For the sake of addressing the issue of extracting multiple features embedded in a noise-heavy vibration signal for bearing compound fault diagnosis, a novel model based on improved adaptive chirp mode decomposition (IACMD) and sparse representation, namely IACMDSR, is developed in this paper. Firstly, the IACMD is employed to simultaneously separate the distinct fault types and extract multiple resonance frequencies induced by them. Next, an adaptive bilateral wavelet hyper-dictionary that digs deeper into the periodicity and waveform characteristics exhibited by the real fault impulse response is constructed to identify and reconstruct each type of fault-induced feature with the help of the orthogonal matching pursuit (OMP) algorithm. Finally, the fault characteristic frequency can be detected via an envelope demodulation analysis of the reconstructed signal. A simulation and two sets of experimental results confirm that the developed IACMDSR model is a powerful and versatile tool and consistently outperforms the leading MCKDSR and MCKDMWF models. Furthermore, the developed model has satisfactory capability in practical applications because the IACMD has no requirement for the input number of the signal components and the adaptive bilateral wavelet is powerfully matched to the real fault-induced impulse response. Rolling bearing is a widely distributed component in a plethora of rotating machinery. As a key component, its availability and reliability are critical to ensure the effective and safe operation of the entire mechanical system ,2. Beari(1)The pre-processing and post-processing are carried out separately, making them computationally expensive.(2)The critical parameters of the procedures are required to be specified by humans, demanding a certain degree of expertise and thus with some randomness.(3)The de-noising capability of these combination methods is inadequate and thus a considerable level of residual noise remains in the extracted fault-induced impulse responses.Vibration-based analysis has become one of the most active and valid means of fault feature extraction for bearings, because a fault vibration signal directly transmits dynamic information regarding not only the fault condition but also the fault type . Many adIn the case of difficult-to-separate complicated signals, the ACMD  employs (1)The IACMD is not only used to separate compound faults; its extracted fault-induced resonance frequencies can be embedded in a dictionary for sparse decomposition, saving computational cost and ensuring the accuracy of the IACMDSR model.(2)The IACMD also enables the separation of compound signals without requiring the number of signal components a priori, making it suitable for engineering applications that do not know the number of signal components in advance.(3)In contrast to popular sparse decomposition dictionaries that adopt the wavelet atoms with a single damping ratio s(t)=With the help of the signal demodulation technique, Equation (1) can be rewritten asi-th signal component, the representation problem can be modeled asi-th modal component. According to Equation (3), the frequency increment can be characterized asMotivated by the VMD algorithm and matching pursuit , the ACMNote that the Equation (8) is expected to be sufficiently smooth. Thus, a low-pass filter is utilized to pre-process the frequency increment in minimizing the interference components. Finally, the IF is shown asThe strengths of the IACMD in extracting the fault-induced resonance frequency is demonstrated in the simulation compound signal in DD to represent all or most of the fault impulse responses. It can be seen from Equation (11) that the sparse representation consists of two main parts: ① dictionary construction, the higher the similarity and matching of the dictionary atom The sparse representation model for a single component fault objective signal lgorithm .X-axis. Wavelet dictionaries have been the subject of extensive research over the past few years due to the flexibility and versatility of wavelet waveforms ,28. AmonN is set as 4 [D is designed, which can be expressed as Equation (14)The resonance frequency for each fault type obtained by the IACMD is adopted as the oscillation frequency  the CFA  is integterature ,30, the With the analysis mentioned above, the novel IACMDSR model is proposed for multiple fault feature extraction based on ACMD and sparse representation, which mainly includes four procedures. The flowchart is shown in (1)The first step is to use the IACMD to adaptively separate the compound fault signal (2)D based on the method mentioned in Second is the design of bilateral adaptive wavelet hyper-dictionary (3)Third is the use of the OMP algorithm and hyper-dictionary to reconstruct each of the signal components decomposed by the IACMD. In which, in order to improve computational efficiency, each signal component is divided into Then, the reconstructed signal (4)The Hilbert transform and square operation is applied to the reconstructed signal A bearing simulation signal is constructed and carried out to illustrate the effectiveness of the IACMDSR model on compound fault feature extraction. The bearing fault signal is made up of two periodic impulse signals and a noise signal. The description of the simulation signal can be described asThe time-domain waveforms, the spectrum, and the envelope spectrum of the simulation compound signals are depicted in For composition, some leading methods are also employed to extract the resonance frequency. The time-domain waveforms of the corresponding in-band signal components are displayed in To demonstrate the effectiveness and superiority of IACMDSR in separating the compound fault of the bearings, the run-to-failure experimental datasets of the XJTU-SY bearing from the group of Professor Yaguo Lei  and the As shown in Next, the IACMD is applied to isolate the compound signal a. Figure(1)The MCKDSR ModelT, the deconvolution signal of interested component is extracted to realize the fault feature separation. The Laplace wavelet is in a shape similar to that of bearing fault-induced impulse responses with signal-sided attenuation [T of the MCKD algorithm is To demonstrate the superiority of the IACMD with sparse representation in this paper, the MCKD algorithm was used as an alternative to the IACMD to process the raw signal a. The MCenuation . Thus, iequency) . The fil(2)The MCKDMWF ModelThe wavelet filter is a versatile tool that can efficiently extract potential fault features ,38. TherIn the early stage of a bearing failure, the fault-induced impulse responses are normally very weak and are accompanied by strong confusion noise. Therefore, the feature extraction and identification of early compound faults are even more challenging. In this section, the efficiency of the proposed approach in early compound fault of bearings is validated. The fault impulse responses of the compound fault signal are irregular, as shown in the time-domain waveform plotted in The IACMD-decomposed components are then sparsely characterized using the sparse representation method outlined in (1)The MCKDSR ModelT of the MCKD algorithm is Similarly, the MCKDSR model was adapted to handle the identical vibration signal a in orde(2)The MCKDMWF ModelThe MCKDMWF model is also used to handle the raw signal a. Finall(1)The effectiveness of the IACMDSR model that incorporates IACMD with sparse representation is validated by simulation signals and two sets of experimental signals, and it exhibits better fault extraction performance than the MCKDSR model and MCKDMWF model. These experiment analyses confirm that the IACMDSR model is powerful and has the capability of detecting bearing weak multiple fault features in the presence of heavy noise, which is the Achilles’ heel of the two other leading rival methods.(2)The performance of the IACMD in extracting fault resonance band is evaluated by the comparison with STFT, CWT, and ACMD, and the comparison results demonstrate that the IACMD exhibits better noise immunity. Additionally, the hyper-dictionary is developed by period-assisted bilateral wavelets, which simultaneously dig deeper into the periodicity and waveform characteristics exhibited by the real fault impulse response. Therefore, it is more suitable for practical engineering applications.(3)The developed IACMDSR model can be extended to those diagnosis fields such as railway axles, high-speed train gearboxes, as well as engine diagnosis. It is worth pointing out that the IACMDSR has outstanding anti-noise capabilities and self-adaptability. Thus, it is completely suitable for the fault feature extraction of railway bearings with heavy noise and a complex working environment, which can play a significant role in maintaining the safety and comfort of the railway transport system.In this paper, the IACMDSR model is put forward for vibration signal denoising and detecting the multiple fault signatures of the bearing. The IACMDSR model can not only automatically extract the multiple resonance frequencies and separate the signal components of a compound fault but can accurately recover the fault-induced impulse response of various signal components. The main conclusions are summarized as follows:Further research will focus on adjusting for the variable amplitude of the reconstructed signal. We would like to embed the feature enhancement algorithm into the sparse representation model. In addition, the characteristics of bearing fault vibration signals under variable speed conditions will be investigated in order to build a sparse representation compound dictionary suitable for vibration signals under variable conditions.


