A plethora of image and video-related applications involve complex processes that impose the need for hardware accelerators to achieve real-time performance. Among these, notable applications include the Machine Learning (ML) tasks using Convolutional Neural Networks (CNNs) that detect objects in image frames. Aiming at contributing to the CNN accelerator solutions, the current paper focuses on the design of Field-Programmable Gate Arrays (FPGAs) for CNNs of limited feature space to improve performance, power consumption and resource utilization. The proposed design approach targets the designs that can utilize the logic and memory resources of a single FPGA device and benefit mainly the edge, mobile and on-board satellite (OBC) computing; especially their image-processing- related applications. This work exploits the proposed approach to develop an FPGA accelerator for vessel detection on a Xilinx Virtex 7 XC7VX485T FPGA device . The resulting architecture operates on RGB images of size  The evolution of FPGAs with respect to the increased hardware resources and the efficiency of their programming tools has significantly affected the applications with real-time specifications. Image-processing tasks, especially those based on deep learning techniques  and CNNsThe proposed approach is effective for CNN applications with relatively low feature space ,4,5 suchConsidering vessel detection as the target application, we exploit the proposed approach to design an accelerator within a single FPGA device, which decides whether there is a vessel , in the CA, USA)  used forCA, USA) ,12 and tCA, USA)  either oThe paper is organized as follows: the following section reports related results in the literature; Researchers have studied and provided FPGA accelerator solutions for CNNs based mainly on the automated software development tools such as the High-Level Synthesis (HLS) ,17,18,19Regarding the results related to vessel detection ,25, whicVessel detection is among the most important tasks of Maritime Domain Awareness ,25 incluMoreover, note that for ships greater than 300 tons, it is mandatory to use shipborne transponders to report their position. Smaller ships though, do not have to own and use these devices. Ships that engage in illegal operations either turn them off or they try to deceive the authorities with false reports of their position. Hence, vessel detection can effectively support maritime domain awareness. Consequently, the exploitation of images, especially satellite imagery, plays a key role for locating vessels on the sea surface. A notable example is satellite-based radar images most often, Synthetic Aperture Radar (SAR), which are commonly used for maritime surveillance because they provide the ability to detect the vessels either in the case of both clear or cloudy skies. The interest in using optical images in the applications of maritime surveillance escalated significantly due to the availability of optical-imaging satellites.Considering the problem definition, vessel detection can be envisaged as a task of detecting an object given that the background in most cases has the characteristics of the surface of the water. Following the latter model, the researchers and the engineers focused on providing solutions in terms of automated analytical methods for remote sensors. These efforts are the consequence of the existence of the large number of Earth-orbiting sensors and their ability to generate and transmit big volumes of data. Hence, the detection systems have to process large volumes of sensor data and in many cases to conform to near-real or real-time requirements. Accordingly, the limitations in the execution time, as well as the restrictions in the power consumption and the resource utilization, call for power and resource-efficient hardware accelerators . A generThe current section introduces the three distinct phases of the proposed image-classification FPGA accelerator design approach. It begins by presenting the first phase with the guidelines for the CNN model design. Then, for the second phase, it describes the development of the fixed-point BAM representation of the CNN floating-point model based on the factors that play a key role in the design of the entire FPGA accelerator. Finally, the third phase introduces the configurable VHDL blocks and the mapping methodology of the CNN layers to the FPGA by utilizing these blocks. The result is a map of the CNN layers on a pipeline of modules, where each module is optimized to the corresponding layer computations. The proposed streamline architecture designs save significantly on the FPGA resources compared to the architectures that implement all the CNN layers on a systolic array  and leavNumber of layers: the neural networks for the low-feature-space image-classification applications can achieve a high accuracy rating even with a relatively small number of convolution layers .Size of convolution kernels: considering the images that are relatively small, the recognized objects tend to occupy a large portion of the image and hence, large and medium-size convolution kernels suffice.Choosing the size of the pooling layers windows: the feature space is relatively limited and hence, the use of Padding avoidance: this is used throughout the CNN because: (a) it does not affect the accuracy and (b) given that the majority of the target applications have objects located at or close to the image center, we do not need to preserve the size of the feature maps.Divisibility: it refers to the divisibility of each convolution layer’s output size by the kernel size of the succeeding pooling layer. If it is applied, it will: (a) allow the omission of padding with no accuracy loss and (b) lead to an efficient pipeline for the contiguous layers.This work focuses on single FPGA device solutions for image recognition applications and more specifically, binary and limited feature space classification tasks. Consequently, the design process has to consider all the factors reducing the resources’ requirements. For this purpose, in the first phase the designer uses the TensorFlow estimator API to design the CNN’s model targeting to fit within a single FPGA’s resources. Focusing on all the key factors of the images under consideration, the designer can develop the model by keeping to the following guidelines for:m and fractional part n are accepted as input parameters to the BAM. This allows the designer to perform a trade-off study between saving on FPGA resources due to the reduced bit width of the CNN parameters and maintaining high classification accuracy as a result of the reduced arithmetic precision.During the second phase, the designer develops the BAM of the designed and trained image classification CNN. The BAM emulates the exact same fixed-point calculations that the hardware accelerator performs. For the BAM, we perform quantization of the CNN model’s trainable parameters, starting from the 32-bit floating point representation provided by TensorFlow to a desired The proposed approach combines the VHDL advantages with an efficient, with respect to the development time, design methodology for CNN accelerators. Multiple instances of configurable and reusable VHDL blocks, each with different configurations, are used for the development of each layer. The following subsections present the reusable VHDL blocks developed in this work.Block RAM that stores one RGB channel of a full image and a Window Generator as shown in Window Generator formulates the input to the following convolution layer as windows of size equal to the convolution layer’s n shift registers with each register containing one image row, in order to avoid the indexing of pixels and thus, lead to improved performance. The Kernel Window Controller FSM of the Window Generator reads n rows from the Block RAM and copies them into the first set of n Shifting Registers. The DSP Decoder formulates the n pixels (memory words) of each of the n Shifting Register, are routed in parallel to the input of the following convolution layer. To create the next window, we shift the n registers by one pixel. There are two sets of Shifting Registers forming a double input buffer. If the following convolution layer uses n shift registers forward an input n registers, (c) the kernel This block consists of a Convolution Block  to produce the The ng Block  receivesVector Multiplier realizes a fully connected Layer neuron; it computes the dot product of the 1-D input vector I (the flattened result of the preceding layer), which is received one point at a time, with the corresponding row of the fully connected layer’s weight matrix. The weight matrix W is stored in a ROM, where each memory word contains the weight of every neuron for each input. At each cycle, the input value of I and the corresponding row of W are multiplied and the block accumulates the result, which is forwarded to the following blocks.The ReLU Block is a 2-to-1 multiplexer. The select bit of the multiplexer is the Most Significant Bit (MSB) of the input value. If the MSB/select is “1”, the input is a negative number and the multiplexer outputs zero, otherwise it forwards the input to the output.The Output Block is the CNN’s final fully connected layer. Its architecture is shown in I with the weights W of the output neurons and then adds the bias. In a pipeline fashion, it is executed once for each output neuron/class.The The current section describes the major considerations and recommendations for mapping the CNN functionality on a VHDL architecture by utilizing the above blocks.(a)High efficiency in resource utilization and computing since all hardware is generated specifically for each CNN layer (module) and the layers are pipelined.(b)Significantly reduced memory requirements for the intermediate results and use of buffers only on the on-chip memory. The extensive pipeline of the proposed approach allows for succeeding layers (modules) to directly consume the data generated by the preceding ones and thus minimize the buffering of the intermediate results.