Transferring a huge amount of data between different network locations over the network links depends on the network’s traffic capacity and data rate. Traditionally, a mobile device may be moved to achieve the operations of vertical handover, considering only one criterion, that is the Received Signal Strength (RSS). The use of a single criterion may cause service interruption, an unbalanced network load and an inefficient vertical handover. In this paper, we propose an enhanced vertical handover decision algorithm based on multiple criteria in the heterogeneous wireless network. The algorithm consists of three technology interfaces: Long-Term Evolution (LTE), Worldwide interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN). It also employs three types of vertical handover decision algorithms: equal priority, mobile priority and network priority. The simulation results illustrate that the three types of decision algorithms outperform the traditional network decision algorithm in terms of handover number probability and the handover failure probability. In addition, it is noticed that the network priority handover decision algorithm produces better results compared to the equal priority and the mobile priority handover decision algorithm. Finally, the simulation results are validated by the analytical model. The algorithm should be reliable; an inaccurate vertical handover decision may cost network resources.The algorithm should be able to distribute mobile devices fairly; this is to balance traffic loads on the networks.The algorithm should be accurate; it must be able to identify the need to increase data transfer rate to mobile devices on the network.The demand of data transfer rate and traffic capacity of mobile communication is growing rapidly; thus, the concept of the heterogeneous network is introduced to meet this demand. In a heterogeneous network, the mobility feature is essential because mobile devices must be able to roam throughout the network and to connect to various radio access technologies. Conventionally, the mobile device is considered the point of attachment based on single criteria, such as Received Signal Strength (RSS). General opinion has been that the simplest algorithm to determine handover is based on RSS , but RSSMoving on, implementing multiple criteria on the algorithm of vertical handover helps to provide an alternative network that might be the best network target. The rest of this paper is organized as follows: a brief related work of the approaches in the network selection area is given in There are several methods of vertical handover decision algorithm, as follows:RSS-based algorithms: This method uses the RSS algorithm as the handover trigger  and to dContext-aware-based algorithms: Handover is decided based on signal quality, the network and the context of the mobile device . ContextCost function-based algorithms: This method can be approached in two ways: network-related cost function and user-related cost function ,10. VariFuzzy logic-based algorithms: The two steps involved are: (a) fuzzification and weighting procedure ; (b) decMultiple criteria-based algorithms: This method combines the multiple criteria-based algorithms to reduce power consumption ,17,18.Generally, RSS-based algorithms are the least complex system , but it The comparison has been simulated in the heterogeneous network environment of WLAN, UMTS  and GPRS. Then, a comparison of network performance  has been carried out. There is also a comparison and a performance evaluation of SAW and the Weighted Product Model (WPM) in terms of processing delay  in the eMoving on, TOPSIS has several advantages over other multiple criteria algorithms. Its concept is simple: it has an efficient computing characteristic and is able to measure relative performance for each alternative . FurtherThere are two mobile station parameters in this study: cost function and mobile speed.Gold cost: A premier user subscription that allows the use of the highest-level of Quality of Service (QoS). The cost function is irrelevant.Silver cost: A medium priority user subscription that would try to balance between QoS requirements and cost function.Bronze cost: A lower user subscription where the cost function is significantly more important than any QoS parameters.There are three types of cost function, listed as follows:There are five different mobile speeds between the low speed vehicular mobile station (5 m/s) and the high speed vehicular mobile station 25 m/s) [ m/s [25]The TOPSIS method provides flexibility in defining the weights of the multiple criteria priority. There are three types of priority in the multiple criteria vertical handover decision: equal priority, mobile priority and network priority. The priority method emphasizes the mobile parameter (cost function and mobile speed); meanwhile, network priority emphasizes network occupancy. Each priority has a certain weight, as presented in i with respect to criterion j.m represents the alternatives .n represents the criteria .Construct the normalized decision matrix: to transform dimensional attributes into non-dimensional ones. This will allow comparison across criteria, using the following equation:W represents the weightConstruct the weights decision matrix. The TOPSIS method is used to multiply each column of the normalized decision matrix by the weights. An element of the new matrix is:(a)Ideal solution:(b)J is the set of benefit attributes or criteria (more is better)Negative ideal solution:Determine the ideal and negative ideal solutions.(a)The separation from the positive ideal alternative is:(b)The separation from the negative ideal alternative is:Calculate the separation measures for each alternative.Calculate the relative closest to the ideal solution.Rank the preference order.In these three types of priorities, we utilize the weights to compute the expected impact of each criterion on selecting the most appropriate network for handover purposes. This is performed by employing the TOPSIS method to compare between the available candidate networks. Suppose there are M alternatives (options) of candidate networks available for handover selection. Building on the previous discussion, TOPSIS will be the method for selecting the most appropriate network for handover. The steps are as follows :ConstrucA set of alternatives can now be preference ranked according to the descending order of To obtain performance results, the proposed algorithm is implemented by using NS-2 simulation  . We implMoving on, handovers are calculated based on the total vertical handover occurrence during an active call. It is crucial in assessing a mobile network’s performance as it is influenced by signaling load and delivery of QoS. It is worth noting that unnecessary handover will waste network resources and time, consequently contributing to inefficiency. The handover failure probability is the average of the incoming handover requests that cannot be serviced due to the lack of resources.The network topology consists of the LET, WiMAX and WLAN networks. The radius of WiMAX is 2500 m, LTE 1000 m and WLAN 300 m. WiMAX covers 75% of the simulation area; meanwhile, LTE covers 65%, and WLAN covers 75%. The radio parameter is presented in Looking at a different point of discussion, there are three types of priorities to be considered in the multiple criteria handover decision. They are equal priority, mobile priority and network priority, and they are implemented in a heterogeneous network environment. Each priority performance is compared to the traditional method, which only considers RSS as its criteria: the multiple criteria method considers the cost function, mobile speed and network occupancy. The number of handovers for network priority multiple criteria is presented in Apart from that, providing larger network occupancy and mobile priority multiple criteria can improve handover failure probability; the average increased by 33.79%. As demonstrated in Call holding time  = the number of a non-blocked calls during a call connection. Then:Apart from that, a non-blocked call handover probability (To find the approximating of network residence times (on (pdf) .The residence time in the first network n as Rr1 .The residence time in all subsequent networks is the gamma distributed random variable that have the same mean Assume that the random variable Hence, the mean and the variance of the random variable Equation , respectSolving Equation  for αianSimilarly, for the residence time in the first network, we have It can be seen that Hence, the pdf of From Equation , we haveWe select the network priority handover decision algorithm, which gives the best results compared to the equal priority and the mobile priority handover decision algorithm for evaluation by use of Equation .Our method of calculating the probability function of the number of handovers and comparing with simulation results is shown in The discussion that has been built demonstrates that the analytical results from the introduced technique and computer simulation agree with the different system parameters. The former also leads to the high accuracy of the results.To overcome the problem of excessive handovers for mobile devices in the environment of a heterogeneous network, we propose an improved vertical handover decision algorithm based on multiple criteria, which will enable the mobile devices to make the right handover decision. From the simulation results, we can observe that our proposal will enhance the number of handovers’ probability and the handover failure probability in comparison with the traditional network decision algorithm. The network priority handover decision algorithm creates good results compared to the equal priority and the mobile priority handover decision algorithm.