The metric table indicated that the 4-cable configuration is a promising design option for a lower limb rehabilitation exoskeleton based on tracking performance, model requirements, and component forces exerted on the limb.The global increase in the number of stroke patients and limited accessibility to rehabilitation has promoted an increase in the design and development of mobile exoskeletons. Robot-assisted mobile rehabilitation is rapidly emerging as a viable tool as it could provide intensive repetitive movement training and timely standardized delivery of therapy as compared to conventional manual therapy. However, the majority of existing lower limb exoskeletons continue to be heavy and induce unnecessary inertia and inertial vibration on the limb. Cable-driven exoskeletons can overcome these issues with the provision of remote actuation. However, the number of cables and routing can be selected in various ways posing a challenge to designers regarding the optimal design configuration. In this work, a simulation-based generalized framework for modelling and assessment of cable-driven mobile exoskeleton is proposed. The framework can be implemented to identify a ‘suitable’ configuration from several potential ones or to identify the optimal routing parameters for a given configuration. For a proof of concept, four conceptual configurations of cable-driven exoskeletons (one with a spring) were developed in a manner where both positive and negative moments could be generated for each joint (antagonistic configuration). The models were analyzed using the proposed framework and a decision metric table has been developed based on the models’ performance and requirements. The weight of the metrics can be adjusted depending on the preferences and specified constraints. The maximum score is assigned to the configuration with minimum requirement or error, maximum performance, and  According to World Health Organization (WHO), stroke is the second leading cause of mortality and the third leading cause of long-term disability worldwide . In 2020via links near the joints and the actuator along with the link induces both inertia and inertial vibration to the attached stroke-affected limb. Furthermore, direct actuation at the joint level requires strong structural support on the acting and reacting parts, which renders these systems heavy and bulky. In such designs, the system also tends to be less transparent as it compensates for the user as well as for the component (support and actuators) weights. In the direct actuation approach, the knee joint is assumed as a pin joint, which oversimplifies its biomechanics and may induce unnecessary moments  and hence mitigates the burden of exact joint alignment.Lower limb exoskeletons with remote actuator locations (distant from the joint), not only enhance the human interaction safety and function of the exoskeleton but also reduce the inertial vibration at the joint . Howevervia two cables allowing for rapid design modification in design. Although the device showed its potential as a knee rehabilitator, rehabilitating the knee joint alone was sufficient to regain a healthy gait trajectory in stroke patients. Bryson et al.  and configuration toward enhancing design options and human safe interaction and comfort remain elusive. In the literature, cable configurations which meet the desired performance specifications and objectives while minimizing the system requirements have not been fully achieved, and there continues to be a lack of generalized methodology of modelling to accommodate a variety of configurations and routings. In this work, we purpose a generalized methodology/framework to evaluate the feasibility of various routings and configuration-based designs of cable-driven exoskeletons and provide metrics to help in selecting the suitable configuration for an optimal design based on the specified constraints.In our previous work , we propM(θ) represents the inertial matrix (2 × 2), G(θ) represents the Gravitational components (2 × 1), and The equation of motion formulated in MATLAB using Euler- Lagrange can be written as:F and E represent the posterior (back) and anterior (front) sides of the leg. The measured angles are assumed positive in the clockwise direction.The model was created such that any cuff placed on the user’s limb  can be defined using three parameters as shown in 1) The distance of the cuff from the nearest joint .2) The length of the cuff.3) The angle of the cuff from the attached limb part (measured by viewing from the cuff to the nearest joint).The three defined parameters arec_ln_F, a_ln_F, and t_n_F respectively. All the other cuffs can be defined similarly.For example, the shank cuff on the posterior side can be fully defined using three parameters The controller used in this study is an impedance controller where a PD controller is employed to estimate the desired torque based on the errors, similar to controllers used in orthoses (impedance control) reported in  Riener  Tucker . The desThe upper control layer is responsible for generating the desired torque required for tracking the next step based on the current motion step. It can employ various controllers such as PID , MPC (model predictive control), SMC (sliding mode control), etc. In the current controller, PD-based feedback linearization was used to transform the error dynamics system into an exponentially stable system and is defined as: via the cable tension distribution module.The above feedback linearization yields the required torque to track the trajectory. The passive elastic joint moment is considered as the user’s voluntary contribution during walking and is subtracted from the required torque to estimate the desired torque, which is then re-distributed among the cables M, C, G), as well as the user’s voluntary input based on the sensor’s data. The sensor data may include kinematics, kinetics, or both depending on the requirements . The matrices are calculated using kinematics data and the 2-link model equations listed in The state estimation layer is responsible for identifying current angles, angular velocities, inertial and other matrices  or via The cable tension vector is projected onto the shank and thigh vectors to find the tangential and normal component of the force as shown in Similarly, the equivalent force components and torque on the hip joint can be estimated as:In F to the hip joint and knee joint torque and is dependent on the kinematics of the C-LREX at any instant.Thus, the relation between torque and force can be written as:B depends on the number of the cables. For example, using 3 separate cables, the control performance matrix (B) can be written from In general, C-LREX employs a combination of cables to generate the desired control effect. The dimension of matrix The cable tension can be distributed either in a priority-based order or in an optimized order. The priority-based algorithm gives full priority to knee control allocation and then to hip control allocation in a single operational step. While the optimization-based algorithm distributes overall control based on a defined objective function in an iterative approach. B) is not square, the values of the corresponding tensions in the cable cannot be determined easily as the inverse of B does not exist. Thus, it is essential to form an optimized control allocation strategy, although, optimization-based distribution requires additional resources. In the case where constraints are not tightly aligned, and faster distribution is required, a priority-based control allocation approach can be implemented.The number of inputs and outputs for the control distribution is not always the same (depends on the number of cables) and results in a rectangular control performance matrix. Depending on the number of cables, the system could be either overdrive (if the number of outputs is greater than the input) or underdrive (if the number of outputs is less than the number of inputs). The tension in each cable for a given torque requirement must be coordinated in such a way that either the cable generates the exact torque or less than the required torque while utilizing the full potential features of the configuration and actuators. Since the control performance matrix .The objective function for optimization can be defined in different ways. find minimum of constrained nonlinear multivariable function (fmincon) function can be implemented. The objective function is defined as the minimization of allocated control to the actuators as:To solve the above problem, MATLAB-based H is the Hessian matrix and f is the gradient matrix calculated from the hybrid objective function.Based on the previous work reported in the literature  in both flexion and extension directions . Failure to provide the moment in either direction at either joint (the hip or knee joint) will result in poor coordination and thus lead to a higher deviation from the desired trajectory . To accConfiguration 1: 4 cables where 2 cable acts for each joint (hip and