Fluxer (https://fluxer.umbc.edu), a free and open-access novel web application for the computation and visualization of genome-scale metabolic flux networks. Any genome-scale model based on the Systems Biology Markup Language can be uploaded to the tool, which automatically performs Flux Balance Analysis and computes different flux graphs for visualization and analysis. The major metabolic pathways for biomass growth or for biosynthesis of any metabolite can be interactively knocked-out, analyzed and visualized as a spanning tree, dendrogram or complete graph using different layouts. In addition, Fluxer can compute and visualize the k-shortest metabolic paths between any two metabolites or reactions to identify the main metabolic routes between two compounds of interest. The web application includes >80 whole-genome metabolic reconstructions of diverse organisms from bacteria to human, readily available for exploration. Fluxer enables the efficient analysis and visualization of genome-scale metabolic models toward the discovery of key metabolic pathways.Next-generation sequencing has paved the way for the reconstruction of genome-scale metabolic networks as a powerful tool for understanding metabolic circuits in any organism. However, the visualization and extraction of knowledge from these large networks comprising thousands of reactions and metabolites is a current challenge in need of user-friendly tools. Here we present  Metabolic reconstructions from whole-genome sequencing and biochemical data aim to determine all metabolic processes occurring within a cell or whole organism, which can then be integrated into genome-scale metabolic models (GEMs) able to predict cellular phenotypes ,2. ConstSeveral tools currently exist for the analysis and visualization of GEMs. The Systems Biology Markup Language (SBML)  is the sk-shortest metabolic paths between two reactions or metabolites, using a user-customizable metric for the link weights based on the computed metabolic fluxes. The graph visualizations are interactive, and the user can select any metabolite or reaction as the root for the flux tree, obtain information about the reactions and metabolites such as their chemical structures, and easily adjust the node labels, weight calculations, and inclusion or not of cofactors, zero-flux reactions or cellular localizations in the visualized graph. Fluxer allows any user with no programming experience to exploit the potential of genome-scale metabolic models in their ability to assist in the understanding of the whole metabolic network of an organism and predict specific phenotypes. Fluxer’s combined use of FBA and graph theory for metabolic network visualization and analysis in a user-friendly web-application can pave the way for advancements in applications for metabolic engineering.Here we present Fluxer, a web-application for the automated computation of GEM graphs based on flux and their interactive visualization with a user-friendly interface. The application is able to take as input an arbitrary GEM specified in SBML format and automatically compute and efficiently visualize the complete network and their metabolic fluxes. The tool performs FBA of the model and includes several algorithms to compute optimized visualizations of complete GEMs, including spanning trees, dendrograms and physics-based force layouts. The application can also compute and display the k-shortest paths and complete graphs with an interactive and user-friendly web interface. Any GEMs in SBML format can be uploaded to the tool, which then performs FBA optimization and calculates and renders the complete model with different interactive graph visualizations. The metabolic networks are encoded as bipartite graphs, where nodes represent metabolites or reactions and edges the involvement of a metabolite in a reaction as a product or a reactant. Nodes can be selected to display detailed information including metabolite structures and reaction fluxes. Furthermore, any number of reactions can be knocked-out through the interface to simulate enzyme gene deletions and their phenotypic effects in terms of growth rates and fluxes. The different available graph representations make use of the computed steady-state reaction fluxes, which together with the stoichiometric coefficients and molecular weights constitute the weight of each edge. The spanning tree graph calculates the input flux pathways toward the root node—the biomass growth or any other reaction or metabolite of interest—visualizing the most important pathways contributing to the root node. The k-shortest paths graph calculates the shortest pathways with highest fluxes between two metabolites or reactions of interest, easily visualizing each separate path in an interactive graph. Finally, the complete graph includes all the reactions and metabolites of the model, which can be visualized as a reaction network or as a tree rooted with a particular metabolite or reaction of interest. The models uploaded to the application can be privately shared with an automatically generated unique web link, and >80 genome-scale metabolic reconstructions from the BiGG Models knowledge base  or any other reaction or metabolite of interest. The spanning tree is recursively connected with the upstream most important reactions and metabolites leading to the root to neatly visualize the whole network. Each metabolic input and output for each reaction (an edge in the graph) is assigned a weight, which is the product of a user-selectable combination of the reaction metabolic flux , stoichiometry coefficient and metabolite molecular weight. The tree includes every node (metabolite or reaction) in the model once, without duplication. Starting from the root node, the algorithm builds the tree by iteratively adding the edge with the highest weight that connects a current node in the tree with a node not yet present in the tree; in case of more than one candidate edge having the same weight, the edge with the shortest path to the root node is selected; and in case of similar depth, the edges are selected according to the alphabetical order of their labels. In addition, precedence is always given to the edges with fluxes toward the root node, so the elements participating in the production of the metabolite or reaction of interest are always added first to the tree. In this way, the selected edge weights determine the layout of the tree. By default, the weights are calculated as the product of the reaction metabolic flux and metabolite stoichiometry coefficient, so the metabolites selected first are those that contribute the most amount  to the root metabolite or reaction. If none of the weight options are selected, all edges would have a default weight of 1 unit, and the spanning tree algorithm is equivalent to an alphabetically sorted breadth-first search, showing the shortest metabolic paths to the root node from every metabolite and reaction in the model.Five different layouts can be used to visualize the resulting spanning tree. A tree layout based on Reingold and Tilford’s tidy drawing algorithm  shows thEscherichia coli BL21  positions the nodes of the acyclic graph using layered techniques and cross-minimization algorithms . Reactions from both the glycolysis and the pentose phosphate pathways are involved in the shortest paths between the two metabolites selected, as it was predicted previously  for the backend, HTML5 and JavaScript for the frontend, and SQLite for the database. FBA optimization is performed with COBRApy  and the k-shortest paths and complete graphs. The different networks can be visualized with tree, dendrogram, radial, dagre and force-based layouts. The web application allows any user to load models in SBML format and interact and customize the different metabolic networks generated. Users require no special training or software installations to access and use the web application. The ability of Fluxer to perform visual comparisons of reaction fluxes makes it a powerful tool for understanding metabolic phenotypes and discovering pathways for metabolic engineering applications.We presented here Fluxer, a user-friendly web application for the computation, analysis and visualization of flux graphs for GEMs. The tool can perform FBA including specific reaction knockouts and compute metabolic network representations based on spanning trees, While Fluxer computes graph visualizations centered on a particular root metabolite or reaction of interest, other approaches have been proposed for the global analysis of GEMs. Extreme pathways  represenFurther future work will extend the current functionality of Fluxer. The capabilities to perform FBA customizations beyond the knockout of reactions will be improved to include different optimization objectives and the ability to change the flux boundaries of any reaction—particularly to specify different uptake rates for different growth media. New graph layouts will be implemented, such as maps to highlight the metabolic composition of a particular phenotype state. Finally, the web server will include the possibility for the user to make public any model uploaded to the application, and then list them in a dynamically updated repository of genome-wide metabolic models.Fluxer provides not only a topological solution for visualizing genome-scale metabolic models, but also specific computational methods to analyze reaction networks and fluxes. The comparison of metabolic network connectivity and motifs across organisms can provide evolutionary insights , an appr


