During the COVID-19 pandemic, the slope of the epidemic curve in Mexico City has been quite unstable. Changes in human activity led to changes in epidemic activity, hampering attempts at economic and general reactivation of the city.We have predicted that where a fraction of the population above a certain threshold returns to the public space, the negative tendency of the epidemic curve will revert. Such predictions were based on modeling the reactivation of economic activity after lockdown using an epidemiological model resting upon a contact network of Mexico City derived from mobile device co-localization. We modeled scenarios with different proportions of the population returning to normalcy. Null models were built using the Jornada Nacional de Sana Distancia (the Mexican model of elective lockdown). There was a mobility reduction of 75% and no mandatory mobility restrictions.We found that a new peak of cases in the epidemic curve was very likely for scenarios in which more than 5% of the population rejoined the public space. The return of more than 50% of the population synchronously will unleash a magnitude similar to the one predicted with no mitigation strategies. By evaluating the tendencies of the epidemic dynamics, the number of new cases registered, hospitalizations, and recent deaths, we consider that reactivation following only elective measures may not be optimal under this scenario.Given the need to resume economic activities, we suggest alternative measures that minimize unnecessary contacts among people returning to the public space. We evaluated that “encapsulating” reactivated workers  may allow reactivation of a more significant fraction of the population without compromising the desired tendency in the epidemic curve.The online version contains supplementary material available at 10.1186/s12889-022-13183-z. COVID-19, caused by the novel coronavirus SARS-CoV-2, first detected in Wuhan, China, in Dec 2020, is a complex disease of infectious origins. The SARS-CoV-2 is transmitted via aerosols and respiratory droplets .Transmission rate: Our base transmission rate was derived from the definition of R0 = TransmissionRate(average)/RecoveryRate.To capture the adoption of general hygiene measures such as widespread use of face masks, we decided to calculate the transmission rate for our model using the average reproduction number (Rt) for June, calculated as described in . We use Infected and recovered nodes at the start of the simulation: We considered the percentage of population already infected and recovered at the start of the simulation, using official figures from the federal government . To addrMexico's initial response to the COVID-19 emergency was a non-mandatory lockdown known as Jornada Nacional de Sana Distancia . During this period, non-essential economic activities were limited.Based on official figures , there wThe pre-lockdown network has all the  occurring connections between people in the non-emergency state. During lockdown, many connections are lost; although some of them that are not subject to restrictions  remain, leading to a sparser network during the lockdown. During the economic reactivation, some of the pre-lockdown connections will be reactivated (highlighted in yellow) as people return to the public space; in addition to the connections that prevailed during the lockdown (grey), giving way to a new post-lockdown network.With this in mind, we represented different economic reactivation scenarios by reconnecting nodes removed in the JNSD network and simulating epidemic dynamics with the parameters mentioned above. For every system analyzed, we ran 100 iterations of EoN dynamics and evaluated the behavior in terms of:Whether a peak in the number of active infections occurs is measured as the number of simulations where at least one day with more infected nodes than day 0 (with a tolerance of 0.1%).Percentage of the infected population at the peak: measured as the average magnitude of the peak for all simulations.Peak time: measured as the average peak time for all simulations.Any economic reactivation following the lockdown period implies that part of the city's population will return to regular work activities. Mobilization will lead to an increase in contact between people returning to the public space. In network terms, this is akin to the reemergence of links adjacent to the nodes representing increased interactions between people returning to the public space; for an schematic representation, see Fig. We present the epidemic risk evaluation under different reactivation scenarios with these ideas in mind.The following pseudocode describes how each of this magnitudes is calculated:The minimum level happened during the lockdown and is represented by the JNSD network. On the other side of the spectrum, the maximum level of communication is represented by the CDMX network, which captures the usual contact patterns of the city without the constraints induced by the pandemic.As we previously mentioned, economic reactivation involves reintegrating a fraction of the population back into the public space. This renewed activity can be represented as a reconnection of contacts removed during the lockdown.For each fraction F of reactivated populationWe selected F nodes of the Mexico City contact networkWe considered that all links adjacent to other nodes were reactivated without restrictions.We considered that the rest of the nodes in F (those with no correspondence to links in F) were the ones in the network as modeled under lockdown conditions as given by the JNSD network.Without any constraint and no additional information, we assumed that the workforce is evenly distributed within the population represented in the contact network . TherefoWe used the resulting network to run an EoN dynamic using the parameters described previously.An alternative approach to economic reactivation consists of leveraging network properties to impose limits on the epidemic dynamic. The concept of modularity in complex networks  is looseInstead of reactivating nodes spread throughout the network in our proposed modular reactivation, whole modules are reactivated until the desired fraction of the population is reactivated. All edges adjacent to the reactivated nodes are reconnected, including links within a given module and a smaller subset of connections beyond the module's boundaries.Figure In the top panel, the contacts of the red nodes may include job and non-job related contacts with similar probabilities. This way, by being randomly distributed in the network, contacts are reactivated both with other essential workers (red) and with the rest of the population (black nodes), which in principle must remain under confinement (as during the lockdown).The lower panel depicts a scenario in which red nodes form a module; hence, it is more likely to connect to other essential workers (red nodes). In case of infection within the module, the general population is, to an extent, shielded since the outbreak has a greater probability of keeping spreading within the module. Hence the attack will become contained more easily.While modular reactivation can be accomplished by arbitrarily activating modules, the interconnection between modules can also further refine the modular reactivation. Dynamic processes will remain within a modular structure; therefore, reactivating smaller, topologically distant modules in the network will further encapsulate the epidemic phenomena and minimize its spread. See Fig. Our current work does not consider the effect of a vaccination campaign on the evolution of the COVID-19 epidemic in Mexico City. We have decided to exclude this from our simulations based on the following considerations: 1) while there are reports that vaccines reduce SARS-CoV-2 transmission , 27, theThis section will present the effects of several reactivation interventions on the trajectory of the COVID-19 epidemic in Mexico City. We report epidemic curves as a percentage of the population to avoid ecological biases.One initial, non-surprising result is that unconstrained reactivation of increasing fractions of the population, with no use of knowledge about the human contact network structure of Mexico City, rapidly approaches the epidemic conditions of full reactivation Fig. .Fig. 4EnTable Reactivation strategies in a randomly distributed population allow only a tiny fraction of the people to be reactivated without a high risk of resurgence of cases. We observe that any fraction beyond 5% of the population rejoining the public space leads to an increased risk of a new wave of infections. The system is quite fragile; The shadowed areas in Fig. A second, less dramatic scenario occurs whenever we make use of some knowledge about the human contact network structure of Mexico City, namely its modular character. The height and duration of the peaks, as shown in Fig. A glance at the summary statistics presented in Table Economic reactivation strategies using the modular structure of contact networks limit the spread of the infectious agent. We can observe that up to 25% of the population reactivation can be achieved with a slight deviation from the complete lockdown in magnitude, timing, and the likelihood of a new peak. In the case of a 50% modular reactivation, subsequent peaks are likely to emerge. However, it is essential to note that the magnitude of these peaks is less than that observed in a complete reactivation scenario.Modular reactivation is thus a better alternative in allowing the incorporation of higher percentages of the population without the risk of massive outbreaks. However, as we can see, later on, there are still better alternatives nurtured by the use of deep knowledge of the topological parameters of the human contact network of Mexico City.By taking into account not only the global topological modularity of the human contact network of Mexico City but also the way the modules are connected , we have been able to develop an optimized set of reactivation strategies, as is presented in Fig. If the population to be reactivated is spread throughout several (more minor) modules, it is possible to control the epidemic dynamics. Even in the case of a 50% population reactivation, this "smarter" modular strategy decreases the magnitude of subsequent possible peaks.Hence, by considering the contact network's global and local modularity structure and simulating epidemic dynamics compliant with these, we have devised an optimized progressive de-containment strategy.Epidemic spread of infectious diseases occurs via chains of transmission, which are dynamic processes shaped by different human behaviors. In the case of COVID-19, the condition is spread through close human interactions , which cHuman interactions  can be mUsing these scaled contact networks and a stochastic dynamics on networks approach, we captured the essentials of epidemic spread in Mexico City. We used the knowledge of said spreading patterns to model de-confinement scenarios to evaluate reactivation strategies after a lockdown in Mexico City. Although these networks exhibit a dynamic dimension, using a representative network as a baseline on which different lockdown and reactivation strategies can be modeled provides an efficient tool to explore different scenarios.Since human contact networks in large urban environments tend to exhibit a modular structure, this invisible compartmentalization is one feature that shapes the transmission chains of an epidemic phenomenon. By optimizing the modular structure of the re-entrant essential worker population, we have proposed schemes that allow for a significant percentage of the population to return to public space without leading to massive outbreaks.This work finds that economic reactivation is feasible without necessarily resulting in a new outbreak. However, for this to work, reactivation must occur within contact communities. Optimally, these modules should be small and exhibit