A commercially available microfluidics flow cell was utilized together with widefield fluorescence microscopy to evaluate the effects of disinfectants on bacterial strains. The flow cell’s inner surface supports the formation of biofilms of numerous bacterial species. The modular setup of the flow cell accessories allows connection to syringes, pumps and collection vials, facilitating aseptic experiments in a controlled fluidics environment which can be documented with precisely timed microscopy imaging. The flow cell is inoculated with a suspension of bacteria in a nutrient medium and incubated for several days allowing bacterial cells to form a biofilm. Shortly before performing an assay, the biofilm is labelled with a dual-fluorescent DNA probe which distinguishes unharmed and damaged bacteria. Then a disinfectant sample (or control) is gently injected and time-lapse imaging is used for quantifying the course of bacterial biomass response. We use a simplified widefield microscopy method that allows intensive recording and quantification of time series of two-dimensional frames for tracking the course of disinfectant action on a variety of microbial strains. This procedure has potential for the rapid evaluation of novel products. Although the capability of biofilm formation has been recognized as a powerful trait of many microbial species for decades , the strThe disinfectant industry has gradually accepted the idea of using biofilm-based methods such as the Minimum Biofilm-Eliminating Concentration (MBEC) assay  for testPrevious pilot experiments suggested that using nucleic-acid fluorescent probes helps visualize the dynamics of disinfectant action in bacterial biofilms ,5. In a 0.4 Ibi Treat; Ibidi, Fitchburg, WI, USA) with approximately a 60-fold larger channel profile which diminishes the negative effects of viscous forces and generation of micro-bubbles.Our goal was to develop a quantitative procedure bridging the gap between bulk methods and detailed  microscopy studies. To this end, flow cells appeared to be the first choice apparatus, as they were considered a favourite tool for biofilm research. However, the most frequently used microfluidics system  was n2) areas of biofilm that captures the emission of the entire thickness of the biofilm, (2) a high-sensitivity sCMOS camera that requires a fraction of the illumination energy normally needed for the CLSM  system and (3), the open source software package  to binarize the entire time series of frames simultaneously. We also acknowledge that a substantial drawback of our initial experiments using time series of microscopic images )Pseudomonas aeruginosa caused the green signal to be 18 times stronger in dead cells than in live ones. In the Gram-positive Staphylococcus aureus, the Syto 9 emission was equal in both live and dead cells in the absence of PI. The Syto 9 signal decay (bleaching) was faster in dead cells over a period of two hours in both P. aeruginosa and S. aureus. We assumed that in our assays, at least photo-bleaching was minimized, as we used a widefield microscope that was highly light efficient and therefore preserved sample viability better than most confocal microscopes [However, the use of fluorescent probes that intercalate in the bacterial DNA as indicators of viability is still subject to ongoing research. Generally, it is assumed that the increase of red (PI) signal is correlated with the plasma membrane permeability of the bacterial population and can be interpreted as a marker of cell death. This phenomenon may or may not be accompanied with the decay of the green (Syto 9) signal, which is partly caused by the displacement of Syto 9 molecules intercalated in the chromosomal DNA with PI molecules , howeverroscopes . With ouHypothetically, an alternative approach would be to run the assays at higher magnification with maximum intensity projections (MIPs) of the CLSM stacks of biofilms. This way individual cells could be resolved for acquiring the real proportion of live and dead fraction. However, aside from greater complexity, this procedure could yield substantially different time responses of either probe emission due to a fundamentally different pattern of exposure to excitation light between the widefield and confocal microscopy.In our study, the fluorescence of bacteria was quantified as the proportion of white pixels in individual binary frame matrices . We used a default global thresholding algorithm of FIJI based on the probability density functions of the foreground and background .(a)Simultaneous start of metering and time-series recording(b)Start of recording just after delivery of the bactericidal agentThe assays sampled a range of agents with different antimicrobial potency, albeit based on similar combinations of basic components. We utilized the advantage of precise agent metering for exploring their action following two approaches:S. epidermidis, the approach (a) helped demonstrate the almost instant decline of the Syto 9 signal when employing both fast- (Product “D”) and slow-acting (Product “C”: In the Gram-positive While approach (a) can detect the onset of disinfectant action and is sensitive to differences among products, its quantification may become very complicated. To simplify the logic, approach (b) starts with a time point when the flow stopped, i.e., the recorded fluorescence emission is mostly independent of fluid motion. Both fluorescence signals start the time series with an initial spike. Then the Syto 9 emission appears to decline invariably and the PI signal typically rises. The rate of its increase is well defined and may be indicative of differences among bacterial strains and disinfection products. Apparently, this approach is not applicable to fast-acting agents  unless they are diluted. Otherwise, all cells would be dead before the time series starts.The agents tested in our assays had more than one way to impact the in vitro biofilm microbiome. The detachment of bacterial cells from the surface being disinfected was an additional major effect. Depending on a particular species or strain, this complicates the testing method based on direct observation of changes at the cellular or micro-colony level. Using approach (b), the delivery of “agents” to the viewing frame had been calibrated so that BacLight probes were not washed out excessively. Still, there was some instability around the start of the time series due to spikes of fluorescence signal and residual motion of the channel contents. These initial spikes of fluorescence signal were generally observed in all series following both approaches. If taken into account, all other values of the sequence would become mostly negligible and the proportional dynamics of the signal would have low resolution. Therefore, we regarded them as outliers and excluded them from the datasets. The remaining values were then recalculated as percentages of the pixel count in the brightest frame for easier visual inspection of graphs.Specifically, in Runs 1–4  happened in tens of minutes. Inspecting PI signal increase in Variation of the white pixel proportion with time allowed us to compute the rate of change and the doubling time (or decay time) of the total red or green signal emitted by the biofilm. This conversion of captured fluorescence values to rates makes the evaluation of cellular-level changes independent of cell density, thus sufficiently robust even when a substantial part of the observed population is removed by the agent. Along these lines, it is also worth noting that the rate of change over a certain longer interval equals the average of the rates over short equal segments of that interval.Using approach (b), the highest value of the PI (red) fluorescence emission (100%) was typically found near the end of the time series. Thus, we were assessing the portion of the final damage achieved at particular points of the sequence. As we chose the length of the assay , we estimated the timing of proportional damage relative to the arbitrary final outcome. It is unclear if the PI emission kept increasing after the time series stopped. The information is also missing on whether all or only a fraction of the population was harmed. On the other hand, the shape of the emission course indicates either its continuation when still increasing at the end (positive rate of change or doubling time) or stagnation when a plateau is reached (rate of change near zero).The results of assays with the simultaneous onset of metering the fluid and the time-lapse record, i.e., approach (a), may reflect a rinsing effect by the steady influx of fresh solvent  above the biofilm, constantly eluting fluorescent probes from the biofilm’s exopolysaccharide matrix (EPS) and the surface of bacterial cells. The probes had been partly removed before they could be absorbed by the cells. When bactericidal agent (“C”) was brought instead of the “solvent”, the cells were made permeable and, possibly, the structure of EPS was altered. Consequently, the biofilm absorbed more PI which caused longer-lasting red emission. At the same time, the Syto 9 signal was quenched faster than in control, as the DNA was being broken down.It remains to be explained, why the fluorescent events happened much earlier than one millilitre of fluid would have been injected, since the leading edge of the agent arrival was expected about 30 min after start according to the “fast flow” calibration (see 3. Recommended Procedure). Given the physical dimension of the Ibidi channel profile , slow flow velocity probably caused major deviation from a laminar (plug-flow) pattern.We presented here a novel technique of using a commercially available microfluidic device and time-lapse fluorescence microscopy to study the effects of disinfectants on bacterial biofilms. This method can be used to bridge the gap between bulk measurements such as MBEC  and the From a practical viewpoint, the test described here is relatively fast, and when used with a well-defined biofilm, it has the potential for high reproducibility. In addition to capturing the course of damage to bacterial cells, it can help quantify the biofilm detachment (sloughing) caused by a tested agent. Since it is based on the dynamics of nucleic acid response, this approach may also be considered for testing antifungal, anti-protozoan or even antiviral products. However, before making conclusions about a particular disinfectant taken to the test, the results must be validated by comparison with alternative methods.


