Congestion control plays an essential role on the internet to manage overload, which affects data transmission performance. The random early detection (RED) algorithm belongs to active queue management (AQM), which is used to manage internet traffic. The RED is used to eliminate weakness in default control of the Transport Control Protocol (TCP) drop-tail mechanism. The drawback of RED is parameter tuning, while adaptive RED (ARED) automatically adjusts these parameters. In this study, the suggested algorithm, the Markov decision process RED (MDPRED) uses the Markov decision process (MDP) to suitably adapt values for queue weight in the RED algorithm based on average queue length to enhance the performance of the traditional RED during TCP Slow Startup phase. This study is conducted based on fluctuations among the rate of service, queuing weight, and the mean queue length by using open-source network simulator NS3. The study shows efficient results by fluctuating end-to-end packet throughput and fast response to the inception of congestion in the network. The modified algorithm achieves a low level of drop packets by evaluating the results with other five algorithms, which is done by increasing the algorithm’s response when the average queue size becomes close to the maximum queue length threshold. Many queue management algorithms have been developed to handle congestion that focused on managing congestion in Congestion Avoidance phase, such as the Random Early Detection (RED) algorithm2, Adaptive Random Early Detection (ARED) algorithm3, Gentle Random Early Detection (GRED)4, NonLinear Random Early Detection (NLRED)5, and Reconfigurable NonLinear Gentle Random Early Detection (RNLGRED)6.The high-speed network and rapid growth of user numbers make congestion control more attractive for researchers. The congestion control algorithm increases the Quality of Service (QoS) by managing the overload in the router queue, increasing the throughput, and reducing the drop of packets in a network. The router in the network has a limited buffer, while congestion makes the buffer full, causing packets to drop. In TCP protocol the congestion effect by two main phases the Slow Startup phase which is responsible on seeking the capacity of path between two end points by increasing the rate exponentially, and then the Congestion Avoidance phase takes place to manage the sending rate with linear increasing rate. However, the default active queue management (AQM)The RED algorithm is used to overcome the drawback of the drop-tail (DT) mechanism used in Transmission Control Protocol/Internet Protocol (TCP/IP). While the ARED is developed to overcome the drawback of RED by dynamically updating the value of In internet today the short TCP flow is widely used such as web traffic objects and Internet of Things (IoT) applications. These type of traffic operate by alternate between active and inactive transmission periods, which mean that these traffics belongs to Slow Startup phase and default AQM algorithms have problems to manage such traffics. In this work the ON-OFF application used to reflect the Slow Startup flow rates.To eliminate the problems in RED and ARED, the Markov Decision Process RED (MDPRED) algorithm has been suggested to give a fast response when many connections send different flow rates that belong to Slow Startup phase. The proposed algorithm used the feature of GRED by duplicate the 7, used MDP to reduce the energy wastage in a wireless sensor network (WSN). Additionally, in8, the MDP was used to manage the network flow risks. In9, software-defined networking (SDN) used MDP as a defense strategy against distributed denial of service (DDoS) attacks. In10, the Wireless Body Area Network (WBAN), an optimal transmission policy between nodes was made based on the MDP to reduce energy consumption. In this study, the MDP is used to manage congestion in the router queue by taking proper action in a specific state to reduce drop packets. This paper examines the new algorithm with RED and ARED by using NS3 as a simulator to observe the number of packets dropped when the number of connections changes. “The MDP has been used in many network fields as a stochastic process that builds an optimal model in uncertain environments to make proper action for a given state. For example11. The most recent AQM algorithms used optimization algorithm solutions to achieve optimal results. In12, the new model was used to control the window size of TCP packets with the help of the MDP to estimate the round-trip-time (RTT). That study used three types of congestion control states: CA-Open (Packets sent and acknowledgment received), CA-Recovered (the packets retransmitted), and CA-Loss (packets lost). The states of MDP are Many algorithms have been developed to reduce the effect of congestion control in TCP/IP connections over the network13 used in the AQM algorithm . In that study, there were two parts: congestion prediction and AQM parameter tuning. The first part used the neural network (NN), which learned from experience and ECN feedback, while the MDP was used to adjust the parameters. The study was used to improve the collision delay (CoDel), which depended on the delay of packets in the queue. The problem in that study was dependent on the ECN that started the congestion in the backward direction of transmission packets, which had some delay to handle congestion.The Markov-Modulated Bernoulli Arrival Process (MMBP)3 was designed to improve the weakness in RED by auto-tuning parameters using the automatic calculation of the drop probability. The drawback of the ARED algorithm was increasing complexity due to the auto tuning of the As mentioned before, the RED algorithm was the earliest algorithm that was developed based on the AQM principles. The RED algorithm needed to carefully tune its parameters; therefore, this algorithm struggled with the difficulty in adopting changes in the type of services and applications. ARED4 was developed to improve the RED algorithm by duplicating the 5 by applying nonlinear change of drop probability, depending on the avg in the router queue. The researcher in15 incremented the queue weight when increasing the avg. The aggressive increase in the value of the queue weight enabled an increase in drop packets when the avg approached the double The Gentle RED (GRED)16 develop AQM that manage the congestion during TCP Slow Startup phase which is interested in this study.All AQM algorithm mentioned above and the research done recently focused on congestion that occur in TCP Congestion Avoidance phase while theThe default of AQM algorithm work is represent in RED algorithm. The RED Inception of the congestion depends on the average queue size (avg), two thresholds (mentclass2pt{minim17 with transitional probability to determine which action needs to be taken for a given state, which can be seen in Eq. (i and j can be 1,2,3, ... that represent states, where j is the next state and i is the current state; and k can be 1,2,3, ... to indicate which action is taken.The MDP depends on the combination of (n in Eq.  as follon in Eq. , and thin in Eq.  as folloThe Point-To-Point Dumbbell network topology built by NS3 with ON-OFF application was used in this study, as shown in Fig. 18. The slow start phase allows the TCP to inform the capacity of links in the transition path; this occurs by duplicating the window size per RTT of each acknowledgment received. If no acknowledgment is received, the TCP indicates that congestion occurs, and the congestion avoidance phase starts; then, the window size increases by one for all successful acknowledgments. Therefore, the TCP slow starts increasing exponentially in each RTT, which means that congestion can occur within a short time.The TCP protocol has a default congestion control with four phases: slow start, congestion avoidance, fast retransmit, and fast recovery2. In the present study, the probability transition matrix 2 as follows:The default values in the RED algorithm parameters are Equation  shows thmentclass2pt{minimBy using the values of the entclass1pt{minima19 by increasing the number of nodes in the Point-To-Point Dumbbell network topology.The NS3 simulation is used to analyze and evaluate the new algorithm, MDPRED, against five algorithms: RED, ARED, GRED, NLRED and RNLGRED based on the total drop, forced drop, and unforced drop packets. In this paper, the performance of the suggested algorithm has been analyzed based on varying of the sending rates and the effect on the average queue lengthIn Table The congestion algorithm can either drop the packets before enqueueing them in the router queue as forced drop or mark the incoming packets with congestion notification of Table The MDPRED is tuning the time response of the queue to the congestion by adopting the queue weight


This work showcases a novel phenomenological method to create predictive simulations of metallic lattice structures. The samples were manufactured via laser powder bed fusion (LPBF). Simulating LPBF-manufactured metamaterials accurately presents a challenge. The printed geometry is different from the CAD geometry the lattice is based on. The reasons are intrinsic limitations of the printing process, which cause defects such as pores or rough surfaces. These differences result in material behavior that depends on the surface/volume ratio. To create predictive simulations, this work introduces an approach to setup a calibrated simulation based on a combination of experimental force data and local displacements obtained via global Digital Image Correlation (DIC). The displacement fields are measured via Finite Element based DIC and yield the true local deformation of the structure. By exploiting symmetries of the geometry, a simplified parametrized simulation model is created. The simulation is calibrated via Response Surface Methodology based on nodal displacements from FE-DIC combined with the experimental force/displacement data. This method is used to create a simulation of an anti-tetrachiral, auxetic structure. The transferability and accuracy are discussed, as well as the possible extension into 3D space. This work showcases a novel phenomenological method to create predictive simulations of auxetic metamaterials made from metal using laser powder bed fusion (LPBF). In the context of this work, the term metamaterial is used to describe a structured material whose properties are very different from the intrinsic properties of the constituting base material used for manufacturing. The mechanical properties of the metamaterial are governed by its geometrical structure on a length scale smaller than the overall dimensions of the overall manufactured object, i.e., its external dimensions, but larger than the microscopic length scales, i.e., the atomistic structure. As this work considers a lattice structure, this new length scale is the size of the lattice unit cell.Auxetic structures exhibit a well-defined