minimal connections to limit the spread of the disease. A set of behavioral and regulatory actions are needed to constrain this contact dynamics tightly, as the alternative shows that even a small fraction of the population being arbitrarily reactivated leads to an epidemic behavior similar to that of implementing no lockdown at all.Our findings might be helpful to provide feedback to public health policies . In clinResidential proximity induces communities .Non-public facing workspaces induce communities.Commuting induces inter-module connections .Public-facing jobs induce intermodule connections.The studies just referred have provided us with the following intuitions regarding modularization of the human contact structure in urban environments such as the one in Mexico City:Based on our results, we have concluded that modular reactivation is a better alternative in allowing the incorporation of higher percentages of the population without the risk of massive outbreaks. If the population to be reactivated spreads through smaller modules, it is possible to control the epidemic dynamics. Therefore, we consider that we may formulate the following recommendations:Interleaved reactivation: different, physically distant neighborhoods every day.Delay the return of workers that require long commutes.logistic complexity (beyond the scope of the paper) that the competent authorities must resolve.Authorities can enforce modularity by closing public transportation needed for intermodular connections; however, leaders should avoid costly penalties to the population. Effective strategies involve a significant degree of Public health authorities must consider logistic complexities to translate the findings of this study into actionable policy. In this regard, measures that must encourage modularity reactivation are presented in Table Limitations of our model and other considerations.Mathematical modeling implies simplification of the "real world." An aspect that we have not considered is mobility data. This factor shows a large number of long commutes within the city, which, as previously mentioned, could be a factor driving inter-module communication. Understanding these commute patterns as a function of residence could be an essential next step or the implementation of directed interventions to reduce inter-module connectivity; the main proposal of the current manuscript. This issue is discussed in the Supplementary Material. A limitation of our study is that the model we are proposing has not been tested. However, we consider it feasible based on mobility studies that have been conducted in Mexico City and surrounding metropolitan areas. Previous studies show most trips originating in municipalities on the periphery of Mexico City and the surrounding metro area have destinations in the central parts of the same municipalities. Therefore, modular networking would not significantly alter pre-pandemic mobility.Mobility metrics have been used as an input for other COVID-19 models. The rationale is that for people to gather and have contacts, they must leave their houses for the public space. However, this is not sufficient: people may enter the public sphere but avoid interacting with other people, therefore having low risk of disease spread; importantly, in populations like Mexico City, where some populations (particularly those in low socioeconomic situations) may live in overcrowded spaces, high, long distance mobility may not even be necessary for disease spreading. In this regard, we consider that our approach, which is based on co-localization contact networks rather than mobility metrics, is better to capture the possible paths through which the virus may spread.A central question at this stage of the pandemic is the role of vaccines in preventing transmission, which remains an open question , with onBy considering mechanisms for the reactivation of economic activities in Mexico City, we have evaluated the risk that the negative slope of the epidemic curve may be reversed by incorporating a fraction of the population into the public space.As null models, we ran the epidemic dynamics on the contact network of Mexico City with no mobility restrictions, representing a return to the connectivity prevailing before the start of the pandemic. The second null model considered was the network corresponding to the JNSD network, a subgraph of the Mexico City contact network with just 25% of active links, capturing the effect of the reported mobility reduction. In these models, reactivated nodes are randomly distributed on the network, which is a conservative assumption due to the lack of further information on the sociodemographic and spatial distribution of the individuals involved in the activities that will be reactivated. The model we propose is a component of epidemiologic surveillance that should include timely detection of active cases and hotspots. In addition, it has the advantage of providing a rapid response to prevent further transmission and inform containment and mitigation measures.We consider that the concept of modularity in network theory may support reactivation economic activities.A module in a complex network is loosely defined as a set of nodes  with a higher number of connections among members of the set than with other nodes of the network, i.e., there are more connections within a module than between modules. An essential property of modular networks is that dynamic phenomena  remain inside a module for longer before spreading outside the module.By translating the findings of network-based analytics and epidemiological models into actionable public policy measures, it is possible to advance into incorporating predicted risks into the assessment portfolio for reactivation large urban conglomerates such as Mexico City after a lockdown in the face of the still ongoing Covid-19 pandemic.Additional file 1. Supplementary Material


The presented study discusses the possible disturbing effects of the electromagnetic field of antennas used in mobile phones or WiFi technologies on the pacemaker in the patient’s body. This study aims to obtain information on how the thickness of skin layers (such as the thickness of the hypodermis) can affect the activity of a pacemaker exposed to a high-frequency electromagnetic field. This study describes the computational mathematical analysis and modeling of the heart pacemaker inserted under the skin exposed to various electromagnetic field sources, such as a PIFA antenna and a tuned dipole antenna. The finite integration technique (FIT) for a pacemaker model was implemented within the commercially available CST Microwave simulation software studio. Likewise, the equations that describe the mathematical relationship between the subcutaneous layer thickness and electric field according to different exposures of a tuned dipole and a PIFA antenna are used and applied for training a neural network. The main output of this study is the creation of a mathematical model and a multilayer feedforward neural network, which can show the dependence of the thickness of the hypodermis on the size of the electromagnetic field, from the simulated data from CST Studio. Approximately one million patients worldwide receive a cardiac pacemaker, considered an implantable medical device able to detect the electrical activity of the heart and stimulate it when the heart is inadequate . The devThe probability of interference is high during the activation of the call signal and, in some cases, 3 or 5 s before. As the distance from the device between the cardiac pacemaker (PM) and the mobile phone increases to a few centimeters, the amount of interference that the electronic system produces at radio frequency (RF) is significantly reduced. The past studies reported an incidence of an electromagnetic interference (EMI) variable of 20–30% between cardiac pacemakers (PM) and cellular phones. The latest studies reported a reduction in the electromagnetic interference at about 1–5% . EMI canThe heart activity depends on the continuous and physiological functioning of the sinoatrial node cells that act as the natural pacemakers. In pathological situations, the natural pacemaker ceases to operate or becomes unreliable, or if the stimulation impulse is not able to reach the cardiac muscle due to a block in the conduction system, the heart’s pumping action is impaired. In these situations, a suitable electrical stimulation can be required to regulate the heart rhythms. This electrostimulation is provided by the pacemaker. The cardiac pacing mode allows to distinguish two types of pacemakers: implantable and external. The external pacemakers are designed to return the normal heart rhythm in case of cardiac arrest, in situations where it is required for a short period of time. Frequently, the external pacemaker is used to correct temporary conduction disturbances due to cardiac surgery . When thAlthough pacemaker implantation is considered to be low risk, problems and technical failures such as pneumothorax and lead dislodgement, superficial phlebitis, and hematomas might occur during the surgery ,26.This procedure is performed under local anesthesia, using the cardiac catheterization techniques, with the electrodes placed on the right hemi-thorax and a pacemaker wire inserted into a vein. The wire is connected to the pacemaker ,28,29. T−1]) given by the following equation:−1), ▿—rotation vector operator, −1), −1), −1), and j—imaginary unit [In recent years, the use of wireless technologies has become very widespread. This has resulted in an increase in the intensity and spatial presence of electromagnetic fields. This is particularly important for pacemaker (PM) implantation patients, who may thus be exposed to excessive radiation. The most commonly used devices include cell phones, Bluetooth, or WiFi devices. They typically operate in the 2.4 GHz center frequency band, which is an unlicensed ICM band. The exact effect of radiation exposure on the human body has not yet been established completely. This is still the subject of research. Hence, the presence of some metallic implants in the superficial layers of tissue (panniculus) can assume antenna functions. In this connection, a possible local increase in the electromagnetic field of the human body by PM is recognized. This occurs in terms of electric field strength or specific absorption rate (SAR) values. This parameter is a dosimetric quantity. It indicates the rate of energy absorption by human tissue during exposure to RF electromagnetic radiation. The local distribution of the electromagnetic field is significantly influenced by the dielectric properties, as well as the size of the metal implant, the thickness of the surrounding tissue, or the shape and orientation relative to the field source ,33. Therary unit .To achieve the computing electric field distributions, the application of the finite integration technique (FIT) was chosen. In the study, the commercial simulation software, CST Microwave Studio, was used. The hexahedral mesh was applied in the time-domain solver based on the FIT.Two sources of RF EMF were used—a dipole antenna and a planar inverted-F-type antenna (PIFA). A generalized tuned dipole antenna model was used because it is easy to model for specific resonant frequencies, but it is rarely used in aforementioned technologies.It is modeled as a long cylinder fed by a discrete port situated in the center of the longitudinal cylinder axis with a reference impedance of 50 Ω .The cylinder was modeled as a lossy copper metal. The geometric properties of the dipole antenna are summarized in On the other hand, the currently prevalent antenna for hand-held devices is the PIFA—due to both its compactness and size. This device is a quarter-wave monopole antenna. It is constructed by two parallel conducting plates, which are connected to the ground through a shorting pin. The resonant frequency of the PIFA depends primarily on the width