The Flory–Rehner theoretical description of the free energy in a hydrogel swelling model can be broken into two swelling components: the mixing energy and the ionic energy. Conventionally for ionized gels, the ionic energy is characterized as the main contributor to swelling and, therefore, the mixing energy is assumed negligible. However, this assumption is made at the equilibrium state and ignores the dynamics of gel swelling. Here, the influence of the mixing energy on swelling ionized gels is quantified through numerical simulations on sodium polyacrylate using a Mixed Hybrid Finite Element Method. For univalent and divalent solutions, at initial porosities greater than 0.90, the contribution of the mixing energy is negligible. However, at initial porosities less than 0.90, the total swelling pressure is significantly influenced by the mixing energy. Therefore, both ionic and mixing energies are required for the modeling of sodium polyacrylate ionized gel swelling. The numerical model results are in good agreement with the analytical solution as well as experimental swelling tests. An ionized hydrogel is a fluid-filled amorphous cross-linked network of polymer chains with an associated ionic charge. The ability of these structures to deform up to orders of magnitude greater than their original volume through the absorption of fluid gives rise to a variety of applications . These aThere have been many published studies modeling the swelling of gels . Each ofWithin this theoretical framework, the mixing and ionic energies contribute to swelling, whereas the elastic energy ensures large deformation of the polymer chains is not possible. The mixing energy describes the attraction of solvent molecules towards the hydrophilic polymer chains through a weak Van der Waals force a. The ioTherefore, the magnitude of the mixing energies contribution to the swelling of ionized hydrogels has not been clearly defined. In this study, the effect of each energy on hydrogel swelling in univalent and divalent solutions is investigated. Furthermore, the effect of including the mixing energy on transient surface instability magnitude is examined. A mixed hybrid finite element method is utilized to model the phenomenon under physiological solution concentrations at varying initial solid volume fractions. The model results are exhaustively validated by comparing back to the theoretical predictions of the swelling pressure and finally to experimental swelling tests.Thermodynamic equilibrium is reached when the difference between the chemical potential inside and outside the gel Πrameters .The ionic energy is associated with the osmotic pressure difference between the inside and the outside the gel as a result of a molar concentration gradient. Differentiating this energy  is defined as,I is a third order identity matrix [G is the shear modulus, J is the Jacobian of the deformation tensor (F) and denotes the volume change (deformation) in reference to the initial state in a three-dimensional model,The elastic energy ceases the gel from swelling largely. The pressure of the fluid in the pores  ,33,(13)C),Differentiating Equation  with resThe dependency of the Poissons ratio on the deformation of the gel states as the solid volume fraction goes to zero (at fully swollen state), the system becomes fully compressible whereas in its dry state, the system is incompressible.x) in any time frame (t) can be connected back to reference frame (X) through a mapping function (F),This system of equations evolves over discretized time resulting in a transient swelling model. The positions (F into Equation (As a result of an increased deformation (subbing Equation ), the thEquation  are re-cEquation ), is calractions . These vquation  is implemented . This meInitially, the gel rests in a sodium chloride solution of high molar concentration conditions so that the pores are saturated without causing the swelling of the gel. Subsequently, the gel is placed in a sodium chloride solution of given molar concentration , causingBoth univalent and divalent external solutions are investigated in order to analyse the effect of divalent ions on the swelling magnitude and dynamics. Pure sodium chloride (NaCl) of physiological concentrations is used for the univalent solution. For the divalent solution, calcium chloride is added to the univalent solution .To validate the implementation of the mixing energy into the numerical model against experimental data, the centrifuge method is used to measure swelling magnitude of a batch of sodium polyacrylate particles, polymerized according to EP2851048A1 . The volIn order to investigate the effect of each energy on gel swelling, numerical MHFEM simulations were conducted using the ionic pressure, the mixing pressure and the total swelling pressure (ionic + mixing pressure) for both univalent and divalent external solutions. The effect of the initial porosity (defined as 1 minus the solid volume fraction) on each swelling pressure as well as the total deformation was also analyzed.The equilibrium volume change, J, a resultiEquation ) resultsEquation , ln a. This lThe difference in initial swelling pressures between univalent and divalent solutions is much larger than the difference between equilibrium deformations for both the mixing and total swelling pressure models. Again, the ionic pressures are identical for both solutions  with and without the mixing energy in a 0.3% wt. NaCl solution (to emphasize wrinkling).http://dx.doi.org/10.17632/95svb4kr2j.1). The concave instability morphology on the side face of the cylinder (first 10 s of the video) in the experiment is repeated for both the total swelling and ionic model simulations. Subsequently, the gel deforms to an irregular geometry which cannot be replicated in the current MHFEM because of uncertainty of the mechanical parameters. Finally, it returns to a cylindrical geometry again, much like both simulations. It is difficult to hypothesize which simulation is more representative without a more quantitative methodology as well as the prescription of perfect material properties. However, these videos together with For numerical verification of the model presented here, the results obtained are compared to the theoretical predictions of the ionic, mixing and total pressures for varying deformation values. The mixing pressure is calculated with increasing deformation by updating the solid volume fraction (Equation .The updated solid volume fraction is then subbed into the Flory–Huggins equation (Equation ). To updEquation  and is sEquation . TheoretEquation ). Only tFrom For exhaustive validation of the proposed ionized superabsorbent polymer swelling model incorporating the mixing energy of the Flory–Rehner theory, comparison of the experimental and numerical volume change magnitudes is conducted. Previous studies have highlighted the insignificance of the mixing proportion of the total free energy within the Flory–Rehner theory when modeling large deformation of ionized hydrogels. Here, full three-dimensional transient numerical simulations of sodium polyacrylate swelling with the inclusion of the mixing energy described by the Flory–Huggins equation are presented. From the model results, the mixing energy significantly affects the swelling magnitude of ionized hydrogels at initial solid volume fractions greater than 0.10 and, therefore, it should be included in the model. At initial solid volume fractions lower than 0.10, the mixing energy can be assumed negligible for both univalent and divalent solutions as its effect on swelling magnitude is minimal. This assumption is in good agreement with deswelling experiments conducted on sodium polyacrylate . However


The paper is focussed on the assessment of cognitive workload level using selected machine learning models. In the study, eye-tracking data were gathered from 29 healthy volunteers during examination with three versions of the computerised version of the digit symbol substitution test (DSST). Understanding cognitive workload is of great importance in analysing human mental fatigue and the performance of intellectual tasks. It is also essential in the context of explanation of the brain cognitive process. Eight three-class classification machine learning models were constructed and analysed. Furthermore, the technique of interpretable machine learning model was applied to obtain the measures of feature importance and its contribution to the brain cognitive functions. The measures allowed improving the quality of classification, simultaneously lowering the number of applied features to six or eight, depending on the model. Moreover, the applied method of explainable machine learning provided valuable insights into understanding the process accompanying various levels of cognitive workload. The main classification performance metrics, such as F1, recall, precision, accuracy, and the area under the Receiver operating characteristic curve (ROC AUC) were used in order to assess the quality of classification quantitatively. The best result obtained on the complete feature set was as high as 0.95 (F1); however, feature importance interpretation allowed increasing the result up to 0.97 with only seven of 20 features applied. Understanding cognitive workload as a mental effort needed to perform a task  is imporThe literature review conducted shows that the most widely used method of assessment of cognitive workload level in the past employed subjective measures, such as NASA Task Load Index (NASA-TLX) ,8. HowevEye-tracking data turn out to be useful in analysing cognitive workload . BenfattMost cognitive workload classification studies presented in the literature are binary approaches. Among them, the Support Vector Machines (SVM) classifier is one of the most popular ,19,20. TMost cognitive workload classification results were achieved in a classical subject-specific approach , for whiPerforming a multiclass subject-independent classification of cognitive workload levels,Examine both classification on the complete feature set and with the application of interpretable machine learning models for feature selection,Carrying out a deeper analysis of the features related to the classification of particular levels of cognitive workload.The aim of the present study includes the following points:The dataset used in the study is eye-tracking and user performance data gathered from 29 participants while solving a computerised version of the digit symbol substitution test (DSST).The digit symbol substitution test (DSST)  is a cogThe rest of the paper is structured as follows. The review of the literature is presented in Almogbel et al. published a study examining the levels of cognitive workload in the process of playing a computer game . The datFridman and colleagues  conducteIn the present study, the computerised version of the DSST test ,51 was aPart 1—low-level cognitive workload: four different symbols to choose from, 90 s test length;Part 2—medium-level cognitive workload: nine different symbols to choose from, 90 s test length;Part 3—hard-level cognitive workload: nine different symbols to choose from, 180 s test length.The number of symbols and the time is defined in the application settings. In the case study, three DSST parts were applied:These three parts correspond to the classes of cognitive workload, defined in The Java 8.0 programming language was the main tool used for developing the application; it was designed to be operated by a computer mouse. The interface of the computerised version is presented in The described experiment was carried out in a laboratory with standard fluorescent light. In order to ensure stable, equal conditions for all participants, the outside light was blocked. The activity of eyes was registered by a Tobii Pro TX300 screen-based eye tracker  utilising near-infrared technology . The vidThe experiment was designed in Tobii Studio 3.2, which is the software compatible with the Tobii Pro TX300 eye tracker, and it is dedicated to