In this paper, we address the problem of vehicle localization in urban environments. We rely on visual odometry, calculating the incremental motion, to track the position of the vehicle and on place recognition to correct the accumulated drift of visual odometry, whenever a location is recognized. The algorithm used as a place recognition module is SeqSLAM, addressing challenging environments and achieving quite remarkable results. Specifically, we perform the long-term navigation of a vehicle based on the fusion of visual odometry and SeqSLAM. The template library for this latter is created online using navigation information from the visual odometry module. That is, when a location is recognized, the corresponding information is used as an observation of the filter. The fusion is done using the EKF and the UKF, the well-known nonlinear state estimation methods, to assess the superior alternative. The algorithm is evaluated using the KITTI dataset and the results show the reduction of the navigation errors by loop-closure detection. The overall position error of visual odometery with SeqSLAM is 0.22% of the trajectory, which is much smaller than the navigation errors of visual odometery alone 0.45%. In addition, despite the superiority of the UKF in a variety of estimation problems, our results indicate that the UKF performs as efficiently as the EKF at the expense of an additional computational overhead. This leads to the conclusion that the EKF is a better choice for fusing visual odometry and SeqSlam in a long-term navigation context. Autonomous vehicles have recently received great attention in the robotics, intelligent transportation, and artificial intelligence communities. Accurate estimation of a vehicle’s location is a key capability to realizing autonomous operation. Currently, the leading technology in this setting is GPS receivers to estimate their absolute, georeferenced pose. However, most commercial GPS systems suffer from limited precision and are sensitive to multipath effects , which can introduce significant biases that are difficult to detect in addition to sometimes being unavailable . To provide alternatives to GPS localization, many sensory devices have been applied to carry out the localization task, including visual sensors that are often desirable due to their low-cost, wide availability and passive nature.Vision-based localization techniques fall into several broad categories including real-time Structure from motion (SfM) or Visual Odometry (VO) and Place Recognition. While VO methods calculate the egomotion by incrementally estimating the rotation and translation undergone by the vehicle using only the input of a single or multiple cameras, Place Recognition methods are based on learning a database of images (the map) and the vehicle consecutively tries to find matchings between this database and the actual visual input (query image(s)). Within the place recognition paradigm, numerous research papers have addressed visual appearance-based place recognition by ground vehicles especially in varying environments ,2,3,4,5.Hence, our motivation is to create a vision-driven localization method in urban environments that uses cues from SeqSLAM and VO. Our method is able to localize vehicles in the global reference system if a georeferenced database of images has been learned beforehand. However, in this paper, we address the problem of long-term navigation where loop closures, referring to when the vehicle has returned to a past location after having discovered new terrain for a while, are used to reduce the drift caused by VO. Such detection makes it possible to increase the precision of the actual pose estimate. To this end, we present the integration of the robust sequence-based recognition capabilities of the SeqSLAM system with the accurate 3D metric properties of monocular VO in a probabilistic framework through the use of the Extended Kalman Filter (EKF), the efficient recursive estimator  and the The paper proceeds as follows: Several approaches have been developed in the past few years, which frame self-localization as a retrieval task. Towards this goal, multiple representations of the world have been adopted, namely, visual Bag-of-Words (BoW), visual features and 3D point clouds. BoW representation has been first used by the popular algorithm FAB-MAP  that relHowever, the success of visual features-based approaches is very dependent on the quality of the visual vocabulary, and in turn on the prior data and the reliability of extracting the same visual keypoints and descriptors in images with similar viewpoints. The latter is particularly problematic when there are large lighting variations and scene appearance changes due either to seasonal changes or to day and night cycles. Significant performance improvements over Fab-MAP under extreme lighting and atmospheric variations have been achieved within the SeqSLAM system, which relies on the use of a whole image regardless of its content by searching in a pre-learnt database of images for the most similar sequence to the query sequence ,2. SeqSLOn the other hand, when the initial position is known, self-localization is achieved by visual odometry that yields relative motion estimates, integrated to obtain an estimate of the vehicle’s current position. High accuracy has been achieved using stereo-based SfM systems . Good peHowever, SLAM methods suffer from issues in terms of speed and map size which limit their application at large scales. Even though efficient optimization strategies using either incremental sparse matrix factorization  or relatOther approaches are based on the fusion of two or multiple algorithms. In fact, data from different algorithms might be combined to derive a more accurate estimate of the vehicle’s pose, as their uncertainties might be complementary. Illustrated in  is the fdIn this section, we briefly present the main components of the state-of-the-art appearance-based global positioning algorithm SeqSLAM and then show some components of the Sequence Matching Across Route Traversals (SMART) system, built upon SeqSLAM to overcome some of its limitations. The SeqSLAM algorithm, using a simple whole-image comparison with Sum of Absolute Differences (SAD), demonstrated impressive place recognition performance across significant condition variance such as seasonal changes and day to night transitions where feature-based methods failed entirely and in the case of low quality imagery . In order to increase the discriminative nature of the observation and to avoid the problem of false-positives, location is represented in SeqSLAM as a sequence of images, rather than a single image from one pose. Images are, beforehand, resolution-reduced and patch-normalised to enhance contrast and are, therefore, converted into visual templates. Then, to compare each query image A next key processing step is to normalize the image difference values u and v are the coordinates in the difference score, However, one of SeqSLAM’s most significant drawbacks is its lack of viewpoint invariance inherited from the use of global image matching. To compensate for a small viewpoint invariance, variable offset image matching and distance-based template learning have been used in the SMART system built upon SeqSLAM . In factectively .On the other hand, SMART learns (creates the database of templates) and queries templates at regular distance intervals  with a topological one . The vehicle tracking is performed by VO leading to drift due to the run-time error accumulation. Loop closure recognition via SeqSLAM allows incremental pose drift  to be ovk is composed by the position in the The goal of this section is to track the state of the vehicle over several images. The vehicle state in time (pdf) of the state in two steps. To compute the prediction update of the Bayesian filter, we use VO. To compute the measurement update, we integrate the topological localization update, whenever it is supplied by SeqSLAM, described in the previous section.We adopt a Bayesian approach  to trackf describes the evolution of the state over time. The measurement model h relates the current measurement x at time k and x at time f and h are in general non-linear functions.The system model Visual Odometry (VO) is usually referred to as the problem of incrementally estimating the egomotion of a vehicle using a single or multiple cameras . In ordek, two consecutive images At time k with respect to the local camera frame is equal toAs we are using monocular visual odometry using only the input of a single camera, we deal with a scale ambiguity where the norm of the translational component cannot be recovered and we only obtain the direction of the translation. nd-plane .Given that we predicted the state of the vehicle Y to an N-dimensional random variable X such as X has a mean Y can be obtained via a Monte Carlo simulation that relies on a repeated random sampling. In fact, a large number of samples X and the function In order to estimate the covariance matrix echnique . In genek and t are saved in a set x. We usually obtain a high number of valid Monte Carlo estimates  and the covariance In the case of VO, the algorithm uses at every step a set of 2D corresponding bearing vectors between images ed using  and 10)k and k−1k, the state The error of the VO is propagated throughout consecutive camera positions as follows. At time We compute the associated covariance i-th component of We compute the Jacobian matrices numerically, the rows of the jacobian matrices n query images S is the set of database images In the SeqSLAM algorithm, the input is the query sequence that refers to the last acquired images converted to visual templates. The algorithm selects the path in the difference matrix that connects the query templates to the database ones. The observations are the sequence of pdf(pdf) is simply a multi-dimensional Gaussian with the mean the estimated state and its covariance. Both the mean and the covariance have to be transformed into the observation space. Hence, the likelihood has the following formn the state dimension and R is the measurement covariance matrix. If the evaluated likelihood is above a preset, empirically determined threshold, the measurement update step is performed. Otherwise, only the prediction is done and the covariance of the state estimate will grow until a successful observation is reported. As well as the case of rejected observations, the dynamics update is still performed in the case of false negatives and they, therefore, do not affect the system performance.The Gaussian assumption underlying the Kalman Filter and its variants implies that when an observation that significantly differs from the state estimate, as determined from the state covariances, is incorporated into the state estimate, the resulting Gaussian deviates significantly from its prior shape. This means that the current estimate of the true state is no longer useful which has a negative impact on the system robustness. Although the filter recovers after some time, several applications depend on the short-term result in which false positives (FP) have a disastrous impact. In fact, even with the use of a sequence matching instead of a single image by the SeqSLAM algorithm, this latter still suffers from false-positives. Thus, to filter them out, we use the state and covariance estimates and the Gaussian basis of filtering techniques to estimate the likelihood of a given observation. The conditional Algorithm 1: False positives filtering.SeqSLAM uses a threshold on similarity between sequences to determine whether a match refers to a true positive determined by precision-recall tests. The lower the threshold is, the more similar are the matched images. However, a too low threshold leads to false negatives . The better compromise has been achieved when using both the similarity score and the likelihood to assess true positives without too many non detections as presented in Algorithm 1.Our aim is to reduce the uncertainty associated to the state estimate by fusing the prediction estimate with the measurement, whenever a valid measurement issued from a recognized loop closure by SeqSLAM is available. The outputs of this fusion step are the updated estimate Then, the measurement update adjusts the prediction results according topdf. The associated weights The Unscented Kalman Filter (UKF) is an alternative to the Extended Kalman Filter (EKF) demonstrating a superior performance and ease of implementation for nonlinear estimation. The UKF uses an alternative form for dealing with nonlinearity based on the sigma points. The time update includes the weights and sigma points calculations, whereas the measurement update uses the sigma points to generate the covariance matrices and the Kalman gain respectively. The augmented state matrix The subsequent step consists in passing the sigma point through the state evolution modelThe mean and covariance estimates for The estimate of the posterior The predicted measurement and innovation covariance are then computedFinally, the posterior belief, The Kitti odometry dataset , recordeIn order to evaluate our method, we conducted experiments on three data sequences from the odometry category with a loopy trajectory. Together, these sequences, presented in www.cvlibs.net/datasets/kitti.We recall that the Kitti dataset provides challenging benchmarks to the computer vision community that are available online The parameters used for SeqSLAM are those reported in the literature and presented in In order to calibrate SeqSLAM and evaluate the uncertainty of the position determination, we used almost We display the ground truth trajectory measured by a precise GPS in green in We show the mean error per travelled meter of position and heading in We show the comparative results between the corrected and non-corrected journeys in The UKF is a superior alternative to the EKF for a variety of estimation and control problems. However, its effectiveness depends on the nonlinearity of the problem. In our case, we are dealing with a nonlinear prediction model and a linear measurement model. As listed in In this section, we briefly describe the storage and computational requirements of the system. In fact, all the templates are down-sampled from igned in , allowinThe template library is created online using navigation information from the VO. That is, when there is not a match in the database, a node is added to this latter with the template and the corresponding information of pose obtained from the VO module. The sequence searching strategy used in this work is based on a regular time interval but could be optimized for a regular distance interval between templates to make the localized sequence searching algorithm more efficient as reported in . In factThis paper presented a solution to localize a vehicle in urban environments by means of the integration of VO and SeqSLAM. SeqSLAM is a well-known successful approach for place recognition in varying conditions such as seasonal changes and day and night cycles when not dealing with a viewpoint change. The localization was performed using a single on-board camera and the template library was created online using navigation information from the VO. The integration was performed with a Bayesian filtering through the use of an EKF. That is, when a place is recognized, the corresponding information is used as an observation of the filter, otherwise the prediction is performed by the VO module. The performance of the system can be increased by using a pre-learnt geo-tagged database of images to develop a reliable alternative to satellite-based global positioning. In such a case, the importance of SeqSLAM is even more important due to its ability to deal with severe lighting differences between the learnt database and the actual visual input. The results showed the superiority of the visual odometry integrated with SeqSLAM algorithm in real-time localization as the navigation errors are greatly reduced by loop-closure detection.


