An exoskeleton, a wearable device, was designed based on the user’s physical and cognitive interactions. The control of the exoskeleton uses biomedical signals reflecting the user intention as input, and its algorithm is calculated as an output to make the movement smooth. However, the process of transforming the input of biomedical signals, such as electromyography (EMG), into the output of adjusting the torque and angle of the exoskeleton is limited by a finite time lag and precision of trajectory prediction, which result in a mismatch between the subject and exoskeleton. Here, we propose an EMG-based single-joint exoskeleton system by merging a differentiable continuous system with a dynamic musculoskeletal model. The parameters of each muscle contraction were calculated and applied to the rigid exoskeleton system to predict the precise trajectory. The results revealed accurate torque and angle prediction for the knee exoskeleton and good performance of assistance during movement. Our method outperformed other models regarding the rate of convergence and execution time. In conclusion, a differentiable continuous system merged with a dynamic musculoskeletal model supported the effective and accurate performance of an exoskeleton controlled by EMG signals. The exoskeleton, a wearable device, was designed according to the physical and cognitive interactions of the users. Exoskeletons have been developed for diverse medical purposes, including strength augmentation , rehabilThus, for patients with stroke or spinal cord injury, decoding motor signals from electromyography (EMG) or neural signals from electrocorticography (ECoG) ,5 would For controlling an exoskeleton, several physiological signal sources, including force sensors  or biomeHowever, in that study, the coordination and interaction between physical activity and cognitive control was limited for the force sensor system because the predetermined trajectory could not synchronize the user’s intension to actively coordinate the muscle movements simultaneously. The force sensor system could only be activated by the predetermined trajectory of the exoskeleton when the actual output was lower than the threshold or deviated from the predetermined trajectory. The unsynchronized interaction, as well as a large time delay, between physical movements and the force sensors increased the risk of tripping . For EEG to be used as the input to control the exoskeleton, direct brain signal recording has been measured on the scalp. In previous studies, the exoskeleton could be controlled via the brain-computer interface by using EEG signals ,13. HoweTo overcome the limitations posed by the time delay between the user’s intentions and the force sensor system, it is crucial to directly decode biomedical signals recorded from the nervous system or the musculoskeletal system. EMG signals are relatively easy to obtain and contain the information about body movement. Other investigators have used machine learning or neural networks to learn and predict the joint angles  and torqIn one example, the artificial neural network technique was applied to EMG signals to predict the joint torque estimation model, and the trained model was used in the arm rehabilitation device aiding limb-paralyzed patients. Although the methods of machine learning or neural networks had performed well on decoding, their “black-box” transformation of the biomedical signals was lacking biomechanical explanation, which remains a source of controversy in these applications.The Hill-type muscle model , built oFor a more advanced application to not only healthy subjects but also paralyzed patients, a Hill-type muscle model, complete with output activation and torque-angles, was used to determine the muscle parameters for biomechanical computer simulations. This method was able to provide adaptive control and generate muscle responses in real-time. Thus, the Hill-type muscle model implements a biomechanical mechanism for natural and reliable interaction between assistance and estimation ,24, and Although the control of neuromuscular stimulation and the exoskeleton can be done cooperatively, it requires an accurate and robust system to identify the parameters. To improve the accuracy of the identified parameters, an iterative method of the sequential least-squares quadratic programming (SLSQP) ,27,28 wiTo make it practicable, the musculoskeletal model had to be simplified . Due to To tackle the challenges described above, we introduced the method of differentiable systems  applied The main biological and engineering contributions of this study are as follows. First, we propose a differentiable continuous system control that achieves assist-as-needed control by integrating the dynamics of musculoskeletal models and analyzing the interaction between the user and the exoskeleton. Second, in this continuous system, we implement an analytical gradient and updating mechanism for the muscular excitation parameters. As a result, and in contrast to previous gradient approximation methods and gradient-free methods, our proposed model is capable of providing the robust and accurate gradients needed to obtain the parameters in the musculoskeletal model.Subjects used a wearable and wireless EMG amplifier  equipped with eight bipolar signal channels that were connected with medical grade button snap cables, common in clinical practice, to pairs of surface electrodes  located on the targeted muscle tissues. Body ground was applied on the leg to increase the signal-to-noise ratio. The acquired EMG signals were amplified by a 12× programmable gain, sampled at 1000 Hz, digitized at 24-bit with delta-sigma ADC , and transmitted to a customized radio-frequency receiver dongle connected to the computer as a COM port input. The baud rate was configured at 921,600 bits/s to enable high-speed transmission. Sampled data was labeled by time stamp and channel counter, passed through an error-control mechanism  to achieTM, FREE Bionics Taiwan Inc., Hsinchu, Taiwan) was applied to a single knee joint in a human-in-the-loop environment as shown in In this study, the knee exoskeleton , assessed on 29 April 2022.While the semitendinosus was assumed to have the same activation as the semimembranosus , the semDuring data acquisition, signals from the exoskeleton and the EMG electrodes were recorded simultaneously. First, the subject was asked to release the force. The knee in full extension (0 degrees) and flexion (100 degrees) was completely supported by the exoskeleton. Furthermore, the subject could move the knee freely without support from the exoskeleton. After analyzing the parameters of the exoskeleton and EMG signals, we evaluated the performance  while the subject was moving the knee with simultaneous exoskeleton support. The schematics of the experiment is shown in To eliminate movement artifacts, the EMG data acquired from the four muscles were processed in the following three steps. First, the raw EMG data were filtered by a fourth-order Butterworth band-pass (20–450 Hz) filter. Second, full-wave rectification and an 8-Hz low-pass second-order Butterworth filter were applied to obtain the EMG signal envelope. Third, the filtered EMG signal was normalized with respect to the muscle’s maximum voluntary contraction level. u(t) and muscle activation a(t), where After the above three steps the smoothed normalized EMG signal ion (1)) ,37,(1)uSince muscle torque cannot be measured invasively, the torque obtained from the motion equation was used as the substitute when adjusting the muscular parameters. This required the three inertia parameters of the motion equation  ,41 as foFor simplicity, we only present the equation derived for one muscle that lets us omit the superscript In this study, a Hill-type muscle model can be simulated by the active (contractile) and passive (noncontractile) muscle components ,43. The traction  and an etraction , where γThe force–strain relationship of a tendon is determined by an exponential function within an initial nonlinear toe region followed by a linear function outside (Equation (10)),The formal relationship between the The fication , in EquaThe force of active muscle contraction is derived, in turn, from the inverse transform of from Equation (12), i.e., by Equation (13):To this end, the force of muscle–tendon contraction required by Equation (7) is demonstrated as adjoint method [However, each user had different muscle properties; therefore, each muscle in the model needs to be calibrated for the parameters. The Hill’s muscle model requires three parameters to scale generic curves for active and passive force generation: the optimal fiber length h lslack . The adjt method , which wMuscle torque cannot be measured directly in an invasive manner. To estimate muscle torque, it was necessary to use statistical inference consistent with physiological and physical explanations. Under the condition that the knee joint of the subject is in the same position as the exoskeleton joint, rearranging Equation (5) yields the functional relationship between muscle torque and the angular acceleration of the joint generated by muscle torque as stated in Equation (14):The angular position of the trajectory can be computed using this equation from the inputs of muscle torque In this system of differential equations, the first component, the velocity of muscle contraction, is calculated from Equation (12). The second component, angular acceleration, requires the muscle length, integrated from first component, to compute the muscle torque ion (16) .