Passive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks. Continuously obtaining human motion data without violating privacy and at low cost is the main challenge for sensor development and data analysis method construction. Long-term accurate human motion trajectory analysis is becoming more and more important for indoor navigation , smart hActive sensors  and paset al. used the MERL dataset to model social behaviors including visiting people, attending meetings and travelling with people with the entropy and graph cuts method [Mitsubishi Electric Research Laboratories (MERL) have deployed a large amount (>200) of cheap, energy-efficient and simple passive infrared (PIR) motion detectors to acquire human motion data continuously for about two years . The sens method . They mos method ,14. Reses method ,16. Becai.e., one person and several people in the cognitive range will produce the same active 1 output from the sensor. As has been discussed extensively in the literature, it is not possible to log the complete track of people moving around the space using only motion detectors [Different from other passive motion sensors like cameras, the PIR sensor is a binary passive sensor that cannot classify and locate individual human . The PIRetectors . Althougetectors ,19. Theretectors .i.e., the same sensor logging may be caused by different human motions, the accuracy of these existing methods can still been dubious. For example, human guidance and carefully defined tracklet construction rules are required for accurate trajectory visualization [It has already been proved that the PIR sensor network cannot provide enough information to recover the trajectory of an individual. Only the statistical behavior pattern can be extracted from the PIR sensor network log data . Technollization . To overlization  to help Besides the direct analysis of the human motion patterns from the sensor log data, there’s another way to analyze the human motions from the generation-refinement paradigm. In the generation-refinement paradigm, all the possible human motion trajectories can be firstly generated and then dynamically refined according to the spatio-temporal constraints and sensor activation logs. Then the people tracking problem can be formulated as how to generate all the possible trajectory with several different spatio-temporal constraints according to the sensor activation log. To summarize, the following advantages can be achieved in the human trajectory analysis under the generation-refinement paradigm: (1) the complete possible human motion patterns can be generated; (2) the human trajectory taking place in the geographic space activates the sensor with unique pattern. Mapping from the trajectory to the sensor activation log is unique; (3) both the spatio-temporal correspondence of the sensor activation and the sensor network topology can be used to reduce the uncertainties of the trajectory analysis ,23. Withi.e., dynamical). Thus, the complete sets of the possible trajectories should be generated dynamically. However, there are rare methods that can support flexible and dynamical trajectory generation according to the sensor network topologies. Second, both the sensor network topology and the sensor activation log data should be formulated as spatio-temporal constraints, but the sensor activation log data and the spatio-temporal constraints data are significantly different. Few method can support the unified representation of both the sensor activation log data and spatio-temporal constraints. Third, the formulated spatio-temporal constraints should be dynamically integrated into the trajectory generation to refine the trajectories. Yet, hardly is there any method that can support such integration of the complex spatio-temporal constraints with the dynamical trajectory generation.The key issue of accurate analysis of the human trajectory from PIR sensor networks is how to reduce the uncertainties of the trajectory reconstruction. Several issues should be carefully studied for the human motion analysis using the PIR sensor network data under the generation-refinement paradigm. First, the human motion trajectory is time-varying  can be coded as algebraic elements  of this mathematical space [In the whole trajectory analysis, there are several concepts that should be defined and analyzed. These are geographical network, sensor activation response sequences, and human behavior semantical sequences. Geometric algebra (GA), founded on the dimensional computation, is an ideal tool for the multidimensional algebraical element representation ,27,28,29al space . Then thal space . With weal space ,29. The al space ,31. Withn > 0, the Clifford algebra/Geometric Algebra system Given any positive number i to the node j, is the i-th row and the j-column of the geometric adjacent matrix. i to the node j. According to the definition, we can formally define the geographical sensor network as follows:Assuming Definition 1: The Geographical sensor network. The geographical sensor network is a physical geographical space where the sensors are located. In this sensor network, each sensor represents one node of the network. The sensors are only connected with the adjacent node according to the geographical spatial topology. A typical representation of a geographical sensor network is depicted in M. The adjacent relations between different nodes are inherited in M. To make the computation more efficient, only the network node of the route is logged in the adjacent matrix M. For example, if ie and je are connected, the i-th row and j-th column element of the adjacent matrix M is logged as elements of je , the output of this sensor in the time period from 1 to t is a time series with a binary active state The PIR sensor network is a set of PIR sensors installed with the intention to cover the floor area completely with little or no overlap between the sensor viewing fields. Assuming there is a PIR sensor L, which can be expressed as L = {L1,L2,iL,…,nL}. The state of the PIR sensor network at time instant t can be seen as an observation, therefore all the observation time can be expressed as T = {t1,t2,jt,…,mt}, and the state series of each time instant can produce a state of the whole sensor network. The observation, notated as X, can thus be encoded as a binary set as X = {0,1}, where 1 means the corresponding sensor is active, 0 otherwise. For the given sensor located at iL, the observation can be represented as a feature vector i.e., topology) of the PIR sensor network, i.e., the person/people cannot move between none adjacent sensors. Similarly, there are also temporal constraints that should make the time intervals between different motions acceptable. e.g., the human motion will not exceed the maximum possible velocities.Given a PIR sensor network composed of Since the PIR sensor only logs the binary response of certain locations and the response time of different responses between different sensors, which can be seemed as activated sequences. It is also possible to represent the sensor activation log as blades. With these blades, the sensor activation sequence can further be projected into the network space. In this way, network structures can be extracted from the sensor activation sequence. So, with the sensors coded with the GA space basis, we define the sensor activation response network as follows:Definition 2: The sensor activation response network. A sensor activation response network is a possible sub-network of the response from the neighbor sensors of activated sensors. For example, in the M, which means the path begins from the sensor C,F and ends at the sensors B,C. From the matrix the possible paths can be also estimated as shown in the The sensor activation response network can be seen as the representation of the spatial constraints of the PIR analysis. It is not possible to directly move a person from one sensor to a non-adjacent sensor in the geographical space. Therefore, it is impossible to construct a real trajectory to represent the human motion, so in this paper, we think it is not suitable to consider the sensor activation sequence between two non-adjacent sensors as a single trajectory, but a combination of several trajectories.In a sensor activation response sequence, the two adjacent sensor activities must happen in acceptable time intervals . BecauseDefinition 3: The human motion trajectory sequence. The human motion trajectory sequence is a sequence that represents the human motion structure from any sensor node to another sensor node in the geographic spatio-temporal space. The human motion trajectory sequence is an orderly sequence that can also be represented as a series path matrix, which applied the oriented join product of timely-adjacent response matrix according to the real human motion.Taking the sensor network in the Therefore, the path matrix can be calculated by the equation:Then the possible human motion trajectory sequence can be calculated as: segments . In thisi.e., the same human trajectory or trajectory combinations will definitely produce the same sensor network activation series. However, the binary sensor activation characteristics of the PIR sensor makes the sensor only able to log the human passing states but not the individual information. If there is only one human motion, the sensor activation sequence  directly corresponds to the trajectory and activation sequence in the time domain. Both the spatial topology of the sensor network and the human trajectory can be revealed from the sensor activation sequence [i.e., the same sensor activation observation may indicate different human motion trajectories. What’s worse, since different trajectories can be intersected, it is not easy to directly determine the starting and the ending nodes of a certain trajectory. A typical example of this uncertainty is illustrated in the L} from the sensor activation data {X} according to both the temporal and spatial constraints C.According to the characteristics of the PIR sensor, the mapping sequence . Howeveri.e., the sensor node in an individual trajectory is adjacent to and only affects the adjacent sensor node. Because each individual human motion has unique map to the activation of the sensor logs, the human motion is restricted by the spatial topology of the real sensor distribution. Therefore, the trajectory construction can be seen as the route generation one node by one node. The sensor activation sequences are not only a fusion of several human trajectory motions, but also the spatial and temporal constraints that will limit the possible and impossible trajectories in the geographical spatio-temporal space. Therefore, the human trajectory analysis can be split into two steps: (1) dynamically generate all possible routes for human trajectories node by node; (2) refine/filter the possible human trajectories to make the trajectory consistent with the sensor activation log data and other spatio-temporal constraints. In this way, the problem can be solved in the generation-refinement paradigm.According to the above problem definition, the sensor network can be seen as a geographical network with each sensor being one node of such a network. Because the sensors