematical models that directly relate the greenhouse climate to the opening of the vents. Instead, this relationship has been studied in the literature for calculating the ventilation flux, which is the airflow that circulates through the vents when they are open ,23. TherFor the reasons explained above, this work aims to develop a data-driven soft sensor to estimate vent opening. To design the soft sensor, Deep Learning (DL) methods  are know2 concentration in the air, and outside wind velocity. The estimation of vent opening with a data-based soft sensor is possible because the evolution of the inside climate variables is affected by the ventilation flux. Every time that the vents of a greenhouse are opened or closed, variations in all or some of the aforementioned variables are measured. A historical dataset from a traditional Mediterranean greenhouse was used to train and test a series of LSTM-based network architectures to reproduce the actual opening of the vents which caused the measured variations in the inside climate variables. The training and testing processes were performed using a progressive elimination procedure (PEP) before selecting the final soft sensor based on an LSTM-RNN neural network. The results show a satisfactory performance, demonstrating that the developed soft sensor can estimate the actual opening of the vents with a reduced error.In summary, the main contribution of this work is the development of a soft sensor using an LSTM-based network to estimate the opening of greenhouse vents from climate variables commonly measured in medium-technology greenhouses. 2 concentration, or air temperature. Other potential applications of the soft sensor may include its integration into IoT platforms, as discussed above, which would be useful for continuous estimation of vent opening from data measured with commercial weather stations installed in greenhouses.The soft sensor’s contribution lies in providing an estimated signal of the opening of vents for its possible use by the existing models to calculate the ventilation flux and predict greenhouse climate variables ,24. In tThe remainder of the paper is organized as follows. In In this section, an experimental greenhouse was used to obtain the dataset needed for this work. The data obtained include a set of inside and outside greenhouse climate variables to be analyzed and selectively used as inputs in the next section, as well as the target, which is the actual vent-opening signal generated by an automatic controller. As a result of changes in the opening of vents, the climate variables inside the greenhouse are affected by the ventilation flux. Thus, the theory of this work is that a soft sensor based on an LSTM neural network can estimate the vent opening that causes those changes in the inside climate variables. The components and equations that constitute the LSTM-based neural network are explained in this section. Furthermore, the potential network architectures and their hyperparameters are preselected.2 (37.80 m × 23.20 m). Tomato (Lycopersicon esculentum “Ramy”) is the crop grown inside this experimental greenhouse, with a plant density of 1.4 plants/m2. The greenhouse is equipped with auxiliary systems, such as different actuators, to control the indoor climate. Particularly, the greenhouse has five roof vents (8.36 m × 0.73 m) and two lateral vents (32.75 m × 1.90 m) for natural ventilation, situated on the north and south sides. The roof vents have an angled opening, as shown in The greenhouse used in this study is located at “Las Palmerillas” Experimental Station of the Cajamar Foundation in Almería, Spain, at an altitude of 151 m. It is a traditional Mediterranean greenhouse see a with a 2 concentration in the air, wind velocity, vent opening, and time variables. The data were recorded during the growth cycle of a tomato crop using sensors installed inside and outside the experimental greenhouse  was used to train, validate, and test the developed soft sensor. This dataset was acquired using a commercial data acquisition system called Compact FieldPoint . It contains 13 climate variables related to inside and outside air temperature, relative humidity, global solar radiation, COouse see . Their aThe period for the selected dataset was from 10 October to 29 December 2020. Due to the large size of this dataset, In artificial neural networks, the LSTM cell is a powerful deep recurrent neural system developed specifically to deal with the vanishing gradient problems that often occur when learning long-term relationships between system inputs and target outputs . This faAn LSTM unit consists of four main components: a cell, an input gate, an output gate, and a forget gate. The cell remembers values over varying time intervals, and the cell gates control the flow of information. The LSTM structure consists of memory blocks, which are recurrently connected subnetworks. The memory block objective is to maintain its state over time while regulating information flow by means of nonlinear gate units. ed below :t is the time instant, Block input, Input gate, Forget gate, Cell state, Output gate, Block output, In Equations (1)–(6), The hyperbolic tangent is often used as the block input and output activation functions, Generally, the vanishing gradient problems can be overcome by using a constant error carousel (CEC), which preserves the error signal within each cell. In a neural network, the role of the LSTM cells is to abstract a meaningful representation of the input time series and then transmit them to the additional hidden layers. Although LSTM-based networks are already performing very well, the potential for improvements is still being explored, as indicated in the comprehensive state-of-art in .The number of hidden layers. It is selected by trial and error between 4 or 5 layers. Three types of hidden layers constituted the initial network architectures that were tested: LSTM cells, Dense (feedforward ANN), and RNN layers, as shown in The number of neurons. The number of network weights, which depends on the number and type of the hidden layers and the number of their neurons, is recommended to be much smaller than the number of data samples to avoid overfitting the network to the training data and to favor the generalization of the network output . Hence, Historical input data. By trial and error, 40 samples (20 min) were chosen as historical input data to capture all the delayed dynamics of the greenhouse climate, knowing that it presents some slow responses to disturbances  and control actions as time-dependent events. It is a fundamental feature of RNNs, specifically of LTSM-based networks, which allows the selective and meaningful mapping of historical input data to the final output.Activation function. As presented in Equation (7), sigmoid is the selected function for all the regular layers. It is proven to be significantly useful in the multinomial logistic regression method, which can model types where the discrete output can have more than two possible discrete outcomes . This isOptimizer. Adam is the selected optimizer. It is used as a mini-batch gradient descent method. It is based on adaptive estimation of first- and second-order moments. It is computationally efficient, requires little memory, and is suitable for problems with noisy and sparse gradients .Learning rate. The default learning rate of 0.001 is used for Adam. Higher and lower values were tested, but 0.001 proved to be more efficient in terms of loss reduction and computation time.Batch size. The batch size defines the number of samples to work within one iteration before updating the internal weights of the network. By trial and error, the batch size was set as 32 samples to accelerate the training process of the network.The number of epochs. An epoch is when all data samples pass through the neural network. By trial and error, 150 epochs were deemed sufficient for this study. In addition, the early stop feature is used to automatically stop the training of the network if no improvement in the validation loss function is shown for more than 50 epochs. In this case, the network with the best weights until that moment is stored. Also, the training can be stopped manually when overfitting is graphically noticed, knowing that the best network is automatically saved after every epoch.The LSTM-based network employs full gradient training to adapt the learnable network parameters (weights). The Backpropagation Through Time (BPTT) technique is used to calculate the weights that connect the network components. The LSTM-based network has a set of parameters, which are called hyperparameters. They are specifically determined to define the network architecture and control the learning process in the training phase before applying it to a dataset. These hyperparameters were selected as follows:In summary, the network architecture obtained in this work is presented in In this section, a data analysis is performed to preselect network inputs. The preselected inputs are then used to test two possible LSTM-based network