(16)τe, the prior probability Here, rameters . The musadjoint method [In order to solve Equation (20) using an iterative algorithm, such as a stochastic gradient descent or adaptive moment estimation (Adam) , it was t method . In brieThe performance of our proposed model was evaluated in two different sets of tests. In the first set of tests, conducted under muscle contraction, the torques and angles predicted in each muscle were compared with real data. In the second set of tests, the efficiency of our proposed method was evaluated by the rate of convergence and execution time and compared with those of other models on an equal basis.In exoskeleton control, the joint torque generated by the muscle force was estimated to predict the movement trajectory. To obtain an estimate of the muscle-generated torque in a non-invasive manner, we identified the parameters of the motion equation (Equation (5)) in advance from the recorded angular position and velocity of the trajectory. The five identified parameters in the motion equation are summarized in  studies .The process used for the optimization of the muscle parameters is illustrated for predicted position in In the model simulation, the time interval of the prediction ranged from 2 to 25 s, the EMG signals were served as inputs to the model, and the model outputs were the predicted trajectory and joint torque. The solid line in The predicated joint torque B was calWe compared the performance of the analytical gradient method and three other gradient update methods. Two gradient-free methods, Nelder–Mead (NM)  and AdapThese noise terms were added to the value of the identified parameters listed in The solid lines represent the average position and torque determined from the 10 sets of the identified muscle parameters, and the shadow area maps the maximum and minimum values for the angle and torque. The dotted lines represent the subject’s measured position and torque, used as the convergence target for optimizing the muscle parameters. As seen in The same is seen for the torque B. Thus, Although the movement trajectories and torques predicted by each method were convergent, the identified muscle parameters may be divergent. Since the movement trajectory was from the same subject, even different initial muscle parameters should converge to similar muscle parameters. We compare the convergence results of the analytical gradient method (Equation (23)) and the gradient-free methods for the optimal muscle length in VM A and ST Predicting the trajectory and torque using Hill’s muscle model was complex because it involved numerical integration. Prior studies usually adopted gradient-free methods to update muscle parameters, thereby, avoiding the complexity of the analysis and the computational effort associated with numerical integration ,25,31. TIn the CG, TNC, and SLSQP, the two-point finite-difference method (FDM) was used to approximate the gradient , the popIn this study, we proposed a differentiable continuous system control that integrates the dynamics of musculoskeletal models and analyzed the interaction mechanisms between the subject and the exoskeleton to achieve assist-as-needed control. This approach has several advantages, including faster convergence, increased accuracy and stability of predicted outcomes. For convergence, both muscle contraction and rigid body dynamic equations were applied in our model to improve calculating gradients. Updating the direction based on the loss value around the parameter made convergence faster in our model than in gradient-free methods, such as the NM method .We considered both the muscle contraction and the rigid body dynamic equation to calculate analytical gradients in our model, and the calculated values of the muscle parameters  were cloIn comparison, the step of inverse dynamics transformation is eliminated in our model, and calculating the torque only requires the measured angle for parameter estimation. According to the results in The iterative update of the muscle parameters was performed using the analytic gradient based on the muscle contraction and rigid body dynamic equations. Unlike the gradient-free methods, the analytical gradients method makes use of the dynamic equation and can generate the specified torque and angle leading to stable convergence of the muscle parameters as shown in The analytical gradient obtained the convergent results by calculating the gradients from the muscle contraction and motion equation with the two dynamic equations putting constrains on the results. In contrast, NM and ANM were gradient-free methods, relying only on the magnitude of the changes in the loss value to determine the direction of parameter update. The benefit of using the muscle contraction and motion equation is that the outputs satisfy the laws of physics. Thus, the use of analytical gradients to estimate muscle parameters could provide a more fault-tolerant and robust initial parameter setting process in practice. We compared the execution time for different parameter-updating methods . The graTherefore, to compare the speed of the different update methods, we selected to evaluate only the loss function  on the same hardware platform . The programs were implemented with the JAX package  and acceOur proposed model merged a differentiable continuous system with a dynamic musculoskeletal model to support an effective and accurate performance for an exoskeleton controlled by EMG signals. This approach could be used to shorten the training time for controlling exoskeleton across different subjects. However, there are limitations to our study. First, this was a pilot study that included a small number of subjects. Second, the EMG signals used to control the model were recorded from only five healthy subjects. More subjects, including healthy and unhealthy ones, will have to be included in a follow-up to our study.To overcome the time lag between the user intention and exoskeleton motion, we applied a differentiable continuous systems approach to a dynamic musculoskeletal model using EMG signals. The trajectories, including the torque and angles during muscle contraction, predicted by the proposed model were similar to the real movement. Moreover, our model outperformed others regarding the convergence rate and execution time. Our study demonstrates an effective and accurate method to decode EMG signals that serves the exoskeleton application. Future extensions of our proposed framework can be for deep neural networks with backpropagation and providing forward inference with biomechanical explanations.


The hypothesis was that HILT would lead to a greater increase in skin surface temperature and vein diameter in the clipped coat group than in the non-clipped group. Variations in vein diameter and skin surface temperature were assessed before and after HILT. The HILT treatment resulted in a greater increase in skin surface temperature in the non-clipped coat group horses compared to the clipped group, while vein diameter increased more in the clipped group horses compared to the non-clipped group. Larger increases in skin surface temperature can be achieved with a non-clipped coat. Coat clipping of the treatment area increases vein diameter, while reducing skin surface temperature. Further research is needed to specify the parameters for treatment of skin areas with clipped and non-clipped coat in order to perform effective laser therapy.The use of high-intensity laser therapy (HILT) has recently been introduced in equine veterinary medicine. The goal of the research was to evaluate the effects of HILT on skin surface temperature and vein diameter in the carpal joint region in 20 racehorses divided into two groups: with clipped (The aim of this study was to investigate the differences in the effects of high-intensity laser therapy (HILT) on skin surface temperature and vein diameter in the carpal joint region in racehorses with clipped and non-clipped treatment areas. The study included 20 Thoroughbreds split into two equal groups: clipped coat and non-clipped coat. Horses underwent thermographic examination to detect changes in skin surface temperature at the medial surface of the carpal joint, followed by ultrasonographic examination to assess changes in the diameter of the medial palmar vein before and after HILT. The increase in skin surface temperature after HILT was significantly lower in the group with clipped coat than in the non-clipped group. The group with clipped coat showed a greater increase in vessel diameter. There was a significantly weak negative correlation between the changes in average skin surface temperature and vein diameter in both groups. In conclusion, an efficient photothermal effect can be achieved in skin with a non-clipped coat and clipping the treatment area increases photobiostimulation of the tissue, while reducing the photothermal effect. Further research is needed to specify the parameters for the treatment of skin with clipped and non-clipped coat in order to perform effective laser therapy. High-intensity laser therapy (HILT) is a treatment method that has gained popularity in equine veterinary medicine and physiotherapy in recent years ,2. The tHILT has a photothermal effect, which leads to an improvement in microcirculation, blood vessel permeability and metabolic process via vasodilatation . IncreasPrevious studies have also reported that HILT has biostimulatory, anti-inflammatory and analgesic properties, without adverse effects on or histological risks to tissue. Photomodulation of HILT depends on the effects of photons on tissue, absorbed by cytochrome c oxidase in mitochondria. Cellular dissociation of nitric oxide from cytochromes increases metabolic turnover and vasodilatation . High-poThere are an increasing number of randomized controlled trials reported in the literature based on clinical use of HILT in equine veterinary medicine. In vivo experiments have demonstrated the biological effects of HILT in the treatment of clinical cases, including tendinopathy and desmopathy ,15,16 anIn the future, research needs to be expanded to evaluate the effectiveness of HILT on healthy tissue to specify the precise parameters for injury therapy more accurately. The question of which parameters of laser therapy are most effective in achieving optimal tissue improvement is currently not clear. Effective laser treatment requires a number of parameters, including power and single irradiation time, which contribute to the total emitted and absorbed photon dose . Based oHowever, none of the above studies addressed the differences between HILT on clipped and non-clipped treatment areas in horses. Accordingly, the objective of the present study was to evaluate the differences in the influence of HILT on skin surface temperature and vein diameter of the medial side of the carpal joint region in clinically healthy racehorses with pigmented clipped and non-clipped coat in the treatment areas. The hypothesis of the study was that HILT results in a greater increase in skin surface temperature and vein diameter in the clipped coat group, compared to the non-clipped coat group. The medial side of the carpal joint was selected for anatomical evaluation to efficiently examine the diameter of the superficial veins.Prior to the start of the study, the project protocol was approved by the Local Ethics Committee for Animal Experiments in Wroclaw, Poland (No. 003/2020).The study was conducted on 20 Thoroughbreds aged 3 to 4 years in August 2021. All horses were trained in the season for flat racing at Partynice Racecourse (Poland) and were housed in individual boxes in one stable with the same management and training schedule. All horses were healthy at the clinical examination prior to the study. None of the horses had an injury to the right forelimb, where HILT treatment was applied. The diagnosis of possible pathological disorders was based on interviews with the trainers and visual and manual assessments performed by a veterinarian (P.Z.).n = 10) and non-clipped coat (n = 10). On the day of the experiment, an electric clipper (blade size 0.8 mm) was used to shave the treatment area in the clipped group. A 10 cm2 area of skin was shaved for all horses.The horses selected for that study were black, bay, and chestnut, without any white marks on the right front limb (close to and in the treatment area). Skin pigmentation in the treatment area was assessed as pigmented. The horses were divided into two groups: clipped coat . To avoid scattering of the laser light, a contact technique was used without applying pressure to the tissue and with the least possible amount of contact of the probe on the horses’ coats or skin.HILT was applied using a class IV Polaris HPS laser , which delivers two wavelengths simultaneously: 808 nm (an AlGaAs laser with a maximum output power