Inadequate postures adopted by an operator at work are among the most important risk factors in Work-related Musculoskeletal Disorders (WMSDs). Although several studies have focused on inadequate posture, there is limited information on its identification in a work context. The aim of this study is to automatically differentiate between adequate and inadequate postures using two wearable devices (helmet and instrumented insole) with an inertial measurement unit (IMU) and force sensors. From the force sensors located inside the insole, the center of pressure (COP) is computed since it is considered an important parameter in the analysis of posture. In a first step, a set of 60 features is computed with a direct approach, and later reduced to eight via a hybrid feature selection. A neural network is then employed to classify the current posture of a worker, yielding a recognition rate of 90%. In a second step, an innovative graphic approach is proposed to extract three additional features for the classification. This approach represents the main contribution of this study. Combining both approaches improves the recognition rate to 95%. Our results suggest that neural network could be applied successfully for the classification of adequate and inadequate posture. Several studies, summarized by da Costa et al. , have reWMSD detection often focuses on monitoring ergonomic risk factors by self-report questionnaires and/or by direct or indirect observational methods ,14. HoweThereby, in this study, we focus on the design of an effective measurement system for differentiating automatically adequate and inadequate postures. As an electronic and automatic assessment tool, we first introduce an enactive insole and smart helmet as wearable devices to better analyze workers’ posture. Since posture analysis can be both an expensive and time-consuming process, previous studies have underlined the need for automatic models that can detect inadequate posture . Some reThe main contribution of the present study is to develop a new methodology for classifying the current posture of workers using the developed instrumentation. More specifically, we establish a new type of features, related to the COP and inertial measurement unit (IMU) data, to classify postures at workstations. To achieve this goal, we focus on prolonged or inadequate postures, since those can constitute a personal determinant of WMSD-related risk. The proposed classification algorithm is optimized by using a hybrid feature selection method.The proposed system consists of two assessment tools as wearable devices: (1) an instrumented safety helmet for recognizing the gestures of the worker’s head and (2) an instrumented insole for assessing local COP displacements under the foot. The architecture of these interactive tools is represented in The safety helmet system is an inexpensive, non-intrusive, non-invasive, and non-vision-based system . It inclThe accelerometer adopted and used in the experimental trials of this work is the MPU9250 from TDK InvenSense .i is the pressure measured on sensor i, and Xi, Yi are the COP’s coordinates for sensor i inside the insole.Worker posture can be analyzed by the COP’s displacement under the foot. This displacement is a virtual site of the plantar surface . More prFrom these measurements, the challenge is to classify the actual worker’s posture (and movement) into one of several predefined groups of postures (and movements), known as being either adequate or inadequate. To find the COP, we suggest one insole containing four force sensors . SeveralCOP) and mediolateral (XCOP) directions, we can use the data provided by the four force sensors located in our instrumented insole according to Equation (1). We begin the next subsection by comparing four types of force sensor technologies. Then, a characterization of several types of conductive supports is performed.Three sensors could also be used in our proposed insole, however the whole surface under the foot would not be covered. To determine the coordinates of the COP in the anteroposterior (YIt is known that the force sensing resistor (FSR) sensor allows measuring the center of pressure parameters  using EqThe capacitive force sensor  has the same shape as the FSR sensor. It is ultra-thin, thus allowing its integration in an instrumented insole without any issues. Its consumption is estimated at 2.5 mA according to its specifications , which iVarious studies have also investigated the use of an optical sensor ,49,50,51The last type of sensor investigated is the Hall Effect sensor. Its operating is based on the shifting of a permanent magnet (N52 3 mm × 1 mm) according to a fixed Hall Effect transistor . When the magnet is closer to the transistor, the output tension is higher.For the Hall Effect sensor, we first designed two magnet supports . At this resistor value (10 kΩ), the FSR sensor consumes maximally 0.33 mA while the Hall Effect sensor consumes 2.3 mA . Due to Classical copper wire could break inside an insole during its use. Conductive textiles provide an interesting solution to prevent this problem. For our insole design, we characterized different types of conductive textiles between an embedded acquisition system (PCB) and the sensors.To compare the conductivity and find the material best suited for our insole, we characterized the resistance of (1) an elastolite, a conductive fabric ; (2) a conductive yarn  and (3) an electric paint . The conductive textile was divided in strips of 68 cm of length. The measuring process was the same for all the conductive supports, except the electric paint since it is more complicated to evaluate. We measured different painting strips to determine which one has the best regularity. The best painting strips were used for comparing against the other types of support. It shows that the conductive yarn coil has the best conduction, followed closely by the elastolite. This interesting finding could be exploited in a future version of the insole. The main difference between these two technologies is their flexibility: the elastolite cannot be bent in every direction, yet the elastolite and conductive yarn coil are rather close in terms of conductivity, hence can be interchanged depending on the insole’s disposition.Due to the higher consumption of the three other types of force sensors, the FSR sensor is a more suitable technology to compute the center of pressure. This section describes the experimental methodology used to validate the proposed measurement system. We present the selected workstation and six series of postures (and movements) that the worker can adopt while accomplishing his or her tasks. We then discuss the representation used for postures (or movements). Finally, the experimental protocol is described.During this phase, we want to represent a posture (or a movement) by the signals collected using our measurement system. Since our aim is to detect inadequate postures during work, this step is important in the learning process where we define the different categories of postures (and movements).The workstation consists of the Flexible Manufacturing System (FMS) used by Meziane et al. . It inclFilling the distributors puts the operator in one of the situations represented in A single participant wore both the helmet  and insoThe signals related to the situations presented in Signals from the helmet in The proposed approach for classifying postures is illustrated in Data acquisition has already been presented in experimental protocol. The preprocessing and classification phases are considered in the following sections.In the processing phase, an initial set of features has to be computed. The goal of this phase is to extract discriminative features from the raw acquired data. Toward this goal, we used two different approaches, a direct approach and a graphic approach, which differ in the nature and the source of extracted features.In this approach, features are determined directly from the helmet and insole recordings. The features extracted from the head acceleration recordings along the three axes AccX, AccY and AccZ are mainly of statistical nature: mean values , maximum values , variances , standard deviations , root mean squares , and kurtosis .COP and the mediolateral XCOP). Various features can be obtained from these representations to analyze the control postural while adopting a certain posture. Among these are global variables [XCOP, YCOP and their resultant in both time and frequency domains [Features extracted from COP recordings are based on two different representations: the statokinesigram  and the stabilogram . To acquire additional characteristics for differentiating between postural Situations, we also proposed a graphic approach which represents COP displacements from the insole as an image (matrix of pixels). To fix the surface of the insole to be discretized, we first computed the minimum/maximum values of XDimensionality reduction is an important step of classification systems to select an optimal set of features and limit the complexity of the model . There aWe performed a selection of features, with five commonly-used filter techniques presented in Unlike the filter one, the wrapper model selects features according to the accuracy of a classifier. As its name suggests, the classifier’s training algorithm is “wrapped” inside the feature selection process, making it this approach suitable for any classifier. However, since the classifier has to be trained and evaluated several times, it is costlier in terms of time and calculation. The wrapper model chosen in this work is the Sequential Forward Selection (SFS) . This moThis method, illustrated in An input layer of i neurons, where i is the number of inputs ;A hidden layer of j = 12 neurons whose activation function is the hyperbolic tangent;An output layer of k = 6 neurons, where k is the number of classes (posture situations presented previously). A softmax function is used to convert the output of these neurons into class probabilities .After feature selection, we proceeded with the classification process. Given the recent successes of Artificial Neural Networks (ANN), in this work, we chose a multi-layer perceptron as classifier. Our network is organized as follows:ij is the desired response of the output neuron i of the sample group j. The specificity of this function is that it strongly penalizes the wrong outputs ) and emphasize those that are correct [As in most classification networks ,69, cros correct .Bestaven et al.  show thaIn We applied dir_hybride_1 and dir_hybride_2  can effectively be used to detect inadequate postures of individuals in a work environment. A direct approach was used to collect features from different postures and movements. We also introduced an innovative graphic approach, which represents movements of the center of pressure, captured by the insole, as an image. A hybrid model was used to select the most discriminative features generated by these two approaches. An important contribution of this study was to combine features from both approaches for the effective identification and prevention of inadequate postures. The proposed methodology allowed us to improve the recognition rate from 90% to 95%, an accuracy superior to that of existing models.A limitation of this study is the small sample size, which was obtained using a single participant. Although our results suggest that the proposed method can be used successfully to assess work-related MSDs, testing on a larger dataset with multiple participants would help to further validate these results. Future research can also apply the Graphic approach to extract features from the helmet recordings. Toward this goal, measurements of the IMU can be exploited to determine the trajectories of the head in the planes formed by its three axes of motion, namely the plane , the plane  and the plane . Subsequently, as with COP displacements, we can use three matrices representing these trajectories. We also plan to integrate other inertial units located in areas of the body, such as the back and the upper/lower limbs. Adding these new sensors in other locations may improve the accuracy of the system. In addition, these sensors  may detect repetitive movements, which are one of the major risk factors aside from awkward postures and movements. Finally, we can extend our detection system to significant muscle efforts, for instance, detected via an electromyogram (EMG).