of the shorting pin and the dimensions of the radiating element. After several parametric simulations, the optimal geometrical parameters have been obtained by maintaining the appropriate reflection coefficients in the desired frequency band b. The maThe PIFA, namely, includes a linear inverted-F antenna (IFA) with the wire radiator element of IFA replaced by a plate in order to improve the performance in terms of bandwidth, suitable to be used in portable wireless devices, especially in mobile handsets. For the mobile devices market, several advantages are offered by it, guaranteeing low profile and manufacturing costs, small size, easy fabrication, simple structure compared to other microstrip antennas, allowing to locate it in structure such as the back cover of mobile terminal, low SAR value (specific absorption rate), and small backward radiation toward the user’s head, reducing the signal absorption and increasing the antenna performance. Typically, the inverted-F antenna (IFA) consists of a rectangular planar element positioned on ground plane, a short circuiting plate or pin, and a feeding mechanism for the planar element. It is an alternative to the monopole antenna, able to reduce the antenna height maintaining the resonant frequency. In the inverted-F antenna, the top section is folded down so as to be parallel with the ground plane, which introduces a capacitance to the input impedance that is compensated by using a short-circuit stub connected to the ground plane. The current intensity in the IFA causes excitation of currents in the ground plane, then the resulting electromagnetic field is produced by the interaction of two contributions: IFA and an image of itself in the ground plane. When the ground-plane size is infinite or much larger than IFA, it acts like a perfect energy reflector.More specifically, the required PCB (Printed Circuit Board) ground-plane length is about one-quarter of the operative wavelength  and the tuned dipole antenna.In the feedforward process, the external input values are first multiplied by their weights and summed. The output The input is defined by the thickness of the hypodermis tissue in terms of the BMI classes: 2 mm for underweight, 5 mm for normal weight, 10 mm for overweight, and 18 mm for obese. The output is expressed by the electric field strength obtained by the tuned dipole antenna. The feedforward backpropagation neural network is composed by an input layer with one neuron arranged in the output layer, using a linear transfer function, and one hidden layer with a hyperbolic tangent sigmoid transfer function.The network training function . The learning curves, shown in The same neural network is used with a network training function It minimizes the combination of the root mean squared errors and weights, and then determines the appropriate combination, so as to produce a well-generalized network. This process is called Bayesian regularization. The learning curve showed the performance of the second neural network obtained after 34 epochs. The MSE is 0.0076424 which shows a good generalization a.In the second case, the relationship between the skin layer thickness input values (x) and the electric field strength distribution (y) of the PIFA antenna proposed as the source of near-field electromagnetic exposure is presented.The same neural network is used with The induced electromagnetic fields can cause interference with ICDs and implanted pacemakers within the body. This study describes a computational mathematical analysis and the modeling of a pacemaker implemented under the skin exposed to different electromagnetic field sources, such as a PIFA antenna and a tuned dipole antenna. The simulation results show the calculated field intensity distribution within the multilayer structure irradiated by the tuned dipole and PIFA antennas performed on four different hypodermis tissue layer thicknesses. In order to accomplish this, the finite integration technique (FIT) implemented within a commercially available simulation software, CST Microwave Studio, is used. Similarly, the equations that describe the mathematical relation between the hypodermis layer thickness and electric field, according to different expositions of the tuned dipole and PIFA antennas, are obtained by training the neural network. In particular, a relationship based on an artificial neural network (ANN) has been proposed between the electric field distribution and the cardiac pacemaker placed under the multilayer structure, characterized by various subcutaneous tissues of the skin exposed to 2.4 GHz RF with a power of 0.5 W in the air. The multilayer feedforward neural network with appropriate weights has approximated the complicated input–output function between the hypodermis layer thickness and the attenuation of the electromagnetic field in the skin tissues. The developed MLP neural network has been useful for modeling the cardiac pacemaker exposed to EMI. In future work, the ANN can be useful to predict the electromagnetic interference with the implantable cardiac pacemaker.


The production of thin-walled beams with various cross-sections is increasingly automated and digitized. This allows producing complicated cross-section shapes with a very high precision. Thus, a new opportunity has appeared to optimize these types of products. The optimized parameters are not only the lengths of the individual sections of the cross section, but also the bending angles and openings along the beam length. The simultaneous maximization of the compressive, bending and shear stiffness as well as the minimization of the production cost or the weight of the element makes the problem a multi-criteria issue. The paper proposes a complete procedure for optimizing various open sections of thin-walled beam with different openings along its length. The procedure is based on the developed algorithms for traditional and soft computing optimization as well as the original numerical homogenization method. Although the work uses the finite element method (FEM), no computational stress analyses are required, i.e., solving the system of equations, except for building a full stiffness matrix of the optimized element. The shell-to-beam homogenization procedure used is based on equivalence strain energy between the full 3D representative volume element (RVE) and its beam representation. The proposed procedure allows for quick optimization of any open sections of thin-walled beams in a few simple steps. The procedure can be easily implemented in any development environment, for instance in MATLAB, as it was done in this paper. In recent years, thin-walled structures have gained more and more popularity, both as thin-walled beam profiles and trapezoidal sheets. They are most often used in constructions as load-bearing elements of structures, among others as roof bolts and purlins, spatial lattice trusses or in transom-column systems. In addition, thin-walled elements are used in the construction of facades and as bracings. In such applications, it is necessary to ensure the appropriate load-bearing capacity of the elements. Another application of thin-walled sections is their use for the production of architectural details. These include window and door leaves, balustrades and fences. Although they are not load-bearing elements, their weight and strength are still important and often optimized.Generally, more and more often the aim is to obtain steel structures with the lowest possible weight while maintaining an appropriate load capacity. This can be achieved by using high-strength steel or by inserting appropriately spaced holes/perforations along the length of the element. Holes obviously reduce weight, but they can also act as mounting holes, which are used to attach a profile to another load-bearing element or, for example, to attach sandwich panels to such a profile, as was shown in . These tDesigning load-bearing elements, including thin-walled purlins usually consists of selecting the appropriate cross-section from the manufacturer’s available catalogs. All that is needed is the beam span, the number of spans and the load value. Cross-section warping and distortions are ignored in this study, although they usually affect the behavior of thin-walled beams. These phenomena are widely discussed in the literature, including buckling assessment of thin-walled steel frames , where GCalculations of geometric or strength parameters for a fixed beam without holes is easy to perform. However, for more complicated structures or elements with holes, more complex calculations are required. In the case of complex cross-section consisting of several materials, the analytical calculations can usually be performed only for the elastic range. The problem arises when one takes into account the plasticity of the material, then the section properties are not sufficient to determine the load capacity of the structure. In this case, the application of general nonlinear constitutive law (GNCL) proves to be useful. Thus far, they have already been used for 2D steel plates , composiApplying the finite element method and creating full three-dimensional models for complex structures is very time-consuming and labor-intensive. In the case of bar elements with perforations or holes, modelling with the use of solid or shell elements is required ,2. In thThe issue of homogenization has been the subject of numerous studies over the last dozen or so years. Consequently, many homogenization techniques have emerged. There are homogenization methods based on the deformation energy , the perThanks to the use of homogenization methods, calculations can be accelerated, which is especially desirable in optimization problems ,22. For In this paper, we use the homogenization technique based on the equivalence of elastic strain energy and traditional gradient-based minimization algorithms for parametric optimization of thin-walled sections of different beams. After the successful implementation of a very efficient algorithm in MATLAB, an offline computation method based on a surrogate was also presented. Its task was to replace the homogenization procedure, which further accelerated the optimization process without significantly losing the accuracy of the proposed procedure. In addition, a properly prepared surrogate can be used on a small portable computer or as a light and fast web application to optimize any parameterized cross-sections of thin-walled beams with holes; of course, after prior training of the model using the procedure proposed here.Optimization problems are common in daily engineering work. Often they are solved intuitively, or by trial and error methods. Highly qualified engineers use advanced optimization methods available in commercial software, developed by in-house codes or possibly implemented as the subroutines/addons to their daily usage programs. One of the key elements is the so-called solver of each optimization task, which must be able to map the output results based on the input parameters.In advanced engineering problems, the solver is rarely a simple analytical or empirical mathematical expression. Most frequently, the output is obtained by heavy and medium-heavy computations, for instance, by employing the finite element method ,33, meshIn this paper, the shell-to-beam homogenization technique  was usedThe load-bearing bending moment In perforated beams, the displacement from shearing can be the important part of the overall displacements. Therefore, in the study, the second order displacements were also taken into consideration:As mentioned earlier, the displacements were computed in To sum up, after considering Equations (2) and (4), the objective function, Such form of Equation (5) enforces the optimization algorithm to find the solution with The main objective of the paper was to verify different approaches to solve the constrained optimization problem. Three cases were selected, namely, (i) traditional