of 8 W) and 980 nm (an InGaAs/AlGaAs laser with a maximum output power of 10 W). To avoid uncontrolled thermal effects, tissue destruction and skin burns, various treatment parameters were utilized for each wavelength. The energy density was 30 J/cm®, Olsztyn, Poland), with a minimal amount of coupling gel and pressure on the tissue to avoid false changes in vessel diameter. Forty ultrasound scans were blindly evaluated by the same person, measuring the vein diameter in the transverse view at the level of the distal half of the accessory carpal bone, where the vessel is located most superficially  could measure with an accuracy of 0.1 cm. All measurements were carried out at the same time, three times, and the average value was calculated.Ultrasonographic examination was performed over the medial palmar vein using a 10 MHz linear transducer . The protocol for the examination was the same as previously described by Zielińska et al.  and SoroThe Shapiro–Wilk test was used to assess the compliance of the empirical distributions of skin temperature and vessel diameter with the theoretical normal distribution. Since in some groups these distributions deviated from the normal distribution, these parameters were given as medians (Me) and the lower (Q1) and upper (Q3) quartiles.p, the power of the test 1 – β was also estimated  was used for the statistical analysis.The hypotheses about the absence of a difference between the average skin temperature values and the blood vessel diameters in the two groups of horses were verified with the Mann–Whitney U test. To assess the strength, direction, and significance of the relationship between changes in skin surface temperature and vein diameter, the value of Spearman’s rank correlation coefficient (rho) was estimated. In addition to the significance level stimated . The satp = 0.089 and p = 0.130, respectively)  .p = 0.006). The clipped coat group had a greater increase in vessel diameter than the non-clipped coat group   .A weak negative correlation (rho = −0.496) was also found between changes in the average skin surface temperature and vein diameter in both groups of horses.One of the biggest challenges in integrating HILT into established medical practice is providing consistent treatment parameters and patient preparation recommendations. There are few studies examining the physiological effects of HILT on different types of healthy tissue or procedure parameters such as wavelength, energy density, treatment duration, and individual patient characteristics ,21,24.Previous studies conducted on clinically healthy horses have shown significant HILT effects in increasing blood vessel diameter and skin surface temperature in the clipped treatment area of a clinically healthy tarsal joint  and haveIn the present work, the hypothesis that HILT leads to a larger increase in skin surface temperature in the group of horses with clipped coat could not be confirmed. However, it could be clearly shown that HILT leads to a significant increase in the skin surface temperature of the non-clipped coat group. The influence of shaving on increased laser transmission has been discussed in previous studies on low-level laser therapy. It is assumed that the lack of a hair coat contributes to a lower scattering of photons and that melanin reduces photon absorption ,23. AccoIn the present work, it was also found that the group of horses with clipped coat showed a greater increase in vascular diameter than the non-clipped group, which confirms the hypothesis established at the outset. Increases in vessel diameter depend on the photothermal mechanisms that take place in irradiated tissue. Nagasawa  has propA weak negative correlation was also found between changes in vein diameter and average skin surface temperature in both groups of horses. Opposite results were found in a previous study, which found a correlation between the increase in skin surface temperature and vein diameter after HILT at the tarsal joint . In anotThe present study was limited by its small sample size. It included only 20 horses, which may have limited the statistical results. Another limitation might have resulted from the short-term HILT effects studied. In the present study, only the thermal and biostimulatory effects immediately after laser therapy were investigated, without evaluating the effects of HILT on the treated areas in the long term.This study has shown a significant increase in skin surface temperature in a group of horses with non-clipped coat at the treatment area compared to a clipped coat group, with a smaller increase in vessel diameter. This suggests that the hair coat plays an important role in the transition and absorption of light energy. It can be concluded that a more efficient photothermal effect can be achieved on the skin with non-clipped coat and that clipping of the treatment area increases the photobiostimulation of the tissue while reducing the photothermal effect. Further research is needed to specify the parameters for the treatment of skin with clipped and non-clipped coat in order to perform effective and safe laser therapy.


Although PBD screening is one of the strongest predictors of breast cancer risk, X-ray-based mammography evaluation is subjective. Therefore, new objective PBD measuring techniques are of interest. A case study analyzing the PBD of thirteen female participants using a bioimpedance-based method, the anomalies tracking circle (ATC), is described in this paper. (2) Methods: In the first stage, the breast bioimpedance of each participant was measured. Then, the participant breast density was determined by applying a mammogram just after the breast bioimpedance measurement stage. In the third stage, the ATC algorithm was applied to the measured bioimpedance data for each participant, and a results analysis was done. (3) Results: An ATC variation according to the breast density was observed from the obtained data, this allowed the use of classification techniques to determine the PBD. (4) Conclusions: The described breast density method is a promising approach that might be applied as an auxiliary tool to the mammography in order to obtain precise and objective results for evaluation of breast density and with that determine potential breast cancer risk. Breast density is a term that describes the relative amount of glandular, connective, and fat tissue, which is evaluated from a mammogram. Dense breasts have relatively high amounts of glandular tissue and fibrous connective tissue and relatively low amounts of fatty breast tissue. Thus, the fibrous and glandular tissue appears whiter in a mammogram because fewer X-rays reach the image detector, and fat areas appear darker because more X-rays reach the detector. The relative amount of white and black determines mammographic density .As breast cancer studies suggest, a high percentage of breast density (PBD) may be related to breast cancer incidence ,3,4. AddAccording to the Breast Imaging Data and Reporting System (BI-RADS), a PBD increase along a three-year period is associated with a higher breast cancer risk, and a PBD decrease with a smaller risk, than the risk when PBD remains unchanged. Two longitudinal BI-RADS breast density measurements (on current and previous mammography) may be better predictors of breast cancer risk than a single measurement . The incAlthough PBD screening is one of the strongest predictors of breast cancer risk, X-ray-based mammography is recommended for women over 40 years old . TherefoThe qualitative methods for visual assessment of breast density have the disadvantage of results variability due to the subjective approach. The breast density can be estimated using two-dimensional (2D) mammograms without considering the breast thickness; it is done with the breast projected area rather than its volume. The implied subjective error can be notably reduced using automated volumetric breast measurement software , but thiCurrently, alternative methods are being developed to improve the breast density measurement. The methods include volumetric measurement of breast density in digital mammography , ultrasoSpearman = −0.52, p < 0.0001) between the EBD and PBD measurements was reported, which stands out the need for further evaluation and EBD device development. An electric breast densitometer (EBD) was used in the reported pilot study in , in whicA case study analyzing the PBD of thirteen female participants using a bioimpedance-based method is described in this paper. The PBD, clinically diagnosed by a specialist in mammary radiology, is compared with the results of applying the ATC) algorithm, which is based on breast bioimpedance measurements for locating breast areas with the minimum bioimpedance and reported for locating carcinoma emulators on breast models . From thBioimpedance measures the opposition offered by a biological medium  to the passage of an ionic current. If these measurements are made within a certain range of frequencies, electrical impedance spectroscopy is obtained, which makes it possible to analyze the impedance behavior on a biological medium according to its basic electrical properties . There are different techniques for measuring electrical impedance, depending on the number of electrodes used. The bipolar technique is very popular due to its simplicity, in which the same electrode pairs are used for voltage and current measurement. In this technique, the electrodes resistance must be considered . In addiBioimpedance of both breasts on thirteen voluntary female participants were measured in the first experimental stage. The participants were women in an age range of 40 up to 60 years old, with a B brassier cup size, and none of them had breast lesions or biopsies in their clinical history. All participants were adequately informed about the applied procedure and asked to sign a letter of informed consent. The bioimpedance measurements were done based on the system described in  but usinIn the second experimental stage, the breast densities for both breasts in each participant were determined. This was done by applying a mammogram just after the breast bioimpedance measurement stage. From the CC and MLO mammograms image, for the right (R) and left (L) breasts, a specialist with twenty-two years of experience in mammary radiology determined the PBD of each participant using the BI-RADS classification. The BI-RADS PBD evaluated by the mammary radiology specialist for each participant is shown in In the third experimental stage, the ATC algorithm  was applThe obtained ATC results and the corresponding mammogram images for each participant, right (R) and left (L) breast, are depicted in The breast zones are defined in quadrants and named from a frontal view of each breast: superior outer quadrant (SOQ), superior inner quadrant (SIQ), inferior outer quadrant (IOQ) and inferior inner quadrant (IIQ). A trend of the ATC results with respect to the BI-RADS PBD was observed after analyzing the obtained ATC data for each participant. Most of the resulting positions of the ATC algorithm for low PBD values (BI-RADS b) are located on the SOQ, while greater PBD values (BI-RADS c and d) resulted on positions near to the