do not overlap, the sensor activation only affects the adjacent sensor. In addition, the sensor node is also a construction node of the human motion trajectories, According to the generation-refinement paradigm, the problem can be further decomposed into the following sub-problems: (1) how to represent and link both the sensor networks and trajectories in the same unified mathematical framework; (2) how to dynamically generate the trajectories according to both the spatial network topology constraints and the temporal constraints from the sensor log data; (3) how to determine the starting and the ending nodes of each individual human motion trajectory. To deal with the sub-problems (1) and (2), we should develop a flexible mathematical expression framework that can represent both the network and human trajectories. Not only the nodes, routes and whole networks should be represented using the same paradigm of mathematics, but also the representation should support the dynamical route generation node by node in the network. In addition, the spatial and temporal constraints should also be represented with the same mathematical tool and integrated in the dynamical route generation. For sub-problem (3), we should determine certain rules that can be used to classify the trajectories to determine the starting and the ending nodes.With the advantages of the spatio-temporal representation properties of GA, we modeled the whole PIR sensor network by GA network coding. The geographical space is first defined and represented as a geographical sensor network. The sensors are coded as the geometric basis of the high dimensional GA space. Then the interaction and response sequence can be embedded as blades. These blades can then represent both the possible human motion trajectories and the sensor activation sequence. By integrating constrains from the real sensor activation observational data, the possible human motion trajectories can be dynamically generated. The overall framework of our basic idea is illustrated in the Different from classical network analysis, which has determined the starting and the ending nodes, the ending node during the trajectory tracking is not constant but changed according to the human motion. Since there is more than one trajectory in the geographical space, the classification of different trajectories is important in our analysis. By considering the trajectory generation as a trajectory tracking problem from the dense adjacent matrix, several different situations can be revealed according to the sensor activation log  Figure . Both tht to determine the granularity of the trajectory segment. In this way, if the stay time in one sensor is larger than t, we can classify them into the state of stop. And we assume the next trajectory starts from the current sensor. With this configuration, the tracking for individual trajectories can be simply achieved by direct adjacent matrix multiplication [To classify the sensor states of the still and stop nodes is important in the trajectory tracking, because it relates to the starting and ending nodes of the trajectories in the foundation of the trajectory generation/tracking. However, it is not an easy task to simply distinguish the two states directly from the sensor activation data. Since the trajectory stills at a node for a very long time, it can be seen that the trajectory stops at the node and a new trajectory is then started from the same sensor node. A more practical way to classify the two states should be defined at a certain time interval of lication ,26,28,30In most common conditions, there should be more than one trajectory in the sensor networks. With more than one trajectory the sensor activation status becomes complex. The simplest condition is that the trajectories are not intersected d. These α and β can take the value 1 or 0, which indicate the connectivity of sensors, for example: if α11 = 0, sensors A and B are not adjacent. Therefore, the trajectories structures are depending on the values of these two adjacent matrix:As the human motion trajectory sequence can be expressed and calculated in the GA space, we can also construct the GA-based classification method of trajectories. Given the sensor activation sequences moving trajectory(1) Continuous moving trajectory is extended with no branch, the path can only have a single starting node and ending node. So, the response matrixes must be the diagonal matrix that avoids having two or more nonzero value in one row or column. The typical expresses of adjacent matrix are: As the continuous According to the Equation (4), the trajectories can be calculated as Join and split trajectories(2) join and split trajectories need the path that have the two or more starting nodes or ending nodes. Therefore, the typical expresses of adjacent matrix are: join trajectories; only if split trajectories.Unlike the Continuous moving trajectory, the Still and stop nodes(3) still nodes can be expressed as ce is the still node. The repetitions of ce suggest the duration time of the still node. If the duration time bigger than predefined threshold value, ce can be also seemed as the stop node. Therefore, the most important is to identify the cce structure in trajectory sequence. According to the definition of human motion trajectory sequence, if there is a diagonal element (which has the same row and column number) in response matrix, this paths must exist a still node.According to the GA-based trajectory representation methods, the trajectory with In a common situation of the human motion, the adjacent sensors will be active and respond continuously. With continuous sensor response log, we can track the spatio-temporal correlations between different sensors in the geographical space and then transform them into possible trajectories. In this paper, we assume that a typical human walks at a velocity of 1.2 m/s. Since no gap exists between different sensors, a human passing through a sensor may need a response time of 2–4 s. So we select the median time window of 3 s to do the object tracking. If any object activates a sensor and activates the next adjacent sensor within the next node, it will probably be a trajectory. If there is no adjacent node activated, then we can consider the node the ending node of a trajectory.We refer to the generative route construction method to extend the possible path according to the sensor network topology as well as the sensor log data. Since the route generation in the GA space may produce redundant or impossible routes in real geographical space, spatial and temporal constraints to each generation are applied firstly to refine the generated trajectories. Based on the spatial object tracking idea, we propose the following route generation and constraints filtering method.(1) The trajectory refinement based on spatio-temporal constraintsC. The spatial constraint matrix C is a diagonal matrix, where ke are the possible active sensor nodes, and we define kkC = 1, otherwise, the kkC = 0. Therefore, we can define the whole spatial constraint matrix as follows:The spatio-temporal constrains should be applied during the route generation process to filter out the real possible trajectories. At first, we apply a time window as temporal constraints to determine the start and end of the trajectory. For the PIR sensor data, a time window of 3 s is suitable to segment the trajectories as individual trajectory. With this configuration, we can separate the responses of the time constraints during the route extension. The spatial constraints are determined by the indoor topological structure and the relations between the tracked nodes. Here, we query any active neighborhood node in the next time window and construct the spatial constraint matrix C is constructed, except when the node queried is considered the ending node of a trajectory. If there are some nodes that are active but not responded to neighborhood nodes, we consider the nodes the new starting nodes of a trajectory. According to the new starting nodes, we can construct a new judgement matrix T, which is also a diagonal matrix, where the element in the new judgement matrix T defines the new starting node ie. The construction rule of the new judgement matrix T is as follows: To extract the trajectory, we apply moving window query to the sensor activation log from the temporal dimension. When there is sensor activation in the next time window, a spatial constraint matrix n is the same with the network adjacent matrix. With the construction of the spatial constraints matrix C and judgement matrix T, we can multiply the adjacent matrix M, the spatial constraint matrix C and the judgement matrix T to construct the new adjacent matrix M’ in this matrix. The non-zero element is the real motion trajectory in the n-th order. This can be formulated as:According to the Equations (9) and (10), (2) The human motion trajectory sequence based route extensionM. For the given sensor activation sequence n-order adjacent matrix can be extended as: The route extension is based on the route expanding using the oriented join product based on the GA-representation of the sensor networks. The adjacent relations between different nodes are inherited in n-order routes, the starting node matrix Q is defined here. Q is a diagonal matrix, in which the i-th row and column has the elements of Q is as follows: To covert the matrix into the According to the above route extension and construction rule, we can then realize the spatial trajectory reconstruction according to the sensor log data. The reconstruction is defined according to the following equations: M that is constructed according to the topology of the sensor network. Clearly, the multiplication of the matrix M can produce all possible routes that the human can walk in the geographical space. According to the sensor network activation data, the starting nodes of the trajectories are first queried to construct the starting node matrix Mn. Then the spatial and temporal constraints can be applied to filter the high order matrix Mn to extract the more accurate trajectories. By determining the completeness of a trajectory and repeating the trajectory generation process, all the possible trajectories can be extracted. The overall process of the algorithm is illustrated in the Based on the above definitions, we can then develop a unified algorithm to generate and refine all the possible trajectories according to the sensor activation log. The algorithm uses the oriented join product to realize the route extension and uses the spatial and temporal constraints to refine the generated trajectories. The algorithm starts with the fundamental adjacent matrix The example of the simple sensor network depicted in (1) According to the time constraint (3s) of human motion trajectory, the sensor activation sequences is Q, and adjacent matrix M, Equation (15) can be written as: (2) Introducing the starting node matrix Q, we can get the n-order routes:Multiply the starting node matrix moving trajectory; in the response matrix of 3-order routes, the matrix split trajectories. So, in the 3-order routes there exist split trajectories ACBe and ACCe. At the same time, the CCe is the diagonal element of the adjacency matrix M, Ce is also a still node here; in the response matrix of 4-order routes, there are not any special structure, so it only inherited the still node Ce of 3-order routes.