preparing and analysing eye-tracking tests. Visual stimuli were presented on a separate monitor (23″ TFT monitor with 60 Hz of refresh rate). During the experiment, the participants occupied a seated position in such a way that the screen was from 50 to 80 cm away from the participant depending on the fact of which position was convenient for the participant to work with the computer. The experiment was preceded by the nine-point calibration procedure realised for each participant, so the distance from the monitor within the mentioned range did not affect the results of the experiment. The calibration procedure is performed separately for each eye. Identical hardware and software settings were used for examining all the participants.Fixations , originaSaccades  are defiBlinks derived as zero data embedded in two saccadic events.The blink was identified as zero data is embedded in two saccadic events ,55,56.Pupillary response understood as pupil size. The Tobii Studio applies the dark pupil eye-tracking method.The Tobii Studio was applied to export eye activities gathered during the experiment. The signal lost was used to check the tracking ratio to ensure the proper quality of data. Eye activities exported from the experiments were related to several measures:Fixations and saccades are detected in the Tobii Studio with the Velocity–Threshold Identification (I-VT) fixation classification algorithm , which cThe experiment was applied on a group of 29 participants aged 20 to 24 . The tested group was homogeneous. Healthy participants  were recruited among second- and third-year students of the BSc degree in computer science. The participants had normal/corrected to normal vision. The acceptable level of data activity recorded by the eye-tracker was set to 90%. Originally, 30 participants were invited to participate in the study; however, one participant was discarded due to poor data eye-tracking quality caused by excessive body and head movement (the data activity level recorded by the eye-tracker for this participant was 70%).The research was approved by the Committee of the Lublin University of Technology . The participation was voluntary, and all participants received information about the study.A single participant was examined for approximately 15 min. The experiment was divided into three parts. Each part was preceded by a calibration phase. The calibration phase was run on the eye-tracker as a built-in procedure. The next step contained the instructions demonstrated to the participants on the screen. The participants were asked to create as many matches as possible by assigning symbols to the appearing letters. The assigning was done by clicking on a particular symbol on the key located at the bottom of the screen see . AfterwaThe dataset consists of files generated from the eye-tracker and files generated from the application. The files generated from the eye-tracker consist of the time stamps and the data that are related to such eye activity as fixations, saccades, blinks, etc. The files generated by application include the time stamps and the data that are related to DSST test results such as number of errors or number of responses. The total number of generated files per participant equals six, i.e., three files from the eye-tracker and three files from the application. The data from each task were saved in a separate file.Fixation-related features: fixation number , mean duration of fixation, standard deviation of fixation duration, maximum fixation duration, minimum fixation duration.Saccade-related features: saccade number , mean duration of saccades, mean amplitude of saccades, standard deviation of saccades amplitude, maximum saccade amplitude, minimum saccade amplitude.Blink-related features: blink number , mean of blink duration.Pupillary response features: mean of left pupil diameter, mean of right pupil diameter, standard deviation of left pupil diameter, standard deviation of right pupil diameter.DSST test results-related features: number of errors , mean response time, response number .Eye activity-related and DSST test results-related data exported from the eye tracker covering 20 features are presented below:The listed eye-activity related features were chosen as the most informative ones available in the eye tracker software. Features related to fixations and saccades are the most common eye-tracking features analysed in the literature  in such Blink-related features as well as pupillary response features were proved to indicate the dynamics of the cognitive process ,62,63. PData acquisitionData synchronisationFeature extractionFeature normalisationFeature selectionTraining and testing classification models.Eye activity data and DSST test results were analysed off-line using custom programs written in MATLAB 2020a and Python 3.6. The procedure of data processing was divided into 6 steps:Six files were generated per participant, three for both parts: one file from the computer application and one from the eye-tracker. The essential part of preprocessing was the procedure of synchronisation, which was necessary to prepare the dataset. It allowed combining the files from the eye-tracker and application and obtaining one file per each part of the experiment. Every participant provided three observations: the first one corresponded to a low level of cognitive workload (file 1), the second one was labelled as moderate (file 2), and the last one had the signal associated with a high level of cognitive workload (file 3). The dataset included 87 observations in total (3 observations per participant). Every observation consisted of independent features and the class label . The values of independent features were obtained by means of a feature extraction step, leading to the twenty features listed in In order to obtain the values of feature importance measure, the logistic regression model was used. Due to its properties, logistic regression is commonly used in the applications where deep understanding of the reasons behind the decision taken by the model is necessary . The modThe result is calculated with the use of the following logistic function:Class 1—observations with low level of cognitive workloadClass 2—observations with moderate level of cognitive workloadClass 3—observations with high level of cognitive workloadThe aim of the classification process was to assign observations into one of the three classes:Classes correspond to three cognitive workload levels applied in the DSST application.Various classification methods, such as SVM, kNN, Random Forest, Multilayer Perceptron (MLP), and Logistic Regression were applied in order to produce initial results, showing the general perspectives of eye-tracking data as the source of information for cognitive workload assessment.kNN—nearest neighbour number: 5Random Forest—tree number: 100MLP—two hidden layers: 32 and 16 neurons; optimiser: Adam; learning rate: 0.0001; activation function: relu (rectifier linear unit)The classifier models mentioned had the following hyperparameters:Afterwards, logistic regression was used for extracting feature weights representing their importance in the process of distinguishing between particular levels of cognitive workload. The influence of selected features was tested for the classifiers mentioned before.The mentioned classification models were chosen due to the following reasons: lower number of parameters (compared to deep learning models), lower tendency to overfit on small datasets (compared to deep learning models), low computational cost allowing running multiple experiments in a reasonable amount of time. Moreover, selection of algorithm was performed considering the other studies in the field of cognitive workload classification.The dataset was shuffled in random order and divided into train and test datasets. Data from every participant could be used only in one dataset: train or test, in order to ensure a truly subject-independent approach, so that the signals of the test dataset persons would be completely unknown for the model. Approximately 20% of the input dataset, which corresponds to 6 participants, was used for testing.A range of classical machine learning models were used. The applied approach included the initial state, where the classifiers were tested on the complete feature set and afterwards the main analysis where the feature selection was conducted. This solution allows optimising calculation, avoiding the models that provide worse results at the very beginning.In order to compare the variance of values (for 20 features) from three DSST parts, one-way ANOVA analysis was used. The Kolmogorov–Smirnov test (K-S test) and the Levene test were used to test the assumptions of the ANOVA analysis. The K-S test was performed to verify that variables had a normal distribution, the Levene test was used to check that the variance of the data from three parts of the DSST test was equal. Finally, the Tukey’s honest significant difference test (Tukey’s HSD) post-hoc test was performed to identify which pairs of the DSST test parts had statistically significant differences. The analysis was carried out in MATLAB 2020a software using Statistical and Machine Learning Toolbox.The three-class subject-independent classification was conducted and the F1 score of a selected classifier for a complete feature set is presented in All the measures, except for accuracy, were computed in a multiclass way; i.e., first, a measure was computed for the separate classes, and the final value was obtained as the mean of class-wise measure values with the weights proportional to the content of specific class samples in the test set.As it may be noticed, the best classification quality was achieved by MLP, SVM with linear kernel, and Logistic Regression. The mentioned models required correspondingly five, five, and four features per class. So, the total number of features was as high as seven, seven, and six—the numbers are obtained as the number of common features in the first five and four rows of p-value 0.05). The Tukey’s HSD post-hoc test identified pairs of DSST parts which presented statistically significant differences.The K-S test revealed three features with non-normal distribution . The Levene test found five features with unequal variances . As a result, the ANOVA analysis was performed for 15 features. It found seven features for which differences between their mean values were significant  of the ANOVA analysis and a post hoc test for selected features. Significant differences were observed mainly between class 1 and class 2, as well as between class 2 and class 3. Only one feature, maximum saccade amplitude, presented significant differences between class 2 and class 3 .The topic of cognitive workload assessment also requires