Long polymeric chains highly stretched and aligned with the flow confer a strong mechanical anisotropy on a viscoelastic solution. The electrically-driven transport of free ions under such conditions is far from being understood. In this paper, we determine experimentally whether the above-mentioned deviation from isotropy affects the electric charge transport across the liquid. To this end, we measure the electrical conductivity in the flow (stretching) direction of the cylindrical liquid filament formed in the elasto-capillary thinning that arises during the breakup of a viscoelastic liquid bridge. First, we examine the behavior of monodisperse solutions of polyethylene oxide (PEO) in a mixture of glycerine and water. For all the concentrations and molecular weights considered, the filament conductivity remains practically the same as the isotropic conductivity measured under hydrostatic conditions. However, we observe a decrease in the electric current at the end of elasto-capillary regime which may partially be attributed to the reduction of the liquid conductivity. Then, we measure the conductivity of bidisperse solutions of PEO with very different molecular weights. In this case, a significant decrease in conductivity is observed as the filament radius decreases. This constitutes the first experimental evidence of ion mobility reduction in stretching viscoelastic filaments, a relevant effect in applications such as electrospinning. Many applications involving liquid handling at microscales take advantage of the inherently clean action of electromagnetic fields. Assuming the most usual case where magnetic effects are negligible, electric fields drive the motion of ions normally present in liquid solvents. In most cases, solvents are molecularly simple, and the classical ohmic conduction model can be adopted to calculate the current density at any location. In this model, the liquid is assumed to be isotropic, and the proportion between the current density transported by ions and the electric field acting on them is a scalar conductivity which depends only on the liquid temperature.In many microfluidic applications such as electrospinning ,2,3, theA is a constant and In the elasto-capillary thinning emerging during the breakup of liquid bridges of polymer solutions , an esseion time . The filion time . As a coion time ,10, and ion time  and blision time  instabilThe capillary breakup of a liquid bridge constitutes an excellent test bench to measure the conductivity in the flow (stretching) direction, al drops . The micng point ,14. The r weight ,15, whicr weight .Polyethylene oxide (PEO) is a biologically inactive yet electroactive polymer, which has found an enormous range of applications from Pharmacy (including COVID-19 vaccines) and Biotechnology to lithium batteries. The PEO molecule contains both hydrophobic and hydrophilic sites with hydroxyl and hydrogen terminations at its two ends. These characteristics endow the molecule with complex behavior and the capacity to respond to numerous mechanical and physicochemical actions. For instance, high-molecular-weight PEO polymers can form relatively big, macroscopic spherulites susceptible to large mechanical deformations .Rubio et al.  have recIn our experiments, a viscoelastic liquid bridge was formed between two horizontal disks 1 mm in radius by injecting liquid with a syringe pump  through an orifice 200 per disk . The edgA direct current (DC) voltage drop was set between the upper and lower (grounded) disks using a power voltage supply . The electric potential was applied during the last phase of the liquid bridge quasi-static stretching to avoid the liquid heating produced by the Joule effect. In addition, a resistor of resistance Digital images of the liquid bridge were acquired with two synchronized high-speed cameras with optical axes perpendicular to each other . One of In the experiments, the liquid bridge was broken with the slow retraction method ,19. A libe small . A voltaechnique  to deterThis liquid bridge configuration has been widely used as a test-bench to study the stretching of a viscoelastic filament. However, other configurations are possible for our study too. For instance, the dripping-onto-substrate configuration ,23 wouldw/w). We used deionized water for synthesis  ) with a conductivity w/w), and then PEO with w/w). We prepared stock solutions by dissolving the polymers in the solvent with a magnetic stirrer at low angular speeds to minimize the mechanical degradation of the long polymer chains. We took special care with the manipulation of our reagents to exclude degradation by contaminants, such as halide ions. As mentioned in the Introduction, PEO is an ideal candidate for our study. Nevertheless, we also examined the behavior of poly(acrylic acid) (PAA). In this case, the filament did not adopt a quasi-cylindrical shape over the elasto-capillary regime, which (as explained below) prevented us from accurately measuring the filament’s electrical conductivity. We also verified that the use of pure water as solvent produced the same undesirable effect.The fluids used in the experiments were solutions of PEO in a glycerin-water mixture 50/50% (ions see . In the The density ) method . The ext samples . We usedAs mentioned above, a quasi-cylindrical liquid filament of radius his work . We madeR and length ℓ, voltage drop across the filament I, and the conductivity Equation  does notℓ, which may produce small spurious variations of that quantity. The main conclusion is that the electrical conductivity is practically the same as that measured under hydrostatic conditions 1/2Rg  at the elues see , and theWalden’s rule  establisR when PEO100K is added to the solution. This is probably due to the early orientation of the polymer molecules in the axial direction as soon as a filament begins to form. We cannot ascertain if the reduction of the solution conductivity in In the second set of experiments, we measured the conductivity of bidisperse solutions of PEO with very different molecular weights. For the sake of illustration, iscosity . There aR larger than about 7–10 We measured the electrical conductivity in the stretching direction of thinning viscoelastic filaments of PEO dissolved in a mixture of glycerine and water. The conductivity of monodisperse solutions of polymers with high molecular weights remained practically the same as that measured under hydrostatic conditions for filament radii Our results constitute the first experimental evidence of ion mobility reduction in thin viscoelastic filaments, an important effect in applications such as electrospinning. In this technique, viscoelastic filaments with diameters much smaller than those analyzed in our study are extruded under the action of an externally applied electric field. Therefore, the reduction of the electrical conductivity is expected to be much more noticeable in that case.As mentioned in the Introduction, isotropic bulk conductivity is the simplest model of charge transport owing to dissolved ions. However, the formation of diffuse electrical double (Debye) layers near the free surface can significantly affect charge transport in that region. On a macroscopic scale, these surface electrokinetic effects are typically accounted for with a surface conductivity ,28,29. Nt radius , and, thOur experiments allow us to measure the electrical conductivity While it has been shown that the presence of macromolecules in electrolyte solutions decreases the electrical conductivity , the eff


Due to their large scales, many biomolecular interactions are difficult to be simulated via all atom models. An effective approach to investigate the biomolecular interactions is highly demanded in many areas. Here we introduce a Structure Manipulation (StructureMan) program to operate the structures when studying the large-scale biomolecular interactions. This novel StructureMan tool provides comprehensive operations which can be utilized to study the interactions in various large biological systems. Combining with electrostatic calculation programs such as DelPhi and DelPhiForce, StructureMan was implemented to reveal the detailed electrostatic features in two large biological examples, the viral capsid and molecular motor-microtubule complexes. Applications on these two examples revealed interesting binding mechanisms in the viral capsid and molecular motor. Such applications demonstrated that the StructureMan can be widely used when studying the biomolecular interactions in large scale biological problems. This novel tool provides an alternative approach to efficiently study the biomolecular interactions, especially for large scale biology systems. The StructureMan tool is available at our website:  Studying interactions between biomolecules is an important but challenging task. In recent decades, many efforts and progresses have been made to study the biomolecule interactions  program to manipulate the biomolecule structures. Four basic and two advanced structural operations were developed to manipulate the structures of biomolecules. These basic operations are developed for two biomolecules (a receptor and a ligand), which include separation, spin, rotation and perpendicular shifting between a pair of receptor and ligand. Furthermore, two advanced operations were developed to study the assembly of multiple biomolecules in pseudo spherical or pseudo cylindrical symmetry. The pseudo spherical operations, including capsid generation, capsid expansion and capsid detachment tools, can be widely used to study the viral capsid assembly problems. With StructureMan, users can easily manipulate the structures of a complex to study the electrostatic interactions for large systems, such as protein-protein interactions in a whole virus capsid or a large piece of microtubule. In this work, we applied the StructureMan on a viral capsid and a molecular motor, which demonstrated that this novel tool is very useful when studying large scale biomolecular interactions.Turnip crinkle virus (TCV) is a plant pathogenic virus which is composed of ~4.0 kb plus-sense RNA and 180 copies of capsid protein subunits , which include separation, spin, rotation and perpendicular shifting between a pair of receptor and ligand. Furthermore, two advanced operations were developed to study the assembly of multiple biomolecules in pseudo spherical or pseudo cylindrical symmetry. These pseudo spherical operations, including capsid generation, capsid expansion, and capsid detachment tools, can be widely used to study the viral capsid assembly problems. For the purpose of demonstration of the advanced operations, the protein capsid of the TCV, and the kinesin-microtubule complex were chosen in this work because of their representative pseudo spherical  and pseudo cylindrical symmetry.With two separated coordinates files of the protein units as inputs, this tool would displace one of the two units in a user given distance away from the other. For clarification, one of the protein units would be fixed in its original coordinates and is referred as the fixed unit. The other protein unit would be manipulated to result in different positions and orientations and is hence referred as the manipulated unit.Cfixed, and Cmanipulated, by averaging the coordinates of each individual atom after weighted by their corresponding atomic mass , xz-plane or yz-plane as users prefer. Rotation in xy-plane is discussed here for a simplified demonstration.fixed using the method demonstrated in the section above. A vector, fixed to a randomly chosen atom A in the manipulated unit can be created  do not actually contain the coordinates of all the protein units within the complexes, making it inconvenient for researchers who study protein-protein interactions among multiple protein units. However, instructions on how to construct the missing units from the given units are given as BIOMT matrices . Within As shown in a to l are provided by the BIOMT matrices , by which the structure of a capsomer and the BIOMT matrices were provided. Using the capsid generation tool, 60 copies of capsomer structures were generated and assembled into the native structure of TCV capsid . These cThe next tool allows the shifting of all capsomers away from the mass center of the whole capsid resulting in a viral capsid expended by a user desired distance .capsid, which would be done in a similar manner to the capsid generation tool. When determining the mass center of TCV capsid, this tool first calculates the mass center of the primary capsomer, and then transformed the obtained coordinates according to the given BIOMT matrices. Sixty copies of coordinates would be generated and presenting the mass center of corresponding capsomers in the TCV capsid. With these coordinates, the coordinates of the mass center of the whole TCV capsid can then calculated by averaging the mass center coordinates of its individual capsomer, as shown in the following equation:The first step in this operation is to find the capsid's mass center, CN is the total number of capsomers, xn, yn, and zn are the coordinates of the mass center of the capsomer n.Where Next, the tool generates the 60 copies of vectors, Ccapsid(x), Ccapsid(y), and Ccapsid(z) are given in Equation (2), CN(x), CN(y), and CN(z) are the coordinates of mass center of the capsomer n.where M, which is defined using the following equation:The shifting (expansion) distance can again be defined by user. To make it more convenient of users who don't have direct measurement of the desired distance, we introduce the concept of a mean radius of mass distribution, the mean mass radius Rxi, yi, and zi are the coordinates of atom i, ri is the distance between a single atom i and the mass center of