architectures using supervised learning. Finally, a network architecture is selected based on statistical and graphical results and using different sets of the selected inputs in a PEP procedure. The methodology to develop the soft sensor is summarized in The available data were standardized and analyzed to study the relationships between the greenhouse climate variables and the vent opening signal for the preselection of the network inputs. The analysis was carried out in two phases: statistically, using different correlation coefficients, and graphically, using regression analysis.The dataset includes the opening signals for roof vents and lateral vents , and the other is a time-dependent control showing fewer changes . The training dataset was selected to include both types of control for the vent opening to enhance the training process with sufficient information. Moreover, this dataset was shuffled to ensure generalization during the training process. It contains 64 days representing 80% of the total dataset, from 19 October 2020 to 22 December 2020.A validation dataset was also used during the training process to provide an unbiased evaluation of the network while being fitted to the training dataset. The validation dataset is also involved in other forms of network preparation, such as feature and threshold selection. The validation dataset was selected to contain 8 days representing 10% of the complete dataset: 4 days from the start of the complete dataset (from 14 October 2020 to 17 October 2020) and another 4 days from the end (from 26 December 2020 to 29 December 2020). These days were selected because they present the different types of control for the opening of the vents.A test dataset is used to perform an unbiased evaluation of the final network. The test dataset was also selected to contain 8 days representing 10% of the complete dataset: 4 days from the start of the dataset (from 10 October 2020 to 13 October 2020) and another 4 days from the end (from 22 December 2020 to 25 December 2020).The development of any ANN requires dividing the available data into three sets  manually or automatically, depending on different techniques. In this work, the time series data were manually divided based on the evolution of the target variable as one of the main factors when manually splitting data. Hence, the data were divided as presented in The network training process was performed using different architectures and inputs. As presented in The preselected architectures were trained, tested, and statistically evaluated with different inputs in a PEP procedure, as presented in The time consumed for the training processes of the LSTM-ANN network is around 6 h, and for the LSTM-RNN network, it is around 9 h, which is considered an acceptable time consumption with a moderate computational cost. According to the results presented in Concerning the graphical evaluation, the training process of the best LSTM-RNN network presented an adequate convergence for the evolution of the training and validation cost function, as shown in A soft sensor based on an LSTM-RNN neural network has been developed to estimate the opening of greenhouse vents using a set of measurable climate variables. A comprehensive statistical and graphical data study was performed using different linear and nonlinear correlation coefficients and regression analyses. Based on the results of this data analysis and trial-and-error training processes, two possible network architectures (LSTM-ANN and LSTM-RNN) and ten inputs were preselected for the soft sensor design. In addition, a series of training and testing processes were carried out in a PEP procedure. It has been shown that the external climate variables The best network architecture is the LSTM-RNN, due to its performance in estimating the actually recorded opening of vents with reduced error values: Estimate and monitor the evolution of the natural ventilation flux.Develop predictive models for greenhouse climate evolution as a function of the estimated vent opening.IoT platforms and decision support systems to provide recommendations to farmers after analyzing the measured data, and for example, alert them to close the vents whenever a high wind velocity is detected to avoid any risk of damage to the greenhouse and crop.Consequently, the results confirm that the soft sensor is suitable for use in greenhouses where farmers manually operate the opening of the vents. In this context, the soft sensor could be applied to:In conclusion, the contribution of this work to the field of greenhouse agriculture lies in the possibility of offering a tool that can be applied to estimate the opening signal of vents without the need to have installed specific sensors on the vents of a greenhouse or control systems for natural ventilation.In future works, the use of larger datasets to improve the performance of the soft sensor will be studied. The focus will be on improving the estimation results when fast changes in the vent opening occur. The soft sensor could also be tested in different greenhouses with different shapes and geographical locations.


Accurate and efficient automatic individual recognition of livestock such as goats is one of the key tasks to achieve smart farming. However, this task is challenging due to factors such as the dense distribution of livestock in a barn. To accurately recognize Chengdu ma goats, a high-quality sheep breed with local characteristics, we construct a Chengdu ma goat dataset containing nearly one thousand images with manual annotation information and propose a method for the automatic individual recognition of Chengdu ma goats using a computer. Experiments demonstrate the usability of our method to accurately recognize Chengdu ma goats under extreme conditions such as dense target distribution and drastic differences in scale. It is worth noting that the method is scalable, which enhances the network’s representation ability for data by learning the structural information of goats’ head area. The location and category information obtained by our automatic individual recognition method is the basis for realizing a series of subsequent intelligent breeding tasks, such as precision feeding based on age and sex. Our method provides a new idea for automatic livestock recognition and can be extended to be applied to additional types of livestock.The Chengdu ma goat is an excellent local breed in China. As one of the breeds listed in the National List of Livestock and Poultry Genetic Resources Protection, the protection of its germplasm resources is particularly important. However, the existing breeding and protection methods for them are relatively simple, due to the weak technical force and lack of intelligent means to assist. Most livestock farmers still conduct small-scale breeding in primitive ways, which is not conducive to the breeding and protection of Chengdu ma goats. In this paper, an automatic individual recognition method for Chengdu ma goats is proposed, which saves labor costs and does not depend on large-scale mechanized facilities. The main contributions of our work are as follows: (1) a new Chengdu ma goat dataset is built, which forms the basis for object detection and classification tasks; (2) an improved detection algorithm for Chengdu ma goats based on TPH-YOLOv5 is proposed, which is able to accurately localize goats in high-density scenes with severe scale variance of targets; (3) a classifier incorporating a self-supervised learning module is implemented to improve the classification performance without increasing the labeled data and inference computation overhead. Experiments show that our method is able to accurately recognize Chengdu ma goats in the actual indoor barn breeding environment, which lays the foundation for precision feeding based on sex and age. The Chengdu ma goat, the research object of this paper, has been listed in China’s National List of Livestock and Poultry Genetic Resources Protection. As an excellent local breed for both meat and skin in China, it has the advantages of fast growth, early maturity, high fecundity, strong adaptability, heat and humidity tolerance and stable genetic performance. Meanwhile, it is characterized by tender meat, no foul smell, a large skin area and excellent quality.However, due to economic interests and the population’s low awareness of breed preservation, foreign breeds have been introduced for crossbreeding with Chengdu ma goats, resulting in a significant decrease in the proportion of purebred Chengdu ma goats, so the seed conservation of Chengdu ma goats is particularly important and urgent ,2. In adThe main reason that the sheep farming industry, represented by Chengdu ma goat farming, has failed to fully realize the transformation of agricultural modernization is that many farmers have no professional knowledge and their farming is only managed in a simple way. However, with the development and modernization of animal husbandry, it is obvious that such a breeding model is unable to meet the demand. Some notable problems are listed