cognitive factor analysis. The term cognitive factors refers to characteristics of the person that affect his/her performance and learning effectiveness. Cognitive factors include such functions as memory, reasoning, and attention. Analysis of eye activity as well as DSST features allows estimating the cognitive factors. The procedure of feature selection run with the use of logistic regression model enabled obtaining the most valuable features, showing that the most important feature subsets are fixation and saccade-related features. Those kinds of features correspond to the intensity of eye movement, which shows a higher degree of attention during the performance of more complicated tasks, which is demonstrated especially well in The aim of the study was to perform the multiclass subject-independent classification of cognitive workload level with both interpretable and noninterpretable machine learning models. A three-class subject-independent classification was performed on the basis of the dataset containing eye-tracking and user performance data. The study assessed mental fatigue with features based on eye-tracking and DSST test results. The data were gathered in a case study of three versions of the computerised Digit Symbol Substitution Test (DSST). An interpretable machine learning model was used for selecting the most valuable features, which allowed improving the result of classification and obtaining insights that enabled understanding the process of mental fatigue.Eight machine learning models were built and compared. It is a common approach to run an initial stage of classification tests with the use of multiple classifier models in order to find the most promising ones for further analysis. The aim of our work was not only to obtain the highest possible result of the classification but also to gain the feature interpretability especially in the case of subject-independent classification. The initial stage was provided by SVM with linear, quadratic, and cubic kernel, Logistic Regression, kNN, Decision Tree, MLP, and Random Forest. The learning process for each model was repeated 200 times.Logistic Regression, chosen in the study as a feature selection tool, is commonly applied as an interpretable machine learning model, as it assigns specific weights to separate features, which allows assessing their importance quantitatively and hence creating a ranking. Logistic Regression was applied for feature selection, as it is commonly used in the cases requiring interpretable machine learning. It allowed improving the result from 0.95 to 0.97 (SVM with linear kernel) using only five features per binary classifier out of 20. Logistic Regression demonstrated a similar result with an even lower feature number (4). MLP also ensured high performance, which required five features. Based on the obtained results, it may be noticed that the most reasonable solution is to use an SVM/logistic regression model in the present problem.The DSST test used in the study was a computerised version of the classical paper-and-pencil test. This cognitive test was chosen in the study as it was sensitive to changes in cognitive functioning, and its performance correlates with the ability to accomplish everyday tasks. Although originally, the test was designed to measure cognitive dysfunction, we decided to apply it in the study, as it is easy to use and it engages the memory and concentration of the participant. A preliminary pilot study was applied in order to define the number of symbols and duration of the test. The original version of the test assumes nine different symbols, which were generated in a random and repeated way to be assigned over a 90 s period. We decided to use this setting for the medium level of cognitive workload, whereas the settings for the low and hard level of cognitive workload were defined in an interview with the pilot study participants. However, the limitation of such an approach is that the level of cognitive workload is set permanently for all participants independently of their abilities and IQ level. It is worth noting that there are some methods dedicated for the evaluation of mental fatigue of particular participants. These methods take advantage of the capacity models  and allow adjusting the cognitive workload assessment to a specific person, taking into consideration his/her mental abilities. On the other hand, these methods are based on surveys  and gather subjective assessment of the participant, which might also be blurred with different factors. Moreover, there are many various aspects affecting the subjective cognitive workload assessment, which are hard to consider and explain. A problem that can appear here is a more complicated structure of the experiment due to a possible lack of balance between the classes in this case. Nevertheless, supplementing the study with a mental fatigue evaluation of each participant might provide new insights into the analysis results and will be performed as a future work.The paper presents the classification process based on eye activity gathered with the eye-tracking technique. This dataset is supported with features retrieved directly from the DSST application. The study includes general eye activity features related to visual fixations and saccadic movements, blinking, and pupil characteristics. Information about location indicating where participants were looking was not included. Such data might be used to map eye position onto the visual objects displayed on the screen; however, it is doubtful whether these results could increase the accuracy of the classification. On the other hand, such data could provide information about the test results, although these results are obtained directly from the DSST application.Classification results and the feature ranking obtained in the study strongly suggest that the performed tasks have a systematic influence on eye movements. The results prove the relation between the participants’ engagement in the task combined with their cognitive state and their eye activity. The results give insights into understanding the dependence between eye movements and cognitive factors. However, further research is necessary to explain these dependences among different participant groups and different stimulus types. Although the results of our study suggest that eye movement-related features might be applied in the process of cognitive state assessment, there is a possibility that the type of tasks, graphical interface, or even initial mental state of the participant might affect the results. However, both sets of features applied in the study (eye movement-related features and DSST test-related features) are objective measures independent of the subjective assessment of individual participants. What is more, the eye tracking-based features chosen in the study are a natural type of response gathered from a non-invasive source and obtained without any training or additional activity. A limitation of the work is the relatively small number of participants. Even if the group of 29 participants turned out to be sufficient for performing the statistical analysis, further studies are definitely necessary to confirm the results over a broader group of participants. What is more, we also plan to check the influence of the task order by randomising it in the experiment.The highest F1 rate was achieved for SVM with a linear kernel after performing feature selection. Three binary classification models were used for distinguishing between three classes, each of them used an individual six-feature set obtained from the ranking built in the stage of feature selection (the rankings are presented in It has to be taken into account that differences in education, performed job, experience, and age can cause complication in the analysis, and results might differ between these groups. Thus, due to the approximate mental homogeneity of the examined group in the present research, the results show the relation between cognitive workload level and eye activity especially adapted to analytical minds of students of technical specialties.The statistical analysis was based on the ANOVA procedure. Statistically significant differences for all classes were revealed for the maximum saccade amplitude feature, but the difference was observed only in the following pairs: class 1 and class 3, class 2 and class 3. The most significant differences were observed between class 1, which corresponds to a low cognitive workload level, and class 3, which is related to a high cognitive workload level, as they are the farthest from each other. However, the differences between average values of the mean response time feature was calculated for class 1 and class 2 but not for class 1 and class 3. It could be explained by the fact that the participants had been better acquainted with the application, so they answered questions faster, despite the fact that the third part of the experiment was more difficult. Presumably, there is no statistical significant difference between class 2 and class 3 for mean response time because the second and third part of the experiment included the same number of elements. What is more, a statistically significant difference has been found only for the maximum saccade amplitude feature between class 2 and class 3. This might be explained by the fact that the third part is the most advanced, and then, the participant saccades had the highest amplitude.The specific aim of the paper was to conduct a deeper analysis of the features related to the classification of particular cognitive workload levels. It is an important task from the point of view of understanding the influence of cognitive workload level and mental fatigue on the brain cognitive process.Interpretable machine learning allows to understand which features provide the most valuable information about the examined process. Generally speaking, it is more profitable to understand the reasons behind the decision taken by a machine learning model rather than using it as a black box. Understanding major processes beneath phenomena of interest allows us to build more robust models and perform more effective monitoring of cognitive workload.For example, in the presented case, due to the interpretable machine learning model, we know which features to focus on and which ones can be neglected, thus allowing lowering the computing cost and obtaining better results. In practice, it is more reasonable to use as few features as possible, since this approach requires less processing power and is more robust.As a general conclusion, the authors may state that the paper can serve as an example for researchers seeking ideas and techniques to investigate the relationships between mental fatigue and various biomedical measures.