optimization with minimization algorithm of sequential quadratic programming (SQP) , (ii) opb—upper flange widthc—web heightd—lower flange widtht—thin-walled sheetw—length of the rectangular part of the holer—radius of the holeIn the study, the three cold formed thin-walled cross-sections were considered, i.e., Z profile, C profile and Σ profile. The Z and C profiles have rounding at the corners and regular holes in the web. The hole shapes considered are circular and stadium type. The geometries analyzed for Z and C profiles were parameterized by a few geometric features:b—upper/lower flange widthc—overall heightj—non-perforated height of the webk—perforation heightl—web perforation depthThe Σ profile was parametrized by:b—bending radius in the cross-section are equal to Moreover, some of the features are dependent on the geometric parameters assumed, that is, a—length of upper flange stiffener is equal to The parametrized models were used to build the representative volume element  for a pAlthough, in the case of thin-walled beam elements, the material from which they are made is rarely heterogeneous, the appearance of holes along the beam means that traditional tools and algorithms cannot be used. To avoid the need for advanced tools and complicated modeling, numerical homogenization can be used ,15,16,17In order to apply the elastic deformation energy equivalence between the full 3D model (which is homogenized) and the model reduced to a structural element, the first step is to correctly construct the relationship between the deformation and the displacement in the selected model nodes. To do this, first one needs to build a full stiffness matrix of RVE and then condense the stiffness of the full model into nodes where kinematic boundary conditions have been applied. Let us briefly go through the homogenization method used in  and adopu is the nodal displacement vector and Starting from a typical finite element method equation, one may extract magnitudes for external , in the method postulated  is the fSequential quadratic programming (SQP) method was used because this optimization method is one of the most efficient, accurate and was tested over a large number of benchmarks. Moreover, it has a high percentage level of successful solutions ,39,40,41The mathematical problem of the optimization is postulated in the following manner:In order to take the constraints into account, the function The quadratic programming subproblem form leads to the following expression:The solution obtained due to quadratic programing is used to generate a new step:function ,39,40.In this work, we used very simple neural networks with radial basis functions. These networks are characterized by zero error on the training vectors. The training parameters of such a neural network are: (1) a set of input vectors As a result of learning, we obtain weights and biases in such a way that the outputs of the network are exactly the values of ollowing :(22)a(n)Each variation in the first layer is set so that for a radial basis function that intersects 0.5, the weighted inputs value is +/− the spread value. This determines the width of the area in the input space to which each neuron corresponds. Second layer weights and deviations are found by simulating the first layer results Thus, the only required condition in this type of neural network is to ensure that the spread is large enough that the active input regions of the radial neurons overlap sufficiently . This makes the approximation function smoother and the results are better generalized for new input vectors located between the input vectors used for training. The spread for each neuron should not be too large to be able to respond effectively to a correspondingly large area of the input space. In our case, the value of this parameter was set to 10; this was the value that generated the smallest error when testing the network with new input vectors.In the work, the systematic sampling data was used in (ii) optimization task, assuming the boundary design parameters In the study, the optimal Latin hypercube sampling was used in (iii) optimization job within the assumed boundary of design parameters The algorithm in order to generate the sampling is using the maximum distance criteria, n et al. : (23)ϕp=In the study, for (iii) optimization job, 75% of the points used for systematic sampling optimization, i.e., (ii) optimization job, was assumed, namely, 2160 points were generated, i.e., set of 2160 design parameters In the methodologies presented above, the artificial neural network with radial basis functions see  is a surThe flow of the computations in the optimization is as follows: design parameters these design parameters these effective stiffnesses are used in Equations (2) and (3) to compute the objective function values The sensitivity analysis of objective functions for each parameter for all profiles used were computed. For each trial step in the SQP optimization the numerical gradient of the objective function was computed. These data were used to compute the mean sensitivity for each parameter. For Z and C profiles the results were similar; for The Z profile results obtained for traditional optimization with SQP minimization algorithm are presented in The example of the design parameters convergence for the fourth case and The C profile results obtained for traditional optimization with SQP minimization algorithm were presented in The example of the design parameters convergence for the first case and The Σ profile results obtained for traditional optimization with SQP minimization algorithm are presented in In The Z profile results obtained for optimization with radial basis function metamodeling feed with systematic sampling data were presented in The example of the objective function convergence The C profile results obtained for optimization with radial basis function metamodeling feed with systematic sampling data are presented in The example of the objective function convergence The Z profile results obtained for optimization with radial basis function metamodeling feed with optimal Latin hypercube sampling data are presented in The example of the objective function convergence The C profile results obtained for optimization with radial basis function metamodeling feed with optimal Latin hypercube sampling data are presented in The example of the objective function convergence The optimization results were presented for three types of optimization tasks: (i) traditional optimization with minimization algorithm of SQP, (ii) optimization with metamodeling by RBF network feed with systematic sampling data and (iii) optimization with metamodeling by RBF network feed with OLHS data. The vast majority of optimization tasks have shown good convergence to find optimal solutions and reached the objective function of less than In most cases, the deflection component in the objective function is greater than bending moment component. This feature is clearly visible in In Moreover, the optimizations with submodelling (surrogate) approach used have shown a great reduction in computational cost. In optimization (i), the time of computation of a single objective function was about The differences between surrogate models and the exact solutions from the optimal design parameters are show in This paper presents a very efficient algorithm for parametric optimization of thin-walled beam sections. The beams analyzed have various openings along their length and are under various loading and boundary conditions. The proposed procedure uses numerical homogenization based on equivalence of the elastic strain energy between the RVE model and the simplified, structural model. RVE was modeled within the FEM framework as a detailed 3D shell structure and then homogenized to obtain its flexural and shear stiffnesses.In order to validate the proposed method, the optimization by sequential quadratic programming was performed in the first case. In the next cases, two surrogate models were prepared based on artificial neural networks, i.e., with systematic training set and taken from the optimal Latin hypercube technique. Both allow use of the proposed procedure offline—without the need for numerical modeling. In the online stage, each type of analyzed cross-section was separately recalculated in different geometrical variants and all flexural and shear stiffnesses were calculated by means of shell-to-beam numerical homogenization. Then, on the basis of the systematic training set as well as the ones generated by the optimal Latin hypercube technique, various neural networks with radial base functions were trained for a different type of the beam thin-walled section.In each case, very precise results, comparable to the reference results, were obtained. This proves the possibility of using artificial neural network models in an offline phase to quickly optimize the cross-sections of various beams with periodic openings under various boundary and load conditions.


Studies have shown that demand-side interventions, such as conditional cash transfers and vouchers, can increase the proportion of women giving birth in a health facility in low-income and middle-income countries, but there is limited evidence of the effectiveness of supply-side interventions. We evaluated the impact of the Subsidy Reinvestment and Empowerment Programme Maternal and Child Health Project (SURE-P MCH) on rates of institutional delivery and antenatal care.We used a differences-in-differences study design that compared changes in rates of institutional delivery and antenatal care in areas that had received additional support through the SURE-P MCH programme relative to areas that did not. Data on outcomes were obtained from the 2013 Nigerian Demographic and Health Survey.We found that the programme significantly increased the proportion of women giving birth in a health facility by approximately 7 percentage points (p=0.069) or approximately 10% relative to the baseline after 9 months of implementation. The programme, however, did not significantly increase the use of antenatal care.The findings of this study suggest there could be important improvements in institutional delivery rates through greater investment in supply-side interventions. This project evaluated the impact of a real-world programme that was implemented at scale on the use of maternal health services across Nigeria.The programme provided trained midwives and facility upgrades to participating primary health centres.Our study combined programmatic data with household survey data to estimate the impact of the programme at the individual level using data from the Nigerian Demographic and Health Survey.Due to the reliance on household survey data that were not collected for the purposes of this study, we were limited by sample size and the types of outcomes we could evaluate.A lack of precise geographic information on households may have limited our ability to directly attribute effects of the programme.The world has made great advancements in reducing maternal mortality: the estimated number of women dying in childbirth declined from 385 per 100 000 live births in 1990s to 216 in 2015.2Over the same time period, the proportion of women giving birth in a health facility had also substantially increased in SSA.While there is a clear need to improve the quality of maternal healthcare in such settings,10Countries in SSA, including Nigeria, rely on a range of cadres of health professionals to provide specialised care to women, infants, and families over the entire continuum of pregnancy from preconception to the early weeks of life and all of these health professionals are classified as SBAs.15Building off this gap in the literature, this study aims to evaluate the impact of the Subsidy Reinvestment and Empowerment Programme Maternal and Child Health Project (SURE-P MCH), a large-scale programme introduced in 2012 that deployed trained midwives and was aimed at