negative Poisson’s ratio . The auxSimulations of LPBF-manufactured metamaterials based on nominal, i.e., construction geometry from the design stage, present a challenge. The reason is the difference between the printed geometry and its nominal CAD configuration  9]. In . In 9]. Metamaterial simulations often employ a discretization using 3D elements. The discretization is either based directly on the structure obtained via CT imaging ,20,21, rIn order to circumvent this issue, beam elements are used to discretize periodic lattice structures ,24. BeamThus, a new approach to solve this issue is needed. The goal here is to design a simulation that is not only predictive but also numerically efficient. The idea is to create a tool for engineers and scientists to setup a reliable simulation with little experimental characterization in order to be able to numerically analyze material kinematics and realistically predict structural behavior in different contexts.A new approach is introduced to setup metamaterial simulations based on a combination of force and deformation consistency. The method is demonstrated using an anti-tetrachiral auxetic structure. This structure is additively manufactured and experimentally characterized using a compressive load. The experiment was analyzed via global Digital Image Correlation (DIC). By exploiting symmetries of the geometry, a simplified parametrized simulation model is created. The simulation model was calibrated based on the data acquired in the experiment, thereby resulting in an efficient simulation. The prediction capabilities of the simulation framework are discussed by investigating the simulation under varying loading and geometrical conditions. Furthermore, the work discusses the applicability to other geometries and the extension in the third dimension.This section introduces the structure analyzed in this work, including the manufacturing process, the base material, and the experimental setup. Furthermore, the section establishes the approach used to analyze the experiments and setup a simulation based on the corresponding data.The chosen metamaterial is an anti-tetrachiral structure. The geometry is based on the structures shown in Ref. . This stSpecimens based on the dimensions depicted in ® was the material of choice, which is a Scandium modified Aluminium-Magnesium alloy. Scalmalloy® stands out due to its increased tensile strength and failure strain. Both properties are similar to the characteristics of some titanium alloys while featuring a lower mass density. Scalmalloy® was created as a base for lightweight crash-absorbing structures, specifically for the automotive sector and aerospace engineering [The manufacturing was executed by Fraunhofer Ernst-Mach-Institute (Fraunhofer EMI) using an EOS M 400 3D printer. Scalmalloyineering . Due to ®, is 29.5%, considering the enclosing cube of 27 × 27 × 27 mm3 as the reference volume.The structure was positioned parallel to the base plate of the printer, with each additional layer extruding the geometry into the third direction in space. This choice ensures identical material properties in all struts. The relative mass density of the 3D-printed metamaterial with respect to the mass density of the base material, Scalmalloy−3/s using a Zwick Z100 testing machine in displacement control mode. The force was measured using a 100 kN load cell with an accuracy better than 0.1% for forces greater than 200 N. The structural deformation is recorded as monochrome images using a Basler ace 2 camera. Approximately 250 images with a pixel resolution of 11.3 μm are acquired per experiment. Images are then post-processed via Digital Image Correlation to yield a quantitative full-field displacement measurement.In order to setup the simulation, experimental data are needed. For mechanical testing, the structure is compressed at a strain rate of 10A quantitative analysis of the structural response creates a base for in-depth understanding of the structural behavior and provides data to setup a simulation of the metamaterial. An optical method to conduct such an analysis is Digital Image Correlation (DIC). DIC computes a displacement field by registering two or more images.Since the images are divided into sub-elements in the process, and the goal is to extract kinematic details, DIC is combined with a finite element mesh based on the nominal CAD geometry. The result is called Finite Element based DIC. The method used in this work was shown in Ref. , appliedIn this work, the displacement field was obtained using Correli 3.0 . The cal®. The corresponding data are provided in Ref. [Compared to other model calibration methods, the goal of this work is not the calibration of a material model. The bulk material behavior is assumed. The material model is calculated from a separate set of quasi-static tensile tests. The specimens were manufactured using Scalmalloy in Ref. . In thisSince the structure is a 2D geometry extruded in the third direction in space, it suggests considering a quasi-2D discretization. This remark raises the question of whether a plane strain or plane stress simulation would be appropriate. It was established, both experimentally and numerically, that a 2D plane strain simulation approach is sufficient. A corresponding simulation model is set up by analyzing the cross-section of a single LPBF printed beam and exploiting symmetries of the geometry. The finished model was calibrated using Response Surface Methodology.The focus of this work is the influence of geometrical variation on structural deformation and strength. As depicted in The rough outer layer does not contribute significantly to the mechanical stiffness or strength of the strut because it does not represent a continuous cross-section that may carry the mechanical load. Thus, the stiffness and strength are mostly a function of the continuous core cross-section only. However, the rough surface contributes to the force response at high compression ratios–within the densification regime. Here, struts come into contact with each other, so the outer surface geometry becomes important. This observation allows the strut to be divided into two components, which have to be numerically defined. Beam elements offer the simplest approach to replicating the composition of the strut. They are a numerically efficient alternative to 3D elements and are used for a variety of structures, especially if the structure is periodic. The stiffness and strength of the solid core are defined by assigning a beam thickness to each node. The values between two neighboring nodes are interpolated to ensure continuity. Each beam thickness is geometrically smaller than the total size of the corresponding printed strut. The lattice structure is subjected to large global deformation. In order to simulate the subsequent onset of densification, the simulation model requires an equivalent to the surface layer. As the influence of the surface layer is only significant in the case of self-contact, it is represented by a contact offset. Besides defining the onset of densification, contact offset does not impact other physical properties of the beam.4-rotational symmetry of the unit cell combined with the reflective symmetry reduces the parameters needed to only two beam thicknesses and one contact offset , also referred to as the outer beam thickness, is the primary parameter that defines the stiffness and strength of the structure. Values lower than 0.2 mm result in buckling effects, while higher values do not significantly influence the deformation field. The second beam thickness, also called inner beam thickness, defines the shear stiffness of the structure. The influence of this parameter is partly dependent on the outer beam thickness. If the value of the inner beam thickness becomes lower than 40% of the outer beam thickness, the structure tends to bend laterally instead of showing an auxetic behavior. For values higher than 40%, no effect of the second beam thickness is observed.The third parameter, the contact offset, only influences the behavior of the structure when it experiences self-contact, i.e., in the densification regime at compressive strains beyond 20%. The value is defined as a one-sided offset starting at the center of the beam. In order to obtain the total size of the beam, the value is doubled. Thus, twice the contact offset should always be greater than either outer or inner beam thicknesses see a. The coTo obtain the model parameters, an optimization approach termed Response Surface Methodology (RSM)  is used The script utilizes a multivariable and constrained optimization approach. After defining the parameters and their respective domains, the parameter space is sampled to create an initial guess. A refinement towards the optimum is iteratively obtained by changing the parameters according to the methods of Central Composite design CCD  and D-opF:This error has two contributions: the force and displacement error. To sum up these two, they are first rendered dimensionless by normalizing with respect to the range of forces encountered in the experiment. The Mean Square Error D, a scalar measure is needed from the displacement field difference between experiment and simulation. To this end, dimensionless root-mean-square averages are calculated along the Cartesian directions, x and y, separately. They are then normalized by the number of nodes m, where the displacement is evaluated. Thus the average error over all nodes is obtained: In the case of the normalized nodal displacement error, NRMSEForce and displacement errors are combined using a weighting factor, a, resulting in the total error of the system. In this work, a = 0.5 is selected:−3 or less.The NRMSEs for a particular set of parameter values are utilized to build the response surface, which is approximated using a quadratic polynomial. By locating the minimum of the response surface, a subdomain is created . This suThis section visualizes and discusses calibration results both qualitatively and quantitatively. Furthermore, the calibrated parameter set is applied to similar simulations with different loading situations in order to analyze its transferability.5 less time to run, terminating after 9 s, rather than 10 days using one CPU core.As depicted in Comparing all resulting parameter values (beam thickness 1 and 2 and contact offset) to each other, the total contact offset was larger than both beam thicknesses. This result confirms the observation in This section analyses whether the calibrated parameters obtained for one unit cell geometry and a specific loading situation can be transferred to similar but different situations. In the first investigated case, the unit cell geometry was kept the same, but the outer dimensions of the sample and the boundary conditions were changed. Following this, a case where the unit cell was similar in many dimensions but had different symmetry elements was investigated. This section analyses if