the capsid Ccapsid, mi is the atomic mass of the corresponding atom, and MT is the total atomic mass of all atoms within the capsid.Where Ccapsid to the mass center of individual capsomer are equal. The calculation of the RM can be simplified to one step using following equation:Thanks to the icosahedral symmetry of the vial capsid, the distances from Ccapsid, and that of the one capsomer n is needed.where only the coordinates of the capsid mass center d, a expansion vector, With the given distance, Finally, the tool generates the expanded structures based on the given primary capsomer, BIOMT matrices and the calculated expansion vector a to l are provided by the BIOMT matrices  from −90Å to 90Å in 2Å interval , strong attractive electric field lines are present between the detached capsomer and the rest of the capsid , which suggests that the electrostatic binding forces guide capsomers from long distance during the viral capsid assembling process.While one chosen capsomer was detached from the rest of the capsid, DelPhiForce was utilized to calculate the electrostatic binding forces, which were represented by orange arrows in  further . The fore capsid . This inThe binding forces between the detached capsomer and the rest of the capsid were also calculated while the detached capsomer was spun  to offer comprehensive operations for the structures in large scale biomolecular interactions, such as interactions in the viral capsid and molecular motor-microtubule complexes. This StructureMan tool contains operations which can be utilized to study the interactions in large biological systems. Combining with electrostatic calculation tools such as DelPhi and DelPhiForce, StructureMan can be used to reveal the detailed biomolecular interactions. Two examples are demonstrated in the results and discussion section, which show that the StructureMan is beneficial when studying the biomolecular interactions in large scale biomolecular complexes.There are four basic and two advanced operations developed in the StructureMan. Note that these basic operations in StructureMan are different from those in existing visualization programs. Those tools in the existing visualization programs can shift or rotate a single biomolecule; However, these four basic operations of StructureMan take two biomolecules as inputs and do the operations between the two biomolecules. For examples, the separation tool in StructureMan shifts the ligand from the receptor in the direction of their mass center connection line, while the existing tools can only shift a single protein. If users want to shift a ligand from the receptor in the direction of their mass center connections, users need to calculate the shifting vector first and normalize the vector, then use the existing tools to shift the ligand. The rotation operation in StructureMan rotates the ligand around the mass center of the receptor. This operation cannot be easily achieved by the existing visualization programs. Instead, users need to write some script to complete such an operation. Advanced operations in StructureMan are even more comprehensive. For example, the capsid expansion operation takes one capsomere as input structure and generates a structure of expanded capsid in which every two adjacent capsomers are separated by a distance defined by the users. Such an operation is not in any of the existing tools. Another advantage is that the StrucrueMan is written in shell script, which can be easily used to handle large number of structures (such as a big number of frames from MD simulations).In this work, we first focused on investigations on electrostatic interactions among capsomers of TCV using the StructureMan. The charge distribution on the inner surface of the viral capsid indicates that the capsomers stabilize the packed genomic RNA, as observed in previous studies. On the outer surface of the viral capsid, strong attractive electric field lines imply that the electrostatic interactions guide capsomers to build the viral capsid. The “expand” operation shifted each of the capsomers away from the rest of the capsid, which reveals that the attractive interaction among the capsomers is a key factor to stabilize the whole viral capsid.M (~97Å), which suggests that the electrostatic binding forces guide capsomers from long distances in the viral capsid assembling process. The spin and rotation operations in the StructureMan show that the electrostatic forces contribute in adjusting the orientations of the capsomers to the native orientations.The StructureMan and DelPhiForce were utilized to further characterize the electrostatic binding forces in the viral capsid. Results demonstrate that the effective range of electrostatic forces between the capsomer and the rest of the capsid is about 0.7RSimilar analyses of the electrostatic binding forces were performed to the kinesin-microtubule complex, where the kinesin motor domain was manipulated by various orientations and distances. The results suggest that when the distance between the kinesin motor domain and the microtubule increases, the binding force toward the neighboring binding site becomes competitive to that toward the native binding site. The calculations from both rotation and spin operations reveal that the electrostatic interaction favors the native or near-native orientations of kinesin motor domain. When the orientation of kinesin motor domain is altered, the binding force reorients it to the native orientation by providing a repulsive binding force. In the operation where the kinesin motor domain was translated along the microtubule, the calculations demonstrate that the electrostatic interactions make significant contributions in guiding the kinesin by favoring the binding on the native binding sites and rejecting the non-native binding positions.http://compbio.utep.edu/static/downloads/script-for-munipulation2.zip.Besides the two examples demonstrated in this work, the StructureMan program is able to help the researchers to study many other large-scale biomolecular interactions. We expect the StructureMan to be combined with DFMD method (Peng et al., The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.LL and WQ proposed the idea, conducted entire research, and wrote the manuscript. YXia and YXie run the calculations, collected the data, analyzed the results, and wrote the manuscript. SS developed and tested the programing code. CK tested the program and revised the manuscript. All authors contributed to the article and approved the submitted version.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.


This paper demonstrates the development of an automatic mobile trainer employing inertial movement units (IMUs). The device is inspired by Neuro-Developmental Treatment (NDT), which is an effective rehabilitation method for stroke patients that promotes the relearning of motor skills by repeated training. However, traditional NDT training is very labor intensive and time consuming for therapists, thus, stroke patients usually cannot receive sufficient rehabilitation training. Therefore, we developed a mobile assisted device that can automatically repeat the therapists’ intervention and help increase patient training time. The proposed mobile trainer, which allows the users to move at their preferred speeds, consists of three systems: the gait detection system, the motor control system, and the movable mechanism. The gait detection system applies IMUs to detect the user’s gait events and triggers the motor control system accordingly. The motor control system receives the triggering signals and imitates the therapist’s intervention patterns by robust control. The movable mechanism integrates these first two systems to form a mobile gait-training device. Finally, we conducted preliminary tests and defined two performance indexes to evaluate the effectiveness of the proposed trainer. Based on the results, the mobile trainer is deemed successful at improving the testing subjects’ walking ability. Stroke is the second leading cause of death in the world , with a Unlike these aforementioned devices, which usually guide the users to follow certain preset movements, Neuro-Developmental Treatment (NDT) is a way to let patients have the feeling of walking with minimal physical intervention . TherapiThe proposed mobile trainer consists of three systems: the gait detection system, the motor control system, and the movable mechanism. First, we apply IMUs to develop a gait detection system that can record the angular velocities of shanks and detect important gait events during walking. Compared with the optical motion capture systems, such as VZ4000  and VICOThe paper is arranged as follows: The mobile trainer is shown in The motor control system is composed of an Arduino Mega2560 to imitate the therapists’ intervention patterns, two MPK569-2.8A motors  to proviThe movable mechanism integrates the gait detection system and the motor control system. It has four casters so that the subject can push the trainer forward when receiving training. It is also equipped with an adjustable handle and a belt for safety. The ropes connect the motors to the user’s pelvis to guide the user based on the therapists’ intervention patterns. The system specifications are illustrated in The gait detection system contains two measurement units and one data logging unit, as shown in Human gaits are regular and periodic. A complete gait cycle is defined as a period from the heel strike (HS) to the next HS of the same leg, as shown in We applied IMU to measure the angular velocity of the shank on the sagittal plane ,31, i.e.The MS eventω is locally maximum and greater than this threshold, as follows:The MS usually accompanies the maximum angular velocity in the gait cycle. As shown in 2.The HS event(1)ω reaches a local minimum.The angular velocity (2)ω is less than The angular velocity (3)The time interval between MS and HS, labelled as The HS usually happens with the first negative trough after the MS, as shown in T is the sampling time (0.042 s). For instance, one positive peak (n=1) was evident between HS2 and the next TO, so that Referring to 3.The TO event(1)The angular velocity (2)The time interval between HS and TO, labelled as The TO event happens after the HS and normally with the minimum angular velocity within one gait cycle. We defined two thresholds, Referring to The system also calculated the average stride time of the previous three gait cycles and set it as an upper limit for The gait detection system applies these algorithms to detect the three gait events  sequentially, as shown in We invited two stroke patients to participate in experiments. Their information is shown in We checked the effectiveness of the gait detection system by comparing its results with a VZ4000 motion capture system . The sucribed in . The walThe trainer is designed to repeat the therapists’ intervention using ropes. As observed in , the theThe block diagram of the motor control system is shown in The robust loop-shaping techniques were applied to design the following controller, as illustrated in We then designed the following pre-compensator Before conducting clinical experiments, we tested the motor control system by attaching the ropes to a subject. The detection system sent triggering signals to the motor control system when it detected the HS events. The motors, when receiving the triggering signals, began to track the sinusoidal commands then followed a minimum force of 1 lb to keep the ropes straight. Note that the motors would follow a force command of 1 lb if the system failed to detect the HS events. This small force would not harm the users. The testing results are shown in We invited five subjects, including three healthy subjects and two stroke patients, to test the mobile trainer. The information of these subjects is illustrated in Each subject received the tests by the following (1)Ratio of the swing time : The ratr to one . (2)Asymmetry of the swing phase : The asyBecause gait symmetry is critical for post-stroke walking rehabilitation, we defined the following two performance indexes to evaluate the effectiveness of rehabilitation:We used the two indexes to analyze the testing subjects’ gaits at different stages. The experimental results are shown in This paper has demonstrated a mobile trainer designed to help with rehabilitation training of stroke patients. The device consisted of a detection system, a motor control system, and a movable mechanism. First, the detection system was equipped with two measurement units and one data logging unit. The former detected the subjects’ gait data, while the latter transmitted and recorded the data. Gait estimation algorithms were then developed to identify three important gait events: MS, HS, and TO. Second, we conducted clinical NDT training by therapists and recorded the data to describe the therapists’ facilitation patterns. The motor control system was then designed to reconstruct these patterns. Lastly, the detection system and the motor control system were integrated on a movable mechanism, so that the users can move at their own preferred speeds during the training. We conducted experiments and defined two performance indexes to evaluate the effects of the proposed trainer. Based on the results, the automatic trainer was shown to improve the subjects’ walking ability. In the current study, we set strict criteria in recruiting subjects who should be in stable states of stroke and have no other musculoskeletal problems in order to reduce the risk of injuries during the experiments. In the future, we plan to invite more stroke subjects with varieties of gait deficits to participate in more ambulation training to evaluate the long-term rehabilitation effects.