(3) The routes in Equation (17) can be classification according to the activated sensor sequences expresses in Equation (16). Firstly, since the response matrix of 2-order routes are diagonal matrix, the results are continuous Clearly, the algorithm we proposed is automated and is clearer to represent the real and possible trajectories from the sensor log data. The spatial constraints, network topologies and the route uncertainties are largely reduced. The route extension is dynamic and generative, which can also be interactively updated. All the possible trajectories can be extracted algebraically, which can be simply computed and analyzed in the future.The algorithm is applied to the MERL sensor database published by the Mitsubishi Electric Research Labs. There are a total of 213 sensors, which logged the human motion in the building from 21st May 2006 to 24th March 2007 and were distributed on the 7th and 8th floor  with ODBFirstly, the overall active frequency is summarized to get the spatial distribution of the active sensors . The hotSince generating all possible trajectories from the whole data set is too complex, we selected one week of data of the 8th floor from 7 August to 13 August to generate all the possible people trajectories from the sensor activation data. There are totally 153 sensors and the total record of the active sensor log is 414,552. Based on the trajectory reconstruction algorithm, we have reconstructed 414,552 possible sensor statues and 563,386 total trajectories. After splitting the trajectories in a one-minute window and removing the duplicated trajectories, which have totally same nodes, we finally have 202,700 complete different trajectories.The overall time cost of both the computation of the statues of each sensor and generation of all possible trajectories is 3 h and 19 min and the peak memory cost is 3.2 GB. The final result database file  occupies a space of 3.07 GB in the NTFS file system. To test the correctness and performance of the method, we use the version 2 tracklets data  as the reference data. Since the original performance of the tracklet graph model are not possible to be accessed, we apply our method onto different numbers of sensor activation logs to evaluate the performance of the method.The time and memory cost of the generation of the sensor status and trajectories from the start to different end time are logged during the computation . The compass, join and split status. The major performance bottleneck in this procedure may be the determination of the still and stop status. Not so restrictedly speaking, the time efficiency of the sensor status query is nearly increased as a linear relation of the total number of sensor logs. For the performance of the trajectory generation, the computation cost is growing much larger, this is because the generation of the trajectories requires one to compute the real oriented join product to generate the path. The generated path should also be compared with the spatial and temporal constraints of filtering the path. The optimization of the oriented join product and optimized data structure of the computation may largely improve the computation performance. Compared with the tracklet graph models proposed by [i.e., our method can provide the join, split, still and pass status of each sensor to provide more detailed descriptions about the human motion. In the tracklet graph model result, several possible trajectories are not extracted  technologies may be applied to help improve the efficiency ,34,35. Ietc. This further detailed analysis may make the GA-based activity analysis a wider potential area. Since the GA representation and analysis is inherently high dimensional, the representation and analysis can be made simple and direct.In our GA-based trajectory reconstruction method, the route extension is not only the real geographical spatio-temporal sequence, but also an algebraical element that can be directly calculated. The clear geometric and physical meaning of the motion can be directly revealed from the algebraical equations. In this way, the representation and analysis method can make the analysis of the motions much simpler and clearer. Since both the outer product and the oriented join product is asymmetric, the route extension has its own orientations, which can be well classified by the spatial and temporal adjacent relations in the sensor activation log. Since the orientation information is very important for the activity identification, our GA method may also be helpful to improve the activity recognition from the sensor data. According to the orientation information, we can reveal not only the classical activity of walking, entering, joining and splitting, but also the activities like turning left and turning right In this paper, we developed a human trajectory refinement method to reveal all the possible human motions from the passive MERL sensor activation log data. Our method unifies the representation of the sensor network, sensor activation data and the human moving trajectory under a unified mathematical framework. All the possible human motion trajectories are tracked according to the dense sensor activation log using the matrix approach. The geometric algebra can well express the absolute and relative coordinates of the human motion. The network and trajectory representation can well express the network and trajectory information in a unified multivector structure. The spatio-temporal constraints as well as the sequence information can be unitedly represented using the outer product. With our method, not only can all the possible trajectories of human motion be extracted, but also the spatial and temporal constraints can be flexibly applied during the route extension. The extracted motion trajectories can more accurately reflect the real human motion in the office environment. Our method provides a new solution that can deal with the uncertain problem of the trajectory reconstruction from the sensor network data. Further integration of our method and the statistical inference method may provide new possibility in passive sensor analysis. In addition, our method is also useful for sensor-network-guided indoor navigation and optimal routing.


The wide band at extremely high frequencies (EHF) above 30 GHz is applicable for high resolution directive radars, resolving the lack of free frequency bands within the lower part of the electromagnetic spectrum. Utilization of ultra-wideband signals in this EHF band is of interest, since it covers a relatively large spectrum, which is free of users, resulting in better resolution in both the longitudinal and transverse dimensions. Noting that frequencies in the millimeter band are subjected to high atmospheric attenuation and dispersion effects, a study of the degradation in the accuracy and resolution is presented. The fact that solid-state millimeter and sub-millimeter radiation sources are producing low power, the method of continuous-wave wideband frequency modulation becomes the natural technique for remote sensing and detection. Millimeter wave radars are used as complementary sensors for the detection of small radar cross-section objects under bad weather conditions, when small objects cannot be seen by optical cameras and infrared detectors. Theoretical analysis for the propagation of a wide “chirped” Frequency-Modulated Continuous-Wave (FMCW) radar signal in a dielectric medium is presented. It is shown that the frequency-dependent (complex) refractivity of the atmospheric medium causes distortions in the phase of the reflected signal, introducing noticeable errors in the longitudinal distance estimations, and at some frequencies may also degrade the resolution. Millimeter wave radar systems operating in multi-frequency or Frequency Modulated Continuous Wave (FMCW) mode were intensively studied over the recent years for a variety of applications such as collision avoidance in automobiles ,2,3, remBroadband FMCW radars enable high resolution in range and depth perception and in the detection of objects with a small radar cross-section. The demand for broadband radar systems and the deficiency of wide frequency gaps within the lower band spectra necessitate the utilization of higher frequencies at millimeter wavelengths in the extremely high frequencies (EHF) range above 30 GHz.The EHF band (30–300 GHz) covers a wide frequency range, which is relatively free of users. It offers many advantages for high resolution radars, enabling detection and imaging of concealed objects with a small radar cross-section. Among the practical advantages of using millimeter and sub-millimeter wavelength radar systems is the ability to employ smaller transmitting and receiving antennas. Millimeter wave radars also operate well under bad weather conditions such as fog and haze, when other sensors fail to detect objects, and thus serve as an alternative to infrared and visible light cameras. They are good candidates for landing assistance instrumentation measuring the height from the ground during dust and haze conditions.3). The calculations were performed using the millimeter propagation model (MPM) developed by Liebe [Some of the key challenges in the development of modern radar in the EHF band are the emerging effects during the propagation of electromagnetic radiation through the atmosphere. When millimeter wave radiation propagates in the atmospheric medium, it is subjected to selective molecular absorption ,12,13,14by Liebe ,16,17,18by Liebe . These eby Liebe ,21,22. WIn the current paper, a general approach for studying ultra-wideband FMCW radars operating in the EHF band under conditions of fog and haze is developed. The study is aimed at pointing out the implications of how dispersive effects in the medium (and not only absorption) play a role on the performances of wideband FMCW radars. The numerical Millimeter-wave Propagation Model (MPM) ,16 is emWe start with a general review of the description of electromagnetic radiation in the frequency domain. The fundamental principles of FMCW radars are also given here, mainly for convenience, defining the terms and notations used in the following discussion. The formulation we have developed is applied for the study of a linear FM (“chirp”) signal detected by a product detector after propagation and reflection from a target. We demonstrate the approach on a FMCW radar operating at millimeter and sub-millimeter wavelengths under fog conditions. We assume that the response of the components within the system is linear; this is a reasonable assumption as, though typical components have non-linear characteristics, it is possible in practice to use an equalizer to reduce the significance of this problem.In a FMCW radar see , a lineaHere Fourier transform of the transmitted field Equation (2) yields:It is useful to present the time-dependent electric field in its analytic form, for which the Fourier transformation is given in positive frequencies . The subThe complex envelope of the received signal