knee) separately in antagonistic configuration. Configuration 2: 3 cables where the posterior cables of configuration are combined as one acting from pelvis to shank. Configuration 3: an extension of configuration 2 where spring is attached to the shank cuff that allows movement along the shank only depending on the magnitude of applied cable tension. Configuration 4: 3 cables where the combined Hip-Knee cable is routed through the thigh cuff and thus acts as two cables with the same cable tension.m, b,andk represent mass, damping coefficient, and spring stiffness respectively. F is the force applied to the spring.A second-order spring with stiffness and damping coefficients is used in the model . The sprThe spring is allowed to slide along the shank direction only in an upward/downward direction. The stiffness and damping constants are assumed as 500N/m and 10 N-s/m respectively. A dead-end (max stretchable length) of the spring is defined to simulate a physical stop or spring stop end.MATLAB model was developed and simulated to track the healthy gait trajectories (performances) and the requirements were analyzed. The models were simulated for 2 gait cycles.The conceptual models in  are tranThe conceptual model’s performance and the requirements during tracking are analyzed and developed as metrics to help in the decision-making process. Further information, such as the exertion of the component forces on the joints due to cable tension, is also included as additional metrics. Throughout the results, the solid line represents the swing phase, and the dashed line represents the stance phase of the gait cycle.Performance is one of the key metrics which plays a critical role in decision-making. Performance parameters, such as joint angle and angular velocity tracking, tracking error, root mean square error (RMSE), and ankle trajectory can be used to assess the performance of the models.The error in tracking the joint angle in all configurations and its RMSE (in degree) are shown in Similar to joint angle error, the joint angular velocity error during tracking  revealedThe tracking of the ankle position (hip joint as origin) during tracking and the RMSE of the error of the distance of the ankle joint from the hip joint (hip as reference) are shown in The requirements to meet the above tracking performances in each configuration differ due to the different number of cables and routings. Requirements such as cable tension, motor torque, motor speed, motor power, and total power requirements can be analyzed to assess the configuration.The cable tension(s) in each configuration is shown in via the same roller diameter. The plots in The cable tension versus cable linear velocity in  curve cop-value in The number of cables corresponds to the number of motors in a configuration. The power requirements of the motor of each configuration  are limiApart from the performance and the model requirements, it is also important to analyse and minimize the force exerted by the system on the user limb joints to ensure human-robot interaction, comfort, and safety. In general, a configuration should exert the least possible additional forces to meet the performance specification.In this section, the joint compressive and shear forces resulting from the cable tensions were studied. These forces depend on the cable tensions and joint kinematics, and they vary with the gait cycle as shown in Based on the metrics above and the design constraints, a suitable candidate from the four configurations studied here can be identified. For instance, in terms of performance , all the configurations are acceptable. On other hand, whilst considering the cable tension distribution throughout the cycle, configuration 1 is the “best”, however, it requires 4 motors as compared to the other configurations where 3 motors meet the demands. Furthermore, in terms of motor requirements, configuration 1 requires 4 motors with less power  compared to others with 3 motors of higher power specification (thus bigger size). The higher power requirement is due to higher speed demand since the maximum torque is the same for all the configurations.Similarly, for the total power required for the different configurations, configuration 2 is the ‘best’. The inclusion of spring has increased the power requirement by 22.43% while configuration 4 holds a reasonable position with a 6.16% higher requirement compared to configuration 2 . In term1) The motors required are of less power and similar specification unlike other demanding higher power motors with different specifications.2) The forces exerted by the device on the user’s limb are minimal.3) The cable tension peak remains minimal throughout the gait cycle .4) The tracking performance is similar to other configurations.In terms of force exerted by the device on the lower limb joints, configuration 1 produces the minimum force and is hence the preferred configuration. Configuration 1 with 4 cables is ‘preferable’ among all configurations despite requiring 4 motors due to the following:Based on the discussed metrics, a table was developed to help identify the most ‘suitable’ configuration for exoskeleton development. A suitable weight is assigned to each metric and can be adjusted depending on the priority and/or constraints given in terms of performance, model requirements, etc. The score assigned in the table is based on the performances and requirements as observed above. The minimum error during tracking, minimum requirements, and higher performances are given maximum scores and vice versa.Configuration 1 has the highest score  and is cThe metric-based identification methodology can be implemented to find the best cable routing of a configuration by creating multiple cases with different cable routing parameters (cuffs parameters). An optimization problem with the combination of the metrics as the objective function can be solved to find the optimal cuff parameters.The actuator torque versus speed curve (T-S diagram) plotted in Cable driven rehabilitation devices are promising and capable of reshaping the field of robotic rehabilitation due to multiple advantages over traditional link-based devices. These include lightweight, exertion of negligible inertia and inertial vibration on the impaired limb, remote actuation capability, flexible routing and reconfiguration options, and flexibility in terms of exact joint alignment. Despite these benefits, the optimal number of cables and cable configurations to achieve the best performance remains elusive.We have developed a generalized methodology/framework to model and assess cable-driven exoskeletons based on various metrics. The model assumes the lower limb as a two link-pendulum with the foot attached perpendicularly to the shank. The voluntary contribution of the user limb is assumed to be the only passive elastic joint moment. The proposed framework can be used to study different cable configurations, identify the suitable one and its associated optimal routing parameters, and estimate the required motor specifications to meet the tracking performance under specified constraints (such as pre-specified actuator T-S specifications). For proof of concept, four different configurations of a conceptual CDRD model that can apply the joint torque in an antagonistic arrangement were studied as potential models to develop a stroke rehabilitation lower limb exoskeleton. A spring was incorporated into one of the models to study the possibility of alteration in the model’s energy requirements along with other performance and requirements. A decision metric was developed based on the performance, requirements, and resulting forces applied to the joints to serve as a qualitative tool for the selection of a “suitable” configuration. Simulation of the conceptual configurations revealed that the 4-cable configuration is “suitable” for a lower limb exoskeleton development, as it generates minimum cable tension distribution, requires smaller individual motor power requirements, and produces a lower component force on the limb joints, as compared to the other configurations. The incorporation of spring in configuration 3 tended to increase the total power requirement of the CDRD (however at the lowest peak power demand pattern) but improved the overall tracking performance.Some of the limitations of the current modelling approach include considering only the sagittal plane motion, assuming the user’s limb contribution as passive elastic joint moment only and assisting the user during the swing phase of the gait cycle. Moreover, the proposed framework is open to parameter adjustments including cuffs, cable tensions, springs, routing, etc., to meet the specified design objectives. Future work includes the extension of the current work to accommodate knee and hip abduction/adduction and cable routings in the frontal plane. Furthermore, the actual user limb contribution can be incorporated into the model instead of the passive elastic joint moment only if the impaired gait kinematic/kinetic trajectory is known.