More and more researches have been carried out recently on Weigh-In-Motion (WIM) technology for solving the traffic safety problems caused by overload. In this article, we aim to study the measurement accuracy of the WIM system. Based on the electromechanical theory and elastic half-space method, we establish a theoretical model of multi-layer structure to investigate the correlation between the output voltage of the piezoelectric sensor and the applied force. In addition, we performed cyclic and moving load experiments to verify the accuracy of the analytical calculations. The load magnitude identified by this theoretical model matched the experiments very well, which shows that this model is effective for the WIM system. In addition, we proved that the load frequency is an important factor affecting the measurement accuracy of the sensor, which further enables us to design more suitable sensors for certain use scenarios. Overloading is one of the main factors of traffic accidents, causing severe economic losses and casualties, and also significantly reducing the service life of roads and bridges. To ensure driving safety, the static weighing scale has been widely used at toll station on highways. The vehicle gross weight can be measured with high accuracy in this way, but it cannot get the single axle weight. Moreover, there are several obvious disadvantages including low efficiency, high expense and even causing traffic jams. During the past decades, the traffic flow has been increasing continuously, which makes the static weighing methods no longer able to meet the requirements in some scenarios.As an improvement, the weigh-in-motion (WIM) method  may direThere are mainly three types of sensors in the aforementioned or other researches used in WIM systems: strain sensor ,13, piezThere are mainly three types of commercial WIM equipment at present: curved plate, scale table and piezoelectric quartz. It is difficult to be widely used due to the expensive price. Therefore, there is an urgent need to resolve the contradiction between the price and accuracy of WIM system. In this paper, we proposed a model based on the electromechanical model and multilayer elastic half-space theory which can correlate the output voltage signals generated by the PZT sensor with the stress applied to it. Then, entering the voltage signals induced by a series of laboratory test experiments, the calculated results showed that high accuracy of identification can be obtained by this model.In laboratory tests, cyclic loads and moving loads experiments were performed to verify the accuracy of the identification theory. The cyclic loads experiment included the wheel tracking test and Mechanical Testing Simulation (MTS) test a. The diTwo different piezoelectric sensors including PZT chip and PZT bimorph were, respectively, used for cyclic loads and moving loads experiments. The positive and negative electrodes of each PZT sensors were connected by wires, which can output electrical signals to the oscilloscope when the PZT sensors were subjected to an external force. To investigate the effect of embedded depth of PZT sensors on the results, the PZT chips were embedded at four different depths . The geometric and electrical parameters of piezoelectric sensors were described in Compared with cyclic loads experiments, the moving loads experiments revealed the effect of the multi-axis effect on the actual road surface. The identification of cyclic loads and moving loads using piezoelectric sensors will be demonstrated based on the general theory of piezoelectricity  and the (1) The PZT’s electromechanical responses do not affect the mechanical behavior of the tracking board;(2) The dynamic effects of the system are ignored and the electromechanical procedure of the system is in a quasi-static state;(3) The PZT chip has the same strain with rutting plate at the same place, that is (4) The contact length (~20 mm) between the wheel and the rutting plate is comparable with the diameter of PZT chip, the PZT chip is therefore assumed in a state of simple compression through thickness direction.V is the voltage drop across the sensor thickness. According to the theory of piezoelectricity, the electric charge generated at the electrode-covered surface of the PZT sensor is R is the electrical resistive load of the oscilloscope. Combining the expression of current and strain correlation with Equation   (8)σz(t)quations , the corified asμ¯33hpmdVThe contact area between the rutting plate and load area of the MTS test is much larger than the wheel tracking test. Therefore, the Possion effect can be ignored and the thickness-wise electric displacement may be simplified asa is the radius of loads. Then, the strain of PZT sensors can be calculated asThe load applied to rutting plate can be simplified as bowl-shaped distribution and along the radial direction can be read aslated asεz,p=εz,bEquation , the cort.The load Equation  with resThe material properties of the PZT sensors used in our experiments are provided by the manufacturer. The material, geometrical, and circuit properties of PZT chips are T sensor a gradualT sensor c agrees T sensor c. FigureCompared with the wheel tracking test, the MTS test increased the loading frequency and magnitude. Besides, the contact area between the load and the rut board covered almost the entire rut board. Therefore, there was no stress attenuation along the depth direction. In moving load identification experiments, the trolley moved on the track at a certain speed with four different loads . The stress applied to each wheel is considered approximately equal and the stresses with different loads are, respectively, 0.34 MPa, 0.59 MPa, 0.85 MPa, 1.1 MPa. The four different voltage peak signals a correspIn summary, we proposed a theoretical electromechanical model that can correlate the output voltage signals generated by PZT sensors with the stress applied to it. A series of laboratory tests were performed to verify the accuracy of this model. Through the MTS test, we showed that the identified load magnitude increases as the loading frequency increases, which indicates that the loading frequency is a significant impact factor on the identified results. In the wheel tracking experiments, the maximum and minimum errors are, respectively 23.01%, 0.64%. In the MTS test, the maximum and minimum errors are, respectively 18.70%, 0.88%. In the moving load experiments, the maximum and minimum errors are, respectively 23.53%, 5.08%. According to the ASTM E1318 Standard Specification for Highway WIM Systems, almost all the relative errors of identified load results of three different experiments were at the range of permissible error ±20%. Both the identified results calculated by the model of cyclic load and moving load experiments exhibited a high accuracy, which suggests that this model is promising for application in improving the measurement accuracy of the WIM system. However, as this model ignores the dynamic effects of load, it might cause different identified results under the same load at different frequencies. Further investigation on the practical influence of load frequency in designing more suitable sensors for certain use scenarios remains an open topic for future study.