the calibrated simulation parameters can be transferred to different loading conditions and different macroscopic sample geometries. The unit cell itself is kept the same, but the outer dimensions of the simulation sample are changed.Two different samples were manufactured, as shown in The first sample is the anti-tetrachiral structure attached to circular plates on top and bottom with a diameter of 54.8 mm and thickness of 3 mm. The structure and both plates were printed together as one sample. This choice circumvented the need for additional means of attachment and prevented weak links in the sample. The plates fit in circular fixtures manufactured using an aluminum alloy. The fixtures are mounted onto the compression platens of a Zwick Z100 testing machine and are designed to limit the motion of the sample perpendicular to the loading direction. Since the structure was attached to the platens, beams on top and bottom were constrained in their motion. This constraint resulted in a different deformation field and strength behavior compared to the experiment used for calibrating the model. Simulation results of the compression test with constrained perpendicular motion showed good agreement with the experimental results at small strains. Both force and deformation started to deviate at 17% compressive strain see . The perThe deviation may have different causes. On the one hand, due to the changed CAD geometry, the structure had to be printed in a different orientation, which may have resulted in different imperfections and symmetry issues. This may lead to slightly different deformation behavior, lower strength, and earlier onset of failure. It was noted that the agreement between simulation and experiment for this loading condition was slightly worse than for the experiment on which the model was calibrated. However, the results may be considered acceptable for many applications where only compressive strain until the densification regime is sought. The mean relative error between experiments was 5%. Due to high repeatability and similar simulation results, only one of the experimental and simulation results is shown.The second sample was analyzed in a 3-point-bending test. The corresponding setup is shown in Results of the 3-point-bending test and the corresponding simulation are shown in This section investigates whether the calibrated model parameters can be transferred to a different unit cell geometry. This case is different from the investigations of An alternative structure was obtained by rotating the unit cell in The structure was manufactured and analyzed experimentally in the same manner as for the preceding results. It was found that the simulation parameters are not transferable to the different unit cell geometry, as shown in To obtain a working simulation model, a new set of parameters was calibrated, which is reported in Similar to the specimen in In this case, the simulation again accurately replicated the deformation field up to 16% compressive strain, similar to its anti-tetrachiral equivalent in This work introduces a novel phenomenological method to simulate quasi-2D lattice structures, which circumvents many of the problems associated with a direct discretization of such structures. An LPBF-manufactured lattice structure is made of many interconnected small and slender beams. Each beam consists of a solid core coated with a rough surface layer. Only the solid core contributes to the stiffness of the structure, and the rough surface only contributes to the strength at large compression ratios within the densification regime. Beam elements with varying beam thicknesses are utilized to discretize the structure. To account for the rough surface, a contact offset is introduced. FE-based DIC is used to measure displacement fields. The displacement fields, in combination with other experimental data, are used to calibrate the model parameters. The usage of global DIC is critical in the calibration process. In comparison to local DIC, global DIC can utilize regularization, which yields a kinematically correct displacement field.The final simulation agrees well with the experimental structural deformation and strength. Comparing calibrated parameters to each other confirms the observation about the composition of the printed structure. Additional experiments with alternative loading conditions confirm the ability of the simulation to predict deformation and strength under varying stress states and boundary conditions. It is noteworthy that the beam model accomplishes recreating complex experimental structural deformation, despite a significant simplification of the simulation, defined by only three parameters in total . The method shows good transferability. The reason is the direct coupling of the parameters with the geometry of the unit cell. As long as the unit cell and the manufacturing setup stay unchanged, the unit cell can be used to predict structural behavior under varying loading conditions. Furthermore, a reliable prediction can also be made in the case of varying numbers of unit cells within a lattice and/or varying overall dimensions of a structure. Therefore, a parameterized unit cell can aid in speeding up the design process of structural components, e.g., energy absorbers.The approach presented herein can be applied to any structure, as it relies only on the minimization between simulation outcome and experimental observables. However, it is computationally only efficient for structures with a high degree of symmetry. It is particularly useful for slender, periodic quasi-2D structures that can be discretized using beam elements. This class of structures includes the majority of additively manufactured lattice structures. It was shown that the approach yields simulations that are faithful for intermediate compressive strains up to approximately 20%. At higher strains, in the region of densification, the predictability is reduced due to failure of the structure and complex contact interactions. Both points require further investigation.Future work will extend the approach shown here to full 3D structures. In that case, volumetric image data need to be obtained from in-situ X-ray CT experiments in combination with Digital Volume Correlation (DVC) rather than DIC. The method introduced herein can easily be extended into the third dimension, as it only requires the addition of a third direction in space in Equation (2). However, this approach yields new challenges. First, a specific setup is required, which allows for measuring force under compression in combination with X-ray CT imaging. Those setups are often limited to a maximum sample size and a maximum force, which restricts the geometry of the structure and choice of the base material. This makes the transferability of the results even more important, as it presents an opportunity to analyze the behavior of a smaller part of a structure, which can be used to predict the behavior of its bigger equivalent. Furthermore, the structure has to be compressed in stages, with each stage being held for the duration of a CT scan. Certain materials, if loaded beyond the point of yield, show an increase in strain without experiencing an increase in stress. Structures printed using those materials are not suitable for this kind of analysis, as the deformation would change over the duration of the CT scan. A conclusive set of deformation data requires multiple images. Dependent on the resolution, each CT image can contain up to several GB of data and takes hours to obtain. The total number of images and, therefore, data points per sample is therefore dependent on available time and storage capacity. Furthermore, computational resources required for the DVC analysis are not only dependent on the resolution of the CT scans but also on the resolution of the finite element mesh used as a base. Therefore, the approach requires a delicate balance between computational resources, time management, and information content.


Along with the steady increase of multi-resistant and extensively virulent microorganisms, the genomic approach has become an essential ally in the search for genetic factors related to microbial pathogenicity . Thus, tThe fact is that genomic information tends to be difficult to interpret due to the high information density present in several datasets . MoreovePanViTa  was developed with the concepts of scalability and agility in mind. It is a tool made entirely in Python3 , focusinhttps://github.com/dlnrodrigues/panvita.The tool is available on GitHub through the link via the command line. BLASTp   and BacM. BLASTp  was sele. BLASTp .For some features of the developed tool, it was necessary to take advantage of some existing libraries and modules in the native language. Therefore, the use of the Python3 version is recommended. Besides intrinsic modules and libraries , the program also needs to import other modules and libraries: wget, to get external data and update databases and dependencies whenever necessary; pandas  for matrTo obtain the final result, the program performs the following steps: (I) extracts the amino acid sequences of the predicted proteome from each GenBank file; (II) extracts the positions of the coding sequences of all proteins in the genome from each GenBank file; (III) aligns the predicted proteome with the selected database using DIAMOND-BLASTp; (IV) filters the results in the tabular output file that match the identity and coverage parameters ; (V) summarizes the results in a similarity-based matrix: X represents the genes with a match higher than the defined cutoff and Y represents the strains given as input; (VI) uses the summarized results to generate a clustermap plot based on Euclidean distance to determine data clusters by proximity; (VII) plots the development of each subpartition in core- and pan-; (VIII) checks and summarizes the gene results by specific strain. The flow chart containing the software steps is shown in Acinetobacter baumannii, Escherichia coli, and Pseudomonas aeruginosa were selected. A. baumannii, E. coli, and P. aeruginosa are presented on The performance of PanViTa was compared with other tools developed with a similar purpose: Abricate  and ResFFor standardization purposes only the CARD database was added to the comparison analysis with Abricate.® Core i5-3570 CPU 3.40 GHz).The analyses were made on a desktop computer with OS Linux-Ubuntu viewing 8 GB of RAM and four cores . In this way, it is possible to assess the presence of certain target factors in more specific portions of the sample.PanViTa also has a genome acquisition module that allows the download of genomes available on the NCBI platform. For this, it is only necessary to use the. csv file generated during the genome search as input. In addition, it is possible to automatically generate the script for annotation using the prokka pipeline, and obtain the host and related disease metadata using the available biosample number.