as follows:Low reproduction rate. Due to the lack of scientific breeding concepts, farmers do not choose good-quality breeding rams for breeding. Moreover, inbreeding is common in the flock and no external good genes are passed on. This will lead to a lower reproduction rate and lower quality of the flock in the long run.Inappropriate feed mix. Due to the lack of professional knowledge, farmers are unable to devise appropriate feeding programs to meet the developmental needs of breeding sheep according to the physiological characteristics and nutritional needs of rams and ewes, resulting in the lower productive potential and reproductive capacity of breeding sheep and a failure to prepare for the production of high-quality lambs, thus reducing the economic benefits for farms.With the rapid development of information technology, combining artificial intelligence technology with animal husbandry can advance the development of animal husbandry in a more efficient and profitable way. Artificial intelligence is such a technology, which needs immediate implementation in the livestock industry. AI has emerged as a tool that empowers farmers in monitoring, forecasting and optimizing farm animal growth, which are some of the key areas in the livestock industry, where the use of artificial intelligence technology can yield rich dividends. As one of the representative innovations in the field of artificial intelligence, computer vision technology can be utilized in a large number of practical production environments, with its relatively manageable price and its non-contact, real-time and long-time working characteristics.In recent years, the application of computer vision technology to the field of livestock production has attracted more and more attention. In 2018, Sarwar et al. combined deep learning algorithms and drone technology to count sheep using convolutional neural networks through video streams captured by drones , but theA synthesis of the above research work reveals that artificial intelligence technology represented by computer vision has brought innovation to the field of livestock production. However, some of the methods require a high degree of mechanization on the farm and require the assistance of other supporting facilities or have certain restrictions on environmental conditions, which do not have good universality and generalization ability. As the number of model parameters becomes increasingly large, the detection of livestock and poultry has obtained high accuracy, but the detection speed is correspondingly reduced. In practical applications, due to the storage space and power consumption limitations of the device, how to optimize the network structure to make the model lightweight and ensure the efficiency of detection is a key problem to be solved.Deep learning-based methods mostly rely on supervised learning when applied to computer vision tasks. Supervised learning requires the use of large, manually labeled datasets for a specific task, and the heavy reliance on manual labeling leads to high labor costs and generalization errors , which iTo assist in the breeding of Chengdu ma goats based on the category information and make the subsequent intelligent tasks, such as precision feeding based on age and sex, possible, we use computer vision technology with deep learning to quickly and accurately identify the category of Chengdu ma goats based on two dimensions: sex and age. The living area of Chengdu ma goats is mainly concentrated in hilly and mountainous regions, which makes the deployment of large mechanized facilities difficult due to the vegetation cover and narrow terrain, and the data collection and detection in sheep barn scenarios cannot be performed by applying Unmanned Aerial Vehicles (UAVs) as in the literature . ConsideThe basic task of object detection is to use rectangular bounding boxes to determine the location and size of the detected objects in the image or video data, and it is necessary to determine the object class and give the corresponding confidence value . With thThe mainstream object detection algorithms based on deep learning can be divided into two categories: two-stage object detection algorithms and one-stage object detection algorithms . Two-staYou Only Look Once (YOLO)  is a wel(1)Backbone: A convolutional neural network is often used here to extract image features.(2)Neck: A series of network layers that combine and reprocess image features and pass image features to the prediction layer.(3)Head: It generates bounding boxes and prediction categories with corresponding confidence values. The confidence indicates the precision of the detection under specific conditions.TPH-YOLOv5  is an obTPH-YOLOv5 adds a detection head for tiny object detection. Combined with the other three detection heads, the four-detection-head structure can mitigate the negative effects of drastic changes in object scale. TPH-YOLOv5 uses Transformer Prediction Heads (TPH) for accurate object location in high-density scenes. To improve the detection performance of the model, TPH-YOLOv5 integrates CBAM  into theAt a time when deep learning is rapidly advancing, it is inevitable that large-scale labeled data are needed to train deep neural networks in order to improve the performance of the models. However, the collection and labeling of large-scale datasets are not easy tasks and require expensive labor and time costs.To reduce the substantial cost of collecting and labeling large-scale datasets, self-supervised learning, an important component of unsupervised learning methods, attempts to learn general image and video features from large-scale unlabeled data without using any manually labeled data . Self-suDesigning efficient and task-appropriate assistive tasks is the key to self-supervised learning. Assistive tasks can be broadly classified into four major categories of methods: generation-based methods, contextual information-based methods, automatic semantic label generation-based methods and cross-modal methods. The self-supervised learning module used in this paper is related to auxiliary tasks based on contextual information, and the design of this class of methods mainly uses the contextual features of image or video data.With the gradual reduction in Chengdu ma goat germplasm resources due to crossbreeding and other factors, only the Chengdu ma goat breeding farm of Chengdu Xilingxue Agricultural Development Co., Ltd. (Chengdu China) has been continuously committed to the conservation of their germplasm resources, and it was approved by the Ministry of Agriculture as a national Chengdu ma goat breeding farm in 2017. On the one hand, Xilingxue Agricultural Development Co., Ltd. has a large number of excellent purebred individuals. On the other hand, the breeding farm has standardized sheep barns with related facilities, which can truly reflect the real state of the sheep under standardized feeding conditions.We acquired images of Chengdu ma goats in the Chengdu ma goat breeding base of Chengdu Xilingxue Agricultural Development Co., Ltd. through manual shooting and camera monitoring. The breeding base is located in Dayi County, Chengdu City, Sichuan Province, China. This dataset was collected by Canon EOS 850D produced in Japan and EZVIZ C3WI wireless surveillance cameras produced in China. The shooting methods included long-distance shooting and short-distance shooting, and the shooting angle included top-down and parallel. The recording of the video included a top view and a forward view of the barns. The data included images and videos captured by the cameras under multiple time periods and lighting conditions, which covered single or multiple individuals and reflected the situation of the sheep house through multiple perspectives. To reflect the actual living environment of the goats as much as possible by considering different light conditions, regional populations and sex ratio distributions, we selected 841 representative images from all of the images that were shot manually or extracted from the video as our experimental data. For the needs of the study, taking into account the practical application scenarios and animal characteristics, we labeled the Chengdu ma goats in the images with the help of professionals from two dimensions: sex and age. We labeled Chengdu ma goats as (adult) rams, (adult) ewes and lambs, and randomly divided the labeled images into a training set and test set according to the ratio of 7:3. Deep learning usually requires a large amount of data to train the model. However, in reality, there are some data that are difficult to obtain, resulting in a small amount of data in this category, which does not meet the demand of data volume for deep learning. For this reason, experts have proposed data augmentation techniques to effectively solve this problem .In the experiments, in addition to the conventional data augmentation methods such as geometric transformation and color transformation, a multi-image fusion method called mosaic was also used. Mosaic crops four images randomly and stitches them