is:The local oscillator reference signal is formed from the same source as the transmitted FMCW signal and it is mixed with the delayed echoes reflected from the target. The mixed signal is filtered to remove higher-order harmonics. The resulting signal at the output of the filter is:The detected signal i.e., when the index of refraction is a constant If the signal is propagating in a vacuum, Here we define It is important to note that the longitudinal range resolution depends on the frequency resolution Since the frequency resolution depends on the sweep duration The range resolution is improved as the radar bandwidth increases. A frequency span of RF chain . A lineaIn order to focus the study only on the atmospheric propagation effects, a completely linear chirp is assumed to be transmitted. It is assumed that the FMCW signal is transmitted and received by a linear system in order to explore the impact of the atmospheric medium. Dispersive behavior of the atmospheric medium will cause deviations in the frequency calculated in Equation (10), and thus produce errors in the measured distance. We now demonstrate the effects of atmospheric fog on the resulting measured distance at millimeter wavelengths.The millimeter wave propagation model is based on a complex presentation of the refraction index:The complex refractivity on (ppm) ,16. The The wavenumber of the field is given by:The group delay at a distance Liebe’s millimeter propagation model (MPM) is used for calculation of the atmospheric frequency response under foggy conditions ,16,17,18 119 GHz ,25,26. A 119 GHz .3). Now we examine the absorptive and dispersive effects of fog on the propagation of an ultra-wideband ‘chirp’ transmitted at millimeter and sub-millimeter wavelengths and received by the FMCW detector. Note that both the attenuation coefficient and haze . Fog andThe intermediate frequency (IF) obtained at the output of the detector is given in Equation (10). It is proportional to the distance to the target, as can be seen in Equation (12). In the following, we consider a wideband FMCW millimeter wave radar transmitting a chirp with Inspection of the graphs in Its average value is explained by the constant component In the following we demonstrate that the deviation in the detected intermediate frequency causes errors in the absolute range estimation and may also lead to some degradation in the longitudinal resolution.We previously showed that according to Equation (14), the longitudinal range resolution of ow power ,28.It was shown in the previous section that in the dispersive atmosphere, the frequency The difference between the measured value in foggy conditions and the real distance to the target (determined by Using Equation (12) we obtain:The graphs of Moreover, it can be seen that during the sweep time, the error deviation varies between a minimum value  systems , the timIn this paper we developed a model to study the effect of fog on the distance accuracy measurements using an ultra-wideband FMCW radar operating in the millimeter and sub-millimeter wave regimes. The developed model can be employed to calculate the radar performance at any frequency from 10 GHz up to 1 THz. The study demonstrates the effects of dispersive attenuation and phase on a detected radar signal. It is found that loss and dispersion in the atmospheric medium cause a deviation in the frequency obtained at the output of the FMCW radar, leading to an erroneous estimation of the distance to the target. We found that foggy conditions significantly affect the accuracy of the ultra-wideband radar. If weather conditions are known  or the transfer function of the medium is estimated, these effects can be overcome by an equalizer and adaptive system. ‘Side information’ about the channel behavior may be used for calibration of the system and compensating for the erroneous measurements. One should note that time-dependent variations in the atmospheric medium lead to temporal fading and turbulence scintillation phenomena. However, in this paper we assume that temporal changes in weather conditions are much slower than the travel time of the signal.The obtained results are the physical bounds for accuracies in distance measurements as well as the resolution. Technical design issues, such as system non-linearities, can cause further degradation that should be considered. It is also interesting to note that in digital wireless communication wideband links operating in millimeter wavelengths, the dispersion of the atmospheric medium is expected to play an equivalent role. The magnitude and phase response of the atmosphere will affect the transfer of M-ary (multi) phase or frequency-shift keying signals, causing inter-symbol interference in the demodulated data.


Acute lymphoid leukemia is a common type of blood cancer and chemotherapy is the initial treatment of choice. Quantifying the effect of a chemotherapeutic drug at the cellular level plays an important role in the process of the treatment. In this study, an oscillating optical tweezer was employed to characterize the frequency-dependent mechanical properties of Jurkat cells exposed to the chemotherapeutic agent, artesunate (ART). A motion equation for a bead bound to a cell was applied to describe the mechanical characteristics of the cell cytoskeleton. By comparing between the modeling results and experimental results from the optical tweezer, the stiffness and viscosity of the Jurkat cells before and after the ART treatment were obtained. The results demonstrate a weak power-law dependency of cell stiffness with frequency. Furthermore, the stiffness and viscosity were increased after the treatment. Therefore, the cytoskeleton cell stiffness as the well as power-law coefficient can provide a useful insight into the chemo-mechanical relationship of drug treated cancer cells and may serve as another tool for evaluating therapeutic performance quantitatively. Cytoskeletal proteins, inside the plasma membrane, are linked by molecular junctions which give the cell a complex and dynamic structure . The cytThe analysis of the drugs with less toxicity on normal cells is indispensable for curing the disease. Studying the effective concentration of drugs on different types of cancers has been extensively studied at the biochemical and molecular levels , 8], , . Many ceIn this study, Jurkat cells, derived and immortalized from an acute lymphoid leukemia which is the most common type of blood cancer in children, was chosen as our demonstrative example . Early t2, and fresh culture medium were added every 2 to 3 days. After five passages, cells were cultured in four different dosages of ART  for 24 hours. Microbeads were adhered to cell membranes and were used as a handle for cell manipulation . T. T27]. T


Purpose. To describe the characteristics of intraocular fluidics during cataract surgery in swine eyes with prior vitrectomy. Methods. We prepared three groups of enucleated swine eyes . Irrigation and aspiration were performed  using a balanced saline solution mixed with a water-soluble radiopaque contrast medium at 1 : 1 ratio. We imaged the eyes using a digital fluoroscopy system (DFS) during phacoemulsification and compared the characteristics of the intraocular fluid dynamics between the groups. Results. The anterior chamber depth (ACD) after the commencement of irrigation differed between groups , as well as the height of the fluid flowing from the anterior chamber into the posterior cavity that was identified by lifting up the iris to correct the infusion deviation syndrome . Conclusions. DFS demonstrated differences in fluid dynamics during phacoemulsification in swine eyes with or without prior vitrectomy. In completely vitrectomized eyes, the large ACD, which developed during phacoemulsification, could be reduced by lifting the iris and allowing the fluid to shift to the posterior cavity. Recognizing the differences in fluidics of vitrectomized eyes as compared to those of the nonvitrectomized eyes may reduce the frequency of intraoperative complications. With progressive refinements in vitreoretinal surgical techniques, an increasing number of posterior segment disorders are being successfully managed with pars plana vitrectomy. However, cataract formation is a frequent complication after vitrectomy, occurring in up to 80% of cases. Well-known potential complications that may arise from cataract surgery after vitrectomy include poor pupil dilation, posterior synechiae, zonular damage, posterior capsular tears, increased mobility of the iris-lens diaphragm, and altered intraocular fluid dynamics as a result of the absence of the anterior hyaloid surface . Upon fiAmong studies on intraocular fluid dynamics, one particular study used a model that evaluated the fluidics of the anterior chamber using microparticles. The anterior flow could be documented with the help of a camera and a computer that monitored the flow of the microparticles .Fluoroscopy is an imaging technique commonly used by physicians to obtain real-time images of the internal structures of a patient through the use of a fluoroscope such as for dynamic digital cardiovascular imaging applications.Modern fluoroscopes couple the screen to an X-ray image intensifier and charge-coupled device (CCD) video camera, allowing the images to be recorded and played on a monitor.The purpose of this study was to investigate the exact mechanism of IDS using a digital fluoroscopy system (DFS) in real time. We also sought to compare the changes in the iris, lens, and anterior hyaloid surface according to the degree of vitrectomy and to compare the intraocular fluid dynamics according to these changes.This study was performed on enucleated eyes of pigs which were 5–8 months old having an axial length of around 24 mm. They were all studied within six hours after enucleation. Nine swine eyes were divided into three groups of three eyes each to compare the intraocular fluid dynamics according to the degree of vitrectomy. Groups 1, 2, and 3 were classified as normal swine eyes that did not undergo vitrectomy , those tImages were taken using iodixanol , which is a nonionic, isoosmolar contrast medium . The conIn each swine eye, the anterior chamber was irrigated with contrast medium mixed with BSS through a clear corneal incision (2.75 mm), using the Infiniti Phaco System (Alcon Laboratories), with the same parameters  for each procedure. We used a 30-degree phacotip with a 0.9 mm outer diameter.In each group, the initial change of the anterior chamber depth (ACD) after irrigation with contrast medium was observed. If present, the degree of IDS was measured and the iris was lifted with a second instrument for 10 seconds. We observed the flow of irrigation fluid from the anterior chamber to the posterior cavity and recorded the amount of contrast medium in the posterior cavity.