The recent interest in 3D printing with concrete has generated great interest on how inhomogeneities arise and affect performance parameters, in particular strength and durability. With respect to durability, of particular interest is how 3D‐printed layer interfaces can impact transport of species of interest, such as moisture, chlorides or carbon dioxide in carbonation processes. This is of particular interest considering that the primary use case of 3D‐printed concrete has been as a lost formwork for a cast structural concrete, and thus it is of interest to determine the carbonation resistance. This study consists of a preliminary look at the microstructure after accelerated carbonation of a 3D‐printed concrete used as a lost formwork. Preferential carbonation is observed in the layer interfaces compared to the bulk of the printed filaments, possibly related to porosity from air voids or a locally high capillary porosity corresponding to the lubrication layer. The concrete of this study is also printed through use of a set accelerator, with a low viscosity and low initial yield stress but a rapid buildability rate. Samples were produced as they would be expected to be used, that is through the use of 3D‐printed concrete as a formwork for a cast concrete. Good structural engineering design would demand the placement of reinforcement bars as close to the edge of the structural element as possible (where tension is highest), but design codes also demand a concrete cover to mitigate corrosion by carbonation. Therefore, it is worth examining if printed concrete could conceivably be used as a cover layer in 3D‐printed components, and our analysis of this 3D‐printed sample is made with this in mind.22.12.1.1w/b of 0.39 and sand to binder ratio of 1.9, before eventually being mixed in the print nozzle with an accelerator paste consisting of calcium aluminate cement (CAC) which is also retarded with sodium gluconate, at an approximate dosage of 10% OPC substitution.The mix used for 3D printing has also been reported elsewhere.2.1.2w/c = 0.6 and maximum grain size of 16 mm.The mix for the cast concrete was a standard C20/25 mix with a CEM I 42.5 N (Holcim Normo 4) at 2.2Printed samples were produced by the 3D printing process at ETH Zurich also described in.2 gas bottle. The CO2 level was maintained between 50% and 90% at a RH of 70% for a period of 2 months before the samples were removed.The samples were carbonated in accelerated conditions in a self‐made carbonation chamber made of a sealed box connected to a CO2.3The produced samples were then cut and immediately had phenolphthalein and thymolpthalein applied to check for carbonation depth. Regions of interest were identified and a polished thin section was produced from layers approximately middle height (as there was no major variation observed in carbonation behaviour from top to bottom) to observe the microstructure with both optical and scanning electron microscopes.2.4A stereo microscope Leica M 60 was used for the observation of bulk samples and a Leica DM750 P in both plane and polarised light mode was used for observing the thin section. SEM‐EDX observation (BSE images) and point analyses were performed with a QUANTA 2003D and EDX system from EDAX company.3Figure In Figure Figure Figure 4At the cast–print interface, one can observe a separation between the printed and cast layers, which could be due to drying shrinkage differences between the two materials and their different production times. The uniform carbonation depth of 0.5 mm in the printed concrete is likely due to the 10 days delay between the printing and the casting, however, because no corresponding carbonation depth was observed in the cast concrete. It is also worth noting that the highly advanced degree of hydration in this zone could be due to sorption of water from the cast concrete. Overall, examination of this region highlights the importance of understanding the relationship between printed formworks and cast concrete in terms of the overall process and performance.In the ‘transition zone’ (Zones 2 and 3), it is quite apparent that carbonation is occurring nonuniformly within the printed concrete, and that it is occurring with greater speed within the layer interfaces, indicative of a difference in the diffusivity between the layer interfaces and the bulk. This higher carbonation speed in the layer interfaces contributes to faster carbonation in the bulk of the print filament as well, essentially by allowing carbonation from the top and bottom of the filament and effectively reducing the diffusion length.It is interesting to note that the thickness of the carbonation layer at the layer interface is approximately 1 mm. It is well known that a ‘lubrication layer’ forms during pumping of concrete, in which a paste rich zone forms at the wall of the pipe,As noted earlier, most studies to date have focused on presence of defects and air voids in the interlayer, with a primary focus on their effect on the strength. Numerous X‐ray micro computerised tomography studies have detailed their presence and extent with respect to processing parameters such as printhead speed, pressure, and height, layer interval time, and ambient conditions with respect to moisture,w/c ratio. The same physical principle that leads to a depletion of aggregate next to the wall will also lead to a depletion of binder particles, which would lead to an effective increase in the w/c ratio within a certain range of the layer interface. This moisture content is enhanced by pressurised bleeding, noted by Sanjayan et al.w/c ratio would manifest itself in a higher degree of hydration, which was observed in this sample, across the width of the layer interface. This is corroborated by Van Der Putten et al.,w/c at the layer interface would lead to a higher capillary porosity and thus enhanced transport.Capillary porosity is strongly influenced by the w/c and concomitantly higher capillary porosity would allow portlandite to precipitate there preferentially, thus also to carbonate there preferentially.The dense carbonation layer at the layer interface could also be indirect evidence of this locally high capillary porosity. One would expect that carbonation products would form generally where there is a high amount of portlandite, such as in the traditional interfacial transition zone (ITZ). For example, Shen et al. demonstrated that carbonation products formed significantly in the ITZ.2 transport.A final discussion point could be made regarding the high degree of microcracking observed. While this could easily be related to sample preparation, it could also bear some relation to material, processing, and shrinkage. The printed material is essentially a binary OPC‐CAC system, relying on ettringite formation to develop rapid buildability strength.5The 3D‐printed concrete of this study showed nonuniform carbonation due to the presence of the layer interfaces, with the layer interfaces showing faster carbonation. This can be from enhanced transport due to either increased air content or capillary porosity in the layer interface. The implications for this require further study, as this would negatively impact the possibility of using a printed formwork as a cover layer.The interface of a structural concrete cast into a 3D‐printed formwork (‘cast–print’ interface) requires attention in future studies, especially as this represents the predominant use case of this technology.A more complete study is needed regarding the generation and evolution of microstructural heterogeneities in 3D‐printed concrete with respect to carbonation, in particular with respect to the capillary porosity at the layer interfaces.The primary conclusions and recommendations that can be drawn from this preliminary look at the microstructure of carbonated 3D‐printed concrete are the following:


A proximal femoral nail using a helical blade (HB) is commonly utilized to treat proximal femoral fracture but cut through failure of the lag screws is one of the devastating complications following the surgery. While controversial, one of the potential risk factors for cut through failure is poor bone strength which can be predicted by measuring bone mineral density (BMD). In this study, we performed a biomechanical test on the fractured femoral head to validate whether the indirectly measured BMD from the contralateral hip or that measured directly from the retrieved femoral head can elucidate the structural strength of the fractured femoral head and thereby can be used to predict migration of lag screws. Our result showed that directly measured BMD has a significant correlation with the HB migration on the osteoporotic femoral head. However, while the BMDs measured from the contralateral femoral neck or total hip is the most widely used parameter to predict the bone strength of the fractured femur, this may have limited usability to predict HB migration. It is estimated that 1.7 million osteoporotic proximal femoral fractures occur worldwide annually, with mortality rates ranging from 22 to 29%4. For their treatment, proximal femoral nails are commonly accepted as an implant of choice. While surgery is mostly successful, failure of lag screws is one of the most severe complications. The typical failure mechanism occurs when the lag screw penetrates or cuts the femoral head; this is termed as “cut through” or “cut out” depending on the type of the lag screw used and on the direction of how the penetration occurs8.Proximal femoral fracture is one of the most common and devastating consequences of osteoporosis10. As the osteoporotic bone may lack mechanical strength, the bone structure of the femoral head may not provide sufficient support, leading to the migration of the lag screw, which may result in the penetration of the femoral head. As bone strength is difficult to measure in vivo, bone mineral density (BMD) is commonly used to quantify and predict the strength of the osteoporotic bone11. In cases of a fractured proximal femur, measurements are provided indirectly from the contralateral hip or the spine. However, there is a paucity of literature on whether indirectly measured BMD from these regions can accurately reflect the mechanical strength of the fractured femoral head. Similarly, it is unclear whether we can predict the excessive migration of helical blade (HB) type of lag screws from this information.A few hypotheses have been suggested to explain why such a phenomenon occurs and to prevent such failure. While controversial, one of the potential risk factors is poor bone strengthTherefore, in this study, we performed a biomechanical test on the osteoporotic femoral head to validate whether the indirectly measured BMD from the contralateral or that directly measured from the retrieved femoral head can elucidate the structural strength of the fractured femoral head and thereby can be used to predict migration of lag screws. More specifically, the current study aims to assess (1) whether the BMD of the fractured femur correlates with the resistance to the HB migration on the fractured femoral head, and (2) whether the BMD of the contralateral hip can be used to predict excessive HB migration.12. The specimens from the patients who agreed to donate the retrieved femoral head for the purpose of this study were selected for analysis. The femoral heads were excluded if acquired from patients (1) who had underlying pathologic conditions that may influence bone quality other than osteoporosis, (2) who had undertaken medication that may potentially influence the quality of the bone, (3) whose BMD was not obtained from the contralateral hip owing to the remaining implants from previous surgery, and 4) with a history of osteoporotic fractures elsewhere.The experimental protocol of this study was approved by our institutional review board before the experiment was conducted (CHOSUN #2020-03-010-0001). All