into a single image as training data. It has the advantage of enriching the background of the images and improving the batch size discretely, so that it can help to reduce the model’s dependence on a large batch size when training. The dense distribution of Chengdu ma goats in some images, coupled with the severe variation in body scale from image to image, poses a great challenge to the effective detection of targets. In order to apply the model to the real-time detection task of Chengdu ma goats, the model needs to have good detection efficiency and deployability while addressing the above challenges with precise localization and classification. Therefore, two main improvements are made in this paper. The first are the improvements of the TPH-YOLOv5 architecture: based on the original TPH-YOLOv5 network, the number and position of transformer encoder blocks are adjusted to reduce the computational cost and make the model applicable to our task of detecting Chengdu ma goats while improving the detection speed; multiple SPP  modules TPH-YOLOv5 uses transformer encoder blocks to replace some convolution blocks and CSP bottleneck blocks in the original version of YOLOv5. The architecture of the transformer encoder is shown in The primary role of Spatial Pyramid Pooling (SPP)  is to soGenerally, the features of shallow layers in the network have strong location information and are suitable for localization tasks; the features of deep layers in the networks have more advanced semantic information and are beneficial for classification tasks. FPN  combinesAfter TPH-YOLOv5 was trained on the VisDrone2021 dataset, it was tested and found to have good localization ability and relatively weak classification performance. Therefore, the authors constructed a training set by cropping the ground-truth bounding boxes and resizing each image patch to 64 × 64, and then selected ResNet18  as the sIn the Chengdu ma goat recognition task, some adult ewes and lambs outwardly expressed minor differences in physical characteristics, with both body coat colors showing russet or brown , while bAt present, most recognition methods based on deep learning are committed to learning the external and significant representative features of the target object, while ignoring the overall structure of the object, which also leads to the traditional methods based on deep learning yielding incorrect results in many cases, such as distinguishing the front and rear wheels of a car and locating the legs of a bird in the branches . While eLook-into-Object (LIO)  understaSpatial Context Learning (SCL) aims to model the location relationships of different parts of the target region by predicting the polar coordinates of the non-central part of the target region with respect to the central part of the target region (the origin of the polar coordinates). The SCL module uses polar coordinates to measure the spatial connection between different regions. Because the targets exist in different forms, modeling the position relationship between different parts using the Cartesian coordinate system requires modeling the absolute position, which is sensitive to the reference coordinate system and can be difficult to model. The polar coordinate system does not have this problem, as it uses relative coordinates; all positions are calculated relative to the pole, and modeling is relatively easy. The SCL module takes the relative distance differences and polar angle differences as the loss, so that the backbone network can recognize the composition of the object.In the sheep barn scenario, sheep are densely packed, and there is often overlap between sheep bodies, so it is not easy to use sheep bodies as recognition information. The SCL, as a self-supervised module, can enhance the ability of the backbone network to extract the overall structural information of the image by learning the spatial contextual relationship of the head region of the sheep without bringing an additional manual labeling cost. ResNet  uses resWe use ResNeXt50 as the backbone network and incorporate the SCL module to construct a classifier for Chengdu ma goats. The feature maps extracted from the backbone network are further fed to the SCL module when training. After end-to-end training, the backpropagation signal from the SCL module can optimize the representation learning of the backbone network. In this way, structural information can be injected into the backbone network to improve object recognition without requiring additional annotation information. In addition, the SCL module can be disabled during the inference time, thus not increasing the computational overhead during inference.We implement all the algorithms using Python 3.8 and Pytorch 1.9.0. All models were trained and tested using an NVIDIA RTX3090 GPU. We used SGD as the optimizer for training. For the object detection models, the initial learning rate was set to 0.01, the momentum was 0.937, the weight decay coefficient was 0.0005, the batch size was 8, and the epoch was 80. For the self-trained classifiers, the learning rate was set to 0.0001, the momentum was 0.9, the batch size was 8, and the epoch was 100.YOLOv5 can be divided into four versions according to the model size, including YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x. The number of parameters, computation and detection performance of these four versions are in increasing order, with YOLOv5s having the lightest network structure and YOLOv5x having the best detection performance. As shown in To further prove the effectiveness of our improvement, the results of the ablation study are shown in The classifier plays an important role in our overall pipeline. The classifier proposed in this paper is ResNeXt50, incorporating a self-supervised learning module. In the experiments, we compared our constructed classifier with several mainstream classifiers, including ResNet and ViT . Figure ViT lacks some of the inductive biases inherent to convolutional neural networks, such as translation equivariance and locality , and theTo help understand and analyze the working principle and decision process of the classifier for Chengdu ma goats proposed in this paper, we generated class activation mapping images using Grad-CAM . As showA review of the literature shows that artificial intelligence technology has been increasingly applied to livestock production in recent years, but limitations still exist. The authors in  explore In this paper, we take the Chengdu ma goat as the research object and discuss the method for Chengdu ma goat recognition in barns. We improve TPH-YOLOv5 to build our detector. Compared with YOLOv5s, with the highest detection efficiency, and YOLOv5x, with the best detection effect in YOLOv5, our detector obtains the best mAP of 83.78% with similar training time. The mAP of our detector even exceeds that of the original TPH-YOLOv5, while our detector needs less computational overhead and a shorter training time. Our experiments demonstrate the impressive and comprehensive performance of our improved detector for detecting Chengdu ma goats in densely packed conditions with large scale variance of targets. Meanwhile, comparing representative classifiers of different architectures, the classifier built by introducing a self-supervised learning module demonstrates effectiveness in improving the classification performance for Chengdu ma goats by learning the structure of the head region when trained on very limited amounts of labeled data.In the head image classification experiments, ViT, representing the transformer architecture, gives poor classification results. With the expansion of the data volume, further research can be attempted to fully exploit its potential learning ability on image features. Our classifier with the self-supervised module works well for classifying specific sheep species and can further learn more generalized features to solve more sheep species recognition problems.In this paper, a Chengdu ma goat image dataset for computer vision tasks is constructed. Precise detection of Chengdu ma goats is challenging due to the dense distribution of the targets in sheep barns and the large differences in body scale in different images. By improving the structure of TPH-YOLOv5 to build a detector and constructing a classifier incorporating a self-supervised module with ResNeXt50 as the backbone network, we propose a general framework for Chengdu ma goat recognition, which provides a new idea for solving Chengdu ma goat recognition problems in barns. The experiments verify the effectiveness of our method. In addition to improving the performance while taking into account the usability of the model, implementing functions such as abnormal behavior detection of livestock will be our subsequent work. As more types and increased quantities of livestock data become available, we will apply our models to a wider range of areas to help develop intelligent technology in animal husbandry. In the near future, automated precision feeding of livestock will become possible with the aid of our method.


