The automated detection of the beginning and ending of nest-building behavior in sows might support the implementation of new management strategies in farrowing pens, i.e., temporary sow confinement in crates. This might improve sows’ welfare and reduce piglet mortality by limiting sow confinement in crates to only a critical period of piglets’ lives. The objective of this study was to predict farrowing with computer vision techniques to optimize the timing of sow confinement. In this study, we developed a computer-vision-based method for the automated detection of the beginning and ending of nest-building behavior in sows. The study included 71 Austrian Large White and Landrace × Large White crossbred sows and four types of farrowing pens. The beginning of nest-building behavior was detected with a median of 12 h 51 min and ending with a median of 2 h 38 min before the beginning of farrowing. It was possible to predict farrowing for 29 out of 44 animals. The developed method could be applied to warn the farmer when nest-building behavior starts and then to confine the sow in a crate when the end of nest-building behavior is detected. This could reduce labor costs otherwise required for the regular control of sows in farrowing compartments.The adoption of temporary sow confinement could improve animal welfare during farrowing for both the sow and the piglets. An important challenge related to the implementation of temporary sow confinement is the optimal timing of confinement in crates, considering sow welfare and piglet survival. The objective of this study was to predict farrowing with computer vision techniques to optimize the timing of sow confinement. In total, 71 Austrian Large White and Landrace × Large White crossbred sows and four types of farrowing pens were included in the observational study. We applied computer vision model You Only Look Once X to detect sow locations, the calculated activity level of sows based on detected locations and detected changes in sow activity trends with Kalman filtering and the fixed interval smoothing algorithm. The results indicated the beginning of nest-building behavior with a median of 12 h 51 min and ending with a median of 2 h 38 min before the beginning of farrowing with the YOLOX-large object detection model. It was possible to predict farrowing for 29 out of 44 sows. The developed method could reduce labor costs otherwise required for the regular control of sows in farrowing compartments. In the societies of European Union (EU) member states, there is a growing concern over the welfare of farm animals. Specifically, phasing out and, finally, the prohibition of the use of cage systems in the EU was proposed in the Citizens’ Initiative “End the Cage Age”, which was signed by over 1.4 million EU citizens and supported by the European Commission  and to aAn important challenge related to the implementation of such a management system is the optimal timing of sow confinement in crates, considering sow welfare and piglet survival. Compared to free farrowing, temporary confinement appears to be beneficial for reducing piglet mortality, but confinement before farrowing prevents nest-building behavior. One of the possibilities is to confine sows in crates after the nest-building behavior has finished but before farrowing starts . HoweverPrecision Livestock Farming (PLF) has the potential to automatize the monitoring of sow behavior in farrowing pens and to indicate the right timing of sow confinement in crates, i.e., at the end of nest-building behavior . To date2 > 0.7) to activity measurement based on an ear-tag accelerometer and a gold standard method, i.e., human labeling. Thus, as a first step in the development of computer vision methods for farrowing prediction, we aim to test if a similar performance of farrowing prediction—as with other sensor technology, i.e., ear-tag accelerometer [Cameras and computer vision have not been applied so far for the automated prediction of farrowing. This technology potentially has multiple advantages over the other sensors that have been used so far for farrowing prediction, i.e., it can be used to monitor animal behavior in a non-invasive way, the whole body of the animal can be monitored, multiple pens can be monitored with one camera sensor, and complex animal behavior can be automatically detected e.g., aggressive behavior ,12. Moreerometer —can be aIn this study, we aim to select the optimal method of the You Only Look Once X (YOLOX) object detection algorithm, i.e., nano, tiny, small, medium, large, or extra large, for sow activity monitoring in real-time. In 2022, YOLOX is a state-of-the-art object detector, designed for real-time applications which surpass YOLOv3  in termsProject PIGwatch was authorized by the Ethical Committee of the Austrian Federal Ministry of Science, Research and Economy and by the Ethical Committee of Vetmeduni Vienna (GZ: BMWFV-68.205/0082-WF/II/3b/2014) according to the Austrian Tierversuchsgesetz 2012, BGBl. I Nr. 114/2012.The observation was conducted in 2 stages at Medau, the pig research and teaching farm (VetFarm) of the University of Veterinary Medicine Vienna, Vienna, Austria. The first dataset was collected between June 2014 and May 2016, while the second one between December 2021 and July 2022. In total, 71 Austrian Large White sows and Landrace × Large White crossbred sows were included in the trials. The parity of animals included in the study ranged from 1 to 8, were between 1 and 4 years of age, and weighed from 198 to 384 kg. The sows were kept in four types of farrowing pens with the possibility of keeping a sow either unconfined or in a farrowing crate. Out of 71 sows, 9 were kept in SWAP (Sow Welfare and Piglet Protection) pens , 9 in trapezoid pens , 9 in wing pens , and 44 in BeFree pens . None of the animals included in the observations were confined in a farrowing crate from the introduction to the farrowing pen until the end of farrowing.2. The pens had a solid concrete floor in the front (lying area) and a slatted cast iron floor in the back (defecation area). The pen had 2 troughs, one for the crated and one non-crated sow , which could vary from 105 to 125 days [The behavior of sows was video-recorded from the introduction to farrowing pens until the end of farrowing with two-dimensional (2D) cameras in order to create a dataset that could be labeled. Each pen in the first dataset  was equipped with one IP camera  locked in protective housing  hanging 3 m above the pen, giving an overhead view. In dataset 2, each IP camera  was installed with a top view of 2 farrowing pens (BeFree). Additionally, above each farrowing pen in both datasets, infrared spotlights  were installed in order to allow for night recording. The images were recorded with a 1280 × 720 pixel resolution in MPEG-4 format, at 30 fps for dataset 1, while at 25 fps for dataset 2.The cameras used for recording the first dataset  were connected to a PC on which the Multicam Surveillance System  was installed. The system allowed for the simultaneous recording of images from 9 cameras. The PC had a processor Intel5, CPU 3330, 3 GHz  with 4 GB of physical memory. The operating system was Microsoft Windows 7 Professional . The first dataset was stored on exchangeable, external 2 and 3 TB hard drives. The cameras used for recording the second dataset (BeFree pens) were connected to a server for the storage of video data  with 4 cores, 8 GB memory, and 260 TB storage.The dataset was composed of video recordings which were collected in a period from the introduction to the farrowing pen until 24 h after the end of farrowing. Dataset 1—which contained recordings of 27 sows in SWAP, trapezoid, and wing pens—was used for the training of YOLOX object detection models and contained 4667 h of video recordings. Dataset 2, which contained recordings of 44 sows in BeFree pens, was used for the testing of YOLOX object detection models, the calculation of activity of sows, and the implementation of KALMSMO farrowing prediction models. It contained 17,713 h of video recordings. The division of recorded videos on dataset 1 and 2 was motivated by the first objective of this study, i.e., to test the performance of YOLOX methods on unseen farrowing pens and animals. This dataset division allowed for the simulation of the expected performance of the models when implemented in a new environment. We could expect a similar division of datasets used for the practical implementation of PLF models, where the initial dataset is collected within a certain period of time, in a limited number of farm environments or pens, i.e., SWAP, trapezoid, and wing pens in our study. On this dataset, the models are trained for a particular purpose, such as the detection of sow locations. Then, the trained models are implemented in a new, previously unseen environment, i.e., dataset 2 in our study. In our study, the breed of sows and sow management, including feeding, were the same between both datasets. However, the type of farrowing pen and climate conditions differed between both datasets as they were collected in different sections, i.e., testing unit and the production unit of the farm.To create a reference dataset on the basis of which further data analysis could be performed, we labeled the time of the beginning of farrowing of each individual sow (n = 71). It was defined as the point in time when the body of the first born piglet dropped on the floor. The time of birth of the last piglet indicated the end of farrowing. Labeling software Interact  was used to label the beginning and ending of farrowing in dataset 1. For labeling dataset 2, we used labeling software Boris (version 7.9.15) .To label the images, which could be later used for training and validation of the object detection model, i.e., YOLOX, we selected the frames from dataset 1 and 2 which contained the most relevant information on sows. This general approach allows for the most efficient use of computational resources for the training of the object detection algorithm, but also reduces the heavy workload related to the manual labeling of objects in the images by human labelers .Frame selection was performed according to the procedure described in Oczak et al. . For theOne object class was labeled by a trained human labeler on each