μm pixel pitch . We compared the changes of the ACD and the amount of contrast medium in the posterior cavity before and after lifting the iris with a second instrument using still images taken from the real-time DFS. In this study, ACD was measured as the distance from the posterior corneal surface to the anterior lens surface, and the height of the contrast medium in the posterior cavity was measured as the distance from the anterior surface of the contrast medium to the anterior retinal surface  at a speed of 30 frames per second. The whole imaging system enables a very high resolution of 184  surface . To compIn the nonvitrectomized eyes , the iriP = 0.135). However, the mean ACD of the totally vitrectomized eyes was significantly deeper than that of nonvitrectomized eyes . However, in the core vitrectomized eyes, the change in the mean ACD was significant (P = 0.023), as well as in the totally vitrectomized eyes (P = 0.009 (P = 0.001). There was no contrast medium in the posterior cavity of the nonvitrectomized eyes . In the = 0.009 ). The mezed eyes .Infusion deviation syndrome (IDS) is caused by a lack of vitreous support and posterior bowing of the iris-lens diaphragm. IDS can result in the blockage of fluid passage from the anterior chamber to the vitreous cavity, causing significant pressure differences between the anterior and posterior compartments . This phIn this study, posterior bowing of the iris-lens diaphragm was not observed in nonvitrectomized eyes when irrigation was initiated, and posterior bowing was only slightly evident in the core vitrectomized eyes. However, in the totally vitrectomized eyes, the iris-lens diaphragm was significantly displaced posteriorly, and the anterior chamber was significantly deepened. Eyes that have undergone meticulous vitrectomy are at a higher risk for this syndrome.IDS can also occur in axial myopia, because of their zonular laxity. The combination of these anatomic factors allow the iris and capsular bag to move posteriorly with the introduction of pressurized fluid into the anterior chamber . To stabIn our study, movement of the irrigation fluid of the anterior chamber into the posterior cavity by lifting the iris worked to stabilize the anterior chamber. This procedure is a safe option for stabilizing the anterior chamber. Once satisfactory anterior chamber depth is achieved, the lens can be removed in the usual manner without difficulty . AnotherUsing the Miyake-Apple technique, one can observe the lens and adjacent structures after cutting the posterior half of an eye and placing it on a transparent slide . The posImaging systems such as computed tomography (CT) or magnetic resonance imaging (MRI) can be used to observe fluid flow without disturbing the intact structure. However, it is difficult to observe the dynamic flow of fluid with CT or MRI as these modalities typically only acquire images at a specific moment in time, as opposed to acquiring them over a period of time. While microparticles can be used to study anterior chamber fluid dynamics, they cannot be used in the studies of intraocular fluid dynamics between anterior and posterior chamber.Diagnostic and interventional radiology have a continuing requirement for dose-efficient X-ray-based imaging techniques to visualize moving anatomic structures, organs, and/or clinical items of interest. Such imaging techniques are commonly associated with contrast medium-aided examinations of the gastrointestinal tract, cardiovascular system, and various other soft tissue organs and structures. One of the advantages of DFS is that it affords the possibility to observe the shape of a structure and the movement of contrast medium while maintaining the intact internal structure . Due to In this study, we could observe the different changes in anterior chamber depth and the amount of contrast medium that had passed through the posterior cavity in real-time DFS. This is the first time that DFS has been applied in this way in ophthalmology.The resolution of this imaging technology can be a limitation, although in this study it offered no challenges to identifying the fluid passage and accumulation in the posterior cavity. Moreover, this DFS method can be used to visualize the experimental outcomes of pharmacologic vitreolysis in vivo .Using ultrasound images to observe the vitreous status in vivo, there is an impedance of the crystalline lens on image quality, especially the area directly behind the crystalline lens. But the crystalline lens did not affect the resolution of DFS in the experimental model and afforded the advantage of examining the reaction process in real time .Iodixanol is the only contrast medium formulated with sodium and calcium in a ratio equivalent to that of blood. Iodixanol, a water-soluble contrast medium, is used in coronary angiography with DFS . While iIn conclusion, in this study we could successfully observe real-time images of anterior and posterior cavity using DFS, demonstrating differences in fluid dynamics during phacoemulsification in eyes with or without prior vitrectomy. In the completely vitrectomized eyes, the significantly deep anterior chamber which developed during phacoemulsification could be normalized by allowing the fluid shift to the posterior cavity. Using the digital fluoroscopy system we could successfully identify intraocular fluidics between anterior chamber and posterior cavity. In the group of vitrectomized eyes, a significantly large amount of fluid flowed to the posterior cavity which then led to a normalized anterior chamber depth. Recognizing the differences in the fluidics of the vitrectomized eye as compared to those of the nonvitrectomized eyes may reduce the frequency of intraoperative complications. This study on vitrectomized swine eyes suggests that DFS represents a favorable experimental method to study the intraocular fluid dynamics in vivo.


In order to achieve more accurate and reliable identification of shearer cutting state, this paper employs the vibration of rocker transmission part and proposes a diagnosis method based on a probabilistic neural network (PNN) and fruit fly optimization algorithm (FOA). The original FOA is modified with a multi-swarm strategy to enhance the search performance and the modified FOA is utilized to optimize the smoothing parameters of the PNN. The vibration signals of rocker transmission part are decomposed by the ensemble empirical mode decomposition and the Kullback-Leibler divergence is used to choose several appropriate components. Forty-five features are extracted to estimate the decomposed components and original signal, and the distance-based evaluation approach is employed to select a subset of state-sensitive features by removing the irrelevant features. Finally, the effectiveness of the proposed method is demonstrated via the simulation studies of shearer cutting state diagnosis and the comparison results indicate that the proposed method outperforms the competing methods in terms of diagnosis accuracy. With the rapid development of China’s economy, the demand for coal is still ongoing and the efficiency and safety of coal production is becoming more and more noticeable in the coalmining industry. However, in some mines, the coalmining environment is extremely harsh and the automatization level of coalmining machines is rather low. The operators cannot accurately estimate the cutting state only depending on their visual and auditory information. This phenomenon will lead to some problems, such as poor coal quality and low mining efficiency. Moreover, many safety accidents in collieries occur frequently. As the key equipment in coalmining face, shearer plays an important role in the process of coal production. Diagnosing its cutting state, which aims at whether the shearer is cutting coal or rock, is an indispensable precondition for improving the automatization degree.In the past decades, some studies have focused on the coal–rock identification technology to roughly estimate the cutting state of shearer and many kinds of coal–rock recognition methods have been proposed successively. The most representative methods mainly consist of γ-ray detection means , radar dIn recent years, due to the inevitable shortcomings mentioned above, the diagnosis methods for shearer cutting state based on coal-rock recognition have gradually left using our field of vision. Considering the fault diagnosis methods for other traditional machines, it is known that sensors can tackle the problem of perception by providing information of machines, and vibration signal analysis is becoming the most commonly used method and also proved to be efficient in various real applications ,12. For The process of state detection is essentially a process of pattern recognition and classification. Several techniques have been successfully used for classification problems, such as neural network (NN) ,14, suppPNN is an effective data classifier proposed in ,25, and In addition, another key step in the process of state diagnosis is how to extract the state features and identify the condition from the signals. Some typical methods for feature extraction are Fourier transform (FT) ,33, waveThe remaining parts of the paper are organized as follows. et al.  to represent the flight distance for food finding of an individual fruit fly i in each sub-swarm. The distance iDist and smell concentration judgment value iS of the ith fruit fly can be calculated as follows: In this subsection, the proposed MFOA is utilized to optimize the smoothing parameters of PNN, which can be named as MFOA-FNN, in order to achieve the optimal classification effect. As the number of smoothing parameters is equal to the dimensionality of the samples, the fruit fly swarm location is set as S. Then, the smell concentration iSmell  should be calculated. We adopt the classification accuracy as the fitness function to represent the classification performance of the MFOA-PNN model. The fruit flies are operated and the sub-swarms are updated through Equations (6)–(8). When t reaches the max iterative number, the termination criterion satisfies and the optimal smoothing parameters of PNN model can be obtained. The procedure structure of the MFOA-PNN classification model is illustrated as In the proposed model, the smoothing parameters of PNN can be represented by The intelligent diagnosis for shearer cutting state based on proposed method is essentially a pattern recognition system, as shown in The cutting state diagnosis of shearer starts with data acquisition to collect the machinery working information. Vibration signal acquisition is the most commonly used method that is realized by sensors. For a shearer, the rocker transmission part delivers the power to the drum to cut the coal and rock, and its vibration can mainly reflect the shearer cutting state. However, the internal space of rocker is very narrow and the sensor can only be installed near the idlers, as shown in In the self-designed experimental system, a multifunctional high-speed collector performs the data acquisition and the data are collected into a notebook computer through the Universal Serial Bus interface. The sampling frequency is set as 12 kHz and the sampling time of each sample is 0.5 s. A group of measured vibration signals in different cutting states are plotted in The feature extraction of signals is a critical initial step in any pattern recognition and fault diagnosis system. The extraction accuracy has a great influence on the final identification results. In this work, the measured vibration signals are firstly decomposed by