A rapid increase in the number of patients with dementia, particularly memory decline or impairment, has led to the loss of self-care ability in more individuals and increases in medical and social costs. Numerous studies, and clinical service experience, have revealed that the intervention of nonpharmacological management for people with dementia is effective in delaying the degeneration caused by dementia. Due to recent rapid developments in information and communications technology, many innovative research and development and cross-domain applications have been effectively used in the dementia care environment. This study proposed a new short-term memory support and cognitive training application technology, a “positioning and shadowing system,” to delay short-term memory degeneration in dementia. Training courses that integrate physical and digital technologies for the indoor location of patients with dementia were constructed using technologies such as Bluetooth Low Energy, fingerprint location algorithm, and short-range wireless communication. The Internet of Things was effectively applied to a clinical training environment for short-term memory. A pilot test verified that the results demonstrated learning effects in cognitive training and that the system can assist medical personnel in training and nursing work. Participants responded with favorable feedback regarding course satisfaction and system usability. This study can be used as a reference for future digital smart cognitive training that allows observation of the performance of patients with dementia in activities of daily living. Memory loss is a natural process of aging, but it greatly affects daily life and work. Dementia is regarded as a symptom of memory loss. It is an irreversible disease, and its prevalence increases with age. According to the Global Action Plan on the Public Health Response to Dementia initiated by the World Health Organization, a total of 47 million people had dementia in 2015, accounting for approximately 5% of the global aging population. The plan estimated that the number of people with dementia will increase to 75 million in 2030 and 132 million in 2050 [Relevant studies have confirmed that cognitive training can effectively improve the cognitive functions of patients with dementia and have an indirect positive effect on functional deficits in activities of daily living ,3. As anFurthermore, a digital age has emerged, with rapid development of digital technologies and the introduction of new digital applications. To escape conventional thinking and care models and to solve the problem of declining birth-rates, technology must be integrated into the lives of older adults. In recent years, information and communications technology (ICT) has been continually upgraded. In particular, the use of the Internet of Things (IoT) in mobile learning has achieved favorable results. The integration of IoT and mobile learning with related technologies is expected to achieve excellent results. On the basis of the IoT in the health care industry, this study proposed a short-term memory multi-sensory learning cognitive training model including Bluetooth low energy (BLE), a fingerprint location algorithm, and received signal strength indication (RSSI) integrated with digital, multimedia rehearsal materials and blended into context, with the aim of developing a set of short-term memory cognitive training courses suitable for patients with early dementia experiencing short-term memory loss. We constructed digital short-term memory training classes and a mobile positioning algorithmic mechanism to integrate the real-time virtual and real cognitive training technology, so the patients of short-term memory loss will be able to achieve basic self-life governing ability with the help of IoT technology and compensatory memory assistance. Furthermore, with the recording and storage functions of the information system, therapists, and practitioners are able to grasp key factors, such as cognitive changes and the learning willingness of older adults with dementia, which provides clinical decision-making support to medical-related units for improved disease control and treatment effects.This paper is the extended version of the conference paper . This exDrugs for the treatment of dementia have not been able to prevent damage or to restore brain cells, but medical and nonpharmacological treatments can delay disease development. Nonpharmacological treatment includes cognitive training, reality orientation therapy, validation therapy, reminiscence therapy, and horticultural therapy. Their purpose is to ameliorate a patient’s symptoms or delay the progression of the disease, enhance the patient’s quality of life, and reduce the burden on caregivers. Cognitive training is considered a means to improving memory. Since the brain controls cognitive functions, repetitive training can strengthen brain cell connections and thus improve attention, memory, language, and executive functions, and it can have an indirect positive effect on functional defects in activities of daily living. Using the aforementioned therapies in appropriate treatment can improve the social and professional integration of patients . CognitiMemory is a mental function used to retain and retrieve past experience for daily use. The process of memory consists of encoding, storage, and retrieval . AtkinsoIn addition to daily diet and daily routine adjustments, strengthening the brain is essential in preventing hippocampus degeneration and improving short-term memory. In clinical trials, numerous memory training methods have proved to be effective. Klingberg confirmed that the cognitive functions of the brain, such as short-term memory, information processing speed, and abstract reasoning ability, can be improved through training, particularly through an online digital training design that can effectively enhance cognitive abilities . In 1965Memory training is a commonly used method in cognitive training. The goal is to adapt to, understand, or reduce the effect of memory impairment on daily life through various strategies and techniques, such as designing memory aids or teaching memory-assisting skills. The focus of memory training is to prevent or slow further degeneration, not to restore memory ,14.Galante et al. conducted a single-blind randomized controlled trial of computer-assisted cognitive rehabilitation of 11 patients with AD and MCI . The comIn summary, relevant studies have confirmed that cognitive training can effectively improve the cognitive functions of patients with dementia. However, no virtual interactive memory training model using IoT has been developed for MCI in Taiwan or abroad. Therefore, a set of short-term memory cognitive training courses suitable for older adults was designed, and a multi-sensory learning environment was constructed through the IoT to increase motivation and willingness for continuous engagement with the courses to achieve cognitive training effects.A real-time location system based on wireless sensor networks is used to locate and track targets in real-time through received information. The modes of the real-time location system can be divided into time of arrival, time difference of arrival, angle of arrival, and received signal strength index (RSSI). RSSI positioning does not require special equipment or strict network synchronization, which renders it particularly suitable for large-scale wireless sensor networks. Li et al. classified three positioning modes or methods of RSSI for indoor use . The firThe third method is the fingerprint location algorithm see . A fingeThe actual environment designed for this study required participants to move using different walking paths, thus, the linear signal transmission states of the reader and the tag could not be constantly maintained. Therefore, triangulation positioning was unsuitable for use in the environment. Since the proximity method requires that numerous tags be used as reference points (RPs) in the environment, it was infeasible due to the cost of actual construction. In consideration of deployment cost and feasibility, a smart device was used to read the RSSI signal value in the environment to determine the location of the target. Moreover, a smart device can learn the current range of the participants and the location of the training point according to the results of mobile computing, which could assist the memory training of the participants.k = 2, with a positioning error of 1.5 m [Using only radio frequency for positioning cannot reflect the actual position due to factors such as diffraction and refraction in the environment. Bluetooth devices with a broadcast frequency of 2.400–2.485 GHz can provide an example. When multiple devices of the same frequency band are installed near the receiver, the devices are prone to conflict with each other, and the receiver is unable to receive signals correctly. Therefore, machine learning algorithms have been added in many studies to estimate the final position with improved accuracy. For example, Stavrou et al.  used a Bof 1.5 m . Subedi of 1.5 m  proposedof 1.5 m .k values to identify the data that produced the lowest median error in all test datasets. Using k = 3 had the optimal positioning results. For the multilayer perceptron method, the lowest error was found when the three hidden layers contained 8, 8, and 6 neurons and when the rectified linear unit activation function was used. In the support vector machine method, the lowest median error was based on the radial basis function kernel γ = 0.01, ε = 0.02, and C = 1. The final results suggested that the fingerprinting localization outperformed the triangulation method. In addition, KNN had the simplest deployment method and fewest parameters of the machine learning methods, and it was therefore chosen as the primary algorithm. Ke et al. used a fingerprinting localization algorithm to construct a BLE beacon-based location system for smart home power management [Cannizzaro et al. integrated triangulation and a fingerprinting localization with machine learning regression methods, including KNN, multilayer perceptron, and support vector machine, and performed analysis and comparisons for an industrial environment . He deplnagement . The stunagement . In accoThis study proposed a new short-term memory support and cognitive training application technology named a “positioning and shadowing system.” The system employed a short-term memory training model using an indoor positioning system to integrate physical and digital learning content. Based on perception and dementia compensatory memory aids, the system was combined with digital multimedia short-term memory and rehearsal materials to create a multi-sensory training environment for visual, auditory, and haptic perceptions. In addition, the intervention with the information system enabled the identification of key factors, such as cognitive change in older adults with dementia and their learning willingness, to achieve satisfactory disease control and treatment effects. The positioning method, training process, calculation mechanism, course module, and management platform designed for the study are introduced separately.The positioning method included two execution programs. First was a physical and digital learning content training model that integrated with an indoor positioning system. Second was a content-driven process design of a fixed-point interactive course. The digital content in the study was based on the digital content generated by the location point, as detailed later.The short-term memory training model environment design of indoor positioning systems integrating physical and digital learning content is shown in The fingerprinting localization was divided into a training phase and a localization phase. The training phase collected the signal characteristic parameters of each RP in the required positioning area and established a fingerprint database of the correspondence between the measured RSSI and its location. The localization phase used the fingerprint database and the parameters of the received signal to match the data in the database and to identify the nearest calibration points, which were ultimately used to estimate the location of the target node.n represents the path loss according to the communication environment and The principle of RSSI is to estimate the distance between the transmitter and the receiver, based on the relationship between signal intensity and distance decay. The distance calculation was as in Equations (1) and (2), where th beacon at the th scan. The range of The signals of the RPs in the experimental field were collected on the basis of Equations (1) and (2), and each RP was represented by a numbering of , which was mainly used to correct the signal for improved positioning accuracy. The RSSI after removing the noise is represented by the vector However, the use of only radio frequency for positioning cannot reflect the actual position due to factors in the environment, such as diffraction and refraction. Therefore, most studies have added machine learning algorithms, such as nearest neighbor (NN), KNN, or WKNN ,25,26, tk data i is the total number of beacons in the environment and n is the number of selected RPs. Calculate the distance between the test sample vector and the RP vector (shown in the yellow block in (2)Calculate the weight (shown in the blue block in (3)Sort in ascending order according to distance.