frame out of the selected 14,242 frames, i.e., body of the sows . The ComThe frame selection was performed similarly as for dataset 1 with the same k-means algorithm. However, we selected 500 frames from all the videos recorded for all sows in dataset 2—recorded within a period from introduction to the farrowing pen until one day after farrowing. Because in dataset 2 there were 2 sows under one camera view d, the nuThe election of a lower number of frames in dataset 2 (1000) than in dataset 1  was motivated by the reduction in the manual workload needed for labeling the frames. The human labeling time was 1 day in dataset 2. As dataset 1 already contained 3 types of farrowing pens and a relatively large number of frames with animals, we assumed that for validating the models and re-training on a fourth environment from dataset 2, a lower number of images was needed.In dataset 2, one object class was labeled on each frame out of the selected 1000 frames, i.e., body of the sows . The COCYOLOX is an updated version of the YOLO model. The updates include switching the detector to an anchor-free manner, the application of a decoupled head, and the leading label assignment strategy SimOTA. These updates allowed for state-of-the-art results across a large-scale range of models . In our The OpenMMLab toolbox was used to train, validate, and test the methods of YOLOX, i.e., nano, tiny, small, medium, large, and extra large . OpenMMLParametrization of the YOLOX methods was used as implemented in MMDetection, i.e., optimizer stochastic gradient descent (SDG), with learning rate 0.01 and momentum 0.9. Similar images were augmented as implemented in MMDetection with mosaic, random affine, mixup, random horizontal flip and color jitter. No changes were made to the architecture of YOLOX methods, optimizer, or augmentations provided in MMDetection. Python version 3.8 was used with MMDetection.We designed 2 experiments to test the performance of methods of the YOLOX algorithm  in termsTraining was set to 100 epochs and was carried out on RTX Titan  on a server with 24 Core CPU AMD 3.2 GHz and 256 GB RAM with an evaluation of validation and test set performances on every 5 epochs. Performance of the models was evaluated with standard 12 COCO evaluation metrics, e.g., Average Precision (AP) and Average Recall (AR) . The optThe two best performing models, one from experiment 1 and a second one from experiment 2, were used to extract sow locations in the videos recorded during the sow observational period in BeFree pens. Bounding boxes indicating sow locations were extracted in 1 fps (60 fpm) out of the videos recorded in 25 fps; later, these data were down-sampled to 40 fpm, 20 fpm, 5 fpm, 1 fpm, 12 fph, and 4 fph. In the next step, the Euclidean distance was calculated between centroids of extracted bounding boxes on the range of frame rates from 1 fps to 4 fph, as described in Oczak et al. . The ranThe Euclidean distance was further smoothed with a mean calculated on a sliding window of 24 h with 15 min steps, similar to the research of Oczak et al.  where thTo estimate the dynamics of activity of sows, the Kalman filtering and fixed interval smoothing (KALMSMO) algorithm was used, as described in Oczak et al. , with thThe impact of the frame rate of video data (1 fps to 4 fph) on the performance of farrowing prediction was estimated by an analysis of proportion of “first-stage” alarms generated within 48 h before the beginning of farrowing. The number of sows for which no “first-stage” alarms were raised was also considered. Similarly, the impact of frame rate on the proportion of “second-stage” alarms raised after the “first-stage” alarm and not later than the end of farrowing was analyzed in the context of the performance of farrowing prediction.Analysis was performed with a commercial software package  and function irwsm of CAPTAIN toolbox  was usedResults of both experiment 1 and 2 revealed, as could be expected, that more complex models of YOLOX  had better AP in both validation sets and test sets . Higher The performance of models in experiment 1 was generally worse than in experiment 2. In experiment 1, in the test set after 100 epochs of training, the AP of most methods of YOLOX was higher than 80, while in experiment 2, it was higher than 90. In The best model for the detection of sows in farrowing pens in unseen environments, i.e., BeFree pens, was YOLOX-medium, which had the highest AP of 84.2 on the test set in comparison to the other models. In experiment 2, the model with the highest AP of 95.4 on the seen environment was YOLOX-large. Thus, for the extraction of centroids of sows, these two models were used. YOLOX-medium trained for 70 epochs and YOLOX-large trained for 100 epochs.The speed of inference of YOLOX models ranged from 21 fps with YOLOX-extra large to 42 fps with YOLOX-nano on recorded videos. With a maximum frame rate of 1 fps used in this study, even with the most complex YOLOX-extra large model, it would be possible to monitor the whole farrowing production unit at VetFarm Medau, with one RTX Titan (20 pens) installed on a modern server.The results of farrowing prediction with the KALMSMO models on the highest sampled frame rate of videos (1 fps) indicated the “first-stage” alarms with a median of 10 h 46 min and 12 h 51 min based on centroids of sows extracted with YOLOX-medium and YOLOX-large, respectively . “First-Similarly, “second-stage” alarms were raised slightly later based on the application of the YOLOX-medium model trained in experiment 1 with a median very near to the beginning of farrowing at 2 h 17 min in comparison to 2 h 38 min based on the YOLOX-large trained in experiment 2. The other metrics of distribution of “second-stage” alarms represented a similar difference in timing between both models, i.e., first and third quartiles . These rHowever, the analysis also revealed that the “first-stage” alarms were not raised for 20 out of 44 (45%) sows when predictions of farrowing were based on centroids extracted with YOLOX-medium trained in experiment 1 . This reAnalysis of the frame-rate impact on the performance of farrowing prediction showed that with the highest frame rate of 1 fps, we obtained the highest number of alarms for a model trained in experiment 1, with “first-stage” alarms raised for 29 animals out of 44 (66%) in the period of 48 h before the beginning of farrowing . With thThe highest number of true positive alarms was achieved with a frame rate of at least 20 fpm. With this frame rate, either the best performance was reached (model trained in experiment 1) or the performance increased only slightly with a further increase in the frame rate (model trained in experiment 1). Generally, a lower number of true positive alarms with lower frame rates of videos was related to increased variability in the calculated activity of sows. With lower frame rates, an increase in activity related to nest-building behavior and approaching farrowing became less distinguishable from normal activity patterns. This was visualized in For all tested frame rates and both object detection models, there was only one sow (2%) for which a “first-stage” false alarm was raised, i.e., earlier than 48 h before the beginning of farrowing.Making PLF algorithms work in real-life conditions is a hard job that all developers will experience before successfully implementing a reliable and accurate real-time monitoring system . Before In our former research, we applied the RetinaNet object detection model to detect sows in three types of farrowing pens . We deci50, tails with 78 AP50, legs with 75 AP50 and udder with 66 AP50. The performance of YOLOX-large used in our study was better in detecting the whole bodies of sows, with a perfect result of 100 AP50.In the study of Küster et al. , YOLOv3 50 of 99.9 was achieved but the authors applied post-processing on the results of object detection, i.e., the removal of false positives and removal of detections with a probability lower than 0.5. It is not clear from the article of van der Zande et al. [50 metric. Nevertheless, such post-processing was not applied in our study and YOLOX had a perfect performance in detecting sows with 100 AP50.YOLOv3 was also used in the study of van der Zande et al. . Howevere et al.  whether These two comparisons of performance of YOLOX with YOLOv3 confirmed that the updates to the YOLOv3 made by the authors of YOLOX, such as switching the detector to an anchor-free manner or application of a decoupled head, improved the performance of YOLOv3 not only on reference dataset MSCOCO test-dev (51.2 AP and 42.4 AP) but also on our dataset with sows in farrowing pens. Moreover, YOLOX was also faster than YOLOv3 on MSCOCO test-dev  by 12.3 In both studies, the first of Küster et al.  and the The analysis of this impact revealed that 11.2 lower AP resulted in 20 sows out of 44 (45%) without the “first-stage” alarm, which is 7 additional sows to the 13 sows that already did not get the “first-stage” alarm when a YOLOX model trained on BeFree pens was used. This means that we can expect approximately a 50% increase in the number of sows without the “first-stage” alarm if we do not train an object detection model with a dataset collected on additional farms. The percentage of sows without the “first-stage” alarm could possibly be even higher on unseen farms and in unseen farrowing pens as additional factors such as different body color of sows of the other breeds could negatively affect the performance of object detection models. To our knowledge, it is the first time that such an analysis has been carried out. In the other studies, the training and validation datasets had the same type of farrowing pens. In addition, computer vision had not been used; instead, the other sensor technologies were utilized ,9,10.The reason for the increased number of sows without the “first-stage” alarm was that the YOLOX model that was not trained on BeFree pens in some of the images falsely detected parts of the farrowing pens as sows. This increased the variability in estimated activity of sows as the Euclidean distance was calculated between correctly detected sow centroids and centroids of false detections in consecutive frames. A similar effect of increased variability in the estimated activity level of sows