Plasma methods are often employed for the desired wettability and soaking properties of polymeric textiles, but the exact mechanisms involved in plasma–textile interactions are yet to be discovered. This review presents the fundamentals of plasma penetration into textiles and illustrates mechanisms that lead to the appropriate surface finish of fibers inside the textile. The crucial relations are provided, and the different concepts of low-pressure and atmospheric-pressure discharges useful for the modification of textile’s properties are explained. The atmospheric-pressure plasma sustained in the form of numerous stochastical streamers will penetrate textiles of reasonable porosity, so the reactive species useful for the functionalization of fibers deep inside the textile will be created inside the textile. Low-pressure plasmas sustained at reasonable discharge power will not penetrate into the textile, so the depth of the modified textile is limited by the diffusion of reactive species. Since the charged particles neutralize on the textile surface, the neutral species will functionalize the fibers deep inside the textile when low-pressure plasma is chosen for the treatment of textiles. Textiles are made from fibers of various materials, typically polymers. The properties of polymeric fibers depend on the type of materials and synthesis procedure. The ability of textiles to capture liquids depends on several parameters, and the most important is the surface wettability of the materials the fibers are made from. The surface wettability depends on the surface chemistry, particularly the polarity of surface functional groups. Most polymers contain an inadequate concentration of polar surface functional groups, so the wettability is usually below the level desired in numerous applications, particularly when attempting to bond chemically any foreign material. In cases when increased wettability is needed, the surface functional groups should be altered by grafting more polar groups. A common technique is the application of gaseous plasma. Although the technique has been used for some types of textiles on an industrial scale for decades, the scientific aspects are still a subject of investigation. Numerous scientific articles have been published on plasma modification of textiles, and the trend is shown in Non-equilibrium plasma is usually sustained with an appropriate gaseous discharge. A voltage source is used to create an electric field strong enough to ensure the acceleration of free electrons, which thus gain appropriate energy for ionization collisions with neutral molecules. Various discharges have been reported by different authors. Low-pressure discharges usually operate with a power generator at high frequency in the range of radiofrequency (RF) or microwave (MW), roughly between 10 kHz and 10 GHz. The use of high-frequency fields is preferred since it ensures a rather homogeneous plasma in a large volume without risking arcing. Furthermore, high-frequency discharges can be operated in the electrodeless mode, i.e., any electrode placed outside the discharge chamber. This is useful because the electrodeless configuration overcomes all problems associated with an electrode placed inside the chamber, such as sputtering  the cathThe microwave discharges (frequency between about 1 and 10 GHz) always operate in the electrodeless mode. The electric field provided by an MW generator will not penetrate deep into plasma because of the skin effect . Namely,The radiofrequency discharges operate either in the electrodeless mode or with an electrode mounted inside a metallic chamber, which is typically grounded . The eleAs explained above, the charged particles are accelerated only in the volume of the significant electric field, which is always limited. In the rest of the discharge chamber, there will be diffusing plasma—plasma of practically zero electric field but rich in plasma species because of the presence of rather energetic electrons. The volume of a large electric field in a low-pressure gaseous plasma is often limited to a sheath next to the electrode, which may or may not be inserted in the discharge chamber. The loss of electrons’ energy in the gas phase at low pressure is rather weak because of the low collision frequency, so practically all loss mechanisms take place on the surfaces. The same applies to other reactive plasma species . Typical examples of high-frequency low-pressure discharges and resulting plasmas are shown in −3 [−3 . The lowRF and MW discharges can also be used for sustaining plasma at atmospheric pressure, but the plasma volume will be limited to a small volume where the highest electric field appears. Namely, the diffusing plasma cannot expand to a large volume at atmospheric pressure due to numerous collisions of free electrons with gaseous molecules or atoms. The electrons lose their energy at inelastic  collisions with molecules, so they do not diffuse far from the volume of a large electric field when the pressure is around the atmospheric (1 bar). Therefore, large spatial gradients in the density of charged particles are typical for atmospheric-pressure plasma sustained with high-frequency discharges, for example, at the industrial RF frequency of 13.56 MHz or MW frequency of 2.45 GHz. In addition, such discharges operate in the continuous mode even at atmospheric pressure, and the majority of discharge power is spent on heating the gas (increasing gas temperature), so the application of such high-frequency plasmas is limited at a pressure below, say, 30 mbar.4 m/s [20 m−3 [Low-frequency RF discharges or pulsed direct-current (DC) discharges are preferred plasma sources at atmospheric pressure. In such cases, plasma is not sustained in the continuous mode but rather in the form of stochastically distributed streamers . The str4 m/s ,34. They4 m/s . Streame [20 m−3 . In the  [20 m−3 .Low-frequency and pulsed discharges at atmospheric pressure come in various modes. The scientific literature usually distinguishes two types of discharge: dielectric-barrier discharges (DBD) and corona discharges. DBD employs a dielectric barrier on an electrode to limit the current of a specific streamer, whereas corona employs a resistor mounted in series with the plasma. The electrical current in a particular streamer sustained with the DBD is limited by the available charge that could be transferred from the dielectric surface to the counter electrode. In the case of the corona, the voltage drop will appear on the resistor rather than on the gas gap when the electric current flows, so the voltage across the gas gap will drop below the value useful for sustaining the gaseous discharge during the current pulse. When the electric current diminishes, the entire available voltage (the source voltage) appears again across the gas gap, so another streamer appears. A schematic of the DBD and corona discharges are shown in The illustrations in Corona discharges useful for treatment in the continuous mode are usually sustained along one or more electrodes mounted next to a grounded roll. The textile runs in the space between the powered electrode and the grounded roll. The preferred embodiment is not much different from the one illustrated in Free electrons and positively charged molecules or atoms;Neutral molecular radicals, including atoms, in the ground electronic state;Metastable atoms and molecules in excited electronic states;Radiation in the range from infrared (IR) through visible (vis) and ultraviolet (UV) to vacuum ultraviolet (VUV);Negatively charged ions (important in the cases plasma is sustained in electronegative gases).Regardless of the type of discharge used for sustaining gaseous plasma, the following species are found in gaseous plasma:The fluxes of these species on the surface of any samples, including textiles, depend enormously on the type of discharge used for sustaining gaseous plasma. Typical numerical values will be disclosed in the following text.Te is the electron temperature, kB is the Boltzmann constant, εo is vacuum dielectric permeability (εo = 8.85 × 10−12 F/m), e0 is the elementary charge (1.6 × 10−19 As), and ne is the electron density. The Debye length is proportional to the square root of the electron temperature. The electron temperature in gaseous plasma useful for the treatment of textiles does not span over a broad range; a typical value is between 10,000 and 100,000 K, which corresponds to the average electron energy of roughly between 1 and 10 eV. On the contrary, the Debye length is inversely proportional to the square root of the electron density, which depends significantly on the type of discharge and other discharge parameters and may be anywhere between about 1014 and 1020 m−3. Obviously, the Debye length in plasma of electron temperature a few 10,000 K assumes any value between about 1 mm and 1 µm for low and high electron density, respectively. The Debye length versus the electron density is plotted in Usually, uniform treatment of fibers within a textile sample is preferred to the localized treatment of fibers stretching from a textile surface, so the penetration depth of species capable of modifying the fibers is important information. By definition, plasma is at least partially ionized gas with a density of charged particles large enough to form a space charge of linear dimension larger than the distance between two pieces of solid material . The space charge is absolutely necessary for sustaining gaseous plasma; otherwise, the electrons would escape to the surfaces and will thus no longer be able to sustain plasma. The voltage is screened by the space charge over the distance with a characteristic thickness of the Debye length, which is defined as15 m−3  and about 1018 m−3 . The electron density of 1018 m−3 corresponds to the Debye length of 10 µm  will cause heating at a power as large as 104 W/m2. Obviously, either the density of charged power is too low to enable plasma penetration inside the textile, or it is too high to avoid overheating of the textile surface. From the plasma-penetration point of view, the low-pressure plasmas do not seem to be useful for the treatment of textiles unless the treatment time is extremely short. The benefits of using low-pressure plasmas will be explained below.Low-pressure plasmas will have electron densities between about 10of 10 µm a. ObviouW is the kinetic energy and m the mass of a particle, so they charge the neighboring fibers negatively . From this point of view, atmospheric-pressure plasma sustained by DBD or corona has advantages over continuous plasmas. The high plasma density will ensure for penetration of streamers in volume between the fibers. The schematic of the penetration of a streamer through the textile is illustrated in gatively b. The negatively c. Any obgatively d, thus rgatively e, so fingatively a is largThe surface discharges powered by generators of frequency up to about a few 10 kHz illustrated in Differently to streamers, continuous plasma of reasonable density of charged particles will not penetrate the porous material because the Debye length is larger than the distance between neighboring fibers in a textile. Instead, the movement of reactive plasma particles will be governed by diffusion. One exception is the penetration of radiation. The interested photon energy is beyond the threshold for breaking bonds in polymer materials, i.e., above several eV, i.e., the UV and VUV range of wavelengths. The absorption depth of photons depends on the type of polymer and concentration of impurities that act as absorption sites. The absorption coefficient is a complex function of the photon energy even for pristine polymers , let aloThe penetration depth of particles such as charged particles, metastables, and radicals in the textile treated with a continuous plasma will depend predominantly on the surface loss coefficients. As already mentioned and illustrated in Once the penetration depths are known, it is possible to provide an illustration of the textile modifications upon treatment with gaseous plasma. Obviously, the inhomogeneous treatment over the entire thickness of the textile cannot be avoided but could be suppressed by the appropriate design of the treatment device and choice of the discharge parameters. Below are listed the effects of treatment by different plasma species.As illustrated in 2+, O+, or (OH)+, the newly formed functional groups will be C-OH, C=O, O-C=O, C-O-C, and so on. The effect will be limited to the very surface of the fiber unless some diffusion of successive surface oxygen occurs. The surface will soon be saturated with polar functional groups. Prolonged treatment will cause oversaturation, formation of low molecular weight fragments, and desorption of such fragments from the surface. Macroscopically, these effects will cause etching. The etching will be enhanced due to the heating caused by the dissipation of the potential and kinetic energy of positively charged ions impinging the surface. The etching is rarely laterally uniform, so nanostructuring of the fibers