Location information is one of the basic elements of the Internet of Things (IoT), which is also an important research direction in the application of wireless sensor networks (WSNs). Aiming at addressing the TOA positioning problem in the low anchor node density deployment environment, the traditional cooperative localization method will reduce the positioning accuracy due to excessive redundant information. In this regard, this paper proposes a location source optimization algorithm based on fuzzy comprehensive evaluation. First, each node calculates its own time-position distribute conditional posterior Cramer-Rao lower bound (DCPCRLB) and transfers it to neighbor nodes. Then collect the DCPCRLB, distance measurement, azimuth angle and other information from neighboring nodes to form a fuzzy evaluation factor set and determine the final preferred location source after fuzzy change. The simulation results show that the method proposed in this paper has better positioning accuracy about 33.9% with the compared method in low anchor node density scenarios when the computational complexity is comparable. The Internet of Things (IoT) is a booming new industry and wireless sensor networks (WSN), as the perception layer of the IoT system , provideTaking into account the actual needs of IoT systems, WSN usually have the characteristics of real-time communication , random Common positioning principles include Approximate Perfect Point-In-Triangulation (APIT) , distanc(1)Frist of all, the system model of low anchor node density is defined. Nodes calculate location and time skew with TOA method in this model.(2)Then, a novel location source optimization algorithm is proposed for low anchor node density scenario. In the proposed method, a location source select structure is established with fuzzy comprehensive evaluation. Distribute conditional posterior Cramer-Rao lower bound (DCPCRLB), distance measurement and direction angle is considered as the most significant factors to select location source.(3)The validity and rationality of the proposed method are verified by experiments.Therefore, it is necessary to optimize and screen out the location source that is helpful to improve the positioning performance. The main contributions of this paper are summarized as follows:The structure of this paper is as follows: In recent years, anchor node selection has been a research topic in cooperative localization. Researchers pay lot of attention to anchor node selection, but the ambiguity of nodes position and distance measurement leads to a low accuracy of node position. So far, domestic and foreign experts and scholars have done a lot of research on anchor node selection from different aspects and have achieved good results. The main work is as follow:In , a localReference  analyzesIn , distancReference  considerReference  analyzedIn , an enhaReference  proposedIn , a methoThe following The above methods only consider part of the factors that affect the positioning performance, and do not include all factors in the positioning source optimization framework. The main contribution of this paper is a novel optimization algorithm of the fuzzy comprehensive evaluation  frameworIn this section, first a two-dimensional positioning scene is shown, and the positioning principle and positioning method used in this article are basically explained. Then the DCPCRLB is introduced to pave the way for the proposed location source optimization method. Finally, the node selection algorithm proposed in this article is introduced, and the steps and operation process of the algorithm are introduced.As shown in the In the actual layout, the anchor nodes in the same scene are usually at the same height, so there is a big error in height measurement. Height measurement is usually achieved by other methods, so two-dimensional scenario is considered. The positioning scene includes anchor nodes and nodes to be positioned. The position vector of node Node Assuming that the change process of the vector  process , accordiAccording to the MAP criterion, the estimated value Cramer-Rao lower bound (CRLB) is the inverse matrix of the Fisher Information Matrix (FIM) of the random variable Among them, e θ^i,l.  gives thIn a 2-dimensional wireless sensor network, the node to be located needs to establish communication with at least three anchor nodes to complete positioning. In the location-time joint estimation problem, it is necessary to establish communication with at least four anchor nodes to complete the calculation of the vector to be estimated. In the scenario of low anchor node density, the cooperative localization method provides neighbor nodes as pseudo-anchor nodes for the node to be located, but the pseudo-anchor node itself has low accuracy as the node to be located, and the inappropriate introduction of too many pseudo-anchor nodes will affect the final estimation result has a serious impact, so it is necessary to optimize the introduced pseudo-anchor nodes.In this section, we will introduce the fuzzy comprehensive evaluation method to design the location source selection algorithm. For node Among them, t method  and analt method . FinallyAs shown in ioned in . FinallyIn order to make our simulation environment close to the actual scene, the simulation scene will be set according to the zone 1 scene in The simulation scene is set as a rectangular area of 20 m × 24 m according to the real size of zone 1. To set the average number of anchor node connections around 3, the number of anchor nodes is 4, and the maximum communication distance is set to 10 m. The number of nodes to be located is set to 20 and all nodes conform to the uniform distribution in the simulation scene. All TOA observations are the line of sight (LOS), and the distance measurement error conforms to Gaussian distribution with standard deviation of 0.3 m according to the maximum error of DW1000 . All simulation results are the average of 1000 independent runs.The initial position measurement error of nodes conforms to the Gaussian distribution, the standard deviation of the anchor node is 0.1 m, the node to be located is 10 m. In the simulation process, the clock slope of node to be located is set as 1 ppm according to the performance of the crystal oscillator used in the hardware . Anchor nodes are static and the velocity of node to be located is set to 3 m/s as almost the fastest speed of human walking. Root mean square error (RMSE) and cumulative distribution functions (CDF) are used to evaluate the performance of proposed method.It can be seen from These results in In yroscope .In the next stage of research, the following aspects will be mainly focused on: First, we plan to analyze other factors which affect position accuracy. Secondly, we shall implement the proposed method based on hardware platform and apply the proposed method in realistic scenarios. The measured results in realistic scenarios will be compared with the simulation results to improve the performances of the proposed method.We propose a new positioning source optimization method for low-anchor node density wireless sensor networks, which comprehensively considers the positioning performance, distance, location and other factors that affect the positioning accuracy of cooperative nodes to select positioning sources. First, each node calculates its own CRLB and transmits it to neighboring nodes through collaborative information. Neighboring nodes calculate the CRLB, distance measurement and direction angle of neighboring cooperative nodes to obtain the optimal evaluation matrix, and obtain the fuzzy evaluation result through weight addition, and obtain the final optimal location source. In the fuzzy comprehensive evaluation framework, the weights of various evaluation factors can be flexibly configured. Therefore, the proposed method can screen out different neighbor nodes as positioning sources according to requirements, and then obtain a combination of positioning sources with good location performance and distribution, which can improve the positioning accuracy of the node. Compared with the DRSL and MBIL method, the positioning signal source obtained by this method can obtain higher positioning accuracy about 33.9% and 19.4% under low anchor node density, and the sacrificed calculation time is almost negligible.


Gait analysis has been widely studied by researchers due to the impact in clinical fields. It provides relevant information on the condition of a patient’s pathologies. In the last decades, different gait measurement methods have been developed in order to identify parameters that can contribute to gait cycles. Analyzing those parameters, it is possible to segment and identify different phases of gait cycles, making these studies easier and more accurate. This paper proposes a simple gait segmentation method based on plantar pressure measurement. Current methods used by researchers and clinicians are based on multiple sensing devices ). Our proposal uses plantar pressure information from only two sensorized insoles that were designed and implemented with eight custom-made flexible capacitive sensors. An algorithm was implemented to calculate gait parameters and segment gait cycle phases and subphases. Functional tests were performed in six healthy volunteers in a 10 m walking test. The designed in-shoe insole presented an average power consumption of 44 mA under operation. The system segmented the gait phases and sub-phases in all subjects. The calculated percentile distribution between stance phase time and swing phase time was almost 60%/40%, which is aligned with literature reports on healthy subjects. Our results show that the system achieves a successful segmentation of gait phases and subphases, is capable of reporting COP velocity, double support time, cadence, stance phase time percentage, swing phase time percentage, and double support time percentage. The proposed system allows for the simplification of the assessment method in the recovery process for both patients and clinicians. The inability to walk normally or walking with erratic patterns in gait manifests direct impacts on life quality, making a person less independent or even generating other health issues. An erratic gait is characterized by asymmetrical patterns that may cause pain or injuries to the back, hips, or lower limbs . For theIn cases were technological tools are absent, clinicians performs gait analysis by bare observation of patients in standardized motor tests. Even though this is a good source of information, it relies on the clinician’s experience and lacks numerical objectivity when comparing between different clinicians, since the majority of motor skill tests are based on qualitative indexes. Modern equipment aids human gait analyses by allowing for the performance of gait cycles in specialized gait laboratories. These laboratories have expensive video equipment and optical markers for body segment tracking. These analyses are much more consistent as they provide objective numerical information, such as plantar pressure, weight distribution, gait phases, timing, and step length, among others. The disadvantages of these methods include the reliance on expensive equipment, periodic maintenance, the use of sometimes uncomfortable equipment by the patient, and the loss of generalization and the representation of real environment situations since measurement conditions in the laboratory are not identical to real-life situations and terrains ,2,3.For the reasons mentioned above, there is a huge interest in the design of low-cost, wearable devices to measure and track human movement in a comfortable way for the patient and