accelerating progress towards Millennium Development Goals (MDGs) 4 and 5, on institutional delivery (ID) and antenatal care (ANC) rates across Nigeria. Our goal with this study is to generate rigorous evidence on the potential effectiveness of comparable large-scale programmes to improve coverage of high-quality health services in other high maternal mortality settings.In January 2012, the Federal Government of Nigeria eliminated a longstanding fuel subsidy and announced that funds raised by the removal of this subsidy would be used to improve public services, including health services. Following this announcement, the SURE-P MCH programme was launched as a flagship initiative to improve MCH outcomes across the country. The programme was implemented by the National Primary Healthcare Development Agency, a parastatal organisation under the Federal Ministry of Health.Following its launch, the programme began the process of upgrading primary health centres (PHCs) and training newly recruited midwives. In order to facilitate the rapid roll-out of the programme, facilities were purposively selected for the project based on need (defined as a persistent lack of midwives) and also conditional on meeting a set of minimum infrastructure and human resource requirements. To be eligible for SURE-P MCH, PHCs had to: offer MCH services; have minimum equipment and basic infrastructure, including potable water supply, power supply and sewage disposal; and operate on a twenty-four-hour basis. These criteria, especially the last condition, explain why our treated facilities and their respective catchment areas had higher rates of ID at baseline than our comparison facilities.Following their training, the programme deployed 1285 midwives to 473 PHCs in high-priority areas across Nigeria’s 36 States and the Federal Capital Territory. Each facility usually received more than one additional midwife and the first batch of facilities began to fully participate in the programme in October 2012. The SURE-P MCH programme also benefited from a wide-reaching mass media campaign, encompassing radio and television adverts, billboards and posters encouraging pregnant women to visit SURE-P MCH PHCs after the deployment of the midwives.Although initially there had been plans to further scale up the programme after the initial phase, unfortunately the programme had been cut short about a year after implementation primarily due a budget shortfall related to a sharp fall in international oil prices. Although the programme was not officially terminated until 2015, news had begun to circulate following the drop in oil prices that the programme would be terminated in early 2014. Programme officials had reported to the research team high levels of midwife attrition, which may have started in late 2013. Due to these factors, we limit our analysis to the first year of programme implementation (October 2012–2013), however, as discussed in the data section, we only had data for the first 9 months of this implementation period to evaluate outcomes.To evaluate the impact of SURE-P MCH, we adopted a differences-in-differences (DID) study design, a method that has been widely used to rigorously evaluate the impact of health and social programmes in other comparable contexts.For the purposes of this analysis, we consider 1 October 2012 as the start date of the programme, based on information obtained from national programme officials. The effect of the programme is then estimated as the interaction of the treatment variable and the post-treatment variable, conditional on a set of covariates. Our regression model is thus the one below, where X is a vector of covariates listed in Y are the outcome variables of interest (IDs or ANC), SUREp is a binary variable that takes the value 1 if the mother (i) lives in a household that is located in one of the SURE-P MCH treatment areas, and Post is a time (t) dummy variable that takes the value of 1 if the birth occurred after October 2012. X is a set of control variables, which are fully described in our result tables. We also account for sample clustering effects (c) in our estimates of CIs and p values.In this equation, The DID approach relies on a common trend assumption . However, this assumption can never be formally tested in the presence of an intervention, thus it is standard to test this assumption in the pretreatment period. In the Results section, we provide estimates of our test of the common trend assumption and show that the parallel trends hypothesis cannot be rejected for the entire pretreatment period.Our data source for the outcome and control variables, which were measured at both the individual and household levels, was the 2013 Nigerian Demographic and Health Survey (NDHS)In order to define the treatment and comparison status of health facilities, we ascertained the location of all the 473 SURE-P MCH facilities using programme data. We then determined a 100 m radius around each SURE-P MCH facility and defined all facilities within such radius as treatment facilities, under the assumption that all facilities within the same proximity of a treatment facility were indirectly affected by the programme. This produced a final list of treatment facilities in the NMIS, and the remaining facilities were defined as comparison facilities.In the NDHS, households were sampled within primary sampling units , and these were geocoded. To determine whether each cluster from the 2013 NDHS was within a treatment or comparison area, we first calculated the distance of each cluster to the nearest treatment facility as well as the nearest comparison facility . We defined treatment clusters as those located less than 2500 m from the nearest treatment facility and comparison clusters as those located within a 2500 m radius of a comparison facility and outside a 2500 m radius of a treatment facility. To avoid comparing households living in very remote locations with those located close to a treatment facility, our analysis excluded a small number of clusters that were located more 2500 m from the nearest treatment facility and over 7500 m from the nearest comparison facility. The selected distances were derived from a literature review on distance-based access to care measures across SSA that accounted for the mean distance of clusters from facilities within the sample.2010.1136/bmjopen-2021-053792.supp1Supplementary dataThe two primary outcomes of the study were the rate of ID, defined as the proportion of deliveries, as reported by women, that took place in either a government hospital, health centre, health post or other public sector medical facility, a private hospital, clinic or other medical sector facility; and the percentage of all pregnancies resulting in a live birth for which the mother reported receiving at least four ANC visits (ANC4).Given that our treatment areas differ from the comparison areas due to the selection criteria of the facilities, a rich set of covariates was also drawn from the DHS dataset including data collected at the individual, household, cluster and regional levels. The covariates were selected based on whether they could have influenced the facility selection criteria and thus represent a potential confounder with the primary outcome variables of interest. A summary of covariates is given in This study was originally registered as an observational study.Patients or the public were not involved in the design, conduct, reporting or dissemination plans of our research.Although it is not possible to statistically prove that trends in IDs were parallel prior to the start of the programme, we used hypothesis testing to ascertain whether our data are consistent with the hypothesis of parallel trends in the pre-policy period. Specifically, we performed a Wald test of the hypotheses of common trend assumption and these were never rejected during the pre-policy period years (2008 to 2012) for IDs (p=0.30) and attendance of at least ANC4 visits during delivery (p=0.24). This is also consistent with the assumption, which is true to the best of our knowledge, that there were no other large-scale policies or programmes in place in Nigeria that would have affected the SURE-P MCH facilities differently than those in the comparison areas, which would be difficult given the very specific criteria that were used to select the treatment facilities. We previously discussed these criteria in our Study Context section.The main estimates were obtained assuming a facility catchment area of 2500 m As a robustness check, we have re-estimated our model for the period October 2012 to October 2013 using all the births recorded in the 2013 NDHS as well additional births from 2013 and 2014 that were captured in the 2018 NDHS. However, only a very small number of additional births (2.8% of the births in the 2018 NDHS) were recorded in 2013. When we again estimate the impact of the programme in the first year, the results are similar to those seen Improving the quality of maternal health services is an important global health priority for many countries, including Nigeria. In 2012, the Nigerian government launched an ambitious programme that dispatched trained midwives to eligible health facilities across the country. This study found that the increased availability of the midwives led to substantial increases in the proportion of women giving birth in a health facility leading to an increase in ID rates by 7.2 percentage points. This represents approximately a 10% proportional increase in women gaining access to health services, a substantial increase obtained after 9 months of implementation. However, the increased availability of midwives did not cause an increase in the use of ANC.To contextualise our findings, we compared our findings to those observed in other studies of other programmes aimed at increasing ID rates in similar contexts. Our findings are smaller in magnitude than those found in evaluations of conditional cash transfer programmes in Nigeria.et al, which investigated the impact of national health facility construction programme on delivery rates in Ethiopia using similar data and study design. The authors find similar effect sizes: the construction of a new health facility led lead to a 7.2 percentage point increase in ID rates among treated facilities and the effects were observed almost immediately after the facilities had been constructed.While there is limited evidence of the impact of supply-side interventions, a notable exception is a recent study by Croke However, while evidence from this study indicates that supply-side interventions that increase the availability of midwives and upgrade health facilities can have substantial effects in the short-run, it also highlights that it can be challenging to maintain large-scale national-level programmes in many international contexts. More evidence is needed to support the development of programmes aimed at the supply-side and efforts to sustain these programmes over the long-run.Our study has a few limitations that must be taken into consideration when interpretating our results. First, the DHS data were collected in clusters which were georeferenced but this locational data were displaced to protect the identity of respondents.Following the MDGs, additional resources were channelled towards, and greater focus was placed, on improving maternal health outcomes globally. While the MDGs for MCH were not met in Nigeria, the results of this study demonstrate that supply-side improvements hold promise for increased rates of ID in Nigeria and, likely, other LMICs. Supply-side interventions, which thus far have been poorly studied, represent an under investigated part of the solution to maternal mortality. Therefore, additional research is needed to understand the impact of other supply-side improvements, including the complementary role they can play alongside demand-side incentives, on health outcomes in Nigeria and in other international contexts.