ensemble empirical mode decomposition (EEMD) method. Then, the Kullback-Leibler divergence (KLD) is used to identify the false components from the EEMD. The decomposed signals and the original signal are estimated by nine feature parameters. Finally, the distance-based evaluation (DE) approach is used to choose some of the most effective features from the entire feature set.The original signal will generate several intrinsic mode function (IMF) components after the EEMD decomposition. However, some of the IMF components, especially low frequency components, are superfluous and can be called the false components. Identifying these false components and deleting them is very meaningful to the state diagnosis of machine. In this work, the KLD method is used to measure the relation levels between the IMF components and original signal. Smaller KLD values show that the IMF components possess tighter relation with the original signal, whereas the IMF components with larger KLD values are false and should be removed. The detailed steps are listed as follows:x(t) is decomposed by EEMD to get N IMF components ic(t) and each ic(t) contains r data points.Step 1: The original signal x(t) and ic(t). We assume p(x) and q(x) be two probability density functions of x(t) and ic(t). The following function can be defined as the kernel density estimation of p(x): K is the kernel function and h is the bandwidth, which can be determined according to , n = 8, m = 120, N1 = N2 = N3 = N4 = N5 = 24, k = 5. After the training phase, the PNN model with the optimal parameters can be obtained and the diagnosis results of the training samples and testing samples are illustrated in Based on the eight selected features, 200 samples for the proposed MFOA–PNN model can be generated. Sixty percent of the samples are used to optimize the smoothing parameters of PNN and the remaining samples are put into the trained PNN model to verify its classification performance. After some tries and simulations, the parameters of proposed method can be set as follows: As observed from u and the number of the output neurons is set as the number of all the possible cutting types k. A single hidden layer structure is adopted and the number of neurons in the hidden layer is determined by an empirical formula To highlight the advantage of our proposed method over the conventional state diagnosis methods, it is compared with other four methods of FOA-PNN (the basic PNN with FOA), PNN (basic PNN), support vector machine (SVM), and back-propagation neural network (BP-NN). For the SVM-based state diagnosis method, the penalty parameter and kernel parameter are optimized by the K-fold cross validation method. The number of the input neurons of the BP-NN-based state diagnosis method is equal to the number of the selected features It can be obviously observed from In the course of simulation, we notice that the number of selected features and the number of training samples are two critical parameters and have some influences on the diagnosis performance of some state diagnosis methods. Hence, the following subsection will present how the performance of above five methods may change with different parameter settings.In order to measure the relation between the number of selected features and the diagnosis accuracy, the number of selected features for the vibration of rocker transmission part is set from 1 to 45 and the corresponding diagnosis accuracies based on different methods are compared. In this study, the number of training samples is set as 120 and the number of training samples is set as 80. The parameters of the diagnosis methods are chosen reasonably according to above simulation. These methods are trained and tested for 10 times and the average values are chosen as the diagnosis results. After the training phase, the diagnosis results of the testing samples for the BP-NN-based diagnosis method, SVM-based diagnosis method, PNN-based diagnosis method, FOA-PNN-based diagnosis method, and the proposed diagnosis method are plotted in As can be seen in the graph, the diagnosis accuracies for all methods show an upward trend initially and then a downward trend with the increasing number of selected features. In detail, the BP-NN-based, SVM-based and PNN-based state diagnosis methods have an obvious fluctuation if the number of selected feature is greater than 10, whereas the mild oscillating phenomena occur for the FOA-PNN-based and MFOA-PNN-based state diagnosis methods. Furthermore, the proposed method always exhibits the highest accuracy than the other four methods for most selected features. The observation from However, using too many feature parameters to train the models will surely consume much time and reduce the computational efficiency for all the state diagnosis methods, although more selected features may increase the diagnosis accuracy. In order to balance the computational cost and diagnosis accuracy and according to the illustration in In this subsection, a comparative study is also carried out to reflect the influence of the number of training samples on the diagnosis accuracy. The first eight features with the greatest values of effectiveness factor are selected as the inputs of the five competing methods. The number of samples is added to 280 in order to obtain enough independent testing samples. The number of training samples changes from 5 to 200 and 80 testing samples are randomly selected from the residual samples for all diagnosis models. Meanwhile, the five methods also run 10 times for more accurate results. Finally, the diagnosis accuracies with respect to different numbers of training samples are illustrated in It is observed from The overall goal of the work presented in this paper is to provide a novel method for the diagnosis of shearer cutting state based on improved probabilistic neural network. The fruit fly optimization algorithm with a multi-swarm strategy is proposed for the adaptive choice of the smoothing parameters of the PNN. Then the Kullback-Leibler divergence is used to identify the false components from the EEMD, whereas the distance-based evaluation approach is employed to choose some effective features. Simulation experiments are provided and the comparisons with other four methods indicate that the proposed method possesses an exceptional performance on the state recognition of shearer and outperforms the competing methods.We are confident that this study makes a significant contribution in the diagnosis of shearer cutting sate and will enable the production of high quality solutions for classification problems. However, this paper only consider the vibration of rocker transmission part to identify shearer cutting state and neglects the vibrations of other parts of shearer, such as travel unit and hydrocylinder. In the near future work, we will install some sensors on other key parts of shearer and acquire more useful vibration signals. Furthermore, some data fusion algorithms may be investigated to further improve the diagnosis accuracy and efficiency.


While the thermo-mechanical problem takes place in the three-dimensional space of the laminate, moisture diffusion occurs in a two-dimensional domain represented by the polymeric layers and by the vertical channel cracks in the solar cells. Therefore, a geometrical multi-scale solution strategy is pursued by solving the partial differential equations governing heat transfer and thermo-elasticity in the three-dimensional space, and the partial differential equation for moisture diffusion in the two dimensional domains. By exploiting a staggered scheme, the thermo-mechanical problem is solved first via a fully implicit solution scheme in space and time, with a specific treatment of the polymeric layers as zero-thickness interfaces whose constitutive response is governed by a novel thermo-visco-elastic cohesive zone model based on fractional calculus. Temperature and relative displacements along the domains where moisture diffusion takes place are then projected to the finite element model of diffusion, coupled with the thermo-mechanical problem by the temperature and crack opening dependent diffusion coefficient. The application of the proposed method to photovoltaic modules pinpoints two important physical aspects: (i) moisture diffusion in  Photovoltaic modules are composite structures obtained by laminating layers of various materials. Some have the role to guarantee protection from the environment by a suitable sealing of the device, others have specific electric features to produce energy. The durability of these devices is a serious concern due to fracture events promoted by the mismatch between the thermo-mechanical properties of the material constituents, often amplified by the severe working conditions they are exposed to. Moreover, their modelling is changelling and it requires a multi-physics framework to gain an accurate picture of their overall working conditions, performance and degradation .damp heat tests in  is time dependent because it contains the diffusivity D which changes with time according to  are herein considered to be temperature-dependent as experimentally evaluated in [E(T) in Fig. Due to a non constant temperature boundary condition, this problem is much more difficult to be simulated as compared to the uated in , see theD(T) for EVA as reported in [Regarding the diffusive properties, we consider the expression of orted in  and showG is the area below the traction-separation curve, the following relation holds:G(T) experimental data can be converted in The critical crack opening,  data in  reportinA sketch of the cross-section of a PV mini-module simulated in the present study and containing 3 solar cells is shown in Fig. mentclass2pt{minimmentclasspt{minimaThe temperature distribution inside a portion of the PV module cross-section near one of the free boundaries is shown in the contour plots in Fig. The evolution of moisture concentration in the encapsulant vs. time by using a time-dependent diffusivity is shown in Fig. mentclasspt{minimahumidity freeze test is humidity freeze test, corresponding to the first temperature ramp from 0 up to The time step used for the simulation of the The proposed numerical method can be also employed to simulate the effect of cracks in Silicon on the evolution of moisture concentration. To have a benchmark experimental result to compare with, minimodules composed of humidity freeze test inside a climate chamber at Politecnico di Torino, Italy. Electroluminescence images taken regularly during the test (the reader is referred to [This minimodule was then subjected to the erred to  for morehumidity freeze test. The presence of cracks enhances moisture diffusion in the central cell, with a contour plot of moisture concentration that correlates very well with the EL image of the electrically damaged areas (compare the red areas in Fig. To prove that cracks can enhance electric degradation due to moisture diffusion through them, a numerical simulation is performed according to the present computational framework by imposing the value of A comprehensive finite element computational framework for the simulation of coupled hygro-thermo-mechanical problems in photovoltaic laminates has been proposed. To achieve the computational efficiency required to simulate large scale commercial PV modules consisting of up to 60 solar cells, the EVA encapsulant layers have been modelled as zero-thickness interface elements whose traction-separation relations take into account the complex thermo-visco-elastic rheological response of the polymer as per experiments. Moreover, a staggered solution scheme has been proposed and implemented in FEAP  to solvedamp heat test and the humidity freeze test. In the latter, due to a continuous variation of temperature during the test, we have shown that coupling between the thermo-mechanical field and moisture diffusion has to be taken into account to correctly predict the spatio-temporal evolution of moisture in the PV module. Moreover, percolation through channel cracks in Silicon solar cells has been found to significantly enhance moisture diffusion. This trend has been confirmed by experimental tests performed by the present authors and showing an increased electric degradation in cracked solar cells with respect to the intact ones.The proposed methodology has been successfully applied to the simulation of the qualification tests required by the International Electrotechnical Commission , namely With the present computational tool available, further developments may regard the simulation of other moisture diffusion phenomena observed in experiments, in addition to percolation through Silicon channel cracks and to diffusion along the EVA layers. As shown in , in fact