methods were carried out by relevant guidelines and regulations and informed consent was obtained from patients or their legal guardians before the femoral head was retrieved. The femoral head was retrieved from female patients who underwent hip arthroplasty owing to femoral neck fracture between March 2018 and June 2021. The patients were given the option to undergo either arthroplasty or primary fixation and the decision was made by the patients and their legal guardians after a detailed description of both surgery's advantages and disadvantagesThirty-two femoral heads that fulfilled our inclusion and exclusion criteria were retrieved, which constituted the basis of our study. Nineteen femoral heads were from the right-side hip. The demographic data of the donors are listed in Table 13. While BMDs from several regions can be measured, the BMDs of the total hip region and the neck region were used for analysis as these are the measurements commonly used clinically14. The BMDs were measured and collected using the Encore program , with the region of interest (ROI) set automatically by the software built in the DXA scanner and adjusted by the radiotechnologist when necessary  at the time of admission. The patient was positioned with a non-fractured hip in 15 degrees of internal rotation which provides the greatest area for measurement15. Precision assessment was also performed before the experiment to measure the least significant difference (LSC), which was 4.2% in the total hip and 5.1% in the femoral neck. The measured LSC in our institution is within the recommendation of the ISCD.All measurements were performed by two radiotechnologists and the mean values were used for analysis. Quality control was performed for both technologists and densitometry devices according to the protocol recommended by the International Society for Clinical Densitometry (ISCD)At the time of hip arthroplasty, the femoral head was retrieved with caution to minimize iatrogenic damage. If the ligamentum teres were intact, it was resected from fovea capitis, an oval-shaped dimple in the superior-medial portion of the femoral head, with a scalpel to prevent avulsion of the femoral head during the dislocation process. The retrieved femoral head was washed with saline and dried at room temperature for 1 h. This was then fresh frozen at − 20 °C for later experiments.16. The fresh frozen femoral head was thawed at room temperature for 24 h before the micro-CT scan. All measurement was made within 10 days of the retrieval.Direct BMD of the retrieved femoral head was measured using the Quantum GX micro-computed tomography (micro-CT) imaging system , located at Korea Basic Science Institute . The decision was made to use micro-CT over DXA as there is currently no standardized way of measuring BMD with DXA when the femoral head is not in its anatomical position. Also, micro-CT has been validated for its high accuracy in measuring BMDFor the scanning process, the X-ray source was set to levels of 90 kV and 88 μA with a field of view of 72 mm and a slice thickness of 0.144 mm. The scanning time was 4 min in a 360° rotation. Using AccuCT™ analysis software , all raw CT values were converted to Hounsfield Units (HU). The intensity of water was defined as 0 HU and that of dry air as − 1000 HU. The calibration of the AccuCT™ analysis software was performed with the use of hydroxyapatite (HA) phantom . The spherical cap region of 30 mm from the fovea capitis was selected as an ROI of the retrieved femoral head . A helical blade-type lag screw from a commercially available proximal femoral nail system  was utilized to test the resistance properties to the helical blade migration. The specifications from the provider indicated that the PFNA-II helical blade has a diameter of 12.2 mm. We used lag screws of 85 mm in length for the experiment. For the setup, lateral locking of the PFNA-II helical blade was released, so that the blade portion of the PFNA-II lag screw can be freely rotated. This was determined to reproduce the failure mechanism of the lag screw where the femoral head is typically rotated along the lag screwInitially, the position of the lag screw was manually adjusted so that the tip is in contact with the resected surface of the femoral head. This was then advanced at 15 mm/min until the lag screw penetrated the femoral head and advanced for an additional 5 mm to replicate the cut through situation Fig. F, G. TheThe resistance properties were defined as (1) peak resistance and (2) total resistance during the 25-mm advancement. The peak resistance was defined as the maximum load measured in the load–displacement curve, while the total resistance was defined as the area under the load–displacement curve during the 25-mm advancement of the lag.19. With a correlation effect size of 0.5, an alpha error of 0.05, and a beta error of 0.2 to ensure power of 80%, the estimation indicates that it will be necessary to include at least 29 cases for the purpose of the current study. The measured results were expressed as means and standard deviations. The correlations between the following variables were assessed: (1) BMD of the contralateral hip and that of the fractured femoral head, (2) BMD of the fractured femoral head and resistance properties of the fractured femoral head, and (3) BMD of the contralateral hip and resistance properties of the fractured femoral head. The normality of the distribution of the data was assessed using the Kolmogorov–Smirnov test20. Correlation analysis was performed using Pearson correlation or Spearman rank correlation test according to the normality of the distribution of each variable. In addition, linear regression analysis was performed to confirm the change in mechanical resistance properties according to the BMD of the contralateral hip and the fractured femoral head.The sample size was estimated a priori using G*power software P values of < 0.05 were considered significant.Statistical analysis was performed using the SPSS software version 27 . All P-values were two-sided, and The mean interval from the time of the fracture to BMD measurement using DXA was 1.9 ± 1.2 days. The mean time from the retrieval of the fractured femoral head to micro-CT measurement and the mechanical test was 7.6 ± 3.3 days.2 in the femoral neck and 0.65 ± 0.16 g/cm2 in the total hip, which corresponds to a T-score of − 2.74 ± 1.21 and − 3.11 ± 1.34, respectively. The BMD of the fractured femoral head measured on micro-CT was 467.8 ± 69.3 mg HA/cm3. The measured BMDs and the correlation between the fractured and non-fractured sides are summarized in Tables The mean BMD of the contralateral hip measured on DXA was 0.61 ± 0.15 g/cmThe load–displacement curve during lag screw advancement through the femoral head showed an initial stiff increase followed by a gradual decrease  (Fig. When correlation was analyzed between measured BMDs and the resistance properties of HB on the femoral head, there was a significant positive correlation between the BMD and the resistance properties of the fractured femoral head (Peak resistance; The results of this study show that the BMD measured directly from the fractured femoral head may reflect resistance to the HB migration but that measured from the non-fractured hip does not correlate with the resistance to the HB migration on the fractured femoral head indicating that this may not be used to predict the excessive migration of the HB lag screw.21. For minimizing the risk of developing lag screw failure, a helical blade-design lag screw was developed to provide better rotational and varus stability by compacting the trabecular bone around the flanges of the blade22. However, a problem of medial migration remained which has been reported to be observed in 0.7–6.3% of patients undergoing surgery with this type of lag screw23. Several factors have been suggested to be a potential reason for this phenomenon, which include inappropriate tip–apex distance, unstable fracture pattern, and the z-effect phenomena25. One additional potential risk factor would be deficient bone strength of the femoral head9. Theoretically, the trabecular bone surrounding the helical blade may function to resist further advancement of the lag screw. Therefore, it can be hypothesized that in the femoral head with weak bone strength, resistance may not be sufficient to prevent medial migration27.A proximal femoral nail with a sliding hip screw is commonly used to stabilize proximal femoral fractures with satisfactory outcomes but the failure of lag screws remains to be fatal complications28. Therefore, Ammann et al. reported that DXA-measured BMD may predict only 60–70% of the variation in bone strength according to established studies that validated the correlation between the BMD and bone strength30.One of the most widely used methods to predict bone strength is measuring the BMD using DXA. However, the BMD largely represents the quantity of the trabecular bone. While bone mass is one of the important factors contributing to bone strength, the mechanical properties of the bone are also configured by other factors, such as architecture geometry, cortical porosity, and tissue mineralization density31 investigated the correlation between the mechanical properties and BMD of 22 femoral heads. The study performed uniaxial compression tests on the cylindrical bone samples which were retrieved from the osteoarthritic femoral head. They reported that there was a weak but significant correlation between the BMD and the mechanical properties of the trabecular bone. Interestingly, in the subsequent study by the same authors, only the structural modulus had a significant correlation with the BMD and not the ultimate compression strength32. Because of different test protocols, we cannot make a head-to-head comparison with these studies, but our findings are somewhat similar in that we found a significant correlation between the BMD and the mechanical properties of the femoral head as measured by the ultimate and total resistance of the HB-type lag screw. On the other hand, our study also showed that the BMDs of the contralateral hip do not reflect the resistance properties of the fractured femoral head as measured by the HB migration. This is likely because the BMD of the fractured head does not correlate with the BMDs of the contralateral hip which is another finding of our study.We are aware of only two studies that investigated the relationship between the BMD and the mechanical properties of the femoral head. Haba et al.33 compared the mechanical properties of 10 paired left–right proximal femurs and reported that no significant difference was found when both sides were compared. Conversely, a larger study by Afzelius et al.34 measured the BMD of the bilateral hip in 133 participants and reported that while there was no difference when the BMD of the femoral neck was compared, the total hip BMD was lower in the dominant leg. Another study by Li et al.35 compared the BMD of the non-fractured hip side and fractured hip side using quantitative CT. They reported that the volumetric BMD of the non-fractured side was higher than that of the fractured side and that the difference was significant when the BMD was measured through the center of the femoral neck. In our study, the BMD of the non-fractured side was measured using DXA with the ROI in the femoral neck and total hip while that of the fractured side was measured at the femoral head using micro-CT. It should be noted that the typical measurement of the total hip BMD does not include the femoral head, as this image overlaps with the posterior acetabular wall, a bony structure located in the posterior part of the hip socket. Therefore, our result arises from the comparison among different parts of the proximal femur which may be the potential reason why no correlation was found. Nonetheless, we believe this