Electrical impedance tomography (EIT) has been applied in the field of human-computer interaction due to its advantages including the fact that it is non-invasive and has both low power consumption and a low cost. Previous work has focused on static gesture recognition based on EIT. Compared with static gestures, dynamic gestures are more informative and can achieve more functions in human-machine collaboration. In order to verify the feasibility of dynamic gesture recognition based on EIT, a traditional excitation drive pattern is optimized in this paper. The drive pattern of the fixed excitation electrode is tested for the first time to simplify the measurement process of the dynamic gesture. To improve the recognition accuracy of the dynamic gestures, a dual-channel feature extraction network combining a convolutional neural network (CNN) and gated recurrent unit (GRU), namely CG-SVM, is proposed. The new center distance loss is designed in order to simultaneously supervise the intra-class distance and inter-class distance. As a result, the discriminability of the confusing data is improved. With the new excitation drive pattern and classification network, the recognition accuracy of different interference data has increased by 2.7~14.2%. The new method has stronger robustness, and realizes the dynamic gesture recognition based on EIT for the first time. With continuous breakthroughs in methods and technologies, methods of human-computer interaction have been extensively enriched. Hand gesture recognition is one way to realize human-computer interaction by letting the computer recognize human gestures and the commands they represent. Hand gesture recognition can be divided into static hand gesture recognition and dynamic hand gesture recognition according to the recognized subject. Compared to static hand gesture recognition, dynamic hand gesture recognition has a more natural and comfortable interactive experience in some practical applications such as sign language recognition , interacIn the past few decades, the recognition scheme of dynamic hand gestures mainly includes inertial motion sensing ,7, electElectrical impedance tomography (EIT) is a non-invasive imaging technology, which restores internal impedance distribution by measuring the response information of the surface electrode pair around the object . In the Machine learning algorithms are not only applied to data prediction  and supeThe contributions of our paper are threefold. Firstly, the most suitable EIT excitation driving pattern for dynamic gesture recognition is proposed. Secondly, the CG-SVM network is designed to improve anti-interference ability of gesture classification. Finally, the loss function of the classification network is improved to better distinguish the confusing gesture features.The rest of the paper is organized as follows. The entire EIT system architecture is shown in The data acquisition mode used by most of EIT systems is cyclic excitation and measurement. As opposed to static gesture recognition, the impedance distribution of the arm changes quickly for dynamic gestures. The simultaneous variation of excitation current and impedance distribution for traditional cyclic excitation and measurement pattern introduces uncertain information and affects the recognition results. In order to keep the consistency of the same dynamic gesture data as much as possible, the fixed excitation pattern is adopted, which can simplify the measurement process to suit the impedance changes of dynamic gestures.We discuss four fixed excitation patterns for the eight-electrode EIT system and compare them with the traditional cyclic excitation and measurement pattern, as shown in The data acquisition board is shown in For each dynamic gesture, 40 measured values can be obtained through measurement. The details of data acquisition for each measurement pattern are shown in We recruited five healthy volunteers to participate in the experiment. The volunteers included 2 women and 3 men, all aged from 22 to 25 years old. Data acquisition can be achieved by volunteers completing hand gestures within the measurement time. In all experiments considered in this study, the left arm was used as the measurement object, and the electrodes were uniformly worn ten centimeters below the wrist, as shown in In addition to collecting the normal measurement data, we also designed three kinds of interference experiments  to verify the anti-interference ability of the network. In the shaking arm interference experiment, the initial position of the arm is perpendicular to the desktop. After the action starts, the arm begins to swing downward until it is horizontal to the desktop. During this process, the collection of gesture data is completed as shown in After the data collection was complete, we split it into two parts (one for training and the other for testing). The data set information is shown in In order to improve the generalization of the classification network, the network structure is redesigned in this paper. A dual-channel feature extraction network based on CNNs and Gated Recurrent Unit (GRU)  is propoFor the upper branch: the input data is sequentially fed into three convolutional neural network modules. The methods of CNNs processing time series can be divided into two types. First, the original one-dimensional data is rearranged into the form of a matrix, and then a two-dimensional convolutional network (2D-CNN) is used for feature extraction, which is suitable for the case that the original data has many eigenvalues. Second, a one-dimensional convolutional network (1D-CNN) is used to directly process one-dimensional data, which is suitable for the case that the original data has few eigenvalues. Each set of gesture data collected by this system contains only 40 measurement values. Due to the limitation of data size, one-dimensional convolution is used to convolve the data. To fully extract deep features from original data, multiple convolutional filters of size 5 × 1, 4 × 1, and 3 × 1 are used in the CNN module to learn features at different scales. In addition, the CNN module introduces sparsity through the ReLU function so as to better extract features and fit training data.For the lower branch, the input measurement data is preprocessed by first-order difference. Then, multiple GRU units are utilized to extract gesture sequence features. GRU is a recurrent neural network that has been widely used for time series forecasting and classification ,32. The In addition, the classifier module also contains two channels: the main classifier and the auxiliary classifier. SVM is used as the main classifier. The auxiliary classifier consists of two Dense layers. The feature vector of the feature extraction module is further passed down to the Dense layer, which is a regular fully connected layer and finally, it is forwarded to the classifier. The role of the auxiliary classifier branch is to adjust the eigenvalue distribution of the Concatenate layer through backpropagation, make it easier to be distinguished by SVM, and improve the classification accuracy.SoftMax loss can make all of the classes have the maximum log-likelihood in the probability space, and is widely used in various classification networks as a loss function . When SoIn this paper, a new loss function is proposed to improve the recognition accuracy of dynamic gestures. For dynamic gesture recognition, some different gestures have similar frames on the motion trajectory, which easily leads to misjudgment between them . The intion item , cyi∈Rd The loss function combining K is a distance coefficient specified through experience. The inter-class spacing is supervised by calculating the distance between the centers of different classes, thereby improving the distinguishability of confusing gesture data. The purpose of adopting the The In the whole loss function (Equation (1)), the balance of the center loss term and the center distance penalty term is adjusted by scalars p < 0.05. Post hoc tests were performed using the Holm–Bonferroni correction if the variances of the average accuracy were homogeneous. Post hoc tests were performed using Tamhane T2 if the variances were heterogeneous.All types of gesture data were successfully collected through the five excitation drive patterns described in p < 0.041, d ≥ 4.7). This shows that our proposed fixed excitation measurement pattern achieves good results in dynamic gesture recognition. Comparing the performance of the three classification algorithms from the experimental results, the SVM algorithm achieves the best classification accuracy under the same excitation pattern.p < 0.001, d ≥ 25). This further reflects the advantages of the proposed fixed excitation measurement pattern in the interference environment. Compared with the fixed interval excitation patternⅡ, the fixed opposite excitation pattern performs better in the classification task of interference data, especially in the shaking arm interference test, the classification accuracy is significantly better than that of the fixed interval excitation patternⅡ .The classification results of normal data and interference data under different excitation patterns are shown in We conducted a simulation analysis to explain the advantages of fixed opposite ex-citation pattern in anti-interference experiments. The simulation result is shown in From the simulation results, the distance between the excitation electrodes for the fixed adjacent excitation pattern a and theIn summary, we can draw the following conclusions: in the dynamic gesture recognition task, fixed excitation pattern has more advantages than the cyclic excitation pattern. The fixed interval excitation pattern Ⅱ achieved the best results under the normal measurement data, but the fixed opposite excitation pattern has stronger anti-interference ability.In order to evaluate the performance of proposed classification network, comparative experiments with different network and loss functions were conducted.The detailed parameters of all networks used in the experiments are shown in p < 0.006, d = 13.8 and p < 0.05, d = 5.7). In the classification task of cross-day data, the classification accuracy of CG-SVM3 network is 14.2% and 6% higher than that of SVM algorithm and CNN network, respectively . This further confirms the excellent feature extraction ability and anti-interference ability of the newly designed classification network.The recognition results of different types of data under different networks are shown in For the fixed excitation mode proposed in this paper, only electrodes with fixed positions (electrode A and E in The whole body movement recognition based on EIT is still a research trend, although there are some challenges. Since the electrode sensor of current EIT system cannot cover the entire body, wearable sensor adapted to different parts of the human body should be designed. The impedance distribution corresponding to the different postures needs to be understood, and the data stability for simultaneous measurement of different body parts needs to be improved. More efficient recognition network with high accuracy will also be studied. Therefore, more research and experiments are needed to verify this in future.We propose an optimal excitation driven pattern for EIT dynamic gesture measurement. We also proposed a dual-channel feature extraction network CG-SVM to extract deep features from gesture data. To solve the problem that some dynamic hand gesture features are not highly distinguishable, the center distance loss is proposed to increase the inter-class spacing as much as possible, so that the distinguishability of various dynamic gestures has been improved. The new network structure and loss function proposed in this paper have achieved higher recognition effect and stronger anti-interference ability.