Objective: Cortical pathology, periventricular demyelination, and lesion formation in multiple sclerosis (MS) are related (Hypothesis 1). Factors in the cerebrospinal fluid close to these compartments could possibly drive the parallel processes. Alternatively, the cortical atrophy could be caused by remote axonal transection (Hypothesis 2). Since MRI can differentiate between demyelination and axon loss, we used this imaging modality to investigate the correlation between the pattern of diffusion parameter changes in the periventricular- and deep white matter and the gray matter atrophy.Methods: High-resolution T1-weighted, FLAIR, and diffusion MRI images were acquired in 52 RRMS patients and 50 healthy, age-matched controls. We used EDSS to estimate the clinical disability. We used Tract Based Spatial Statistics to compare diffusion parameters  between groups. We evaluated global brain, white, and gray matter atrophy with SIENAX. Averaged, standard diffusion parameters were calculated in four compartment: periventricular lesioned and normal appearing white matter, non-periventricular lesioned and normal appearing white matter. PLS regression was used to identify which diffusion parameter and in which compartment best predicts the brain atrophy and clinical disability.Results: In our diffusion tensor imaging study compared to controls we found extensive alterations of fractional anisotropy, mean and radial diffusivity and smaller changes of axial diffusivity  in patients that suggested demyelination in the lesioned and in the normal appearing white matter. We found significant reduction in total brain, total white, and gray matter ,  of patients compared to controls. The PLS analysis revealed a combination of demyelination-like diffusion parameters  in the lesions and in the non-lesioned periventricular white matter, which best predicted the gray matter atrophy (p < 0.001). Similarly, EDSS was best predicted by the radial diffusivity of the lesions and the non-lesioned periventricular white matter, but axial diffusivity of the periventricular lesions also contributed significantly (p < 0.0001).Interpretation: Our investigation showed that gray matter atrophy and white matter demyelination are related in MS but white matter axonal loss does not significantly contribute to the gray matter pathology. Multiple sclerosis is an inflammatory, demyelinating, neurodegenerative disease of the central nervous system. Besides the white matter lesions, which are the diagnostic cornerstones of the disease, increasing attention is being paid to gray matter atrophy, which has recently become a tool for the follow-up of the therapeutic efficacy  and presents in the form of ribbons, often affecting several adjacent gyri. This kind of cortical demyelination proved to be associated with meningeal inflammation. Earlier studies found that a non-targeted general immunopathological response arising from this meningeal inflammation and mediated by the cerebrospinal fluid is responsible for the cortical pathology  are almost as abundant as subpial lesions , and hence the diffusion profile of the water can reveal microscopic details about the tissue architecture. Importantly, axon loss and demyelination cause different diffusion profile alterations. The changes in axial diffusivity relates to axon damage, while the alterations of radial diffusivity refer to myelin damage, which in our study we will allude to as demyelination-like diffusion parameters.Gray matter atrophy defined by demyelination-like diffusion features would support the common origin of white matter demyelination and gray matter atrophy . Gray matter atrophy related to an axon loss-like diffusion pattern would suggest the causative factor of remote axonal transection in gray matter atrophy (Hypothesis 2).The contribution of pattern of diffusion parameters to the gray matter atrophy was investigated by model-free partial least square approach. When the predictors are highly collinear, as it is expected in case of diffusion parameters, the use of conventional regression analysis is not recommended. Partial least squares not only deals with the issue of collinearity, but offers to identify a pattern of parameters that best predicts the variable to be explained.The study was conducted on 52 patients with a diagnosis of relapsing remitting multiple sclerosis and 50 healthy, age-matched volunteer controls with no history of any neurological or psychiatric diseases. Patients were recruited from the Multiple Sclerosis Outpatient Clinic at the Department of Neurology. The diagnosis was based on the 2005 revision of the McDonald criteria .2, NEX: 2, ASSET) were acquired for all subjects.MR imaging were carried out on a 1.5T GE Signa Excite HDxt MR scanner. 3D spoiled gradient echo , FLAIR  and 60 direction diffusion-weighted images with 6 non-diffusion-weighted reference volumes . Fractional anisotropy, mean diffusivity  to the principal diffusion direction were computed for the whole brain.Diffusion data were corrected for Eddy currents and movement artifacts by 12 degree-of-freedom affine linear registration to the first non-diffusion-weighted reference image. Diffusion images were processed by using FDT  method to reduce possible errors resulting from misalignment of the images: A non-linear registration tool (FNIRT), which uses a b-spline representation of the registration warp field, aligned all fractional anisotropy images to a 1 × 1 × 1 mm FMRIB58_FA standard space. We brought the data on all patients into the standard space, and created the mean fractional anisotropy image, which was then fed into the fractional anisotropy skeletonization program, thresholded at fractional anisotropy 0.2 to create a mean fractional anisotropy skeleton that represented the centers of all tracts common to the group. The aligned fractional anisotropy data on each subject was then projected onto this skeleton, which resulted in the 4D skeletonized fractional anisotropy image. The resulting data was fed into voxel-wise cross-subject statistics.p < 0.05 was chosen as threshold and the results are corrected for multiple comparisons across space).With the use of a non-parametric permutation-based cluster analysis  as implemented in FSL, modeling and inferring, we accomplished the standard general linear model (GLM) design. The design encoded for group membership. Statistical tresholding was carried out with Threshold Free Cluster Enhancing (TFCE) approach .We performed volumetric comparison across groups and the correlation tests with the Statistical Package for Social Sciences . To identify a global white matter damage we calculate the averages of these z-scores:X is the measured diffusion parameter  in the nth voxel in the skeleton. Indices i and j are for controls and patients, respectively.where The average diffusion parameters were calculated for each patient in the following compartments: periventricular lesions, non-periventricular lesions, periventricular normal white matter, and non-periventricular normal white matter.To define the periventricular space the ventricles were manually segmented on the 1 × 1 × 1 mm FMRIB58_FA image dilated by three voxels similar to Jehna et al. . The nontbss_non_fa algorithm. The mask was finally thresholded at 0.5 and binarized to avoid any size increment arising from the interpolation.The lesions were projected to the fractional anisotropy skeleton. The manually segmented lesions were brought to the diffusion data space with 6 degree-of-freedom linear registration. Through use of the warp field and the skeleton projections of the TBSS analysis of the fractional anisotropy images, the lesion mask was brought to the skeleton with the FSL Y is an n × q matrix of dependent variables over n observations and X is an n × p matrix of predictors, Partial least squares successively extracts latent variables (factors and loadings) from X and Y in such a way that covariance between the factors and loadings is maximized. With this approach, Partial least squares reduces the dimensionality of the data by providing a weighted linear combination of X variables to form orthogonal components that predicts the dependent variable. In mathematical terms, Partial least squares is a linear decomposition of X and Y such thatWe used Partial least square regression analysis to estimate the contributions of the calculated compartmental diffusion parameters to the EDSS, gray matter and global brain atrophy. If T and U is maximum  is the normalized brain or gray matter volume or EDSS, the predictors (X) are the diffusion parameters in the different compartments  and the lesion load normalized to the intracranial volume  was 17.087 ± 22.509 cm3 (mean ± SD). The normalized lesion volume showed a negative correlation with the normalized gray matter volume , but no correlation was found with the normalized brain volume. The lesion load did not correlate with the EDSS of the patients either.The average native space lesion load was 12.328 ± 16.100 cmThe lesions were distributed across widespread white matter regions, but the lesion probability was highest in the periventricular white matter , total white matter , and total gray matter  volume in the multiple sclerosis patients compared to controls  in the multiple sclerosis patients: in essentially all the white matter fiber bundles, with the exception of the area of the corticospinal tract  in the majority of the white matter bundles but not in the corticospinal tracts and the inferior longitudinal fasciculus  in all examined fibers in the skeleton, apart from the corticospinal tracts  in the corpus callosum, the superior longitudinal fasciculus, the superior corona radiate, the inferior fronto-occipital fasciculus, the posterior and anterior thalamic radiation, and the internal capsule  and accounted for 47.3% of the variation of the dependent variable and 76.5% of the predictors. The X loadings and the corresponding Variable Importance in the Projection scores indicated that the mean diffusivity and radial diffusivity of the lesioned and non-lesioned periventricular and the non-periventricular lesioned white matter contributed significantly to the gray matter atrophy  and the permutation test revealed a non-significant latent variable. The permutation test indicated that the first latent variable was significant . The X loadings showed that the predictors of EDSS were very similar to those of the gray matter and the brain volume: the radial diffusivity and the mean diffusivity of the lesions had the largest effects on EDSS, irrespective of the distance from the ventricles. Furthermore, the radial diffusivity of the non-lesioned periventricular white matter and the axial diffusivity of the periventricular lesions contributed significantly  and disability (Llufriu et al., Our previous investigation also revealed that the pattern of various diffusion parameters can describe a pathology better than can the individual parameters (Kincses et al., In our study we found extensive alterations in diffusion parameters in the patients that suggested demyelination in the high lesion burden periventricular white matter and in the normal-appearing white matter. In the patients we found significant brain, gray and white matter atrophy. The partial least squares analysis revealed a combination of demyelination-like diffusion parameters, in the lesions and in the non-lesioned periventricular white matter which best predicted the gray matter atrophy. Similarly, EDSS was best predicted by the radial diffusivity