on the decrease in performance of farrowing prediction can be observed in our study with lower frame rates than 20 fpm .A promising approach to improve the performance of object detection models and further farrowing predictions on additional farms and farrowing pens is to generate synthetic datasets with, e.g., farrowing pens or other breeds of sows and train the models on these synthetic datasets. In this way, we can potentially generate training examples in a controllable and customizable manner with various light conditions or camera perspectives and avoid the challenges of collecting and labeling data in the real world. A rendering engine requires computer time rather than human time to generate examples. It has perfect information about the scenes it renders, making it possible to bypass the time and cost of human labeling .In our current study, we focused on the detection of the whole sow bodies for activity estimation. We decided to not focus on the detection of parts of sow bodies because in our previous study , detectiThe performance of the farrowing prediction model KALMSO applied for the first time on ear-tag accelerometer data in Oczak et al.  was confIn summary, the KALMSMO algorithm seems to be robust when applied for the purpose of farrowing prediction. The performance of the model is similar when applied for estimation of changes in trends in sow activity independently of two sensor types, i.e., ear-tag accelerometer or a 2D camera. However, for a better performance, the object detection algorithm which is necessary for calculating the activity of sows must be trained on images recorded in the farrowing pens where the farrowing prediction system will be implemented.The proposed practical application of the “two stage” alarms for farrowing prediction was described in detail in Oczak et al.  in the cAccording to the first objective defined for this study, we selected the optimal methods of YOLOX for the implementation of the farrowing prediction algorithm at VetFarm Medau and the other farms. In the next stage of our research, we intend to implement the YOLOX-large trained in experiment 2 of this study for the estimation of location of sows at VetFarm Medau. With 20 farrowing pens in the farrowing unit of the farm and 1 fps frame rate of videos, our server with 24 Core CPU AMD 3.2 GHz, 256 GB RAM, and 2× Nvidia RTX3090 24GB should be sufficient for real-time farrowing prediction. Data on the activity of sows and alarms on approaching farrowing will be stored in InfluxDB time series database . A Grafana  dashboard will be used for data visualization. InfluxDB with Grafana was previously used by Kupfer et al.  for the To implement the farrowing prediction methodology on farms other than VetFarm Medau, we recommend the application of YOLOX-medium trained in experiment 2 of our study for 70 epochs. This model seemed to generalize better than the other models on new, unseen farrowing pens and it was trained on all four types of farrowing pens available in our dataset. The presence of all farrowing pens in the training set might improve the performance of the model in new, unseen environments.The optimal YOLOX method for the implementation of the farrowing prediction algorithm at the research farm on which the dataset was collected in this study was the YOLOX-large. For implementation of the farrowing prediction methodology on farms other than the research farm, we recommend the application of YOLOX-medium trained in experiment 2 of our study for 70 epochs. The KALMSMO algorithm confirmed the results of farrowing prediction obtained in the previous study on ear-tag accelerometer data, considering that the object detection model had to be trained on the farrowing pens present in the validation dataset. The developed method could be applied to warn the farmer when nest-building behavior starts and then to confine the sow in a crate when the end of nest-building behavior is detected. This could reduce labor costs otherwise required for the regular control of sows in farrowing compartments.


The COVID-19 pandemic has led to unprecedented social and mobility restrictions on a global scale. Since its start in the spring of 2020, numerous scientific papers have been published on the characteristics of the virus, and the healthcare, economic and social consequences of the pandemic. However, in-depth analyses of the evolution of single coronavirus outbreaks have been rarely reported.In this paper, we analyze the main properties of all the tracked COVID-19 outbreaks in the Valencian Region between September and December of 2020. Our analysis includes the evaluation of the origin, dynamic evolution, duration, and spatial distribution of the outbreaks.We find that the duration of the outbreaks follows a power-law distribution: most outbreaks are controlled within 2 weeks of their onset, and only a few last more than 2 months. We do not identify any significant differences in the outbreak properties with respect to the geographical location across the entire region. Finally, we also determine the cluster size distribution of each infection origin through a Bayesian statistical model.We hope that our work will assist in optimizing and planning the resource assignment for future pandemic tracking efforts. Since March of 2020, the COVID-19 pandemic has put our society under tremendous pressure on a global scale, revealing vulnerabilities and pre-existing structural limitations in the public administrations and healthcare systems of most countries in the world. Unprecedented amounts of socio-sanitary and mobility data were made available to scientists, government officials, and decision-makers to inform and support their policy-making efforts . The gloDespite this wealth of COVID-19 publications, there are few reports about the characteristics and evolution of individual SARS-CoV-2 outbreaks within a wide region over a sustained time period. Most previous work on infection clusters has analyzed the evolution and characteristics of a single COVID-19 cluster within a social group –26. HoweIn this paper, we focus on analyzing the origin, duration, spatial distribution, and temporal evolution of all tracked COVID-19 outbreaks in the Valencian Community of Spain for a period of 16 weeks between September 15th and December 29th, 2020. To the best of our knowledge, this is the longest study of SARS-CoV-2 outbreaks to date. The main research questions (RQ) that we address in our work are:RQ1: What is the dynamic evolution and duration of all the tracked COVID-19 outbreaks within the Valencian Region of Spain?;(1) RQ2: What is the predominant origin of such outbreaks?;(2) RQ3: Are there any differences in the outbreak characteristics among the 24 health departments in the region?;(3) RQ4: What mathematical function best describes the relationship between the outbreak duration and frequency?(4) RQ5: Can the cluster size distribution of each infection origin be modeled?(5) The paper is structured as follows: Next, we summarize the most related previous work. Section 2 describes the data used to model the COVID-19 outbreaks. The main results of our work are presented in Section 3, followed by our conclusions and lines of future research.In this section, we describe the most relevant published works that study the evolution of individual COVID-19 outbreaks within a region or country.Several contributions describe the number of cumulative cases of COVID-19 outbreaks resulting from the celebration of public events, such as religious gatherings , or outbAdditionally, the clusters' size and their relationship with mortality rates in hospitals and other facilities in Japan have been analyzed in . In 32)32), the Some published works detail the size distribution of individual clusters at local, regional and even national levels. However, some of these contributions present aggregated data  that areThe overall duration of individual transmission chains in a small number of outbreaks is reported in a few publications , 39, 41 Mathematical tools can shed light on the intrinsic characteristics and dynamics of COVID-19 outbreaks. Several mechanistic models studying outbreak dynamics were already available before the COVID-19 pandemic, such as theoretical work based on stochastic Markov chain modeling for isolated populations . New modvia Bayesian statistics to better understand their dynamics and if there are disparities among the COVID-19 outbreaks with different infection sources.Given all previously reported related work, the main contributions of this paper are three-fold. First, we study the main characteristics—namely duration, dynamics, origin source, and relation with other public health data—of 3,365 individual COVID-19 clusters tracked over 3.5 months in the Valencian Region of Spain. Second, we assess the mathematical properties of the cluster size distribution and third, we model the temporal evolution of the COVID-19 clusters The dataset analyzed in this paper consists of the temporal evolution of 3,365 COVID-19 outbreaks detected in the Valencian Autonomous Community or Region of Spain during the period of September 15th till December 29th, 2020. This region is the fourth most populous autonomous community in Spain after Andalusia, Catalonia, and Madrid, with more than five million inhabitants. Its capital, Valencia, is the third-largest city and metropolitan area in Spain. It is located along the Mediterranean coast on the East of Spain. . The Valencian Community consists of three administrative provinces: Castellón, Valencia, and Alicante. Their official name in Valencian language is Castelló, València, and Alacant. From a public health perspective, the Valencian Community is divided into 24 health departments (HD), which are the geographic areas served by a major hospital, as displayed in The outbreak dataset comprises 16 weeks of outbreak information in the three provinces, 24 health departments and 230 municipalities of the region. It classifies the outbreaks in six different types depending on their origin: educational center, healthcare center, nursing home, vulnerable collectives , workplace, and social origin. The following variables are associated with each outbreak: outbreak identifier, outbreak origin, detection week, health department, municipality, province, and the