with the capability of measurement in unrestricted environments. These wearable devices are achieving fast and easy human movement analysis based on objective and repeatable measurements. Wearable devices may also benefit cases were disease requires continuous monitoring during daily-life conditions for longer periods of time, which is not achievable though video analysis tools . In the In this paper, we propose a new in-shoe embedded system composed of an instrumented shoe insole with custom-made capacitive pressure sensors capable of performing automated human gait segmentation and analyses. The sensors are placed over the most anatomically representative spots in the foot. The proposed system embeds all electronic components within the insole, making the system more comfortable and easy to use.Heel strike: considered the starting and finishing event of a gait cycle. Corresponds to the first contact between the heel and the ground.Foot flat: The plantar surface touches the ground.Midstance: The contralateral foot passes the position of the stance foot.Heel-off: The heel comes off the ground. Higher pressures are present in the metatarsal heads.Toe off: The toes lose their contact with the ground. The least amount of foot is in contact with the surface during this event.Acceleration: quick acceleration of the swinging foot.Midswing: The swinging foot passes the position of the stance foot.Deceleration: The swinging foot decelerates as it approaches the ground. The heel takes its position for a new heel strike.The human gait is defined as a locomotion method generated by the use of both legs in an alternated way, allowing the support and propulsion of the human body. During walking, the gait can be divided into phases . Gait exAnalyzing the stance phase, four subphases can be defined, delimited by the events from one to five. The initial contact phase (ICP) occurs when the heel contacts the surface the first time, then the forefoot contact phase (FFCP) starts when the metatarsal heads contact the ground for the first time. Next, the flat foot phase (FFP) is when the middle foot contacts the surface. Lastly, the forefoot push-off phase (FFPOP) starts when the metatarsal heads and the toes are the only support in contact with the ground. The toe-off event is considered the transition between the stance and swing phases ,17.Plantar pressure measurement: This method measures the reaction forces that are generated between the sole of the foot and the ground. To measure this interaction force, transducers are positioned between the support surface and the sole. Several transducers are used for this application: force resistive sensors (FSR), piezoresistive sensors, capacitive sensors, and optoelectronic sensors. Using this method, it is possible to detect gait events by using the sensors as switches and indicating the presence of certain events according to a pressure or non-pressure state. It is also possible to generate pressure distribution maps by quantifying the pressure exerted on the force sensors ,18.Angular displacement and accelerometry: This method measures and quantifies angular displacement, velocity, and acceleration in different points in the body, usually the lower limbs and trunk. In this application, gyroscopes and/or accelerometers are widely used. Attaching one or more of these sensors to body segments, it is possible to model and describe the movements of lower limbs ,19.Camera tracking: The methods with cameras use opto-electronic systems to capture the execution of gait in video images. To track body segments, passive reflective markers or active markers such as LEDs are used. With the markers positioned on the body, the system is able to calibrate positions and keep track of movement. Image processing software uses this information to generate a representation of the movement. Its accuracy depends on the number of cameras used and their resolution, which determines the spatial quality of the obtained images ,20.The cyclical patterns of gait allow us to obtain parameters and indexes that characterize the gait, which has relevant impacts in clinical applications. If it is possible to define normal ranges of those parameters, these can be characterized as constituting a normal gait. Significant deviations with respect to normal values could indicate the presence of pathological conditions. Using this information, clinicians could have more accurate methods to determine and characterize pathologies and treatment follow-up. Gait is represented by temporal and spatial characteristics. It is possible to analyze these components in order to describe, represent, and/or evaluate the way in which people walk. Temporal components are associated with the time intervals that a person needs to execute each gait phase or subphase. The spatial components refer to relative positions between different segments and/or joints of the lower limbs . There aCamera-based systems are not suitable to analyze gait in non-laboratory environments. Recent studies compare the usability and acceptance of textile wearables versus angular displacement devices for activity monitoring. As a result, these studies conclude that textile wearables  are more acceptable and show a better potential for usability in clinical fields than inertial measurement units (IMUs) ,22. For The most relevant parameters that can be calculated using the pressure-sensing method are listed below.This refers to reaction forces produced due to the contact between the plantar surface and the ground. This effect is explained by Newton’s third law. The plantar surface produces a vertical force in the direction of the ground, in response, another force with the same intensity but in the opposite direction is generated .This determines the magnitude of the vertical ground-reaction forces as a body weight representation and its distribution in different points of the plantar surface. Weight distribution changes along the gait. It depends on the contact between the plantar surface and the ground in each gait subphase. While standing, toes provide stability, changing the weight distribution. This parameter has relevance in studies of pathologies , where is important to identify abnormally high pressures ,31.This is defined as the location in the plane where a resultant force vector is generated. This force represents different forces that are applied in different points of the plane. It is calculated as the mean of all forces weighted by the applied position. It reveals useful information about stability and balance during the gait . The cenHeel strike and toe off are the two most important events in order to identify the transition between the stance and swing phases. The heel strike determines the beginning of the stance phase and the end of the swing phase. Otherwise, toe off determines the end of the stance phase and the beginning of the swing phase ,17. In aGait detection consists in determining and recognizing the different periodic patterns during a gait. Segmentation is the use of those spatial and temporal patterns in order to identify the gait phases and subphases ,32,34,35Corresponds to the steps executed in a segment of time, usually one minute. The normal values of cadence depend on different factors such as age, height, leg length, and gender .The proposed system consists of three functional parts: capacitive pressure sensors, acquisition and data transmission hardware, and an external device where algorithms analyze the data. Capacitive sensors were manufactured with two superimposed flexible copper films , separated by a flexible dielectric film. The chosen dielectric material was “Electroactive Ferroelectret Film” by EMFIT. This dielectric material was chosen for its properties shown in Sixteen sensors (eight in each insole) were manufactured using an LPKF ProtoLaser S laser structuring machine and then placed in an EVA foam shoe insole. The positions of the sensors were chosen to match the main support and balance plantar points of the body . Figure The sensors were calibrated using a mechanical press (ProLine table-top Z005 Zwick/Roell). The sensors were submitted to a linear compression force over the total sensor area in the range from 0 to 1200 kPa, three times each. This range of characterization was chosen according to the typical ranges of plantar pressures during walking in the literature . Figure Two Texas Instruments FDC1004 integrated circuits (ICs) were used to measure sensor capacitance. These ICs communicate by I2C protocol to an NXP micro-controller (MCU). This MCU (LPC824) configures the ICs to adjust the measurement capacitive offset and configures the sample frequency (100 Hz for each channel). During operation, it acquires the pressure measurements with 24 bits of resolution and send them by UART protocol to an ST Microelectronics SPBT3.0DP2 Bluetooth module configured as a serial port profile (SPP) device. The PCB design was done in Autodesk EAGLE 9.4.2 and was manufactured in a 0.8 mm thickness rigid PCB with the LPKF ProtoLaser S laser structuring machine. A Shenzhen Sunbang Technology Co. lithium-polymer (Li-Po) battery of 400 mAh is used to power the system, and a battery charge circuit is included in the PCB design. Digital acquisition hardware PCB was also embedded inside of the EVA foam insole, in the same way as the capacitive sensors. To avoid battery-related risk, the Li-Po battery was carefully placed under the arch area in the foot as the pressure in this anatomic zone is minimal and almost negligible. The sensors are connected to the hardware through electrical wires carefully routed through the EVA foam, avoiding intersection between channels and keeping spacing to reduce parasitic capacitance. In addition, signal+ and signal- wires are wrapped by a shield copper tape electrically connected to the active shield signal. Two instrumented insoles were implemented in order to perform experimental walking pressure validation tests and implement the gait analysis algorithms.Calculations to obtain different parameters for gait analysis were implemented, such as CoP coordinates. The algorithms take the plantar pressure signals of each sensor implemented (eight in each insole) as inputs and based on the anatomical locations of the sensors, CoP coordinates are calculated over time. The CoP calculation methodology and gait-phase segmentation algorithm is detailed in the following steps:These events mark the beginning and end of the stance phase, respectively. This detection allows us to segment the human gait into stance and swing phases. To achieve this, adaptive thresholds for the heel and the hallux pressure signals are calculated using the moving average between the local maximum and minimum of each signal. Initial contact will be detected when the heel pressure signal exceeds its threshold. On the other hand, the toe-off event is detected when the hallux pressure signal goes below its threshold.CoP over the total surface was calculated in order to determine the trajectory of the CoP. For this, Equations   CoP overCoP positions were used. The segmentation was performed by dividing the anteroposterior plane of the foot into four zones that correlate with each subphase. The divisions were considered according to the normal 41 EU foot size (US 10) and the described characteristics of each subphase [To segment the stance phase into the four subphases described in subphase .CoP velocity calculation, Equations , obtaining the instant velocity.To perform the quations  and 4)  CoP veloTo calculate the period time of the double support phase, the intersection between the times of the stance phase of both feet is realized. The algorithm