The findings show the insufficiency of focusing merely on the distributional factors, such as location in a food desert and number of facilities, to capture the disparities in access, especially during the preparation and impact/short-term recovery periods. Furthermore, the characterization of access by considering combinations of access indicators reveals that flooding disproportionally affects socially vulnerable populations. High-income areas have better access during the preparation period as they are able to visit a greater number of stores and commute farther distances to obtain supplies. The conclusions of this study have important implications for urban development (facility distribution), emergency management, and resource allocation by identifying areas most vulnerable to disproportionate access impacts using more equity-focused and data-driven approaches.Natural hazards cause disruptions in access to critical facilities, such as grocery stores, impeding residents’ ability to prepare for and cope with hardships during the disaster and recovery; however, disrupted access to critical facilities is not equal for all residents of a community. In this study, we examine disparate access to grocery stores in the context of the 2017 Hurricane Harvey in Harris County, Texas. We utilized high-resolution location-based datasets in implementing spatial network analysis and dynamic clustering techniques to uncover the overall disparate access to grocery stores for socially vulnerable populations during different phases of the disaster. Three access indicators are examined using network-centric measures: number of unique stores visited, average trip time to stores, and average distance to stores. These access indicators help us capture three dimensions of access:  In particular, better access to grocery stores is critical for access to food and water during and in the aftermath of disasters. Therefore, disrupted access to grocery stores could result in adverse effects on the well-being of residents.Natural hazards can disrupt access to healthcare, pharmacies, and grocery stores. Households with easier access to these critical facilities could achieve a higher level of preparation and short-term recovery effort6. Sub-populations already facing access inequality could be further exacerbated during disasters due to disruptions in road networks8, as well as residents’ capabilities and lifestyle patterns10. Certain vulnerable areas face a greater impact from road closures and damage to the stores, which may disproportionally disrupt the access to the grocery stores14. To add on, people of a lower socioeconomic status usually have fewer resources and capabilities to find alternatives to food and water to compensate for such disrupted access18.In non-disruptive periods, not all residents enjoy equal levels of access to grocery stores which result in disparate impacts on the well-being of residents in their day-to-day lives. For instance, areas with a dearth of retail establishments selling nutritious food, known as food deserts, are endemic to lower socioeconomic neighborhoods. However, these measures of food inaccessibility are rather limited in examining disparate impacts during disasters when compared to normal conditions3 and disruptions in infrastructure and supply chains19. In preparation for an impending disaster, people who choose to shelter-in-place tend to stockpile supplies in anticipation of several days of disruptions with the adverse effect of depleting grocery store inventories20. This surge in demand for supplies leads grocery stores to run out of stock and force people to visit multiple stores at farther distances. When faced with local shortages of supplies, households with personal vehicles have the means to travel further to obtain their needs from more distant stores compared to households relying on public transportation. Thus, while the number of stores nearby and the physical distance could provide indications of levels of access during normal times, they are inadequate in examining disparities in accessibility during the preparedness stage. Due to disruptions in road network21 and power outages, as well as supply chain impacts, the time span for returning to pre-event conditions can be a few weeks to more than a year. The extended period of disruption can significantly impact sub-populations who are unable to access facilities due to increasing trip distance and duration.Indeed, the disaster setting can cultivate a supply–demand imbalance among different sub-populations due to protective actions of residents26 or a combination of number of stores and distance from a specified area28. However, these measures are primarily based on physical distance and only partially characterize access to facilities. Approaches focused on physical distance, as done in the majority of current studies, is not a reliable measure of accessibility during disasters as disruption influences the availability of stores and duration of trips. To begin, less distance from a household to a grocery store may imply better accessibility since the household does not have to travel as far. However, examining distance as an isolated variable limits the holistic perspective needed to understand equitable accessibility. For instance, households living in more affected areas may have a higher number of closed stores due to storm damage which decreases the availability of stores. Flooding events also exacerbate traffic congestion which significantly increases the total trip duration. Although a household is normally a set distance from certain grocery stores, traffic congestion from the closed roads or even construction repairs in damaged roads could significantly increase the travel time. Though these households may have similar distances to grocery stores, they may still spend more time reaching a grocery store to meet their needs. Failure to account for these multiple dimensions of access limits the characterization of access to facilities both during normal times as well as during disasters24. Thus, the research channels the unique challenges brought on by disruption to properly capture the multiple dimensions of access. In contrast to previous approaches, the research will use a combination of access indicators to better understand characteristics of access.The majority of the current literature lacks the nuanced approach to accurately measure accessibility to grocery stores given the unique nature of the disaster context and through physical and social factors. Previous literature defines access based on the number of available stores31, there is limited empirical and observational insights to inform about the impacts of disasters on access to grocery stores32. In order to address the knowledge gap of using empirical and observational data to capture accessibility, the research presents an innovate method which harnesses and analyses location-based data to examine the equitable access to grocery stores during different phases of the disaster.A more nuanced characterization of access based on measures related to population-facility network interactions reveals a more accurate picture of access. Therefore, there is a multitude of physical and social factors in the disaster context that impact the equitable access to grocery stores. The research will specifically focus on the inaccessibility of grocery stores through multiple access indicators while accounting for the specific disproportionate impact on socially vulnerable populations. While the extant literature recognizes importance of access to facilities for community resilience in disastersredundancy in access; (2) rapidity of access; and (3) proximity of access. Redundancy is based on the number of unique stores visited as in the number of available stores open to consumers. This greater availability of critical facilities can lead to increased potential accessibility to important resources and needed services26. Rapidity of access is based on the duration of trips to stores which accounts for traffic delays and road disruption. Quicker trips can represent the functionality and restoration of the system35 Proximity of access is based on distance to stores visited. Shorter distances could mean that consumers do not have to travel as far to reach important resources28.In this study, we constructed and analyzed the population-facility network for examining access disparities to grocery stores in the context of Hurricane Harvey in 2017. In reviewing literature of accessibility to critical facilities and functionality of critical systems, we characterized access to facilities based on three distinct but complementary dimensions: (1) Accordingly, this research aims to answer the following research questions: (1) What are the characteristics of access to grocery stores for different sub-populations at different stages of disasters? (2) What is the extent of disparity in access among different sociodemographic groups in different disaster phases? (3) What are the contributing factors to unequal access to grocery stores, and to what extent are facility distribution inequities, such as food deserts, indicative of access disparities? To address these research questions, we defined three distinct indicators for examining access and quantify these indicators based on location-based data related to people’s visits to grocery stores in the context of Harris County, Texas.We examined disparities in access to grocery stores based on the structure and attributes of population-facility networks. The three steps of this methodology Fig.  were:  visit data from SafeGraph, sociodemographic information from a 5-year estimate of the American Community Survey of the U.S. Census Bureau, Federal Emergency Management Agency (FEMA) Flooding Data, and the Food Access Research Atlas developed by United States Department of Agriculture. These data sources were aggregated at the census-tract level to construct the population-facility network models and to calculate access indicators and subsequent analyses. A detailed description of these data sources is provided below.38. The O-D network of visits to grocery stores in Harris County was examined in this study from August 1 through September 30, 2017. Figure 39, StreetLight Data is capable of reaching approximately 23 penetration rate40, covering distinct census divisions in North America’s road network. Thus, the data provide a proper sample of human mobility in Harris County for examination of grocery store access disparity in the face of Hurricane Harvey.The mobility data are provided by StreetLight Data, a commercial platform that provides origin–destination (O-D) analysis data. Our analysis aggregated anonymized data from cell phones and GPS devices to create travel metrics, such as duration and distance41. In this study, the top facility categories related to grocery stores, specialty food stores, and general merchandise stores  were considered as the grocery stores