number of diagnosed and suspected COVID-19 cases each week after the start of the outbreak.This dataset was shared with the authors by the Public Policy and Analysis Directorate within the Presidency of the Valencian Regional Government, by virtue of a collaboration agreement between the Valencian Government and the authors in the context of the Data Science against COVID-19 taskforce which was established in March of 2020 and where the authors were members of. All the data is fully anonymized and in compliance with existing data protection regulations. The data sharing was approved by the Government's Data and Privacy Protection Officer. See also  for otheNext, we briefly enumerate the non-pharmaceutical interventions (NPIs) adopted in the Valencian Region during this period. We indicate the level of intensity of each applied NPI according to the COVID-19 Government Response Tracker : School We first pre-processed the data to amend typographic mistakes and other sources of noise. For example, the number of new positive and suspected cases was wrongly annotated as the cumulative number was provided instead of the new positive and suspected cases. We transformed the three location variables  and the outbreak origin into factors. We excluded the outbreaks labeled with the origin “other”, which corresponded to 22 outbreaks of the total number of 3,387.Mean (standard deviation) and median  values are reported in the case of numerical variables and relative and absolute frequencies in the case of categorical ones. We complement these basic figures with a variety of descriptive graphs, such as boxplots and scatterplots. The geospatial distribution of the outbreaks is depicted in choropleth maps of the Valencian Community. Statistical modeling of the evolution in number of cases of the outbreaks is performed using Bayesian negative binomial models with a monotonic effect for the week variable. Our models include each specific outbreak as a random factor with both a random intercept and a random slope for the week variable. The monotonic effect for the week variable is parameterized as introduced by , followii is a simplex , D is the number of unique values of the predictor minus one and b takes any real value and sets the global scale of the effect of the predictor on the response variable. x is the monotonic predictor (week) with n different observations. This method was proposed for modeling ordinal predictors in situations where their effects are assumed to be monotonic. Such models prevent an incorrect treatment of ordinal variables as nominal and avoid to overestimate the information provided by the variables. In the case of the COVID-19 pandemic, they have been used to estimate unreported COVID-19 deaths in the United States  value using 10-fold cross-validation. All statistical analyses have been performed using R (version 4.0.1) and the brms (version 2.16.3) and clickR (version 0.8.0) R packages.RQ1 to RQ3. Next, we tackle RQ4 and RQ5 and model the characteristics of the outbreaks to shed light on their growth and the role that they play in the evolution of the pandemic.In this section, we describe the main results of our analysis. We first present a general descriptive analysis of the data, followed by a temporal and spatial description of the outbreaks to address We analyze 3,365 tracked COVID-19 outbreaks in the Valencian Community of Spain. A heatmap visualization of the distribution of weekly new positive cases per outbreak is shown in Geographically, almost two-thirds of the outbreaks (65%) were detected in the province of Valencia, 25% in the province of Alicante and roughly 10% in the province of Castellón. Note that the relative population size of these provinces is: Valencia (51%), Alicante (38%), and Castellón (11%). Thus, there was a larger presence of outbreaks in the province of Valencia than what one would have expected given its population. The areas without any confirmed coronavirus infections correspond to sparsely populated, rural municipalities in the interior of the Valencian Region. This is not surprising since most of these villages and small towns are located within forestry and hilly areas. The municipalities with the largest number of outbreaks per capita correspond to the three largest metropolitan areas in the region, namely Castellón (39°59'N 0°2'W), Valencia (39°28'N 0°22'W), and Alicante-Elche . In general, more outbreaks are reported in the coastal areas than in the interior regions , probablRemarkably, the number of outbreaks ranges between 0 and 2.5 clusters per thousand inhabitants. In addition, the median outbreak size in each HD ranged between 4 and 6 confirmed cases, except for the HD 19, Alicante, with a median value of 7 positive cases (data not shown). Moreover, there were no relevant differences among the health departments regarding the number and distribution of positive cases (data not shown).With respect to age, the SARS-CoV-2 virus is more likely to severely impact the elderly and individuals with compromised immune systems. Therefore, we analyzed the relationship between the percentage of elderly population (aged 65+ years old) in each province and the number of outbreaks per capita. As shown in We also studied the relationship between the number of COVID-19 cases linked to outbreaks and all reported positive cases for each municipality in the Valencian Region. This relationship captures the mean coverage of confirmed cases of the outbreak tracking system with respect to all detected cases for each municipality . There aThe temporal evolution of the type of origin of the COVID-19 outbreaks is shown in In this section, we first study the relationship between the number of outbreaks and their duration. The latter is defined as the period of time (in weeks) between the outbreak identification and closure. This means between the week an outbreak is first identified and that when no additional cases have been reported for that outbreak. When this happens, the outbreak is labeled as closed.2-value for the linearized logarithmic values is close to 98%, indicating a strong power-law relationship between the duration and the number of outbreaks. The exponent is lower than −3, which is the lowest expected exponent in many natural and physical phenomena. This may be due to missing information and to the fact that parts of an outbreak may be reported independently. When we split the outbreaks by their infection origin, we also obtain power-law relationships as depicted in Interestingly, we find that this relationship follows a power law: most outbreaks last less than 2 weeks before they are controlled, whereas a few last for more than 2 months before they are fully contained (data not shown). Such pattern has been previously reported in –61. The Finally, we model the evolution of the outbreak cases for each infection source using a Bayesian negative binomial function with monotonic effects. The main statistics of each obtained distribution and the parameters of the model are shown in However, the modeled interval contains the horizontal trend without new cases in all outbreak sources. Thus, the outbreaks can be considered to be fully controlled in the last weeks. For social-origin and work-related outbreaks, the model seems to underestimate the number of cases. However, this is not the case as most of the outbreaks just contain less than 10 people. Conversely, we do not observe this effect for outbreaks with vulnerable-collectives and nursing home origins probably due to a larger variance of the number of cases in the case of outbreaks with these infection sources. Internal validation of the models using 10-fold cross-validation yielded the following RMSE values: 1.83 for the vulnerable collectives model, 1.19 for the social origin model, 2.78 for the health center model, 3.64 for the educational center model, 7.58 for the nursing home model, and 1.25 for the work-related model.We also estimate the probability distribution of the cluster size for each infection source using posterior draws from the posterior predictive distribution of each model. These distributions are displayed in In this paper, we have analyzed the characteristics of one of the largest COVID-19 outbreak datasets containing all the COVID-19 outbreaks reported in the Valencian Autonomous Community of Spain over a period of 16 weeks between September and December of 2020, right before the emergence of the third wave of COVID-19 infections in January-February 2021.From our analyses, we draw several insights that could inform the design of public policies in future waves of this pandemic.1. Social and workplace infections are key: Concerning the outbreak origin, most outbreaks are linked to social or workplace infections, with a contribution of 80% to the overall identified outbreaks. This is in line with the increased probability of infection in poorly ventilated indoor environments, especially inside buildings , by virtue of a collaboration agreement (Convenio Singular). MR, JC, and NO have been partially funded by grants from the BBVA Foundation through the IA4COVID19 research project and from the Valencian Government, grant VALENCIA IA4COVID (GVA-COVID19/2021/100) research projects, technological development, and innovation (R+D+i) by COVID-19.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