inconsistency of BMD in the bilateral hip may be the explanation for why BMD of the contralateral hip does not correlate with the resistance properties measured in the fractured femoral head.A number of studies have reported a correlation of the BMD from the bilateral hip with conflicting results. Banse et al.36. Similarly, the factors that may influence the outcome in real patients, such as body mass index or walking habits, are not considered in our study design. Therefore, the application of our results to actual practice should be made with caution. Another limitation is the potential effect of freezing and thawing on the specimens. After retrieval of the femoral head, we froze the samples at − 20 °C until we had access to micro-CT and the MTS. Numerous studies have shown that the freeze and thaw process has a minimal effect on the cortical bone; however, there is limited evidence of its effect on the trabecular bone38. We think the immediate test on the femoral head following retrieval may have provided results that may more accurately imitate the condition of the femoral head in the body.We acknowledge that there are a number of limitations to our study. First, the study is largely limited by the number and characteristics of the specimens. The retrieved femoral heads were mostly from elderly patients with an osteoporotic femoral neck fracture, and it is unclear whether the current conclusion is applicable to younger patients with stronger bone. Therefore, data from different age groups and a wide range of BMD would provide us with a better understanding of the relationship between the BMD and the mechanical strength of the femoral head. Second, our study tested the axial compression load of the HB on the femoral head but the direction of the lag screw in the study does not correspond to the physiologic load applied in vivo. Owing to the anatomical axis of the lower leg, the load against the femoral head should be applied to the superior-medial direction toward the acetabulum but we were unable to reproduce this in our studyNevertheless, this is the first study to validate the correlation between BMDs and the resistance to HB migration on the femoral head.Our study indicates that directly measured BMD has a significant correlation with HB migration on the osteoporotic femoral head. However, while the BMD measured from the non-fractured contralateral femoral neck or total hip using DXA is the most widely used parameter to predict the bone strength of the fractured femur, our findings suggest that this may have limited usability to predict helical blade migration.Supplementary Information 1.Supplementary Information 2.


It is possible to detect damage in structures based only on vision-system-based assessment of their deformation shape under load. There is, however, a gap between available methods designed to detect damage in beam-like structures and engineering needs for monitoring structures of many different shapes. In this article, a new Aligned Marker Space method of morphing vision data is introduced. The method allows damage detection of any engineering object with one fixed support as if it were a cantilever beam. The paper also presents a new fusion technique to combine the results of several damage-detection methods for an increase in accuracy and sensitivity. The methods are tested based on numerical simulation of various structures, a blender-based simulation, and a set of practical experiments in which crane structures are subjected to damage of different sizes and locations. The optimization of damage detection methods’ metaparemeters is performed using an evolutionary algorithm designed to find the Pareto front of the solutions. The assessment of the influence of different factors, like camera position, damage position, or repetition of the experiment, is provided. Vision systems are very popular monitoring methods due to their full-field measurement and noncontact nature, and for these reasons are often used in many areas of engineering ,2,3,4. IIf the environmental conditions, including load causing the deflection, are similar, the differences in acquired DSs are an indication of damage. These differences are usually analyzed through calculation of Deflection Difference (DD) signal, being a subtraction of reference DS and potentially damaged DS. The DS-based approaches have several limitations, including necessity to provide a good point of reference for reliable mounting of a sensor or compensation of its location difference, and they work in situations when the damage developing in the structure affects structural behavior locally and in the plane that is observed by the vision sensor. For this reason, they can not be used e.g., for corrosion detection—for which other methods, including deep learning are of great interest . HoweverVarious algorithms based on the analysis of irregularities in the 1D displacement, deflection, or curvature signals have been developed. Gauthier et al.  experimeAbdo  applied One of the most significant drawbacks of the crack localization methods based on curvature analysis is the sensitivity to noise in the measurement data. Bakhtiari-Nejad et al.  proposedAn interesting emerging group of damage detection methods is the approach utilizing moving loads for damage identification. It found an application in the deflection analysis of bridges under operation. O’Brien et al.  proposedIn all these methods, usually, a simple linear cantilever beam is used as a proof-of-concept. It is caused by the fact that geometry changes alter the deflection and, in many cases, render it difficult to analyze. Additional assumptions include constant stiffness over the whole structure under investigation and the uniform marker spacing—as changes in each of these parameters are often reflected in breaks or dents in the DD as well, causing false-positive damage indications.In this paper, a novel Aligned Marker Space (AMS) method of transforming the deflection data is proposed, such that it can be used to build easy-to-analyze DSs even if the markers are not uniformly spaced, the structure’s stiffness is not constant, and the structure’s geometry is complex. These DSs are analyzed using a variety of deflection-based damage detection methods, namely: a Second Derivative (SD) method, a Wavelet (WV) method, a Line Difference (LD) method, a Line Segment (LS) method and, finally, a Field Fusion (FF) method that combines them all into one decision engine. A Blender-based simulation is introduced as a means of methods’ configuration and evaluation and to facilitate the practical implementation of the approach for new structures. Then, the configured methods are tested in a series of experiments using a crane structure of varying stiffness that is damaged in several locations.1The AMS method allows us to use beam-based damage detection and localization methods to various non-beam configurations 2Decision fusion of various damage detection methods increases the reliability of the solution. The fusion can be performed both in the sub-method space and measurement space by aggregating results from different methods and from different measurements.3It is possible to optimize meta-parameter values for the FF method based on beam data and then, with the help of the AMS, use it to detect damage in a new structure.The main novelty of the article is based on three claims:The remainder of this article is organized as follows: One of the main limitations of the existing deflection-based damage detection methods is the requirement of the structure to be cantilever-like. This can be solved using the AMS approach.x-axis is built from coordinates of points so that each consecutive point is placed on the axis, and its distance to its neighbor to the left (previously considered point) is equal to the euclidean distance between these two points in measurement space. The method thus straightensa curvature of the object to a straight line. Future baseline-based displacements will be recorded perpendicularly to this line. Algorithm 1 for obtaining a vector of new coordinates is as follows:Algorithm 1: AMS transformation.1:set pointer p to 02:set3:whiledo4:d between measurement points k and    calculate euclidean distance 5:C   Store coordinate . The order of points in the matrix is used to build a new, non-euclidean transformation of measurement space into AMS. The new As a result, AMS is defined by adding a new separate spatial dimension to measurement space for organizing the points and then shrinking all three initial spatial dimensions into one for storing displacement of the point from its initial location. It is worth noting that instead of shrinking the 3D space into a 1D displacement axis, it is possible to calculate displacements separately for all three axes and storing all of them in a 4D AMS: the first dimension for the organization of points followed by three dimensions for displacements in 3D. An illustration of the process is provided in Three numerical models were developed and computed to illustrate how the method allows for straightening of various structures and reduction of markers under observation. The FEM was adopted to calculate the position of points on investigated structures—as depicted in Curved beam—see Hook—see L-shape crane—see The numerical models of the following shapes were taken into consideration:The Finite Emelent (FE) models were formulated using the Altair HyperMesh pre-processor and solved in MSC.Marc solver. The 2D element types were chosen because the thickness of modeled components is significantly lower when compared to the other dimensions. The boundary conditions and defects introduced in each model are presented in A#1 Calculation of a vertical displacement difference as a function of marker position along horizontal axis (a standard approach).A#2 Calculation of the euclidean displacement difference as a function of marker number.A#3 Mapping the displacements using AMS.The calculated DDs were analyzed using the following approaches:The DDs obtained for the three structures and the proposed methods are shown in The A#1 approach was provided here only for the sake of consistency—as visualization of vertical displacement along a horizontal axis is efficient only for beam-like structures. Here it failed to provide meaningful information for all the cases investigated. It either provides a complex plot in which it is hard to denote where the damage is located  or shrinThe method based on the article by Dworakowski et al.  requiresThe method is again based on previous work by Dworakowski et al. , and it The basic idea of the LD method involves calculating a coefficients for a line connecting both ends of a DD curve. This line is then subtracted from data resulting in a deviation from a straight line. The maximum of this deviation is treated as a damage indicator, while its location is marked as the damage location. A method in-operation is depicted in the The WV method involves calculating a wavelet transform of results of a LD method. Maximum of the absolute value of scalogram averaged over the selected range of scales is treated as damage indication . A method in-operation is depicted in the Finally, the last method (FF) fuses all of the above-described components into one decision. The idea involves calculating positions and values for damage indications from respective methods and then using these to generate Gaussians of signal in AMS. For each component, the Gaussian has an amplitude equal to damage indication multiplied by a weight and pre-defined width—centered at the source method’s damage location. The method thus has 8 metaparameters which are denoted as weights To optimize the internal weights of the FF data obtained in the previous experiment was used . These dAs a solution for optimization of FF metaparameters a Evolutionary Algorithm (EA) was designed. The two-criterion problem was solved