Search Results (41)

Stroke is one of the leading causes of long-term disability worldwide, often resulting in motor impairments that limit the ability to perform daily activities independently. Conventional rehabilitation exoskeletons, while effective, are typically rigid, bulky, and expensive, limiting their usability outside of clinical settings. In response to these challenges, this work presents the development and validation of a novel soft exosuit designed for elbow flexion rehabilitation, incorporating a multi-wire Shape Memory Alloy (SMA) actuator capable of both position and force control. The proposed system features a lightweight and ergonomic textile-based design, optimized for user comfort, ease of use, and low manufacturing cost. A sequential activation strategy was implemented to improve the dynamic response of the actuator, particularly during the cooling phase, which is typically a major limitation in SMA-based systems. The performance of the multi-bundle actuator was compared with a single-bundle configuration, demonstrating superior trajectory tracking and reduced thermal accumulation. Surface electromyography tests confirmed a decrease in muscular effort during assisted flexion, validating the device’s assistive capabilities. With a total weight of 0.6 kg and a fabrication cost under EUR 500, the proposed exosuit offers a promising solution for accessible and effective home-based rehabilitation. Full article

This work presents the validation of a low-cost measurement system based on an open-ended coaxial SMA (SubMiniature version A) probe for the characterization of complex permittivity in the microwave frequency range. The system combines a custom-fabricated probe, a vector network analyzer, and a dedicated software application that implements three analytical models: capacitive, radiation, and virtual transmission line models. A comprehensive experimental campaign was carried out involving pure polar liquids, saline solutions, and biological tissues, with the measurements compared against those obtained using a high-precision commercial probe. The results confirm that the proposed system is capable of delivering accurate and reproducible permittivity values up to at least 6 GHz. Among the implemented models, the radiation model demonstrated the best overall performance, particularly in biological samples. Additionally, reproducibility tests with three independently assembled SMA probes showed normalized deviations below 3%, confirming the robustness of the design. These results demonstrate that the proposed system constitutes a viable alternative for cost-sensitive applications requiring portable or scalable microwave dielectric characterization. Full article

Stone mastic asphalt (SMA) is the most widely adopted asphalt mixture on highway pavement in China. However, the cost of SMA is rising continually due to the increasing shortage of high-quality basalt aggregate. On the other hand, China’s steel slag and reclaimed asphalt pavement (RAP) stock is abundant, and steel slag has excellent strength and wear-resistant performance, which can fully or partially replace part of the basalt aggregate. The content of asphalt may be increased due to the porosity of the steel slag. If fine RAP rich in asphalt is also used for SMA, it can partially fill the voids of steel slag and reduce the amount of new asphalt and fine aggregate. For this objective, SMA 13 was designed with two particle sizes of coarse steel slag aggregate (5–10 mm, 10–15 mm) and one fine RAP (0–5 mm), named SR-SMA. The fundamental pavement performance of SR-SMA was evaluated through a wheel-tracking test, low-temperature beam bending test, freeze–thaw indirect tensile test, and four-point bending fatigue test. For comparison, the mix design and performance tests of two SMAs involving coarse steel slag and fine basalt aggregate (named SB-SMA), and coarse and fine basalt aggregates (named B-SMA), respectively, were conducted. The results indicated that SR-SMA (dynamic stability of 4865 passes/mm) shows the best rutting resistance, followed by SB-SMA (dynamic stability of 4312 passes/mm), and B-SMA (dynamic stability of 4135 passes/mm) comes in last. Additionally, the dynamic stability values of three SMAs have significant differences. SR-SMA has better low-temperature cracking resistance with a failure strain of 3150 με, between SB-SMA and B-SMA (failure strain values are 4436, 2608 με). Compared to B-SMA and SB-SMA, the moisture stability of SR-SMA is relatively poor but meets Chinese specification. While the fatigue resistance of SR-SMA is the worst among three SMAs, their differences are insignificant. Furthermore, SR-SMA reduces material cost by approximately 35% per ton compared to conventional B-SMA. Overall, SR-SMA is cost-effective and can be used as an alternative material to traditional B-SMA. Full article