The monitoring of nuclear safeguards measurements consists of verifying the coherence between the operator declarations and the corresponding inspector measurements on the same nuclear items. Significant deviations may be present in the data, as consequence of problems with the operator and/or inspector measurement systems. However, they could also be the result of data falsification. In both cases, quantitative analysis and statistical outcomes may be negatively affected by their presence unless robust statistical methods are used. This article aims to investigate the benefits deriving from the introduction of robust procedures in the nuclear safeguards context. In particular, we will introduce a robust estimator for the estimation of the uncertainty components of the measurement error model. The analysis will prove the capacity of robust procedures to limit the bias in simulated and empirical contexts to provide more sounding statistical outcomes. For these reasons, the introduction of robust procedures may represent a step forward in the still ongoing development of reliable uncertainty quantification methods for error variance estimation. The monConcerning this second point, two approaches are available: the bottom-up and the empirical top-down approach . The forIn empirical application, the outcomes of the bottom-up and the empirical top-down approach could significantly diverge. Specifically, bottom-up estimations are often larger than their top-down counterparts . The neeThe presence of outliers in the data may in fact seriously affect the reliability of the estimations. In random effect models, even one single anomalous observation may greatly influence the properties of the statistics . The biaSuitable breakdown point: they have the capacity to handle a consistent proportion of outliers in the data before returning an incorrect result;High efficiency: they provide estimates with desirable properties even when there are no outliers in the data;Closed expression: they do not depend on optimization algorithms and are rather easy to calculate.The aim of this article is to propose an alternative approach for the UQ problem through the introduction of robust estimation of the between and within group variance estimators. The robust analysis is expected to limit the influence of outliers in past data on the outcomes and to improve the quality and the reliability of the statistical outcomes. For this purpose, we will compare in a simulation exercise the accuracy of the classical estimator with respect to one of the many methods available for estimating the variance components of a random effect model. In particular, we focus on the robust estimators proposed by  and baseThanks to these properties, the proposed estimators are extremely flexible, and may represent a step forward in the still ongoing development of reliable UQ methods for error variance estimation .This paper proceeds as follows. In the next section, the measurement error model and the classical estimation of its variance components are introduced. Then, in I) and operator (O) measurements of the item k during inspection j is the following:Given a set of data from past inspections and assuming the same number of measurements in each inspection , a typical model for representing the inspector   2) repreemployed . It is aThe parameters that need to be estimated in both  and 2)  2) are tgiven by :5)σ^ρ2=σ^ρ2=(3)yFinally, it could happen that (a)Anomalous values of the group error(b)Anomalous values of the individual measurement errorClassical estimation of model  through ∞. Following the definitions provided by [block breakdown point for contamination (a) and measurement breakdown point for contamination (b). Actually, [This double nature of contamination is reflected also in a double definition of breakdown point, defined in general as the smallest proportion of outlying observations that can lead to estimated values which tend to 0 or vided by , we willctually,  proposedp-percentile of set In statistical terms, the price to pay when using robust estimators is a loss of efficiency, given that they are in general less efficient than their classical counterparts. However, an accurate choice of the robust method could sensibly mitigate this drawback, allowing to select the robust estimator that offers the most convenient payoff between breakdown point and efficiency. At this proposal, different choices are available for robustly estimating ribed in ,19,20,21ribed in  and the ribed in . Among aribed in  and baseribed in . The defThe reasons that motivated the choice of these particular estimators are the following:Suitable breakdown point: [n point:  proved tpression ), the blHigh asymptotic efficiency: defining the asymptotic efficiency as the ratio between the variances of asymptotic distributions of the robust estimators and those of the classical estimators, [n and n increases.imators,  proved tClosed expression:N and g increase. However, for moderate values of N and g as the ones considered in this article, this does not seem to be an issue. Moreover, In this section, we want to study and compare through a simulation exercise the behavior of the classical and robust variance estimators defined in –7). At . At 7). (i)the total number of observations (ii)g, that in nuclear safeguards context corresponds to the number of calibration or inspection periods;the number of groups (iii)balanced and unbalanced samples. The values of (iv)the ratio between the systematic variance component and the total variance: pression , the val---In order to have an exhaustive view on the main patterns that characterize the estimators in a finite-sample environment, the contaminated and uncontaminated simulations will consider different combinations of settings regarding:(A)contamination of 10% of the random errors (B)contamination of 10% of the short-term systematic errors (C)10% of the random errors Similar to , in eachThe programming environment used for the simulation exercise is MATLAB (release R2021a). The codes for running and replicating our experiment are available upon request. They are described in the Before presenting the results, it is important to notice that condition  may leadN and g increase. Similar patterns characterize the values of both classical and robust estimates, with the only difference that the classical estimates always offer better performances than the robust ones. There are only marginal differences between the MAB in balanced and unbalanced samples, with the former slightly outperforming the latter. The value of N and g: increasing g (for a fixed N) worsens the accuracy of classical and robust estimators, whereas the opposite happens if we increase N (for a fixed g). The estimates of N and g. Finally, it is worth pointing out that N and g. Even in small samples, the estimated efficiency of N and g are small) to 85% . The value of ollowing , in orderoved in , the panConcluding, simulations results on noncontaminated data provided a comprehensive description of the finite-sample properties of classical and robust estimators of variance components. Even though classical estimates resulted more accurate on average, the gap with respect to the respective robust counterparts is limited. Also in terms of efficiency, the choice of the robust alternative does not imply a remarkable loss, especially for  than σρ .Results concerning contamination (B) are displayed in Finally, classical estimates for differently from classical one, robust estimation does not detect any relevant short-term systematic error component.Classical estimates obtained for Coherently to what we observed in the simulations, the presence of outliers in the sample yields an overestimation in the variance components that the robust methods is capable to limit. The The availability of an efficient and unbiased estimation of the uncertainty components in nuclear safety measurements is a fundamental element for a reliable detection of possible data falsification by operators that could mask diversion. Top-down methods for UQ rely fundamentally on modeling measurement data as a one-way random effect model with within and between groups variance components, which are usually estimated through the classical ANOVA expressions. However, the reliability of these estimates could be seriously affected by the presence of outliers in the data.The aim of this article was to introduce the use of robust approaches in the UQ of nuclear safety measurements, and to analyze the general benefits in terms of estimates accuracy and bias reduction. For this purpose, we proposed a robust estimator of the variance components that combines several desirable statistical properties, i.e., suitable breakdown point, high efficiency, closed expression. Simulation results and the empirical application proved that the presence of even on single outlier may significantly affect the reliability of classical estimates. The robust estimators, instead, have the capacity to handle anomalous data, and to remarkably limit their negative effects on the final estimates.Robust statistics offers many other alternative methods for estimating within and between group variance. The promising outcomes obtained through the simulation exercise and the empirical application may be considered as a starting point for a broader investigation aimed to compare different robust estimators for one-way random effect models in simulated and empirical contexts, and to identify the more suitable one. Such analysis will undoubtedly represent a solid contribution to the still ongoing development of reliable UQ methods for error variance estimation.