will occur. The combination of polar surface functional groups and rich morphology on the sub-micrometer scale will cause a super-hydrophilic surface finish [If the negatively charged electrons are retarded by the surface charge, the positively charged ions are accelerated toward the surface, as illustrated in e finish  and, thuAs already mentioned, the surfaces facing non-equilibrium gaseous plasma are biased negatively against the plasma because of the adsorption of free electrons from gaseous plasma. The negatively charged ions (relevant only for plasmas sustained in electronegative gases) will be retarded by the surface potential and be directed back to bulk plasma. Therefore, the negatively charged ions will not reach the fiber surface, so any discussion of interaction with the fibers is not necessary.Electrons of adequate energy will cause the excitation of both neutral molecules and atoms, as well as ions, into electronically excited states. The excitation occurs in the gaseous plasma where the electron density and temperature are rather large. The excited states may be resonant, meaning that they will immediately (in a time measured in nanoseconds) relax by emitting a photon. Many states, however, are metastable because of the rules of quantum physics, which prevent the relaxation by electrical dipole radiation. The lifetime of such excited states may be long enough to enable the metastables to reach the surface. On the surface, the excitation energy may be transferred to an electron bonded to the polymer surface, thus causing surface modification. The effect is yet to be elaborated because very few results on the interaction between oxygen metastables and polymer materials have been published, although plasma scientists often take them into account when explaining mechanisms in oxygen plasmas ,46,47.19 m−2 [21 m−3, the saturation occurs in 0.1 ms! Much larger doses are needed for saturating the surface with other oxygen-containing functional groups, but 1022 m−2 is enough for many polymers, including fluorinated ones [The neutral reactive particles usually govern the surface chemistry upon treatment of polymer materials with oxygen plasma . In almo19 m−2 . If the ted ones .The unique advantage of using neutral radicals over positively charged ions is excellent efficiency for surface oxidation. An atom will either interact chemically with a polymer surface or will experience an elastic collision and will not cause surface heating. The heating also occurs at surface recombination, but the coefficient is low for most polymers , so the 21 m−3 [At atmospheric pressure, the loss of radicals in the gas phase is efficient in molecular gases and less efficient in noble gases with a small admixture  of molecular gas. That is one of the reasons for using noble gases in atmospheric-pressure plasmas. In any case, the maximal achievable atom density at atmospheric plasmas is practically the same as in low-pressure plasmas, i.e., roughly about 1021 m−3 . The ene4 m/s. The ionization wavefronts leave behind non-equilibrium gaseous plasma, which is of short duration because of numerous collisions which lead to the gas thermalization and thus the loss of chemically reactive particles useful for surface functionalization of polymers. An advantage that makes the atmospheric-pressure plasmas attractive for the treatment of textiles is the ability to sustain plasma even inside the textile, in the space between the fibers, providing the electron density within the streamer is large enough so that the space charge can suppress their loss on the surfaces. By using streamers of high electron density, it is possible to obtain the desired surface finish of the entire textile despite the localized character of the streamers. On the contrary, low-pressure plasmas assure a large density of molecular radicals, in particular atoms, which may diffuse in the space between the fibers and cause surface chemistry even on fibers rather deep in the textiles. The charged particles will always cause significant heating of the materials facing them, so the plasma of a large density of charged particles may cause overheating of the fibers on the textile surface while leaving the fibers deep inside the textile inadequately treated.An insight into the plasma techniques for the functionalization of textiles from the perspective of plasma science was presented. Plasma sustained in pure noble gases does not contain chemically reactive particles but is a source of VUV and UV radiation which breaks bonds in the polymer material and thus provides dangling bonds to be occupied upon exposure to reactive gases. The efficiency is rather inadequate because the photons are not absorbed in the very-surface film useful for functionalization with desired surface functional groups but rather in a thicker film, typically about 100 nm for very energetic photons of 8 eV and above, more for less energetic photons. The effect of other plasma species is limited to the very surface, especially when the textile is kept at a floating potential . The reactive plasma particles are predominantly formed at an impact with an energetic electron in gaseous plasma, but the loss will be in the gas phase at elevated pressure (atmospheric and above), and on surfaces at low pressure (a few mbar and below). In between atmospheric and low pressure, there is a range of pressures rarely tackled by scientists or users of plasma technologies. The energy dissipated at the neutralization of charged particles and the recombination of radicals, including atoms, to stable molecules will cause heating. Gaseous molecules will be heated in atmospheric-pressure plasmas, while in low pressure, the surfaces will be heated. Low-pressure plasmas are usually sustained in the continuous mode and are rich in neutral reactive particles that are useful for chemical interaction with polymer surfaces. While the atmospheric-pressure plasmas can be sustained in the continuous mode, they are not very useful for the treatment of textiles due to the high-power density needed for sustaining the continuous plasma at high pressure. From this aspect, it is advisable to use atmospheric plasmas, which operate in streamers—current pulses of duration of the order of µs occurring stochastically in the discharge gap. The streamers are actually ionization wavefronts progressing from the powered electrode at a speed of roughly 10


This paper analyzes the power flow solution in bipolar direct current networks with radial structures considering multiple monopolar and bipolar constant power loads. The electrical configuration of the bipolar DC grid considers that the reference pole is non-grounded along the feeder, which produces important neutral currents and voltage imbalances along the DC grid. The power flow problem is formulated through the triangular-based representation of the grid topology, which generates a recursive formulation that allows determining the voltage values in the demand nodes through an iterative procedure. The linear convergence of the triangular-based power flow method is tested through multiple load variations with respect to the nominal grid operative condition. Numerical results in the 21- and the 85-bus grids reveal the relevant variations in the voltage profiles and total grid power losses when the neutral cable is solidly grounded or not. Recently, the direct current (DC) distribution network has gained attention in the research and industrial communities since they do not require generation synchronization, have better voltage profiles, present lower power and energy losses, and in these grids, reactive power and frequency concepts are nonexistent ,2. AdditElectrical distributions with DC technologies can be constructed with two possible configurations: monopolar and bipolar ,7. The fTo examine the DC distribution under steady-state conditions, even if this works with monopolar or bipolar configurations with multiple constant power loads, it is mandatory to implement the power flow methodologies owing to the nonlinearities introduced by the constant power terminals ,14. In tOwing to the existing gap in the current literature regarding numerical methodologies in solving the power flow problem in bipolar DC grids with constant power terminals, this research proposes the extension of the triangular-based power flow method expounded initially in  for bipoThis document is organized as follows: p, neutral o, and negative n poles from an initial voltage value provided in the first iteration [A bipolar DC distribution network with multiple constant power loads can be analyzed through a recursive formulation that allows the determining of the final voltage profile in the positive teration . To illuteration .k, it is possible to have loads connected between the positive and neutral poles, i.e., d is used in referring to demand nodes.l is named k is defined as b being the total number of nodes/buses the network), and Now, if the first Kirchhoff’s law is applied to each node, where the current through the line Now, if the second Kirchhoff’s law is applied to each closed-loop trajectory from the slack node to each demand node, then the following set of linear equations is obtained:l, which produces the following set of linear equations:To relate branch current and branch voltage drops, Ohm’s law is applied to each branch Equation (6)El,poNow, if Equation  is substFor simplicity in the formulation, the resistance-like nodal matrix, Equation  into 8)Rdd,pon, k for each pole as follows [Note that Equation  is indee follows :  (9)idk node can be calculated as presented below:To arrive at a compact formula for the calculation of the demand current at each node, as defined in Equations –11), le, le11), k defined in  this pole is solidly grounded at each one of the nodes of the network; and (ii) this pole is only solidly grounded at the substation bus.The numerical data provided in The main issue when the neutral pole is not solidly grounded in all the nodes corresponds to the total grid power losses since these are increased with respect to the solidly grounded case. For example, in the 21-bus system for the first simulation case, the total power losses are 91.2701 kW, while for the second, this number increased to 95.4237 kW. These values indicate that there is about 4.1536 kW that is transformed into heat owing to the currents flowing through the neutral cable.As evidence of the convergence properties of the studied triangular-based bipolar power flow method, we plot the logarithm value of the convergence error, i.e., The numerical performance of the triangular-based method for bipolar DC grids in the 21-bus system depicted in After the implementation of the proposed triangular-based method for bipolar DC grids in the 85-bus system under the nominal operating conditions, we found that (i) when the neutral wire is solidly grounded in all the nodes of the grid, then the total power losses is On the other hand, in Voltage profiles in Note that when positive and negative voltage profiles are compared see , it is o✔In the solidly grounded scenario after 1000 consecutive evaluations of the power flow methodology, the average processing times for the 21- and 85-bus grids were 0.6498 ms and 6.4858 ms, respectively. In the non-grounded case, these times were ✔Comparative simulations with the classical backward/forward power flow method adapted for bipolar DC grids demonstrated that both methodologies reach the same voltage profiles and power losses calculations. However, the main advantage of the triangular-based power flow method is the processing time required, since for both tests feeders were 20 % faster than the backward/forward power flow method, which confirmed the results presented by authors of  in the cThe following results were also obtained for both test feeders:The power flow problem in bipolar DC grids with multiple constant power loads was addressed in this research through the application of the triangular-based power flow formulation. The proposed formulation is applicable in radial configurations, considering the possibility that the neutral cable is or is not grounded in all the nodes of the system. The numerical results in the 21-bus system demonstrate that when the neutral pole is solidly grounded, the maximum voltage regulation bound under normal operative conditions was about In the case of the 85-bus grid, the minimum voltage profile occurs in the negative pole for the solidly-grounded and non-grounded operative scenarios with magnitudes of For future research, it will be possible to execute the following works: (i) propose a recursive power solver for bipolar DC grids by linearizing the demanded currents around the initial voltage values and then updating this point until the desired convergence error is reached; (ii) determine via metaheuristic or convex optimization the optimal subset of nodes where the neutral cable must be solidly grounded to reduce the total grid power losses; and (iii) design an experimental scenario where the power flow methodologies for DC bipolar networks can be validated.