of the lesions and the non-lesioned periventricular white matter. The periventricular lesion axial diffusivity also contributed significantly to the clinical disability.The pathology of multiple sclerosis is a heterogeneous process, but our results demonstrate that even spatially remote processes may have common roots. The substance mediating between such processes might be the cerebrospinal fluid. Further studies are needed to reveal the factors that are responsible for the gray and white matter demyelination. We summarized our results in a Graphical presentation Figure .The authors concur with the submission of the manuscript and both authors have approved the final version. Everyone who was working on the manuscript is listed between the authors. ET and ZK: Design of the work, interpretation of data for the work, Drafting the work, Final approval of the version to be published, and Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy. NS: Interpretation of data for the work, Drafting the work, Final approval of the version to be published, and Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy. GC, AK, PF, and TS: Analysis, Revising the work critically for important intellectual content, Final approval of the version to be published, and Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy. KB and LV: Substantial contributions to the conception, drafting the work, Final approval of the version to be published, and Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Starch digestibility may have an effect on the postprandial blood glucose profile. The aim of this meta-analysis was to analyze the relationship between Slowly Digestible Starch (SDS) levels and plasma glucose appearance and disappearance rates, as well as other parameters of glucose metabolism, after healthy subjects consumed cereal products that differed in SDS content. Three randomized controlled clinical trials that included a total of 79 subjects were identified. Using binary classification for the variables , we found that there was a 15-fold higher chance of having a low rate of appearance of exogenous glucose (RaE) after consumption of a high-SDS product. A high SDS content was also associated with a 12-fold and 4-fold higher chance of having a low rate of disappearance of exogenous glucose (RdE) and rate of disappearance of total plasma glucose (RdT), respectively. The RaE kinetics were further analyzed by modeling the contribution of SDS content to the different phases of the RaE response. We show that the higher the SDS content per portion of cereal product, the higher its contribution to the incremental area under the curve (iAUC) of the RaE response after 165 min. Using the association rule technique, we found that glycemic iAUC and insulinemic iAUC values vary in the same direction. In conclusion, this meta-analysis confirms the effect of the SDS level in cereal products on the metabolic response, and shows for the first time that the degree to which SDS affects the RaE response differs depending on the SDS content of the food product, as well as the phase of the postprandial period. Elevated postprandial glycemia may be associated with a greater risk of developing chronic metabolic diseases such as obesity, type 2 diabetes mellitus, and cardiovascular disease . CarbohyThe absorption kinetics of dietary CHO-derived glucose provide information that is complementary to postprandial changes in circulating plasma glucose and insulin concentrations. Although the moderate postprandial glycemic response induced by low-GI products may indicate a slower rate of appearance of ingested glucose in the systemic circulation, as well as slow tissue uptake , this reStarch is one of the most important glycemic CHOs present in cereal products. Starch structure, nutritional composition, and food processing methods applied to cereal products influence the rate of starch digestion ,23,24. NThe aim of this meta-analysis was to compare the kinetics of RaE and metabolic responses after the consumption of cereal products that differed in their CHO digestibility, as represented by their content of slowly digestible starch (SDS). Complementary statistical analyses were performed to better understand the impact of SDS at different time points. The main outcomes were the relationship between the SDS content of ingested test products and 1/the RaE level, including its contribution over time to the iAUC(RaE), and 2/the postprandial glycemic response. Additional links between SDS and parameters related to the kinetics of glucose absorption (the rate of disappearance of exogenous glucose (RdE), the rate of appearance of total glucose (RaT), the rate of disappearance of total glucose (RdT), and insulinemia) were explored to better understand the mechanisms of action. In addition, the link between glycemia and insulinemia was investigated to obtain a more detailed understanding of these relationships.Studies were selected if they were randomized clinical trials in humans that compared the effect of consuming low or high-SDS cereal products, as measured by the method developed by Englyst et al. , on the We planned to include three studies previously performed by our group in the meta-analysis ,30,31. AThe three identified studies were randomized controlled clinical trials that analyzed the metabolic responses of healthy subjects after the consumption of a breakfast that included cereal products with either a high (biscuits) or a low  SDS content. The studies followed the kinetics of both RaE and RdE in the blood using the double isotope labeling method, as well as the glycemic and insulinemic postprandial responses. The characteristics of the studies are shown in Sixty-six subjects  and 79 subjects  were included in the isotope and metabolic response studies, respectively . All of The breakfasts were consumed over a maximum period of 15 min after an overnight fast. The tested cereal products were consumed with a dairy product  and with decaffeinated tea or coffee without sugar (or with intense sweetener) . The breBlood samples were taken at baseline (before eating breakfast) and then regularly during the postprandial period (9 to 17 samples), as described in Vinoy et al. (2013) . Glucose2H2) glucose enrichment, and RaE and RdE were determined from plasma (13C)-glucose enrichment [RaT and RdT were calculated from plasma D-, there were 126 and 152 data points available from the isotope and metabolic response experiments, respectively.The study subjects were compared to determine whether the study populations had similar glycemic responses, by testing the iAUC (0–120 min) for glycemia using a Welch’s ANalysis Of VAriance (ANOVA) test. The distribution of the iAUCs for glycemia was comparable in the three studies. Complementary statistical methods were employed to detect relationships between SDS and glucose kinetics and metabolism parameters. For the first two methods, the continuous measures required dichotomization. In order to take full advantage of continuous outcome data, a partial least squares (PLS) regression method was also used to detect relationships between descriptive and target variables.The iAUC values for glycemia, insulinemia, RaT, and RdT represent the incremental area under the curve, ignoring the area beneath the fasting concentration, and were calculated using the trapezoidal method. The same mathematical principle was used to determine the iAUC for RaE and RdE, except that their baseline values were null. The iAUCs were calculated from T0 to the end of postprandial phase.The studied variables  were categorized according to a binary classification scheme, and were considered to be qualitative variables for the purposes of the meta-analysis. The key variables to assess (related to RaE and RdE in the blood) in the two studied groups  were categorized. A median split procedure was used to convert the continuous variables into categorical variables. This method is not affected by outliers, and enables the creation of equally sized groups. The statistical analyses were performed using MIX 2.0 Pro , a professional software program for performing meta-analyses. Significance tests were performed to control the risk of being misled by chance. The degree of heterogeneity in the meta-analysis was assessed using the I(2) index. As the I(2) value was 0, the studies were considered to be homogenous and to vary in the same way. A fixed effect model of SDS on the glycemic response was used to estimate the summary effect. Confidence intervals were calculated by odds ratios (OR) to assess the magnitude of the variable dependencies . A z-tes2). The quadratic term SDS2 was not retained, as its variable importance in projection (VIP) was under the set limit . A prediction profiler was built with Excel software (version 2010)  using the prediction formula obtained from the model that was developed based on the PLS analysis.This analysis was performed to determine the contribution of the SDS content to RaE kinetics across the postprandial period, using continuous data. The SDS contribution was calculated by dividing the iAUC value for each 30 min period by the iAUC value over the whole postprandial period for each study. Based on these data, a PLS analysis was performed using Jump 10.0 software  to determine the contribution of SDS content and time to RaE, and to describe their interaction and quadratic terms . The OR indicated that there is a 15-fold greater chance of having a low RaE after consuming a high-SDS food product compared to a low-SDS food product  during the late phase of the postprandial period . In contl period a. Converl period b.p < 0.0001). A high SDS content was associated with a 12-fold greater chance of obtaining a low RdE and a 4-fold greater chance of obtaining a low RdT, compared to a low SDS content (p = 0.0418), with a 2-fold greater chance of having a high RaT after consumption of a low-SDS product compared to a high-SDS food product. Thus, the results indicate that the SDS content is significantly associated with the kinetics of CHO absorption (confirmed by a z-test performed for each parameter), and that this association is even stronger for RaE and RdE kinetics.The association between the SDS content and RdE, RaT, and RdT was significant for all three of these kinetic parameters . The mos content a,c. Therp < 0.0001). A similar association was found between the SDS content and the insulin response, with a 3-fold greater chance of having a low insulinemic response in response to consumption of a high-SDS product compared to a low-SDS product; this was confirmed by a z-score of 2.8 (p < 0.004).The analysis of the combined studies indicated that there is a significant association between the SDS content and the glycemic response, with a 5-fold greater chance of inducing a low glycemic response with a high-SDS product compared to a low-SDS product . These rThe results of the associations between the glycemic and insulinemic responses are shown in This meta-analysis shows that consuming a high-SDS cereal product is associated with a 15-fold greater chance of having a low RaE in the blood and a 5-fold greater chance of having a low glycemic response, compared to consuming a low-SDS cereal product. The novelty of our meta-analysis comes from the further statistical analyses focused on the contribution of SDS levels at different time points along the postprandial RaE response curve. This is the first demonstration of the tight relationship between SDS levels in cereal products and RaE kinetics. Different