Biological expression language (BEL) is one of the main formal representation models of biological networks. The primary source of information for curating biological networks in BEL representation has been literature. It remains a challenge to identify relevant articles and the corresponding evidence statements for curating and validating BEL statements. In this paper, we describe BELTracker, a tool used to retrieve and rank evidence sentences from PubMed abstracts and full-text articles for a given BEL statement (per the 2015 task requirements of BioCreative V BEL Task). The system is comprised of three main components, (i) translation of a given BEL statement to an information retrieval (IR) query, (ii) retrieval of relevant PubMed citations and (iii) finding and ranking the evidence sentences in those citations. BELTracker uses a combination of multiple approaches based on traditional IR, machine learning, and heuristics to accomplish the task. The system identified and ranked at least one fully relevant evidence sentence in the top 10 retrieved sentences for 72 out of 97 BEL statements in the test set. BELTracker achieved a precision of 0.392, 0.532 and 0.615 when evaluated with three criteria, namely full, relaxed and context criteria, respectively, by the task organizers. Our team at Mayo Clinic was the only participant in this task. BELTracker is available as a RESTful API and is available for public use.Database URL:http://www.openbionlp.org:8080/BelTracker/finder/Given_BEL_Statement Biological networks are powerful and expressive means of representing biological information and knowledge . BiomediThere have been considerable efforts made by domain experts to design formal representation standards to allow for a seamless transition from the natural language texts to structured representation without any loss of information. Biological Expression Language (BEL)  and SystGiven textual evidence for a BEL statement, generate the corresponding BEL statement (information extraction [IE] task)Given a BEL statement, provide, at most, 10 evidence sentences At the right juncture, the BioCreative V challenge has been organized, with a goal of addressing the dearth of tools to formalize information in natural language to BEL representation in a limited context of causal relationships. The organizers defined the following two tasks, one centered on information retrieval and the other on information extraction , 5. The We participated in both tasks , 7. In tThe paper is organized as follows: First, we briefly review related work, where we discuss the gaps in the current state-of-the-art tools and cover how they are unsuitable for the BioCreative V task. Second, we briefly describe the BEL framework. Third, we explain our approach and present the results of the system on the test dataset, which was evaluated manually by the organizers. Finally, we conclude the paper with a brief discussion on the performance of the system against the test data, its limitations, and possible future directions of the current work.Biological networks and pathways contain rich semantic information on various cellular processes and how they determine various physiological and pathological conditions of an organism. Despite various collaborative efforts, there is considerable lag in the curation of biological pathways from the biomedical literature. It takes a substantial amount of time for the experts to manually review the literature and infer the right information , 9. Therhttp://www.ncbi.nlm.nih.gov/pubmed) and iHOP  keyword-based retrieval, (ii) concept-based retrieval and (iii) IE-based retrieval. PubMed , and change in the cellular processes. The first-generation tools described above did not pay attention to any concepts beyond simple entities. GoPubMed  is an insubject-verb-object (SVO), where subject and object are biomedical entities and verb indicates relation type, such as: activate, induce, and cause. MedIE is a considerable advancement over the earlier-generation tools in the following ways: (i) It uses deeper syntactic analysis to extract relationships from the biomedical literature, and (ii) It uses a triple-store representation (SVO triplets) for indexing. MedIE opened avenues to query documents based on the preindexed metadata extracted from the documents, in addition to paving a way for IR to mature to handle sophisticated causal relation queries. To some extent, the IR paradigm of MedIE is similar to the SPARQL query directed against semantic triple stores.Ohta et al  developewhere clauses directed against a natural language text. A typical BEL query, as outlined earlier and in the following sections, consists of entities, functions, and relationships involving various ontological classes. In the past, several challenges, such as Genomic TREC  parsing a complex semantic query such as BEL and translating it into a simple keyword/concept retrieval without any loss in the semantics, (ii) identifying the relevant literature that best describes the semantics of the query and (iii) ranking the retrieved sentences in the order of relevance to the query.et al.  (cs (MGI) , Chemicacs (MGI) , Gene Oncs (MGI) , Medicalcs (MGI)  and HUGOcs (MGI) . The purRelation type: Type of link between subject and object. There are four relationship types in the provided dataset: increases, decreases, directly increases, and directly decreases.Function: There are five categories of BEL functions: abundance, modifications, activities, processes and transformations.Arguments for functions: BEL functions can have up to three arguments, depending on its type. For example, the function protein modification (pmod) can have three arguments: (i) type of modification, (ii) modified residue and (iii) sequence position of the modification.Query translation: Translating a given BEL statement into a queryRetrieval: Retrieving the relevant citations from PubMed abstracts and full-text articles from PubMed Central (PMC)Ranking: Identifying the appropriate evidence sentences (at most 10) from citations and ranking their relevance to the given BEL statementBELTracker has three main components:https://www.elastic.co/), Weka , Weka , LibSVM /), Weka  and Stan/), Weka  as compoincreases will be expanded to words such as induce and activate that best characterize the relation often used by authors to describe increase. Similarly, we also have an expert-curated dictionary of synonyms for BEL functions and their arguments.As an initial step, BELTracker translates the given BEL statement into a query suitable to retrieve information from natural language indexed in a search engine. First, we identify the individual components in a given BEL statement  highlighted in AND) and others optional (by using OR). The presence of entities or their synonyms in the retrieved citations is mandatory, while the presence of other elements, such as functions and relationships, are optional. Similarly, synonyms were assigned lower weights when compared with the original entities that occurred in the BEL statement. BELTracker currently assigns the weight in an ad-hoc manner, namely assigning a weight score of two to the names expressed in the BEL statement, while it assigns a weight score of one to their synonyms.However, we distinguish the source for expansion by assigning different weights to the individual components of a query. We assign greater importance to externally curated standard resources widely used by the community against the internally curated resources. Hence, BELTracker assigns higher weights to entities when compared to functions and relations. We assigned lesser weights to internally generated resources due to their limited vocabulary. As a final step in query translation, we combine the expanded query elements with appropriate Boolean logic, making some mandatory  is used to query against the ElasticSearch index to retrieve the most relevant citations (up to 1000 from abstracts and 1000 from full texts). The ElasticSearch assigns relevant scores to each citation. BELTracker uses this score to rank the citations. The task requirement is not to retrieve just the full-text article or abstract, but also the evidence sentence that has the most marked evidence of the BEL statement. After retrieving the relevant citations, the abstracts and full-text articles are split into individual sentences using the Stanford Core NLP tool . The ranEntities: The main element of a BEL statement is an entity. First and foremost, an indicator of relevance between an evidence sentence and the given BEL statement is the appearance of the BEL entities within the sentence. Therefore, the system assigns the highest weight to those sentences that contain the entities mentioned in the BEL statement. Even among the entities, higher preference is given to those sentences that exactly contain the same entities mentioned in the BEL statement when compared to the occurrence of synonyms of the entities.Elements of functions: Certain functions, such as protein modifications, often contain such elements as residue modified and the residue position that occurs in the protein sequence. For example, consider the function pmod. While the presence of the word phosphorylation or its variants and the mention of the amino acid residue serine is important, the mention of position 520 in the sentence makes it extremely relevant, and it should therefore be ranked higher. The system assigns differential weights to even functional elements identified in the evidence sentences. Based on our observation of training data, we assigned such differential weights to the function elements.Functions: Functions themselves carry less weight when compared to the presence of their