center, regardless of the quadrant. Hence, a concentric zones distribution of the resulted ATC positions according to the BI-RADS classification is proposed and graphically depicted in kstest function of MATLAB 2018a (R), resulting in h = 0 (acceptance) with a p-value = 0.994 of the hypothesis test.The resulting ATC positions for all participants were subjected to the standard Kolomogorov-Smirnov distribution null hypothesis test. The test was performed with the RN and LN represent the right and left resulting ATC position for the THN participant, respectively. A K-means clustering with the horizontal and vertical components of the obtained ATC positions as input variables was employed. A three-cluster classification with their corresponding centroids is shown in A classification of the ATC resulting positions based on the minimum distance to a cluster centroid is proposed. Hence, the radius of the circle generated by the ATC resulted position for each participant’s breast is proposed as the classification parameter. The obtained ATC positions corresponding to all the participants, expressed as the absolute value of the vertical and horizontal components from the breast center, are shown in Considering the assumption that the higher the PBD value, the smaller the radius of the resulting ATC, K-means clustering method to the obtained ATC positions for all the participants are depicted in The three defined classification clusters applying It is possible to observe from the resulting centroid position of each cluster that the closer its distance to the center, the larger the breast density. In this way, the centroid for a BI-RADS PBD a would be located even further than the corresponding to BI-RADS b. According to the PBD results evaluated by the specialist and the described clustering outcomes, As it is noted, the same BI-RADS PBD for both breasts in all the participants was obtained by the specialist; meanwhile, a 61.5% was achieved by the described ATC clustering. An important uncertainty factor to be considered is that the electrodes ring position in most of the cases is not exactly the same on both breasts for each participant. As a consequence, different bioimpedance values are measured because the breast tissue distribution is not the same. For instance, the specialist evaluation for both breasts in participant 9 is BI-RADS c, while the described ATC classification resulted in BI-RADS b-c and c for the right and left breast, respectively. Such difference may be explained by analyzing the relative electrode ring positions on both breasts. The CC mammogram images of both breasts are shown in The PBD evaluated by the specialist for participant 1 is BI-RADS c for both breasts, and the outcome for the described clustering method is BI-RADS d and c for right and left breast, respectively. The CC mammography images for both breasts are presented in In the case of participant 5, both breasts are BI-RADS b and c according to the specialist and the ATC classification algorithm, respectively. Such resulting greater PBD is due to the presence of large mammary gland covered by the measurement plane of the electrode ring in both breasts, as shown in As it is observed from the obtained results, the ATC algorithm PBD outcomes agree with the ones diagnosed by the mammary radiology specialist when a representative homogeneity zone is covered by the electrodes ring plane for breasts having homogenous breast tissue distribution (HBTD). This is the case for both breasts of participants 4 and 12, whose CC mammograms images are shown in It is important to note that a woman’s BI-RADS classification may change over time. According to studies by US radiologists, recategorization of sequential screening examinations, e.g., from dense (BI-RADS c or d) to not dense (BI-RADS a or b) or vice versa, occurs in approximately 13–19% of women ,31. HoweA new breast density evaluation method using the ATC algorithm, which is based on breast bioimpedance measurements for locating minimum bioimpedance breast zones, is described in this paper. The proposed methodology was applied to thirteen female participants in one case study. The obtained ATC positions were classified according to the closest distance to a cluster centroid, where three clusters were defined. The clustering outcomes for each participant were compared with the BI-RADS breast density given by an experienced mammary radiologist specialist. The three defined clusters in the described ATC position clustering agree in most of the cases with the three BI-RADS density found by the mammary radiologist specialist. The general trend was the higher the BI-RADS breast density, the smaller the resulting ATC radius and vice versa.The BI-RADS breast density defined by the described method depends on the breast tissue covered by the plane of the measurement electrodes ring. The electrode ring position in some cases does not cover a representative area of the global breast density. In such cases, the described method outcomes do not agree with the BI-RADS classification given by the global breast density evaluation carried out by the mammogram radiologist expert. Hence, it is possible to state that the BI-RADS density classification with the described ACT clustering method depends on the relative electrode ring position on the breast and on the woman’s global mammary gland distribution. One potential solution to this issue is to increase the bioimpedance measurement area covering more breast tissue in order to achieve a global BI-RADS classification. Currently, the authors are researching the described methodology using a greater number of measurement electrode ring arrays (at least two) in order to achieve a breast bioimpedance measurement in parallel transverse breast areas, expecting in this way a greater precision in the determination of the breast density percentage.None of the participants felt discomfort or pain during the bioimpedance measurement procedure, unlike the breast pain or discomfort suffered throughout a mammogram procedure.The described breast density method is a promising approach that might be applied as an auxiliary tool to the mammography in order to obtain more precise and objective results for evaluation of BI-RADS breast density and potential breast cancer risk. The proposed methodology is suitable to be implemented as a portable and minimally invasive solution applicable to women of any age because it does not imply radiation exposure.


Reasonable exercise is beneficial to human health. However, it is difficult for ordinary athletes to judge whether they are already in a state of fatigue that is not suitable for exercise. In this case, it is easy to cause physical damage or even life-threatening. Therefore, to health sports, protecting the human body in sports not be injured by unreasonable sports, this study proposes an exercise fatigue diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN). The method analyzes and diagnoses the real-time electrocardiogram, and obtains whether the current exerciser has exercise fatigue according to the electrocardiogram. The algorithm first performs short-time Fourier transform on the electrocardiogram (ECG) signal to obtain the time spectrum of the signal, which is divided into training set and validation set. The training set is then fed into the convolutional neural network for learning, and the network parameters are adjusted. Finally, the trained convolutional neural network model is applied to the test set, and the recognition result of fatigue level is output. The validity and feasibility of the method are verified by the ECG experiment of exercise fatigue degree. The experimental recognition accuracy rate can reach 97.70%, which proves that the constructed sports fatigue diagnosis model has high diagnostic accuracy and is feasible for practical application. In recent years, competitive sports have increasingly developed, and many people have gradually realized the importance of sports to health and began to exercise regularly in their daily lives. However, it is not uncommon for ordinary people and professional athletes to be injured during sports, and most of them are caused by physical fatigue due to inappropriate exercise methods and inappropriate exercise intensity . ReasonaIn recent years, researchers have turned their attention to other more informative physical indicators for detecting fatigue during exercise . PadhmasMost of these studies have used electrocardiographic signals (ECGs) from the heart, which contain rich exercise-related features, and high diagnostic accuracy has been achieved based on these features. The miniaturized ECG monitor has a small size and can be carried around during daily exercise training. Exercise is a continuous process, and it is not comprehensive to analyze the state of exercise fatigue only based on a certain moment. There is a close relationship between the fatigue state of the human body at different times . CNN is Therefore, this paper proposes an exercise fatigue diagnosis method based on short-time Fourier transform and convolutional neural network, and monitors the fatigue degree of the human body during exercise according to the ECG signal. Firstly, the Visual Geometry Group (VGG) convolutional neural network model used for training was established, and the attention mechanism imitating biological eye attention behavior was added into the model to improve accuracy; then the signals collected by the sensor are converted into spectrograms using STFT, and the set divided into training sets and validation sets. The set is fed to the VGG network for training and recognition, and an accuracy of 99.9% is obtained. The experimental results show that the real-time ECG signal can be used to monitor the fatigue degree during exercise with high accuracy. On the other hand, the identification data only needs to be derived from the spectrogram by STFT, and no additional data preprocessing is required. The spectrogram is directly sent to the neural network for classification and recognition, which has better robustness and generalization ability.In this part, short-time Fourier transform and convolutional neural network will be briefly introduced to diagnose fatigue degree in motion, mainly including the structure of VGG neural network and the principle of ECG signal processing as spectrum graph. In other parts of this section, the evaluation index of neural network model and the overall method flow of using ECG signal to diagnose exercise fatigue degree are also introduced.STFT is a commonly used signal processing method to quantify the time-varying frequency and phase content of a nonstationary signal. By adding a window function (the length of the window function is fixed), the time domain signal is firstly windowed, and the original time domain signal is divided into multiple segments by sliding windows, and then each segment is STFT transformed to obtain the signal time spectrum. Time and frequency are expressed by STFT as two-dimensional functions, as follows: d on τ . The calThe most basic CNN model consists of four parts, including convolution layer, pooling layer, full connection layer and classification layer. The convolution layer