(c)Reduced latency for shallow CNNs designed for the target limited feature space classification tasks. This is achieved by the parallelization strategy, the pipelining between the VHDL implemented layers (modules) and the use of only low-latency on-chip RAM.The proposed approach uses the mapping to result in a streamline architecture that implements all the layers of the CNN as a pipeline of modules: each module implements a CNN layer’s computations. This allows flexibility in the parallelization strategy of the computations of each layer (implemented as a module); our proposed approach aims at parallelizing the layers in a way that enables extensive pipelining between them and minimizes the use of intermediate buffers. In more detail, for the acceleration of binary and limited feature space classification tasks with shallow CNNs that this approach targets, the streamline architectures and the proposed design approach have the following benefits:N filters in each convolution layer (except the first). Assuming that a convolution layer is designed with N filters, then the accelerator can have K parallel Convolution Blocks to complete the N convolution filters in Buffers between layers and speed-up: The effort is given to parallelize the N feature maps and apart the first accumulates these in N memories. The size of each of these N memories depends on the size and the number of the preceding pooling layers. We denote by p pooling layers of sizes N memories of the current layer) has size N parallel filters with their individual memories.Reduce the memory of each layer: each convolution layer produces Structure consisting of one convolution block per channel; these blocks compute the convolution of all the input image’s channels (3 channels and 3 corresponding blocks in the case of RGB). Each block completes the convolution in real time and it forwards each result to the following pooling layer without a buffer, a design feature that significantly improves the memory requirements since the first convolution layer operates on the full-size input image (without any downsampling). The use of one (1) Structure to complete all the filters of the first convolution layer is resource efficient. It creates a parallel Structure by using first, 3 Convolution Blocks to compute the convolution of all the input image’s channels (3 in the case of RGB) and second, a tree of adders adding each channel’s convolution result in real time, forwarding it to the following pooling layer without a buffer. Avoiding this buffer is advantageous because it significantly improves the memory requirements considering that the first convolution layer operates on the input image without any downsampling. A single parallel Structure has to repeat this operation for every filter of the first convolution layer, but is the most resource efficient. If the following pooling layer has size k instances  obtained from the camera sensor.The model was trained with the “Ships in Satellite Imagery” Kaggle dataset. It contains 4000 80 × 80 RGB images in total, labeled with either “ship” or “no-ship” binary classification: 3K images were selected for the training process and the remaining for model validation.Number of convolution layers: The proposed CNN with only two convolution layers achieves an accuracy of 97.6%, which is close to that of CNNs with more, e.g., a CNN with three convolution layers before any of the proposed optimizations achieved 98.5% accuracy.Ship orientation: The ship orientation is limited and, along with the proportion of the 80 × 80 image that the ship occupies, it leads to the use of 32 filters per convolution layer for achieving the best accuracy–computational cost trade-off.Max pooling layer: size The kernel’s size for each convolution layer: the first achieved improved accuracy with a Fully connected layer’s neurons: 128 neurons of the fully connected layer is the minimum number to use in order to avoid prediction accuracy loss.A variety of training processes was performed with the TensorFlow Estimator API in Python to create a CNN model close to optimal with respect to prediction accuracy, number of operations and resources requirements. The CNN design space exploration  that represents the exact operations and calculations in integer arithmetic that takes place in the FPGA. We note here that the input image is represented in RGB with 8-bits per pixel and the BAM keeps (does not reduce), for each pixel, the input bit-depth. Each parameter of the CNN model  is represented as a fixed-point number with 1-bit for the sign, 1-bit integer part and 6-bit fractional parts (Q2.6). Throughout the BAM, we preserved the 6-bit fractional part by truncating the result of each multiplication. In order to avoid accuracy losses due to the overflow after consecutive additions, the integer part is increased and the final results are represented in Q11.6.The proposed accelerator’s architecture consists of eight structural blocks on which we map the functionality of the software model blocks: (a) the input layer, (b) the first convolution layer, (c) the first pooling layer, (d) the second input layer, (e) the second convolution layer, (f) the second pooling layer, (g) the fully connected layer and (h) the output layer. The overall architecture is illustrated in two figures, Window Generators and the first convolution layer including three Convolution Blocks. Their output is forwarded to the ReLU and the first pooling layer consisting of one pooling block configured for Channel Block RAM (on-chip memory), so that we can read a whole row in a single clock cycle. These blocks, along with the three Window Generators of the three Input Blocks, constitute the input layer. The Window Generators are configured to accept one Channel Block RAM, the three Window Generator blocks operating in parallel load the three distinct channel windows of size Channel Convolution Blocks of the first convolution layer, as shown in Convolution Blocks in a fully pipelined operation.Another key element of the proposed accelerator’s architecture is the input layer, which is shown in Filter Convolution Blocks, each block configured for Filter Convolution Blocks, each of which accumulates the result in a dedicated Accumulator RAM of size Filter Convolution Block stores the kernel weights associated with each input feature map in an internal Block RAM. The results of this layer are complete when every feature map of the previous layer is received and processed. At the final accumulation step, each filter’s bias is added and the Accumulator RAM contents of each Filter Convolution Block are forwarded, in a continuous stream (in filter order) to the second pooling layer. The second pooling layer is similar to the first pooling layer, also configured for Vector Multipliers, one for each neuron. When all the multiply–accumulate steps are complete, the 128 parallel multipliers and a tree of adders calculate the inference result.Although the CNN vessel accelerator improves the performance of CPU, GPU and edge processors, as is shown in This section presents the results of the proposed accelerator’s implementation on the Xilinx VC707 board and a comparison with the corresponding performance of our code executed on: (a) the low power Intel’s Myriad2 processor, (b) the edge-computing NVIDIA’s Jetson Nano Jetson’s ARM processor and (c) the Jetson Nano GPU.The development and validation of the proposed accelerator targeted the Xilinx Virtex 7 Development board (XC7VX485T) with the use of the Vivado development tool. The resource utilization of the FPGA on the Virtex 7 board is presented in The FPGA implementation of the proposed accelerator has achieved a maximum operating frequency  at 10 W includes an ARM processor and an 128-core Maxwell GPU targeting computer vision and deep learning applications. The Myriad2 processor is being utilized for on-board satellite computing applications in missions  and reseThe comparison is based on a sequential C code for vessel detection. This was executed on a single core of the Jetson’s Nano ARM CPU and measured at 440 ms. From this point, we developed a custom CUDA-accelerated application taking advantage of the 128 CUDA cores. The mapping of calculations to the grids of thread blocks optimizes the scheduling of warps on the 128 CUDA cores. The shared memory is used to store global data in a thread block and the intermediate results. The execution time of the CUDA application is 20.3 ms.The development on the Myriad2 starts with the optimization of the sequential C code, using the CMX, DDR and cache memories efficiently; this single core application took 56.27 ms with less than 0.5 W. The parallel Myriad2 code uses the 12 SHAVES, by dividing the CMX memory between them, minimizing the required memory of each SHAVE by pipelining the operations of each processor, the parallel code takes 14.6 ms at 1 W.The detailed results are presented in (a)The same metrics between different FPGA accelerators may not be suitable for direct comparisons due to different FPGA platforms, benchmarking methodologies, etc.(b)While the majority of related works focus on accelerators for well-known CNN models, this work proposes a design approach that includes guidelines for designing CNN models from scratch, resulting in a custom model for vessel detection application.(c)This work focuses on accelerator designs for shallow CNNs suitable for binary and low feature space classification tasks while most works in the literature study complex and larger CNN models and result in substantially different architectures.This subsection aims to provide more context to the proposed approach by showcasing where the proposed accelerator stands in the field of FPGA accelerators in the literature. A straightforward comparison of the resulting accelerator to FPGA-based CNN accelerators is a challenging task  because:Regarding these architectural differences, the proposed streamline architectures in this work use contiguous modules for each layer of the CNN in a pipeline fashion. These architectures have particular benefits for our target applications (described in Taking into account the aforementioned considerations, The current paper presented a design approach for FPGA accelerators for image classification CNNs with limited feature space targeting the edge, mobile and on-board satellite computing applications. The objective of this work was to achieve real-time performance by placing all the inference task computations and memory within a single FPGA device. The benefits of the resulting architecture are the low-power consumption, the higher operating frequency and the improved resources utilization. These advantages are shown by the FPGA accelerator for vessel detection that compared favorably to the performance of notable edge and low-power processors. Finally, the benefit of introducing the approach for the image classification on a single FPGA device, whenever this is feasible, can be shown by the vessel detection accelerator performance and compared to the optimized FPGA CNN accelerators and also to low cost ones.