The paper analyses the autonomy of a wireless body sensor that continuously measures the potential difference between two proximal electrodes on the skin, primarily used for measuring an electrocardiogram (ECG) when worn on the torso. The sensor is powered by a small rechargeable battery and is designed for extremely low power use. However, the autonomy of the sensor, regarding its power consumption, depends significantly on the measurement quality selection, which directly influences the amount of data transferred. Therefore, we perform an in-depth analysis of the power consumption sources, particularly those connected with the Bluetooth Low Energy (BLE) communication protocol, in order to model and then tune the autonomy of the wireless low-power body sensor for long-term ECG monitoring. Based on the findings, we propose two analytical models for power consumption: one for power consumption estimation in idle mode and the other one for power estimation in active mode. The proposed models are validated with the measured power consumption of the ECG sensor at different ECG sensor settings, such as sampling rate and transmit power. The proposed models show a good fit to the measured power consumption at different ECG sensor sampling rates. This allows for power consumption analysis and sensor autonomy predictions for different sensor settings. Moreover, the results show that the transmit power has a negligible effect on the sensor autonomy in the case of streaming data with high sampling rates. The most energy can be saved by lowering the sampling rate with suitable connection interval and by packing as much data as possible in a single BLE packet. With the advancement of electronics and constant densification of microchips, the development of small and extremely low-power wireless sensors became possible. The energy consumption of wireless body area network (WBAN) sensors is an important factor to be considered in the design phase. For practical reasons, WBAN sensors are generally powered by small batteries with limited overall size and dimensions. Therefore, the available energy is mostly a very limited resource. The available energy can be increased and device lifetime prolonged using different techniques for energy harvesting from radio frequency (RF) energy, thermal energy, solar energy, and mechanical energy .Different technologies suitable for low-power wireless sensor communications were developed during the past years. In 2003, IEEE defined a technical standard for low-rate wireless personal area networks (LR-WPAN)—IEEE 802.15.4—which focuses on low-cost and low-speed communications enabling the emergence of upper-layer communication protocols such as ZigBee, 6LoWPAN, Thread, WirelessHART, MiWi, ISA100.11a, and others [BLE, a proprietary wireless interface, is often selected mainly due to its general ubiquity in modern personal mobile devices. Unlike all the other previously mentioned communication technologies, BLE is the only one readily available in nearly every new smartphone, tablet or smart watch. The main advantages of using standardized and widely spread technology are great availability, standardized communication interfaces, great amount of support available in the community, etc. Despite the ever-present possibility of creating slightly better wireless communication technology regarding the power efficiency per transferred information, the greater availability in consumer devices, and consequently lower price, outweigh small differences in performance.One particular example that we focus on in this paper is the energy consumption of a miniature smart wireless ECG sensor, which is an important topic when providing long power autonomy in the scope of a framework for long-term ECG monitoring , e.g., tEnergy consumption in battery-powered wireless sensors is important because of its impact on the sensor’s operating time. Having a model to estimate the energy consumption based on the sensor’s activity patterns and workload can help engineers to develop energy-efficient applications and obtain quick estimations without thorough testing with actual devices. It can also help the end user or technician to obtain a good estimate of the expected device operating time according to the device settings. These are the reasons for energy consumption modeling in wireless sensor networks (WSNs) being an active research topic. Energy consumption models have been proposed and evaluated for various communication protocols in WSNs ,6,7,8, aWe perform an in-depth analysis of the power consumption sources, particularly those connected with the BLE communication protocol, in order to model and then tune the autonomy of the given wireless low-power body sensor for long-term ECG monitoring. Power consumption estimations are the basis for the sensor autonomy estimation and we thoroughly analyzed the sensor from this angle.Based on the findings, we propose analytical models that enable the power consumption estimations for all wireless operating modes of the ECG sensor. The proposed models are validated with the measured power consumption of the ECG sensor at different ECG sensor settings, such as sampling rate and transmit power.The resulting models and autonomy estimations enable the engineers to find the optimal wireless ECG sensor settings to optimize the autonomy as well as the hardware of future sensor revisions, given the possible scenarios of sensor use.Having in mind the above said, in the paper, we demonstrate how a general model can be put in place and evaluated for a special case of low-power sensor for long-term ECG monitoring. Moreover, in this paper, we also shortly illustrate the task of providing guidelines for further development of the ECG sensor, based both on the experience gathered from real-life usage of the sensor and on the analysis of the production version of the sensor circuitry. The real-life experience was gathered from volunteer users  and fromThe paper is organized as follows. In the next section, the related work from the area of modeling and analyzing energy consumption models is overviewed. In that section, the wireless body sensor for continuous ECG measurements is described as well. Following this, in In this section, the related work regarding energy consumption modeling in wireless sensor networks is initially presented, including models and consumption analysis for various communication protocols, such as LoRaWAN and BLE. After that, we present the background and the related work of the considered wireless ECG sensor, focusing of its design and its intended use.Energy consumption modeling in WSNs is an active research topic. The authors in  have proOne of the widespread technologies for low-power WSNs is LoRaWAN, with its unique narrow-band modulation which enables low-power, low-rate and long-range narrow-band communications. The authors in  proposedA widespread technology that enables low-power WSNs is the BLE technology that is already available in a vast majority of modern smart devices . The omnipresence of BLE technology enables its quick adoption in consumer-oriented battery-powered low-power wireless devices. The authors in  presentehttp://savvy.si/, accessed on 29 April 2022). Physically, the sensor is small (dimensions: 130 × 35 × 14 mm) and light (weight: 21 g). It acts as a body gadget that can be worn on the skin by attaching it with two standard self-adhesive electrodes  to the chip with a radio transceiver. The signal is then wirelessly transferred to a personal digital assistant (PDA), such as a smartphone or a tablet, by using a custom wireless protocol built on top of the BLE. The selected BLE radio for the sensor is the nRF8001 module by Nordic semiconductor, which supports Bluetooth 4.0 low-energy specifications. On the PDA, the data is stored and visualized in real time . The ECGThe two electrical connectors of the sensor, additionally to connecting the electrodes to perform measurements, are also used to plug the sensor on a battery charger. A charger circuitry in the sensor detects when the battery charger is connected to the input snap terminals and puts the sensor in charging mode. Moreover, the battery charger is implemented as a charging dock designed to prevent reverse connection of the sensor on the charger. Additional components of the sensor, contributing to preserving sensor’s energy, are: the circuit breaker that isolates all the other circuits from the battery in order to minimize power consumption when the sensor is stored for a longer period of time and the power delivery circuitry used to lower the power usage while the sensor is not active by enabling the MCU to selectively deliver power to other building blocks.As a compromise among sustainable power consumption, acceptable measurement quality, and acceptable bandwidth for most smartphones to handle, a moderate resolution of 128 samples/s is used and enforced by the software, but the hardware allows for it to be increased up to 1024 samples/s. The sensor has found its application in several areas in medicine and in every-day life , prospecThe ECG sensor supports different operating modes to extend autonomy as far as possible. In this section, all operating modes are described in detail. The operating modes are later broken down to individual power consumption events. The individual power consumption events are measured and analyzed in order to identify the important parameters needed for power consumption modeling.Shutdown modeSleep modeIdle mode (disconnected mode): fast and slow advertisingActive mode (ECG sampling).The wireless ECG sensor supports several operating modes to comply with the power efficiency requirements imposed by its small size and battery operation:In shutdown mode, all the electronic components on the wireless sensor board are disconnected from the battery for maximal power conservation, particularly for storing the device for longer periods of time. In particular, the device consumes fewer than 2 nA in shutdown mode, and this consumption is constant as there are no events in this mode of operation. The ECG sensor exits the shutdown mode when it is connected to the charging station.When an ECG sensor is powered-on, it switches to idle mode, where it starts advertising itself by periodically transmitting BLE advertising packets. First, it starts in a fast advertising idle mode, where the BLE radio periodically transmits advertising packets. After the fast advertising period is over, the ECG sensor switches to slow advertising idle mode. The faster advertising rate during the fast advertising idle mode enables faster discovery of the ECG sensor by the central device , while the slow advertising idle mode, on the other hand, enables a bit slower discovery of the ECG device while also reducing the power consumption, thus significantly prolonging the sensor’s autonomy.The central device acts as a BLE scanner device that listens for a few seconds for connectable advertising packets containing the ECG sensor service identification; more than one second is typically required, but five seconds is advised to ensure that the ECG sensor can be discovered in its slow advertising mode. After the ECG sensor is discovered by the scanner, the central device initiates a standard nonencrypted BLE connection with the sensor. The ECG sensor then makes a transition to the active mode of operation: the ECG sensor is connected to the central device and actively streams ECG measurements.Transmit (TX) power setting . The default in our case is 0 dBM.Sampling rate selection, which is limited to values that divide 32,768. The default in our case is 128 Hz.Following a successful connection attempt, the central device issues several commands over a proprietary protocol defined on top of a client-writable GATT. GATT here stands for Generic Attribute Profile, which is a protocol most commonly used with the BLE. Among others, there are two commands that influence the sensor’s power consumption:If the ECG measurement is stopped by the user, then the central device triggers a disconnect. In such an event, the sensor stops sampling and switches to idle mode. In case the sensor battery is drained down to its minimum allowed voltage, the sensor disconnects and switches to shutdown mode, from which it can only be awakened by its battery charger. The last possibility is that the sensor moves out of the radio range and the connection on both devices times out. In this case, the sensor also stops sampling and switches to idle mode.The focus in this paper is on the events consuming considerable amounts of energy, i.e., events in idle and active mode. Therefore, in the following, these operating modes are described in more detail regarding their power consumption profiles. First, we give details about the measuring equipment used to obtain the power consumption measurements.To measure the power consumption of the wireless ECG sensor, we used the measurement circuit presented in B lower than half of the sampling frequency For average power consumption analysis, we used the HP3478A benchtop digital multimeter (DMM) with Resistor-Capacitor (RC) low-pass filter  with BLE capability was used. The SBC acts as a central device and