Hip ultrasound for neonates is a standard procedure to prevent later critical outcomes, such as hip dysplasia. Additionally, the SARS-CoV-2 pandemic has put worldwide stress upon healthcare units, resulting often in a lack of sufficient medical personnel. This work aims to develop solutions to ease and speed up the process of coming to a correct diagnosis. (2) Methods: Traditional medical procedures are envisaged, but they are enhanced to reduce diagnosing errors due to the movements of the neonates. Echographic noise filtering and contrast correction methods are implemented the Hyperanalytic Wavelet Transform, combined with an adaptive Soft Thresholding Filter. The algorithm is tailored to infants’ structure and is tested on real ultrasounds provided by the “Victor Babes” University of Medicine and Pharmacy. Denoising and contrast correction problems are targeted. (3) Results: In available clinical cases, the noise affecting the image was reduced and the contrast was enhanced. (4) Discussion: We noticed that a significant amount of noise can be added to the image, as the patients are neonates and can hardly avoid movements. (5) Conclusions: The algorithm is personalized with no fixed reference value. Any device easing the clinical procedures of physicians has a practical medical application. Neonates are prone to affections, which typically do not produce visible effects while the infants are less than one year old, due to their reduced movements. Hip dysplasia is one of the most occurring affections . DevelopA pediatric hip exam was carried out with the help of an ultrasound machine ,9, whichIn any ultrasound exam, a transducer is pressed against the skin and sends high-frequency sound waves inside the body-tissue, cartilages, and bones, all of which react differently (different density), aiming to form images of the hip joint .Usually, problems appear when the child moves and the images are not sharp enough. Software filters should be applied for a reliable diagnosis. ,12,13.-a linear probe/transducer in the frequency range 5–7 MHz. Probes/transducers with a higher frequency than 7 MHz, show a better resolution but worse penetration depths, thus worse ultrasound images. However, neonates smaller than 4 weeks of age must be examined with the 7 MHz transducer, while in children over this age a 5 MHz transducer is used;-there must be a possibility of rotating electronically the image (by 90°) on the screen to visualize the “standard projection”;-there must be a thermal printer available for direct image printing or the possibility of storing the images without loss of resolution;-the ultrasound device must be performant enough to possess software, which should allow line-tracing to measure the angles formed and thus provide a clinical diagnosis.For the ultrasound device some elements are compulsory:The clinical exam relies on a single coronal image of the hip. The child is placed in a lateral decubitus position and the transducer is brought parallel to the studied hip and parallel to the vertebral spine. This is aimed at obtaining a coronal/frontal through the middle plane of the acetabulum (hip socket) .-the inferior side of the ilium bones of the pelvis, inside the acetabulum fossa ;-the middle plane of the hip socket;-the acetabular labrum (a cartilage ring that surrounds the acetabulum);This “standard plane” is identified through the presence of three points :-the infThe paper is structured as follows: in Several noisy images from neonates were checked, the images were acquired in Timisoara, Romania. The proposed filtering algorithm based on the hyperanalytical wavelet has been tested on several newborn hip-images, both male and female patients. Ultrasound images that were not correctly acquired were excluded from the study, as well as images of neonates with known musculoskeletal malformations or pathologies. For each noisy ultrasound image, two clinical aspects  were checked.-an initial line is traced parallel with the lateral wall/side of the ilium bone (constituting the baseline);-a second line is traced in the middle of the bony margin (with the fibrocartilaginous margin) surrounding the acetabulum, forming the angle “beta” with the baseline: this is a measure of the cartilaginous covering of the acetabulum;-a third line is traced from the inferior side of the bony margin of the acetabulum to the triradiate cartilage , tangentially to the bony margin of the acetabulum. The angle formed with the baseline is the “alpha” angle: this is a measure of the bony covering of the acetabulum. It is usually greater than 60°. An angle less than 55° is considered not normal.During measurements, the physician focused on two coronal angles (acetabular inlet planes) “alpha” and “beta”, some computers displaying a software for the calculation of the Graf angles. Of course, the lines defining the angles must be correctly identified and tracked by the physician :-an initWavelets are an adaptive, powerful, but easy-to-implement mathematical tool which allows the construction of effective denoising filters. During propagation, the waves associated with the input signals are reflected by obstacles, creating echoes. The ultrasound is generated by measuring the strength and the round-trip time of the received echoes . As the Speckle noise affects the mean grey level of an area, causing a difficult image interpretation . It is mRemoving random noise from the original image is still a challenging research in image processing. The presence of speckle noise severely degrades the signal-to-noise ratio (SNR) and contrast resolution of the image, making human interpretation and computer assisted detection techniques difficult and inconsistent. Therefore, a speckle reduction process is quite necessary in low SNR, low contrast ultrasound images for enhancing the visualization of organ anatomy and improving the accuracy of object detection without affecting the important diagnostic features of the image.In this paper, we aim to remove noise induced by unwanted echoes, which affects the main grey levels of the area, making medical interpretation and diagnosis difficult. A shorter exposition time and a faster diagnosis are also beneficial for the neonate. We propose a novel wavelet transform, the Hyperanalytic Wavelet Transform (HWT) ,18, asso2 = j2 = −k2 =−1.The HWT is used for decomposing the signal on different levels to check different frequencies (layers) of the same input signal, a filter being applied at each level. Finally, the signal is recomposed after denoising . Each waThe HWT of the input sonogram sono is obtained as the scalar product between this image and the hypercomplex MW (Equation (2)):useful × sononoise) is transformed into an additive one through logarithm computation (Equation (3)):During image acquisition, the acquired data are often corrupted by noise. The noise randomly affects the intensity values of each pixel in a noisy image. The aim of the proposed algorithm is to reduce the noise level while preserving the main features of the image. The multiplicative noise component  of the same input signal at each level applied the ASTF. The advantages of using wavelets are the improvements of the processing speed and the decorrelation of the noise wavelet coefficients.noise and σuseful. As realistic assumption, based on previous studies [noise, while the useful distribution is tailored as a Laplacian repartition with the standard deviation σuseful. The input–output relation of the ASTF filter to the wavelet coefficients of the sonographic image sono:We propose an ASTF for filtering, which is a soft thresholding filter whose threshold is adaptively selected in moving windows in the function of the noise level. Another parameter is the current standard deviation of the useful image. This filter is suited for additive noise applied in the wavelet domain ,23. The  studies ,25, the sono is computed with the following relation:The algorithm is personalized and tailored to the characteristics of each scanned neonate, as no fixed threshold value is applied. The threshold TNoisy recordings of two male and one female patient have been chosen to exemplify the performance of the proposed HWT filtering procedures. HWT has been applied on four levels of decomposition, using the Daubechies orthogonal mother wavelet with a number of two vanishing moments . For the noisy echographic image two clinical aspects  are observed.The original recording is displayed in We can remark, when analyzing the zoomed parts of The second image  is a hipAn ultrasound (Image 3 ) was takAfter applying the HWT filtering , the aceWe may conclude, when comparing last two images, that after filtering several details can be seen, easing the process of diagnosis for a trained physician. The denoised images are brighter and more details  can be distinguished.f represents the input image, g represents the denoised image, p is the number of lines, and q the number of columns of that images. Thus, for the three cases of application of the proposed denoising method we obtained the PSNRs in To improve the analysis of the results we add the values of some quality measurements as well. One of the most used quality measurements for the evaluation of an image processing method is the Peak Signal to Noise Ratio (PSNR), defined as:These high PSNR values indicate that the proposed denoising method is indeed optimal, realizing a very good treatment of homogeneous regions. The best result was obtained in the third experiment and the poorest result in the case of the second experiment. Usually, performance is related to the noise level contained in the processed image . Nevertheless, the PSNRs are quite high in all cases.The goal of the study was to reduce exposition time for neonates during necessary procedures, such as hip ultrasounds, and ease the obtainment of a diagnosis. Noiseless images are seldom encountered in practice, as a child is naturally moving constantly, thus adding unwanted echoes. This type of noise is named speckle and has a multiplicative nature. We propose a homomorphic filtering method based on the HWT, transforming multiplicative noise into additive noise through logarithm computation. Additionally, an adaptive ASFT filter is applied, with a threshold tailored to suit the neonate’s specific structure. The algorithm is thus personalized, searching for the optimal results of each recorded echogram. The ASTF’s optimality is due to the equivalence of this type of filter with the LASSO regulator. We selected a filtering method applied in the wavelet domain because wavelets have some advantages, such as a good time–frequency localization, the speed of the WT algorithm, and the ability to decorrelate