(4)Select K points that have the smallest distance to the current point.(5)Determine the occurrence frequency of the category where the K points are located.(6)x-axis coordinate value of the ith RP and y-axis coordinate value of the ith RP).Calculate the last coordinate  and the medication order of the user. The process is depicted in The digital shadowing system can be divided into a frontend memory training application content design and a backend management platform. A multi-sensory learning environment was constructed through the IoT and a software platform. Multi-sensory learning allowed the participants to use digital voice learning materials and physical operation  for training. The inclusion of multiple memory types, including auditory, visual, and haptic stimulation, can improve memory performance . This seThe design of short-term memory cognitive training courses for dementia was based on the principles of safety, simplicity, and challenge. Only by reducing the generation of negative emotions in the older adults can the effectiveness of cognitive training and the motivation to participate in activities be improved. Because the degree of degeneration of each patient differed, assistance from professionals was required to make adjustments and confirm the needs and goals of the patients, as well as to provide strategies and information to assist in the use of compensatory memory aids to prevent cognitive decline and social withdrawal situations and thus delay dementia .(1)The participant logs into the application.(2)The participant scans the NFC tag.(3)The application reads a number series.(4)The participant shadows the content.(5)The application plays interference sounds of a car and a butterfly (shadowing with interference).(6)The participant shadows the interference sounds (shadowing with interference).(7)The participant operates the physical objects.(8)The participant says “confirm” upon completion of the operation.The content of the digital shadowing exercise designed for this study was based on the shadowing and shadowing with a twist methods , combineUpon completion, the system wrote the operating time into the database as the basis for assessment by professionals and the adjustment of subsequent courses. This set of applications was developed for the Android 8.0 system by using the Java programming language. All training commands were interactive with text-to-speech and automatic speech recognition technologies. The participants controlled the learning steps at their own pace.The purpose of developing a learning management system was to provide case managers with an effective management tool to continuously track the progress and performance of patients in various training and learning activities. The development tool used Apache  as the main server software and MySQL as the database engine. The web backend pages were designed using JavaServer Pages, and the frontend was designed with a combination of HTML, JavaScript, and CSS.The innovative, digital short-term memory training management platform for dementia is usable on any browser to facilitate content management by medical personnel. The platform also contained a learner list management function. Compared with conventional cognitive training activities, the digital cognitive training system is more efficient in quantifying the training data and served as a tool for remote monitoring and cloud management by medical personnel; thus, case managers can accurately grasp the progress of patients. As shown in This study was conducted in a professional medical environment. A beacon signal intensity test was performed to identify the most appropriate deployment point and to solve positioning error problems such as signal interference and drift in the indoor positioning method. The experiment used beacons deployed in the environment to divide the space into the following areas: the starting point, number memory operation (N), daily tool training (D), and cognition of leisure activity (C). Two beacons B1 and B2 were deployed in area N, B3 was deployed in area D, and B4 was deployed in area C, with seven training points and one cloud voice performance evaluation point in each area, as shown in k value parameters. The experimental results indicated that the mean error was approximately 2 m, and k = 3 was the minimum standard deviation. Therefore, k = 3 was used for the experiment.The experiment was conducted in a 48 m × 32 m plane space to evaluate the positioning accuracy of the fingerprint positioning algorithm method. The frontend positioning equipment used Bluetooth 4.0 beacons as signal transmitters and an Android mobile phone as the receiver. Regarding the experimental design, in order for the participant to perform the training correctly, they must receive the learning content at the active zone and then start training. When the participant started the training mode, the beacon in the environment and the RSSI value received by the mobile phone were compared using the fingerprint positioning algorithm. When the participant was located in the default active zone at the backend of the system, the user ID and the beacon media access control address were used as the primary key to search for the corresponding data in the database, and information from the cloud server was transmitted to the smartphone. The active zone was defined according to the signals collected during the training phase. Ideally, each active zone signal would be independent without overlap. Nonetheless, B3 and B4 partially overlapped in the experiment (as shown in the dashed intersection range in (1)Understanding the user’s satisfaction, ease of use, and feedback with regard to the system for possible future improvements, and(2)Understanding physician views of the system.The proposed training mechanism was to develop virtual and physical integrated, digitized cognitive training content to provide patients with MCI appropriate training stimulation, and to understand the training of each patient through systematic and continuous recording. To examine the performance of the system, we conducted functionality and acceptance testing as part of the system development process. The specific objectives of the pilot test included:The pilot test was conducted from May to June 2020 in Taipei City. Participants with mini-mental state examination scores less than or equal to 23 points, normal vision and hearing, sufficient cognitive ability, and the ability to understand sentences were recruited. The sample included 10 participants with mild dementia . Basic information for the participants is provided in During the study period, the participants received two training sessions per week over 5 weeks. They were required to complete the training course for that day in accordance with the course list of the system command and to answer a 5-min Mandarin voice satisfaction questionnaire after the session. The questionnaire included questions (one open-ended) about their experience in using the hardware, software, and applications and about their satisfaction with the hardware and the system. In addition, the participants were required to complete a System Usability Scale (SUS) in week 5.The interactive voice satisfaction questionnaire was designed to understand participant acceptance of the system, with a total of eight closed questions and one open-ended question. The closed items were measured using a five-point Likert scale, with a maximum score of 40 points; higher scores indicated greater satisfaction. The mean score for the survey was 4.093 points . However, regarding system usability, more encouragement and interaction were needed to actually reduce the time necessary for caregivers or technicians to assist the participants to complete training courses independently. In sum, however, compared with other interventional tools, the digital cognitive training intervention developed for this study was practical for patients with MCI and did not incur prohibitive costs. The proposed system can provide a reference for future clinical digital smart nonpharmacological management and achieved the goal of allowing observation of activities of daily living of patients with dementia. However, it is important to note that our testing is preliminary and limited given the purpose of the system testing and the small sample size. We plan to conduct a comprehensive and in-depth study for future work.The training course content designed for this study can be revised, and two specific suggestions are made to improve the model. First, multiple signal overlaps and interferences occurred because the measurement environment was contained in a small space. Positioning accuracy and associated benefits can be greatly improved if the study is implemented in a larger space. In addition, the training process was designed mainly to measure the smooth completion of each stage. Therefore, participants were required to complete all the tasks simultaneously in each training session. Although the preliminary results received excellent satisfaction feedback, medical personnel were required in the long-term to establish different training prescriptions and assess difficulties for each patient. By that time, the training path of each participant will be different during the activity. In terms of longitudinal observation and recruitment, this method is more likely to strengthen the specific contribution of the innovative application of the indoor positioning system to short-term memory cognitive training. Second, the assessment was essentially concerned with the ease of use and usability of the system. However, the brevity of the training program meant that the researchers were unable to observe whether the intervention tools were highly positive and significant for the current situation of the participants. Future studies with a longitudinal research design are recommended to track changes in short-term memory and to explore the empirical effects of the memory training intervention.


Unmanned aerial vehicles (UAVs) allow better coverage, enhanced connectivity, and elongated lifetime when used in telecommunications. However, these features are predominately affected by the policies used for sharing resources amongst the involved nodes. Moreover, the architecture and deployment strategies also have a considerable impact on their functionality. Recently, many researchers have suggested using layer-based UAV deployment, which allows better communications between the entities. Regardless of these solutions, there are a limited number of studies which focus on connecting layered-UAVs to everything (U2X). In particular, none of them have actually addressed the aspect of resource allocation. This paper considers the issue of resource allocation and helps decide the optimal number of transfers amongst the UAVs, which can conserve the maximum amount of energy while increasing the overall probability of resource allocation. The proposed approach relies on mutual-agreement based reward theory, which considers Minkowski distance as a decisive metric and helps attain efficient resource allocation for backhaul-aware U2X. The effectiveness of the proposed solution is demonstrated using Monte-Carlo simulations. Unmanned aerial vehicles (UAVs) are considered to be powerful and versatile in nature, which supports various opportunities related to navigation, surveillance, emergency response, disaster management, health care, urban planning, agriculture, and telecommunications ,3,4,5,6.UAV networking is one of those applications which have enhanced the utility of these vehicles. However, their fully functional usage is limited to certain criteria and network constraints, which include coverage abilities, mobility laws, lifetime, connectivity, hazard tracking, and resource allocation ,10,11,12Using UAVs in layers is another approach prominently studied by several researchers, such as Sekander et al. , Sharma This article focuses on the issue of resource allocation in multi-layered U2X, and presents evaluations on the number of offloads/transfers which are required to handle the entire area load without any reconfigurations or additional UAVs. Mutual agreement reward theory is used for effectively resolving the optimization constraints. Furthermore, this article considers the backhaul formation between one of the layers of UAVs and the core, which helps to make a decision on reshuffling the UAVs. Such a situation allows for better conservation of energy and a lesser demand for additional support from the network.A backhaul-aware U2X scenario with the support of multi-layer UAVs is presented along with a resource allocation problem with the constraints on the number of transfers.Mutual agreement reward theory is applied to understand the problem of resource allocation in U2X.The entire problem is resolved using coordinated resource allocation, which is accounted for using a reward-jump mechanism.Hazard tracking and ownership procedures are used to decide the control over the network and reshuffling amongst layers.Monte-Carlo simulations are used to evaluate the proposed approach by generating scenarios with various numbers of failures for different numbers of UAVs.Based on the problem of resource allocation for backhaul-aware multi-layered U2X, the contributions of this article are summarized below:The remaining parts of the article are organized as follows: Several articles have addressed the issue of resource allocation in general UAV networks. Yang et al.  focused Nguyen et al.  worked oUAV backhauling into the core network requires an effective wireless channel for connectivity. The backhaul can be supported by the free space optical (FSO) or millimeter wave (mmWave) connection with a ground station. Galkin et al.  used a sUAV-to-X Communications have increased rapidly and have been effectively applied in military, public, and civil applications. Zhang et al.  gave a cThis section presents a detailed system model and formulates the actual problem to be solved as a part of energy-efficient resource allocation. A coordinated proposal is used for solving the resource allocation problem while considering the scenarios with increasing as well as decreasing failure rates. This section also explains how the traffic is handled and how the system adjusts itself to accommodate failures while keeping intact the constraints defined in the system.N tiers into which the UAV network is divided. These UAVs communicate with a set M devices, which includes user equipment or base stations. UAVs in each layer are represented by a set U refers to their numbers. The entire system is presented as an inverse problem for offloading, which decides the number of transfers of load H=eηM and ith instance. Here, distance , which huch thatR=limτ→∞∑distance  with a vinitions ,46, the The optimization variable in  follows The proposed approach aims at maximizing the resources handled by UAVs while decreasing the exchange or limiting the shifting users across the UAVs, except for the scenarios wherein UAVs fail in batches. Irrespective of such failures, the proposed approach controls the network and offloads the traffic with limiting iterations.For multiple tiers scenarios, the UAVs which support the backhaul links are the ones which make a decision on the load sharing capabilities of fronthaul UAVs by considering The proposed approach supports efficient resource allocation while considering backhaul-enabled multiple UAVs connected to every entity in the network. The backhaul facilitates continuous support for UAVs to take appropriate decision on sharing the load and it helps to resolve the constraints associated with the optimization problem expressed in the earlier subsection. Following points should be considered while deploying the proposed coordinated resource allocation solution and attaining an efficient resource allocation scheme:Let Once The value of To facilitate the entire process, the reward-jump associations are formulated based on the earlier expressed mutual-agreement reward theory. According to this, each entity in the network accumulates certain reward values, which are helpful in shifting load across each other. This shifting is referred to as jumping, hence the name reward-jump mechanism. An illustrative diagram for this is shown in All the above aspects help to provide coordinated resource allocation that involves coordination between the core, layers of UAVs, and the end-user entities.The failures are caused in the network because of improper management of resources. In particular, when the network entities do not consider the backhaul, the decision for offloading may look fine from fronthaul’s perspective, but it may induce extra overheads on the companion nodes. Thus, it is desired to consider resource allocation as a joint problem throughout the network. To resolve this, we employ the use of intermediate UAVs help to perform inter-cell coordination. This allows for better decision making to edge UAVs, which primarily serves the end-user entities. Furthermore, with the use of intermediate UAV layers, the single point of failure and excessive loads on a common node can be avoided, as in certain cases, the UAVs can even exchange their positions between the layers to facilitate better connectivity along with the maximum conservation of energy.Furthermore, in the proposed approach, the backhaul coordination is done only by using UAVs, which have better lifetime and maximum coverage. Here, coverage refers to the awareness of a UAV with respect to the other components and their operational metrics. Such a mechanism helps to generate accurate reward functions. This can be understood with the following example: consider a situation wherein the number of end-users increases beyond the capacity of near-user UAVs. In such a situation, the network optimization criteria may fail and there is no longer facilitation of constraints. Thus, the choices may involve either increasing the capacity of UAVs or deploying more UAVs. The second solution is widely preferred in the majority of the existing solutions; however, in the proposed approach, a similar strategy is used, but the UAVs are selected from the ones which support the backhaul and have better reward function values. Overall, the network tries to maintain a balance of reward function across the layers so as to provide better load-sharing whenever such a situation arises.Once the backhaul is set to facilitate requests for moving UAVs between layers, the system is set to perform resource allocation while lowering the overall consumption of energy. Moreover, To allow this functionality, the resource allocation module is installed on all the UAVs where a special flag is used to check if the UAV operates for fronthaul or backhaul operation. As a part of backhaul, the system maintains an account of the overall consumption of the entire layer of UAVs. This helps to check whether the network can continue to operate or additional resources are required. Once this condition is met, the network traces individual UAV and checks for requests handled by them while offering reward points after certain time intervals. Once the UAVs have sufficient reward points, it advertises itself for offloading. Finally, UAVs with fears of reaching thresholds check for the advertised UAVs and offload traffic in such a way that minimum energy is consumed and maximum reward points are saved.Each time an exchange is done, the reward-jump value of the involved entities is decremented as per the reward function, and the system continues to settle for the minimum operational requirement. In no instance will the value of the reward function refer to the situation of zero-tolerance, as new UAVs have to be accumulated or load has to be increased on certain UAVs. In such a situation, the overall reward value of the system may decrease, but this can prevent the loss of connectivity and avoid any isolation. The decrease in the values leads to penalties and its far lower value can result in major shutdowns. Thus, it is desirable to transfer load appropriately for which the thresholds for the reward function must be selected effectively.It is required to quantify the network performance, which can be done using hazard tracking and penalties. These help ascertain that the proposed approach always makes an accurate decision for resource allocation and the correct entity is in-charge of the decisions made. To further understand the hazard tracking and penalties, two conditions, as shown below, are derived. These conditions help to understand how ownership of decision making is decided and the optimal points at which the given network can perform without inducing additional failures.Condition-1: This condition deals with the optimal ownership with hazards (failures) for the UAVs across which the load is to be divided. This operates alongside reward-jump function, and according to this, the entities with ing η in  to the auch thatα=∫τU−minCondition-2: This condition deals with the maximum number of transfers for allocating the maximum resources in contrast with the optimal ownership with hazards. For this, limits are set for The proposed approach is verified by using Monte-Carlo simulations for its efficacy in handling requests and allocating resources while conserving the maximum amount of energy at an optimal number of transfers. The Monte-Carlo simulations were fixed in checking whether more UAVs should be deployed or not. Further, the inclusion of reward functions is done with probabilistic variation in shifting services across the UAVs based on the induced number of failures. These evaluations allow for the understanding the impacts of constraints which affect the mutual agreement reward theory amongst the UAVs and everything. The details of configurations used for evaluations are given in The results were achieved with 100–1000 runs. However, the variations based on the number of runs were limited; hence, all the major evaluations are presented for 100 runs. To support this argument, a graph is presented in It is to be noted that with the use of reward-based mutual agreement, the optimization problem converges far better and the probability of handling users at the given number of resources increases if the network is allowed to reconfigure. However, such a state is also affected by the number of failures and the current range of active links. Despite that, the proposed approach shows an increasing value in the probability of handling users with an increasing number of failures against the fixed number of UAVs, as shown in In case the network wants to continue without reshuffling to avoid replacement latency and decision overheads, a certain number of reward values are required. As stated earlier, these reward values are accounted for by the number of times the UAVs in the network obey the constraints of the network. For tracing up to milliseconds at a total run time of 1000 s, the number of rewards required to maintain the state of the network is 92.75% higher for a general scenario than the proposed situation, which performs better resource allocation at lower values of reward function for an increasing number of UAVs, as shown in The results for the number of transfers with an increasing number of failures for different scenarios of UAVs are shown in The entire network functions towards maximum allocation of resources. But these issues have a huge impact on the energy model of the network, and it is required to understand how well the network utilizes its energy while allocating the resources. As shown in Finally, to present the efficacy of the proposed solution, results are traced for the probability of resource allocation with random and the fixed numbers of links for an increasing number of failures over the increasing number of UAVs, as shown in This paper considered the issue of resource allocation in backhaul-aware U2X. The proposed approach is useful in deciding the optimal number of transfers amongst the UAVs, which can conserve the maximum amount of energy while increasing the overall probability of resource allocation. The proposed approach allocates reward-jump points based on mutual agreement reward theory which helps shuffle UAVs across multiple layers. Such an approach effectively reduces the requirements of additional network support and UAVs and decreases the number of transfers to handle all the user requests. Monte-Carlo simulations were conducted to prove the effectiveness of the proposed approach. Results were traced for the number of rewards required to attain high probability in resource allocation at a lower number of transfers. Furthermore, the results showing impacts on the probability of handling users, resource allocation, and energy consumption with an increasing number of failures over the varying number of UAVs were also presented. The results suggest that the proposed approach can readily decrease the rewards’ requirements by 92.75% compared to a general setup with no-reshuffling and impact the number of transfers by 92.99% by using layered UAVs along with reshuffling in comparison with the fixed number of UAVs without reshuffling.In the future, this work will be extended to include the study of latency aspects, optimal ownership, and sustainability against the number of failures.