Magnetic Resonance Imaging is a powerful non-destructive tool in the study of plant tissues. For potato tubers, it greatly assists the study of tissue defects and tissue evolution during storage. This paper describes the MRI analysis of potato tubers with internal defects in their flesh tissue at eight sampling dates from 14 to 33 weeks after harvest. Spatialized multi-exponential  Magnetic Resonance Imaging (MRI) is a powerful non-destructive tool to study the internal structure and composition of plants and foods . Among t tissues ,4, provi tissues  and/or h tissues ,6, assum methods ,8 now maPotato tubers are consumed in fresh and processed form and are used for starch extraction and potato seedling production. Their quality traits are mainly defined by tuber dry matter content and composition, and by the absence of external and internal defects. Factors affecting quality at final harvest include the choice of cultivar along with growth conditions such as soil characteristics, the availability of macro- and micronutrients and climate . The colA recent MRI study has investigated internal defects in potato tubers and their evolution during storage . Multi-eIn the present study we applied a recently proposed spatialized multi-exponential n scheme  to potatDetails concerning tuber production and storage, and MRI analytical protocols are provided in . ConsequSolanum tuberosum grown in a greenhouse  under water deficit conditions comprising soil humidity levels equivalent to 20% of field capacity, interspersed with 3 short periods (2–3 days) of rehydration at 33, 46 and 60 days after shoot emergence. After final harvest, following a protocol recommended by the potato industry, tubers were prepared for long-term storage and were then stored at 5°C. At the beginning of the storage period, a set of tubers with defects were visually selected from previously recorded MRI morphological images. Four tubers with large internal rust spots in their flesh tissue were analyzed by spatialized multi-exponential The potato tubers were harvested from plants of the Rosanna cultivar of Images of potato tubers were recorded on a 1.5 T MRI scanner  equipped with a circular polarized head array radiofrequency (RF) coil. Tubers were analyzed simultaneously. They were placed in a temperature regulating device within the RF coil, allowing the temperature to be set at the same value as for storage (5 ± 1 °C). Transverse relaxation parameters of the middle section of the tubers were measured using a multi-spin echo (MSE) sequence  with theThe centers of the virtual tuber slices analyzed by MRI were marked with a permanent marker to ensure their position remained the same for the 8 subsequent sampling dates.Signal As proposed in , the opt account . Figure riterion . This leF between the measured and the estimated signal at 14 WAH. ribed in . The latre given . The twore given . Figure The relative fitting error did not exceed 0.05 (5%) of the signal. The spatial variation of this error could be explained by the spatial variation of the signal linked to the different kind of tissues. The error was lower for the region where the signal to noise ratio was the highest, which was expected. Similar results were observed for the other sampling dates.The estimation step produced four parameter maps. A post-processing scheme that clusters voxels with similar multi-exponential relaxation parameters was applied in order to reduce the complexity of the information, as suggested in . For thirameters  was usedK-means imposes a number of classes and, in no way, guarantees that these classes are statistically different, differences between classes were studied after the classification stage. The differences between the classes were assessed statistically using a univariate approach. We focused on the difference between each pair of classes, which corresponds to the univariate post-hoc analysis of a multivariate analysis. The Kruskal–Wallis test was performed. The alpha level was set to 0.01 and the Bonferonni correction was used as the control for multiple comparisons between each pair of classes.The selection of the number of classes was based on a compromise between the need to classify the defects from the tuber tissues separately and the need to obtain statistically different classes. Since classification by In order to assess the evolution of the individual classes over time, each class was assigned a color with respect to two characteristics  had low In this study, the use of multi-exponential d method  it was pThe classes’ spatial distribution over time was not consistently recorded for three of the nine sampling dates, leading us to discard these points for the analysis. A possible reason for the discrepancy is that the virtual MRI section may not have always been positioned in exactly the same place, despite all the precautions taken, due to a possible slight variation of the position and angle of the tuber in the container. Other problems may be added to that of positioning but they remain to be elucidated However, for the six remaining sampling dates, it is interesting to note that the classification scheme did not include any spatial information but still led to spatially coherent classes, within each tuber and also over time. The pith, the defects and the cortex were correctly detected and the classification managed to distinguish three different tissues within the flesh. Moreover, the relaxation parameters of these tissues were found to be statistically different. This confirmed that different areas were present within the tubers, with different relaxation parameters.Regarding the method itself, the choice of the number of classes is open to discussion. The choice was based on a comparison of the results from different numbers of classes, for which some criteria could be considered subjective. Indeed, the process called for user expertise, and is comparable in this respect to other user interventions such as the manual delimitation of ROIs performed on images in previous studies ,14. NeveThis method of estimation relies on a model that uses a discrete number of relaxation components. Use of the BIC criterion revealed that two components was the best choice, defined as the most frequently-occurring optimal value across all voxels for all sampling dates. However, for some voxels, the optimal number was smaller or greater. Modeling the signal as the sum of a continuous distribution of The flesh could be divided into three classes that were statistically different for almost all parameters and at all measurement dates, contrasting with the cortex and the pith which appeared clearly as homogeneous regions. Using the classification scheme, we were able to confirm and quantify the heterogeneity in the flesh already observed in a previous study . This prThrough its capacity to access information on water status and, consequently, on the latter’s organization at cell level, using the dedicated methods that enable spatialized relaxometry MRI provides a unique perspective on differences in healthy tuber tissues, tissue heterogeneity and the appearance of defects. Interpretation of the relaxation times in terms of tissue structure and composition is beyond the scope of this paper, but some interpretations have already been proposed elsewhere ,19, HajjSpatialized multi-exponential


Microfluidic preconcentration enables the collection or extraction of low-abundance analytes at specific locations. It has attracted considerable attention as an essential technology in bioengineering, particularly for detection and diagnosis. Herein, we investigated the key parameters in the preconcentration of fluorescent dyes based on electrophoresis in a microfluidic electromembrane system. Commercial ion-exchange membrane (IEM)-integrated polydimethylsiloxane microfluidic devices were fabricated, and Alexa Fluor 488 and Rhodamine 6G were used as fluorescent dyes for sample preconcentration. Through experimental studies, the effect of the channel concentration ratio  on the performance of the sample preconcentration was studied. The results show that the preconcentration of the target sample occurs more effectively for a high CCR or high salt concentration of the main channel when the CCR is constant. We also demonstrate a phenomenon that the salt concentration in the electrolyte solution increases as the preconcentration progresses. Our results provide consolidated conditions for electrophoresis-based sample preconcentration in electromembrane systems. Preconcentration is a process that involves the removal of unwanted substances, such as solvents, from chemical compounds to increase the concentration of the key solid constituents. It has been actively investigated in relation to microfluidic systems. To ensure the precise handling and detection of low-abundance analytes in a lab-on-a-chip (LOC) device with bio-samples, the development of preconcentration techniques for sample pretreatment is essential . MicroflRecently, numerous preconcentration techniques that apply ion concentration polarization (ICP) , which iHerein, we present an effective methodology for sample preconcentration by examining various salt concentrations in the main (w/sample) and buffer (w/o sample) channels in the electromembrane system. To visualize the electrophoresis, we fabricated a scalable PDMS microfluidic device with two CEMs/AEMs and introduced two fluorescent dyes (negatively charged Alexa-Fluor 488 and positively charged Rhodamine 6G) with different polarities. It should be noted that our study aims at preconcentration of each positively and negatively charged analyte rather than separating fluorescence-labeled analytes and fluorescent dyes themselves. For this, we need to trace movement of target sample via fluorescent visualization and examine various operating parameters  as well. For an efficient comparison of the sample preconcentration with quantifiable values, we define the concentration factor (CF) based on visualization and image processing. To verify the relationship between the CF and the electrolyte concentrations and the concentration ratio between the main and buffer channels, a wide range of salt concentrations  were applied to the main and buffer channels and analyzed.An IEM is a membrane that can selectively transfer specific ions. IEMs are classified into two types: AEM and CEM. When an electric field is applied across the IEM, the AEM selectively allows only anions to pass, whereas the CEM allows the passage of only cations depending on the electric polarity. In this study, as shown in Here, Recovery is the width of the particle -enriched stream compared to the total width of the outlet channel. The determination of the enriched boundary during image processing was performed in the same way as that in a previous study . However2) and electrodes  were installed between the top and bottom PDMS blocks, followed by plasma treatment for facilitating PDMS block bonding. A voltage was applied to both carbon electrodes to apply an electric field to the entire fluidic channel, and an Ag/AgCl tip was added to measure the effective voltage (Veff) across the main channel. All channels of the device used in the experiment were 15 mm long, 1.5 mm wide , and 0.2 mm deep, and the width at the inlet and outlet was 0.4 mm.BC/MC where BC and MC are the salt concentration of the buffer and the main channel, respectively) is newly defined. Here, the preconcentration experiment was conducted for cases CCR = X1, X10, and X100. To observe the preconcentration of fluorescent dyes for different concentration ratios between the main and buffer channels, the channel concentration ratio , the CF is higher for lower concentrations in the main channel  and CCR (X10) come out similarly at a low level within the margin of error.Following the CCR analysis, the CFs were re-arranged based on the overall voltage (V) applied between the anode and cathode and the VFinally, we examined the CFs with changes in the salt concentration of the electrolyte solution. In general, as the preconcentration of the fluorescent dye in the main channel occurs, the salt concentration in the main channel increases because of the continuous ion transport through the IEM. Accordingly, the salt concentration in the electrolyte solution inevitably increases upon the preconcentration of the target sample. This was measured using a conductivity meter to identify the correlations. In this study: experiments were conducted on the electrophoresis-based preconcentration of two well-known fluorescent dyes—the negatively charged Alexa Fluor 488 and the positively charged Rhodamine 6G. A commercial IEM-integrated microfluidic electromembrane device was fabricated and visualized. The preconcentration of the fluorescent dye was investigated by varying the salt concentration in the main channel containing the sample and the buffer channel without the sample. The following conclusions can be drawn from the findings of this study. First, the fluorescent dye preconcentration is more effective when the CCR between the main and buffer channels is higher. This is because the value of Veff across the main channel with the sample increases as the CCR increases. Second, when the CCR remains constant, the fluorescent dye preconcentration is more effective for a higher main channel concentration because of the higher Veff across the main channel with the higher absolute salt concentration in the buffer channel. For Alexa Fluor 488, a more effective preconcentration is possible for lower CCR and lower salt concentration in the main channel, presumably because the electrophoretic mobility is higher at lower ionic concentrations. Finally, the salt concentration in the electrolyte solution increases in the main channel as the fluorescent dye preconcentration increases, and the effect is likely to be higher for a lower solution concentration in the main channel. Based on the results of this study, we expect to achieve significant findings for a variety of bulk solutions and concentrations. Furthermore, the experiments conducted in this study can be a basis for the electrophoresis-based preconcentration of fluorescent dyes as well as other particles in electromembrane systems with IEMs.