uses convolution kernels to extract features and contains multiple convolution kernels. Each neuron in the network maintains a one-way connection with the neuron at the next layer, which is called “receptive field”, and the size of this area depends on the size of the convolution kernel. The pooling layer is a subsampling operation, and its main objective is to gradually reduce the number of features contained in the feature graph. In this paper, Max Pooling is used, and its function is The fully connected layer reintegrates the local features extracted in the previous steps. In the following The classification layer (SoftMax) is a general form of logistic regression, which can implement multi-classification problems. The input data has The mainstream CNN models include AlexNet, GoogLeNet and VGG network. In 2014, the Visual Geometry Group at the University of Oxford proposed an even deeper neural network with 13 convolutional layers and three fully connected layers. The proposed network structure is stacked by multiple  kernels . The netThe pooling window of the pooling layer has a size of The VGG network is used to build a signal recognition model. The activation function of the model uses the ReLU function. The number of weight parameters of VGG-16 is as high as 70303557, of which the three fully connected layer parameters account for a large proportion. The original parameter setting of VGG-16 is to complete 1000 classification, and the classification of processing signals is less. Therefore, the first two fully connected layers only use half of the original number of nodes, that is, 2048 nodes, and the third fully connected layer has 6 nodes corresponding to the classification category, which further improves the training efficiency and recognition accuracy of the neural network model.m represents the number of categories, In the process of continuous training, CNN needs an evaluation index to evaluate the training effect of the current CNN model and decide whether to terminate the training. In general, loss function and accuracy function are used to evaluate the model. The smaller the loss function is, the higher the accuracy value is, and the better the model effect is. In network constructed in this paper using the evaluating model of cross entropy loss function, this kind of loss function is suitable for the multiple classification problems. Accuracy is usually used to evaluate the evaluation results of the current model in the test set during model testing. In multiple categories, a model that identifies a category correctly will be defined as positive, and a model that identifies a category as any other category will be defined as negative. The formula is defined as Visualization method is simple while intuitive observation model prediction results, the multiple classification problems, and the predicted results are usually use confusion matrix visualization. Confusion matrix uses value of accuracy for visualization. As shown in T-sne was proposed by Laurens van der Maaten and Geoffrey Hinton in 2008. It is a machine learning algorithm mainly used for dimensionality reduction of data. After dimensionality reduction, data can be better visualized, so that experimental results can be observed and model parameters can be adjusted according to the experimental results. To improve model performance, T-sne was used to reduce the dimensionality of the data of the whole connection layer in the prediction process, and the relations between categories were represented by two-dimensional images .The main process is to use VGG convolutional neural network for feature extraction and classification of ECG signals, and then detect the fatigue degree of human movement according to the trained model, as shown in Step 1First, ECG signals in motion are collected by ECG sensors, and then STFT is used to process the ECG raw data to obtain a two-dimensional spectrogram.Step 2The spectrum graph data set is divided into training set and verification set, and then the training set is fed to the VGG neural network for training.Step 3According to the obtained loss function, accuracy value and visualization results, the parameters of the VGG neural network model are constantly adjusted to make the neural network model convergent. Finally, the validation set is sent into the model for testing, and after satisfactory test results are obtained, the neural network model can be used for fatigue monitoring.Step 4The neural network model is applied to real movement fatigue monitoring.www.physionet.org/content/ephnogram/1.0.0/. The biomedical engineering review committee of Shiraz University approved the acquisition experiment of ECG and PCG data. Before the experiment, each subject signed a written consent to participate in the study in learning about the subjects’ physical condition through structured interviews. According to the structured interview, the subjects can be determined with good physical condition, without cardiovascular diseases. Three hours before the test, the subjects’ diets are controlled, that is, they can drink, but eating and, alcohol intake and caffeinated beverages are banned. Then data are acquired. 24 male subjects between the ages of 23 and 29 took part in the experiment, and the experiment was conducted indoors.In this study, synchronous ECG and echocardiogram databases from Physical Web were used as validation data to verify the validity of the method . The datA 3-lead ECG acquisition device with a sampling rate of 8 kHz and a resolution of 12 bits was used. Each volunteer performed a specific task and 69 ECG and PCG recordings were collected. In the few cases where the data quality was poor, the test was repeated to obtain acceptable data. Poor quality samples were also included in the dataset for noise research purposes and marked as low quality in the spreadsheet accompanying the dataset. In our validation experiments, poor quality data were discarded for data balance. The data set was divided into six different fatigue levels according to the exercise in the test. 1) Fatigue level 1: ECG signal in a calm lying state. 2) Fatigue level 2: Subjects remained calm, sat in a chair, and did nothing else. 3) Fatigue level 3: Subjects performed light activity and walked at a constant speed of 3.7 km/h 4) Fatigue level 4: Subjects rode at a constant speed on a fixed bicycle. 5) Fatigue level 5: The subjects rode on a fixed bicycle, but increased the load until they were tired. 6) Fatigue level 6: Subjects were tested running at an increasing pace until they became fatigued. During the training, the data set was divided into 6 levels according to the motion state of different fatigue levels. The lowest level of fatigue was when the subject lay flat on the bed in a calm state, and the subject accelerated continuously until the tired ECG signal dataset was defined as the highest level of fatigue, at which the ECG signal would be identified as overtired and should be stopped for rest. Only records with a time of about 30 min are selected in the experiment. At the sampling frequency of 8KHZ, each data file contains about 14400,000 data points, which are too large for the experiment. In order to improve the calculation efficiency, the original data is down-sampled and the sampling frequency of the down-sampled data becomes 0.08khz. At this sampling frequency, the Numerical Python library function in Python is used to divide 1000 data points into a sample. The hardware device for the experiments was a Lenovo laptop with an Intel Core i7-10875H CPU and an Nvidia GeForce RTX 2060 GPU with 16 GB of RAM. The notebook uses a 64-bit Windows 10 operating system, and the system uses the python runtime environment and the keras deep learning framework to build the required CNN structure.The spectrogram dataset of ECG signals will be divided into 90% training set and 10% test set. The number of training samples per iteration of the VGG model is 32, the number of training iterations is 40, and the learning rate is set to 0.0001.plt function in the matplotlib package in Python to convert it into a two-dimensional image. When it contains multi-channel signals, the ECG signal is fused into a two-dimensional image. The Gram Angular Difference Field (GAF) method encodes the time-domain signal by the Gram Angular Difference Field to generate a Gram Angular Field Image (GAF) containing motion fatigue features. The Markov transition field (MTF) coding method uses the MTF matrix to encode the time series into a two-dimensional image, which uniquely corresponds to the one-dimensional (1D) time series and contains the features in the time series (The ECG data was processed using the STFT two-dimensional method, and the data set was divided according to the ratio of 1:9, and the sample data set shown in e series .Four different 2D methods are trained using the same VGG neural network, and the training results for four different methods are presented in The existing results cannot intuitively see the impact of different signal processing methods on fatigue recognition, so it is necessary to visualize the recognition results of the model on the validation set to better observe the classification results. The prediction results are shown in The clustering results also prove that STFT and VGG neural networks have stronger classification performance. In ECG signal is the physiological signal that can best reflect the fatigue state during exercise. ECG signals also have different characteristic axes under different exercise fatigue degrees and different fatigue states, so ECG signals have the characteristics of diversity and complexity. With the application of high-precision heart rate sensors in wearable devices, while traditional methods only use part of ECG signal information, it is difficult to meet the requirements of accurate monitoring in the face of these huge data. To solve these problems, a motion fatigue diagnosis method based on short-time Fourier transform and convolutional neural network was proposed, and the fast classification and diagnosis of ECG signals was realized by using VGG convolutional neural network. First, using STFT to process the 1D ECG signal to obtain a 2D spectrogram, then divide the data set, and use the VGG neural network for training and diagnosis. In this study, different two-dimensional processing methods were used for experiments. The combination of STFT and VGG neural network has the highest classification accuracy, and obtained a fatigue level diagnosis accuracy of 97.7%. Compared with the common 1D data processing method, it has the highest accuracy and the fastest convergence speed. It can be seen that when converting 1D time series into 2D images for fatigue level diagnosis, the STFT conversion method can effectively represent the characteristic information in the signal. The VGG network structure has better classification performance for fatigue diagnosis based on ECG signal. The combination of STFT data processing and VGG convolutional neural network can make full use of human ECG signals in exercise, reduce the complex process of extracting features, and quickly diagnose the current human fatigue level with strong robustness and effectiveness.