levels of SDS in cereal products affect RaE kinetics differently at different time points during the postprandial period. The PLS analysis showed that the RaE response curves obtained from high-SDS and low-SDS foods crossed at 165 min after consumption of the cereal products. Products with a low SDS content had a strong impact on RaE kinetics in the first part of the postprandial period, whereas products that are high in SDS contributed less to RaE kinetics in the first part of the morning and more strongly in the second part of the morning. As both the low-SDS and the high-SDS products contained similar amounts of available CHOs, the products with a low SDS content had higher levels of rapidly digestible CHOs, which contribute strongly to the glucose kinetics during the first part of the postprandial phase. These novel results show that altering CHO digestibility alone, without modifying the macronutrient content of cereal products, is sufficient to impact the RaE.Interestingly, our analysis showed that consumption of the high-SDS products was also strongly linked to a lower glycemic response, which was induced by the slower RaE kinetics. Previously published studies with partially comparable designs have also found a link between the slowly digestible fraction of starch and RaE ,38,39,40Finally, the positive associations found between high SDS levels and low glycemic and insulinemic responses strengthen the argument for increasing SDS levels in food products to prevent the development of metabolic diseases in healthy subjects ,5,42,43.For the purpose of this meta-analysis, we had to make some methodological choices. A systematic review was performed to select studies that compared glucose kinetics (using the double-isotope labeling method) after the consumption of cereal products with high or low SDS contents , as well as analyzing the glycemic and insulinemic responses. The double isotope labeling method was used to measure the blood levels of endogenous and exogenous glucose originating from food, which could influence the postprandial glycemic responses. Despite an extensive literature search, the number of relevant human interventional studies fulfilling the inclusion criteria was low. Eleven studies were identified that measured glucose kinetics in blood using the double isotope labeling method. Five of these studies did not investigate the relationship between SDS levels in cereal products or the glucose kinetics and the glycemic response . To ensuThe three selected studies used two types of cereal products , in which the CHOs represented the major macronutrient . Additional data from a wider range of small meals that vary in SDS content, macronutrient composition, or proportion of CHOs would reinforce this body of evidence. Indeed, other studies have shown that the macronutrient content and the digestibility of CHOs can dramatically influence the metabolism of glucose derived from cereal products ,27,45.In the current meta-analysis, we chose to categorize the SDS values according to a binary classification scheme, because there were two clear ranges of values found in the cereal products used in the three selected studies. Indeed, the smallest difference in SDS content per meal among the selected studies was 11 g. In addition, the complementary analysis that modeled the relationship between SDS content and RaE kinetics using the continuous data yielded similar results. In conclusion, the results from this meta-analysis confirm the relationship that has already been observed in individual studies between a high SDS content in cereal food products and a low RaE in blood, as well as a moderate postprandial glycemic response. We show for the first time that the contribution of SDS to the RaE depends on the phase of the postprandial period. Starchy foods with a high SDS content contribute much more to the RaE at the end of the postprandial period than cereal products with a low SDS content. This simple modulation of cereal products to increase their SDS content reduces the challenge to plasma glucose following the meal and the associated plasma glucose and insulin excursions, and could potentially regulate additional, physiologically interesting effects, such as the prolonged elevation of gut hormone levels . These p


Initiated by studies on the mechanism of formation of the skeletons of the evolutionary oldest still extant multicellular animals, the sponges (phylum Porifera) have provided new insights into the mechanism of formation of the Ca-phosphate/hydroxyapatite skeleton of vertebrate bone. Studies on the formation of the biomineral skeleton of sponges revealed that both the formation of the inorganic siliceous skeletons (sponges of the class of Hexactinellida and Demospongiae) and of the calcareous skeletons  is mediated by enzymes . Detailed studies of the initial mineralization steps in human bone-forming cells showed that this process is also controlled by enzymes, starting with the deposition of Ca-carbonate bio-seeds, mediated by carbonic anhydrases-II and -IX, followed by non-enzymatic transformation of the formed amorphous Ca-carbonate deposits into amorphous Ca-phosphate and finally hydroxyapatite crystals. The required phosphate is provided by enzymatic  degradation of an inorganic polymer, polyphosphate (polyP), which also acts as a donor for chemically useful energy in this process. These new discoveries allow the development of novel biomimetic strategies for treatment of bone diseases and defects. Suberites domuncula as a model system [With the elucidation of the functional genome of the sponges (phylum Porifera), using the demosponge l system , it becal system . Even thl system . Analyzil system  and provl system  that a fl system . An imprl system ; the expThe introduction of the molecular cloning of genes, which are—and this is imperative—coding for informative proteins, has rapidly increased our knowledge on the evolution of the multicellular animals from the common ancestor with the Porifera. In addition, it became possible to understand the steps of the evolution in this phylum, from the siliceous sponges with the class of Hexactinellida as the phylogenetically oldest one and the younger class of Demospongiae ,9, to thIt was surprising that these simple metazoan animals comprise almost all structural and functional metabolic circles, e.g., the kinases , the celTethya aurantia [S. domuncula [The major problems which had to be solved to understand the different facets of the complex Bauplan of the siliceous sponges and the calcareous sponges included the formation of their skeletons. Driven by general scientific approaches concerning the basic principles of metabolic processes in living systems, we asked for enzymes that are underlying and driving the inorganic mineralization in the two sponge taxa, the siliceous Demospongiae and calcareous Calcarea. A series of thorough studies disclosed the first enzyme, termed silicatein, involved in the deposition of the inorganic siliceous skeleton of aurantia  S. domunomuncula ,21. Siliomuncula ). Two isomuncula . The bioomuncula . FinallySycon raphanus. The skeletal elements of this sponge are composed of almost pure calcium carbonate. This mineral is formed in reactions that are catalyzed by this enzyme. In a biomimetic approach using a recombinant carbonic anhydrase, we succeeded to form amorphous pat-like particles that subsequently rearrange to crystalline rhomboid/rhombohedroid crystals with a dimension of about 50 µm. Surprising was the finding, based on light microscopical inspection and scanning electron microscopy studies, that the newly formed calcitic crystals associate with the calcareous spicules from S. raphanus in a highly ordered manner. Even more surprising was the observation that the calcitic crystals almost perfectly arrange in an array orientation along the two opposing planes of the spicules, leaving the other two plane arrays uncovered [The next challenge that has been addressed was the elucidation of the formation of the calcareous skeleton. Again it was disclosed that it is an enzyme that forms those inorganic deposits. It is a carbonic anhydrase that is the initial driving molecule ,25. The w/w) and an organic matrix  and 10% of water . It iewed in . Hydroxysupplied . During 2+-binding proteins exist in the vestibular system, which initiate Ca2+-deposition [Very interesting to note is the fact that in vertebrates, besides of the hydroxyapatite-composed bones, biomineralized otoliths exist that are found in the vestibular labyrinth of the vertebrate ear. Surprisingly, they consist, besides of organic matrix proteins, to 90–95% of Ca-carbonate in the aragonite form ,33. As aposition .2+-deposition in osteoblasts starts intracellularly in calcium-containing vesicles which substantially contribute to the bone apatite formation [2+ and phosphate, perhaps as Ca2+-polyphosphate (Ca-polyP) [Likewise, intricate and compelling is the fact that Ca-carbonate also co-exists with Ca-phosphate in the vertebrate bone . Under pormation . Finallya-polyP)  promotina-polyP) .Both Ca-phosphate formation  and Ca-cIn a comprehensive review solid experimental evidence has been presented documenting that the amorphous phases are the precursors for any kind of crystalline deposits in animals . This alInterestingly enough we found that sponges with the examples of the siliceous sponges Ephydatia muelleri and Tethya lyncurium contain inorganic polyphosphate (polyP) that is involved in the control of growth control and mineralization and contain enzymes that are able to degrade this polymer ,49,50. TIn a biomimetic approach we succeeded to fabricate polyP into amorphous nanoparticles, similar to those found in vivo; e.g., Refs ,57; FiguThese findings allowed us for the first time to outline the pathway of bone mineral formation including enzymes that control crucial steps during Ca-phosphate/hydroxyapatite formation, starting at the stage of amorphous Ca-carbonate bio-seeds ; see FigThe studies with siliceous and calcareous studies disclosed that—as a rule—also inorganic depositions in Metazoa are the result of enzymatic processes. There are the silicatein(s) in the siliceous sponges that are the drivers of the inorganic silicification of the skeleton  or tAccepting that evolution drives in a conserved manner the progress of animals from the more simple to the more complex state allowed the formulation of new strategies even to decipher the complex mineralization stages of bone formation. From studying the skeleton of the siliceous sponges the knowledge was obtained that—basically as expected, but not clarified—even inorganic minerals are formed enzymatically. Progressing further to the calcareous sponges it became overt that it is Ca-carbonate and not Ca-phosphate from which mineralization of hydroxyapatite starts. Even more, the data obtained introduced the new concept that the abundantly present inorganic polymer polyP serves, after enzymatic hydrolysis via the ALP, as donor for the phosphate components required for bone formation and also as energy supply for extracellular endergonic reactions. The discovery of polyP as an extracellular system for the storage and delivery of metabolically useful energy may also contribute to the understanding of other energy-dependent processes occurring in the extracellular space and may progress the development of further applications based on this inorganic polymer in the biomedical field, in addition to bone repair and regenerative therapy of the bone diseases.