using the Pareto optimization approach: Solutions were arranged in order of raising the number of misses and decreasing accuracy . In this two-criterion space, a Pareto front was calculated. Individuals located at the front were passed without modification to the next generation. The remaining space in the offspring population was filled by random mutations of the individuals located at the front. The selective pressure was relatively high and variable, as only a few individuals were usually located at the front. The initial population was generated so that weights and radii starting range was . No algorithm meta-parameter optimization was performed because the problem was relatively easy with consecutive runs of the code resulting in relatively similar solutions. Parameters of EA are provided in the In For the first-stage evaluation presented in this paper, a numerical setup that is based on the results of FE Analysis (FEA) and a computer graphics program (Blender) was developed. The algorithm’s result is an image rendered in a graphic program, which presents the examined structure. It is synthetically produced based on the deformation determined by the FEM solver. Numerical models allow us to significantly reduce the time for vision data creation compared to real-life experiments.The developed algorithm workflow is as follows: nodal displacements of the structure under study are determined as a result of the FEM analysis. For the evaluation presented in this paper, the L-shape crane model was created. The deformed mesh of the model is exported to the *.STL format, and then loaded in the Blender graphic program. Materials are next assigned to the appropriate parts of the mesh, characterized by a given color, texture, and the means of light diffusion. Additionally, the camera system settings are configured: lighting, camera position and orientation, camera sensor parameters, and the focal length of the lens. The camera system settings modeled in Blender are as follows: camera resolution: 5616 × 3744 pixel, focal length: 30 and 32 mm, sensor size: 24 × 36 mm—those parameters correspond to full-frame DSLR  Canon EOS 5D Mark II. Image rendered using the developed setup is shown in The procedure was repeated for the loaded state with damage and without damage. The intact scenario was used for the reference deflection state calculation. The obtained photos are input for further processing using the image processing and damage detection algorithms.The resulting deflection difference curves for both locations of damage, mapped into AMS space and subjected to diagnosis using all the tested solutions, are depicted with the respective damage locations in The second stage of the evaluation was experimental. The research was carried out using the same L-shape crane structure as shown in Each experimental case involved introducing ten damage sizes starting from 2 mm and going up to roughly 20 mm. After each damage introduction, about 1 min time interval was used in order to dampen vibrations. Then, at least 20 images were captured using each camera. Sample images captured in the experimental tests are shown in A result for one particular setup  is shown in It can easily be seen that that all the methods’ damage indications tend to correlate with damage size. It is also clear that the higher the damage is, the more accurate the methods are in its location. Note that in In the remainder of the paper, the damage detection capabilities will be evaluated in a non-normalized manner to allow for threshold setup based on preoptimization routine verification. Due to that, the base methods  will be omitted to maintain clarity and because there is not enough data in the testing subset to calculate thresholds for each of the underlying methods in an unbiased manner.In this subsection, the camera location’s influence (located at a straight angle or tilted), specimen used (so repeatability of the experiment), and influence of damage location are investigated.Cases C#3 and C#4 were used to evaluate how camera location influences the results. The results arranged similarly as in the previous example are provided in In this section, cases C#3 and C#5 are compared with each other, as presented in Finally, cases C#1 and C#3 are considered. The results are provided in The aggregated results are presented for all the methods in In this article, a new approach to deflection-based damage detection was introduced. Its primary contribution relies on introducing the AMS method for morphing data from any structure fixed at the end into a form similar to beam-like data. Therefore, it enables the usage of methods aimed at damage detection for beams. Five different damage detection methods were tested in the article: two of them (namely: SD and WV) are based on local deformation, two of them (namely: LD and LS) are based on general deflection shape and one fusion method that can incorporate in various extents input from all these sub-methods. Even though the fusion method was optimized based on historical data obtained for a different, beam-like structure, it was still able to detect damage in crane structure in most cases. The fusion method was compared with the previously reported algorithm and was proven to be much more efficient. While the method was proven to be generally successful and accurate, some problems have arisen regarding detecting damage in a horizontal part of a beam. While both the numerical simulation and some practical results suggest that the approach is efficient and could be of use, many cases were unsuccessful due to the relatively low signal-to-noise ratio. Therefore, the authors conclude that further work is required to determine the method’s efficiency for complex geometries.


Due to the demographic aging of society, the demand for skilled caregiving is increasing. However, the already existing shortage of professional caregivers will exacerbate in the future. As a result, family caregivers must shoulder a heavier share of the care burden. To ease the burden and promote a better work-life balance, we developed the Digital Case Manager. This tool uses machine learning algorithms to learn the relationship between a care situation and the next care steps and helps family caregivers balance their professional and private lives so that they are able to continue caring for their family members without sacrificing their own jobs and personal ambitions. The data for the machine learning model are generated by means of a questionnaire based on professional assessment instruments. We implemented a proof-of-concept of the Digital Case Manager and initial tests show promising results. It offers a quick and easy-to-use tool for family caregivers in the early stages of a care situation. The aging population in Germany is resulting in a growing need for care. According to the Federal Statistical Office, 4.1 million people in Germany currently require care under the German Long-Term Care Insurance Act (SGB XI). Of these, approximately 80% are cared for at home  indicateIn light of these developments, interactive technologies are becoming more popular as a means of providing medical and nursing care to those in need. According to the German Federal Ministry of Education and Research (BMBF2022), these technologies may significantly improve the quality of care by relieving the physical and mental strain that informal and formal caregivers experience in the course of their daily duties and by raising the standard of care given by the care profession. Instead of replacing trained caregivers, these technological tools are designed to complement and support them in their duties, lightening their workload and streamlining their job. This method considers both the people receiving care and the larger network of healthcare professionals. Glauner  sees greAI in healthcare is primarily used by healthcare professionals, but there is an increasing amount of research on this topic . Our goaI.What machine learning-based systems or tools in healthcare can support family caregivers in navigating the healthcare system, identifying the best care options for their family members, and balancing their work and caregiving responsibilities?II.What type of data is required to train a data-driven tool for initial guidance, and how can it be obtained?III.What is the underlying machine learning challenge and how can this challenge be addressed in the context of a care situation?IV.How accurately can the data-driven tool predict the next steps in the care process for a patient during the initial phase, based on answers to a questionnaire completed by the family caregiver, given that the training dataset is familiar with similar care situations?Therefore, we have formulated the following research questions:Machine learning can support family caregivers in navigating the healthcare system, identifying the best care options for their family members, and balancing their work and caregiving responsibilities. Examples include recommender systems ,18, healRecommender systems assist family caregivers in finding care options tailored to their family members’ needs by considering factors such as medical conditions, preferences, and location. They provide recommendations for care facilities, services, and providers that are appropriate for the individual’s situation. Health management tools, such as apps, help family caregivers in managing the health and care needs of their family members. These tools provide real-time information on vital signs, adherence to medication, and other health factors, empowering caregivers to spot possible issues and take timely action. Decision support systems facilitate family caregivers in making well-informed decisions about their family members’ care by providing information on different care options, including potential risks and benefits, in addition to guidance on how to pick the best option. They can also assist caregivers in assessing the suitability of different care providers.We chose to create a recommendation system for the initial stage of a care scenario because it offers valuable guidance to family caregivers who are overwhelmed. Particularly, caregivers who are trying to balance work and caregiving obligations may not have the time or resources to spend hours scouring the web and weighing up different care options. A recommendation system provides them with personalized advice rapidly and effortlessly, saving them time and effort and allowing them to concentrate on their work and caregiving duties. This is especially beneficial for caregivers who are dealing with complex medical conditions or other challenging situations in their lives.The development process for the Digital Case Manager is schematically depicted in A dashed line indicates the workflow of the Digital Case Manager application. Here, the procedure is laid out, starting with a care case and ending with the application of the ML model and the prediction of the next steps. The Digital Case Manager is highlighted with a solid line and is located in the center. It is built upon the ML model, which discovers the connections between a care situation and the next steps. This enables the model, after receiving the answers from the family caregiver on a questionnaire, to determine the next steps for the specific care situation and to offer initial guidance to those affected. Below is more information on each stage of this process.Data are essential for the successful implementation of machine learning. In the healthcare sector, it is challenging to obtain real data from sources such as health insurance companies due to data protection regulations. In our scientific work, we encountered this issue and were forced to generate artificial data for our research. It is crucial that the generated data accurately reflect the characteristics and patterns of real-world data so that machine learning models trained