It turned out that the addition of molybdenum helps to create a tight oxide coating that inhibits the further growth of corrosion processes. Increasing the share of molybdenum in the alloy also reduces the thickness of the oxide layer.The resistance of cast iron to chemical and electrochemical corrosion depends on the exposure conditions, chemical composition and the distribution of alloying elements in the microstructure. The article presents an attempt to describe the formation of a corrosion layer under controlled chemical corrosion conditions for SiMo ductile iron. In the experiment, a (HT-HRD) chamber for heating the samples with a controlled atmosphere was used, at the same time providing the possibility of testing the sample surface using the XRD method. The analysis was both qualitative and quantitative. It allowed us to capture the sequence of the oxide layer growth on the tested samples. The beneficial effect of molybdenum on the corrosion resistance of SiMo cast iron has been proven. For all cases under analysis, the phenomenon of an increase in the share of hematite (Fe Many components in the automotive industry are still manufactured as castings. One of the examples is an exhaust manifold made of SiMo cast iron. This material is also used to manufacture other components exposed to high temperatures . Iron-based cast parts are readily used because they are relatively cheap and display good mechanical properties. Corrosion phenomena and elevated operating temperatures of such components are a certain problem here. The resistance of cast iron to chemical and electrochemical corrosion depends mainly on the chemical composition and the distribution of alloying elements in the microstructure. The authors Xiang, S., et al. , Çelik, 2O3, Fe3O4) and an inner oxide layer (Fe2SiO4), which provides a protective effect [The diffusion of oxygen under the operating conditions of the SiMo cast iron exhaust manifold causes the pores left after the spherical graphite precipitates to be filled with oxide scale, hence the amount and morphology of graphite in the casting matrix is important. In addition, too much carbon in the alloy causes the unfavourable phenomenon of graphite flotation ,31,32. Oe effect ,10.Considering the above mentioned, it is important to analyse the very mechanism of the oxide layer formation on the surface of the casting using the high-temperature X-ray diffraction technique (HT-XRD). The solution proposed in the article allows us to analyse the formation of a passive layer on the casting surface under strictly controlled conditions. At the same time, we can observe the growth of the oxide layer both in terms of its chemical composition as well as in quantitative approach.The experimental melts were conducted in the induction furnace with a medium frequency and a capacity of 25 kg according to . The chaIn the studies, samples of spheroidal cast iron with Si content of 5% and Mo content of 0–2% were used. The chemical composition was determined on the basis of the Leco spectrometer  and the Leco carbon and sulphur analyser CS-125 . The chemical compositions of the tested samples are presented in The samples for metallographic tests were cast from the experimental melts. These samples were cast in resin-coated sand moulds, the shape of which corresponds to the standardized samples to determine the impact toughness. Metallographic tests were performed using scanning electron microscopy .The sections of the samples analysed were included in the conductive resin .The microstructures of the investigated cast iron are presented below. There are visible particles of spheroidal graphite and several particles of vermicular graphite as shown in Kα λ = 0.179 nm). The X-ray qualitative phase analysis was performed using the dedicated Panalytical High Score Plus (v. 3.0e) software based on Inorganic Crystal Structure Database . The quantitative share of the identified crystalline phases was calculated using the Rietveld method.In order to assess the mechanism of oxide layer formation on the surface of SiMo castings, the high-temperature X-ray diffraction (HT-XRD) technique was applied. The oxidation and X-ray diffraction (XRD) measurements were taken by using a Panalytical X’Pert Pro MPD diffractometer  equipped with a high-temperature chamber Anton Paar HTK 16  with a platinum heating unit. The oxidation process was conducted at 700 °C, in cycles (from 1 to 3 cycles) for each SiMo sample, with free air flow to the HTK chamber. The parameters of the oxidation process in a single cycle are shown in 3Mo3C phase. A higher Mo addition in cast iron promotes the formation of Fe-Mo-C-type carbide phases [Investigation of the phase composition of SiMo samples before the oxidation process , confirme phases .2O3—Hematite, crystallizing in the hexagonal crystal system (ICSD: 98-002-2505) and Fe3O4—Magnetite, crystallizing in a face-centred cubic (98-008-5806). Peaks from the oxide phases, magnetite and hematite, were identified for all diffraction patterns except for sample 3 after the first oxidation cycle, where no Fe3O4 peaks were observed. For all diffractograms, iron alpha peaks were identified with a slight shift towards higher angular values. By analysing the diffractogram from sample 1  caused a significant increase in the thickness of the oxide layer.The conducted analysis allowed us to determine the growth dynamics of the outer oxide layer components for the alloys under analysis. For alloy without molybdenum addition  is 21.7%, while for sample 3 (2% Mo) is 9.8%. For cast iron with the addition of 2% Mo, FeIn one study , C. K. CIt should be noted that the increase in the corrosion resistance of cast iron is achieved by adding into cast iron elements with high electrochemical potential  or elements that make up the passive layer , which is consistent with the description contained in articles ,9,10,11 Adding molybdenum to silicon cast iron stabilises the corrosion process and reduces the thickness of the oxide layer.For the addition of 1% Mo, the decrease in the thickness of the oxide layer is 80% compared to the alloy without molybdenum.Increasing the Mo concentration from 1% to 2% causes a decrease in the oxide layer thickness by about 31% .The reduction in the oxide layer thickness for the alloy with 2% Mo compared to the alloy without molybdenum addition is about 87%.For the alloy without molybdenum addition (sample 1), it is clearly visible that the oxide layer is formed with the decrease in silicon ferrite, the loss of which is 50%.For the sample with the addition of 1% Mo, the decrease in the share of silicon ferrite in the top oxide layer is 25.1%, while for the sample with the addition of 2% Mo, the decrease is only 9.7%.Increasing the Mo content in the alloy causes a decrease in the share of magnetite and hematite in the oxide surface layer.The 1% Mo concentration for cast iron provides an optimum-quality oxide layer.The conducted research allows us to draw the following conclusions:


It is shown that diffusion permeability depends on the structure and composition of anion-exchange membranes, as well as on the nature of amines. The technical and economic characteristics of the electromembrane processes for the production of amines and sulfuric acid from amine salts are determined. It is shown that when using Ralex AMH anion-exchange membranes in an electrodialyzer together with bipolar aMB-2m membranes, higher concentrations of diethylamine and sulfuric acid are achieved, compared with the use of MA-40L anion-exchange membranes.The influence of the nature of amine solutions on the frequency spectrum of the electrochemical impedance of the bipolar membrane aMB-2m is investigated. Moreover, the effect of the circulation rate of solutions in the electrodialyzer chambers on the volt-ampere characteristics of the Ralex AMH and MA-40L anion-exchange membranes and the aMB-2m bipolar membrane has been investigated. The diffusion characteristics of various types of anion-exchange membranes in a system containing dimethylammonium sulfate ((DEA) Due to the growing demand for the production of organic substances using electromembrane processes, a fundamental understanding of the transport of organic substances in ion-exchange materials is needed. An important issue to study is the question of changing the physicochemical properties of membrane materials due to contact with solutions containing organic substances. Electrodialysis of solutions containing organic ions may differ significantly from the treatment of solutions containing inorganic salts ,2,3,4.For the first time, the process of separation of mixtures of weak organic electrolytes by electrodialysis was described in . Investin-butane) was studied in [In , the eleudied in .The need to obtain amines from their chloride salts to return to the technological cycle arises when obtaining potassium, ammonium and sodium chlorides, potassium and ammonium salts of phosphoric acid as fertilizers, sodium bicarbonate and potassium carbonate salts ,11,12,13With the advent of bipolar ion-exchange membranes ,19,20,21Despite the abundance of works on the production of acids and alkalis using electrodialysis with bipolar membranes, there are few publications devoted to the production of amines from their salts, and they have not investigated the effect of solutions adjacent to bipolar and anion-exchange membranes on their electrochemical characteristics.In this paper, the influence of the nature of amines on the overvoltage of the bipolar region of a modified bipolar membrane containing an ion polymer with phosphoric acid groups catalytically active in the dissociation reaction of water molecules, the effect of the circulation rate of adjacent solutions to bipolar and anion-exchange membranes on their current-voltage characteristics, as well as the calculation of technical and economic parameters of the processes of producing amines from their salts using bipolar electrodialysis are investigated.The bipolar membrane under study was an aMB-2m bipolar membrane modified with a phosphoric acid cation-exchange resin, which was synthesized earlier by the authors . The bipHeterogeneous membranes MA-40, MA-40L, MA-41 , and Ralex AMH-PES membrane  were selected as the anion-exchange membranes under study.The MA-41 membrane is reinforced with a nylon fabric, and has strong basic quaternary ammonium bases in its composition. Moreover, the Ralex AMH membrane contains strong basic quaternary ammonium bases, but is reinforced with polyestersulfone. The MA-40 and MA-40L membranes contain secondary, tertiary amino groups and quaternary ammonium groups, their difference lies only in the reinforcement fabric: For the MA-40 membrane it is nylon, for the MA-40L it is lavsan.The simplest and most accessible method of studying diffusion permeability  is the d2), deionized water (140 mL) was poured into the cell chamber (3), a membrane (1) with a working area of 0.19 dm2 was located between the solutions. By means of an Expert-002 conductometer connected to a computer, the electrical conductivity of an aqueous solution was measured in chamber (3) in time. To eliminate the influence of diffusion layers near the membrane and uniform distribution of the diffusing electrolyte in the solution, the solutions