I-consistency and I-consistency results were more acceptable than the Persistent organic pollutants (POPs) pose serious threats to human health. Increasing attention has been paid to POPs to protect the environment and prevent disease. Humans are exposed to POPs through diet (the major route), inhaling air and dust and skin contact. POPs are very lipophilic and hydrophobic, meaning that they accumulate in fatty tissues in animals and can biomagnify. Humans can therefore be exposed to relatively high POP concentrations in food of animal origin. Cooking animal products can decrease the POP contents, and different cooking methods achieve different reduction rates. Here, a consensus decision-making model with interval preference relations is used to prioritize cooking methods for specific animal products in terms of reducing POP concentrations. Two consistency mathematical expressions ( Persistent organic pollutants (POPs) are a serious environmental problem. POPs are toxic, can be transported long distances in the environment and can accumulate in animals and humans and biomagnify through the food chain. POPs are persistent in various environmental media, including soil, air, water and organisms. Many scholars have studied the harm to the human body resulting from the exposure of POPs by analyzing experimental data ,2. The mIndividual preferences can be described using a series of uncertain fuzzy numbers, such as an interval number, an intuitionistic fuzzy number and a hesitant fuzzy number, to represent uncertain fuzzy preference relations, such as the interval fuzzy preference relation, the intuitionistic fuzzy preference relation and the hesitant fuzzy preference relation, respectively, to express the degree to which one alternative is preferred over another. The study presented here is focused on how the individuals’ uncertain preference information can be fully integrated into the consensus preference relations based on SCF to give a satisfactory group decision-making solution. Consensus decision-making (CDM) with preferences is used to investigate consistency and reach consensus. Consistency in individual preferences is the basis of CDM, and this reflects the logic of the decision-makers (DMs). Consensus is the combined view of a decision-making problem, and the CDM rules are essentially reflected by social choices and group preferences. The roles and meanings of consistency, consensus and rules in CDM in the model are described below.(1) Consistency: The consistency of an individual judgment is used to determine whether the judgments of the DMs are logical. The aims of the study include evaluating the ability of the DMs to make judgments and prioritizing the alternative decisions. The use of interval fuzzy preference, triangular fuzzy preference or intuitionistic fuzzy preference by the DMs depends on the specialty and knowledge of each DM supplying judgments about the decision that should be made. Every type of preference relation has a consistency definition because of discrepancies in the mathematical structure of the model. This is also related to certain logical formulae. Similar studies have been focused on combining consistency and preference relations, for example interval fuzzy preference relations ,8, trian(2) Consensus: The DMs reach consensus through mutual cooperation and compromise. The objective of CDM is to determine the priority (weight) of each alternative, to determine the degree of consensus, to identify disagreements and to control the convergence of views. In terms of the protocol involved, in 1978, Bezdek et al.  stated tConsensus with uncertain preference relations requires not only the consistency of the opinions of an individual DM to be determined, but also differences between the opinions of different DMs to be minimized. Many researchers have used a distance and similarity function to measure the degree of consensus. For example, Zhang  and Gonz(3) Rule of CDM: The aim of CDM is to coordinate all of the individuals and to combine the opinions given, balancing the behaviors of different DMs with different backgrounds in studying complex and variable environments. CDM can help the DMs harmoniously reach a decision. Different distance functions (such as minimization of the consensus deviation or minimization of the maximum deviation) are used to achieve this. In reality, some SCFs can be identified using these functions . The aimPrevious studies of consensus with uncertain preference relations have mostly only taken consistency conditions or the SCFs into consideration. In the study presented here, uncertain consensus models combining consistency and the social principles  are developed. Fuzzy relations, such as the intuitionistic fuzzy preference relation, hesitant fuzzy preference relation, triangular fuzzy preference relation and linguistic fuzzy preference relation, have similar mathematical structures to the interval fuzzy preference relation, so consensus models with interval fuzzy preference relations will be explored. The goal is to minimize the distance between the individual judgments and the ideal consensus judgment, and consistency is the constraint.I-consistency and I-consistency and Two interval fuzzy preference relations with For simplicity, we use In decision-making, pairwise comparisons  are often used by DMs to compare a set of alternative decisions with respect to a specified criterion. For a set of alternatives Definition 1. The inteis called the interval fuzzy preference relation if:whereI-consistency and The Definition 2.For all, ifsuch thatthen R is called the consistent interval fuzzy preference relation with I-consistency.I-consistency in interval fuzzy preference relations is derived from the consistency definition for fuzzy preference relations [Note: The definitionof elations ,26.Definition 3. Ifis the priority vector for the fuzzy preference relations, then:I-consistency, for which the priority vector is obtained from this definition.In Definition 4. The inteis called the consistent interval fuzzy preference relation with-consistency if the priority vectorof R satisfies:whereI-consistency emphasizes the logical relationship between the matrix elements, and m DMs Assuming that there are We assume that there is an ideal DM Form I: Element Form II: Element As the research in , let us m DMs p  social choice mode. This is achieved by linearly combining the three specific ethical rules, simultaneously considering the freedom, fraternity and equality principles.We will now assess the ideal fuzzy preference relations I-consistency, although the values for the judgments made by the DMs are intervals, the ideal values are crisp numbers, which allow the weight values for these preference relations (using Definition 3) to be determined.Considering U is the weighted average deviation between the ideal judgment m DMs. U is the sum of the consensus deviations and should be as small as possible. The optimal ideal preference relation using the freedom principle is:When The objective function of Model U is the maximum deviation between the ideal judgment m DMs, where m DMs. Under these circumstances, the smaller the deviations are for all of the DMs the better, i.e., the value should be as small as possible, and When The optimal ideal preference relation using the fraternity principle is:D value.The parameters used in Model (8) have the same definitions as those used in Model (7). Formula (8d) indicates that all of the consensus deviations are no more than the maximum The objective function in Model (8) indicates that the ideal judgment If we let:then Equation  indicateThe optimal ideal preference relations for the equality principle are constructed as shown below.The parameters used in Model (11) have the same definitions as the parameters used in Model (7). Formula (11d) indicates that the weights for the deviations between the ideal DM and each DM will be as equal as possible. The objective function of Model The ideal fuzzy preference relations using the three individual principles  were established in The objective function of Model (12) indicates that the sum of the products of all of the deviations and corresponding weights using different social choice patterns are minimized, and the deviation in the consistency is also minimized. For the mixed principle, the optimal  consensus with appropriate consistency is achieved using Model (12).The consensus model described above is relatively simply solved because the procedure turns nonlinear functions into linear models.I-consistency are used, and the ideal fuzzy preference relations with Using U is the weighted average of the consensus deviation between the ideal DM Similar to Model (13) is nonlinear, but it would be convenient to convert it into a linear model to allow a solution to be determined. We will multiply both sides of the constraint Equations (13a) and (13b) by We let:The objective function of Model (15) indicates that the model can minimize the consensus deviation between judgments U is the maximum consensus deviation. We let Similar to The parameters used in Model (16) have the same definitions as the parameters used in Model (15). Formulas (16g) and (16h) imply that the lower and upper limits are no more than The objective function of Model We let:Equation  shows thThe optimal ideal preference relation for the equality principle is determined using Model (19). The parameters used in Model (19) have the same definitions as the parameters used in Model (15).Equations (19g) and (19h) indicate that the weights for the deviations between the ideal DM and each DM are as equal as possible. The objective function of Model Similar to The parameters used in Model (20) have the same definitions as the parameters used in the three models described above. The objective function of Model (20) indicates that the model can minimize the consensus deviation between The cooking method used can affect the POP contents and distributions in food. We will only consider frying, baking, poaching and pressure cooking , (8), (11) and (12) are shown in Explanations of the priorities found using the other principles are omitted.The ideal preference relations using all of the social principles are shown below.The ideal preference relations using the freedom principle are:The ideal preference relations using the fraternity principle are:The ideal preference relations using the equality principle are:The ideal preference relations using the mixed principle are:The optimal solutions to Models (15), (16), (19) and (20) are shown in Explanations of the priorities found using the other principles are omitted.The ideal preference relations using all of the social principles are shown below.The ideal preference relations using the freedom principle are:The ideal preference relations using the fraternity principle are:The ideal preference relations using the equality principle are:The ideal preference relations using the mixed principle are:I-consistency) and I-consistency were all different. Therefore, even though more experts suggest using I-consistency, we found that I-consistency is more acceptable and robust.The results for the two consistency conditions were compared to support our conclusions. Some data in the matrices were changed (changing one matrix each time) to seek to identify a pattern. The results for the two types of consistency with the modified matrices are shown in I-consistency is more appropriate than Improving the POP contents and distributions in food of animal origin can protect human health. Different cooking methods should be selected to decrease POP concentrations in food, which depends on the environment and the food type. We investigated the CDM problem using interval preference relations and two consistency definitions. We used the goal programming approach to minimize the distances between ideal judgments and the judgments of individual experts and developed the ideal interval preference relation models to prioritize different cooking methods. A numerical example indicated that (1) In the first model class, based on freedom, fraternity, equality and mixed principles, we considered deviations between the ideal judgment and the judgments of individual experts and achieved the minimum deviation in consistency.(2) In the second model class, also based on freedom, fraternity, equality and mixed principles, the goal was to achieve the minimum deviation between the ideal expected judgment and the judgment of each individual expert. Consistency was used as a constraint.I-consistency is more appropriate than The results of the numerical example indicated that This research could be extended to consensus decision-making models based on triangular fuzzy preference relations, intuitionistic fuzzy preference relations, hesitant fuzzy preference relations, linguistic preference relations and other relations. The model can take the views of different experts expressed in different ways into consideration, making it appropriate for use with complicated ecological systems and cooking methods to allow cooking methods to be prioritized to minimize human exposure to POPs.