Background: SMEs have made a major contribution to the national economy, but Pandemic covid-19 made the decline of SMEs which directly affected the economy in Indonesia. The purpose of this research focus to explore the innovation of business models the novelty of the research model in the business model of innovation research models is to include the variable self-efficacy. This research emphasizes self-efficacy and Islamic financial literacy as independent variables, business model innovation as an intermediate model and business performance variables as dependent variables, with the focus of research on SMEs.Methods: This quantitative study uses partial least square structural equation modeling (PLS-SEM) and a questionnaire survey to analyze the 40 Street Vendors which is categorized as SMEs around Unisba. In the initial stages, the Questionnaire was tested with the Validity and Reliability test. In the PLS-SEM analysis, the validity of the indicators / items forming latent variables is based on the significance value of t arithmetic obtained from the loading factor value divided by the standard error. After being collected, the data is then processed and analyzed. The analytical method used in this study is the simultaneous equation model, namely Partial Least Square (PLS). The statistical tool used to test the hypothesis is Smart PLS.Results: The results showed that self-efficacy and Islamic financial literacy through business model innovation, had a positive effect on SMEs business performance simultaneously or partially.  Conclusion: So that by having good self-efficacy and good Islamic financial literacy plus an appropriate business innovation model, it will improve the business performance of SMEs. Small Medium Enterprises (SMEs) play a very important role in economic growth, in both developing and developed countries. In developing countries the perspective of employment opportunities and sources of income for the poor, income distribution and poverty reduction and rural economic development .The Ministry of Cooperatives and Small and Medium Enterprises (KemenkopUKM) shows that in 2018 there were 64,194,057 SMEs in Indonesia  . The devBased on this phenomenon, SMEs are facing rapid changes resulting in the current business model is no longer sustainable. The driving force for changes in the SMEs industry - self-efficacy and Sharia financial literacy - is increasing because these facts are interrelated . Self-efThe Covid-19 pandemic phenomenon causes the problem of decreasing assets, SMEs market share in Indonesia . This phThe issue of MSMEs never stops to be studied. This has become a joint PR for MSME activists, especially since almost a year has been hit by the COVID-19 pandemic, MSMEs are getting worse, as well as row stall traders around the Unisba campus. The trading system is too conventional so that when the Unisba campus is carried out an online lecture system, there is no buying and selling process anymore. There are 40 traders around Unisba, they sell various kinds of food and drinks. Meanwhile, based on interviews with street vendors via telephone, they are currently selling at home, it’s just not clear what income they get because only a few neighbors are buyers.This research emphasizes self-efficacy, Islamic financial literacy as independent variables, business model innovation as an intermediate model and performance variables as dependent variables, with a focus of research at the SMEs level in Indonesia, so that the aspects of concern are dominant aspects affecting business model innovation and SMEs performance. This study assumes two main variables that influence or become determinants of the innovation of the SMEs business model. So the purpose of this research is whether it influences or determines the innovation of the SMEs business model.The variables in this study are self-efficacy (SE), Islamic financial literacy, business model innovation.So that the research examines self-efficacy, Islamic financial literacy, its influence on the performance of SMEs by moderating business model innovation. Several previous studies related to the relationship of the variables studied contained contradictions or differences in research results, so that it became a research gap that could be studied further from the limitations of previous research.In addition to self-efficacy, SMEs face certain difficulties, due to the limited financial literacy of their owners. According to Lusardi and Mitchell  financiaAccording to  Islamic The relationship between socio-economic concerns and business success is not something that happens automatically. A business case for sustainability must be created . The conTheoretical considerations show that various approaches to integrate aspects of sustainability into the business model and also its expansion must be present and that this must be directly related to the extent to which environmental and social aspects have been embedded in the performance of companies that underlie the business model. In general, it seems clear that because environmental and social issues gain relevance in strategy, modifications to the broader business model must be made; both, regarding the modification of existing models and the development of new sketches of existing business models.Two considerations that explain why business performance and business models are closely related. First, if companies implement strategies aimed at business cases that are sustainable, for example  businessThe results show that companies that are trying to improve their sustainability performance must change their business models. Changes in business models can be incremental or radical can turn into determinants (ie limit or support) to successfully create one or many business cases for sustainability  and a questionnaire survey to analyze 40 SMEs especially row stalls that are managed by 3IKB (women association of Unisba) around Unisba. The research location was determined purposively, from January to April 2021.The sampling technique used is saturated sampling, that is, all of the population is used as a sample, namely 40 SMEs at the location of the Unisba Row stall managed by 3IKB. The primary data used are measurement and data collection directly by researchers with questionnaires. Filling out the questionnaire was carried out by filling in directly by the research subject.Instruments which is developed by Bandura  was usedIn the initial stages, the Questionnaire was tested with the Validity and Reliability test. In the PLS-SEM analysis, the validity of the indicators/items forming latent variables is based on the significance value of t arithmetic obtained from the loading factor value divided by the standard error. Validity and reliability tests were conducted to measure the validity and reliability of the variables proposed by the researchers. Overall, the validity test determines whether the external loading of each indicator exceeds 0.4 , while the reliability test determines whether the value exceeds Cronbach's alpha 0,6, which shows acceptable reliability.Evaluation of external models is done by examining two elements, namely: convergent validity and discriminant validity. In general, convergent validity is determined by examining the loading factor and extracted average variance (AVE), while discriminant validity is measured by examining cross-loading values. In this study, all factor loading meets the standard value of 0.4, while all AVE values exceed 0.5, thus indicating that convergent validity is achieved. Based on the inspection of cross-load values, the discriminant validity also increases. The model is evaluated according to R-square for endogenous latent variables and path coefficient values, the results of which are shown inAccording toAfter being collected, whereas instrument of financial. The analytical method used in this study is the simultaneous equation model, namely Partial Least Square (PLS). This method is also the development of path analysis. The statistical tool used to test the hypothesis is SmartPLS.The process of processing statistical data with PLS is done by 2 approaches, namely (i) estimation of the measurement model and (ii) structural model estimation. In the measurement model test, the conformity test with indicators included Chi-Square, Root mean square residual (RMR), Roor Mean Square Error of Approxation (RMSEA), Adjusted good of fit index (AGFI), and so on. In structural model tests, the influence of exogenous latent variables to endogenous latent variables that are direct and indirect can be determined, as well as determining the direct and indirect effects of path analysis. The direct effect shows the direct effect of a variable on other variables. Indirect effects indicate the indirect effect of a variable on other variables through other variables. Total effect is the sum of direct effects and indirect effects.This study focuses on the influence of self-efficacy (X1) and financial literacy sharia (X 2) at Innovation Business Model (Y) on the Performance of Business (Z) on SMEs in Unisba row stall Indomesia are analyzed based on research data using Analysis of Structural Equation Modeling (SEM) Smart PLS software tools.After evaluating the outer and inner models, a bootstrap is carried out to determine the impact of each variable on the performance of SMEs. SE, and Islamic financial literacy on SMEs performance is shown inAccording toOverall, the majority of respondents indicated that SE increased their confidence in running their business. According to , high SEWith regard to social cognitive theory  found thBased on the modeling of the least square’s structural equation (PLS-SEM) in this study, Islamic financial literacy has a positive impact on the performance of SMEs. This is in line with his research  financiapositively influencing the performance of SMES businesses in Indonesiasimultaneously or partially.Meanwhile, the results of the study found that the influence given the variable self-efficacy (X1), Islamic financial liters (X2) on Business Model Innovation (Y) was 89.9 % and the influence of other variables not included in this study was 10.1 %. While the influence given by the Business Model Innovation Variable (Y) on Business Performance (Z) is 75.7 % and the effect of other variables not included in this study is 24.3%. then the hypothesis is accepted, namely self-efficacy and Islamic financial literacy through business model innovation,et al.  to successfully create one or many business cases for sustainability (regarding the intensity of modification and innovation of different business models .This research was approved by the Ethics Licensing Committee of the Universitas Islam Bandung (Protocol number: 660/B.04/Bak-k/XII/2020) after consultation, a letter of approval was given by the researcher to all respondents. Written approval to participate from the Unisba 3IKB Management (Unisba Large Family Mothers Association) was obtained in accordance with document 097/3IKB-K/XII/2020. Written consent to participate was obtained from respondents through the Traders Stall Row. Respondents have given their consent without coercion from anyone. The informed consent was given before the research was conducted by providing a consent form to become a respondent. Respondents who are willing, then they must sign the consent form. Furthermore, to protect the rights and privacy of respondents, all forms of data obtained will be kept confidential.This quantitative study examines the effects of self-efficacy and Islamic financial literacy through business model innovation on the performance of SMEs around the Unisba campus. Based on questionnaire surveys and related analysis, self efficacy has a positive effect on the performance of SMEs, because it motivates owners to evaluate their business-related activities. Similarly, Islamic financial literacy has a positive effect on the performance of SMEs. Islamic financial literacy will improve or influence the performance of their business. It's just that the owner of SMEs still lacks an adequate level of Islamic financial knowledge. Product innovation also has a positive effect on SMES performance. So that the novelty of SMES products will make business continuity continue in any condition and situation, especially in pandemic covid-19.The implication of this finding is that in general the Resource Base View is an important thing in competitive strategies that come from internal organizational sources, especially human resources, so SMEs must improve their SE as best as possible, while integrating their Islamic financial literacy into SMEs to improve overall performance. In addition, MSMEs should ideally implement Islamic financial literacy initiatives, which will not increase the confidence of MSME owners in running their business, but will also improve the performance of their SMEs in achieving long-term competitive advantage.Further research could include refining Islamic financial literacy resources and data. Limitations of the study include the variety of characteristics of respondents, the sample size is still limited.https://doi.org/10.6084/m9.figshare.17082419 .Data are available under the terms of the This is very interesting research examining the roles of self-efficacy and sharia financial literacy on SMEs performance. However, a minor revision is required to improve the quality of the paper. 1. Some abbreviations used in the article was not written in full. Example: MSME, UNISBA should be written in full when it first appears in text before using abbreviations. 2. Please replace the keyword "literacy Islamic finance" to "sharia financial literacy" and "The performance of SMEs" to "SMEs performance". 3. In the methodology section, the author should first discuss the research design  then followed by the type of analysis . 4. This article must be submitted for proofreading.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:Economics; Econometrics; Data analyticsI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. She paper has been improved significantly. So, I recommend for publication.Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?PartlyIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:business management and accountingI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you very much. The research gap is not clearly stated.The justification for choosing the mediating variable of business model is needed.The sample size is too small for the study of SMEs.The reasons for choosing street vendors in Bandung is not clearly described.Introduction:The use of RBV is not specifically explained, what is its relevance for the current study.No hypotheses are developed for this study. Literature Review:Sample size equals to population (40) vendors (SMEs) is insufficient for SMEs study.How to deal with common method variance (CMV) is absent in this study. Method:The interpretation of statistical attributes need correction .Discussions need elaborations, especially the relevance of the findings (using 40 samples) to the wider audience.It is difficult to discuss the findings without the development of hypotheses. Results and Discussions: The implications of this study needs further elaboration, to SMEs and their stakeholders (in both discussion and conclusion).A thorough proofreading of the manuscript is recommended. Language:Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?PartlyIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:business management and accountingI confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Thanks for the feedback and review.