the noise. In particular, the HWT has some noticeable advantages over other WTs, including the quasi shift invariance, the better directional selectivity, and the ability for hyperanalysis.We evaluated the proposed approach on real images, acquired in Timisoara. Through filtering, more accurate images were obtained, displaying more clearly the areas of interest . The denoising method proposed rejects a part of the speckle noise and as a consequence it increases the contrast of the image. Both effects facilitate the visual analysis of the echogram. This contrast enhancement effect can be improved by making a non-linear transformation of the magnitudes of the HWT coefficients after the use of the ASTF . The ultFuture research directions envisage the checking of the algorithm’s performances with other mother wavelets  or even a new family of mother wavelets, such as the biorthogonal mother wavelets, which use different filters for the decomposition and reconstruction of the signal.The goal of this paper is to propose an HWT-based filtering algorithm for neonate ultrasound images. The HWT transform is flexible and quasi-invariant to translation, with good directional selectivity. We performed a simple implementation of this transform, based on the reduction to the input principle. Any orthogonal or biorthogonal real mother wavelet can be used for the computation of HWT.As a first pre-processing step, we added a palette color to the original image to enlighten the areas of interest. Our goal is to make ultrasound images easier to interpret and diagnose, so the time necessary for acquisition is reduced (a benefit for the neonate patient). Additionally, the images need to be reliable, and a filtering method has been established to remove the main artifact echoes (due to involuntary movements or the breathing of the patient). After filtering, several details can be noticed, easing the process of diagnosis for a trained physician. The images are brighter and more details  can be distinguished after processing. We also computed the PSNRs of the three denoising results obtained and we obtained high values, objectively showing the high quality of the denoising method proposed.Future studies will aim further than the validation of the processing method and will be applied to larger populations of neonates. It is possible to find a mother wavelet other than the Daubechies mother wavelets with the two vanishing moments used in this study, which improved the PSNR of the medical ultrasound images of the hip. Any medical device which eases medical diagnosis, especially if difficult situations arise, such as over-fatigue or lack of sufficiently trained personnel , is of p


As a result, accurate estimation of the binding stability and the effects of mutations on changes of binding affinity is a crucial step to understanding the biological functions of proteins and their dysfunctional consequences. It has been hypothesized that the stability of a protein complex is dependent not only on the residues at its binding interface by pairwise interactions but also on all other remaining residues that do not appear at the binding interface. Here, we computationally reconstruct the binding affinity by decomposing it into the contributions of interfacial residues and other non-interfacial residues in a protein complex. We further assume that the contributions of both interfacial and non-interfacial residues to the binding affinity depend on their local structural environments such as solvent-accessible surfaces and secondary structural types. The weights of all corresponding parameters are optimized by Monte-Carlo simulations. After cross-validation against a large-scale dataset, we show that the model not only shows a strong correlation between the absolute values of the experimental and calculated binding affinities, but can also be an effective approach to predict the relative changes of binding affinity from mutations. Moreover, we have found that the optimized weights of many parameters can capture the first-principle chemical and physical features of molecular recognition, therefore reversely engineering the energetics of protein complexes. These results suggest that our method can serve as a useful addition to current computational approaches for predicting binding affinity and understanding the molecular mechanism of protein–protein interactions.The cellular functions of proteins are maintained by forming diverse complexes. The stability of these complexes is quantified by the measurement of  It is related to the change of Gibbs free energy after binding  dK and available 3D structures Here, we computationally assessed the stability of a protein complex by decomposing the binding affinity into the contributions of interfacial residues and other non-interfacial residues in the complex. The energetic contributions of both interfacial and non-interfacial residues in our model are related to the structural features of interacting proteins, such as secondary structural types and solvent-accessible surfaces (SASs). We used the Monte-Carlo algorithm to search the parameter space of these energetic contributions and optimize the weights of the parameters. Our model was tested against Structural database of Kinetics and Energetics of Mutant Protein Interactions (SKEMPI), a large-scale database that contains 158 wild-type protein complexes and 3048 associated mutants with experimentally determined i and j can be denoted as follows:i and j at the protein binding interface iχ is the mole fraction for residue type i at the interface. Two residues form a contact if the distance between any atoms from the sidechains of these residues is less than the defined cut-off distance (5.5 Å).A knowledge-based potential is first constructed to evaluate the energies of protein complexes. The potential gives the energetic parameters for all combinations of residue pairs that appear at the protein binding interface. Specifically, the binding energy e.g., Protein Data Bank (PDB) index, residue range, and chain ID] is provided to both interacting protein domains. From each IDP in the 3did database, only one representative 3D item is selected to reduce sequence redundancy in the library. As a result, there are 4960 entries of homodimeric or heterodimeric PPIs in the final library.The specific values of Eq. A and protein B can be described as follows:We hypothesize that the stability of a protein complex is dependent on both the residues at the binding interface by pairwise interactions and all other remaining residues that do not appear at the binding interface. These non-interfacial residues could be either on the surfaces or in the interiors of the protein complex. Moreover, the contributions of both interfacial and non-interfacial residues to the binding affinity are dependent on their local structural environments such as the secondary structural types and SASs. Consequently, the binding affinity between protein i and any atom from the sidechain of residue j is smaller than the cut-off value of 5 Å, these two residues form a contact at the binding interface i and residue j belong to different proteins in a complex. The weight of this interfacial energy term not only depends on 20 different types of amino acids i and j. The secondary structure of a residue is determined by the standard DSSP algorithm A and B, respectively. The weights of these non-interfacial energy terms not only depend on 20 different types of amino acids and 3 different types of secondary structural elements, but also are sensitive to the SAS for each residue in the corresponding protein. The Shrake-Rupley algorithm is applied to calculate the SAS of a residue nw is used to scale the relative contributions between interfacial and non-interfacial residues.In Eq. ρ is calculated as follows:N is the total number of protein complexes used in the refinement. The variable i-th protein complex calculated by Eq. To retrieve the energetic contributions of PPIs and accurately calculate their binding affinities, the sampling of parameter space in Eq. The refinement begins from an initial condition in which all parameters are given random weights ranging between −1 and 1. A Monte-Carlo movement is applied by randomly selecting a parameter with equal probability in the parameter space and updating the weight of the selected parameter by a different new value among the same range between −1 and 1. The correlation coefficients are calculated before and after the movement. The updated weight is accepted if the correlation is higher. Otherwise, the movement is rejected, and the weight of the selected parameter maintains its original value. The system is iterated by a series of such Monte-Carlo steps until the calculated correlation coefficient becomes stabilized or a pre-defined number of simulation steps is reached. Finally, to avoid overfitting during the refinement, cross-validation is applied to test the robustness of the procedure. This procedure is described in the next section.https://life.bsc.es/pid/mutation_database/).The experimental values of binding affinity used in this study are derived from the SKEMPI database Other databases also contain information on protein complex structures and measured binding affinities. For instance, Kastritis et al. The Monte-Carlo-based refinement is combined with a leave-one-out cross-validation strategy to test whether the optimized weight parameters can predict binding affinities for unknown protein complexes. The cross-validation contains 158 separate runs, and each run further consists of independent refinement and testing phases. During each run of cross-validation, one of the 158 wild-type protein complexes, together with its associated mutants, is selected for testing, while the remaining data in the SKEMPI database are used for refinement. There are always more than one data point in the testing set. In addition to the binding affinity of the wild-type protein complex, each testing set also contains the binding affinities of various mutants, in which one or multiple residues of the complex are replaced by other types of amino acids. For instance, the testing set for the Barnase/Barstar complex (PDB ID: 1BRS) includes 95 different mutants, so there are 96 data points in total.Given the organization of the datasets, cross-validation is conducted as follows. In the refinement phase, the values of the parameters in Eq. versus the total number of predictions that are reported to increase binding affinity. Finally, the overall accuracy is defined by calculating the ratio between the number of mutants that are correctly predicted to either increase or decrease binding affinity versus the total number of