on it are able to make accurate predictions and provide useful recommendations. To achieve this, we developed a data generation concept taking into account the various factors that influence care situations and necessary care steps. This concept involves input from professional care experts and aims to provide a comprehensive and realistic representation of care situations and necessary steps. The workflow of the entire data generation process is depicted schematically in To generate data, a questionnaire is needed that captures the relevant care situation. This allows the physical situation to be translated into data being processed by a computer algorithm. In addition to the questionnaire, we also need use cases that describe and depict specific care situations. These care situations are documented by healthcare experts using the questionnaire, and the recommended care steps are also recorded. Based on the use cases, schemas are constructed that serve as templates, which also allow slight deviations from the original use cases. By means of the schemas, the algorithm generates randomized data, which is a multifaceted and realistic representation. The data generation algorithm is described in depth in The questionnaire is a vital component of our data collection process. It is based on the survey methods of the German Medical Service of the Health Insurance Funds (MDK) , which iThe questionnaire is comprised of seven modules and includes 47 questions with a 4-point Likert scale. These questions are the minimum necessary to cover all important areas of the care situation and thus enable a prediction of the next steps. All of the questions can be found in Mobility;Cognitive and Communication Skills;Behavioral and Psychological Issues;Self-Care;Managing Health and Therapy Needs;Managing Daily Life and Social Interactions;Organisation of the Care.The seven modules are the following:The first module, “Mobility”, evaluates whether outside assistance is required for actions like standing up or climbing stairs. There are seven questions on it. The ability to communicate is assessed in the second module, “Cognitive and Communication Skills”, which also looks at other mental impairments. Seven questions are also included in this module. The third module, “Behavioral and Psychological Issues”, contains four questions and focuses on issues like aggressive behavior, anxieties, and depressed moods. Eleven questions make up the fourth module, “Self-Care”, which evaluates the capacity to carry out duties linked to household management and personal hygiene. The fifth module, ”Managing Health and Therapy Needs,“ has eight questions and covers abilities including the capacity to take medication and assess blood sugar on one’s own. The sixth module, ”Managing Daily Life and Social Interactions”, contains four questions that determine the capacity for organizing the day and interacting with others. Six questions make up the last module, “Organisation of the Care”, which focuses on the assistance or care given to the recipient of the assessment.These modules cover a wide range of important aspects of a care situation, allowing us to collect comprehensive and accurate data that can be used to make recommendations for further care and next steps. They are grounded in best practices and expert techniques from geriatric care, ensuring that the questionnaire is both reliable and effective in assessing the care needs of older individuals.Changing position in bed ;Holding in the sitting position;Getting up out of the seat;Transferring from the bed, e.g., to the wheelchair;Moving around inside the home;Moving outside the home;Climbing stairs.To provide further insight into the questionnaire, here is an example of the questions contained in Module 1, “Mobility”:These questions are selected and adapted to obtain a thorough understanding about the person’s ability to perform physical activities. They capture the relevant aspects and allow for accurate predictions of the next care steps.Possible without outside help;Some outside help needed;Outside help predominantly needed;External help completely needed;Not required.The answer options are as follows:The answer options in the questionnaire allow the person to accurately and thoroughly describe their care situation. This ensures that the recording of the person’s needs and abilities is consistent and follows a standardized way.The use cases are an essential component of our data generation process, as they provide specific scenarios and context for the questionnaire responses. Based on the ICF  from the World Health Organization (WHO) , these sThese chronic disease patterns capture the complexity and long-term nature of care situations and aim to depict medical and nursing care as accurately and comprehensively as possible. The case scenarios on the topic of caregiving were deliberately chosen to distinguish them from medically diagnosed diseases in order to crystallize any differences with regard to the care steps. Initially, the Digital Case Manager is only applicable to these specific areas. However, it can be easily expanded to include data from other care situations, such as Parkinson’s disease, as needed.To expand the data, we developed schemas based on the use cases that serve as templates for our data generation algorithm. These schemas allow for slight variations from the original 28 use cases, while still leading to the same next steps in the care process. The allowed variations were carefully reviewed by care experts to ensure their realism and relevance. The inclusion of such variations in the generated data allows us to capture the diversity of care situations more effectively. By allowing multiple answer options for a given question, even in similar care situations, we are able to reflect the complexity and uniqueness of each individual case. This enhances the quality and realism of the generated data and helps improve the learning of the ML model. Despite the initial focus on certain areas of care, our approach allows us to generate a range of complex care scenarios and enrich the ML model’s knowledge and capabilities.Algorithm 1: Data Generation Algorithm.Require: use cases U, schemas S, questions Q, labels Ln   Set the number of data points to be generated,    Initialize a counter whiledo            Randomly select a use case u         Select a schema for q do         a for question s for use case u             Randomly select an answer end for         u         Add the labels          Increment the counter: end while   return Generated data   The use cases and their schemas described so far do not provide enough information for machine learning purposes. To properly train and evaluate a machine learning model, we need a dataset that includes multiple care situations. Each row in this dataset should represent a different care situation and include the answers to the questions from the questionnaire. To create this dataset, we use the schemas as templates and vary the answers to the questionnaire based on the allowed variations specified in the schema. To automate this process, we have developed a data generation Algorithm 1.The algorithm starts by randomly selecting one of the 28 available use cases. It then uses the corresponding schema as a template and runs through all the modules and questions of the questionnaire, randomly selecting one of the options for each question. After all questions have been answered, the algorithm stores the next steps in the last column of the data set. This process is repeated by randomly selecting a new use case and creating a new care situation until the requested number of care situations is reached. Each row of the data set therefore corresponds to one generated care situation.To improve the understanding of the data structure, the generated data are displayed in The dataset is organized in a tabular format, with rows representing individual care situations and columns corresponding to the various features and labels. The features consist of the 47 questions posed to the patient or caregiver, while the labels represent the next steps or recommendations for care. To provide a concrete example, consider the first row of the dataset, which pertains to a care situation involving stroke. The first two columns pertain to the “Mobility” module and ask whether the patient requires assistance with tasks such as changing position in bed or holding in a sitting position. This pattern is repeated for all 47 questions. The final column contains the labels, which may include recommendations for outpatient care, care and accompaniment, speech therapy, occupational therapy, household help, and medication. A list of all of the next steps used can be found in The underlying problem of determining the appropriate steps to take in a given care situation can be represented as a multi-label classification task in machine learning. This means that multiple classes  can be assigned to a single instance  in the Digital Case Manager. Multi-label classification is a type of multi-task learning, a subfield of machine learning where multiple tasks are solved simultaneously . It invoAlgorithm 2: Model Learning Algorithm.Require: data DEnsure: model ML    Transform the labels of each row of the data into a list I    Convert the answers to the questions into integers D into training     Split the data     Train a Multi-Output Classifier     Set the number of estimators in return    The data generated from the questionnaires were used as input for the model learning algorithm, which is based on the Random Forest Classifier . The RanThe labels are first transformed from a string to a list on an individual basis. This allows us to approach each step as a separate classification problem, which is the prerequisite if we want to apply the Multi-Output Classifier for prediction. The responses to the questions are also converted into integers in order to simplify processing and the application. Next, training data and test data are separated. This enables us to evaluate the model’s capacity to generalize to novel, unobserved data and produce reliable forecasts on that data. The Multi-Output Classifier is a machine learning model that consists of multiple binary classifiers, each responsible for predicting a specific label or target. In this case, we employ the Random Forest Classifier as the binary classifier and set the hyperparameter for the number of decision trees per Random Forest to 10 to balance computational efficiency and accuracy. To train the model, we utilize a set of labeled data known as the training data. Once the model is trained, it can make predictions on new input data, which includes answers to questions and other relevant information. The model employs the trained binary classifiers to predict the values of each target label, and these predictions can determine the next steps in the process.Before using the model for prediction, it is important to evaluate its performance. To do this, we use unseen test data, which are not used for training the model. The test data are already in the correct format for the model, as shown in Algorithm 2. The evaluation involves comparing the model’s predictions on the test data to the known ground truth labels. This allows us to measure the model’s accuracy and determine how well it generalizes to new, unseen data. If the model performs well on the test data, we can have confidence in its ability to make accurate predictions on other similar data. We evaluate the model by comparing performance for three different data sets to examine the impact of insufficient data on model performance: Dataset 100, which contains 100 generated care situations; Dataset 500, which contains 500 generated care situations; and Dataset 1000, which contains 1000 generated care situations. For each dataset, the data are separated into training and test sets using a ratio of 