In this article, we present a method for extracting the complex permittivity of high-conductivity solutions up to 9 GHz. Microwave measurements were performed using a low-cost SMA connector, employed as an open-circuit coaxial probe, which was subsequently brought into contact with the liquids under characterization. Compared to state-of-the-art techniques, this method offers the advantage of good accuracy while remaining simple to implement with a low-cost sensor. The affordability of the sensor is crucial because the sensor must operate in a corrosive environment. The use of existing but expensive commercial solutions is prohibitive. Therefore, sensor replacement must be straightforward and inexpensive in case of damage. Two permittivity extraction methods were studied, both relying on a straightforward experimental approach and knowledge of the complex permittivity of reference liquids (deionized water, ethanol, methanol). The technique was initially validated on saline solutions (NaCl) known from the literature before being applied to aqueous lithium bromide (LiBr water) solutions. Eight LiBr water solutions, known to be highly corrosive, were measured for LiBr mass concentrations ranging from 1% to 54% and for conductivities up to 14 S/m. The high conductivity of these solutions brings challenges to extract the real part of the permittivity, which is underestimated by both methods. In contrast, the imaginary part exhibits consistent results with variations strongly correlated to the concentration. Notably, an inversion of the direction of variation was observed for mass concentration in LiBr exceeding 35% aligning with the conductivity curve. Full article

This study introduces a novel technique called Synchronized Multi-Augmentation (SMA) combined with multi-backbone (MB) ensembling to enhance model performance and generalization in deep learning (DL) tasks in real-world scenarios. SMA utilizes synchronously augmented input data for training across multiple backbones, improving the overall feature extraction process. The outputs from these backbones are fused using two distinct strategies: the averaging fusion method, which averages predictions, and the dense fusion method, which averages features through a fully connected network. These methods aim to boost accuracy and reduce computational costs, particularly in Edge Intelligence (EI) systems with limited resources. The proposed SMA technique was evaluated on the CIFAR-10 dataset, highlighting its potential to enhance classification tasks in DL workflows. This study provides a comprehensive analysis of various backbones, their ensemble methods, and the impact of different SMAs on model performance. The results demonstrate that SMAs involving color adjustments, such as contrast and equalization, significantly improve generalization under varied lighting conditions that simulated real-world low-illumination conditions, outperforming traditional spatial augmentations. This approach is particularly beneficial for EI hardware, such as microcontrollers and IoT devices, which operate under strict constraints like limited processing power and memory and real-time processing requirements. This study’s findings suggest that employing SMA and MB ensembling can offer significant improvements in accuracy, generalization, and efficiency, making it a viable solution for deploying DL models on edge devices with constrained resources under real-world practical conditions. Full article

Enzyme-based portable amperometric biosensors are precise and low-cost medical devices used for rapid cancer biomarker screening. Sarcosine (Sar) is an ideal biomarker for prostate cancer (PCa). Because human serum and urine contain complex interfering substances that can directly oxidize at the electrode surface, rapid Sar screening biosensors are relatively challenging and have rarely been reported. Therefore, highly sensitive and selective amperometric biosensors that enable real-time measurements within <1.0 min are needed. To achieve this, a chitosan–polyaniline polymer nanocomposite (CS–PANI NC), a carrier for dispersing mesoporous carbon (MC), was synthesized and modified on a screen-printed carbon electrode (SPCE) to detect hydrogen peroxide (H₂O₂). The sarcosine oxidase (SOx) enzyme-immobilized CS–PANI–MC-2 ternary NCs were referred to as supramolecular architectures (SMAs). The excellent electron transfer ability of the SMA-modified SPCE (SMA/SPCE) sensor enabled highly sensitive H₂O₂ detection for immediate trace Sar biomarker detection. Therefore, the system included an SMA/SPCE coupled to a portable potentiostat linked to a smartphone for data acquisition. The high catalytic activity, porous architecture, and sufficient biocompatibility of CS–PANI–MC ternary NCs enabled bioactivity retention and immobilized SOx stability. The fabricated biosensor exhibited a detection limit of 0.077 μM and sensitivity of 8.09 μA mM⁻¹ cm⁻² toward Sar, demonstrating great potential for use in rapid PCa screening. Full article