mutants in the dataset.Sensitivity is defined as the percentage of correct predictions among all mutants in the dataset that increase binding affinity. Specificity is defined as the percentage of correct predictions among all mutants in the dataset that decrease binding affinity. Precision is defined by calculating the ratio between the number of mutants that are correctly predicted to increase binding affinity We first assumed that the stability of a protein complex could be determined by adding the interactions between residues across the binding interface. Based on the residue-based interactions derived from the statistics of structurally available protein complexes, we calculated the binding energy of a protein complex using our constructed knowledge-based potential. The potential gives the energetic parameters for all types of residue pairs at the binding interface, and the calculated binding energy of a protein complex with a known 3D structure is the summation of all residue pairs that form contacts at the interface (see Method for details). We compared our calculated binding energies with the experimentally measured binding affinities for all protein complexes in the SKEMPI database. As shown in The reason for this low correlation is mainly due to the fact that not all residues at the binding interface play an equal role in regulating affinity. Certain critical residues at the binding interface, called hot spots, contribute most to binding Given that the stability of a protein complex cannot be directly reflected by the parameters derived from statistical potential, we reconstructed the calculation of binding affinity by Eq. nw was first changed to investigate the relative impacts of interfacial and non-interfacial residues on binding affinity. In detail, we increased nw from 0 to 1 and carried out the same refinement and testing procedures during this process. For each specific value of nw, we calculated the maximized PCC after refinement as well as the predicted PCC for testing. nw. Not surprisingly, the PCCs calculated for refinement were always higher than those for the actual prediction. Moreover, the testing results improved when nw was increased from 0. The best performance, however, was only obtained when nw fell into a small range between 0.7 and 0.8. The testing PCCs of prediction reached the highest values within this range. Further increases of the contribution from non-interfacial residues, however, caused negative effects on the testing PCCs. These results indicate that the energetics of non-interfacial residues positively contribute to the binding affinity, and that this contribution is balanced by the inter-molecular interactions at the binding interface of a protein complex.The value of n =w 0.7), we compared our calculated binding affinities with the experimental measurements for all 3205 protein complexes in the dataset. As shown in Given the optimal weight between the interfacial and non-interfacial energetics . The distribution of these PCCs is plotted in P value lower than 1 × 10−6, and the alternative hypothesis was accepted, i.e., the correlation between the experimental and calculated data was significant.To assess the statistical significance of the derived correlation between the calculations and experimental data, we first tested our results against a null hypothesis in which the experimental and calculated data show no correlation. We applied the permutation tests for the null hypothesis. In the permutation tests, the experimental binding affinities were fixed, and the calculated binding affinities were randomly reshuffled. The PCC was then calculated between the experimental and randomized data. This process was repeated 1 × 10nw was fixed at 0.7. All other weights were first refined by Monte-Carlo optimization, and the binding affinities were then calculated against the testing sets, as described above. The final testing PCC was obtained after completing the cross-validation for the entire dataset. This procedure was repeated 10 times. The average value and SD of the PCCs were calculated. The PCCs obtained during refinement and testing for each run of cross-validation are listed in To further estimate the confidence interval of our calculated PCCs, we conducted 10 runs of cross-validation. The leave-one-out test was performed during each run. The weight between the interfacial and non-interfacial energetics versus our prediction for all mutants in the dataset. The relative change of binding affinity for a mutant was derived by calculating the difference between the absolute binding affinity of this mutated complex and that of its wild-type counterpart.The dataset contains both wild-type protein complexes and complexes with one or more mutant residues. To assess whether our computational model is able to estimate the absolute binding affinities of wild-type complexes and/or their relative changes due to mutations, we separated 158 wild-type protein complexes from the dataset and identified 3048 mutants corresponding to these wild-type complexes. We calculated the absolute binding affinities for all wild-type complexes as well as the relative changes of binding affinities for all mutants, and then compared them with the experimental data. As shown in i.e., dividing a protein complex into five structural regions (Mutations might occur at any location of a protein complex. They affect the binding affinity even if they are not directly involved in the binding interface. To study the sensitivity of our model in calculating the changes of binding affinity to different locations of mutants, we followed the definition of a previous study  regions A by comp regions C, which We further decomposed all single mutants into five groups by the aforementioned definition of structural regions and calculated the PCCs between the calculated and experimental binding affinities for these groups B. The reThe SKEMPI database contains 158 native protein complexes. As described in Method, during the leave-one-out cross-validation, one of these native protein complexes, together with all associated mutants, was excluded from refinement for testing purpose, while the remaining data in the SKEMPI database were used to optimize the weights. As a result, the cross-validation included 158 runs of the individual refinement process. All refinement processes were independent from each other, and each run led to a set of different values at the end. To test if these optimized parameters are biologically meaningful, we calculated the average values for all weights in Eq. i.e., positively charged residues have an average tendency to form pairwise contact with each other. In contrast, much lower propensities were found for the negatively charged amino acids to pair with each other. It has further been illustrated that the hydrophobic contacts in the extended configurations between the aliphatic sidechains of these positively charged residues are responsible for this pairing propensity (The values of the optimized parameters for pairwise interactions of interfacial residues were highly dependent on the local secondary structural elements, suggesting that the secondary structural types at interfaces of protein complexes contribute differently to binding free energy. Among all parameters of pairwise interactions, we focused on the data that had the lowest values, which indicate significant contributions to binding affinity. We noticed that pairs of amino acids, which formed specific types of interactions, were often found in the list. For instance, the weights of the pair between Ile and Val as well as the one between Val and Leu showed very negative values that were averaged over all secondary structural types A, indicaopensity C. Finallopensity D.Figure The parameters for the non-interfacial residues were also found to be highly dependent on their local structural environments. This indicates that residues located in different parts of a protein complex cause different effects on binding stability, even when they are remote from the binding interface. Different from the weights of interfacial residues, however, no specific patterns can be recognized by the weights of non-interfacial residues. This is partially true because these non-pairwise terms are degenerated more by mixing various energetic factors. Nevertheless, through an unbiased and blind refinement process, our study showed that the searching was converged to a weight space in which the values of many parameters can be interpreted by basic physical and chemical properties. This demonstrates the effectiveness and reliability of our method in exploring the determinants of binding affinity.in silico approaches that can predict binding affinity or mutation-induced affinity changes of PPIs. Based on the hypothesis that the stability of a protein complex is contributed by both the pairwise interactions of interfacial residues and all other non-interfacial residues, we reconstruct the calculation of binding affinity by decomposing it into the energetic contributions of both interfacial and non-interfacial residues in a protein complex. We further assume that the contributions of both interfacial and non-interfacial residues to the binding affinity depend on their local structural environments, such as secondary structural types and SASs. After refining the weights for all corresponding components against a large-scale dataset, we show that the model not only shows a strong correlation between the absolute values of the experimental and calculated binding affinities, but also can effectively predict the relative changes of binding affinities from mutations. Upon examining the values of weights in the optimized parameter space, we observe that many capture the chemical and physical principles of molecular recognition, which are not incorporated in a statistical potential derived from the structure of protein complexes. In summary, we demonstrate that our computational model reversely engineers the energetic components of protein complexes. Therefore, the proposed model can be a useful tool to predict binding affinity and understand the molecular mechanism of PPIs.PPIs determine the functions of most cellular processes https://github.com/wulab-github/AffPred.The source codes for cross-validation of binding affinity estimation against the SKEMPI dataset can be found in the GitHub repository: The authors declare no conflict of financial interests.Bo Wang: Methodology, Software, Data curation, Formal analysis, Validation. Zhaoqian Su: Data curation, Software, Validation. Yinghao Wu: Conceptualization, Methodology, Software, Supervision, Writing – original draft, Writing – review & editing, Funding acquisition.