Adaptive signal processing algorithms play an important role in many practical applications in diverse fields, such as telecommunication, radar, sonar, multimedia, biomedical engineering and noise control. Recently, a group of adaptive filtering algorithms called partial update adaptive algorithms  has gathered considerable attention in both research and practical applications. This paper is a study of the application of PUs to very demanding, structural active noise control (ANC) systems, which are of particular interest due to their ability to provide for a global noise reduction. However, such systems are multichannel, with very high computational power requirements, which may be reduced by the application of partial updates. The paper discusses the modifications necessary to apply PUs in structural ANC systems and the potential computational power savings offered by this application. As a result, leaky versions of the PU LMS algorithms are introduced to the general public. The paper also presents two simulation examples, based on real laboratory setups, confirming high performance of the proposed algorithms. L coefficients requires at least L signal read, L coefficient read and L coefficient store operations [Many applications of adaptive filtering are computationally too expensive to be implemented in real time, even using modern hardware. Examples of such applications include wireless communication systems , radar serations . Other aGenerally, the idea of partial updates can be applied to any iterative descent algorithm, e.g., Recursive Least Squares (RLS). However, in this publication, we will concentrate on the application of partial updates to the family of LMS algorithms. The purpose of this paper is to extend the idea of PU LMS algorithms over the group of LMS algorithms with leakage. To the best of our knowledge, a general analysis of such a combination has not been presented to the general public yet . In tThe idea of partial updates is not new, as the first publications within this category are dated back to 1994 . A good There are two main groups of PU LMS algorithms: time-domain algorithms and transform-domain algorithms. This publication is concerned only with the first group. Time-domain PU LMS algorithms can be further divided into data-independent and data-dependent algorithms. An interesting observation is that while application of a data-independent algorithm always results in performance degradation , the application of a data-dependent algorithm does not necessarily result in such degradation. Moreover, under some circumstances, it can even result in performance improvement . On the Data-dependent PU algorithms analyze the input data to select those filter coefficients, which will result in the best filter improvement after the update. This operation usually involves sorting of the input vector g period ,11.The only disadvantage of data-independent PU LMS algorithms is slower convergence rate; therefore, those algorithms cannot be used in applications where fast convergence is critical. The level of convergence rate decrease depends on the number of an adaptive filter weights excluded from update. The disadvantage of data-dependent algorithms, on the other hand, is the necessity of implementation of the sorting (or finding the maximum). In this case, the number of parameters excluded from the update must be high enough to overcompensate the computational power of the sorting. This will probably be hard to achieve in case of short adaptive filters.Data-independent algorithms:−Periodic partial update LMS algorithm (periodic LMS),−Sequential partial update LMS algorithm ,−Sequential partial update NLMS algorithm ,−Stochastic partial update LMS algorithm (stochastic LMS),Data-dependent algorithms:−M-max partial update LMS algorithm (M-max LMS),−M-max partial update normalized LMS algorithm (M-max NLMS),−Selective partial update  LMS algorithm (selective NLMS),One tap update LMS algorithm (OTU LMS).In this publication, we will focus our attention on the following, popular PU algorithms:several sampling periods. The properties and behavior of this algorithm are very similar to the full update LMS algorithm (except for the convergence rate); therefore, it will not be considered below.The periodic LMS algorithm is probably the most intuitive of all: it updates all the filter coefficients once per The one tap update LMS algorithm is in fact the selective NLMS with the extreme selection of an update of a single filter tap only in every sampling period. It was enumerated separately due to a very important property: instead of sorting the whole input vector, this algorithm is based on finding the maximum absolute value of this vector. As searching for a maximum value is computationally more efficient than sorting , this altap-delayed vector. As mentioned above, this specific structure can be utilized to sort the input vector more efficiently. For details, refer, e.g., to [Before describing each of the above PU LMS algorithms, we will recall the (full update) Leaky LMS (LLMS) algorithm, which is given by :(1)w(n+1lgorithm ) or varie.g., to .The error signal The general equation describing PU LMS algorithms with leakage discussed in this paper, which applies to all but the periodic LMS, can be given as:n-th iteration; the number of the elements in such set is equal to, or less than M. At least one of the sets has the number of elements equal to M, which is the maximum number of filter weights updated in any iteration.The coefficient selection matrix Similarly, the leakage matrix Equation  applies The difference between particular PU LMS algorithms is in the way the elements of the coefficient selection matrix (and the leakage matrix) are selected, as described below.sequentially [The sequential LLMS organizes the filter weights into several subsets, which are then updated entially . The numn matrix  can thanB iterations, all the weights are updated.The Sequential LLMS algorithm operates with a constant step size  NLMS algorithm, the normalization can either be considered as a modification of the LLMS algorithm, with the step size dependent on the energy of the input signal, or may be developed formally as a solution of a constrained optimization problem . RegardlIt may be proved that the sequential LLMS algorithm is permanently unstable (regardless of the step size) for some input signals, e.g., for cyclostationary signals . TherefoPlease note that it is necessary to randomize the subset selection rather than the decision if to update every individual parameter. This assures that the complexity reduction is attained during each sampling period. Please note also that the computational cost of the Stochastic LMS algorithm is slightly higher than in case of the Sequential LMS, as the random selection mechanism requires more time than the sequential selection. For further details, refer to .Provided the simulation time is long and the random selection is uniform, the stochastic LLMS algorithm properties and behavior are similar to the sequential LLMS, with the exception of avoiding instability for some signals. Therefore, the algorithm will not be included in the experiments presented below.M those entries of the input vector M maxima of the elements One of the simplest ideas about the data-dependent PU algorithms could be to select lgorithm , and itsorithm [1ik(n)=1ifThe M-max LLMS algorithm requires ranking (sorting) of the input vector elements, based on their absolute values. Therefore, the time savings this algorithm offers are smaller than in case of the data-independent algorithms. On the other hand, computationally efficient sorting algorithms can be applied to lessen the sorting computational burden .Similarly to the sequential LNLMS, the idea of M-max PU can be applied together with the step size normalization. In this case, the weights selection matrix is defined identically as in the case of M-max LLMS algorithm (Equation ), and thThe M-max LNLMS algorithm has a subtle drawback: it uses the power of the whole input vector to normalize the step size in spite of the fact that only some of its elements are used for the