Wireless capsule endoscopy (WCE) has been widely used in gastrointestinal (GI) diagnosis that allows the physicians to examine the interior wall of the human GI tract through a pain-free procedure. However, there are still several limitations of the technology, which limits its functionality, ultimately limiting its wide acceptance. Its counterpart, the wired endoscopic system is a painful procedure that demotivates patients from going through the procedure, and adversely affects early diagnosis. Furthermore, the current generation of capsules is unable to automate the detection of abnormality. As a result, physicians are required to spend longer hours to examine each image from the endoscopic capsule for abnormalities, which makes this technology tiresome and error-prone. Early detection of cancer is important to improve the survival rate in patients with colorectal cancer. Hence, a fluorescence-imaging-based endoscopic capsule that automates the detection process of colorectal cancer was designed and developed in our lab. The proof of concept of this endoscopic capsule was tested on porcine intestine and liquid phantom. The proposed WCE system offers great possibilities for future applicability in selective and specific detection of other fluorescently labelled cancers. The wireless capsule endoscopy (WCE) system is becoming increasingly popular for examining the entire human gastrointestinal (GI) tract, as this method eases patients’ discomfort and pain compared to other endoscopy methods . HoweverA typical WCE system consists of a pill-shaped electronic capsule, a data recorder with radio frequency (RF) antenna, and a workstation computer with software, as shown in Colorectal cancer is the third commonest type of cancer diagnosed in both men and women. It is also the third commonest cause of cancer-related deaths in the United States . About 1Fluorescence imaging is a technique which is widely used in medicine, biology, and biochemistry. It is the process which visualizes fluorescent dyes or proteins as labels to study the biological molecules and phenomena . The visAutofluorescence is the process of detecting fluorescence emissions arising from endogenous fluorophores. Various tumor types are detectable both by autofluorescence techniques as well as by the use of exogenous fluorescent markers . Tumor-sIn this work, a complete WCE system is designed based on the principle of fluorescence to detect colorectal cancer. This system has potential to automate the detection process and improve the overall efficiency and accuracy of diagnosis. The developed capsule prototype consists of the following blocks: an optical block, a microcontroller, telemetry, and a power module. The hardware diagram of the proposed system is shown in The proposed device consists of four main electronic printed circuit boards (PCBs): a sensor board with illumination components, a control/processor board, a telemetry board and the power module as shown in A comprehensive analysis was performed before selecting the complete illumination system. The choice of these components depends upon several factors, but trade-offs were considered between power consumption, miniature size, and the light intensity that would be required for the capsule to perform efficiently. The chosen LED is 2 mm × 1.25 mm in size. Furthermore, the absorption/emission spectra of the target fluorophore were considered while selecting these components. The spectral sensor is situated in the middle of the LEDs, equidistant from all LEDs. The electrical connections with the second layer (microcontroller) were achieved through pins 23 and 24 via a 11 × 2 header that helps the board to correctly align with the optical sensor. Each LED is driven via digital pulse width modulation (PWM) pins of the microcontroller, which will help to control the brightness, if needed. The capsule prototype uses an nRF24L01 + radio transceiver for wireless communication. This transceiver has a good range and can communicate for a considerable distance through walls with negligible data loss. The nRF24L01 + module operates at 2.4 GHz. At this working frequency, the wireless transmission module shows the characteristics of having long transmission range and strong penetrability, which is helpful for WCE application. This module is an important bridge for wireless communication (both transmission and reception) with the external data logger. The power board contains miniature low dropout regulators (LDO), each having a rated voltage of 3.3 V and current of 150 mA. Any kind of silver oxide or Li-ion battery with a voltage range over 3.7 V could be used to power up the capsule prototype. The data logger is based on an Atmel microcontroller, which is capable of receiving the capsule’s signal and transmitting the received data to a computer. It has two modes of operation. First, when the data logger is plugged into a computer via a USB port, it detects the “software mode” operation, which means that the data logger can receive signals from the capsule and transmit them to the computer in real-time. On the other hand, when the data logger is powered up by an external power source without having connection to the computer, the device enters a “stand-alone mode”. In this mode, the data logger will still receive data from the capsule, but stores the received data in the internal memory and transmits them to the Raspberry Pi board for further processing. By connecting the data logger to the computer, our software can read the data from the memory. One of the major objectives of this work is to demonstrate the ability to detect the increasing levels of fluorescence emitted by the cancer cells compared with normal cells. Fluorescein is a safe-dye and commonly used in vivo. It has found increased use in many applications, such as: ophthalmology and neurosurgery ,33. FluoIn order to further assess the performance of the proposed WCE device, we used the following four indices: sensitivity, specificity, accuracy, and precision, as expressed below:When the capsule is turned on, it starts to sense the signal through the sensor module (AS7262), and begins to capture the data and send to the data logger via RF transceiver. The data logger receives and stores the data. The software on the data logger is a Python script which communicates through the serial port. Operational parameters, such as spectrum capture interval and capsule illumination mode can be controlled via the software. There are several wireless transceivers available, each having their pros and cons depending on the application. A Nordic transceiver is used in the proposed system. One of the major challenges in wireless communication is the possibility of data corruption during transmission and reception. The Nordic transceiver has a built-in capability for cyclic redundancy check (CRC)-based error detection and retry with acknowledgement , which makes it a reliable choice. However, several retries may hamper the data transmission rate in a noisy environment . In order to verify the performance of our prototype further at 2.4 GHz ,49, we pMoreover, the frame rate and resolution of the GI image in the existing system is not optimal for proper diagnosis of GI abnormalities. There are few non-camera based WCE systems which can measure different physiological parameters, such as pH, temperature and pressure. Removing the cameras will significantly increase the battery life of the capsule, and reduce the cost. In addition, there are other capsules at the research stage which aim to detect bleeding and automate the detection of a cancer. Some of them utilize the property of autofluorescence to automate the detection process. However, autofluorescence comes with reduced specificity and sensitivity in automating cancer detection. As a result, this technique does not provide enough contrast when compared to normal and abnormal cells, because the signal from the cancerous cells are often buried under the autofluorescence of healthy cells. In addition, the intensity of autofluorescence is dependent on diet . To the Targeted fluorescence-based WCE systems have the advantage of improving specificity and sensitivity over the current generation of conventional WCE systems. They improve the diagnostic as well as screening procedures of the entire GI tract. In addition to being non-invasive, these capsules present us with an opportunity to enable automatic detection of abnormalities. One of the main challenges in designing such a system is to make these devices small enough that a patient can swallow it. The components used to build the proposed prototype are available off-the-shelf. The developed capsule size is 16 mm × 34 mm. Further miniaturization can be done by using custom-designed components. Moreover, this device also removes the need for bulky components  which reduces the cost of the device.Most colon cancers begin as pre-cancerous polyps. Therefore, screening for cancer in a timely manner will allow physicians to find and treat different types of cancer at an early stage before they cause symptoms. This will lower the burden on both the patient and the healthcare system. After the screening procedure is completed and a positive sign of cancer is suspected, other confirmatory tests  can be performed to confirm the findings and locate the cancerous cell . The folDue to the invasive characteristics of the traditional endoscopic system, most patients are reluctant to go through the procedure, adversely affecting the early-detection process and the possibility of efficient treatment. Therefore, the non-invasive WCE technology can help with early detection. The proposed device can detect multiple fluorescence signals from multiple fluorophores; therefore, it may be possible to detect other types of cancers by using different fluorophores that would bind to the specific antigen of a specific cancer cell. The development of a single WCE device for detecting multiple cancers may be possible in the future. In the future, a camera can also be placed on the other end of the WCE device which will only turn on to capture the GI images when an increased level of fluorescence is detected. This would help the physicians examine the abnormality visually, thereby increasing the efficiency, as well as the reliability of the system. The battery life of the proposed capsule can be improved by using the adaptive illumination technique . MoreoveIn order to provide the best and most effective healthcare, the Internet of Things (IoT) offers new ways of improving the system by presenting novel facilities and enhancing the functionalities of the existing system. It is estimated that the hospital-centered healthcare system will be transformed to a home-centered system by 2030 . Thus, iWCE has enabled a pathway towards pain-free diagnosis and screening of the entire GI tract. This system has encouraged patients to go through the examinations, ultimately paving the way for mass screening and enabling early diagnosis of GI abnormalities. The current generation of WCE systems has several limitations which restrict its wide application. Hence, in this paper, we have presented the design and development of a complete WCE system, which utilized the principle of fluorescence imaging, capable of performing targeted endoscopy. The developed capsule consists of four major units: illumination, processor, transceiver and power. Each of these units was separately tested before integrating into a complete system. Initial experiments with various solutions and phantoms have demonstrated that the proposed device is capable of selective and specific detection of abnormal colorectal cancerous cells.