70:30. This allows us to train the model on the training data and evaluate its performance on the test data, using the known ground truth labels. By comparing the model’s performance on the different datasets, we are able to investigate the influence of the amount of data on the model’s accuracy and generalization ability.We have calculated a variety of metrics to evaluate the performance of our model including classical measures such as accuracy, F1-score, and 2-fold and 10-fold cross-validation, as well as metrics specifically tailored to the task of multi-label classification, such as hamming loss, coverage error, label ranking average precision, and label ranking loss. Accuracy is a commonly used metric that assesses the proportion of predictions made by the model that match the labels assigned by experts for a particular care situation. The F1-score is a metric that balances precision and recall, and is calculated as the harmonic mean of these two values. In the context of multi-label classification, the F1-score is the average F1-score for each label, with the weights of each label determined by the number of samples. This allows us to assess the overall performance of the model across all labels, taking into account both precision and recall. Cross-validation is a resampling technique that is applied to evaluate the performance of a model by training and testing it on different subsets of the data . This meTo compute these metrics, the ML model was firstly learned by using the training data. Then, the test data are used as new input to create predictions of the next steps by the model. These predictions were compared with the actual steps, which were determined by care experts. The results of this evaluation are presented in The model trained with Dataset 100 achieved an accuracy of These findings indicate that the models developed using 500 or 1000 generated care situations are effective and appropriate for the task at hand. The model chosen for the Digital Case Manager’s implementation was the one that performed the best in the 2-fold and 10-fold cross validation after being trained with 1000 generated care situations. The model’s strong performance in the label ranking metrics, high accuracy, F1-score, low hamming loss, and coverage error support this decision. These outcomes demonstrate the effectiveness of using machine learning to predict the next steps in healthcare scenarios.A proof-of-concept implementation of the Digital Case Manager was developed as part of the research project work and care. This implementation was done in Python 3 using the sklearn and streamlit libraries ,34,35. TThe prototype of the Digital Case Manager starts with an introduction that explains how to use the tool and what it is for. This is followed by a closed window, which can be opened with the plus symbol in the upper right corner. Here is detailed information on the individual modules of the questionnaire. Next comes the section with the seven modules and their questions. For each question there are answer options that can be selected by means of a radio button. After the question area comes the analysis area. Here is a button called “Start Analysis”, which the user has to press to start the prediction of the next care steps. After the model has determined the next steps, they are listed at the bottom of the page.In a typical use case of the Digital Case Manager, a healthcare situation arises where the user (the family caregiver) feels overwhelmed and needs initial guidance on the next steps to take. To provide this guidance, the user completes the questionnaire and initiates the prediction of the next steps using the machine learning model. The model calculates the next steps in care and displays them to the user. The user can then click on specific care steps, such as ergotherapy, and be directed to service providers who offer that service in their local area. This creates an optimal care path that allows the user to access the help they need quickly and reliably, around the clock.In this evaluation we focus only on the technical aspects of the Digital Case Manager, such as development and implementation. An evaluation with family caregivers about the user experience is planned and we will report findings in a future publication.The Digital Case Manager demonstrated its potential, particularly in the use cases outlined in What machine learning-based systems or tools in healthcare can support family caregivers in navigating the healthcare system, identifying the best care options for their family members, and balancing their work and caregiving responsibilities? Machine learning can be a valuable resource for family caregivers, as our literature research has shown. We identified three different kinds of systems that are able to assist caregivers in their duties: recommender systems, health management tools, and decision support systems. Recommender systems use algorithms to provide personalized recommendations for care facilities, services, and providers that meet the specific needs of family members. Health management tools help caregivers track and monitor the health of their family members, providing real-time updates and alerts for potential issues. Decision support systems offer caregivers valuable information about various care options, supporting informed decision-making. In our research, we focused further on a recommender system, which we found to be particularly promising and effective in supporting family caregivers with regard to balancing work and care.Regarding the question: What type of data is required to train a data-driven tool for initial guidance and how can it be obtained? High-quality data that reflect the characteristics and patterns of real-world scenarios are necessary in order to create a trustworthy data-driven tool for giving initial guidance in care settings. Unfortunately, acquiring such information can be tough. In order to overcome this difficulty, we developed a model for data generation that includes input from professionals in the field of care and seeks to provide a realistic representation of care needs and the steps required to address them.Regarding the question: What is the underlying machine learning challenge and how can this challenge be addressed in the context of a care situation? The machine learning challenge presented in the context of care situations is a multi-label classification problem, in which multiple classes (next steps) can be assigned to a given instance (care situation). The multiple classes correspond to the next steps in care and one instance corresponds to one care situation. The answers to the 47 questions are accordingly the features. To address this challenge, we employed a multi-label classifier based on the Random Forest algorithm to learn the relationship between the care situation, which are described by the features and the next steps of care.Regarding the question: How accurately can the data-driven tool predict the next steps in the care process for a patient during the initial phase, based on answers to a questionnaire completed by the family caregiver, given that the training dataset is familiar with similar care situations? Our research revealed that the machine learning approach we developed had high accuracy when trained with Dataset 1000, with an accuracy of 99.33% and a F1-score of 0.9993. Cross-validation results demonstrated a high level of accuracy, with the 2-fold cross-validation achieving 99.7% accuracy and the 10-fold cross-validation achieving 99.8% accuracy. The model also had a low hamming loss of 0.00039, implying its error in predicting the next steps in the care process was very low. These results demonstrate the effectiveness of the machine learning approach in accurately predicting the next steps in the initial phase of a patient’s care process, provided that a similar care situation was included in the training dataset.Regarding the question: The Digital Case Manager is a tool that provides advice and support to assist family caregivers in navigating the complex healthcare system and making informed decisions about care options for their family members. It offers valuable information about existing support structures and facilitates the identification of appropriate care options, leading to cost and time efficiencies. The Digital Case Manager streamlines the caregiving process, helping family caregivers remain in the workforce by freeing up their time and addressing the challenges of demographic change in the Western world. However, it is important to recognize that the Digital Case Manager may not be accurate in all care situations and is just one resource among many that caregivers can utilize. Additionally, while it provides quick and convenient support for family caregivers facing a challenging situation, it is not able to fully replace the support and guidance of a human care manager. So, if in doubt, it is advised to consult a professional.One limitation is that the tool’s machine learning model was trained using information from only 28 care scenarios based on stroke, dementia, and caregiving. As a result, it might not provide reliable recommendations for other care scenarios. In order to address this issue, more data of other care situations need to be gathered in collaboration with a care facility or health insurance provider. This will not only improve the tool’s data realism, but also help to ensure that the Digital Case Manager is able to provide recommendations for a wider range of care scenarios. Another limitation of the Digital Case Manager is that it does not sufficiently consider the perspectives of patients and the family members who care for them. In the future, we intend to address this by evaluating actual care scenarios with the patient and the family caregiver present. Their knowledge and experience will be added to improve the tool further. Our preliminary evaluations, conducted with both family caregivers and professional experts, suggest that the Digital Case Manager has the potential to be a valuable tool, but further development and field testing are required. The tool’s current version was specifically designed for family caregivers who are not familiar with the medical field as the target group. To increase the effectiveness of the Digital Case Manager for professional healthcare personnel, it will need to be customized to their specific needs and preferences.In the future, an idea is to integrate the Digital Case Manager into a health platform and to connect the individual steps recommended by the model to the services and resources offered by providers such as pharmacies and care facilities. This would allow users to easily access the services and resources they need to follow the recommended next steps and manage their healthcare more effectively.We intend to explore additional possible applications for the Digital Case Manager in addition to improving its functionality. Healthcare professionals might employ it, for instance, to simplify their workflows and devote more time providing care to patients. We are aware that the current recommendations are not comprehensive enough to offer assistance to professional caregivers. We plan to develop more precise recommendations in the future that will offer the required level of detail and support to address this issue.The flexibility of the Digital Case Manager to adapt to particular addressing scenarios, such post-stroke care, may serve as a further starting point for future development to provide more tailored assistance. Here, the tool would have the potential to increase the standard of procedures and care while streamlining patient care throughout the whole healthcare system. As a result, stroke victims may receive better, more efficient care in the long run, and the healthcare system may have an easier time allocating resources for its medical and nursing services.