Modern applications of Internet of Things (IoT) devices require cheap and effective methods of measurement of physical quantities. Cheap IoT devices with sensor functionalities can detect a lack or excess of substances in everyday life or industry processes. One possible use of tension sensors in IoT applications is the automated replenishment process of fast moving consumer goods (FMCG) on shop shelves or home retail automation that allows for quick ordering of FMCG, where the IoT system is a part of smart packaging. For those reasons, a growing demand for cheap and tiny tension sensors has arisen. In this article, we propose a solution of a small flexible tension sensor fabricated in an amorphous InGaZnO (a-IGZO) thin-film process that can be integrated with other devices, e.g., near-field communications (NFC) or a barcode radio frequency identification (RFID) tag. The sensor was designed to magnify the slight internal changes in material properties caused by mechanical stress. These changes affect the dynamic electrical properties of specially designed inverters for a pair of ring oscillators, in which the frequencies become stress-dependent. In the article, we discuss and explain the approach to the optimum design of a ring oscillator that manifests the highest sensitivity to mechanical stress. Existing methods for measuring strain in integrated circuits (ICs) commonly use silicon piezoresistors as sensing elements, thereby, giving rise to a variety of microelectromechanical system (MEMS) solutions . A diffe process .Despite the low cost and relative popularity of the a-IGZO fabrication process, there is limited literature concerning mechanical strain sensors using this technology, whereas most authors investigating a-IGZO properties have focused on the mechanical stress impact on the durability and electrical characteristics ,4,5,6. FA similar issue arises when considering nanomaterial-based sensors , as thesWhile the strain-dependent resistance or capacitance is easy to measure in a laboratory setting, it requires additional mixed-signal circuitry to incorporate into a larger digital system. An interesting solution to this problem can be found in , where tThere are several articles in which the authors reported mechanical stress-induced changes of a-IGZO material, though these are presented in terms of changes in the electrical performance of flexible displays, rather than sensors applications. The authors in  reportedIn this article, we propose a practical use of a-IGZO stress-induced parameter deterioration in a stress (tension) sensor. For this purpose, we use a process described in  to manufThe technology offers  only 3V .The advantages of the flexible IGZO process result in faster design and higher customization capabilities, allowing fitting ICs to exact products and needs. Lower chip thickness and elasticity are also beneficial in the later stages of integration. They give the chip more durability while incorporating it into the inlay and the final product, raising the final production yields.The rest of the article is organized as follows: The implementation of the flexible tension sensor that can be designed in the same process as the microcontroller or an RFID tag requires a suitable flexible substrate TFT-based technology ,17,18. DThe flexible TFT a-IGZO The example cross-section of active layers and a polyimide substrate in the process chosen for a target design is shown in The wafers are manufactured on a glass substrate and delaminated after production. After the delamination, the thickness of the active layers forming the electronic devices is still very small (less than r (30μm) . In ordeW) to-length (L) ratio  caused by bending on a jig of a particular radius, whereas the electrical connections to the bent wafers were made with the use of either a 12 contact The impact of mechanical stress on the transfer characteristics of NMOS transistors with various I–U) characteristics, we used an Agilent 33600A  function generator acting as a programmable gate-source voltage source  change significantly, which leads to the conclusion that the stress-induced capacitance changes cannot be large. For the applied mechanical stresses, the observed effect was predictably small, similar to the change in the dimensions of the structure resulting from the According to the above considerations, one can see that the mechanical compression of the flexible wafer caused the NMOS drain current to drop and the resistance to rise, whereas the capacitors maintained almost constant parameters. Based on these results, we made adjustments to the Cadence SPECTRE environment models of the NMOS, resistor, and capacitor components. In this way, we obtained an appropriate model version with adjusted parameters for the mechanically-stressed devices. Therefore, we were able to optimize the electrical behavior of more sophisticated circuits in two extreme conditions—stressed and unstressed.Mechanical compressive stress shifts the orted in , where tdistance . Moreovedistance ,6 reporte states .The ic (RTL) ,25. Constion see a.This means that tion see b. MoreovThis phenomena can be used for mechanical stress measurements, where the stress-induced Based on the previous considerations and measurements of electronic circuit elements, we propose a sensor circuit utilizing a pair of identical ring oscillators consisting of logical gates  and a frequency mixer built of a D flip-flop (DFF). The block diagram of the device is shown in In this way, the difference (deviation) of generator (ring oscillators) frequencies The inverters and DFF were implemented in the pseudo-CMOS logic and RTL, which resulted from the lack of PMOS devices in the a-IGZO process . TherefoRing oscillator inverters .(2)UouAn inverse transform of Equation  is troubThe initial (unstressed) parameters taken as the starting points in the macromodel simulation (with the MATLAB Simulink environment) were The =5μm see b. The RkThe initial The example fmincon function with the interior point algorithm [For the purpose of the numerical optimization, we used a MATLAB lgorithm . The macThe obtained optimum set for the linear model was The optimization of ange see .The optimization results indicated a nontrivial solution in which the Miller capacitance, together with a low According to these outcomes, we fabricated a frequency response sensor with the design parameters corresponding to the results obtained above according to the block diagram in The bottom and side views of the sensor with the corresponding layouts of the DFF and ring oscillators with the optimum physical parameter set see c are shoThe whole sensor structure is to a PCB , which aF) to the sensor, which resulted in the sensor bending with radii (r), and this varied along the sensor’s length. In this way, an F) is applied to the other end. In this way, we were able to obtain the local strain distribution in the sensor.The physical experiment results that are described in this section required applying load forces  of a sensor deflection is depicted in As the strain near the r. Therefore, the measurements of the r and The use of an additional PP layer increased the strain r measurements of the sensor arc near the ZIF connector, we used a picture analysis (edge detection) algorithm of the sensor test bench , force (F), and SolidWorks F and its estimates Based on the measurements of the bending radii . Based on this method, the estimated Considering the measured The other important observation during the measurements was that, despite the relatively high sensitivity of the device to mechanical stress, the initial relative frequency deviation for low strains, i.e., The edges of the first inverter output waveforms  corresponds to a digital signal frequency.One of the disadvantages of the proposed design is the initial differential offset frequency for a sensor with no mechanical stress applied. This nonzero offset results from the local mismatches and, during measurements, did not exceed However, flexible packaging constitutes a tiny part of the smart packaging market. There might be another niche for goods that are sensitive to deformation while at the same time being infeasible to store in rigid containers. The main advantages of the proposed sensor are its high sensitivity (Some hysteresis is to be expected for


Experimental characterization of the bulk resin properties and curing behavior was made to setup the models. The higher micro-residual stresses occur at the thinner fiber gaps, acting as triggers to failure propagation during mechanical loading. These micro-residual stresses achieve peak values above the yield stress of the resin 55 MPa, but without achieving damage. These micro-residual stresses reduce the transverse strength by at least 10%, while the elastic properties remain almost unaffected. The numerical results of the effective properties show a good agreement with the macro-scale experimentally measured properties at coupon level, including transverse tensile, longitudinal shear and transverse shear moduli and strengths, and minor in-plane and transverse Poisson’s ratios. A sensitivity analysis was performed on the thermal expansion coefficient, chemical shrinkage, resin elastic modulus and cure temperature. All these parameters change the micro-residual stress levels and reduce the strength properties.The manufacturing process in thermoset-based carbon fiber-reinforced polymers (CFRPs) usually requires a curing stage where the material is transformed from a gel state to a monolithic solid state. During the curing process, micro-residual stresses are developed in the material due to the different chemical–thermal–mechanical properties of the fiber and of the polymer, reducing the mechanical performance of the composite material compared to the nominal performance. In this study, computational micromechanics is used to analyze the micro-residual stresses development and to predict its influence on the mechanical performance of a pre-impregnated unidirectional CFRP made of T700-fibers and an aeronautical grade epoxy. The numerical model of a representative