The distant tubed pedicle skin flap concept was introduced almost 100 years ago. It was previously considered the workhorse flap to reconstruct the face and upper limbs. Despite being considered an ancient reconstruction method, it is still widely used by many reconstructive surgeons. It is considered a versatile flap with a good outcome. This article presents two cases of soft tissue injuries in the upper limbs reconstructed using distant tubed pedicled groin flaps, one of the types of tubed pedicle flaps. This article highlights the relevance of the tubed distant pedicle flap in reconstructing the upper limb defects and its effectiveness in restoring function, even though the cosmetic outcome is not favorable. McGregor and Jackson first proposed the tubed pedicle groin flap in 1972 . The groCase 1A 33-year-old gentleman was referred to our center, where he was involved in an industrial injury. The left hand was crushed under a piece of huge wood. It was in contact with the hot chainsaw metal for more than one minute. A full-thickness burn injury was noted on a presentation involving the dorsal aspect of the left second to fifth fingers, as shown in Figure The wounds were exposing the distal phalanges of the second to fourth fingers. Evaluation at one-week post-injury noted the ring and little fingers were gangrenous. The exposed bones were desiccated and necrosed; hence, they were amputated at the level of the neck of the phalanx and distal interphalangeal joint, respectively. The left middle and index fingers were debrided on the dorsal part, exposing the external tendons and bones that are still healthy. The wound was almost circumferential post-debridement with only a small area of intact skin on the radial side, as shown in Figure The left pedicled groin fasciocutaneous flap was designed based on the ipsilateral superficial iliac circumflex artery and raised from laterally to medially. The lateral part of the donor site closed primarily, leaving the fasciocutaneous flap with the pedicle within. The flap enveloped the left middle and index fingers, making a tube appearance in Figure Post-operation, the left arm was adducted to the left groin, and the flap was divided after 21 days. Because of the thick and bulky flap, it was thinned and subsequently grafted with a full-thickness skin graft harvested from the right inguinal area. Figure Case 2A 24-year-old gentleman was involved in an industrial-related injury. His left upper limb was in contact with hot steel for almost an hour, leading to a full-thickness burn on the left forearm with compartment syndrome. He underwent an emergency fasciotomy and multiple subsequent wound debridements for an infected fasciotomy wound. Because of that, he had a significant wound on his left forearm with an exposed left ulnar bone. The wound was on the anteromedial aspect of the distal left forearm and measured 11 x 4 cm with the presence of pink granulation tissue. Also, there was an exposed distal part of the left ulnar bone with a length of 7 cm, but the bone was still viable, as shown in Figure He underwent soft tissue coverage with a left pedicled groin flap for the area of exposed ulnar bone. The rest of the wound was managed by dressing. On day 21 post-operation, the flap was divided, and the underneath raw areas were grafted with a split-thickness skin graft. Three months post-operation, the flap appeared bulky; however, there was no residual area of defect or exposed bone, as shown in Figure Filatov, Ganzer, and Gillies were the earliest surgeons to apply the tubed pedicle flap and were reported in the literature in 1917 . The skiAlthough the distant tubed pedicle flap has been used for nearly a century, it is still widely used in reconstruction, particularly in upper limb defects . PedicleThe pedicled groin flap that McGregor introduced in 1972 has several advantages over other pedicled flaps. First, the inconspicuous donor site gives a better cosmetic perception, especially to women. For example, as in the deltopectoral flap, the donor site may have a large scar in the upper chest. Second, the distal portion of the groin flap, which is the part set into the recipient site, is usually hairless, even in hirsute individuals. Third, the groin flap appears less bulky than the abdominal flap since it has thinner subcutaneous tissue. Last, tissue edema is less likely to occur because of the excellent venous drainage at its base [Pedicled flaps have many disadvantages, like being uncomfortable for the patients as they have to keep the limb attached to the flap for 21 days, their bulkiness, and the need for multiple stages of surgery . In our One of the major downsides of a pedicled flap is its lack of sensation. This is particularly disabling to a patient if both the thumb and fingers are involved. To overcome this, patients may require subsequent microsurgical procedures like wrap-around flaps or plantar sensated skin transfers .Despite the fact that the pedicle-tubed groin flap is considered one of the old fashions for soft tissue coverage, many surgeons believe it is still relevant and practically used for reconstruction in upper limb trauma instead of the more popular free tissue transfer because it is versatile, has less demand on infrastructure, and is cost-effective. In addition, it is generally an efficient method and still able to achieve the aim of reconstruction with a good coverage outcome.


PLA (polylactide) is a bioresorbable polymer used in implantable medical and drug delivery devices. Like other bioresorbable polymers, PLA needs to be processed carefully to avoid degradation. In this work we combine in-process temperature, pressure, and NIR spectroscopy measurements with multivariate regression methods for prediction of the mechanical strength of an extruded PLA product. The potential to use such a method as an intelligent sensor for real-time quality analysis is evaluated based on regulatory guidelines for the medical device industry. It is shown that for the predictions to be robust to processing at different times and to slight changes in the processing conditions, the fusion of both NIR and conventional process sensor data is required. Partial least squares (PLS), which is the established ’soft sensing’ method in the industry, performs the best of the linear methods but demonstrates poor reliability over the full range of processing conditions. Conversely, both random forest (RF) and support vector regression (SVR) show excellent performance for all criteria when used with a prior principal component (PC) dimension reduction step. While linear methods currently dominate for soft sensing of mixture concentrations in highly conservative, regulated industries such as the medical device industry, this work indicates that nonlinear methods may outperform them in the prediction of mechanical properties from complex physicochemical sensor data. The nonlinear methods show the potential to meet industrial standards for robustness, despite the relatively small amount of training data typically available in high-value material processing. Polylactide (PLA) is a bioresorbable polymer derived from plant sources, which breaks down in vivo to Hperature . The bioperature . For appPressure and temperature sensors are routinely applied for continuous real-time monitoring of extrusion processes, but these provide limited information on physicochemical changes such as polymer degradation, which may occur during the manufacturing process. Over the last decade, many researchers have exploited the development of robust fibre-optic probes to apply vibrational spectroscopy techniques such as near-infrared (NIR), Raman, and UV–Vis, which are sensitive to changes in the molecular bonds present in polymeric materials while they are being extruded ,5,6,7. NNIR spectral data contain a very high number of variables (absorbance of light at hundreds of wavelengths); however, the absorbances of adjacent wavelengths are highly correlated, and so a large number of variables are redundant. Hence, some form of feature reduction is required for regression. The classical approach is dimension reduction via a subset of features derived from a linear transformation of the original variables, such as in principal component regression (PCR) or PLS. PLS dominates as a soft-sensor method for spectroscopy data in polymer extrusion processes, with very little exploration of other methods in the field to date ,9. PLS hNonlinear soft sensors using, e.g., artificial neural network (ANN) or kernel-based methods such as support vector regression (SVR) or Gaussian process regression (GPR) have commonly been applied in other fields and can outperform linear approaches ,14. WithThere has recently been increasing interest in monitoring/predicting parameters such as polymer degradation and mechanical properties in melt processing of bioresorbable and medical polymers. McKinley et al.  used ANNHence, while both NIR and conventional pressure and temperature sensors appear to yield useful information on the subsequent mechanical properties of melt-processed PLA, the validity of using such sensor data for in-line monitoring of product quality in an industrial process has not yet been explored. It should be noted that in the previous NIR-based studies, process conditions were constant or the NIR imaging was performed on samples post-processing. However, NIR is sensitive to changes in process conditions including melt temperature, screw speed, and polymer rheology , and thitm biopolymer 2003D from NatureWorks LLC. A slit die was attached to the end of the extruder and housed the sensors used to capture in-line process data. Data were recorded from pressure, temperature and NIR spectroscopic sensors. The pressure drop between two pressure transducers spaced along the slit die allowed for the shear viscosity of the material to be estimated during extrusion processing, as described in our earlier work [A Prism twin-screw extruder with four barrel zones was used along with a calender roll-off unit to manufacture extruded PLA sheet. The PLA grade was Ingeoier work . PressurA design of experiments (DoE) methodology was applied to explore the effect of process conditions on the mechanical properties of the extruded PLA sheet within the processing window of the material. An initial set of 24 process trials consisted of twelve different processing conditions, each replicated using different combinations of extruder feed rate, screw speed, and temperature profile. The temperature profile included four barrel zones of the extruder , the adaptor zone, and the die zone. The factor levels for the feed rate were 1160 g/hr (Low) and 1600 g/hr (High) and for screw speed were 56 rpm (Low) and 83 rpm (High). These were chosen at the extremities of the processing window for the material and extruder that was identified in the scoping trials. Three levels of temperature profile were applied: Low, Mid, and High, as presented in The initial process runs were full factorial, i.e., all combinations of factor levels were investigated with two replicates. The details of these experiments, together