Cartilage defects can impair the most elementary daily activities and, if not properly treated, can lead to the complete loss of articular function. The limitations of standard treatments for cartilage repair have triggered the development of stem cell-based therapies. In this scenario, the development of efficient cell differentiation protocols and the design of proper biomaterial-based supports to deliver cells to the injury site need to be addressed through basic and applied research to fully exploit the potential of stem cells. Here, we discuss the use of microfluidics and bioprinting approaches for the translation of stem cell-based therapy for cartilage repair in clinics. In particular, we will focus on the optimization of hydrogel-based materials to mimic the articular cartilage triggered by their use as bioinks in 3D bioprinting applications, on the screening of biochemical and biophysical factors through microfluidic devices to enhance stem cell chondrogenesis, and on the use of microfluidic technology to generate implantable constructs with a complex geometry. Finally, we will describe some new bioprinting applications that pave the way to the clinical use of stem cell-based therapies, such as scaffold-free bioprinting and the development of a 3D handheld device for the in situ repair of cartilage defects. Cartilage defects, due to trauma or progressive joint degeneration, can impair the most elementary daily activities, such as walking or running. Due to the limited self-repair ability of cartilage, these lesions can easily evolve into osteoarthritis (OA), leading to the complete loss of articular function and to the subsequent need for joint replacement . In the In the development of stem cell-based therapies for tissue regeneration, bioprocessing optimization is required to exploit the remarkable potential of stem cells. In particular, efficient cell differentiation protocols and the design of proper biomaterial-based supports to deliver cells to the injury site need to be addressed and overcome through basic and applied research . In thisin vitro maturation of bioprinted constructs. In addition, we will describe studies whereby microfluidic and 3D bioprinting technologies have been applied to generate implantable constructs with a complex geometry. Finally, we will describe some new bioprinting applications that pave the way to the clinical use of stem cell-based therapies, such as scaffold-free bioprinting to generate clinically relevant constructs using 3D cell spheroids as building blocks and the development of a 3D biofabrication handheld device for the in situ repair of cartilage defects.In this review, we will describe how microfluidics and bioprinting can provide different insights in the field of mesenchymal stem cell-based cartilage repair and contribute to the development of novel therapeutic strategies. Specifically, since microfluidic and bioprinting technologies share the use of hydrogel-based materials, in the first section, we will focus on the optimization of these materials to mimic the composition and the mechanical properties of the articular cartilage. We will then describe the use of microfluidic devices for the identification of biochemical and biophysical factors driving stem cell chondrogenesis that could be implemented during the In several clinical applications, stem cells are directly injected into the target tissue without any biomaterial carrier. This process leads to limited stem cell engraftment at the treatment site, mainly due to leakage of cell suspension during injection . Since tBecause of their intrinsic features, hydrogels have been applied as a 3D matrix for cell culture in microfluidic devices  as well in vitro maturation before implantation or reinforcement of a supporting material. Indeed, hydrogels show low compressive stiffness resulting to becoming unsuitable for application in the fabrication of load-bearing tissues. For this reason, several strategies have been exploited to reinforce hydrogels using stiffer materials . Th. Th75]. ϕ 8 mm) in an ovine model [Another interesting approach that shares some fundamental principles with 3D bioprinting, such as the use of living cells and biomaterials as building blocks, is represented by a 3D bioprinting pen, called “Biopen”, which was developed by O'Connell et al. . This nene model . Differene model , 79 and ne model  may paveAlthough stem cell-based therapies have emerged as a novel treatment in cartilage-based repair, their success is often limited due to multiple factors, such as inefficient differentiation of stem cells towards the chondrogenic lineage and/or poor stem cell engraftment and survival after transplantation. In addition, an important aspect that is often neglected is that stem cells are usually delivered to an inflamed environment and, hence, have to face a plethora of catabolic signals that may negatively affect the outcome of cell-based approaches.in vitro culture may pose obstacles in the clinical translation of stem cell-based therapy, the possibility to direct stem cell fate uniquely using biophysical factors, such as shear stress, appears to be fascinating. Microfluidic models hence represent an invaluable tool to define the biophysical stimuli that should be used to direct stem cells towards the chondrogenic lineage without using growth factors. Furthermore, microfluidic devices can be used to develop organotypic models of the whole articular joint capable to recapitulate either a physiological or a pathological environment [In this scenario, microfluidics can provide important advances in the selection of biochemical and biophysical factors able to direct the fate of stem cells that can be implemented in new protocols for stem cell differentiation and in the design of dynamic culture systems. It is also possible to envision an exploitation of microfluidic models personalized with patient-derived stem cells for the screening of the most suitable differentiation protocol for each patient-specific stem cell population. This would allow the optimization of personalized differentiation protocols. Additionally, since the use of growth factors during ironment , perfectironment , 83. Theironment . This teironment . Bioprinironment , 87.On the other hand, microfluidics-based 3D bioprinting approaches could be used to overcome the problem of poor cell engraftment at the lesion site, by transplanting stem cells embedded in an ECM-mimicking environment. 3D bioprinting is rapidly becoming a first-choice approach for several advanced applications in tissue engineering. Despite the appealing advantages offered by this technology, such as rapid production of cellularized constructs with high accuracy and repeatability independently of scaffold geometry, a major ongoing challenge that still needs to be addressed is related with bioink formulation. In fact, an ideal bioink should provide, on one side, a proper matrix for cell maturation, differentiation, and neomatrix synthesis while still keeping, on the other side, a high printing resolution. So far, this issue has been addressed only to a minor extent and a continuous research is carried out to find new solutions. In the case of cartilage regeneration, 3D bioprinting represents a suitable technology to recapitulate the tissue structure in terms of chondrocytes and ECM organization. In particular, the complex zonal organization might be reproduced in the future through the development of more accurate systems for multimaterial deposition. Furthermore, the formulation of bioinks should be refined to better promote stem cell differentiation towards the chondrogenic lineage with the synthesis of new polymers or with the formulation of blends or composite bioinks that would eventually result in enhanced quality of the neodeposited matrix. Another major issue that must be overcome in order to boost the translation of engineered constructs for cartilage regeneration into the clinic is related to their poor mechanical properties. In fact, native cartilage has a Young's modulus of around 700–800 kPa, which is between one and two orders of magnitude higher compared to the constructs obtained via 3D bioprinting. This is a key issue that would require many efforts to be overcome. A possible solution may be found by employing more sophisticated culture systems that may lead to more functional cartilage tissue by providing controlled mechanical and biochemical stimuli. However, then, we need to be sure that biofabricated mature tissue will properly integrate with surrounding natural cartilage. So far, 3D bioprinting has already demonstrated its capacity to build complex artificial structures. However, the future work must be focused on enhancing the functionality of such constructs to prompt applications in real clinical scenarios. Finally, in situ 3D bioprinting can enable the achievement of thick tissues directly into the lesion site in a one-step approach, by translating bioprinters in the surgery room. Despite challenges, this computer-aided technology holds a great potential since it would allow overcoming the need for preshaping or reshaping of the scaffold based on the defect geometry and achieving high precision in the deposition of cells and biomaterials. Because of these features, we envision that in situ 3D bioprinting will produce significant advances in the regeneration of the entire articular units or in the treatment of complex articulations, such as the carpometacarpal joint.