volume element (RVE) was developed in the commercial software Abaqus Fiber-reinforced polymers (FRPs) are commonly used in aerospace and high-performance applications due to their higher strength-to-weight ratio. They are made with a fiber reinforcement embedded in a polymeric matrix. This reinforcement ranges from short fibers, continuous long fibers and fabrics. The most common fiber reinforcement materials are glass fibers (GFRPs), carbon fibers (CFRPs) and other organic fibers such as Kevlar, Dyneema, etc. . ComposiTwo families of polymeric matrices are commonly used in FRPs, namely thermoplastics and thermosets. While thermoplastics are processed by melting the polymer, and cooling it down to its final configuration, thermoset-based composites are manufactured with a non-reversible thermo-curing process where the polymer is transformed from a gel to a monolithic material. The latter have been the preferred option in aerospace applications due to their easier processing characteristics.Inadequate manufacturing processes in composite materials generate low-quality products and performance reduction in as-manufactured conditions due to residual stresses, voids formation, or incomplete curing. This performance reduction can lead to design failure because material properties were overestimated, or because the geometry does not fit the requirements ,6. LinkiFocusing on carbon fiber-based thermoset composite systems (CFRPs), micro-residual stresses appear at the microstructural level due to the mismatch between the thermal expansion coefficients of the fiber and the polymeric matrix, and due to the chemical shrinkage, that takes place during the curing process ,9,10,11.The curing process is inherently a thermo-chemical phenomenon for thermoset polymers where an exothermic chemical reaction starts at a given temperature and finishes when the polymerization process ends. One of the most commonly used approximation models for polymer curing was proposed by Kamal . This moExperimental works to measure actual residual stresses at micro-scale level are not easy to perform due to the reduced length scale at which they occur. For instance, Minakushi  performsNumerical simulation of the properties evolution with curing is a current research field. To the best of the authors’ knowledge, Ersoy et al.  is amongA similar approach was followed by Hui et al. , who alsAnother relevant topic relates to the methodologies used to estimate the effective macro-scale mechanical properties and the imposed BCs. Volumetric average measures of stress and strain fields are the most commonly used technique to extract the effective strain and stress response . Second Considering first order approximations, several authors have studied the influence of the BCs in the effective strain–stress response, concluding that the periodic boundary conditions (PBCs) give the best compromise between accuracy and computational simplicity ,33. Howe® and user-material (UMAT) subroutines to simulate the curing process and estimate the micro-residual stresses and its influence on the mechanical performance of an aerospace grade epoxy resin. A distinction between residual  and micro-residual  stresses is made because the micro-residual stresses are developed inside the RVE with traction-free boundary conditions.Here, a micromechanical model is developed using the commercial software AbaqusUsing the methodology proposed by Danzi et al.  that couThe effective strain and stress measures are extracted directly from the PBCs output, i.e., the strain is obtained from the strain of the master node, and the stress from the force-conjugate measure of the applied strain, instead of using volumetric averages that are not appropriate when strain localization is achieved. The predictions obtained are then compared with experimental data obtained from standardized composite macro-scale tests.A detailed analysis of the RVE response during the curing process is also performed using three different RVE geometries. Instantaneous measures of the effective macro-strain are extracted to see effective material expansion and contraction, besides the typical stress analysis. This analysis shows how the constituents are subjected to local stresses while keeping the macro effective stress equal to zero.Additionally, due to the uncertainties of the material parameters at micro-scale level, a sensitivity analysis is performed in this work, to assess the influence of the thermal expansion coefficient, chemical shrinkage, resin elastic modulus and cure temperature on the residual stresses and the posterior effective mechanical properties, to complement the overall analysis of the micro-residual stresses.The following section describes the materials, the constitutive modeling approach, the finite element procedures and the micro-residual stress analysis methodology. Then, the results are analyzed to highlight the micro-residual stresses distribution inside the RVE, and to link their influence on the effective mechanical properties. Finally, conclusions are drawn based on the discussion of the principal findings of this study.The fiber-reinforcement system selected for the experiments and simulations consists on the Toray T700 standard modulus carbon fiber. The parameters used in the current analysis are taken from the datasheet  and otheThe polymer matrix used in this study corresponds to an aeronautic grade epoxy resin. terature ,27. The The fibers are modeled using a transversely isotropic elastic constitutive relation. This is a common approach for RVE modeling of composites whose transverse failure is controlled by the polymer matrix and its interface with the fibers. Equation (1) shows the elastic compliance matrix considering that direction 1 is the fiber direction. The Poisson’s ratio The curing process in thermoset resins is usually an exothermic reaction process. It is normally characterized by the total curing reaction enthalpy The model proposed by Kamal  is used i et al. . The curThe temperature dependent functions The parameters of the curing kinetics model are calibrated from differential scanning calorimetry tests.On the other hand, it is also necessary to relate the mechanical properties evolution with the curing state. A phenomenological approach  is propoThe parameters Therefore, the instantaneous elastic modulus The properties at completely cured state The initial elastic behavior is defined by a linear isotropic relation between the stress tensor, A paraboloidal yield criterion The flow rule The yield strengths are used to define the yield surface. Hardening is considered to affect both yield strengths and the increment of equivalent plastic strain is:The yield stress hardening curve is approximated with a potential law as:The damage evolution model proposed by Melro et al.  enforcesThe thermodynamic free energy potential The damage initiation criterion The damage evolution condition is given by Equation (19). The damage parameter An exponential damage law is proposed to relate the damage parameter The damage dissipated energy The total dissipated energy due to the fracture process The total strain mechanical strain To couple the curing phenomena in the material with the mechanical response, the elastic constitutive tensor The temperature dependence function i et al. . βE is eDue to the nature of the curing problem, the equilibrium stress rate should be written in rate form similar to an hypoelastic law perature ,40. WithThe instantaneous stress is approximated by Finally, during the curing process the elastic strain measure cannot be directly related with the initial/undeformed configuration. Hence, an instantaneous elastic strain measure can be calculated using the compliance tensor (inverse of elastic tensor) for the current stress state. With this approach the undeformed configuration is kept as the stress-free configuration which is not the initial configuration. This formulation is compatible with the plasticity theory but without damage. At damage onset, plasticity and curing are not allowed to develop .(28)εe=The stress increment is calculated using a trapezoidal integration scheme as shown in Equation (29). The RVE geometry is defined using a constant fiber diameter atanotti  is impleAdditional constraints for the RVE generation include a maximum fiber separation of PBCs are implemented to guarantee continuity in the displacement field locally without over-constraining the boundary faces. The PBCs are derived from the strain-displacement definition, applied to opposite faces as given in Equation (31).The numerical implementation follows the procedure proposed by Barbero , where tThe effective composite properties, incl. elastic tensor components, yield strength and ultimate strength, are identified from the force-displacement response. The material parameters are calculated by enforcing that the load-displacement behavior of an equivalent monolithic RVE matches the full model RVE. Instead of volume average strain or stress measures, the effective strain corresponds to The solution procedure to analyze the cure residual stresses begins with a thermomechanical analysis where the temperature curing cycle is imposed in the RVE domain. A pure mechanical analysis is considered because the temperature gradients inside the RVE are negligible. During this analysis step, the curing is simulated to determine the elastic properties evolution. The micro-residual internal stresses develop as a consequence of the interaction between the resin and the fibers. The PBCs are imposed in this step with a traction-free condition to allow the RVE to expand and shrink freely but conserving the periodicity. The constitutive model is completely implemented in a UMAT user subroutine, the shrinkage and thermal expansion via a UEXPAN user subroutine and the PBCs are implemented using a Python script to generate the input file. After the curing step is completed, another analysis step is made to perform the test loading cases given in The nominal curing cycle recommended by the material supplier, given in After a preliminary RVE size analysis considering