were intensively mixed. Before starting the experiments, all membrane samples were brought into equilibrium with the corresponding 0.5 mol-eq/L electrolyte solutions at 25 ± 1 °C during the 24 h, and then washed with deionized water until the electrical conductivity of the washing waters was constant. The characteristics of diffusion transport were calculated from the experimentally obtained dependence of electrical conductivity on time [An electrolyte solution (145 mL) was poured into the cell chamber ( on time : Diffusi2H2SO4), dimethylamine ((DMA)2H2SO4), diethylamine ((DEA)2H2SO4), and ethylenediamine (EDAH2SO4) were used as the studied electrolyte solutions. The concentration of diffusing amine salts was calculated based on the calibration dependencies of their specific electrical conductivity at a temperature of 25 °C. For this purpose, a series of calibration solutions with different concentrations of amine salts in solutions was prepared beforehand and the electrical conductivity of these solutions was measured at a temperature of 25 °C, calibration dependencies were plotted and approximating equations were found.Neutral sulfuric acid salts of methylamine ((MA)j is the diffusion flux density, mol/(m2·s); V is the volume of water in the chamber into which diffusion occurs, m3; C is the concentration of electrolyte, mol/m3; S is the working surface area of the membrane under study, m2; τ is the time, s.Furthermore, according to Equation (1), the diffusion fluxes of amine salts through the membrane under study were calculated:β (Equation (2)) was found, characterizing the shape of the concentration profile inside the membrane during electrolyte diffusion [According to the dependence of the diffusion flow on the concentration in bilogarithmic coordinates, the parameter iffusion :(2)β=dlgPm is the integral coefficient of diffusion permeability, m2/s; d is the membrane thickness, m; P* is the differential coefficient of diffusion permeability, m2/s.Then, the integral coefficients (Equation (3)) and differential (Equation (4)) coefficients of diffusion permeability were calculated :(3)Pm=jdThe diffusion permeability coefficient is a quantitative characteristic of the ability of a particular substance to penetrate an ion-exchange membrane under the influence of a concentration gradient. The integral coefficient of diffusion permeability is determined experimentally from the diffusion flux of the substance, and the differential coefficient is calculated taking into account the parameter β.2H2SO4, dimethylammonium sulfate (DMA)2H2SO4, diethylammonium sulfate (DEA)2H2SO4, and ethylenediammonium sulfate EDAH2SO4.The electrochemical characteristics of the aMB-2m bipolar membrane were studied by impedance spectroscopy in a four-electrode electrochemical cell  as descr2. The frequency range of the electrochemical impedance spectra was from 0.1 Hz to 1 MHz, measurements were carried out in a stationary mode at a temperature of 25 ± 2 °C. The amplitude of the alternating current voltage drop on the membrane was 50 mV.The frequency spectra of electrochemical impedance were obtained using an AUTOLAB 100N potentiostat-galvanostat with a FRA32M frequency impedance measurement module in galvanostatic mode at DC densities from 0 to 2.6 A/dmRb  [R0 is the differential resistance, obtained by extrapolating the low frequency part of the impedance spectrum, where the dissociation of water molecules in the bipolar membrane appears, at the ReZ axis, Ohm dm2  :(6)Ub=∫0 Ohm dm2 ; R∞ is t Ohm dm2 ; i is thThe calculated overvoltages of the bipolar region were used to obtain a partial current-voltage characteristic of the membrane bipolar region.–1.The method of Fourier-transform infrared spectroscopy was used to evaluate changes in the structure of the ion exchange membranes. The IR spectra of the samples of the initial membranes and membranes after use in the electrodialyzer were recorded on the Bruker Vertex-70 device  using the module to distribute the total internal reflection in the range of wave numbers 4000–400 cmPreliminary preparation of membrane surface for the analysis consisted in converting all samples into a salt form, holding in 0.5 mol-eq/L sodium chloride solution, washing with deionized water until the electrical conductivity of the washing waters is constant, and drying the samples to an air-dry state. The interpretation of the spectra was carried out using the literature data in ,41,42.To study the electrochemical characteristics of the Ralex AMH and MA-40L anion-exchange membranes, as well as the aMB-2m bipolar membrane, before and after use in electrodialysis processes for the production of amines from their salts, the dynamic current-voltage characteristics of the membranes were measured at different circulation speeds of a mixture of 0.25 mol-eq/L diethylamine and 0.25 mol-eq/L diethylammonium sulfate and 0.5 mol-eq/L sulfuric acid solution in the chambers of the laboratory electrodialyzer. The study was carried out in the same laboratory electrodialyzer that was used to study the process of amines production . To meas2 at a scan rate of 6.6 × 10−4 A/c and a temperature of 25 ± 2 °C, at linear speeds of circulation of solutions equal to 0.5, 1.5, and 2.5 cm/s.The measurements were carried out in a galvanodynamic mode in a current density range of 0–3.3 A/dmTo study the processes of amines production in a laboratory electrodialyzer, sulfuric acid salts of methylamine, dimethylamine, diethylamine, and ethylenediamine were obtained.The membrane stack of the electrodialyzer contained five two-compartment unit cells. Each unit cell of the electrodialyzer comprised an acid compartment and a base–salt compartment formed by sequentially located aMB-2m bipolar membranes and Ralex AMH or MA-40L anion-exchange membranes. The choice of this configuration is attributed to the low degree of dissociation of organic bases. In the case of using a three-compartment unit cell, this factor leads to a low electrical conductivity of the solution in the base compartment and the high energy consumption of the process . In the 2; the distance between the membranes of 1.2 mm was set by the thickness of the frames and mesh spacers. The hydraulic circuit .2. The initial amine sulfate concentration in the salt solution was about 0.5 mol-eq/L; the sulfuric acid concentration was about 0.1 mol-eq/L. At the beginning of the experiment, the temperature of the salt–amine and acid solutions was 25 °C; by the end of the experiment, the temperature increased to 29–30 °C.The electrodialysis process of producing amines and sulfuric acid from sulfuric acid salts of amines was carried out in galvanostatic mode at a current density equal to 2 A/dmAt certain intervals throughout the experiment, samples from vessels with base-salt and acid solutions were obtained for subsequent analysis.The concentration of amine and amine salt in the samples of the solution of the base-salt chamber and the concentration of sulfuric acid and amine salt in the samples of the solution of the acid chamber were determined by titration using an automatic titrator Titroline 6000. The amine concentration was determined by direct titration of the aliquot with 0.1 hydrochloric acid solution. To determine the concentration of the amine salt in the base-salt chamber, an excess of a sample of swollen anionite AB-17-8 in hydroxyl form washed with deionized water was introduced into the sample of a solution of sulfuric acid containing an amine and amine salt and remained under stirring on a magnetic stirrer until the specific electrical conductivity of the solution, measured using a conductometer Expert-002, was constant in time. At this stage, the sulfuric acid was completely removed from the sample and the amine cation was converted to amine. The concentration of the amine in the sample obtained in this way was determined by direct titration with 0.1 mol-eq/L hydrochloric acid solution. Furthermore, the difference was used to find the volume of the titrant used for salt titration, and the concentration of the amine salt was calculated.The concentration of sulfuric acid in the acid chamber was determined by direct titration of the aliquot with 0.1 mol-eq/L sodium hydroxide solution. The concentration of the amine salt in the acid solution was determined similarly to the concentration of the amine salt in the base-salt chamber.The time dependencies of the amounts of sulfuric acid and amines in solutions circulating through the compartments of the laboratory electrodialyzer were used to calculate the main technical and economic characteristics of the device in the process of amines and sulfuric acid production from salts.η were calculated using Equation (7) [I is the current supplied to the electrodialyzer, A; N is the number of unit cells in the membrane stack of the electrodialyzer; n is the amount of the resulting acid or amine, mol-eq; τ is the time elapsed since the beginning of the experiment, s; and F is the Faraday constant, 96485 (A·s)/mol.The integral current efficiencies of sulfuric acid and diethylamine in the electrodialyzer tion (7) :(7)η=FINE, kWh/mol-eq) was calculated by Equation (8) [U1 is the voltage supplied to the electrodialyzer, V; U2 is the voltage supplied to the electrodialyzer containing electrode chambers separated by an anion-exchange membrane at the same electric current at which U1 was measured, V.The integral energy consumption required for the production of acid and amine (tion (8) :(8)E=U1−P, mol-eq/(h·m2)) was calculated by Equation (9) [S is the active area of each membrane, m2.The integral productivity of the electrodialyzer with respect to sulfuric acid and diethylamine (tion (9) :(9)P=ηIFIt is shown that the differential coefficient of diffusion permeability weakly depend on the concentration when studying the diffusion permeability of various natures of amine salts through different types of anion-exchange membranes . This isThe MA-40L membrane has the greatest diffusion permeability . This isThe diffusion transport of amine salts of various natures decreases with an increase in their molar mass. Therefore, the salt of the simplest representative of aliphatic amines—methylamine—diffuses through the anion-exchange membrane about five times better than diethylammonium sulfate .2. The hydrogen and hydroxyl ions determine the resistance of the bipolar region of the membrane, since in this case, they are the main carriers of electric current.To assess the influence of the nature of amines and the range of current densities in which the bipolar membrane aMB-2m can be used in electrodialysis processes, the dependencies of the resistance of the bipolar membrane region on the current density and the partial overvoltage current-voltage characteristics of the bipolar region in systems with different amines were calculated . It can be observed from the dependencies  that theThe small value of the current density, at which a maximum is observed on the dependence of the resistance of the bipolar region on the current density, indicates a sufficiently high selectivity of the bipolar membrane aMB-2m in solutions used in the process of producing amines from their salts.