with their run order and factor level combinations are available in .A second set of experiments comprising six process runs forming an independent test set, was conducted in a separate trial several months later. The factor levels for the feed rate and screw speed were the same as in the initial experiments; however, as presented in Data were recorded for four minutes in all 30 runs once a steady state was reached (as indicated by a steady average pressure reading). Three samples were cut from the sheet extruded in each process run. The yield stress  is primarily used to reduce the dimensionality of data sets which contain a number of highly correlated features . Highly Y. One of the problems associated with PCR is the selection of the optimum number of PCs. Generally, the first few PCs (major PCs), which capture most of the variability in the data, are selected, and the minor PCs are excluded in the final model. However, while the major PCs constitute the optimal low-order approximation of the input data set (minimum loss of information); there is no surety that these selected PCs are also relevant to the prediction of the dependent feature Y [Principal component regression (PCR) uses a subset of these PCs in a linear regression model to predict the response eature Y .X and Y such that these components successively maximise the covariance between the independent matrix X and the response feature Y [Partial least squares (PLS) regression also involves linear regression on a set of uncorrelated predictor features which are created from a linear transformation of the original set of variables. In PLS, the new features—the so-called ‘latent variables’—are formed from a simultaneous decomposition of eature Y . As withRegularisation is a popular method used to solve the multicollinearity problem of linear regression, whereby the Equation  is closex as an independent feature and y as a response feature.A regularisation method imposes a penalty on the size of the regression coefficients in the loss function of ordinary least squares (OLS). In ridge regression, a penalty proportional to the sum of the squares of the regression coefficients is added . The losn is the number of observations in the data set and m is the number of features in the model. Here, As a result, a positive constant is added to the diagonal of An alternative regularisation technique is LASSO (least absolute shrinkage and selection operator), which imposes a penalty on the sum of the absolute magnitudes of the regression coefficients. The LASSO technique has some advantages over ridge regression in that it drives some of the coefficients to exactly zero, resulting in a more parsimonious model which may be more interpretable . HoweverX is substituted by an , also p example  for moreC, and the Gaussian kernel parameter The Gaussian radial basis function (RBF) given by Equation , is regam. The parameter m is a number controlling the size of the subset input features sampled at random at each node of the RF decision tree for its split decision. The model then searches for the best feature and split value from within that subset. By selecting an m value which is less than the total number of input features, only a subset of input features is considered at each split. This is known as decorrelation of the trees and results in a variance reduction in the model. This is achieved because averaging a number of uncorrelated features results in greater variance reduction than averaging a number of positively correlated features. The algorithm is described in the following steps:1.T).Choose the total number of trees to grow in the random forest Generate a bootstrap data set from the training data.(b)Grow a random forest regression tree (i)Select (ii)Pick the best feature/split-point among the (iii)Repeat until reaching terminal nodes that have ≥5 observations. If a node has >5 observations, it becomes a terminal node if a split will leave subsequent nodes with <5 observations.(c)T trees are grown.Repeat until 4.Output the ensemble of trees {5.Average the predictions from each regression decision tree to predict using the forest.Random forest (RF) is an ensemble learning method whereby several decision trees are grown using bootstrapped data . The regThe data split for training and testing the models was prepared as follows. The data from the 24 initial experiments were split in a ratio of 65:35. The 65% set was used as the training data for each of the models. The 35% set, referred to as the ‘internal validation’ set (referencing the language in the FDA guidance for NIR-based soft sensors ), was usFor PLS and PCR, the number of components included was increased in increments of 1 until a clear minimum in the RMSE on the internal validation set was identified. PLS performed optimally on the internal validation set with 30 components, while the optimal PCR model had 290 components . For ridge regression, C were tuned, while Multiple SVR models were trained using original features or principal components. An SVR model using the original features was trained using default parameters, i.e., m value equal to 204 was used, i.e., one third of the number of features, which is the default value. To reduce the dimensionality, the model performance with a subset of PCs was investigated, similarly to the approach used in the SVR model training. The number of PCs to include and the model m value were both tuned in a two-step process. First, an RF model was trained for each of a different number n of the major PCs. Values of n of 5, 10, 15, 20, 30, and 50 were tested. One hundred trees were used in each model, where an m value equal to, or rounded up from, m from 1 to 20. The number of trees was fixed at 100 as it was observed that there was no performance gain above that threshold. An m value of 5 resulted in the lowest RMSE value on the internal validation set, and this model was selected for performance evaluation.Random forest models were trained using all 612 original features, all PCs, or subsets of PCs. For each of the models, either with original features or with all PCs, an euristic . The modm value of It is worth noting that we also investigated whether there was an advantage to using an RF model to select a subset from all the PCs (not just the major ones), as an alternative feature selection step. Here, an RF model was trained using all 612 PCs, and an internal feature importance score was generated by the model. The model ranked features by permuting the last feature in each subset used to split tree nodes and measuring the effect on the model error during the training phase. Those which had the greatest effect on the model error were ranked in order of importance. RF models were then generated using each of the top 30 and top 50 important features from the model ranking, and the heuristic FDA guidelines endorse the use of predictive models to realise the potential of in-line analytical sensors for quality assurance, provided the model reliability is adequate for the purpose . AssessmModel accuracy was evaluated based on the mean predicted value of the yield stress for each process run, as given by Equation . That isRMSE values of all models for the internal validation set and the external test set. A normalised root mean squared error (NRMSE) was also calculated by dividing the RMSE by the range of the mean yield stress values in the data set to illustrate the magnitude of the errors relative to the range of variation in the true values. The NRMSE is illustrated for all models in Model linearity was determined by plotting the predicted yield stress against the measured yield stress, and a coefficient of determination , with fixed processing conditions and almost always using the linear method of partial least squares. In this paper, we showed that combining in-process NIR with pressure and temperature sensors can allow reliable prediction of the mechanical properties of a bioresorbable polymer product under different processing conditions. While previous work indicated the potential of using low-cost pressure and temperature instrumentation for soft sensing of mechanical properties in PLA extrusion, it was shown here that model robustness is significantly improved with the addition of NIR data, which provides information on molecular bond activity in the polymer melt as well as information on the physical state . The results of the low-cost method may be satisfactory for process control in non-critical applications such as biodegradable packaging, but they are not sufficient to satisfy quality assurance demands in highly regulated industries. Due to the cost of the NIR equipment, the method proposed here is most suitable for high-value PLA applications with stringent quality demands, such as in pharmaceutical and medical device products. Further, the approach described here performs significantly better on predictive accuracy relative to other studies that predicted the mechanical properties of PLA using PLS regression applied to hyperspectral NIR images of post-processed samples . Imagingm) require tuning, compared to SVR where the number of PCs and three hyperparameters require tuning. However, it should be noted that other nonlinear methods have also been proposed in the literature and may also perform well for such a task. In particular, kernel-PLS has been found to have a similar performance to SVR and may be a more attractive alternative in the industry, which has a level of familiarity with PLS methods and their interpretability [We showed that a nonlinear regression method is needed in this application in order to satisfy the reliability demands outlined in regulatory guidelines for quality analysis in the medical and pharmaceutical industries. Both support vector regression and random forest regression approaches worked well when preceded by a principal component dimension reduction step. While the PCA–RF approach gave the best performance, further tuning of the PCA–SVR method is possible. However, an advantage of the random forest method is the simplicity in model tuning, where only the number of PCs to be included and a single hyperparameter (tability .A significant constraint on model development in medical and pharmaceutical applications is the cost of generating training data using very high-value raw materials; however, the nonlinear methods proposed here perform well against all industry-standard reliability metrics for analytical procedures, even with the constraint of limited training data. Further reliability testing should be carried out if the method is to be applied outside the range of conditions analysed in the external test set used here. However, the results indicate the potential for in-process measurements to give rapid feedback for process control purposes, and may reduce the amount of off-line destructive testing of the product required for quality assurance purposes. A challenge to industrial acceptance in highly conservative, regulated industries such as medical devices is the ’black box’ nature of such models. The interpretability of nonlinear approaches should be addressed in future work.