RVEs with 9, 16, 25 and 36 fibers, an RVE with cs works ,28, leadoperties , the oneA set of three different statistically representative RVE geometries are used to perform all simulations, reducing the geometry dependence effects. The fact that interface failure is not considered in these analyses can lead to over-estimations in the effective strength prediction of the material, because the interface strength is typically lower than the bulk resin strength ,42. AlthA sensitivity analysis was performed to analyze the influence of the chemical shrinkage, thermal expansion coefficient, elastic modulus and cure temperature in the micro-residual stress state and mechanical properties. In order to calibrate the curing kinetics model, differential scanning calorimetry (DSC) tests were performed to un-cured resin samples following ASTM D3418. Next, to measure the instantaneous evolution of the storage modulus with curing, three DMA tests to un-cured resin samples are made, keeping the curing temperature fixed at 120 °C.Transverse tensile tests following ASTM D3039.Longitudinal shear tests following ASTM D3518.Transverse shear tests following ASTM D5379.The coupons were extracted from composite plates manufactured following the curing cycle presented in Additionally, coupon level tests were performed to measure the macro-mechanical properties of the corresponding composite system. The following tests were performed:The curing model parameters for the epoxy resin used in this study were obtained by fitting the experimental data of the curing process measured with DSC tests. The parameters are given in Using the maximum obtained storage modulus and Equation (7) to obtain the instantaneous values of the shift function β = 4 and γ = 0.8. A comparison between the experimental relation and the relation from Equation (7) is given in Accordingly, the shift function given in Equation (7) is fitted, obtaining the parameters The tests coupons after mechanical testing are shown in A summary of the experimental results is given in During the curing process, the elastic modulus is developed at the same time the material expands due to the effects of the thermal expansion, and contracts due to chemical shrinkage. If the matrix material is completely free to deform it is not possible to generate stresses at the micro-mechanical level. However, in the case of fiber-reinforced composites, the fibers introduce local restrictions to the free deformation of the resin leading to the generation of local micro-residual stresses, the first stress generating mechanism. The second stress generating mechanism is the CTE incompatibility between the two constituents that becomes more relevant during the cooling-down stage.To analyze the generated micro-residual stresses, The regions between the fibers concentrate higher deviatoric (von Mises) stresses in the matrix, being the most favorable regions to damage initiation and propagation. On the other hand, the hydrostatic stress distribution in the matrix is predominantly negative, which means that the bulk is in tensile state , until the curing process stops, achieving a stress equilibrium state with a smaller volume. A cooling stage follows, where additional straining is retrieved, developing more micro-residual stresses.The strain measure evolution with time and the degree of cure present trends similar to the strain measured experimentally with optical fibers by Minakushi .Therefore, the matrix volumetric shrinkage is the precursor of the stress development during curing because it occurs at constant temperature, while the thermal contraction (CTE) is the stress precursor during cooling. The longitudinal strain deformation of the RVE is very low because it is dominated by the high stiffness of the fibers and their low negative CTE.The chemical shrinkage for this resin is 2%, while the thermal contraction during cooling is only 0.6% (CTE multiplied by the temperature amplitude—from curing temperature to room temperature). However, the final volume reduction is around 0.9% estimated from the final volume of the RVE as presented If a full restrained analysis is performed, the volume contraction is enough to cause material failure, because the failure strain is lower than 0.7%. For the current example, no damage is retrieved from the numerical results, and only small plastic straining is found in the stress concentration zones.Other results that can be extracted from the curing analysis are the failure index measurements in the RVE given by Equations (11) and (18). For this purpose, To study the influence of the micro-residual stresses in the mechanical performance of the composite material, several simulations were performed using the selected RVEs subjected to different loading conditions as planned in The stress–strain response for the transverse tensile loading case is presented in A similar response is retrieved for the shear loading simulations in both transverse and longitudinal directions, as shown in To facilitate the comparison of results between both analysis sets, analysis ,42. Anotanalysis ,10.A similar trend is observed for the predicted transverse and longitudinal shear moduli, which are similar to the experimental results, while the strength measures present a reduction of 22% and 12%, respectively, when compared with the “no-cure” condition. Similarly, the experimental measured values are lower than the numerical predictions. The effective properties estimated considering the cure condition are closer to the experimental results.These results show evidence that the micro-residual stresses in the RVE after the curing process can reduce the material strength and should be considered when performing homogenization procedures. Regarding the fracture response, further work is required to understand the damage mechanisms and the appropriate size effects that must be considered, because the application of the material models and mechanical properties from the macro-scale experimental measurements are straightforward for the elastic and strength parameters, but not for the fracture properties.A sensitivity analysis of the relevant material and process parameters that influence the curing micro-residual stresses is presented in this section. After a preliminary study following previous literature results , the matA clear tendency to increase the micro-residual stresses arises from increasing the CTE and the chemical shrinkage. For higher levels of micro-residual stresses, damage is achieved, with the failure index becoming higher than unity.The elastic modulus also has a relevant influence on the micro-residual stresses. The tendency shows that micro-residual stresses increase with increasing elastic modulus, including scenarios of damage onset. It is worth to note that the strength properties are kept constant during the sensitivity analyses.The cure temperature has a different influence on the micro-residual stresses because it changes the degree of cure evolution and the final curing level in the matrix and not the mechanical response directly. This difference in the curing level changes the final elastic modulus, Poisson’s ratio and overall mechanical behavior of the material. For lower temperatures, keeping the cure cycle time unchanged, the cure level is reduced to 20%, a very low value, but (as known) the final properties would not be suitable for a composite material.The cure temperature reduction leads to a reduction in the elastic modulus followed by a loss of material strength . The variation of the elastic modulus has a direct effect in the composite stiffness, as expected, and leads to a tensile strength reduction due to the higher micro-residual stress levels.The sensitivity results show that the strength reduction can be mainly attributed to the micro-residual stress state that was characterized by the average von Mises stress and hydrostatic stress. Using all the data obtained above and plotting the tensile strength against the von Mises stress and the hydrostatic component, as shown in Although it is recognized that additional work is required to appropriately understand the influence of process parameters on the mechanical response of the matrix resin at the micromechanical level, the difficulties associated to experimental work at this scale make the numerical insight a valuable tool to understand better the effective macro-mechanical response of composite materials, as demonstrated by the methodology proposed in this work.The micro-residual stress analysis showed that stress concentrations are retrieved between the fibers in the narrower gaps, while the higher hydrostatic components are present in the resin-rich regions. This effect is clearly attributed to the constraining effect that the fiber arrangement imposes on the matrix, because the whole RVE is in traction-free condition.During the curing process, the whole RVE experiences volume changes that follow the superposition of the CTE effects during heating, chemical shrinkage during curing, and the final thermal contraction during cooling down. This superposition gives a final volume reduction for the current example of 0.9%, approximately half of the chemical shrinkage (2%).The micro-residual stresses have almost no influence in the effective stiffness of the composite system, but they have a more considerable effect in the strength properties, for example, reducing the transverse tensile strength by 11%. However, the experimental measured strength properties with macro-scale coupons are still slightly lower than the predicted properties considering the curing conditions.The sensitivity analysis shows that increasing the CTE, chemical shrinkage and elastic modulus contributes to an increase of the micro-residual stresses. The cure temperature influences the final degree of cure of the material, degrading the elastic modulus.A direct analysis of the transverse tensile strength as a function of the micro-residual stresses shows a linear relation between the von Mises stress and the hydrostatic stress. Therefore, the loss of performance can be attributed to the micro-residual stresses.