calculates this intersection using the stance phase time of each foot.Cadence correlates with gait velocity, but its measurement unit is steps per minute. To calculate it, the difference between the detection of initial contact in one foot with the detection in the other foot is used. Then, this calculation is normalized to express it in steps per minute. To calculate cadence, Equation  is used:Using all of the previously described indexes, some percentages of the gait-phase times with respect to the total cycle time are calculated. These are the double support time with respect to the total cycle, the stance phase time with respect to the total cycle, and the swing phase time with respect to the total cycle.The system was tested with six healthy male volunteers  in 10 m walking distance trials. The test consisted in standing still at the beginning of a straight 10 m track then walking across the track at a “normal” (defined by each volunteer) speed and finally standing still at the end for a few seconds. With this simple walking test procedure, the system is capable of analyzing the gait in the most natural way. During the test, participants were asked to wear the instrumented shoe insoles in both shoes. The authorization was requested through informed consent, which was approved by the Biosecurity, Bioethical, and Ethical Committee of the University of Concepción (Number 3180551).Using the instrumented shoe insoles with the embedded capacitive sensors, plantar pressure data was registered during the walking test trials. After the trials, no volunteer reported discomfort from wearing the instrumented shoe insoles. With the logged data, the indices described in The results show that by using the developed system, it is possible to determine gait events and parameters, as well perform an automated segmentation of the gait cycle phases and subphases. This allows us to evaluate gait symmetry, characterize the gait of a subject, and obtain relevant gait parameters. The main limitation of this preliminary study is the amount of people tested. In order to validate and characterize abnormal gait patterns, an extensive study with more subjects would be needed.The proposed system (as well as other smart textile devices) has the advantage of enabling a simple gait assessment method with minimal intervention on the user in comparison with multiple camera or IMU systems (camera markers and IMUs in each body segment). In addition, wearable systems enable us to measure gait parameters in a non-confined environment, as required for platforms and camera-based gait laboratories. Even though less slippage and bending issues have been reported in smart socks than in-shoe insoles, they show more variable and noisy signals during static and dynamic performance because of the construction process . On the Similar works only detect some events in the gait cycle, such as the toe off, heel lift, and heel strike . Others Moreover, the detection and classification of the different phases and subphases of gait may be used to compared these with known parameters of healthy subjects in order to assist in pathological condition diagnosis. The implemented system has the major advantage of achieving gait analysis under arbitrary non-controlled environments.2. Tests were performed using two instrumented insoles. The volunteers walked 10 m at “normal” gait velocity. Pressure data was recorded and analyzed with the proposed algorithms. The system segmented the gait phases and subphases in all subjects. The calculated percentile distribution between stance-phase time and swing-phase time is almost 60%/40%, which is aligned with literature reports on healthy subjects [Capacitive custom-made sensors were designed and manufactured using flexible PCB and EMFIT dielectric sheets. The resulting sensors are 0.41 mm thin with an area of 1 cmsubjects . In addi


The severity of the novel 2019 Coronavirus leaves much trepidation, anxiety and desperate measures are taken to curb the pandemic. Such measures according to WHO include hygiene, isolation and social distancing. If clustering of people is considered a major catalyst in the spread of corona virus, social distancing is therefore important for its control. But compliance has remained a concern, especially in Nigeria. We examine the concept and global trends in social distancing in infectious disease control and the negative feedback on public health as revealed in current body of knowledge from news media and other literatures. The risks associated with failure to comply with social distancing as a result of ignorance or defiance are highlighted. Human survival has often been threatened by diverse plagues since existence. One of such recent threat is an infectious disease, the pandemic of SARS-CoV-2 also known as the Coronavirus disease 2019 (COVID-19). The first case of the novel strain of the Coronavirus was reported in Wuhan, Hubei province in China on November 17, 2019 according to the South Morning China Post [The concept of socio-distancing in infectious diseases control: social distancing combined with good respiratory hygiene and hand washing are considered the most feasible way to reduce or delay a pandemic that is on course. The Centers for Disease Control and Prevention (CDC) described social distancing as a set of “methods for reducing frequency and closeness of contact between people in order to decrease the risk of transmission of disease”. In the course of the 2019-2020 coronavirus pandemic, the CDC revised the definition of social distancing as “remaining out of congregate settings, avoiding mass gatherings, and maintaining distance (approximately six feet or two meters) from others when possible”. Therefore, in basic terms, social distancing entails physical distancing. The key reason for physical distancing is to reduce the spread of the virus by contact. WHO elucidates that the instance an infected person coughs or sneezes, droplets containing the virus are deposited on objects and surfaces where people may likely touch and as such anyone in close proximity of about 1-2 meters may be at risk. When appropriate physical distance is maintained, the potential to contract and spread of the virus is reduced . Each coGlobal socio-distancing and impact on the spread of COVID-19: in a study to ascertain the cause of the unprecedented global spread of the coronavirus, Hendrick Streeck and colleagues studied the first COVID-19 cluster in Heinsberg, Germany following infection of hundreds of people . They noIn Korea, a 61-year-old woman infected 37 people at her church, in Daegu, South Korea [Social distancing scenario in Nigeria: the first confirmed incident of the COVID-19 in Nigeria was announced on February 27, 2020, when an Italian citizen arriving Nigeria through the Lagos Airport tested positive for the virus . On MarcSeveral cases abound where there were partial or zero adherence. For instance, a typical scenario plays out in most public places such as banks where customers seeking to gain access into banking halls clustered outside. In more organized societies, in helping people maintain the required distance apart during this pandemic, standing boxes measuring 2-3 meters are drawn for queue in most public places  In stateRecent local news that made rounds during the lock down involved celebrities and politicians hosting a house party in the city while under lock down. After being duly arraigned, it was also observed that the caution of physical distancing was also not respected at the court hearing as journalists and onlookers milled together. Another audacious show of defiance to the call for social distancing was on display on a national television, TVC during the funeral of the Chief of Staff to the President. Prior to his death he had tested positive to the COVID-19 virus after a return trip from Germany on March 24, 2020. Despite government´s initial announcement that the burial would be conducted in private to show compliance with the guidelines against the spread of COVID-19, it was observed that a cross section of the sympathizers in attendance were crowded with only very few observing physical distancing. The carelessness did not go without public criticism though the federal government has since apologized. Adherence to the rules and precepts is more important to safeguard lives.Negative feedback in the enforcement of socio-distancing: in restricting movement and encouraging social-cum-physical distancing, the lock-down having its positives also has its fair share of negative feedback. With cases of civil attacks resulting into deaths of people in the hands of law enforcers. A Nigerian Television Station on April 9, 2020 reported the killing of about six people in Kaduna State by suspected law enforcement agents. Similar case was also reported in Delta State where there was an altercation between the natives and soldiers resulting in the death of a native. Instances of defaulting, defiance and violence bring to question whether the lockdown is really serving its purpose. The apparent psychological state of the people coupled with the harsh socio-economic situation in the country may have contributed to the unrest and resistance. Hence, in the face of provocation and rebellion, law enforcement agents should be humane with civilized sense of duty.Another critical point of dialogue during a lockdown has to do with insufficient socio-economic cushion for the people especially as it concerns the indigents and the vulnerable in the society. A country where more than 85% of its population survives economically on a day-to-day basis, their means of livelihood is sure to be hampered by lockdown. Considering this reality, challenges of inadequate supplies and meagre palliative measures or none from government raises major concerns. Though the government has reeled out laudable initiatives, very little impact has been made. While some complain about marginalization due to the lopsided delivery of these palliatives, others feel its administration and distribution is grossly politicized and not transparent. More so, in some centers where these distributions are conducted, it is observed that social distancing is usually the last thing on the mind of the beneficiaries a succinct description of the struggle between life and livelihood. In combating this COVID-19 pandemic, the government, civil society organizations and humanitarian agencies can ameliorate the living conditions of the less-privileged by being proactive and deliberate in meeting their basic need so as to prevent needless death caused by poverty and hunger. Though there remain a genuine and exigent need for ‘social distancing´, but what this really calls for is ‘physical distancing´ as people can remain socially active whilst maintaining physical distance. Instead of the usual visit to/by family and friend, the social media affords people the opportunity to connect, stay in touch and be entertained. As imperative as it, it is hard to ask people to abruptly alter their social norms and instinctive habits. However, desperate time calls for desperate measures. Maintaining physical distancing is one of the proven measures to limit the spread of coronavirus, so people must learn to change their lifestyle to stay alive.There is an obligation to intentionally stay safe during this pandemic period. This is because the global impact of COVID-19 pandemic, including socio-economic fallout of the scourge, is far-reaching than most can imagine, hence doing the needful in abiding by preventive processes should not be difficult. As it is often said, prevention is better than cure. It is better to stay safe.The author declares no competing interests.