−1 corresponding to aliphatic amines (C-N bond vibrations)  . This ma−1 confirms the presence of quaternary ammonium bases in the Ralex AMH membrane –N+(CH3)3 3 . The disTo estimate the effect of the circulation rate of amine and sulfuric acid solutions through the chambers of a laboratory electrodialyzer in the electrodialysis process, the current-voltage characteristics of the bipolar membrane aMB-2m and the anion-exchange membranes Ralex AMH and MA-40L were calculated. It is shown that there are no electrodiffusion limiting currents on the membranes in the region of the studied current densities . This inIt is shown that the circulation rate of solutions adjacent to the anion-exchange membranes under study almost does not affect their current-voltage characteristics. This is due to the fact that the used current densities are significantly lower than the limiting electrodiffusion current (there is no “plateau” of the limiting current) on the membrane. The rate of solutions circulation adjacent to the ion-exchange membranes under study mainly affects the value of the limiting electrodiffusion current and very weakly affects the slope of the current-voltage characteristics of the membranes.The analysis of the dependencies shows that the circulation rate of solutions does not affect the current-voltage characteristics of bipolar membranes. However, it can be noted that at the same solution speed (2.5 cm/s), the current-voltage characteristics of the bipolar membranes before use in electrodialysis processes for the production of amines (1) and the membranes that were used in electrodialysis processes  are markedly different. For bipolar membranes used in amine production processes, there is a decrease in voltage drop compared to the original membranes. This may be due to the breaking of the polyethylene interlayers in the bipolar region during the use of the membrane in the electrodialysis process, which in turn leads to the appearance of new generating contacts in the bipolar region of the membrane .Comparison of the dependencies of the solution concentrations on the operating time of the electrodialyzer with aMB-2m bipolar membranes and Ralex AMH anion-exchange membranes during the process of amine and sulfuric acid production from appropriate salts shows that the concentrations of amines a and sulContamination of the produced sulfuric acid solution with amine cations increases with the increasing concentration of sulfuric acid . The pre2H2SO4–H2SO4» system are confirmed in [The integral current efficiencies of sulfuric acid and amines decrease with an increase in their concentration . In the irmed in .Similar dependencies are observed in the case of the use of the bipolar membrane aMB-2m and the anion-exchange membrane MA-40L in the process of producing diethylamine from diethylammonium sulfate .However, it is worth noting that the process using MA-40L membranes as anion exchange is more intensive at the initial stages of the process, which is confirmed by an increase in the slope of the dependence of the concentrations of salt, amine, acid, and the electrical conductivity of the base-salt solution on the operating time of the electrodialyzer. At the same time, the current efficiency of diethylamine when using MA-40L membranes drops sharply with an increase in the concentration of the resulting amine .The main technical and economic parameters of the process are presented in . The intDespite the high effective transport numbers of hydrogen ion through the anion-exchange membrane under the conditions of the process of producing amines and sulfuric acid from amine salts and not too high current efficiencies of amines and acid, it is possible to achieve almost complete conversion of the amine salt into amine. An additional decrease in the current efficiency of methylamine is caused by its high coefficient of diffusion permeability through the anion-exchange membrane. The use of bipolar aMB-2m membranes and heterogeneous anion-exchange membranes in the conversion process of amine salts into amines makes it possible to produce sufficiently pure sulfuric acid solutions with an equivalent amine fraction not exceeding 12% for methylamine and not exceeding 5% for DMA, DEA, and EDA.


This TiO2/Ag/HAp composite coating has a developed surface relief at the micro- and nanolevels and has a significant cytological response and the ability to accelerate osteosynthesis, and also has an antibacterial effect. Thus, the developed biomaterial is suitable for production of dental and orthopedic implants with improved biomedical properties.When creating titanium-containing bone implants, the bioactive coatings that promote their rapid engraftment are important. The engraftment rate of titanium implants with bone tissue depends significantly on the modification of the implant surface. It is achieved by changing either the relief or the chemical composition of the surface layer, as well as a combination of these two factors. In this work, we studied the creation of composite coatings with a two-level (the micro- and nanolevel) hierarchy of the surface relief, which have bioactive and bactericidal properties, which are promising for bone implantation. Using the developed non-lithographic template electrochemical synthesis, a composite coating on titanium with a controlled surface structure was created based on an island-type TiO The duration of the recovery of a damaged bone after implant placement affects the quality of the patient’s life. Therefore, the implant engraftment rate is one of the key factors in implantation. This could be improved by the high biological activity of its coating, determined mainly by the chemical composition and topography of its surface.Usually, implants are metal-based, e.g., alloys of titanium, cobalt, etc., which is due to their high mechanical properties and economic profitability. However, some implants may be rejected due to susceptibility to corrosion, susceptibility to infection, low biological activity and, consequently, inability to effectively integrate into the hostile environment of the body. This means that new bioactive systems for implants are needed ,2. BioacAt the same time, it is well known from the literature data and clinical practice that there is a relationship between the morphology of the implant surface, biocompatibility and osseointegration ,5,6. It The presence of a micro-rough surface on the implant and nanoscale irregularities that mimic natural bone structure provide improved implant osseointegration ,8, and, The two-level hierarchy of the coating relief (roughness both in the micron and nanometer range) triggers the process of osteoblast differentiation and improves adhesion, respectively, increasing its material bioactivity ,6,9,10.It is a non-trivial task to obtain coatings with a two-level hierarchy. A promising strategy for the production of implants with biologically active hierarchical coatings, consisting of inorganic bioactive materials, includes a combination of several technological methods  .The implant engraftment is slowed by unwanted infection of the implant area . AntibioThe usual inorganic antibacterial substances are metals and metal oxides, for example Ag (silver), Cu (copper), Au (gold) and Zn (zinc), in their micro- or nanoforms ,14.Ag is the most beneficial antibacterial substance due to its broad spectrum of bactericidal properties, as well as a high level of biocompatibility and stability ,16. Its 10(PO4)6(OH)2 (HAp) or amorphous calcium phosphate, because HAp is one of the main components of the mineral part of the bones [To increase the speed and improve the quality of implant engraftment, the implant surface is often modified with hydroxyapatite Cahe bones ,24,25. Che bones ,27,28. Hhe bones .2/Ag sample using pulsed electrochemical deposition, and we carried out preliminary comparative cytological studies. As a result, it has been shown that electrochemical deposition is not cytotoxic for osteoblast-like cells.In our previous work , we show2/Ag/HAp composite coating with controlled composition and structure at the micro- and nanolevels using a combination of sol-gel synthesis and electrochemical deposition from a solution.In this work, we continued the study begun in  and crea2 coating on the early adhesion of osteoblast cells.This bioactive island-type coating at the micron level improves the adhesion of osteoblast cells on the surface of the Ti implant and eliminates the negative effect of Ag in the TiOThe prospects of using this coating, which has antibacterial and high bioactive properties, for bone implantation have also been investigated.2 template, post-synthetic heat treatment temperatures between 150 °C and 500 °C in 50 °C increments were tested. The optimal temperature for creating a perforated (insular) oxide coating is 400 °C. At this temperature, the perforation areas are commensurate with the osteoblast cells, which positively affects the rate of the osteoblast cell monolayer formation on the implant surface.During the deposition of the TiO2 film with the presence of micro- and nano-roughnesses. The two-level hierarchy, as noted above, significantly increases the bioactivity of the implant surface.The structures were analyzed using SEM. 2 coating  with an electron probe using an X-ray microanalysis attachment on a scanning electron microscope. It can be seen from the Ti and Ag element maps  and adhesive properties of MG-63 