Despite a direct ban on charging interest, interest-based benchmarks are used as a pricing reference by a majority of Islamic banks, due in part to the absence of stable and widely- published alternatives. Benchmarking interest rate exposes Islamic banks to the problems of conventional banks, particularly the interest rate risk. Against this backdrop, the present study empirically examines the dynamic linkage between the interest rate volatility and the financing of Islamic banks. The empirical analysis is carried using evidence from the Islamic banking industry of Pakistan during the time period 2006–2020. The multivariate Johansen and Jusiles Co-integration test and Vector Error Correction Model (VECM) are used as the baseline econometric models. Moreover, the DCC-GARCH model is employed for robustness and ensuring the consistency of results. The results indicate that a significant long-term and short-term relationship exists between the interest rate volatility and the financing of Islamic banking industry providing significant evidence for co-movements and convergence. These findings suggest that paradoxical as it may seem, the financing of Islamic banks operating within a dual banking system is subject to interest rate risk, mainly due to benchmarking interest rate, which in-turn makes Islamic banks vulnerable to the rate of return risk and withdrawal risk. Moreover, corporate financing, in particular, is more vulnerable to interest rate risk. Shari’ah ′ is a 2x1 vector of the data series. The conditional means equations can be presented as the following reduced-form VAR:Suppose A(L) presents a matrix in the lag operator L, and εt = ′ is the vector of innovations with the following variance-covariance matrix:Where Ht represents the conditional variance matrix, Rt represents the time varying correlational matrix containing conditional correlations, while Dt is a n x n diagonal matrix of time varying standard deviations form univariate GARCH models having Where hit) for different investment portfolios as indicated in the following equation:The univariate GARCH  model is used to estimate the conditional variance . Rather it can include any GARCH process having normally distributed errors that fulfills the appropriate non-negativity constraints and the stationarity conditions [Where i = 1,2,…., n with the usual GARCH restrictions of stationarity and non-negativity being imposed such as nditions . The prost stage estimation . Thus, the Rt (in Eq 8) can be decomposed into:Where, Where,Qt. We primarily focus the conditional correlations in Rt. Thus, unlike constant conditional correlation (CCC)-GARCH, the DCC-GARCH does not suffer with the problem of constant correlations. Similarly, unlike multivariate GARCH (M-GARCH), it does not have the difficulty of dimensionality. This is because the DCC-GARCH eases the assumption of constant correlation and enables time-varying correlations that can be measured with respect to the variables’ past values.So, I(1). Hence, the Johansen and Juselius (JJ) co-integration test can be applied to check the long run association among the variables under consideration.The Augmented Dicky-Fuller (ADF) test  and PhilSince, the financing of Islamic banks, extended to different sectors, and interest rate reveal cointegrating (long-run) relationships, VECM was assessed to model short-run dynamics of each scheme. Results from the VECM model are shown in The p-values for each co-integrating equation are presented in Shariah compliant non-interest based benchmark for Islamic banks that is significantly decoupled from the conventional benchmarks and could be used as a robust indicator of the average expected cost of short term interbank market funding.The results of VECM reveal that seven equations are negative in sign and significant which indicates that the short run association exists between the time series i.e., they are capable to adjust their errors from long run equilibrium in a sufficient way. These findings provide strong evidence for co-movements and convergence. Therefore, Islamic banks, though operating on interest free ideologies, are prone to interest rate risk. Thus, our findings confirm the view of Archer and Karim , Bacha , and RosThe cointegration and VECM indicate the presence of long term co-movement between interest rate volatility and financing of Islamic banks, and the short term error correction respectively. However, these models are limited in terms of estimating the time variant volatility of the said time series and the dependence in their co-movements. Furthermore, these models do not indicate the pairwise linkage between interest rate and financing of Islamic banks in different sectors. Therefore, it is difficult to conceptualize which sectors are more vulnerable to the interest rate risk. To cover these limitations the multivariate Dynamic Conditional Covariance (DCC)-Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model has been employed for the analysis of the data.Unlike alternative methods, the DCC framework offers a flexible approach for the estimation of the multivariate GARCH models and allows to model separately the conditional correlations and conditional variances . Thus, dThe GARCH family has various assumptions including the presence of data stationarity, autocorrelation, and ARCH effect in the data. Furthermore, the conditional distribution in the GARCH models is supposed to follow a parametric distribution. To test these assumptions and justify the usage of DCC GRACH several preliminary tests have been performed including the ADF test, Ljung-Box Q-stat test, Lagrange multiplier (LM) test, ARCH effect test, and the Shapiro-Wilk test. The data was transformed using the first difference transformation to resolve the unit root problem. The ADF test is used to check the presence of unit root or stationarity in the data. The underlying null hypothesis of this test is that the time series has unit root. The result of ADF test indicates that all variables are significant at 5% level of confidence, which denies the presence of unit root in the data, and thus the data satisfies the assumption of the stationary time series. Similarly, the Ljung-Box Q-stat test is applied to check the serial correlation/autocorrelation in the data. This test has the null hypothesis that time series variables do not have autocorrelation. Results show that null hypothesis is rejected for all the variables at 5% significance value which indicates that the time series have autocorrelation. The LM test also confirms the results of Q-statistics.Similarly, the ARCH test is used to check heteroscedasticity or ARCH effect in the data. The results of this test highlight the presence of ARCH effect in the data. On the other hand, the Shapiro-Wilk test is used to assess the normality of distribution. The null hypothesis of this test is that the distribution is normally distributed. Since, probability value of each variable is insignificant at 5% level of significance. Therefore, we can conclude that all variables are normally distributed. Hence all preliminary conditions are satisfied for the DCC-GARCH Model.The time varying volatility has important role in understanding the behavior of portfolio diversification among different streams of Islamic financing. Therefore, the DCC-GARCH model developed by Engle  is selecδ1 and δ2) and the volatility decay parameters (λ1 and λ2) for both variant of the DCC-GARCH. The results suggest that the decay parameters are highly significant for both models. The sum of the volatility decay parameters (λ1 and λ2) are less than 1 for each series which indicates that conditional volatilities are mean reverting and the volatility tend to decay gradually. Similarly, the sum of correlation decay parameters (δ1 and δ2) is also less than 1 for each time series which indicates the presence of mean reversion in the conditional correlations. This indicates that the system has tendency to return gradually to the normality. Thus, both variants of the DCC-GARCH model suggest that interest rate volatility has a relationship with the volatility of other financing portfolios and this relationship is mean reverting.Consequently, benchmarking interest rate may lead to several implications for the financing (assets side) of Islamic banks, particularly in the dual banking system . For exaSecondly, due to opportunities of such risk free arbitrage through fund flows, Islamic banks become vulnerable to the consequences of interest rate movements that normally apply to conventional banks. For example, when the cost of funds changes in the conventional banking system, the costs of funds to Islamic banks will also change. Thus, bank may end up paying higher return to the depositors in the wake of avoiding the outflow of existing deposits and attracting new deposits. Consequently, the bank’s income margin may shrink due to increase in cost of funds on the liability side if the earnings from the assets do not keep pace with the interest rate movements. Thus, though the effect of interest rate variation may be indirect on the Islamic banking industry, the consequences would be similar , 34.The results indicate that interest rate has the highest correlation with the corporate financing 1.3498) and the lowest correlation with other sector financing (0.9559). This indicates that corporate financing, which constitute more than 70% of the overall financing portfolio of Islamic banking industry in Pakistan, is more vulnerable to interest rate risk followed by the SMEs financing. This is because the financial managers usually opt for an optimal capital structure, determined by the cost of financing and a trade-off between tax shield benefits and bankruptcy costs associated to debt financing including the financing from banks [ and the Compared to other sectors commodity financing is relatively less affected by interest rate volatility. This is because commodity financing is predominantly composed of financing extended to government departments to support the prices of agriculture commodities via commodity operations. Whereby, its financing decision is not contingent upon the cost of financing or its volatility. Rather, the commodity operations are part of the government policy to support and sustain the prices of commodities in the market. Furthermore, government departments do not choose between Islamic and conventional banks on the basis of the relative difference in their cost of financing. Rather the government subsidiaries, upon the recommendations of finance division and SBP, acquire a portion of funds for commodity operations from Islamic banks to solve the excess liquidity problem being faced by Islamic banking industry in Pakistan. This is because, being in its nascence, the assets creation of Islamic banking industry is weak which induces liquidity management problems particularly in the short run .The results of Cointegration test suggest a significant long-term relationship while the VECM suggest short-term relationship between the interest rate volatility and the financing of Islamic banking industry. Similarly, the results of DCC-GARCH model also confirm the relationship between interest rate volatility and the volatility of financing portfolios of Islamic banking industry. These results invariably confirm the presence of strong links between the Islamic and the conventional banking systems due to benchmarking interest rate. Consequently, Islamic banks are prone to adverse effects of the interest rate movements.These findings confirm the view of Archer and Karim , Bacha , and RosEven the Islamic banking industries of the GCC countries, despite using the Islamic Interbank Rate (IIBR) for price reference, are also prone to the interest rate risk. This is because the IIBR too is closely associated with the conventional benchmark rates , 44–46. Furthermore these results are also consistent with the previous studies that suggest the adverse effect of benchmarking interest rate on the performance of Islamic banking in various ways [See for example Musharakah, or leasing. In case of credit sale , the return is determined in advance and is included in the pre agreed selling price which cannot be changed subsequently in response to variation in the market cost of capital. The diminishing Musharakah and lease contracts, on the other hand, may allow periodic adjustments in the lease rentals based on an agreed benchmark. However, the