in Harris County. Then we manually filtered the facilities to ensure that these POIs are stores used by the Harris County residents to obtain grocery needs. Figure POI data by SafeGraph were used to identify the location of facilities in this study. SafeGraph obtains location data by partnering with several location-based mobile applications. Data includes the basic information about the names, geographical coordinates, addresses, and North American Industry Classification SystemSociodemographic characteristics of census tracts were determined by collecting data from the demographic characteristics estimate over the 2015–2019 period of the American Community Survey. The sociodemographic characteristics of the TAZs were determined from their overlap with census tracts. Figure 42. The dataset was developed using the gage points from the National Weather Service and the terrain data from USGS. In this study, the flood data were examined to determine the flooding extent in each TAZ. Then those areas with a flooding extent greater than 6.5% of the area, which is the 75th-percentile flooding level in Harris County, were marked as flooded to further examine the relationship between flooding status and disrupted access to grocery stores.The Harvey Flood Depth Grid/Flood data from FEMA were adopted in this study to identify the flooded areas in examining the impacts of flooding on access43, a census tract is considered to have poor access to grocery stores if at least 500 or 33% of residents live more than 1 mile (in urban areas) or 10 miles  from the closest supermarket, supercenter, or a large grocery store. This information was implemented to interpret the findings and to understand the extent that poor access to grocery stores contributes to the variation of access in the face of a disaster.The Food Access Research Atlas developed by USDA was used to identify areas labeled as having poor access to supermarkets in Harris County. According to the definition provided by the atlasThe analyzed data is an aggregation of the trip information from the users in home TAZs to the visited grocery stores. Based on the duration and time that a user spends in a particular location, Streetlight determines the home TAZ of the user. As an example of the aggregation, Streetlight first processes that there are 100 users residing in home TAZ A. Each user has a unique, one-way trip which is used to calculate the duration and distance between the home TAZ to the visited grocery store. An aggregated outcome could be that 40 users from home TAZ A attended Grocery Store A and 60 users from TAZ A attended Grocery Store B, where each user has a distinct duration and distance for their trip.First, we constructed the population-facility network models of the study area. Population nodes are TAZs, facility nodes are individual POIs, and the links represent trips. Three separate network models were created: (1) one with unweighted links; (2) one with weighted links representing trip duration; and (3) one with weighted trip distance links. Accordingly, network-based metrics were used for examining the spatial and temporal patterns of access to grocery stores. Then, the variations in access to grocery stores during the different disaster phases were analyzed to characterize access to grocery stores, to examine access disparities, and to identify the factors contributing to such disparities.redundancy for having access to grocery stores; the weighted degree based on duration captures rapidity of access and the weighted degree based on distance captures proximity. Table In this study, we used network metrics for quantifying three distinct access indicators. These metrics, derived from the population-facility network models of visits to grocery stores, comprise topological and structural properties of the network of grocery visits. The three access metrics are: 1) the unweighted degree of TAZs (capturing number of unique POIs visited by the residents of a TAZ); (2) weighted degree of TAZs: trip duration ; and (3) weighted degree of TAZs: distance to stores . These metrics capture complementary characteristics of access, and are indicators of different access dimensions. The unweighted degree of TAZs captures  the unweredundancy in the face of disasters. Other access indicators (duration and distance), which capture the rapidity and proximity access dimensions, are influenced by human activities (lifestyle patterns) and infrastructure conditions (facility locations and road congestion). Table The access indicators Table  capture The examination of access to grocery stores based on the adopted access indicators was conducted across three disaster phases: prior to the event (without a disturbance to the network), during the period from the formation of Hurricane Harvey to prior to landfall (focus on preparedness activities), and during the landfall and recovery. The spatial and temporal patterns of impact and short-term recovery are evaluated to discover disparate access during different disaster phases. Then unsupervised learning approaches for time-series clustering characterize patterns of impact and short-term recovery on access indicators. Then, we performed statistical analysis to evaluate the influence of different factors  on residents’ access to grocery stores at different stages of disaster. The analysis steps are explained in the remainder of this section.44. The constructed network represents trips from TAZs to POIs as the links with weights  includes three weeks to define the weekdays’ baseline values. Then the percentage change for each TAZ was calculated based on the following equation:45. The Kalman filter method uses the structural time series ideas where the system is outlined by a well-defined model with unknown parameters46. The maximum likelihood approach was implemented to determine the time-dependent model parameters47.To deal with missing data on certain dates for the three indicators, we first filtered those TAZs which did not have data for 5 consecutive days. Then, the Kalman imputation method was applied to the data to deal with the missing data in the time series. The ImputeTS package in R was used to implement the algorithm on the univariate time-series for all the access indicators48 was used to perform the time-series clustering. Partitioned procedures were considered to be optimization problems that maximize the inter-cluster distance and minimize the intra-cluster distance49. DTW is a widely used approach for defining the distance in time-series data50. In this study, we implemented a multivariable time-series clustering approach for classifying the TAZs. The 3 day moving average time series related to the access indicators for each TAZ were used to identify the TAZ clusters. Then, two cluster validity indices, COP and modified Davies-Bouldin, were used to determine the number of clusters51.We implemented time-series clustering algorithms on the access indicators to characterize and examine the spatial and temporal variations. First, a 3 day moving average was applied to the time-series data of each access indicator to extract trend and limit noise. Then, a partitional clustering using dynamic time warping (DTW) distance in dtwclust in R The calculated access indicators for the TAZs (described in the Method section) were used to examine the access at three disaster phases: pre-disaster condition, during preparation, and during disturbance and recovery of the affected areas. The analysis covers a period between August 1 and September 15, 2017. The pre-disaster period  covers the period immediately prior to landfall; however, no disturbance had occurred to the residents’ access to grocery stores. This period captures access to grocery stores during normal conditions. Following the formation of Hurricane Harvey and the issuance of the hurricane watch on August 23, preparation activities were initiated, and some grocery stores were out of stock due to increased demand. Finally, Hurricane Harvey made landfall in Harris County on August 25, 2017, causing several road inundations and disrupting access to grocery stores, which affected Harris County residents’ access.Redundancy dimension of access, shows a spatial clustering pattern in some areas. The TAZs in the East and South of Harris County show a lower number of visited stores, which translates to a low redundancy of access to grocery stores. The difference in the redundancy in access to grocery stores could be related to the number of nearby stores, the size of the stores, and households’ lifestyles and shopping behaviors.To understand the key characteristics of the access in the normal condition, we analyzed the period before the Harvey landfall. Dynamic clustering was used to identify the distinct clusters of TAZs with similar access characteristics for each indicator. The results show the patterns of access to grocery stores for the different TAZs in Harris County. Figure redundancy indicator. Proximity and rapidity indicators also show the presence of some spatial clusters, which suggests TAZs would have similar access indicators to their neighboring TAZs. Examining the three access indicators together, however, shows little overlap among the clusters for the three indicators. This result shows the spatial heterogeneity of TAZs in terms of their three access indicators. Figure rapidity and proximity access are more concentrated in high-income areas west and southwest of downtown. The large TAZs in the north (highlighted in red) are the next most frequently occurring clusters with high levels of duration and distance and a low level of number of visits, which shows poor access in terms of proximity, rapidity, and redundancy.On the one hand, the more stores available in the proximity of a TAZ, the more the visits would take place during normal conditions. On the other hand, the size of stores could also affect the frequency of visits to stores, as larger grocery stores with a larger, more varied inventory could meet diverse needs of the households; one visit would be adequate to satisfy the weekly needs for groceries and other supplies. Furthermore, some households rely on coupons or have lifestyles that require them to follow certain shopping behaviors that affect the number and type of grocery stores they visit. Therefore, the properties of the facilities, their distribution, and the lifestyle characteristics of the households could influence the access The association between the sociodemographic characteristics of the TAZs, as well as the facility distribution characteristics  with the access indicators, were examined to evaluate factors influencing spatial variations of access patterns. The results show that the identified clusters have distinguishable demographic and facility distribution characteristics. Table proximity and rapidity access dimensions do not show a clear disparity with respect to the sociodemographic characteristics. In fact, the results show that TAZs with a better socioeconomic status have lower proximity and rapidity access dimensions. This pattern could also be due to the fact that these TAZs are more likely located in residential areas, and their residents have a greater capability to commute longer distances to obtain their needed supplies from specific stores. In addition, these clusters are located in areas with a lower number of stores and a higher chance of being located in a food desert.The results for the number of visited POIs indicator show that the cluster 1, which the highest number of unique stores visited (highest redundancy), had a lower socioeconomic status. These groups of people have diminished access to big supermarkets and are more likely to buy their needed supplies by using coupons from different stores to save money. In addition, cluster 1 has more stores in the TAZs and also a lower chance of being located in a food desert (being far from a store) compared to other clusters. The availability of stores also partly explains a greater number of unique grocery stores visited by residents of TAZs in this cluster. The results related to the The percentage change in access indicators in comparison with the defined baselines was examined to assess the disparities in access to grocery stores in the preparation phase. In the time span between the identification of Hurricane Harvey until landfall in Harris County, residents attempted to store supplies, such as food and bottled water, to protect their households against hardship. Since there was limited mandatory evacuation by public officials, most residents sheltered in place; thus, storage of food and bottled water was critical to riding out the storm. Many grocery supplies were out of stock due to the high demand and the shelter-in-place protective behavior of the residents, which disturbed the access patterns to grocery stores. We analyzed the percentage change in the access indicators to seek out disparities in access during the preparation phase. The percentage change of the access indicators on the day before the hurricane landfall  was chosen to examine disparities in access patterns. The examination of the daily patterns of access indicators showed that their greatest percentage change occurred on this date, and residents showed a high level of activity to obtain grocery supplies for their households.rapidity dimension, as the TAZs with a longer trip duration (lower rapidity) in the normal condition showed a lower increase in the percentage change. The increase in the trip distance also shows that those TAZs which have shorter distance (better proximity) in the normal condition have a greater increase in the preparation period.To explain the variations in the access indicators during the preparation period, we first examined the patterns in the pre-disaster level for the three indicators (as discussed in the previous section) together with income level. The examination of the association of these two factors with the percentage change in the indicators reveals that there is a statistically significant relationship between the access patterns in the normal condition with the percentage change of these indicators during preparation stage. Results show that those with lower accessibility based on the three indicators, meaning fewer unique POIs visited, longer duration, and longer distance, had a higher percentage change during disaster when compared to normal conditions. This means that those who visited few number of POIs (redundancy) greatly increased the number of unique visits. A similar pattern exists in the proximity (long distance) in the normal condition.Within the low, median, and high levels of accessibility in the normal condition, the low income (bottom 33%) and high income (top 33%) TAZs were compared to their new levels of accessibility in the preparation stage. The results show disparate access to grocery stores during the preparation phase of Hurricane Harvey. Figure proximity. Thus, those residents  who could not increase their access distance may not be able to adequately prepare for the impending hazard.In the next step, we examined the effect of facility distribution factors, namely the number of POIs in a TAZ, on the access indicators. First, the correlation between the numbers of POIs in a TAZ with the access indicators Table  shows thIn the next step, we categorized TAZs into four categories based on income in conjunction with location in a food desert to understand the effect of these factors on access indicators. Figure redundancy); however, the cluster shows a lesser increase for the distance (decrease in proximity) and duration (decrease in rapidity) indicators during the recovery period. In particular, the proximity indicator seems to show a lesser level of decrease (increase in distance) in cluster 2 compared to cluster 1. The patterns suggest that while cluster 2 visited more stores, their duration and distance did not increase significantly compared to cluster 1. This result shows that cluster 2 TAZs had better access, as they could meet their needs by visiting more stores without the need to significantly increase their trip distance. Increased redundancy without the need for decreasing proximity is an indication of better access for cluster 2 .To understand the dynamic spatiotemporal patterns of impact and short-term recovery of access to grocery stores, we conducted multivariable temporal clustering on the three access indicators. The results indicate the presence of two clusters of access patterns to grocery stores in TAZs whose access was affected by flooding. Figure Figure Figure p = 0.33). The insignificant difference in flooding shows that the diminishment of access to grocery stores goes beyond the extent of flooding; however, the sociodemographic characteristics and the facility distribution characteristics explain the disparate access to grocery stores for the two groups.The distribution of income level within a food desert in the two identified clusters was examined in a proportion test analysis. The results of the proportion test show that in cluster 2, the proportion of high-income TAZs in food deserts is significantly higher than that of low-income TAZs in food deserts with proportions being 0.53 and 0.25, respectively . However, such a distribution did not exist in cluster 1, with proportions being 0.57 and 0.47 for the high-income and low-income TAZs, respectively . Thus, the high proportion of higher-income areas in food deserts allowed for better access in cluster 1 than in cluster 2 despite a higher level of being in a food desert. These results suggest that the food desert designation is not adequate to capture residents’ access to grocery stores in the aftermath of disasters, and more data-driven insights (such as the results of this study) are needed to evaluate the access of different sociodemographic groups to critical facilities in times of disaster. Furthermore, the results from the comparison of the extent of flooding in the TAZs do not show a significant difference between the two clusters  and the distance traveled (proximity). This reveals the higher capability to take protective actions and seek essential resources for preparation in comparison to lower-income areas. This phenomenon changes in the impact/ short recovery stages of the disaster setting. Areas with higher income and a lower percentage of racial minorities maintained their higher redundancy, meaning they were able to access more stores similar to the preparation periods. In this case, though, these areas had a lesser decrease in distance (proximity) and less trip duration (rapidity) compared to those of lower income and larger minority population. Thus, these groups could visit more stores without longer and more lengthy commutes.The key findings of the research show the importance of characterizing access based on the three dimensions of The examination of different dimensions of access in the context of disasters confirmed the inadequacy of physical measures of access, such as location in a food desert and the number of available stores in an area, for understanding access in disasters. The research findings show that while these factors are better capable of explaining the variations in access during normal times, they fail to explain access characteristics during different stages of a disaster. The multivariate analysis of access showed that the areas with higher income and a lower percentage of racial minorities have better access to grocery stores. However, higher income areas tended to have a lower number of stores in their area and a higher likelihood of being in food deserts. This could be because higher income areas live in residential areas, where they are more separated from grocery stores; however, they have the capability to travel further distances and visit more stores. These findings indicate that, while focusing on facility distribution characteristics is useful in understanding the disparities during the normal condition and for purposes of urban development, the standard physical measures of access, such as location in a food desert and the number of available stores in an area, is not sufficient for understanding access to facilities in the context of disasters.Although the research captured an element of disparity in the accessibility to grocery stores, it is important to acknowledge the complex metrics that cumulate in food inaccessibility at the individual household level. Thus, future research could integrate the significant findings into other physical and social indicators. For example, financial ability, particularly the capabilities or purchasing power of households to understand the challenges and decision-making of individuals. Data-driven evaluation of access and disparities in disasters could inform future emergency response, preparedness, and mitigation plans and actions. For example, the placement of temporary distribution centers in areas with lowest levels of access could help with better and more equitable preparedness for shelter-in-place populations. In using the current case study, it was established that Cluster 1 had less accessibility metrics when compared to Cluster 2. Temporary distribution centers could be prioritized in the highlighted areas of cluster 1 Fig. . Additio


Around 10% of all hematologic malignancies are classified as multiple myeloma (MM), the second most common malignancy within that group. Although massive progress in developing of new drugs against MM has been made in recent years, MM is still an incurable disease, and every patient eventually has relapse refractory to any known treatment. That is why further and non-conventional research elucidating the role of new factors in MM pathogenesis is needed, facilitating discoveries of the new drugs. One of these factors is the gut microbiota, whose role in health and disease is still being explored. This review presents the continuous changes in the gut microbiota composition during our whole life with a particular focus on its impact on our immune system. Additionally, it mainly focuses on the chronic antigenic stimulation of B-cells as the leading mechanism responsible for MM promotion. The sophisticated interactions between microorganisms colonizing our gut, immune cells , and intestinal epithelial cells will be shown. That article summarizes the current knowledge about the initiation of MM cells, emphasizing the role of microorganisms in that process. Multiple myeloma (MM) is a hematological neoplasm deriving from clonal plasma cells. In almost every case, it is preceded by a premalignant stage called monoclonal gammopathy of undetermined significance (MGUS) , 2. In 3+/CD56-/CD45+/CD38+, while the malignant plasma cells are losing CD19 and CD45 and acquiring CD56  or allergic diseases (Th2) , 46. TheThere is also one other proof of how vital well-balanced gut microbiota is for maintaining the immune system in shape. Experiments on germ-free mice, free of any microorganisms, showed that gut microbiota is obligatory for Tregs differentiation . Other eThe process of immunoglobulins (Ig) production starts within the germinal centers (GCs) of secondary lymphoid organs. This is where naïve B cells encounter T cells accountable for selecting B cells eligible for future combat against pathogens or antigens . Given tIn the GCs, B cells are selected based on the higher affinity of B-cell receptors (BCR) towards the antigen. This is the initial step in immunity organization that will last for long years . During Helicobacter pylori  include Paneth cells, absorptive epithelial cells, and goblet cells, and their two central roles are to segregate and mediate between the microorganisms colonizing the gut and the immune system. The former function of IECs is possible because of the physical and chemical barriers which prevent the intestinal inflammation that could start because of conflict between these “two armies’’ of cells. The latter function means that IECs can forward signals deriving from the gut microbes and their metabolites and transform that signal for the language “understandable’’ for the immune cells . One exavia the production of a proliferation-inducing ligand (APRIL) through toll-like receptor (TLR) signaling  between the gut microbes and the immune cells that are staying on the two sides of the wall and by secretion of chemokines, cytokines, and hormones they maintain the balance between “both armies.”Immune cells engaged in the crosstalk with the gut microbiota are predominantly seen in the lamina propria. The most common ones are T regulatory cells, NK cells, and invariant T cells. Dendritic cells infiltrate very deeply into villi and closely contact with the IECs   may be the site of a growing repertoire of B cells . Interesvia BCR recognition of the carbohydrates and proteins produced by the gut microbes, which act as antigens  is currently a standard of care for patients in good condition. Until the engraftment, the patients are in critical pancytopenia and prone to opportunistic infections and therefore very usually treated with antimicrobials. However, before that, patients receive conditioning therapy that influences the composition of the gut microbiota and has a gross impact on intestinal epithelium. That altogether leads to the dysbiosis and monodominance of microbes such as Enterobacteriaceae . Researcy for MM .Regarding the proteasome inhibitors (PIs), which are commonly used to treat MM, it is worth noting that their adverse event is diarrhea. Unfortunately, the pathophysiology of gastrointestinal toxicity of PIs is poorly understood. However, as it is known that the SCFAs and PIs regulate the NF-kB pathway, the gut microbiota probably influences the risk of adverse events after PIs .Prevotella heparinolytica induced differentiation of Th17 cells. They then migrated to the bone marrow of Vk*MYC mice (which are the transgenic mice that develop disease mimicking MM) and favored the progression of MM. That agrees with the notion that SMM patients with a higher level of IL-17 in the bone marrow have faster progression of the disease  the gut or state of dysbiosis when particular species of bacteria overgrowth could be responsible for the chronic antigenic stimulation and development of at least part of MGUS and MM cases.Additionally, it would be interesting to see whether the gut microbiota composition influences the cytokines produced by the leukocytes in the blood. For example, maybe the state of dysbiosis provokes the production of proinflammatory cytokines by leukocytes and, therefore, indirectly promotes the progression of MM or MGUS.As it was said in the previous paragraph, it is worth checking whether the treatments targeting IL-17 or IL-17R could work by lowering the risk of progression of the MGUS/SMM/MM. Such drugs are already registered by the FDA (anti-IL-17A antibodies), making it even easier to conduct such a study .Preclinical studies suggest that some species of bacteria colonizing the gut promote the progression of MGUS or MM. Papers are mounting about the role of the gut microbiota in the pathogenesis of many diseases, and great hope is seen in the procedure of fecal microbiota transplantation to restore the balanced gut microbiota. Maybe such a procedure or probiotics/prebiotics could lower the risk of progression of MGUS to more advanced stages of the disease. Currently, the patients with MGUS or SMM are offered with watchful waiting strategy since the risk of progression, especially in the case of MGUS, is particularly low. Perhaps in the future, the gut microbiota composition of these patients is going to be known in detail. The patients with an exceptionally high risk of progression could be treated with prebiotics/probiotics or even with fecal microbiota transplantation (FMT) to diminish the risk of progression completely.Klebsiella and Streptococcus. The researchers assume this is because of the progression of MM associated with the excessive accumulation of urea. Then the urea reaches the intestinal wall and selects nitrogen-recycling bacteria for overgrowth. In turn, microbes can produce L-glutamine, which is then delivered to the host and promotes the proliferation of myeloma cells since they cannot produce it on their own. Thus, the authors propose that the gut microbiota alterations, namely reduction of Klebsiella and Streptococcus populations, which are also present in the normal microflora, could lower the risk of progression of MM (Klebsiella or Streptococcus (Jian et al. showed recently that the bacterial diversity of the gut microbiota of newly diagnosed MM patients is significantly increased with enrichment of nitrogen-recycling bacteria such as on of MM . Furthertococcus . Therefotococcus .Klebsiella and Streptococcus were shown to play a role in the progression of MM, it seems that further studies on gut microbiota in the treatment of MM are warranted. Additionally, these bacteria are often responsible for infections in that population of patients. Therefore, the possible efficacy of FMT in the elimination of these “microbial partners in crime’’ would be multidirectional.To sum up, it seems highly probable that there is a role of the gut microbiota in the pathogenesis and treatment of MM. With ever-growing numbers of papers published in that field, the hope for an entirely new type of prophylaxis of progression of MGUS or treatment of MM is growing in parallel. Our previous work shows the remarkable efficacy of FMT in preventing colonization of a single, in that case, antibiotic-resistant bacteria . Also, iMJ and JB wrote the paper, GWB reviewed the paper. All authors contributed to the article and approved the submitted version.JB and GWB are the founders of the fecal microbiota bank and laboratory named the Human Biome Institute.The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Early detection of high fall risk is an important process of fall prevention in hospitalized elderly patients. Hospitalized elderly patients can face several falling risks. Monitoring systems can be utilized to protect health and lives, and monitoring models can be less effective if the alarm is not invoked in real time. Therefore, in this paper we propose a monitoring prediction system that incorporates artificial intelligence. The proposed system utilizes a scalable clustering technique, namely the Catboost method, for binary classification. These techniques are executed on the Snowflake platform to rapidly predict safe and risky incidence for hospitalized elderly patients. A later stage employs a deep learning model (DNN) that is based on a convolutional neural network (CNN). Risky incidences are further classified into various monitoring alert types . At this phase, the model employs adaptive sampling techniques to elucidate the unbalanced overfitting in the datasets. A performance study utilizes the benchmarks datasets, namely SERV-112 and SV-S2017 of the image sequences for assessing accuracy. The simulation depicts that the system has higher true positive counts in case of all health-related risk incidences. The proposed system depicts real-time classification speed with lower training time. The performance of the proposed multi-risk prediction is high at 87.4% in the SERV-112 dataset and 98.71% in the SV-S2017 dataset. Falls are indicators of feebleness, immovability, and severe chronic injury in elderly people. Fall risks reduce normal tasks by producing injury and movement limitations. Most injuries in the elderly are caused by falls that can produce hip fractures and forearm injuries ,2,3.The prediction of fall risks must consider the range of ages and fitness status within the elderly patients and report the variety of reasons for falls. Fall risk factors are listed in controlled studies, which agree to the detection of patients at risk. Older people with several health deficiencies are at the highest risk ,5,6.An introducing event that relocates the patient’s center of mass away from its base of support which can be caused by environmental hazards or unstable joint weakness.The second stage of a fall is the inability of the patient to sustain an upright posture to perceive this movement in time to evade a fall due to loss of sensory function.The third stage is the effect of the patient’s body on solid surfaces, which yields to the conduction of forces to the patients’ organs. The prospective for damage is a function of the magnitude and direction of the fall forces.A fall is defined as an unintentional occasion that leads the patient to rest on a ground level. Falls are designated in three stages, as follows :An introResearch is moving towards automation, deep learning (DL), and artificial intelligence. Dynamic monitoring networks speed up the stream of health-related risk information ,2,3. TheMany researchers have utilized deep learning models for the monotonous prediction of unusual monitoring movements for hospitalized elderly patients . Many moThere are several hybrid monitoring prediction models that unite several deep learning techniques such as support vector deeps (SVM) and C-means ,11,12,13Distributed intelligent learning techniques and deep techniques are presented by many researchers ,20,21,22https://www.snowflake.com/en/) accessed on 23 June 2022, for real-time prediction of risk. Snowflake’s Data Cloud is powered by an advanced data platform provided as Software-as-a-Service (SaaS). Snowflake enables data storage, processing, and analytic solutions that are faster, easier to use, and far more flexible than traditional offerings .Data normalization is the computation to convert the multi-dimensional feature into a one-dimensional value. Preprocessing confines the values to definite ranges to reduce the effects done by the difference in variance values. In this article, all representative values are normalized to , where The incidence labels of the SERV-112 and SV-S2017 are numeric values, and binarization labeling of is utilized to define categories into vectors of bits. In the SERV-112 dataset, we have eight categories representing the severity of immobility. In the SV-S2017 dataset, we have the same eight categories, in addition to two categories of health-related risks, namely previous injuries and previous falls. This research performs label binarization for the immobility features and the health-related risks categories.The second phase is for rapid prediction of health-related risk incidences while guaranteeing the performance of health-related risk incidences. If a high number of health-related risk incidences are misclassified, the monitoring system’s false negative count will rise, and the monitoring systems will not be operative. Thus, the objective of this phase is to isolate the health-related risks promptly. The enhancement of the monitoring system’s performance is governed by the legitimacy of the input. The data composed by the monitoring systems are diverse collected from patients’ behavior and aptitude. This multiplicity that we employ can produce the operative processing of monitoring incidences. These data of multi-dimensional monitoring systems need to predict the risky incidence before classifying health-related risk classes. The health-related risk is clarified by removing safe incidence, which decreases the effort and time complexity of the in-depth analysis of health-related risks in the final phase. The prediction at this phase is depicted in The binary monitoring prediction system integrates the DC technique and the Catboost technique using Snowflake. The DC technique is utilized to group monitoring prediction incidences, and Catboost is utilized to categorize the clusters. The input is separated into several subsets according to the structure of the Snowflake system and the data size. All different data subsets subsist as DSet in Snowflake. Each transmutes and action function in the technique will be incidences and will be applied to all DSets. Thus, the incidence sets in In the last phase, the incidences classified as safe by the Catboost technique in the previous phase will leave the system directly. Many health-related risk incidences are positively separated, and the incidences classified as unsafe will enter the last phase. Using these schemes, unsafe incidences and safe incidences can be extracted. The model platform of the DC and the Catboost techniques are depicted in In this phase, three architectures are used, namely: “DNN”, “LS”, and “DNN + LS”, and are utilized to validate the accuracy of the system. Each system structure comprises from one to three hidden layers. ReLU classification is used, and the final phase is fully connected (FC). The classification is performed using Softmax. These structures are utilized to discover the most appropriate multiple risk health-related incidence prediction method. All health-related risk incidences are classified using Softmax. The whole configuration of this phase is depicted. In the last preprocessing stage, SERV-112 has 120 attributes and SV-S2017 has 80 attributes. The last layer has six dense layers for the SERV-112 dataset and eight in the SV-S2017 dataset, as depicted in https://www.ti.com/lit/pdf/spvu015 accessed on 23 June 2022), which utilizes Tensor as the prime platform of Keras. This permits us to decrease the training time of the DNN system. After the binary prediction by the DNN model on the Snowflake framework, the safe and unsafe incidences are directly computed by the distributed model to the master side. In the final phase, the monitoring prediction data saved on the master side can be utilized for prediction straight away as depicted in The deep learning model is depicted in Evaluation methods for prediction results of unsafe incidences are used. Precision and true positive rate and true negative rate are mostly measured. The ratio of incidences in the test subset diverges, and the classification outcomes may be inclined to incidences with higher occurrence data. Thus, the precision is not enough. The classification outcomes produced by the monitoring system can be partitioned into several groups.TP) is defined as the correctly classified health-related risk incidences, and TN denotes the properly classifies safe incidences. FP depicts the mistakenly classified positive incidences and a false negative is defined as the incorrectly classified negative incidences.The accuracy is the percentage of correctly classified incidences to the total incidences count. Accuracy is employed for all safe incidences and unsafe incidences in the test dataset. The true positive rate is the percentage of incidences defined as health-related risks, which are properly classified as health-related risks in the datasets for all items defined as health-related risks as depicted in the equations below.The prediction by the deep DNN prediction system, the results are depicted in The multi-risk health-related incidences prediction employs several deep learning frameworks: DNN, long short-term memory, and DNN + long short-term memory. The factors of the training system affect the prediction experimental ratios. After several epochs, the deep layers of the DNN system are made equal to 2056, the FC size is made equal to 6, and the pooling layers are made equal to 4. The factors for the long short-term memory system are set to 520. The parameter of the DNN + long short-term memory system is computed using the previous systems. The preliminary learning rate is equal to 0.02.The precision rate of the loss rate of the training systems of eight convolutions is depicted. The three deep learning systems can accomplish high accuracy and low loss rates. It is observed that the count of epochs needed to predict only health-related risk incidences is 100 epochs, which is considerably low compared to the 500 epochs needed by the model in  to prediIn ,33 the sResults depicted in The Bland-Altman plot of the real ratio of the risk incidence in elderly patients from a medical personal prognosis in comparison to the calculated ratio of risk incidence of the proposed system is depicted in The true positive rates of health-related risk incidences used for the three used models are depicted in The computational load is challenging due to the fact that these systems were realized on various platforms. Thus, the computational load can be depicted by simulation models. The final phase of the system is grounded on the statistic that testing datasets are partitioned into unsafe related risk incidences and safe incidences from the second phase. The incidences predicted as unsafe-related risks from the second phase were gathered on the server. The DNN prediction system of the final phase is predicted from the server, and testing will be converted frequently on various platforms. Snowflakes’ distributed hardware finalizes the data of the training input in the final phase and will save training time in this phase. The prediction system utilizes Azure Machine, which utilizes the speedup technique for faster training, so the computational load of the final phase can be enhanced. Comparison between the proposed model (DNN + LS) model and other fall prediction models is displayed in In deep learning neural networks, training time is one of the metrics for defining the model performance. In addition, classification CPU time is very crucial, especially for real time applications. The fall monitoring system is one of the real time applications where classification has to be done in real time from video captured by the camera. In the following table we display the CPU time comparisons for both training time and classification time.As we can see from This article presented a cascading monitoring prediction technique based on distributed C-means, Catboost, and deep learning. We presented a methodology to solve monitoring systems problems that are computationally time-consuming and that have low prediction accuracy. The distributed methodology is utilized to accomplish the time-efficient processing of the monitoring prediction dataset. The unsafe related risk incidences and safe incidences were alienated by incorporating the distributed C-means and the Catboost technique. The isolated unsafe related risk incidences are fed to deep learning systems that extract hidden features of multi-risk incidences. The final phase performs the prediction of various related risk incidences promptly. The prediction system presented in this paper is evaluated using the SERV-112 and SV-S2017 datasets. The experimental results depict that the presented method can efficiently recognize the prediction of health-related risk incidences. The performance of safe incidences and the other three types of unsafe related risk incidences is 87.24% in the SERV-112 dataset, while the accuracy of unsafe incidences is 98.9% in the SV-S2017 dataset. We also compared the proposed model (DNN + LS) with the separate deep learning monitoring prediction systems (DNN and LS)), and the system presented in this paper achieved a higher true positive rate for most risk incidences irrespective of the incidence number of these related risk incidences, which determined that more elderly related risks could be precisely predicted. The proposed model maintains average time that is lower than the other models in training time. This is due to two factors. The first factor is the Snowflakes’ distributed hardware where the proposed system is designed and implemented to the distributed system. The second factor is the application of the C-Means from the beginning to cluster the fall incidence from the No-Fall incidences before the deep learning training phase. The same two factors contribute significantly to less classification time for alarms to notify health personnel about a future fall incident.The limitation of this study is the lack of posture information in the learning stage, as it might help in the classification process, but it might take require more classification CPU time.