osteoblast cells cultured on the surface of samples  responsiThe results of the evaluation of the proliferative activity of MG-63 cells cultured on the surface of the samples are shown in 2 xerogel for better adhesion of osteoblast cells. In addition, based on micrographs, it can be concluded that the resulting composite coating has no cytotoxicity and has the capacity for of more than 85% in 24 h for MG-63 osteoblast cells to adhere to it.Based on the obtained microphotographs of samples with osteoblast cells on the surface , it is cCell differentiation in the osteogenic direction was studied using markers of early  and late (osteopontin—OPN) differentiation ,32.During three weeks of differentiation of MG-63 cells cultured on the surface of samples (no. 1–4), the level of ALP activity in the conditioned medium was higher compared to the control values (cells culture under standard conditions) .At the same time, a gradual increase in the level of ALP activity was observed during cell differentiation on the surface of two samples (no. 3 and 4), where ALP activity increased by 24 and 105%, respectively. The level of ALP activity in the medium obtained from control cells (culturing on plastic) was taken as 100%. When cells were cultured on the surface of samples no. 1 and 2, the level of ALP activity gradually decreased. For example, after the first week of cell differentiation on the surface of the sample no. 1, the values of the ALP activity level were 34% higher compared to the control, and then decreased and no longer had significant differences with the control .2/Ag, TiO2/Ag/HAp coatings) of the surface of samples no. 3 and 4 had the most significant effect on the differentiation of the cell line.Thus, during the differentiation of MG-63 cells on the surface of the samples, there was an increase in the level of ALP activity compared to the control (cell culture on plastic), which indicated the influence of the nature of the surface treatment of the material on the ability of MG-63 cells to differentiate in the osteogenic direction. At the same time, the treatment (TiODuring three weeks of differentiation of MG-63 cells cultured on the surface of samples (no. 1–4), a gradual increase in OPN content was observed . The OPN2/Ag/HAp significantly enhanced the differentiation of osteoblasts in the osteogenic direction  on the TiO2/Ag/HAp composite sample and the control sample. It can be seen that in the presence of Ag in the composition of the sample, there is a 100% absence of culture growth.It can be seen from the table that the growth of microorganisms was observed in the seeded control samples and in control samples without silver containing only the titanium oxide coating. In the composite coating of the island-type TiOAs a result of the study of samples for antibacterial activity, the presence of antibacterial properties of the samples was confirmed. The results of this study allow us to conclude that it is advisable to apply an insular composite silver coating on titanium implants.+ ions into the peptidoglycan layer or bacterial wall, which leads to damage to DNA and bacterial proteins [The mechanisms of the antibacterial action of silver can be described in several ways . One of proteins . The forproteins . In addiproteins .2/Ag/HAp composition with a developed surface relief at the micro- and nanoscale has a significant cytological response and the ability to accelerate osteosynthesis.Based on a large number of in vitro and in vivo studies and our experimental data, we came to the unequivocal opinion that for the fastest and most successful osseointegration of the implant, a developed relief is necessary both on the micro- and nanoscale scale ,37,38. M2 was coated on Ti as the template for further operations [Firstly, TiOerations . A techn2 film.Secondly, Ag was coated over the TiO3, sulfosalicylic acid, 25% aqueous ammonia to reach pH 9. Template deposition was carried out in a polypropylene cell with stirring, using a potentiostat-galvanostat Elins P45X. A Ti substrate coated with TiO2 was used as a working electrode, and silver was used as counter electrode.Silver was precipitated from an aqueous electrolyte containing AgNOThe silver was deposited from the solution at pulsed current, since constant current gave insufficient repeatability of the template pattern without repeating the geometry of the perforations. Preliminarily, the optimal number of deposition cycles was determined. Each of 300 cycles included the impulse sequences 1 V—5 ms, 0.3 V—3 ms, 2 V—10 ms, 0 V—65 ms.2/Ag composite coating as the template. As above, potentiostat-galvanostat Ellins P45X was used for electrochemical matrix synthesis (TiO2/Ag composite serves as cathode and matrix and graphite serves as anode). Based on HAp stoichiometry (Ca/P = 1.67), we used 1.18 g of Ca(NO3)2,  and 0.411 g of KH2PO4  per 100 mL of deionized water. HAp was deposited at pulsed current [At the third stage, HAp structures were obtained on the surface of the TiO current .5/cm2 of the sample. Samples with a suspension of cells deposited on their surface in this way were placed in a CO2 incubator at a temperature of +37 °C for 3 h. After this time, during which cell adhesion on the surface of the samples is supposed to occur, a nutrient medium was added to the Petri dishes. As a control, the application of a suspension of cells on the surface of cultural dishes was used.All samples were placed in Petri dishes and sterilized by ozonation. The suspension of the MS3T3-E1 cell line was applied in a small volume of nutrient medium (100 µL) in such a way that a “drop” formed on the surface of the sample. The concentration of cells during sowing was 1 × 10The assessment of the state of cells (the nature of adhesion and spreading of cells on the surface of the samples) was performed by the SEM method using a JSM-35.7 microscope .n = 5. After cell adhesion, the medium was changed to the medium obtained after the samples’ incubation. The cytotoxicity of the material on the cells was evaluated after 24 h using a Vibrant MTT Cell Proliferation Assay Kit  according to the manufacturer’s protocol.Titanium samples were sterilized under ozonation conditions, then incubated in 1 mL of nutrient medium for 24 h at 37 °C. Two samples were used for each variation. A suspension of MG-63 cells with the same concentration (5000 cells/well) was sifted into the wells of a 96-well board . The number of repetitions 5 in 20 µL of culture medium DMEM/F12 containing 10% fetal cow serum and gentamicin  was applied to their surface . After 2 h of incubation, the cells were adhered to the surface of the samples, then 180 µL of nutrient medium was added to each sample so that the cells on the surface of the samples were completely covered with the medium.Ti samples were sterilized using ozonation. The samples were placed in a Petri dish , and a suspension of MG-63 cells with a concentration of 1 × 102 incubator for 3 weeks with a change of medium once a week to a medium that induces differentiation . Standard conditions of cell culture on the surface of culture plastic were used as a control. At each change of medium, a cell-conditioned medium was sampled, which was placed at −80 °C for subsequent assessment of the presence of alkaline phosphatase (ALP) and the content of osteopontin in it using a Human ALP and SSP1 (Osteopontin) ELISA Kit in accordance with the manufacturer’s protocol. The measurements were carried out on a multimodal Thermo Scientific Varioskan LUX reader with a wavelength of 450 nm.Cells were cultured on the surface of the samples in a CODuring the experiment, titanium plates covered with silver structures as well as pristine titanium samples were examined. The samples were not subjected to additional processing, including sterilization.6 and 107 CFU/mL: Enterococcus faecalis ATCC 29812, Staphylococcus aureus ATCC 25912, Klebsiella pneumoniae ATCC 19882, Acinetobacter baumannii 987, Pseudomonas aeruginosa ATCC 27853.For the study, daily cultures of reference strains were used, in the amount of 106 CFU/mL on a Muller-Hinton agar for the reliability of the contamination of the suspension; and (2) Ti plates without Ag coating to confirm the negative antibacterial effect of samples on microorganisms in the absence of Ag.The controls were taken: (1) seeding of a suspension of microorganisms in the amount of 10To assess the presence/absence of microbial growth, the quantitative growth of bacteria on a dense nutrient medium was studied after sowing suspensions on agar from samples without silver after 24 h of incubation. The experiment was repeated twice, and the results are reproducible without errors.This study was conducted according to ISO Standard 22196 “Measurement of antibacterial activity on the surface of plastics and other non-porous materials”.The structure and morphology of the coatings were studied using a Zeiss Merlin scanning electron microscope . For X-ray spectral microanalysis and the construction of element maps, we used the EDX console (Oxford Instruments INCAx-act) for a Zeiss Merlin scanning microscope. The MiniFlex II X-ray diffractometer  was used for X-ray phase analysis (λ = 1.5406 Å).2/Ag composite coating obtained by sequential application of the sol-gel method using dip-coating technology and electrochemical deposition. The resulting coating has a two-level orderliness of the relief, consisting of micro- and nano-irregularities, and due to this increase, the bioactivity of the implant, since it improves adhesion and promotes an accelerated process of osteoblast differentiation. It was also shown that the combined approach used to obtain coatings based on the sol-gel technology and electrochemical deposition method is not cytotoxic for osteoblast-like cells. The study of coatings containing a deposited layer of silver in the form of islands has shown that the preservation of the antibacterial effect of such coatings also leads to a reduction in the period of implant engraftment. Cytological studies of the obtained TiO2/Ag/HAp coatings have shown that coatings of this composition lead to accelerated osteosynthesis and are promising in the development of implantation products in dentistry and orthopedics.In the course of this work, a technique was developed for electrochemical template synthesis of a TiO