Thermal energy accumulation is one of the ways how to optimize heat production processes and how to balance the supply and demand of heat in distribution systems. This article presents a design of a fin-and-tube latent heat thermal energy storage (LHTES), which combines high thermal energy storage density and scalability. A computational model that used lumped heat capacities was tuned using the experimental data. The numerical model proved to be simple yet precise. A new constant mixing temperature test was designed and performed with the LHTES. Unlike standard constant flow rate charge/discharge test, this test provided valuable information about what to expect in the real-life operation conditions. From the tests and data from simulations, it was concluded that the LHTES would perform better in terms of its capacity utilization if it operated at lower output power than in the laboratory circuit. This indicates that a smaller, and thus more cost-effective, LHTES could be employed in the laboratory circuit with virtually the same utility to the system if its heat transfer characteristics were improved. Unfortunately, the supply and demand for heat usually do not meet at a given time. At times of heat abundance from solar systems, the demand for heat is very low, and vice versa3. Of course, heat sources based on fuel combustion operate independently of weather conditions, but they have other drawbacks. Heat and electricity are often co-generated to increase overall fuel efficiency. Cogeneration is, without a doubt, a beneficial concept, but it may lead to a mismatch between heat production and consumption again. Moreover, fuels are costly nowadays and generate (at least) local emissions. To run these heat sources economically and ecologically, their heating power output should be close to the nominal value. However, this is often not the case because the heat sources are designed to cover the maximal demand, so they run on relatively low power settings most of the time.The demand for heat in housing, industry, agriculture, and other facilities naturally varies. There are seasonal differences and daily patterns, which lead to huge differences between consumption peaks and valleys5.Of course, heat accumulation solves some problems addressed in the previous paragraph. Thermal energy can be stored for months or just for a couple of hours. There are various principles of heat storage with different properties, such as storage temperature level, energy density, efficiency, charge/discharge time, and cost6. However, the downside of using PCM is that the PCM must be kept separated from the heat transfer fluid (HTF), which limits the charging/discharging times because PCMs are usually relatively poor heat conductors. Many heat transfer enhancement techniques have been proposed to tackle this problem8. A popular way is to add some materials like graphite powder, carbon fibers, graphene, or metal foam. However, these solutions appear prohibitively expensive or unreliable in long-term performance and are thus impractical for real-life applications.This paper focuses on a particular design of a latent heat thermal energy storage (LHTES) intended for demand side management . The application of phase change materials (PCMs) for heat accumulation is beneficial from the energy density perspective, allowing a smaller storage volume when compared with commonly used stratified water storage13. The main advantage of this approach is technological simplicity, but at the cost of relatively low energy density and usually long charge/discharge times. Long charge/discharge times can be avoided by microencapsulation, where the interface area between the PCM and HTF is relatively large9. The second option is to use the inspiration from the heat exchanger design. Shell-and-tube LHTES11 usually offer high energy density, but they are lacking in heat transfer rate. Therefore, various types of LHTESs with fins protruding to the PCM have been designed18. Fins can increase the heat transfer rate significantly, but they are usually complicated to attach to the pipes reliably, which makes them expensive19. Many numerical studies using fins have been published, too20. However, they often focus too much on the optimal geometry without considering the manufacturing technology aspects of the design22.Therefore, two types of more robust solutions are often used. The first one is to encapsulate the PCM into small containers of various shapes surrounded by the HTF17. The outcome of this method is a high peak of maximum power (and outlet temperature) at the beginning of the process, followed by a gradual descent. This approach gives relevant data for numerical model validations, but it is very unlikely that the heat demand of a system will follow the gradually descending profile. Real charging/discharging is likely to be governed by advanced control algorithms like model-based predictive control (MPC)25.A crucial step in designing an LHTES is the evaluation of its performance. The most common way of doing it is by charging or discharging the LHTES by constant flow rate and constant inlet temperature9 tested constant power charging/discharging of their LHTES designed for a CHP unit. However, they controlled the HTF inlet and outlet temperature difference while keeping a stable HTF flow rate, which might be impractical in real applications. Zauner et al.26 also reached almost constant charging power in their tests, but it was due to the power limit of their heating equipment. Xu et al.27 coupled their numerical model of an LHTES with a heating system model and compared various discharging strategies. Gradually increasing the flow rate through the LHTES was found to be the most effective. However, only numerical simulations supported the conclusion.Charging/discharging strategies that would emulate real-life conditions are rarely employed. For example, Nuytten et al.This article presents a demonstrator of a fin-and-tube LHTES. The performance of the prototype was evaluated employing a standard constant flow rate testing, and then a newly proposed constant mixing temperature method was tested on the demonstrator. Another part of the article deals with the mathematical modeling of the designed LHTES. The effective heat capacity approach was used for modeling the PCM. A lumped capacity model of the LHTES demonstrator was developed, and its heat transfer parameters were tuned using the constant flow experimental data. Finally, the analysis of the conducted experiments led to the execution of a set of simulations under different conditions, which showed the LHTES’s sensitivity to its power load.One of the main contributions of this article to the ongoing research in the field of heat energy storage is the fin-and-tube LHTES demonstrator itself. It provides excellent stored energy density, which was found superior to previously published designs. At the same time, it has a decent power output rate, and it is easily scalable. The second contribution is the new constant mixing temperature method with a fixed total flow rate for storage performance evaluation. The experimental circuit for the method was designed, including control algorithms, and the experiments proved that the method worked as intended. The purpose of the method is to emulate real-life operation conditions. No similar approach was found in the literature. The third important part of the work is the lumped parameter simulation model. This kind of model was found to be highly time effective and precise when some heat transfer parameters were tuned using the experiments. The numerical model could be easily made a part of a simulation of a larger system. The fourth valuable result of the paper lies in the analysis of the experiments and the subsequent simulations. It showed that the LHTES would perform better in terms of its capacity utilization if it operated at lower power output than that experienced during the experiments. By application of this knowledge, a smaller, and thus more cost-effective, LHTES can be designed while the utility for the system remains the same.The design of the test device strictly follows the essential condition for the highly effective stored energy diffusion from the PCM areas to the walls of the tubing containing the flowing HTF. The main features of the design are displayed in Fig. The tubes for the heat transfer fluid are of an external diameter of 28 mm and a wall thickness of 1 mm. The material of the tubes is copper (EN 1057). The set of fins  of the hexagonal shape made from stainless metal sheets of 1,5 mm thickness is firmly connected to the cooper tubes to provide intensive heat conduction from accumulation areas to the tube walls. There are dents on the fins to prevent contact between the fins and the “V” shaped spacers. All fins are dented the same way for simple manufacturing. The dents have virtually no influence on heat transfer. The storage vessel contains 42,5 kg of PCM. The space for the heat storage is delimited by the cylindrical vessel body with the “V” shaped spacers from the sides and by the flat round lids from the bottom and the top.A set of 13 tubes comes through the top and bottom lids via brass bushing parts; tubes are firmly connected to the bushing bodies which are part of the bottom lid. The tubes inside of the bushing parts installed on the top lid are axially moveable as the provision for tube thermal expandability effect elimination. On both ends, the system of tubes is mutually interconnected by the “U” shaped fittings into one flow line.The internal space of the heat accumulator is filled with the PCM via the feed port located on the top lid; the PCM content is drained via the exhaust port located on the bottom lid. For the lab testing, the whole thermal storage vessel assembly is duly thermally insulated from the outside.Many latent heat storages have been designed. Therefore, a brief comparison with some previously proposed storages is presented in Table 16 and Kabbara et al.15. However, both designs employed higher temperature differences, which is favorable. Therefore, the current design would perform better than these for the same temperature range in the field of energy density. Moreover, Kabbara et al. used unevenly located fins, leading to prohibitively long charging/discharging periods. In general, LHTESs with encapsulated PCM tend to have lower energy density. It is mostly due to a relatively larger portion of the HTF and capsules, which are poor heat-storing media.The current design achieved an energy density of PCM1, TPCM2, TPCM3) and three temperature sensors installed on the outer surface of the storage . Once they all reached the desired temperature (with W1), and the lower one collects the water leaving the system.The LHTES was integrated into a circuit that allowed various modes of operation and data collection Fig. . For theTwo modes of operation are presented in this work: constant flow rate through the storage and constant mixing temperature (with a constant flow rate through the whole system).The constant flow rate experiments started with charged storage and mixing valve V2 set to fully closed (B-AB opened). Then valve V1 control algorithm was activated, and after about 60 s, when the flow rate measured by Q1 reached the required value, valve V2 was set to fully open (A-AB opened). The change in setting V2 takes about 60 s, and valve V1 compensates for any changes in system hydraulics, so the flow remains constant during the valve member position change and afterward.The constant mixing temperature experiments started with charged storage and mixing valve V2 set to fully closed (B-AB opened). Then valve V1 control algorithm was activated, and after the flow rate measured by Q1 reached the required value, valve V2 was set to 20% open. After about 30 s, the control algorithm for valve V2 was activated and