actual weight update. To correct this issue, it is possible to calculate the normalized step size as:derive the selective LLMS algorithm as a result of instantaneous approximation of the Newton’s method, or as an application of the minimum disturbance principle [It is also possible to rinciple . Both thThe difference between the selective LLMS and the M-max LNLMS algorithms may appear to be small, but it will be shown that it results in substantial differences in performances of both the algorithms.n. The maximum absolute value As previously mentioned, the selective LLMS algorithm with the extreme choice of quations  and 17)M=1 will k-th filter coefficient, the algorithm may be expressed as:Remembering that  Douglas , updatesmaxline algorithm [does not increase with the filter length. Of course, calculation of the filter output still requires L multiplications and Considering the shift structure of the input vector, the lgorithm  can be ushown in  that thesortline algorithm can be used to sort this vector. Therefore, the cost of the sorting was assumed to be L for full update and M for PUs).The computational demands of the discussed algorithms are given in refer to , but alsPractical implementation of the leaky PU algorithms is similar to the implementation of other LMS-family algorithms. The architecture of modern microprocessors, especially those which are referred to as “digital signal processors”, is optimized to perform the MAC operation ; therefot, e.g., .As was already mentioned, the data-dependent algorithms require sorting or calculation of a maximum value of a vector. Practical aspects of implementation of such algorithms are discussed in .Noise is one of major civilizational issues and therefore is of great scientific attention . PassiveStructural ANC systems are the systems that apply active control to the noise-emitting structure rather than to an additional loudspeaker ,23. ThesSeveral techniques can be used to lower the computational demand. The most intuitive way is to use shorter adaptive filters, but this attitude may result in poor attenuation results for noises more complicated than sinusoidal signals. Another attitude is to use a computational power saving algorithm. There are a few such algorithms, e.g., the switched error algorithm ; howeverThe first experimental setup consists of an active casing with rigid corners and 1 mm thick aluminum plates, with 420 × 420 mm dimensions, as presented in The above setup consists of 15 actuators and 5 error microphones; thus, the number of secondary paths to consider is 75. The models of those secondary paths were identified in the form of finite impulse response (FIR) filters with 256 parameters. The identification experiments were performed using white noise as an excitation and using the sampling frequency of 2 kHz. In addition, a loudspeaker was placed inside the casing and the primary paths were identified as well, in the same form. Finally, a reference signal path was identified between the loudspeaker and a microphone placed inside the casing.j is the corresponding error microphone number. This reference signal forms the input signal to the control filters—three such filters, i is the actuator number  were used to design a simulation system allowing for rapid testing of adaptation algorithms. A part of the block diagram of this system, associated with one of the casing walls, is presented in All 5 error signals are used by each of the adaptation algorithms, denoted as LMS in Exemplary frequency characteristics of the secondary path TFs from one of the actuators mounted on the front plate to all the five error microphones are illustrated in Both the above mentioned figures suggest that the identified models are accurate and contain a variety of phenomena present in real environment, contrary to some other, simplified models  used in simulations presented in the literature. Moreover, the secondary path TF estimates . It can be noticed that updating only eight parameters results in slightly worse performance, compared to simulations with The second experimental setup uses a commercial washing machine, as presented in Similar to the case of the first experimental setup, impulse responses of primary and secondary paths were identified, each with 256 parameter. There were eight primary paths from the reference microphone to the error sensors, and 104 secondary paths, from the 13 actuators to each of the error sensors. The same sampling frequency of 2 kHz was used. Again, the simulation system was implemented in the Matlab environment with the block diagram similar to this presented in The simulations were performed using a signal recorded during the washing machine spinning cycle, which is the loudest part of each washing. The spectrogram of this signal is presented in The active filter length used in the following simulations was 256. Based on the results obtained with the rigid casing, it was assumed that it will be sufficient to update eight filter parameters in each iteration , which might have given a little advantage to the PU algorithms. Nevertheless, the results obtained with the algorithm that updates only 1 filter weight out of 256 (OTU) are impressive.Finally, The goal of the research presented in this paper was to study usefulness of several partial updated LMS algorithms in a very demanding application to structural ANC. From a practical implementation point of view, overestimated multichannel ANC systems usually require adaptation algorithms with leakage, because leakage allows us to equalize individual actuator control efforts, thus resulting in stable adaptation and higher attenuation levels. Therefore, to allow for application of PU LMS algorithms in such systems, leaky versions of the algorithms were introduced. In case of PU algorithms, the leakage must be implemented in a way that does not ruin the computational power savings. The main contribution of this paper is the presentation of the proper attitude towards solution of this problem, and the presentation of the resulting leaky PU LMS algorithms . The computational power savings offered by these algorithms with the respect to the full update LLMS algorithm are also presented and discussed.To test the proposed algorithms in a rapid development environment, two simulation systems were constructed, based on the structural ANC real laboratory setups. The first system was based on an active casing with rigid walls; five panels were controlled by three actuators on each wall. The second was based on a commercial washing machine placed close to the laboratory room wall, so four washing machine walls were controlled with a total of 13 actuators. Both the systems were multichannel, with 75 and 104 secondary paths, respectively. Contrary to many other publications, the secondary path estimates used in these simulations were not perfect. The simulated noises included two sinusoids embedded in noise and a real recording of a washing machine spinning cycle.The simulations confirmed that the proposed leaky PU algorithms can be used in structural ANC systems. All the leaky PU LMS algorithms tested were capable of providing between 8 and 40 dB of attenuation. It must be emphasized that in case of the simulated structural ANC system, the attenuation effect is global and is not concerned only with the error microphone location. Therefore, the obtained results are satisfactory and prove the leaky PU LMS algorithms have a high potential for real application.The main disadvantage of PU LMS algorithms is possible degradation of the convergence speed, which is an inherent feature of all data-independent PUs. Therefore, data-independent algorithms should be used with caution when fast convergence is important. Data-dependent algorithms, on the other hand, do not result in the convergence speed decrease, but require the implementation of sorting of the input vector. This in turn lowers the computational power savings these algorithms offer. An interesting compromise may be the leaky One Tap Update algorithm, which instead of sorting utilizes maximum values searching—the algorithms that can be implemented in a very optimal way.