elements within those functions. Nevertheless, their appearance is often more than the relation and, hence, should be weighed higher than the relation keywords.Relations: The lowest weight is assigned to the appearance of any term related to the BEL relations due to thedifficulty of defining and identifying these terms. In addition to the appearance of any related term, we use a binary classifier to classify sentences into increase or decrease classes (the two main types of BEL relations). The result of the classifier is another factor in the ranking component. We describe the role of the classifier later in this section.The presence of the number of irrelevant entities within the sentences dilutes the ranking of the sentence for a given query. The system filters out those sentences that do not contain all the entities (or their synonyms) mentioned in the given BEL statement. It then ranks the remaining sentences based on the number of matching elements.increase and decrease. For training the classifier, we used the training data provided by the organizers, which contain 11 073 BEL statements extracted from 6359 sentences. We used unigrams (after removing stop words), bi-grams, entities within sentences, and part-of-speech tags of all words between the entities as features. Using 10-fold cross validation, we compared the performance of several learning models such as the Support Vector Machine (SVM), Naïve Bayes and Random Forest, on the training dataset. The class label assigned to the sentences by the classifier became another parameter in the ranking.To identify relationship types, we trained a binary classifier to classify sentences into two classes: The organizers provided a training dataset that consisted of 11 073 BEL statements extracted from 6359 sentences. Several BEL statements were curated based on the information from a single sentence from both abstracts and full-text articles. The training data was common to both the tasks.Full: Used if the identified evidence sentence contains the complete BEL statement.Relaxed: The retrieved sentence may not have all the evidence for extracting the complete BEL statement. However, it may have the necessary context and/or biological background to enable extraction of the full BEL statement.Context: Even though the complete or partial BEL statement cannot be extracted from the sentence, it provides the necessary context for the BEL statement. The entities or their synonyms mentioned in the BEL statement are also identified in the sentence, but the context description (function or the links) may not accurately reflect the actual BEL statement.For the evaluation, they provided two separate corpora for tasks 1 and 2. The test data for the second task contained 97 BEL statements. The organizers defined the following three criteria for evaluating the performance of the system  are ranked after all false positives (FPs).Random:Randomly reordered the results 2000 times and computed the average MAP for all these variants.Best:All TP are ranked before all FP.In addition to precision, organizers also computed the mean average precision (MAP)  to evaluBELTracker, on average, retrieved 612 citations for each BEL statement, of which 222 were from abstracts and 390 from full texts. As per the task requirements, we provided, at most, the top 10 evidence sentences for the 97 BEL statements. BELTracker returned 806 sentences for 97 BEL statements at an average of 8.3 sentences per BEL statement.K =  1:10). To analyze the impact of the ranking component, we calculated the system precision for most top K evidence sentences (“p(HGNC:IL1A) increases r(HGNC:DEFB4A)”Out of the 97 statements in the test data, our system was able to retrieve at least one evidence sentence for 96 BEL statements. BELTracker failed to find any evidence sentence for only one BEL statement (given below):IL1A and DEFB4A, with their synonyms included along with the term increase. Performing a similar query in PubMed resulted in only one abstract, which seemed to be relevant to the above BEL statement. Preliminary analysis indicates that the IR capabilities of BELTracker may be far limited when compared to the Entrez retrieval engine from NCBI. One way we plan to address this issue is to use the results of Entrez. In the following sections, we analyze the system performance under the Full and Context criteria.The query for the retrieval engine had the following components, Group A, where the system identified at least one evidence sentence within the top 10 that was relevant for constructing the complete BEL statement, even though they may not be among the top ranked.Group B, where the system failed to identify at least one evidence sentence that is relevant for constructing the complete BEL statement.To streamline our discussion, we grouped our results into two categories:As per the manual evaluation by the organizers, there were 72 statements that fell into group A and 27 that fell into group B.In group A, the system ranked 53% (38/72 BEL statements) of the evidence sentences that provided evidence for extracting complete BEL statement at the top, while the remaining 47% were ranked within the top 10. This result indicates that while the system was successful in retrieving at least one fully relevant evidence sentence, its inability to rank those sentences on top of the lists (for 34 [47%] of the BEL statement) reveals the limited capability of the ranking component of the system.HGNC entities, which is higher than the other namespaces, especially when compared to MGI entities, which was only 19%. One possible explanation for the lower performance in finding evidence statements for humans (HGNC) compared to mice (MGI) may be due to the bias in existing literature towards mice (more than humans). These findings suggest that there is enough scope to further improve the ability of BELTracker to identify and rank sentences that contain evidence for extracting a complete BEL statement.We further investigated why the system was unable to retrieve any sentence that will help construct the complete BEL statement within the top 10 for group B statements. We counted the number of retrieved citations from PubMed abstracts and full texts and compared that number to group A. The average number of retrieved citations for statements was higher (650) for group A compared to group B (510). We speculate that our decision to make the entities the only mandatory component to match in the evidence sentence was a key reason for this difference. We further analyzed the performance of BELTracker by observing its trend across different entity namespaces. The performance of BELTracker was better when evaluated for finding sentences that contain necessary context for extracting a BEL statement. First, under context criterion, the system was able to retrieve at least one relevant evidence sentence for 81 out of 97 statements, which is 9% higher than the full criterion. The system has the potential of extracting more contextual information for BEL statement curation than identifying the correct sentence to extract a complete BEL statement.Subject and object are concepts from the UMLS Metathesaurus and predicate is a relation from the UMLS Semantic Network. These semantic predications will allow BELTracker to take advantage of the already extracted relationship between concepts to judge further their relevance to a given BEL statement.We also observed a considerable difference in the performance of the system (61% for context vs. 39% for full) during manual evaluation by organizers under the two criteria. In both At this moment, the system employs various heuristics, based on the lexical features, to rank the evidence sentences. As pointed out in an earlier section, we plan to address this by extensively taking into account the semantic features embedded in a BEL statement in order to further improve the accuracy of the system. BELTracker has a very long response time for a given query, usually on the order of a few minutes. This slower response time needs to be addressed, given that users typically expect responses closer to a few milliseconds from IR. All the ranking methods operate at the sentence level. In the initial design, most of the pre-processing, such as sentence tokenization etc., were handled as they happened, which resulted in a very slow response from the search engine. Recently, we revamped the implementation by pre-indexing all the PubMed abstract sentences. Pre-indexing sentences considerably improved the response time of the system from a few minutes to very few seconds. We hope to improve the processing time further by pre-indexing all metadata components of BEL statements, including entities, functions, and relations.We participated in the BEL track of BioCreative V and developed an information retrieval system called BELTracker to retrieve and rank evidence sentences for a given BEL statement. Our system contains three components: (i) query translation, (ii) retrieval and (iii) ranking components. BELTracker retrieves evidence sentences from PubMed abstracts and full-text articles available at PubMed Central. Our system was able to rank at least one evidence sentence among top 10 returned sentences for 72 out of 97 BEL statements of the test set. The precision of our system under full, relaxed, and context criteria was 39, 53 and 61%, respectively. Our error analyses indicated the need to explore a more sophisticated ranking mechanism that considers semantics to improve the precision of the system. We were the only group to participate in this task.This work was supported by the grants from the National Institutes of Health .Conflict of interest. None declared.


Connexins (Cxs) constitute Gap Junction Channels (GJCs). GJCs connect the cytoplasm of adjacent cells providing a hydrophilic path between cells that allow the movement, by passive diffusion, of water, cations and small molecules. The opening or closing of GJCs is dependent on the voltage difference between the apposed cells and/or the membrane potential. An approach to understand the voltage gating mechanisms of GJCs is to study a simplified system that can account for the basic features of a GJC.In this work, we have devised a series of simple systems bearing in mind that idea. The systems here presented are: i) a dual membrane, ii) a dual membrane with a pore on each membrane, iii) a dual membrane with a channel connecting both membranes and iv) a dual membrane with a channel having explicit charges inside.In all cases, membrane and pore were built solely with carbon atoms. Both equilibrium and non-equilibrium MD simulations were performed in all systems. Non-equilibrium simulations were produced by applying a uniform external electric field in order to produce a potential difference across the membranes. We then performed detailed analyses of the electrostatic potential, ionic current and the potential of mean force of an ion through the system pores.This study provided important insights regarding the behavior of the electrostatic potential and ion currents inside simple dual membrane systems with or without a connecting channel, and will be useful in understanding the voltage effects and ion transport mechanisms of GJCs.