exposes the options to set a desired connection interval as well as a desired sampling rate.sleep mode, with both of them consuming almost no energy at all. Low current spikes near the noise floor represent the MCU activity bursts. In the following subsections, we describe all possible energy consumption modes that can be identified from the current profile recordings. Additionally to the active mode events, the consumption measurements for the advertising event in idle mode and in the sleep mode are also described.Two types of communication events are defined in the BLE specification: advertising events and connection events. During the BLE connection event, devices exchange all the data that was prepared during the last time between two consecutive connection events. The number of messages exchanged during one connection interval can differ, but for the given ECG sensor, most connection events exchange the same amount of data. One example of a BLE connection event current consumption profile recording is presented in Each time an ECG packet buffer in the MCU becomes full, the MCU transfers the Application Controller Interface (ACI) packet to the radio for transmission. The message-processing event looks very similar to a BLE connection event, except there is no RF communication involved during the event. A communication controller inside the BLE radio processes the incoming ACI packet and updates the on-board BLE services accordingly to prepare for information exchange during the next connection event. The transmitter and the receiver stay in an idle mode during the ACI packet processing event. All the activity in the nRF8001 radio is concerned with the BLE stack message processing and preparation for transmission during the next nearest BLE connection event. The measured mean message-processing event energy consumption The ECG sampling is performed in equidistant ECG sampling events. The ECG sensor uses a The energy consumption of an ECG sampling event Equation ). The meThe MCU uses a system tick timer for real-time event execution. A tick timer is set for continuous operation with a predefined time interval based on a clock from the During one BLE advertising event, the BLE radio transmits the advertising packet on three consecutive advertising channels and listens for a short period on each channel for possible requests for connection from a scanner device (initiator), which searches for the available ECG sensors. We averaged the current consumption profiles for 10 advertising events to suppress the effects of slight deviations between them. The energy consumption of a BLE advertising event Equation ). The avAmong all the events, both the BLE radio and the MCU wait in either idle or sleep mode, which are both power-saving modes. According to the official documentation, the nRF8001 BLE radio consumes 2 g events . The MSPan 1 μs) . The meaThe proposed power consumption models describe two main operating modes of the sensor: the idle mode and the active mode. In the idle mode, the sensor sends advertisement packets at regular intervals to be discoverable by the PDAs for data acquisition. Active mode, on the other hand, acts as a data collection mode where the MCU collects ECG samples and forwards them to the BLE radio for transmission to the connected PDA. The models are based on the parameters identified in the previous section, i.e, measured during the events that constitute a certain mode, idle or active. The events are, however, standard from the perspective of BLE specification. Different hardware and software implementations of BLE transceivers can have different event power profiles, but the events are equal from the protocol perspective. The only requirement for another BLE sensor platform to use the proposed energy consumption model is to measure the energy consumption for the identified events previously.The proposed analytical power consumption model for the idle mode Equation . Tadv isThis model gives us the average power consumption during a selected idle mode setting: fast advertising or slow advertising. If we want to calculate the overall average power consumption Equation  should bEquation .(4)P¯idT which we are measuring the average power consumption for. The total energy consumed consists of the energy consumed in connection events T and the sum of time spent in all other events.In the active mode, the ECG sensor connects to the controlling device, such as a mobile phone, and starts sampling and streaming the ECG signal. The average power consumed during the active mode The proposed analytical model for the power consumption in active mode is presented with Equation , where eEquation  gives anThe proposed power consumption models from All measured idle mode power consumption parameters presented in Equation .In For the active mode power consumption analysis, we selected 12 possible settings: all combinations of 4 TX power settings and 3 ECG sampling rates measured in samples per second (sps). All measured model parameters for power consumption estimation of the 12 settings are summarized in To translate power consumption to sensor autonomy, we divide the battery capacity by the average current consumption in the selected sensor mode. The analyzed sensor has an integrated 240 The most influencing parameter on the sensor autonomy is the ECG sampling rate. Sampling rate directly imposes requirements on the length of connection interval, which has to be, on one hand, short enough to enable transfer of all recorded ECG data, while, on the other hand, long enough not to waste energy with unnecessary empty connection events. By decreasing the length of the connection interval, the density of transmission events increases, which consequently increases the average power consumption. The optimal connection interval for the selected sampling interval Before providing guidelines for further development of the ECG sensor, additionally to taking into consideration the power anatomy analysis, we also consider the experience gathered from users who bought/rented the device for themselves  or used Having the power consumption model available, as well as the above experience, both user-subjective and medical, we are able to make recommendations regarding the sensor redesign. Our recommendations are to increase the sampling frequency to 512 In this paper, we thoroughly investigated the sources of power consumption in a modern wireless ECG sensor and proposed analytical power consumption models for the most important activity modes. We designed a model for active ECG streaming mode, which slightly overshoots the measured average current consumption for higher ECG sampling rates, but nevertheless serves as a good predictor of the wireless sensor autonomy.The limitation of the presented approach is that for each new platform which we want to estimate power consumption for, we need to make measurements of all the parameters used in the proposed models. This arises from the variability of the platforms available on the market for BLE sensors. Different BLE modules may have different base power consumption due to different implementations of the communication stack. The central processing unit (CPU) used for the application can also have a large impact on the power consumption, depending on the CPU performance and the complexity of the application running on it. The power consumption estimates can be used for similar platforms, but without the measurement of base current consumption events, significant deviations from the results presented in this manuscript are to be expected.Based on the presented results, we can conclude that it is not possible to make a power consumption model for the general use of BLE devices. There are some useful guidelines though regarding the manner the current consumption of BLE devices can be tamed. The results show that the transmit power has a negligible effect on the sensor autonomy in the case of streaming data with high sampling rates. The most energy can be saved by lowering the sampling rate and by packing as much data as possible in a single BLE packet. Finally, selecting a suitable connection interval for the applied sampling rate can have the strongest energy-saving effect.Further optimizing such a sensor is a difficult task. We propose two scenarios in which the battery could be either increased or decreased according to current usage patterns. Apart from the battery capacity, other sensor hardware and software parameters cannot be optimized for significant power consumption reduction.


A 49-year-old man sustained an accidental injury when he was changing and inflating the tyre of a truck, and there was a sudden explosion of the truck tyre at the service station, which was by the roadside of the highway. With the pressure of air generated due to the exploding tyre, the victim was blown to around 6 feet away. He was declared dead on admission. The medico-legal examination was conducted, and death was determined to be multiple organ injuries  caused by the shock wave produced due to tyre explosion. Tyre blast injuries are not so common. A meticulous post-mortem examination is fundamental in formulating and recording the pattern of traumatic injuries. Preventive occupational measures should be put in place. Tyre burst injuries are relatively rare but are known for causing severe injury patterns. Tyres can be thought of as air tanks, but the same safety rules that apply to pressure tanks do not apply to tyres. Blast overpressure is described as an increase in pressure above atmospheric values, as witnessed in a blast of explosive weapons. Air compression before the blast wave accelerates air molecules and generates a pressure peak . This hiThe tyre explosion has a high potential for destruction, yet it has garnered little attention in the medical literature,  commonlyA 49-years-old man sustained an accidental injury on the 31st of December when he was changing and inflating the tyre of a truck and there was a sudden explosion of the truck tyre at the service station which was by the roadside of the highway. With the pressure of air generated due to the exploding tyre, the victim was blown to around 6 feet away. He was taken to the hospital in an unconscious state and was declared brought dead on admission at the All-India Institute of Medical Sciences in Delhi in the emergency department. The police were intimated and accordingly, the case was registered as a medicolegal case. Inquest papers were prepared by the Investigating Officer and the body was shifted to the Morgue associate with the Forensic Medicine and Toxicology Department at AIIMS, Delhi for medico-legal post-mortem examination.The Incident occur at about 4:00 PM on 31st Dec. Postmortem conducted at 12:30 PM and concluded at 1:45 PM on 1st January. A detailed complete and meticulous post-mortem examination was performed, rigor mortis all over the body in the supine position, cadaveric lividity on the back, dependant parts of the body. The lividity was fixed. There was the presence of clotted blood and oozing on left ear ossicles  in exterOn internal examination, subarachnoid haemorrhage with underlying brain parenchyma, contusion in patches were present on both cerebral hemisphere , the cheThe situations surrounding the incident, as well as the location of injuries anatomically, suggested that a middle-aged mechanic was injured accidentally as a result of a tyre explosion while changing and inflating at a service station along the highway. Because such work-related unintentional injuries are underreported, the forensic literature may be deficient in this regard. Multiple organ injuries  caused by the shock wave produced due to tyre explosion was concluded as the cause of death. An inflated tyre contains a high amount of potential energy.A 63000 ft-lb of energy is released when a truck tyre inflated to 90 psi explodes. The estimated force is around 2000 g, which is enough to raise a 3000 lb car 21 ft off the ground . The blaIn literature there have been instances in certain cases wherein a tyre burst did not result in a fatality, however, reports of cases with fatal incidents have been reported by certain authors. Fatal pneumatically induced injuries are rare in forensic literature; high-energy released due to blast of tyre results in fatal injuries .Deforming injuries and fatalities caused by explosions among mechanics servicing trucks, in the existing literature, it’s only been mentioned a few times. The size of the tyre’s air pressure and the distance between the tyre and the victim at the moment of the blast influence the severity of the injury. Explosions are common during tyre servicing, particularly when inflating the tyres; bystanders are also injured and may sustain fatal injuries , 22.A tyre burst can happen while servicing or inflating tyres. High inflating pressure and a short safety distance are the two most common causes of fatal injuries. The high energy produced by a large tyre burst can result in severe injuries with high morbidity and mortality. A meticulous post-mortem examination is fundamental in formulating and recording the pattern of traumatic injuries. Preventive occupational measures should be put in place.