In this paper, machine learning models for an effective estimation of soil moisture content using a microwave short-range and wideband radar sensor are proposed. The soil moisture is measured as the volumetric water content using a short-range off-the-shelf radar sensor operating at 3–10 GHz. The radar captures the reflected signals that are post processed to determine the soil moisture which is mapped to the input features extracted from the reflected signals for the training of the machine learning models. In addition, the results are compared and analyzed with a contact-based Vernier soil sensor. Different machine learning models trained using neural network, support vector machine, linear regression and k-nearest neighbor are evaluated and presented in this work. The efficiency of the model is computed using root mean square error, co-efficient of determination and mean absolute error. The RMSE and MAE values of KNN, SVM and Linear Regression are 11.51 and 9.27, 15.20 and 12.74, 3.94 and 3.54, respectively. It is observed that the neural network gives the best results with an R2 value of 0.9894. This research work has been carried out with an intention to develop cost-effective solutions for common users such as agriculturists to monitor the soil moisture conditions with improved accuracy. The estimation of soil moisture has gained significant interest amongst researchers recently. The different applications where moisture detection is crucial are hydro-meteorological, agricultural research and climate change . It is eRemote sensing eliminates the disadvantage of the previous method and is the process in which the physical characteristics of an area are detected and monitored from a certain distance above the ground. Microwaves are electromagnetic radiations with frequencies in the range of 300 MHz–300 GHz and are commonly used in remote sensing due to its long wavelength and easy penetration capabilities through different materials. The infiltration reduces with the rise in the amount of moisture content in the soil and vegetation. Additionally, soil moisture can be determined at any time and in almost all weather and environmental conditions ,5,6.Microwave remote sensing can be classified into active and passive microwave remote sensing . First, In the remote sensing community, the machine learning methods for the estimation of earth system parameters has recently received much interest . MachineHowever, using machine learning algorithms on data collected with a short-range radar opens the possibility of new and effective applications with accurate results, and this has been explored in this research work. The short-range radars can be easily mounted onto vehicles, agricultural machinery, and drones. The soil moisture can be determined for a small piece of land rather than over a large area. Even though there are few methods  where caIn this paper, accurate soil moisture is determined using different machine learning models with a short-range and wideband radar sensor operating in a range of 3–10 GHz. The efficiency of the models is computed using root mean square error, co-efficient of determination and mean absolute error. The paper is organized into three different sections. This section introduces the system setup and methods for measuring the soil moisture using the wideband microwave radar.We have selected an off-the-shelf Walabot radar sensor that operates at 3.3–10.3 GHz frequency . This raThe soil moisture is the amount of water present in the soil and can be represented as Volumetric Water Content (VWC), the soil  as givenThe maximum value of volumetric water content in a soil can be 45%, beyond which there are no more air pockets to hold moisture and this soil is referred to as saturated . AdditioA two-step approach is adopted to determine the volumetric water content. First, using dielectric constant, and then using a regression-based model. In the first process, perature . Thus, t−4 mm to 70 mm   found inThe short-range image profile is selected to quantify the dielectric constant of the soil sample. For the estimation of the dielectric constant of soil, no filter is required, and the arena settings are set based on the dimensions of the sample holder. The sample is placed above the microwave absorbers to minimize the reflection from the surroundings. The Walabot radar first captures the reflected signals of the metal reference which is an aluminum plate placed on top of the empty Styrofoam plate. The next step is to capture the reflected signals of the soil sample.Metal reflects almost all the signals that falls on it, so this serves as the reference and is then compared with the reflected signal from the soil as shown in The reflected signal values of the metal reference and the sample, as shown in In the second process, the volumetric water content values determined in the first process are input to the machine learning algorithms for training. Different machine learning algorithms like neural network, support vector machine, linear regression and k-nearest neighbor are implemented. The regression-based model fitting is used to find the relationship between the response variable against input variables. The response variable being volumetric moisture and the input variables being mean, standard deviation, mean of the instantaneous phase, skewness, mean absolute deviation, co-efficient of variance and kurtosis. Out of these input variables standard deviation, mean of the instantaneous phase, mean absolute deviation and kurtosis were chosen as input features. The data collected with the help of the experiment is used to train a model which is able to predict the soil moisture content accurately without any prior knowledge on the soil properties.In this experiment, the VWC values are varied by gradually adding water to the soil sample to increase the VWC. The VWC of the soil is also measured using the Vernier soil moisture sensor and the values are displayed on the LabQuest2 interface. Few statistical parameters are extracted from the reflected signals. In this paper, the moisture values measured for the soil samples are −1.5% (dry soil), 2.2%, 19%, 25% and 37%, respectively. For every VWC value, 3 to 4 different readings are obtained and used for the purpose of training machine learning model.The dielectric constant has been found using the procedure explained in To test the validity of this method, the same procedure above is repeated for the beach soil taken from the East Coast Park (ECP) of Singapore which falls under Kallang formation . Table 2The data set is used to train three different models, namely K Nearest Neighbor (KNN), Linear Regression and Support Vector Machine (SVM) in Python. Mean absolute error (MAE) and root mean square error (RMSE) are used to compare the VWC values that are predicted by the three models to demonstrate their efficiency. The measured vs. the predicted values of the three different models are shown in 2 value equal to 1 represents that the model perfectly fits the data.In 2 values obtained for KNN, SVM and Linear Regression models are 0.41, −0.02, and 0.93, respectively. Since the amplitude of the reflected signals decreases as the soil moisture increases, there is a linear relationship between the reflected signals and the soil moisture. KNN and SVM are the classification algorithms, so they work better if the number of target values is lesser (two or three) [2 are chosen since the final output is a number and they are used for regression algorithms. These metrics were also used to compare the machine learning algorithms in [The Rr three) . Here, tr three) . Also, aithms in  for soilithms in  to compa2 of 0.93. However, when a neural network model is trained using MATLAB, it outperforms the other models and achieves the best results with an R2 value of 0.9894, while predicting the soil moisture values as very close to the actual values. This research work can further be expanded in the future by exploring different windowing techniques, different soil types and by training the model with different features which could potentially improve the accuracy of the results. After improving the model’s accuracy, this sensor can be installed on the vehicles in the agricultural field to measure the soil moisture content.Machine learning models for an effective estimation of soil moisture content using a microwave short-range and wideband radar sensor have been presented. The soil moisture is measured as the volumetric water content using short-range off-the-shelf radar sensor operating at 3–10 GHz. The rectangular window has been applied to the reflected signals from both the metal and the soil. Thus, the dielectric constant of the soil has been found out for the frequency range of 3–10 GHz. The experiments have been carried out by varying the moisture content gradually to determine the dielectric constant and in turn, the volumetric water content to collect the data for the training of the models. The results from different models, namely KNN, SVM, linear regression and neural networks are analyzed with statistical parameters from the reflected signals serving as input features and volumetric water content as the output for the purpose of training. Comparison results show that linear regression is superior to the other two models with an R