Clinical potential and safety are presented as novel criteria to evaluate neutron beams designed for boron neutron capture therapy (BNCT). The presently used figures of merit are a set of physical quantities calculated in air, related to the neutron flux, the collimation, and the spectral characteristics. However, the capability of the beam to deliver an effective and safe treatment to patients should be the most important criterion in view of the clinical application. This work presents the design of a neutron beam produced by a proton accelerator coupled to a beryllium target and the use of new figures of merit to choose the best beam among different candidates. These figures of merit use tridimensional dosimetry simulated in phantoms and patients, to calculate the probability of tumor control without affecting healthy tissues, employing proper radiobiological models. Moreover, the dose absorbed by out-of-field healthy organs is used as a criterion to establish the safest beam for clinical treatments. Results show that beams that would be rejected by physical in-air quantities demonstrate a clinical performance comparable to existing neutron beams successfully used for patients, and that the presented criteria allow a clear selection of the most adequate beam among the ones presented.(1) Background:The quality of neutron beams for Boron Neutron Capture Therapy (BNCT) is currently defined by its physical characteristics in air. Recommendations exist to define whether a designed beam is useful for clinical treatment. This work presents a new way to evaluate neutron beams based on their clinical performance and on their safety, employing radiobiological quantities. (2) Methods: The case study is a neutron beam for deep-seated tumors from a 5 MeV proton beam coupled to a beryllium target. Physical Figures of Merit were used to design five beams; however, they did not allow a clear ranking of their quality in terms of therapeutic potential. The latter was then evaluated based on in-phantom dose distributions and on the calculation of the Uncomplicated Tumor Control Probability (UTCP). The safety of the beams was also evaluated calculating the in-patient out-of-beam dosimetry. (3) Results: All the beams ensured a UTCP comparable to the one of a clinical beam in phantom; the safety criterion allowed to choose the best candidate. When this was tested in the treatment planning of a real patient treated in Finland, the UTCP was still comparable to the one of the clinical beam. (4) Conclusions: Even when standard physical recommendations are not met, radiobiological and dosimetric criteria demonstrate to be a valid tool to select an effective and safe beam for patient treatment. Boron Neutron Capture Therapy (BNCT) is a form of hadrontherapy that exploits the products of neutron capture reactions in  Section 837 b andBNCT has been applied in institutions equipped with research nuclear reactors. Through appropriate Beam Shaping Assemblies (BSA), thermal or epithermal neutron beams with fluxes of the order of 10 patient ,5. Accel patient . Other a patient ,12,13.The Italian National Institute of Nuclear Physics (INFN) designed and constructed an RFQ proton accelerator delivering a 5 MeV, 30 mA proton beam in Continuous Wave (CW) mode . The neu 3.2 MeV . This spThe optimal neutron beam for BNCT of deep-seated tumors has a spectrum peaked between 1 and 10 keV ,17,18, rminimum epithermal neutron flux, minimum degree of collimation as neutron current over epithermal neutron flux maximum gamma dose rate epithermal neutron flux maximum thermal neutron and epithermal neutron flux ratio maximum fast neutron dose rate over epithermal neutron flux IAEA published the TecDoc-1223 in 2001  describiTogether with some in-phantom quantities , these FIn this work, beams obtained with different BSA configurations have been first evaluated according to the classic IAEA FOMs. For a deeper analysis, we then exploited a radiobiological figure of merit as an indicator of their clinical performance. The same beams were then tested on a human whole-body phantom to evaluate the dose delivered to peripheral organs. The information retrieved from these evaluations led to the selection of the best BSA configuration. The candidate beam was finally used to simulate a treatment plan in a patient bearing head and neck tumors, one of the most promising targets in present BNCT clinical trials. The result of the radiobiological figure of merit in the treatment planning was compared with that of the original BNCT treatment performed at FiR1 in Finland.The goal of these evaluations is to provide a more realistic clinical assessment of the designed neutron beams, allowing the selection of the optimal candidate in terms of therapeutic potential and safety.Presently many reactor and accelerator-based neutron sources are available, all with characteristic neutron spectra, needing ad hoc BSA materials and geometrical configuration to make them suitable for BNCT. The INFN RFQ accelerator produces neutrons by interactions of 5 MeV protons with a Be target. The target is described in , and it 3) , which iFive different beams based on these three geometrical structures were selected changing the materials. The described beams were chosen among others using the FOM listed in To introduce a criterion related to the BNCT treatment outcome, we tested the beams in a phantom representing a human neck bearing a tumor. The phantom is a cylinder (radius of 10 cm and height of 24 cm) of mucosa, which is the tissue at risk in Head and Neck (H&N) BNCT treatments. A 2 cm radius sphere centered 3 cm from the phantom equator surface represents the tumor. Boron concentration was assumed to be the standard in Finland treatments: 15 ppm in blood, which results in 30 ppm in mucosa and 52.5 ppm in tumor .When a clinical facility is projected, it is critical to select the safest beam for the patient, i.e., the one delivering the lowest dose to the organs outside the irradiation field. To study this, the beams were tested using the anthropomorphic phantom MIRD ,38. An eBoron concentration was assumed to be 15 ppm in all the normal tissues except for the skin and kidneys, set to absorb 22.5 ppm  and 75 pThe combination of therapeutic potential and suitability assessment allowed a clear selection of the best neutron beam design.The selected beam was finally used to simulate a BNCT treatment plan. The chosen clinical case is a Head and Neck cancer patient (squamous cell carcinoma) treated with BPA-mediated BNCT in Finland . The perMultiCell was used as the Treatment Planning System, to obtain the voxelized patient model shown in It is important to note that UTCP is not used here to establish an absolute medical criterion, but as a FOM to inter-compare beams on the basis of their therapeutic potential. Notably, when the classical physical parameters would not promote any of these beams, UTCP demonstrates that they are all comparable to the reference clinical beam, used for many patients. Thus, with this criterion, they are all potentially useful for treatment. Still, the UTCP alone is not enough to establish a ranking, as all beams give the same UTCP within the uncertainty.All beams allow reasonable treatment times. Moreover, the maximum dose to the healthy tissue at risk (D) is very close to the clinically prescribed dose of 6 Gy in Finland protocol .The anthropomorphic male MIRD phantom was positioned in a representative irradiation position for H&N cancer treatment . For allThis result together with the therapeutic potential is a clear demonstration that BSA #5 is clinically the most performing beam in terms of therapeutic potential and safety.We compared the results of out-of-beam dosimetry of BSA #5 with those obtained in a similar irradiation position with both a designed ab-BNCT beam and with a reactor beam used in BNCT clinical applications  ,50. To tThe BSA #5 is showed in detail in The beam from BSA #5 was used to simulate the treatment planning of a real clinical case treated in Finland. The results of the treatment planning simulation for the optimum irradiation time and the corresponding data for FiR 1 are shown in At the maximum UTCP value for each beam, for approximately the same NTCP , the TCPWe used the selected beam to compare its performance with the reference neutron facility FiR 1, in a clinical case that was successfully treated there. If clinical decision is to select the treatment time that maximizes the UTCP, the therapeutic potential of the selected beam—quantified by the most probable value of UTCP—is 15% higher than the one obtained for FiR 1. The real irradiation time of the H&N cancer patient treated with FiR 1 was 110 min, a value slightly higher than the one that maximizes the UTCP. If we now simulate an irradiation time to equate the NTCP of the real treatment, the candidate beam achieves a TCP value 15% higher than the one obtained with FiR 1 in an irradiation time almost identical .therapeutic potential, i.e., the capacity to treat a deep-seated tumor without damaging the tissue at risk. UTCP is a radiobiological FOM which condenses the 3D dose distribution into one value related to the clinical outcome. This FOM demonstrated that beams that did not satisfy the physical recommendations were indeed comparable with a reference clinical beam on the basis of the therapeutic potential. For the selection of a suitable beam, we also evaluated safety, calculating the dose delivered to the other organs of the patient. This figure describes the suitability of a BNCT neutron beam. It is not possible to define a threshold above which the dose absorbed in an organ is considered too high for the treatment because the priority is the clinical outcome of the tumor irradiation [This work aimed at designing a clinical BNCT facility based on the accelerator built by INFN, employing innovative methods to select the most performing and the safest candidate among different possibilities. The first step was to choose the BSA generating a beam suitable to treat deep-seated tumors. The free-beam in-air parameters proved to be a descriptive tool and a guide to select possible candidates which however did not allow a fair ranking of the beams. We then introduced the concept of adiation . Hence, The obtained results confirm that it is possible to produce a neutron beam for clinical BNCT from the INFN RFQ. The chosen beam satisfies only some of the IAEA recommendations. However, it proved to ensure a comparable clinical performance to that of our reference beam FiR 1 for H&N treatment.Our approach adds to the standard evaluation of the in-air physical parameters, the calculation of suitable radiobiological FOMs and out-of-field dose that directly account for the clinical performance and safety of these beams. The possibility to extend this approach to other tumors, testing the therapeutic potential of the beams for a wider class of treatments, requires the availability of the computational tools, the radiobiological data and the models to calculate the 3D dose distribution and the FOMs to predict the clinical outcome. It is thus particularly important and urgent to produce in-vitro and in-vivo experiments to feed the models. Moreover, suitable phantoms, with simple yet significant geometrical characteristics, and real clinical cases must be selected to allow for a broader evaluation and comparison with existing beams.An important discussion is now ongoing within the BNCT community on the necessity to establish common guidelines to evaluate BNCT beams. This is a non-trivial issue because the evaluation criteria strictly depend on the type of tumors that are addressed, i.e., shallow or deep-seated, and thus on the spectra that are preferable. Nevertheless, the biological effects of the overall dose distributions in patient must be taken into account. For this reason, radiobiological figures of merit such as TCP, NTCP, and UTCP, give a deeper insight into the clinical effectiveness and outcome of the simulated treatment planning thus providing a robust criterion to predict the beam performance.