kept the mixed temperature  unit.The volumetric flow rate was controlled by one valve and one three-way valve. The two valves were driven by electromagnetic actuators SIEMENS SSB61, which were controlled by the voltage signal from the data acquisition and control unit.As the data acquisition and control unit, the NI cDAQ 9188 with NI 9216 Temperature Input Module, NI 9203 Current Input Module, and NI 9263 Voltage Output Module were chosen. The measured data were preprocessed and stored using a PC which was connected via an Ethernet link to the main data acquisition and control unit. The software for the control and DAQ was created in LabVIEW. The temperature was recorded at a rate of 28. Most simple models of phase transition assume that the liquid phase mass fraction is a direct function of temperature in the mushy zone29. The liquid phase mass fraction is defined as29Melting or solidification can occur under a single temperature or within a temperature interval. The latter case relates to typical PCM, where enthalpy varies continuously within a mushy zone. A phase change is limited by solidus temperature in the lower limit and by liquid temperature in the upper limit. There coexist both solid and liquid phases in the mushy zone. The Gumbel minimum cumulative distribution function is used in this model29The effective specific heat capacity model needs liquid phase mass fraction as a function of temperature. This function can be obtained by fitting experimental partial enthalpy data provided by the PCM manufacturer. The crucial step is the selection of this monotonously increasing function that realizes a smooth transition from 0 to 1. An unsymmetric probability density function fits the requirements29It is convenient to model the distribution of real PCM as a superposition of cumulative distribution functions30 was used as the PCM in the LHTES. Specific partial enthalpy was obtained by the T-history method and its details have been previously published12. Figure RT35HC from Rubitherm12. The advantages of this type of modeling are listed above, but there are also disadvantages. For example, heat transfer between PCM and the heat transfer surface (pipe surface and ribs) is complicated, and some of its parameters must be found by fitting the model with experimental data.A numerical storage model was developed. The priorities of the modeling were computational effectivity, simple scalability for solving larger storages, and the ability to be part of more complex models. To get all the listed properties, a lumped parameter technique was chosen. The authors presented a similar approach inThe necessary parameters of the model were tuned using the constant flow rate experiments In "" sectionThe schematic of the model is shown in Fig. The HTF inside the pipe is represented by a chain of control volumes. The HTF has constant density, so the mass balance is simplySpecific enthalpy in the volume is balanced using the following equationwhere the first two terms on the right-hand side of the equation represent convective transport of enthalpy, and the last one is heat flux through the wall of the volume. Enthalpy is a function of temperature.There is no heat transfer in the first volume and the last one because they represent the short portions of pipe that are between the inlet and the inlet temperature sensor and between the outlet and the outlet temperature sensor, respectively. These short parts proved functional, especially at the beginning of the simulations, where they improved the dynamic behavior of the storage model.n interconnected volumes that take part in heat transfer. Each of these represents an equal portion of the pipe inside the storage. Heat transfer between the HTF and the inner wall of the pipe is described by Newton’s cooling law31, so an appropriate correlation should be used. For thermally developing flows, the determining parameter is nondimensional pipe length, defined as32. The whole range of Between the first and the last volumes, there is a series of The pipe itself, the ribs, and the lids through which the pipes go  are represented by heat capacitors. Their enthalpy rate of change is given by12. A linear function yielded an acceptable outcome, but the results presented in this article use a function with one more parameter describing its shape. The chosen function isThe heat transfer coefficient between the PCM and the heat transfer surfaces (pipe outer surface and ribs) is modeled as a function of liquid phase mass fraction. It respects the fact that the intensity of heat transfer depends on the state of the PCM. The authors employed a similar approach successfully before using a linear functionThe reason for the form of the function in Eq.  can be aPCM is modeled according to Equation   with heaquation  . The outAn important numerical parameter is the previously mentioned number of volumes of HTF and respective sections of other components. A simple convergence study was performed, and there was virtually no difference in results for five and more volumes (for a pipe length of about 13 m). The further presented results are for 15 volumes because the extra computational cost was found negligible.12 was used for the evaluation of the state of charge (SoC) of the storage. For discharging it isOne of the methods used in26. In this work, two flow rates were measured (0.9 m3/h and 0.45 m3/h) with the initial storage temperature  close to 55 °C and the inlet water temperature of about 15 °C.Constant flow rate is a standard heat storage test, which was performed with many designs many times, for example, inIn both cases, the outlet temperature profile was very similar Figs.  and 7. TThe parameters for the numerical description of heat transfer between the PCM and the heat transfer surfaces were found by fitting with the experimental data, as mentioned in the previous section. For both flow rates, the level of agreement was excellent, which indicates an appropriate choice of the structure of the heat transfer function for the studied configuration [Eq. ].3/h and 0.9 m3/h. Only three of them are presented here because for the highest power settings (25 °C and 0.9 m3/h), the used control algorithms and hardware could not reach the desired outlet temperature for a technically relevant period. This experiment showed the limits of the used hardware and algorithms. For the tested setup, the maximal reasonable power setting was found to be around 8 kW, which is close to case 2 experiment.Constant mixing temperature experiments were designed to test the storage performance in a scenario that is closer to real-life operation conditions than the constant flow rate. Four settings were measured: Outlet temperatures 20 °C and 25 °C with total flow rates of 0.45 mThe results are presented in Figs. 27, and their simulations indicated that it is close to the ideal flow rate profile for discharging an LHTES. The experimental investigation presented here thus confirms their findings from numerical simulations.The total flow through the system was kept constant during the experiments with no extensive fluctuations. The flow rates through the storage are presented in Figs. The comparison between the cases is presented in Fig. Of course, the heat released after the mixed temperature drops below the desired temperature is not lost, but it is of less utility because of its lower potential. It can be used only for pre-heating, and some other measures must be taken, like starting a conventional heating or another charged heat storage.11.The dominant reason for the fact that the faster the storage charges/discharges, the less portion of its capacity can be effectively used is the limited heat transfer rate within the storage, particularly in the PCM. Unfortunately, changes in the design leading to heat transfer enhancement  are usually costly and decrease the heat storage energy densityThe range of parameters investigated by the experiment seemed inappropriate for the designed LHTES. The SoC at the end of the discharging period was too high, and most of the stored heat was not extracted. Therefore, simulations of discharging at lower power settings were performed. The goal was to find if lowering the power output would lead to an appropriate discharge time and if the capacity of the storage is utilized sufficiently, i.e., if the SoC at the end of the discharge is adequately low.mix). Even though the power was the same in cases 4 and 6 and 5 and 7, respectively, higher Tmix led to lower heat utilization. The extracted heat was 23% lower for the higher power (2 kW), and it was 15% lower for the lower power (1 kW) when compared between Tmix = 20 °C and Tmix = 25 °C. Secondly, there was the desired output power effect. When comparing cases 4 and 5, there was a 29% increase in heat utilization, and for cases 6 and 7, there was an increase of 43%. Not surprisingly, the best heat utilization was found in case 5 with the least demanding conditions. However, the overall discharging time, in this case, was almost four hours, and it might be too much for some applications.The parameters and the results of the simulations are summarized in Table Finding the optimal design of an LHTES for an intended application does not involve only finding a suitable capacity and phase change temperature of a PCM. The design has to respect the charging times and powers usually achieved in the system. Doing this can achieve significant savings in the required space, construction materials (including PCM), production hours, and, thus, costs. Making an LHTES that can exploit the maximum of its heat storage capacity at usable temperature levels and not making it unduly complicated and thus expensive is anything but a trivial task.The fin-and-tube LHTES presented in this article has an excellent value of energy density and a relatively simple scalable design. The comparison with other designs showed that, in general, fin-and-tube storages are superior to storages with capsules in terms of energy density, and the current design is among the best considering this parameter. Between temperature levels 15 °C and 55 °C it can store A new constant mixing temperature test that is much closer to an actual LHTES operation mode was proposed and tested with the special laboratory circuit. The used algorithms for controlling temperature and flow rate proved to be sufficiently robust for the task. It was found the constant mixing temperature test is a useful tool for the evaluation of thermal storages at the stage of initial design in computer simulations and on laboratory prototypes for verification of their qualities.The computational model based on the effective heat capacity PCM modeling and lumped heat capacity approach provided results that agreed with the data from the experiment very well. The computational simplicity of the model allows it to be used as part of more complex models of whole distribution systems. It is also easily expanded into greater scales, and more storages can be simulated together, possibly with different PCM phase change temperature levels.The analysis of the constant mixing temperature tests revealed that the experimentally tested settings were not ideal for the LHTES, being too demanding on the power output and thus not exploiting the capacity of the storage effectively. Additional simulations illustrated how changes in performance parameters of the LHTES operation can improve the utilization of the stored heat. The amount of heat extracted from the storage at the desired temperature parameters was only between 25 and 45% for the experimental cases (cases 1–3). The simulations confirmed this finding. Additional simulations (cases 4–7) showed that between 47 and 79% of stored heat could be recuperated from the storage if the heat power demand was lower. While the output temperatures remained the same, obtained heat was almost double.