adjustment period is too long  to allow the Islamic banks to respond to the changes in the market cost of funds. Consequently, conventional banks, being able to lend at floating rates and also having recourse to interest rate swaps, are clearly at an advantageous position compared to Islamic banks.In fact, unlike conventional banks that can transfer the burden of interest rate rise to their borrowers in most cases (depending on their terms of lending) due to their basic business model of borrowing and lending, Islamic banks have very limited ability to do so. This is because, the modes of financing used by Islamic banks on the assets side either involve sale on credit, diminishing Wakalah) based fund management model on the deposit side, where Islamic bank acts as a managing partner while depositors act as the sleeping partners. This model allows the depositors to withdraw funds at short notice. Given this model, the asset-liability management because a challenge for Islamic banks, particularly when the benchmark rate rises, due to their inability to reflect this rise in their return on investment in the short run. Consequently, Islamic banks, not being able to offer a comparable return that could meet the market expectation of depositors, get exposed to the withdrawal risk and the resultant financial distress.This inability to adjust promptly to the changes in the benchmark rate makes Islamic banks vulnerable to the so-called "withdrawal risk". Since, Islamic banks typically use the partnership  which refers to the transfer of risk associated with deposits to the shareholders of the bank. This risk arises when the bank has to forgo a part of their profit, under commercial pressure, to pay the depositors in the wake of preventing withdrawals due to a lower return on deposits . Thus, tThe present study empirically examines the dynamic linkage between the interest rate volatility and the financing of Islamic banking industry. The findings suggest that a significant long-term and short-term relationship exists between the interest rate volatility and the financing of Islamic banking industry providing significant evidence for co-movements and convergence. The study therefore concludes that invariably strong links exist between the Islamic and the conventional banking systems due to benchmarking interest rate. Therefore, Islamic banks, though operating on interest free ideologies, are prone to interest rate risk (IRR) which ultimately induces the rate of return risk, withdrawal risk, and displaced commercial risk in Islamic banking.Shariah compliant model contracts for Islamic finance that allow for periodic adjustments of lease rentals or profit rate. This would help the banks in dealing with the adverse effects of monetary policy, particularly the exposure to rate of return risk, which would consequently lead to financial stability. Furthermore, based on the findings it is strongly recommended that Islamic banks should have their own unique Shariah compliant non-interest based benchmark that is significantly decoupled from the conventional benchmarks and could be used as a robust indicator of the average expected cost of short term interbank market funding. This would not only help in establishing a unique identity of Islamic banks but would also provide a cushion against potential losses resulting from probable future financial crises. Furthermore, it would help in resolving the issue of Islamic banks’ exposure to the interest rate risk, rate of return risk, and withdrawal risk.Thus, the benchmark rate risk, if not managed properly, could pose a significant threat to the capital base, financing, and earnings of Islamic banks. Moreover, it can induce systematic risk and financial instability in the market. Therefore, the central banks should introduce and allow Efficient local non-interest based benchmark rates are also desirable for the Islamic banking industries coexisting in the dual banking systems like Pakistan keeping in view the changing dynamics in the international and local financial environment whereby, the LIBOR has finally been phased out starting 2022, while its counterpart Euribor is also losing its attraction. Similarly, their alternative benchmark rate like SONIA, SARON and SOFR are already being experimented with. Similarly, the prevailing Islamic interbank benchmark rate (IIBR) has also failed to prove as independent reference rates due to its strong linkage with the conventional benchmark rates. This calls for the focused and serious coordination among the digitization experts, regulators, practitioners and academia to come up with more innovative and workable solutions. However, to have such a benchmark the Islamic banking industry has to offer unique and distinctive financial products having unique risk-return profiles. If Islamic banks continue to compete with the financial products of conventional banks by mimicking their products, the Islamic banks would remain a sub-set of the conventional banks with similar products and rates.S1 Data(XLSX)Click here for additional data file.


Low-strain tests are widely utilized as a nondestructive approach to assess the integrity of newly piled foundations. So far, the examination of existing pile foundations is becoming an indispensable protocol for pile recycling or post-disaster safety assessment. However, the present low-strain test is not capable of testing existing pile foundations. In this paper, the torsional low-strain test (TLST) is proposed to overcome this drawback. Both the upward and downward waves are considered in the TLST wave propagation model established in this paper so that a firm theoretical basis is grounded for the test signal interpretations. A concise semi-analytical solution is derived and its rationality is verified by comparisons with the existing solutions for newly piled foundations and the finite element results. The main conclusions of this study can be drawn as follows: (1). by placing the sensors where the incident wave is applied, the number of reflected signals can be minimized; (2). the defects can be more evidently identified if the incident wave/sensors are input/installed close to the superstructure/pile head. Among many structure health monitoring approaches  soil layer, respectively. Based on the continuum theories, the equilibrium equations for the soil medium in a cylindrical coordinate system can be written asThe three-dimensional rod theory can better reveal the wave propagation during the TLSITs. However, its adoption would significantly increase the mathematical complexity of the problem, resulting in terrible computational efficiency. Further, it was reported by Zhang et al.  that thejth pile segment.For the part extending out of the soil, the equilibrium equation can be written asThe displacement and stress in the soil medium diminish at the radial infinite so that the following boundary conditions can be acquired:The interactions between the soil layers are simulated by a distributed Kelvin–Voigt model, whose formulas can be presented asThe transient impulse is subjected to the side of the extending part. Considering that the stress distribution inside the pile shaft is continuous, these boundary conditions can be written asThe pile end soil-pile end interaction is modeled by the fictitious soil pile. At the end of the fictitious soil pile, the displacement is supposed to be zero.The interaction between the pile and the upper structure is simplified to elastic springs and dashpots:When conducting the low-strain integrity test, both the soil and pile only go through tiny deformations, under which circumstance the motions of the soil and pile can be regarded as simultaneous.At the initial moment, the system has no velocity nor acceleration, and the transient pile-side impulse is the only reason for the system vibration.By performing Laplace Transform on both sides of Equation (1) and conducting the variable separation method, Equation (1) can degenerate to the following two differential equations:Submitting Equation (20) into Equations (4) and (5), one obtainsFurther considering the interaction between soil layers, as listed in Equations (6) and (7), the following transcendental equations can be established:Through numerical iterations, the above transcendental equations can be solved with a series of numerical answers, which can be denoted as t. It can be found that Equations (26) and (27) are non-homogeneous and homogeneous functions, respectively. The general solution of the corresponding homogeneous function of Equation (26) can be given asSimilarly, by performing Laplace Transform on both sides of Equations (2) and (3), one obtainsEmbedded pile segments: Extending pile segments:Based on the small strain assumption, the displacements at the soil-pile interface for embedded pile segments are continuous. Next, Equation (30) is substituted into (13), herein introducing the orthogonality of the following equations:With the utilization of Equation (32), one obtainsIn addition, the undetermined coefficients After the above derivation, the general solution for the embedded pile segments can then be given in a homogeneous equation form.To acquire the undetermined coefficients  can be further simplified toThen, the undetermined coefficients of the near-ground pile segment can be acquired through the inverse transfer function asThe twist angle and velocity response of the near-ground pile segment can be obtained asTo verify the correctness of the proposed model, the results calculated from the present solution are compared with those derived from the TLST theory aimed at the newly piled foundation and those computed from the finite element method (FEM). The soil-pile parameters utilized in this section are presented in As mentioned, the classic TLST theory is established for newly piled foundations. Consequently, it is only capable of simulating the specific testing case in which the incident wave is input at the pile head. Unlike the newly piled foundations, the selections of incident wave input and signal receiving locations can be diverse for the testing of existing high-pile foundations. To verify the accuracy of the proposed solution in simulating the simultaneously propagating upward and downward strain waves, the results calculated from the present model are compared with those computed from FEM. The finite element model is established and solved using Abaqus Explicit solver and C3D8R is utilized as the elements for both the soil and pile, the mesh of which is depicted in As shown in Based on the above analysis, a preliminary conclusion is drawn: by inputting the incident wave as close to the pile head as possible, the difficulty in signal identification and interpretation can be reduced. This section investigates the influence of the layouts at the input and signal receiving locations on the velocity response, aiming to find the optimal layouts for the TLSTs. In order to simulate the test for floating piles, the length of the fictitious soil pile is set as 5 m.As shown in Defect identification is one of the major tasks for the integrity examination of the existing pile foundations. Further, the neckings and concrete segregations are the two most commonly found defects in practice. This section investigates the identification ability of these two defects utilizing the TLSTs. The results shown in By placing the sensors where the incident wave is applied, the number of reflected signals can be minimized to acquire a more precise signal spectrum.The optimal layouts of the TLST are inputting the incident wave close to the pile head and installing the sensors close to the pile head as well. By doing this, the defects can be more easily identified from the signal spectrum.The existence of concrete segregation defects would influence the occurrence time of each reflected signal, while the necking defects would not. Hence, this is a helpful tip for distinguishing the concrete segregation defects  from the necking defects.This paper establishes a rigorous mathematical model to simulate the strain wave propagation during the torsional low-strain test (TLST) for existing high-pile foundations. In the proposed model, the simultaneous propagation of the upward and downward strain waves inside the pile body is considered. The parametric analysis reveals the optimal layouts of the TLSTs for the existing high-pile foundation. The main conclusions can be drawn as follows: