The unstoppable adoption of the Internet of Things (IoT) is driven by the deployment of new services that require continuous capture of information from huge populations of sensors, or actuating over a myriad of “smart” objects. Accordingly, next generation networks are being designed to support such massive numbers of devices and connections. For example, the 3rd Generation Partnership Project (3GPP) is designing the different 5G releases specifically with IoT in mind. Nevertheless, from a security perspective this scenario is a potential nightmare: the attack surface becomes wider and many IoT nodes do not have enough resources to support advanced security protocols. In fact, security is rarely a priority in their design. Thus, including network-level mechanisms for preventing attacks from malware-infected IoT devices is mandatory to avert further damage. In this paper, we propose a novel Software-Defined Networking (SDN)-based architecture to identify suspicious nodes in 4G or 5G networks and redirect their traffic to a secondary network slice where traffic is analyzed in depth before allowing it reaching its destination. The architecture can be easily integrated in any existing deployment due to its interoperability. By following this approach, we can detect potential threats at an early stage and limit the damage by Distributed Denial of Service (DDoS) attacks originated in IoT devices. The 5G paradigm aims at defining “universal networks” connecting devices of all kinds. Billions of devices with different needs in terms of throughput, latency, reliability, security and density of users will be connected through 5G networks. The Internet of Things (IoT) is explicitly considered by the 3rd Generation Partnership Project (3GPP) in the design of new networks . In factIoT devices have very different characteristics and requirements compared to traditional smartphones or other devices under the enhanced Mobile Broadband (eMBB) class, which form the majority of legacy mobile network terminals. They are simple devices with low computing power, so they are highly vulnerable to security issues. They usually have low encryption capabilities and only support simple authentication procedures . Due to Since their origins, cellular networks have been the target of numerous attacks. These increased in number with the deployment of the fourth generation of mobile technology. The IP-based architecture of 4G core networks led to the emergence of attack vectors which had never been seen before, whereas the increasing dependence on cellular networks also raised the interest of the attackers. The introduction of new use cases in 5G networks will foster this interest. In fact, IoT Distributed Denial of Service (DDoS) attacks are envisioned as one of the worst security threats 5G networks will need to face. IoT botnets have reached a high degree of maturity, up to the point that the most powerful and frequent DDoS attacks are performed by IoT botnets conformed by vulnerable commodity devices ,4. There5G networks are being designed around new concepts and paradigms that may be exploited to improve their security. Software-Defined Networking (SDN) ,7 allow In this paper we propose a novel architecture for an efficient detection of DDoS attacks originating from massive IoT devices in mobile networks. Instead of applying Deep Packet Inspection (DPI) to all the data flows in the network, our solution, which relies on the SDN, NFV and Network Slicing concepts, applies a two-step detection procedure. A first detection procedure, executed by a lightweight SDN application, performs a coarse-grained identification of suspicious sets of end devices which are believed to include malicious nodes. The data flows of the suspicious nodes are diverted to a quarantine slice, in which they can be analyzed in depth to set malicious devices apart from legitimate ones. This two-step process reduces the computational load of the DDoS detection system of the mobile network, since traffic is only inspected in depth on demand on a reduced set of data flows.A two-step SDN-based architecture for detecting and mitigating DDoS attacks in IoT cellular networks.The analysis of the detection capabilities of the proposed solution through simulations.The evaluation of the reassignment of IoT devices to quarantine slices in a real-world testbed.Summing up, the main contributions of this paper are:The rest of the paper is organized as follows. DDoS attacks from IoT botnets are a main concern in 5G networks security. SDN, one of the most significant paradigms in 5G core network design, has different advantages to build better defense systems for modern networks . Its gloThere is abundant work in the literature on the detection of DDoS attacks on networks by relying on SDN. In , the autThese works take advantage of the global knowledge of the SDN controller for detecting malicious devices; the centralized programmability of the network control plane, which permits creating consistent policies to drop all the data frames that match certain filters; or the sampling capabilities of SDN networks, which allow mirroring part of the traffic towards an analysis entity. Although the delay in acting on malicious traffic can increase the severity of the problem, immediate blocking of all suspicious traffic can lead to the disruption of legitimate user services since false positives may occur. In  the conchttp://www.anastacia-h2020.eu/) seeks to develop a solution for a network serving IoT devices to act automatically and intelligently in the face of threats. This project introduces an architecture to move users to a quarantine slice if suspicious behavior is detected. However, anomaly detection is carried out by specific entities that perform deep packet inspection of user traffic to determine if a flow is potentially malicious.Suspicious traffic isolation is relevant not only at academic level but also at industrial level. Research projects  and pateIn this paper we present a novel two-step DDoS attack detection solution for current mobile networks supporting massive IoT use cases. A coarse-grained detection procedure running in an SDN application is in charge of detecting abnormal growth of throughput from aggregated sets of IoT devices. Aggregation of IoT devices allows coping with the flow table limits of SDN switches, so that it becomes possible to handle the vast number of devices that 5G networks will support in massive IoT scenarios. In case a set of suspicious devices is detected, the data flows of these devices are diverted to a quarantine network slice in which fine-grained detection, based on DPI, can be performed to set apart malicious devices from legitimate ones. By isolating suspicious devices, the operator can ensure that the QoS requirements of legitimate devices with a regular behaviour are fulfilled, whereas the performance of suspicious devices can still be guaranteed subject to the resource constraints in the quarantine slice. As in-depth traffic analysis is only performed when an abnormal behaviour is detected and only over a reduced subset of IoT traffic, the computational requirements of this detection solution is lower than previous solutions in which all the data flows are continuously sampled and deeply analyzed.We propose an architecture based on Network Slicing for quarantining malicious IoT devices in mobile networks. The solution relies on NFV for creating multiple Network Slices with independent user-plane resources and on SDN for configuring the traffic flows in the network and redirecting each device seamlessly to the proper network slice. Furthermore, the proposed architecture is interoperable with 3GPP entities and therefore can be easily integrated in existing deployments.Using this architecture, the SDN network interconnecting the RAN and the CN can assign UEs dynamically to the slices. This way, IoT devices are initially attached to the IoT NS, which provides full access to the network services the IoT devices require. An SDN application running on top of the SDN controller is in charge of identifying potentially malicious nodes and reassigning them to the Quarantine NS for further inspection. After a deep inspection of the traffic in the Quaratine NS, a device can be deemed malicious in which case its traffic is blocked or, conversely, it can be considered legitimate and consequently moved back to the IoT NS.Overall, the proposed quarantine architecture employs a two-stage system for detecting malicious IoT devices: The first stage performs lightweight and coarse-grained early detection, which can involve some legitimate devices, while the second stage is a fine-grained in-depth traffic analysis for individually identifying malicious devices.This architecture presents two major challenges that must be addressed: the detection of potentially malicious devices by the SDN controller and the dynamic reassignment of these devices to the different slices. On the one hand, the detection of potentially malicious nodes can be addressed by an SDN application monitoring the SDN flow rules installed on the SDN switches. As we show in this paper, the accuracy of this approach depends on the granularity of the flow rules in the SDN network. On the other hand, the dynamic reassignment of nodes to different slices can be achieved by redirecting their user-plane traffic to the desired slice using the SDN network. Moreover, this requires maintaining a state replication of the user-plane context among the different slices, so that the traffic of any device can be properly handled by any of them.An SDN application running on top of the SDN controller periodically samples the flow tables of the SDN switches, obtaining the numbers of packets and bytes transmitted in the sampling period for each flow rule in each SDN switch. Since the number of users connected to each base station can be up to 300,000 , installA flow will be considered suspicious when its throughput exceeds a certain threshold. Thus, this approach requires knowing the typical traffic in a flow without malicious devices. Fortunately, there are several models for the behavior of IoT devices, and in particular their traffic patterns. IoT sensors are often sending data in a deterministic periodic manner ,31,32, aAn undesired side-effect of this aggregation is that legitimate devices may be temporarily assigned to the Quarantine NS due to the aggregation of nodes inside a flow. Nevertheless, those devices will not lose connectivity nor session continuity while they are diverted to the Quarantine NS, and they will be returned back to the IoT NS after actual malicious nodes are identified.Another side-effect of the aggregation is that some flows containing malicious devices may not be properly detected if their activity does not exceed the threshold, so that they will never be sent to the Quarantine NS. However, although it is desirable to detect all suspicious devices, they will cause no harm if they are few. Furthermore, this problem can be alleviated by simply setting lower thresholds for tagging a flow as suspicious. These threshold values will determine the number of flows that are sent to the Quarantine NS.The dynamic reassignment of UEs to slices can be carried out in a transparent and seamless way both to the devices and to the mobile network elements by extending our previous work for transparent session and service continuity in dynamic Multi-Access Edge Computing (MEC)  to NetwoThis section evaluates the proposed architecture for quarantining malicious IoT devices. We analyze the solutions presented for each of the challenges that we have identified. First we evaluate the detection of potentially malicious devices through a set of simulations and then analyze the performance of the dynamic reassignment of devices to slices in a real implementation.We conducted simulations to evaluate the performance of the solution for detecting IoT DDoS attacks in mobile networks described in To represent a realistic scenario and for simplicity the category of factory automation devices in  was consn: Number of devices per flow.i (independent of other devices).i legitimate devices.i legitimate device.Different scenarios were simulated, each of them characterized by the following input parameters:We performed lable at .n factory automation devices generate an aggregated flow that matches an entry in the flow table in an access SDN switch. The behaviour of each node is defined by its device type, which is selected by randomly picking one of the categories in On each round S attack ,39.To simulate the traffic, the devices in the aggregated flow transmitted independently and asynchronously in an ideal non-saturated channel where data frames were always transmitted successfully. To emulate this behaviour, the arrival time of the first transmission since the beginning of the simulation followed a uniform U. The fewer devices are aggregated per flow, the greater the accuracy of the system.n) on the detection of malicious devices in the simulated scenario is not intuitive. Any malicious device is detected for n increases. The cause of this effect is as follows. For a low value of n, the probability that there is no more than one malicious device within the aggregated flow is high  begin to go unnoticed and, consequently, detection drops. But, on the other side, increasing the size of the aggregated flow also decreases the probability of flows containing only malicious nodes of manufacturing cell type, thus raising the detection .A closer observation of n in the scenario described in n, validating our previous arguments. Analytical formulas used for studying this scenario are elaborated in As an example to better understand this effect, we have analytically studied the probability of detecting a flow which contains malicious devices as a function of n and x, given by Equation (1).That is, there is a value for the threshold from where detection decays. Such threshold is high enough to hide the traffic of a specific type.In the same way, the number of legitimate users quarantined also decreases with the threshold, but at a slower pace, as depicted in The effect of the SDN application sampling period  modem connected to a laptop (Intel®Core™i7 4510U CPU @ 2.0 GHz).Radio Access Network: eNodeB software implementation provided by OpenAirInterface running on a commodity PC (Intel®Core™i7-6700 CPU @ 3.40GHz) connected through USB-3 to an Ettus USRP B210 SDR board.Core Network: The network slices of the Core Network were deployed as Docker containers on top of an Intel®Xeon®CPU E5-2603 v3 @ 1.60GHz.-IoT NS: Three Docker containers implementing the main Evolved Packet Core (EPC) entities using OpenAirInterface software, Home Subscriber Server (HSS), Mobility Management Entity (MME) and Serving/Packet Data Network (PDN) Gateway (S/P-GW).-Quarantine NS: One Docker container implementing an OpenAirInterface S/P-GW.https://www.pcengines.ch/apu.htm).SDN switch: Open vSwitch bridge with extended General Packet Radio Service (GPRS) Tunneling Protocol (GTP) encapsulating and decapsulating capabilities  ,44) crea crea44])SDN controller: ONOS SDN controller deployed as a Docker container in an Intel®Xeon®CPU E5-2603 v3 @ 1.60GHz. The SDN applications for moving the devices/UEs were implemented on top of ONOS as described in . These SIn detail, the elements used in the testbed were:Dynamic creation of the Quarantine NS: Deployment of the S/P-GW Docker container in the Core Network (S/P-GW application initialization (State replication of the UE context in the S/P-GW of the IoT NS to the S/P-GW in the Quarantine NS (Data-plane reconfiguration of flow rules in the SDN switch for forwarding the traffic of the device/UE to the Quarantine NS (The experiment we conducted on this testbed was to measure the time required for reassigning a device/UE from the IoT NS to the Quarantine NS (The first step was performed by the Docker engine at the Core Network whereas the last two steps were performed by an SDN application running on top of the ONOS SDN controller. Note that these steps must proceed sequentially for a successful reassignment without service interruption. Thus, The methodology we followed for the experiment consisted in first deploying the SDN controller and the IoT NS in the Core Network and then connecting the eNodeB for a functional 4G network. Then, one static UE is connected to the 4G network generated using the OpenAirInterface software. The UE is intially attached to the IoT NS. Then, we complete different steps and measure their times: Instruct the Docker engine to deploy a S/P-GW container and wait for the container to be deployed, wait for the S/P-GW container to be properly initialized, instruct the SDN application running on top of the ONOS SDN controller to replicate the context of the UE in the S/P-GW of the IoT NS to the new S/P-GW corresponding to the Quarantine NS and finally instruct the SDN controller to reconfigure the flow rules of the SDN switch to send the traffic of the UE to the S/P-GW in the Quarantine NS. We verified that the UE continued attached to the network during the whole process.For complete clean-up of the experiment before subsequent executions, we disconnected the UE, stopped the eNodeB and removed all Docker containers corresponding both to the Core Network entities of both slices and also the SDN controller.This experiment was run 100 times for statistical significance.switches .This reassignment time in the order of one second determines the reaction speed of the first stage of our proposed solution. Since the legitimate devices redirected to the Quarantine NS will be reassigned back to the IoT NS, the time this takes is also relevant for our solution. However, note that this action simply requires a data plane reconfiguration, because the IoT NS is already deployed, the S/P-GW is initialized and it has the required state. As a result, moving a device back to the IoT NS is a lightweight process taking less than 10 Furthermore, the process of moving a device to the Quarantine NS as described is fully reactive, comprising the deployment of new data plane resources on demand. As analyzed, this part is indeed the most time consuming in the whole reassignment process. We thus propose as an alternative a proactive approach for reducing reassignment time: Instead of creating the S/P-GW in the Quarantine NS as part of the reassignment process, a S/P-GW can be proactively created and initialized simultaneously to the IoT NS. This way, the reassignment time (Massive IoT networks are challenging for 5G networks, not only in terms of scalability, but also of security. However, conventional security solutions have focused on eMBB scenarios and do not fit well into massive IoT communications, due to their huge density of devices. Hence, operators need tailored solutions for this use case.In this paper we propose an architecture based on Network Slicing, NFV and SDN for detecting malicious devices and mitigating their impact in massive IoT scenarios, which can be easily integrated into any existing deployment due to its interoperability. The solution in this architecture is a two-step process: the first step, which we analyze in depth in this paper, performs a coarse-grained lightweight detection of potentially malicious devices through SDN techniques. In the second step, suspicious devices can then be seamlessly redirected to a Quarantine Network Slice for fine-grained in-depth inspection.We have validated the flow analysis mechanism proposed through simulations and evaluated the performance of the redirection with a real testbed implementation. The results of the simulations reveal a trade-off between the probability of successful identification of malicious devices and the number of legitimate devices that are sent to the quarantine slice, which depends on the number of devices in the aggregated flows of the SDN switches and the detection threshold. These parameters can be configured in the SDN application to fine-tune the desired performance of the application. On the other hand, the implementation of the proposed architecture in a real testbed allowed us validating its feasibility and also measuring that moving a device to the quarantine slice takes about 1 s when the quarantine slice is reactively deployed on demand. The results also show that this time can be further reduced to less than 10 Our proposed architecture is particularly efficient in scenarios with a large number of IoT devices because it does not need to inspect all the traffic in depth, but just the devices corresponding to aggregated SDN flows with suspicious behavior. Moreover, unlike conventional mechanisms, our solution does only trigger an in-depth inspection of the traffic in the event of a potentially malicious attack, thus reducing the computational load in the operator network. This architecture is specially useful and adequate for environments with a small number of malicious devices. Our experiments and models demonstrated how malicious devices were quarantined for further analysis even for low ratios of compromised devices or devices transmitting small amounts of information.Although our method also quarantines legitimate devices that are in the same flow of malicious devices, their communications are kept active while they are being inspected and also when legitimate devices are returned to the original slice, making our approach transparent and non intrusive.As future work we plan to implement and evaluate a mechanism for performing in-depth traffic inspections in the quarantine slice.


For historic reasons, industrial knowledge of reproducibility and restrictions imposed by regulations, open-loop feeding control approaches dominate in industrial fed-batch cultivation processes. In this study, a generic gray box biomass modeling procedure uses relative entropy as a key to approach the posterior similarly to how prior distribution approaches the posterior distribution by the multivariate path of Lagrange multipliers, for which a description of a nuisance time is introduced. The ultimate purpose of this study was to develop a numerical semi-global convex optimization procedure that is dedicated to the calculation of feeding rate time profiles during the fed-batch cultivation processes. The proposed numerical semi-global convex optimization of relative entropy is neither restricted to the gray box model nor to the bioengineering application. From the bioengineering application perspective, the proposed bioprocess design technique has benefits for both the regular feed-forward control and the advanced adaptive control systems, in which the model for biomass growth prediction is compulsory. After identification of the gray box model parameters, the options and alternatives in controllable industrial biotechnological processes are described. The main aim of this work is to achieve high reproducibility, controllability, and desired process performance. Glucose concentration measurements, which were used for the development of the model, become unnecessary for the development of the desired microbial cultivation process. Theoretical aspects for the practical application of adaptive control systems that operate in unknown, nonlinear, and time-varying biotechnological environments are still to be developed, investigated and implemented. For historic reasons, and reproducibility and restrictions imposed by regulations, the open-loop control approach dominates in industrial cultivation processes. The scope of industrial challenges, when dealing with adaptive control, consists of but is not limited to adequate (sufficient) modeling methods, the lack of direct measurements of the state variables and their indirect estimation tools, and the reliability of sensors.quality cannot be tested into products, it should be built-inor should be by design” [To speed up the implementation of advanced control systems for the control of biotechnological processes the USA Food and Drug Administration (FDA) has announced a well-known Process Analytical Technology (PAT) Initiative . One of  design” .Feeding of glucose solution serves as the main limiting factor to assure the controllability of an aerobic cultivation of Escherichia coli ,3. The ppO2 [Recent modeling approaches of varying complexity have aimed to speed up the development of biotechnological processes ,13,14,15pO2 . This avThe development of desired bioprocess should involve designing a feeding rate time profile by relying on Occam’s razor . ConsequAfter choosing the gray box model to be used for fitting and smoothing the glucose consumption data, the authors’ choice is to apply the maximum (relative) entropy (ME) as a method to update from the prior probability distribution to the posterior distribution . A similIn this study, Gaussian prior distribution represents the data noise but with the additional assumption that the model’s uncertainty is equal to the square of the model’s local mean. This idea originates from an optimal resource allocation study by Renaldas Urniezius (R.U.)  where thR project of statistical computing [The current work’s convex optimization routines’ analogy to statistical mechanics is important: Lagrange multipliers and their estimation procedures are treated similarly to how convex optimization routines using ME criterion are implemented in model fitting problems. In 2013, R.U., in cooperation with Adom Giffin, applied the ME numerical convex optimization implementation. That was the first attempt of a generic implementation of any number of boundary constraints for the criterion of the ME on any prior distribution. The effort involved the development of a generic ME package for the omputing . As a reomputing ,31. LateA study of optimal resource allocation  introducThe implementation of numerical semi-global optimization routines is analyzed simultaneously, paying attention to enforcing initial and boundary conditions. Practical benefits in new industrial process design are also highlighted. Finally, options and alternatives are discussed, which help to achieve high reproducibility, controllability, and desired process performance. The software tool and its graphical user interface, which were developed for this purpose, are also introduced in this paper.In this paper, X is the total biomass. The specific growth rate the biomass yield on substrate ) is performed [After the substrate consumption data is known, the selected gray box model is fitted to the experimental data. This model contains a partial form of the theoretical information about microbial strain growth properties and additional nuisance/perturbation dimensionless parameters. In the beginning of the process, exponential growth is assumed. This can be explained by the fact that, in the beginning of the process, specific biomass growth rate usually increases or remains constant. After the exponential phase of a microbial cultivation, the induction  is multiplied by both a coefficient of biomass concentration (approximately 0.4 g/L/o.u.) and cultivation medium volume in the bioreactor, and is then divided by a biomass yield of glucose (approximately 0.5 g/g) . SubtracThe first step of process design is usually to estimate the maximal available specific growth rate at different process phases. For this purpose, a common approach is to apply fed-batch cultivation with dosed glucose feeding, i.e., to obtain both the glucose cumulative consumption data and high biomass yield, fed-batch cultivation with feeding carried out in portions is a common choice . At the Choosing between glucose consumption calculation techniques depends on practical considerations. Usually, due to the bioprocess lag phase immediately after inoculation to a bioreactor, glucose concentration measurements are skipped. This happens at the beginning of a bioprocess. The main reason for this is that it is sufficient to monitor the dynamics of a dissolved oxygen level to decide whether the substrate feeding control must be started . In suchUntil this point, the procedure consisted of the calculation of the glucose consumption profile in liters per hour. The feeding substrate solution has a certain density and glucose concentration in the solution. Therefore, the retrieval of the substrate solution weights still requires multiplication of the current profile by substrate density value (g/L) and its division by the glucose concentration of the feeding substrate. This finally yields the feeding solution profile to be processed by an industrial controller with weighing scales attached. This profile represents the maximum oxidation capacity, which does not necessarily mean the maximum expression of a product. Therefore, the design of the glucose feeding rate time profile would help to avoid glucose overfeeding and the metabolism pathways associated with it. For this purpose, a time dependent model is necessary to effectively fit the feeding profile data.Time dependence is one of the key properties of the “black box” model to assure that slowing down the time variable with respect to actual physical time would help to preserve the shape of a feeding profile. A parametric construction of model structure makes this model a gray one since some of its structure is inherited from fundamental knowledge about the bioprocess. In other words, the basic course of the process can be described by a family of exponential relationships, and this partially matches fundamental principles of cell biomass growth. The whole bioprocess consists of two stages, and may be described by two gray box models. The first one covers both lag and exponential stages of the cultivation process:c growth ). Even iThe second stage model defines how the process behaves after the time of induction (or after a time close to the induction time stamp):k-th oscillation cycle has been calculated. Similarly to the procedure in [k-th oscillation cycle is:The application of the gray box model for time series probabilistic variables  in ME fiedure in , Equatiok.A probability density function defining the prior distribution is assumed also to be Gaussian, represented by a joint prior :(7)PlikeThe exponential stage is one of the main stages of the whole bioprocess. One of the exponential distribution properties is that its variance matches the squared mean. The same applies to gamma distribution, which is a special case of exponential distribution.Measurements of optical densities at higher densities involve increasing measurement errors due to dilution of the samples, which might cause exponentially increasing bias.It is normal for optical densities to increase by more than 300 times during the process. Consequently, any local or microscopic measurements, such as glucose concentration and OD measurement, involve increasing bias from the macroscopic point of view.From the point of view of classical control theory, there are nonlinear system parts which will influence the feedback, and their effect intensifies as the concentration of biomass increases—for example, the permeability of the feeding hose, any external disturbance on the weighting scale equipment, overpressure changes in bioreactor, etc.Equation (7) shows that the mean of each observation  is proportional to its deviation value. An idea of evaluation of the biomass prediction error or bias through the percentage to the actual observed value is not new. Mean absolute percentage error (MAPE) frequently serves as a tool for biomass prediction or estimation accuracy ,40. Inte(1)There will never be zero biomass concentration after inoculation into a bioreactor. (2)The lag phase covers smaller OD values and smaller estimation errors are literally expected during this period.(3)The maximum OD values usually depend on the strain cultivated, so there are no expected comparison issues when considering the repeatability of the estimator.MAPE evaluation has statistical disadvantages :The idea of having process variable participate as uncertainty originates from financial applications where a fair game condition, between banks of variable sizes, was achieved by treating the annual bank turnover as the uncertainty for the bank’s remittances to ATM network. This showed both performance and optimum wise satisfactory results .Construction of prior and posterior distributions allows shifting to the actual model fitting.k starts with:Prior to model fitting, the resolution of expression of relative entropy for a single discrete time moment Integration of Equation (8) yields:There are two pieces of information that have still to be accounted for in the relative entropy: initial and boundary conditions. The initial biomass concentration condition is relevant, because the shake flask procedures, prior to inoculation, are usually standardized in such a way that biomass concentration is known with smaller uncertainty at the beginning. Since The main boundary condition of the optimization task is making certain that the model fitting curves originating from Equations (2) and (3) intersect smoothly. The time at which both model curves intersect usually corresponds to the induction time, however this is not a strict requirement. The mismatch sometimes indicates that the bioreactor system failed or was planned improperly to induce the IPTG at the right time. However, such discrepancy might be an expected designed behavior which is subject to the requirements of the process development.Three soft restrictions on the rate of change of substrate feeding profile eventually all lead to satisfactory model fitting results. The first ensures that both curves should have the same rate of change at the intersection. The uncertainty of this variance is divided by a factor of 2 compared to Equation (10). The most recent substrate feeding value, prior to the intersection, and the first value immediately after it, both conclude the remaining two loose data constraints. However, the variance is multiplied by a factor of 100 this time, because there is more uncertainty introduced by a considerable time difference. It is anticipated that these two last soft constraints improve smoothness from an acceleration perspective at the intersection.The rate of change of the statistical means Equation (5) has closed form parametric expressions by differentiation:Thus, the final form of ME Equation (10) criterion becomes:Equation (12) recovers the weighted sum of residuals ,42 and m physics ,44, whicThis work’s principle of convex optimization lies in the updating of a prior probability density function to the posterior one by employing:ented in . Equatior images  and vectr images , where aλ configuration space, where circles filled with a color portray the states in a pseudo time, which is defined as a nuisance time. These states are the optimization stages of the entropy maximization, in case of sampling it while updating from prior to posterior function. The meaning of nuisance time and nuisance trajectories is similar to that of nuisance parameters [rameters , and theThe nuisance time and its trajectories define, from an algorithmic point of view, how numerical convex optimization must be implemented.Similarity of the nuisance time and its implications exists to the parsimony principle , where aA trade-off between goodness of fit  and parsThe workflow of numerical identification of the gray box model parameters consists of two proactive parts . The fir3) and (4) and consists of two major steps:The entropy calculation of numerical blocks #1 and #2  uses Equ1. For historic reasons, the resolution of This ends with the closed-form 2. This step is the major convex optimization step which follows the trajectories of nuisance time and deals mainly with the determination of At the start of program execution, the initial base step sizes are Then, At the beginning, numerical block #1 in There are other sampling window strategies which depend on the threshold dedicated for relative entropy , howeverThis revision procedure of When three base steps all reach a convergence precision tolerance of lower than 0.0001 . This allows the user to not only qualitatively change the composition of the feeding solution, but also makes it possible to use smaller, more economical weighing solutions for the cumulative glucose profiles that exceed the maximum measurement range of the weighing mechanism.The last option includes an actual limitation tool for the feed rate limitation. This is a key feature of the gray box model-based design allowing the avoidance of changes in metabolic pathways during the cultivation process and tracking the desired glucose consumption trajectory. Scenario 1 contains a single limiting percentage factor for the whole cultivation, and Scenario 2 has two separate limiting percentage factors—the first one for the model prior to the intersection of the profile curves and the other one for the remaining time after the intersection moment. In Depending on the product to be maximized, the strain cultivation capabilities, and process duration trade-offs, different limitation alternatives should be designed. A general recommendation is to start with at least three parallel cultivations prior to proceeding with any scale-up. Depending on the output, the gradient direction on what to change next will become evident.The substrate feeding profile design software tool has a separate dialog for scaling up or scaling down procedure, where the profiles can be scaled relying on the ratio between the initial volumes in a bioreactor vessel. This procedure is also determined by the same gray box model and its optimization using ME. An efficient scale-down procedure has to create conditions that will be representative for the conditions occurring at a large scale.In conclusion, the gray box model and convex ME optimization both help to design the profile and allow the design of the scale-up, scale-down and prediction strategies in the profile, which is essential for the adaptive control system of bioprocesses.Three types of verifications are provided by this paper’s gray box model approach. Firstly, OD curves were compared between multiple cultivation processes that had same substrate feeding profile applied. Such a test verifies the reproducibility of the approach and, thus, also its controllability, as defined by the Pareto principle earlier in this paper. Secondly, gray box model fitting to the third party  30-min dAs shown in After the discussed application of the gray box model, the fed-batch cultivation with feeding carried out in portions produces the cumulative glucose feeding profile  that wasE. coli strain inoculated at the beginning of the process. All three curves of optical densities are shown in Each cultivation had an identical initial bioreactor volume, initial glucose concentration, medium OD, and Identical substrate feeding solutions were prepared for all three cultivations and their bioreactors were equipped with identical means to perform the biosynthesis. The resulting optical density curves show that there is a reproducibility, as all optical curves have similarity and controllability since the final OD at the end of the limited growth process matched.For illustrative purposes, the third party’s  first cuThe gray box model parameters were estimated from the dataset consisting of 30-min data. Then, those parameters were used to decompress the profile to nine-second data and compare it with the dataset provided in . The resThe next task is to assess the sensitivity of the numerical procedure described in this work to the choice of initial seed values for the iterative procedure.E. coli strains, which are positive and are usually less than 1. The parameter Both of the parameters In conclusion, the MAPE values do not increase more than 0.32%  at diffepO2, PH, temperature, and substrate feeding parameters, the signals of which all provide information for the implementation of gain scheduling algorithms in such control systems. To find the gray box model parameters in both of the feedback control scenarios, the cumulative glucose volume is introduced as uncertainty into the convex optimization task. A description of nuisance time is provided, which explains how to efficiently arrive at the multivariate solution. Additionally, experimental data analysis was conducted with practical illustration of bioprocess reproducibility and controllability, assessment of goodness of fit to a dataset acquired from a third party, and sensitivity analysis of the numerical routines to the seed values of initial parameters, which shows one more practical benefit of the presented approach.In this study, a generic bioprocess gray box modeling approach is presented that uses entropy maximization to derive an optimization criterion for planning and/or prediction of the feeding solution profile. The proposed design procedure has benefits for the improvement of existing industrial feed-forward and adaptive feedback control systems. Because of the proposed procedure, process controllability is gained, ensuring good control qualities of E. coli cultivation processes.In the scenario of a regular open-loop biotechnological system, in order to evaluate the specific growth rate profile, the first fed-batch cultivation process with feeding carried out in portions is performed. The volume of the substrate fed during fed-batch steps and observations of glucose concentration in the bioreactor media all help to evaluate the substrate consumption. After the gray box model parameters are identified through ME, the procedures to realize industrial cultivation processes under growth-limited conditions are described, with emphasis on applying different limitation options and alternatives. Glucose concentration measurements become unnecessary during these subsequent cultivations to identify the best run time profile, which corresponds to the desired trajectories of process variables . In the adaptive control system, the authors’ approach serves as a short-term forecasting tool, which helps to both design the feed rate time profiles and predict the induction time in the recombinant


Electronic devices that transmit, distribute, or utilize electrical energy create electromagnetic interference (EMI) that can lead to malfunctioning and degradation of electronic devices. EMI shielding materials block the unwanted electromagnetic waves from reaching the target material. EMI issues can be solved by using a new family of building blocks constituted of polymer and nanofillers. The electromagnetic absorption index of this material is calculated by measuring the “S-parameters”. In this article, we investigated the use of artificial intelligence (AI) in the EMI shielding field by developing a new system based on a multilayer perceptron neural network designed to predict the electromagnetic absorption of polycarbonate-carbon nanotubes composites films. The proposed system included 15 different multilayer perception (MLP) networks; each network was specialized to predict the absorption value of a specific category sample. The selection of appropriate networks was done automatically, using an independent block. Optimization of the hyper-parameters using hold-out validation was required to ensure the best results. To evaluate the performance of our system, we calculated the similarity error, precision accuracy, and calculation time. The results obtained over our database showed clearly that the system provided a very good result with an average accuracy of 99.7997%, with an overall average calculation time of 0.01295 s. The composite based on polycarbonate−5 wt.% carbon nanotube was found to be the ultimate absorber over microwave range according to Rozanov formalism. Since 2019, artificial intelligence (AI) is in the spotlight. This technology is developing at high speed, and its use cases are increasing in all sectors. According to experts, AI is set to overturn all aspects of our society in the years to come [An immense variety of static phenomena can be characterized by a deterministic relationship between causes and effects; neural networks are good candidates for modeling such relationships from experimental observations, given that there are sufficiently numerous and representative data .AI algorithms and machine learning are making headlines in several areas, such as commerce, medicine, education, autonomous driving, entertainment, etc. AI does not leave aside the field of scientific research, where it imposes itself as an essential tool for scientific discovery. The new AI method has been developed by an interdisciplinary team of chemists, physicists, and computer scientists from the University of Warwick, the Technical University of Berlin, and the University of Luxembourg . AccordiArtificial intelligence and machine learning algorithms continue to catalyze discoveries in the field of scientific research. A recent method developed by researchers has shown that AI can be used to predict the quantum states of molecules, called wave functions, which determine all the properties of molecules. According to the researchers behind the discovery, their algorithm could also significantly speed up future simulation efforts in the design of drug molecules or new materials . AlgoritAmong the methods of artificial intelligence is the multilayer perceptron (MLP), which is a computer system inspired by the functioning of the human brain to learn . In prinThe MLP works as follows: typically, a neural network relies on a large number of processors operating in parallel and organized into three stages. Input and output layers shown in Unlike other types of algorithms, neural networks cannot be directly programmed to perform a task. Like a developing child’s brain, the first assignment is to understand the basic mechanism of their environment to get useful information. Through an algorithm, the artificial neural network allows the computer to learn from new data. The computer with the neural network learns to perform a task by analyzing examples for training. These examples have been labeled beforehand so that the network knows the output corresponding to a specific input set and then fine-tune the network.Polymer nanocomposites, consisting of nanoparticles dispersed in a polymer matrix, have gained large interest due to the attractive properties of nanostructured fillers, which are embedded in low volume fraction (1–5%) into the polymeric matrix, providing important electrical properties, which allow the insulating matrix to pass from dielectric to semi-conductive class . Multi-wBased on the multiplicity of electromagnetic absorption index values over a different large of frequencies for various polycarbonate/MWCNTs film composites characterized by a VNA instrument, the implementation of a computing new design is needed. It aims to give the same performances as a VNA analyzer and a prior knowledge of electromagnetic absorption index for researchers who investigate on EMI shielding field of polycarbonate (PC)/carbon nanotube (CNT) microdevices. This method can suppress multiple series of tests, as well as calculus procedure of raw data conversion to physical parameters , and calibration of the machine prior to use, adding to all of these characteristic parameters: time measurement dependency and requirement of a qualified operator having the ability to resolve the problems that may survey during the measurement procedure. The development of a new numerical method consisting of a multilayer perceptron neural network, aiming to predict the electromagnetic absorption index at an identified frequency, is designed to surpass the above issues. This method has been chosen due to the large and different scale of input data as it is presented in This paper is organized as follows: Any electronic device that produces, transports, and consumes electrical energy generates electromagnetic interference (EMI); this latter can cause malfunctioning and device damages, as well as an increase in electromagnetic pollution, affecting human health if no shielding is provided . EMI shiIn this article, we investigated the use of AI in the EMI shielding field by developing a new system based on a multilayer perceptron (MLP) neural network (NN) method, designed to predict the electromagnetic absorption of PC/CNT composites films reinforced by different weight percentage of carbon nanotubes.The state-of-the-art shows that machine learning techniques have never been used in the absorption index prediction of polycarbonate/CNT composites. This can be explained by the lack of sufficiently large databases.The originality of this work is described hereafter. The objective was to obtain by predictions using the AI MLP algorithm the absorption index in an automatic way without having to go through the measurement procedure . The idea was to apply a machine learning method to the prediction process, exploiting the data values of PC/CNT composites films’ information of different samples. As we assumed to have a priori knowledge of the absorption index of each input, it was possible to predict the absorption index of a new composite by means of supervised machine learning. Finally, the optimal concentration of carbon nanotubes for maximum EM absorption was determined by a method independent from geometrical aspects (such as thickness) and frequency band. The efficiency of any absorber was determined by Rozanov formalism, which is used as the reference in the domain . The obj® OD2015 polycarbonate. The CNTs were multi-walled NC 7000 nanotubes provided by NanoCyl SA, Belgium. The nanocomposite compounds were melt-blended at 280 °C for 5 min at 150 RPM in a micro 15 DSM micro-compounder. The composite pellets were twice hot-pressed under six different operating conditions adapted to the viscosity of the pellets in a Fontijne press to produce films with various thicknesses, as shown in PC-xxxCNT and T°/press./time represents the composite name and the pressing conditions .The polymer matrix used for the conductive nanocomposites was Bayer MakrolonThe electromagnetic characterization is made by an Anritsu M54644B in waveguide configuration described in Ref. . The calIn the current work, 34,362 experimental data points for the electromagnetic absorption index of PC/CNT composite films were derived using the method described in Ref.  from thePC-xxxCNT film the user has chosen.The complete system included 15 different MLP networks; each network was specialized to predict the absorption value for each composite. Optimization of the hyper-parameters using hold-out  validatiThe capacity of nonlinear classification/prediction methods seems to be particularly interesting and powerful. However, before implementing such models, it is necessary to ensure the necessity of nonlinear properties of the proposed model. Indeed, if the prediction does not require a particular complexity, the use of linear methods would be more judicious. If the use of nonlinear methods is necessary, the choice between several machine learning methods cannot primarily be decided before an understanding of their applicability in modeling a specific issue, as applied in this article .For our study, beforehand, we reported results of a comparative study of widely used machine learning algorithms applied to predictive polycarbonate-CNT composites absorption index. The machine learning algorithms involved were chosen in terms of their representability and diversity, were extensively evaluated with seven data sets that were taken from real-world applications . Some interesting findings relating to the data and the quality of the models were obtained in advance; MLP seemed the best for the several machine-learning methods tested in which we obtained the best accuracy with a minimized calculation time.We chose to work with the MLP network because it gives us the advantage of the following three main pointsFlexibility of neural networksMLP has the capacity to deal with a wide variety of problems. The result can be a prediction, classification, or data analysis. They make it possible to deal with unstructured problems, where no information is available beforehand .Yielding of important experimental resultsMLP gives good results because, even in very complex fields, they are more efficient than statistics or decision trees. By discovering the relationships between the variables, the implemented system does not force us to question the form of the function to be estimated ,27,28.Relevance of the analyzed dataMLP can work on incomplete or noisy data. This data imperfection can be filled by adding additional neurons to the hidden layer .-Step 1: Setting the number of hidden layersThe implementation of multilayer neural networks includes two design parts: first, the objective is to lead toward the best choice of architecture, then a part of numerical calculation carries out the learning of a neural network. The procedure can be divided into four stages:Aside from the input and output layers, the analysis must decide the number of intermediate or hidden layers. Without a hidden layer, the network offers only weak possibilities of adaptation. With one hidden layer, it is capable, with a sufficient number of neurons, of approximating any continuous function. A second hidden layer takes into account any discontinuities.-Step 2: Determining the number of neuronsA larger number of neurons makes possible a better stick to the data presented but decreases the generalization capacity of the network. Consequently, we varied the number of neurons and calculated the mean square error (MSE) to choose the optimal number, leading to the best performance and minimizing the calculation time.-Step 3: Choosing the activation functionIn this work, we considered the sigmoid function  for the -Step 4: Choosing the learningSupervised learning is applied by providing the network with the inputs and, at the same time, the desired outputs. The network must be trained by adjusting its weights so as to reduce the difference between the desired output and its actual output; this procedure is repeated until a performance criterion is satisfied. The learning algorithm used in our work was that of backpropagation of the error because the latter is the most suited for learning MLP-type neural networks .In general, to evaluate the performance of a machine learning system , we diviDifferent measures can be calculated to assess the performance of a system; for our study, we were interested in the similarity error between the actual absorption index and that predicted, thus, the response time of the system. So-called x is the actual value of the absorption index, y is the predicted one, L is the number of tested samples; the similarity error (Err) and the precision accuracy (P) are calculated as follows:1.The measurement of the reflection coefficient of PC/CNT films back coated with a PEC was performed using a vector network analyzer (VNA) and a waveguide configuration.2.The Rozanov performance was calculated according to:The previous section investigates the role of machine learning in predicting the EM absorption index of PC/CNT composites over a wide range of frequencies. As can be seen in fficient .3.This equation was used to calculate the theoretical thinnest film of PC/CNT composite following the Rozanov formalism.4.Finally, a graph of the figure of merit (FOM) defined by the ratioBy introducing the constant parameters (Many arrangements were tested to find an MLP architecture that provides a satisfactory performance without high computational effort and minimizing the calculation time.It was observed that when using more than ten neurons in the hidden layer and more than one hidden layer, there were no significant changes in the performance. Therefore, architectures with more than one hidden layer should be eliminated, as this would only increase the computational effort.The networks were trained on an Intel core i5-4200m 2.5 GHz CPU with 4 GB of RAM. We stopped the training when there was no longer decrease in the loss.Arbitrary use of hyper-parameters in different machine learning algorithms leads to overfitting and underfitting problems, the main cause of the poor performance of predictive models. From the Additionally, the FOM based on Rozanov formalism described in This paper proposed the study of polycarbonate-carbon nanotubes conductive composite films as absorbers of electromagnetic interference at a large band of frequencies. Prediction and simulation of the electromagnetic absorption using AI were the purposes of this study. For this reason, the design of a new system based on an MLP method was considered. A series of evaluation and optimization was realized, aiming to minimize both mean fitting error and calculation time to ±0.002003 and 0.01295 s, respectively. The use of AI models is a new insight into the next studies of PC/CNT composites for EMI shielding applications. The present work showed that the composite PC-5 wt.%CNT reached the best absorption index following the Rozanov formalism with the implementation process by hot extrusion. Future work will be carried out, focusing on the modeling of the degree of percolation and dispersion of the CNT in the polycarbonate matrix using image processing and deep learning. Besides, a deeper real-time validation of our approach could be carried out via the comparison between time-dependent VNA measurements of absorption with its simulation time using our MLP algorithm.Overall, the main contribution and originality of our work compared to the state-of-the-art are the combination of MLP algorithm and Rozanov formalism, allowing us to gauge and optimize the performance of CNT-based absorbers, prior to their fabrication.


Urinary tract infection (UTI) is the most common reason for antibiotic prescription in nursing homes. Overprescription causes antibiotic-related harms in those who are treated and others residing within the nursing home. The diagnostic process in nursing homes is complicated with both challenging issues related to the elderly population and the nursing home setting. A physician rarely visits a nursing home for suspected UTI. Consequently, the knowledge of UTI and communication skills of staff influence the diagnosis.The objective of this study is to describe a cluster randomized controlled trial with a tailored complex intervention for improving the knowledge of UTI and communication skills of nursing home staff in order to decrease the number of antibiotic prescriptions for UTI in nursing home residents, without changing hospitalization and mortality.The study describes an open-label cluster randomized controlled trial with two parallel groups and a 1:1 allocation ratio. Twenty-two eligible nursing homes are sampled from the Capital Region of Denmark, corresponding to 1274 nursing home residents. The intervention group receives a dialogue tool, and all nursing home staff attend a workshop on UTI. The main outcomes of the study are the antibiotic prescription rate for UTI, all-cause hospitalization, all-cause mortality, and suspected UTI during the trial period.The trial ended in April 2019. Data have been collected and are being analyzed. We expect the results of the trial to be published in a peer-reviewed journal in the fall of 2020.The greatest strengths of this study are the randomized design, tailored development of the intervention, and access to medical records. The potential limitations are the hierarchy in the prescription process, Hawthorne effect, and biased access to data on signs and symptoms through a UTI diary. The results of this trial could offer a strategy to overcome some of the challenges of increased antibiotic resistance and could have implications in terms of how to handle cases of suspected UTI.ClinicalTrials.gov NCT03715062; https://clinicaltrials.gov/ct2/show/NCT03715062DERR1-10.2196/17710 Urinary tract infection (UTI) is the most common reason for antibiotic prescription in elderly individuals living in nursing homes in Europe, and many of these prescriptions are considered inappropriate -3. OveraTwo dominant factors associated with elderly individuals living in nursing homes contribute to overtreatment. First, it is widely accepted by nursing home staff that unspecific changes in behavior indicate UTI . HoweverIn the Capital Region of Denmark, most nursing homes are public, but private homes also follow municipality regulations, making labor coverage similar. A home with 60 residents employs 2 to 3 nurses and 20 to 25 health care helpers and health care assistants . Health care helpers and assistants attend to the residents’ everyday needs. Only if a resident appears unwell, a nurse gets involved. When UTI is suspected, the staff usually contacts the residents’ general practitioner (GP), the affiliated nursing home physician, or the out-of-hour service. In addition, physicians often prescribe antibiotics for UTI without directly evaluating the patients themselves, and therefore, rely on the clinical history provided by the nursing home staff ,16.Owing to the interaction between the properties of the elderly population and the nursing home setting, diagnosis and treatment are directly influenced by the knowledge and communication skills of the staff . If nursWe hypothesize that a tailored complex intervention improving the knowledge of UTI and communication skills in nursing home staff will decrease the number of antibiotic prescriptions for UTI in residents, without changing hospitalization and mortality. The aim of this study is to describe a protocol for measuring the effect of this intervention.The protocol describes an open-label cluster randomized controlled trial with two parallel groups and a 1:1 allocation ratio. Originally, we planned recruitment and allocation of nursing homes to be completed by the end of 2017. We aimed to complete the planning of the start-up phase  by the summer of 2018 and finish the start-up phase by the fall of 2018. The trial was planned to last for 4 months. Data collection, analysis, and dissemination were to commence in the spring of 2019 and end in the spring of 2020.There is no central registry of nursing homes in Denmark; therefore, we use a convenience sample method. We seek volunteers through the network of hygiene nurses in the Capital Region of Denmark, who organize local meetings and conferences and have direct contact with key hygiene personnel at nursing homes.Nursing homes enrolling in the trial should not participate in other UTI projects during the trial period. Nursing homes should be situated in the Capital Region of Denmark. They should have common areas with attending staff 24 hours a day. The living spaces for residents with dementia are included, but living spaces for other special needs are not included. Nursing home eligibility screening is to be completed during the recruitment period. Residents should be over 65 years of age and should permanently occupy a living space at an eligible home. According to Danish law, this study is a communication study. Therefore, residents meeting the eligibility criteria are included in the study, unless they or their legal guardians decline access to health information for the trial. Residents’ eligibility screening is to be performed during collection of informed consent permitting sharing of health care data.The primary components are a dialogue tool and a workshop. They target all staff members who have nursing responsibilities in the intervention group. The supporting components relate to communication with stakeholders .Each component of the intervention is described below, and the components received by the intervention group and those received by both the intervention and control groups are presented. Examples of the components are provided in The dialogue tool consists of a reflection and a communication section. The reflection section has three parts. First, a form that enables systematic gathering of signs and symptoms exhibited by the resident. Second, a flowchart to determine if UTI is likely or unlikely. These two parts are based on the revised Loeb Minimal Criteria for ordering urinary culture . Third, The tool is tailored through an iterative process that involves focus groups with stakeholders. Whenever UTI is suspected, a paper copy of the tool will be available for use at the staff office. If any adverse events occur because of the tool, the local coordinators will contact the principal investigator (PI). At least two members of the study group will evaluate the main cause of the adverse event. If the tool is deemed responsible, the entire group is gathered to determine if the trial should be terminated.The tool is introduced to the staff in a 75-minute workshop. At each nursing home, we aim to include as many staff members as possible within three separate workshops. The PI will facilitate all workshops. The focus of the workshop is threefold. First, the staff members learn the distinction between UTI and asymptomatic bacteriuria. Consequently, diagnostic caveats regarding urinary testing, odor, and urine clarity will be discussed. Second, an approach to evaluate unspecific symptoms is discussed. This approach specifically considers the importance of excluding other reasons for the observed signs and symptoms. Finally, the staff members receive training on how to use the tool for test cases.All nursing home physicians in the intervention group receive letters that include an open invitation to participate in the workshop, information about the trial, and contact details of the study group. The letters are emailed to the nursing home physicians.All staff members receive letters that describe the trial and their role from the management of the home and the PI.Posters are provided in each home to remind the staff when and how to use the tool. The posters also direct the staff to local coordinators for questions. Posters are visible at staff offices.In 2016, the state, the regions, the municipalities, and the General Practitioners’ Organization in Denmark agreed to employ affiliated nursing home physicians in nursing homes . By 2019Each home appoints a local coordinator. There are no prespecified eligibility criteria. This pragmatic approach is adopted owing to the heterogeneity of the internal organization of the home. Local coordinators in each arm receive letters describing the trial and the UTI diary.Residents and relatives are informed about the trial through letters posted in the nursing home newspaper or website containing a description of the trial and contact information of research group members.Posters containing information about the trial are prepared for residents and relatives. They are visible in common areas.To prevent dropout, unedited trial data are returned to the homes when data collection is complete. Optionally, the PI can facilitate workshops in the control group after evaluation.In this trial, data are collected from three different sources as presented below.number of living spaces for residentsnumber of livings spaces designated for normal care needs, dementia, and psychiatryowner status of the home (public/private)availability of dipstick (yes/no)affiliated physician (yes/no)Background information about nursing homes is collected from the management during the enrollment period and includes the following:social security numberuse of catheter (yes/no), and if yes, type of catheter use of incontinence aids  (yes/no)mobility status capability of providing informed consent to share health care data (yes/no)treatment for diabetes (yes/no), and if yes, the type of treatment (generic drug name)number of acute and prophylactic treatments for UTI  1 year prior to the trialprophylactic treatment for UTI (yes/no), and if yes, the type of treatment (generic drug name)Collection of background information on residents commences during the trial and is collected from the nursing home medical records. The following information is obtained:generic drug namestart date (yyyymmdd)duration of treatment (number of days)strength, dosage, and frequency prescriber background indication (curative/prophylactic UTI)With regard to all-cause hospitalization, the following information is obtained: date of hospitalization (yyyymmdd); date of discharge (yyyymmdd); and suspected cause of hospitalization according to staff. With regard to all-cause mortality, the following information is obtained: date of death (yyyymmdd).Data on prescriptions for UTI, all-cause hospitalization, and all-cause mortality during the trial period are also retrospectively registered. With regard to antibiotic prescriptions for UTI, the following information is obtained:Signs and symptoms on days 1-8new urinary symptoms other new severe symptoms new onset signs of other infectious diseases other new observations temperature [degrees Celsius]blood pressure [systolic/diastolic]pulse [beats per minute]urinary dipstick result Events on days 1-8increased observation/triage [yes/no]preventive measures [yes/no]dipstick test [yes/no]physician contacted [yes/no]urinary sample [yes/no]change of catheter [yes/no]antibiotic [yes/no]change of antibiotic [yes/no]antibiotic discontinued [yes/no]result of urine culture [positive/negative/unknown/not done]Prescriptions for UTI on days 1-8generic drug namestart date [yyyymmdd]duration of treatment [number of days]strength, dosage, and frequency Hospitalization on days 1-8 Death on days 1-8 (day of death)During the trial, local coordinators complete a diary for each resident with suspected UTI during an 8-day period. The diary is introduced to local coordinators at the nursing home during a 30-minute meeting with the PI. The diary includes the following:The UTI diaries are collected once during the trial period and once after the trial has ended. Examples of UTI diaries are presented in The primary outcome is the number of antibiotic prescriptions for acute UTI per resident in 4 months following contact between the staff and a prescriber. Antibiotics prescribed during hospital visits are not included, because prescriptions by hospital physicians are independent of those by nursing home staff. Prophylactic treatments are also excluded.The secondary outcomes are as follows: number of all-cause hospitalizations per resident in 4 months; number of all-cause deaths per resident in 4 months; and comparison of suspected UTI during the trial. Groups are compared with regard to the numbers of acute antibiotic treatments for suspected UTI, antibiotic treatments for urinary tract symptoms, UTI-related hospitalizations, and UTI-related deaths.Data collected from medical records and diary entries are subjected to double data entry. A data manager performs merging, anonymization, and range checks. Trial data are stored in accordance with the data policy of the University of Copenhagen . Data wiOur primary outcome involves clustered count data, and we determined a minimum sample size according to the method for clustered Poisson regression . In DenmThe study statistician randomly assigns the nursing homes to the two study arms with a 1:1 computer generated randomization schedule stratified by municipality. Owing to the nature of the intervention, the trial is open labelled. The statistical analysis is blinded. Primary and secondary outcomes are analyzed using a Poisson regression model and generalized estimating equation.Because the study is not a health science project as defined in the Danish Committee Act § 2, the Research Ethics Committee of the Capital Region of Denmark has waived the need for full ethical approval . The Danish Patient Safety Authority has approved data collection for those residents unable to participate in informed consent for sharing of their health care information . We have collected informed consent from the rest of the residents from the start-up phase until the data collection phase. The study has been reported to the Danish Data Protection Agency.The timeline of the trial deviated slightly from the original plan. Particularly, enrollment and allocation were completed in June 2018. The trial ended at the end of March 2019; data have been collected and are being analyzed. We expect the results of the trial to be published in a peer-reviewed journal in the fall of 2020. The original idea for the intervention was to provide nursing home staff with a biomarker for severe infection (C-reactive protein as a point-of-care test) in addition to the dialogue tool. Moreover, the primary outcome was originally appropriate prescription for UTI, but this was changed to number of prescriptions for UTI. The changes had been decided prior to registration at ClinicalTrials.gov.The aim of this study is to describe a protocol for evaluating the effects on antibiotic prescription, hospitalization, and death of a tailored complex intervention to improve the knowledge of UTI and communication skills among staff caring for residents with suspected UTI. The dialogue tool is based on a decision aid and a communication tool tested in clinical trials that have previously reduced antibiotic prescriptions ,22. The We apply a tailoring process to develop the intervention and decrease barriers to implementation. In order to facilitate the collection of data and to tailor the intervention process to nursing homes, the research group decided to modify the original project plan slightly. This strategy is recommended to increase the impact of complex interventions . We primThe greatest strength of the study is the randomized design. On the other hand, blinding can only be introduced in the data analysis. Owing to the format of the intervention, the open-label design is indispensable but introduces the control group to the potential risks of contamination and selection bias. This bias cannot influence the primary outcome, because it is obtained from the nursing home medical records. The medical record extracts information on prescribed drugs from “Fælles Medicin Kort,” which registers all prescriptions in the past 2 years . Thus, aThe trial is pragmatic. First, the eligibility criteria for both homes and individual residents are broad, which may result in a heterogeneous population. Second, the implementation of the intervention is left to the individual homes after completion of the workshop. Third, during the trial period, the homes are visited only once by the PI, unless they initiate contact. As the homes differ in organizational structure, a uniform and strict design was not feasible.Nursing home medical records only include information about all-cause hospitalization and all-cause mortality. The drawback is that the information may be unrelated to UTI and that a relevant change may be diluted. We gain access to the social security numbers of residents, thereby permitting a long-term follow-up of residents through national registries. This could reveal any indirect effects on hospitalization or death. However, it is beyond the scope of this study.Because of the Hawthorne effect, participating in a trial poses a challenge when evaluating the effect of the intervention . ControlThe use of a UTI diary may induce diagnostic bias. The diary is used for suspected UTI in both groups, but staff in the intervention group may be more alert to the discovery of potential UTI. There is a risk that staff members in the intervention group complete more diaries than staff members in the control group. The diary is extensive, and both groups may find it too bothersome to complete. This is why the research group changed the primary outcome from appropriate prescriptions to number of prescriptions, which can be found in the medical records. Nevertheless, a UTI diary is the best way to capture important clinical data about patients. Among other things, the diary includes information about signs and symptoms, which are rarely reported in medical records.The problem of excessive and inappropriate antibiotic use is common in nursing homes. The study offers a strategy to tackle the challenges of increased antibiotic resistance and has implications in terms of handling suspected UTI. Some retailoring should be expected when transferring this intervention to an alternate setting.In conclusion, this trial will test the hypothesis that a tailored complex intervention targeting the knowledge of UTI and communication skills of nursing home staff will improve antibiotic prescription for suspected UTI in residents. The results may provide new insights and prove to be an important addition to the current strategies to limit superfluous antibiotic treatment for suspected UTI in nursing homes.


In this study, we use the characteristic infected cluster size to investigate theinhomogeneity of the epidemic spreading in static and dynamic complex networks. Thesimulation results show that the epidemic spreads inhomogeneously in both cases. Also, theinhomogeneity of the epidemic spreading becomes smaller with increasing speed of movingindividuals and almost disappears when the speed is high enough. The epidemic has always been a threat to the human health. Therule of the epidemic spreading as an important issue has been quite extensively studied formany years, but has not been mastered well. The epidemic spreads by the interaction betweenpeople, and complex networks, whose nodes represent individuals or organizations and link theinteractions among them, can characterize this interpersonal interaction perfectly. In orderto conveniently study the epidemic spreading, researchers usually adopt the homogeneousnetwork structure and the homogeneous mixing hypotheses. Recently, researchers began todiscuss the credibility of the homogeneous network structure hypothesis and consider theinhomogeneity of the network structure. However, the credibility of the homogeneous mixinghypothesis has not been well discussed until now. In this paper, we try to discuss thiscredibility and carry out numerical simulations on an epidemic spreading model. The resultsshow that the epidemic spreading is inhomogeneous whether the individuals are stationary ormoving. However, the inhomogeneity decreases with the increase of individuals’ speed, andalmost disappears when the speed is high enough.I.In recent years, physicists and biologists have been attracted to the spreading of theepidemic. Undoubtedly, the successive large-scale outbreaks of SARS (Severe AcuteRespiratory Syndrome) and H1N1 make them more enthusiastic about this study. Complexnetworks as a new branch of statistical physics provide a reliable model for the intensivestudy of the epidemic spreading. When studying the epidemic spreading in complex networks,researchers usually adopt the homogeneous network structureWe will focus on the susceptible-infected-susceptible (SIS) modelIn this work, the inhomogeneity of the epidemic spreading is characterized by thecharacteristic infected cluster size (hereinafter referred to as “CICS”), where the clusteris the subnet whose nodes are connectedII.In our model, We assume that the update of the individuals’ positions and states is simultaneous with thefixed time interval et al.et al.Researchers have analyzed the epidemic thresholds of mode 1 in various complexnetworksIn the following, the number of individuals In this work, we only discuss the cases when In static networks, the individuals are distributed randomly and keep still over time, soeach individual connects to another with the same probability. Thus the degree distributionIII.In order to characterize the homogeneity of the epidemic spreading, we introduce the“homogeneous mode.” In this mode, the infected individuals, whose number isWith the increase of Correspondingly, the number and density of the infected individuals and the CICS in mode 1(mode 2) are denoted by For each value of The initial infection density is 10%, so the epidemic will be persistent with time, onlywhen the infection density is larger than 0.1 after a long spreading. Therefore, we make theepidemic spread in the square for a long time and then discuss the situation whenIn these cases, when see Fig. . They arFigure IV.In this section, we discuss the inhomogeneity of the epidemic spreading while theindividuals walk randomly in the square For each value of In order to further study the impact of speed As can be seen in Fig. As also shown in Fig. V.In this paper, the inhomogeneity of the epidemic spreading in two spreading modes of theSIS model is investigated. The simulations in the static and dynamic networks show that theinfected individuals are usually prone to gather into large clusters as the infectedindividuals always infect their neighbors. For such a reason, the epidemic usually spreadsinhomogeneously. Even in dynamic networks, the inhomogeneity can be kept well. And, thesmaller the infection density, the more inhomogeneously the epidemic spreads. However, theinhomogeneity decreases with the increase of the individuals’ speed in the dynamic networks,and the epidemic spreading becomes almost homogeneous when the speed is large enough.


OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of “hotspots” of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socio-economic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of “hotspots” and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination ( The ever-growing availability of large scale data sources pertaining to human activities in contemporary cities and the fact that the socio-economic and technological systems lend themselves adequately to representation through discrete elements and interactions between them have led recent years to witness an unprecedented increase in modelling of such complex systems using network theory .In urban science, there has been a significant research interest towards understanding urban systems particularly through modelling road structures, human mobility, traffic flow, and economic activity through a complex networks approach –4. In suAn area of research of particular interest in complex network theory is the study of the importance of nodes or edges in a network through centrality measures. Such measures are typically based on local and global network connectivity structures and include a variety of types: degree , closeneAnother important line of research in complex networks is temporal network theory: the study of the evolution and behaviour of networks over time. Temporal networks integrate network science with time-series analysis and contribute greatly to the modelling of epidemic spreading, transportation optimization, biological systems, as well as social networks .Although some recent work has focused on analysing the spatial patterns of different urban features , 21, stuOD) networks. More specifically, we focus on the spatial arrangement of the most central nodes of the OD network as identified by the Adapted PageRank Algorithm (APA) most impoented in  and furtented in , in whicthors in  and 41]most impothors in  also stuOD networks built from cellular traffic data. [OD networks with change detection techniques for identifying “change points” in time, in which the entire structure of the graph changes.So far, we have discussed static networks as the object of study with tools from network theory. However, since many real-world phenomena require modelling their behaviour over time, temporal network theory has become a valuable tool in many fields. This is the case with urban mobility which demonstrates important temporal patterns, the study of which could greatly inform urban planning, policy making, and management. A number of studies has attempted to analyze urban mobility from a temporal perspective.  use centic data. , study tThere have also been recent applications of graph neural networks on temporal sequences of graphs, mostly in a prediction setting. For instance, based on the length of prediction windows, previous studies of traffic forecast can be divided in dynamical modelling  based onOD networks, statistical dispersion measures describing the centrality value distributions, as well as a measure of spatial spread are presented in detail.In this section, the centrality measure applied to rank the attributed nodes in the PageRank model [PageRank vector) which establishes a ranking of all the pages analyzed according to their importance.The nk model  was propGoogle matrixG′  be a graph representing a network with n nodes, let be D the data matrix associated to nodes of G and let be Output: GbeginP from the graph G according to  is the distance between cell i and cell j, and spreading index is essentially the average distance between cells with Another downside of measuring heterogeneity of the obtained APA values with the Gini approach is that it does not offer the possibility to study the spatial arrangement of the “hotspots”—locations with very large APA values. The “hotspots” are defined as the grid cells with an APA value above a certain threshold x→* see . For choduced in  for measThis paper studies the spatio-temporal characteristics of urban mobility in Rome and London, two of the most emblematic and active cities in the world, both from socio-economic and touristic points of view.n Cartesian grid cells of size 1 × 1 km, and each such quadratic cell is considered a node in the graph.The urban territory has been subdivided into The raw GPS trajectories of around 10000 private cars spanning two years have been obtained from proprietary car insurance data for research purposes. The data have been cleaned, processed, and superimposed on the grid. Then, trip origin and destination GPS positions have been identified by interpreting the engine ignition on/off interval for each vehicle. Time intervals between 10 and 35 minutes showed robust outcomes, and 20 minutes were chosen for identifying car trips. The extracted origin-destination points are then mapped to the respective grid cells.OD networks have been built from the extracted origin-destination pairs described by the weighted adjacency matrix, i and ending at node j.The p = 3. The first column collects the number of car displacements from each node to the other nodes. The second column collects the data related to the shopping or commercial activity of each node. In this column we have added the following attributes: shops and shopping malls. The third column summarizes the data referring to the food-services economic sector. We have added the following attributes: restaurants, fast food, bars, cafes. All the features corresponding to food and retail activity per cell has been extracted from geo-referenced data from OpenStreetMap [Data matrix treetMap .The workflow of building the data sets is as follows:https://figshare.com/s/d61592a380dd508102b2.All the data used in this study can be consulted in the following doi: OD networks  subsection. The methodology can be summarized in After the city territories have been tessellated into 1x1km grid cells, the raw GPS data has been processed, trip origins and destinations have been extracted and the In this section we conduct the numerical experiments for the study and outline the principal findings. We then undertake a detailed discussion of the results in the forthcoming section.Mobility flow network only.Flow network with nodes attributed with information related to retail .Flow network with nodes attributed with information related to food services .We proceed to compute the APA values using Algorithm 1 for the following three kinds of networks:D that is in accordance with the economic activity being evaluated.Note that for each of these networks we use the corresponding data column of matrix The APA values of the Rome and London grid cells at different times of the day can be seen in p = 0.004), and a log-normal-like distribution in the case of London (p = 0.06). Although the exact distribution is irrelevant here, this finding suggests that different data-generating mechanisms might be in place in the two cities.The spatial as well as empirical cumulative distributions (ECDF) of the computed APA values in Rome and London are presented in Gini Coefficients subsection. In particular, as it is shown in We now proceed to analyzing the heterogeneity of the APA values in both cities, as described in With the aim of finding whether the Gini and Spatial Gini coefficients capture any difference between working days and weekends, both coefficients computed daily are represented in Gini Coefficients subsection, it tells us nothing about the spatial distribution of the APA values.Despite the fact that some conclusions can be drawn from observing a relatively higher Gini coefficient during the night hours in both cities and on the weekends in London, the temporal evolution of the Gini coefficient, as can be seen in Figs Gini Coefficients subsection. In essence, we are interested in finding how much of the Gini coefficient is due to non-neighbour heterogeneity. To achieve this, we follow the approach described in [In order to understand the temporal behaviour of the spatial component of the Gini coefficient, we resort to decomposing the Gini coefficient as described in ribed in  and use GI2 obtained from the observed data to the distribution of GI2 values obtained from random spatial permutations. It should be noted that this inference based on random spatial permutations is on the spatial decomposition of the Gini coefficient given by the expression . This method, called “LouBar”, is inspired by the classical technique for determining the scale for an exponential decay. Indeed, if the decay from F = 1 were an exponential exp − (1 − F)/a where a is the scale to be determined, the described method would yield For a given distribution of data, the construction of the Lorenz curve proceeds as follows. For a set of values of cardinality ribed in , we noteIn In fact, the thresholds oice see .In this section, we present the results of studying the spreading index profiles on a typical day in Rome and London, and build hypotheses regarding their interpretations.OD networks across the working days and obtain sampling distributions and corresponding 95% confidence intervals of spreading indices at each hour with the aim of testing our results for robustness  change in the index for London, while no significant change appears to be present in Rome.In spreading indexη over a typical day in Rome and London. The spreading index, being based on Euclidean distances between the cell centroids, represents geographic space, but fails to capture urban mobility. In particular, due to congestion in cities at peak hours, travel times can be said to distort the perception of space. If travel times are considered as a measure of distance, geographically very close locations in the city center might turn out to be further away than geographically further placed locations in the city periphery with low traffic. For this reason, we enable the spreading index to capture urban mobility by introducing the time-space spreading index (TSI), essentially weighting the distances in the calculation of the spreading index η by the pairwise average travel times:t is the average travel time from cell i to cell j, and is obtained using the Google Distance Matrix API.We have previously computed and tracked the This constitutes an important dimension for studying the spatio-temporal characteristics of the “hotspots” in the mobility networks.TSI of the three activities during a typical day in Rome and London.Therefore, we then proceed to analyze the time-space spreading index TSIs for Rome and London are shown in TSI in both cities is considerably higher than the spreading index, hinting at the above-mentioned space-time distortion, in which geographically close central locations become further apart because of longer travel times due to traffic, effectively increasing the TSI compared to the spreading index. This effect is shown in TSI are visualised with multidimensional scaling (MDS) [The spreading indices and ng (MDS) .TSI values are wider than those of the spreading indices since additional uncertainty is introduced in the calculation of the TSI by including travel times contingent on volatile traffic conditions  as well as other socio-economic activities in cities over both short and long terms.