This paper examines the effectiveness of a pumped storage hydropower plant (PSHP) when combined with other plants. System 1 examines the contribution of the PSHP to reducing fuel costs for thermal power plants. System 2 examines the optimization of operations for power systems with energy storage and uncertain renewable energies to maximize total profit based on four test system cases: Case 1: neglect the PSHP and consider wind and solar certainty; Case 2: consider the PSHP and wind and solar certainty; Case 3: neglect the PSHP and consider wind and solar uncertainty; and Case 4: consider the PSHP and wind and solar uncertainty. Cases 1 and 2 focus on systems that assume stable power outputs from these renewable energy sources, while Cases 3 and 4 consider the uncertainty surrounding their power output. The presence of a PSHP has a key role in maximizing the system’s total profit. This proves that Case 2, which incorporates a PSHP, achieves a higher total profit than Case 1, which does not include a PSHP. The difference is USD 17,248.60, representing approximately 0.35% for a single day of operation. The total profits for Cases 3 and 4 are USD 5,089,976 and USD 5,100,193.80, respectively. Case 4 surpasses Case 3 by USD 10,217.70, which is about 0.2% of Case 3’s total profit. In particular, the PSHP used in Cases 2 and 4 is a dispatching tool that aims to achieve the highest profit corresponding to the load condition. The PSHP executes its storage function by using low-price electricity at off-peak periods to store water in the reservoir through the pumping mode and discharge water downstream to produce electricity at periods with high electricity prices using the generating mode. As a result, the total profit increases. A modified slime mould algorithm (MSMA) is applied to System 2 after proving its outstanding performance compared to the jellyfish search algorithm (JS), equilibrium optimizer (EO), and slime mould algorithm (SMA) in System 1. Full article

Mechanocaloric cooling/heat pumping with zero carbon emission and high efficiency shows great potential for replacing traditional refrigeration with vapor compression. Mechanocaloric prototypes that are developed using shape memory alloys (SMAs) face the problems of a large driving force and high cost. In this work, we report a low-crystalline thermoplastic polyetherurethane (TPU) elastomer fiber with a low actuation force and good mechanocaloric performance. We fabricate the TPU fiber and develop a multifunctional mechanical tester to measure both the elastocaloric and twistocaloric effects. In the experiments, the applied stress required to induce mechanocaloric effects of the TPU fiber is only 10~30 MPa, which is much lower than that of widely used NiTi elastocaloric SMAs (600~1200 MPa). The TPU fiber produces a maximum twistocaloric adiabatic temperature change of 10.2 K, which is 78.9% larger than its elastocaloric effect of 5.7 K. The wide-angle X-ray scattering (WAXS) results show that the strain-induced amorphous chain alignment and associated configurational entropy change are the main causes of the good mechanocaloric effects of the TPU fiber, rather than the strain-induced crystallization. This work demonstrates the potential of achieving low-force heat-efficient mechanocaloric cooling using thermoplastic elastomer fibers. Full article

Porous Ni-Mn-Ga shape memory alloys (SMAs) were prepared by powder metallurgy using NaCl as a pore-forming agent with an average pore size of 20–30 μm. The microstructure, phase transformation, superelasticity, and elastocaloric properties of the porous alloys were investigated. The prepared porous alloy had a uniform pore distribution and interconnected microchannels were formed. Cu doping can effectively improve the toughness of a porous alloy, thus improving the superelasticity. It was found that porous Ni-Mn-Ga-Cu SMAs have a flat stress plateau, which exhibits a maximum elongation of 5% with partially recoverable strain and a critical stress for martensite transformation as low as about 160 MPa. In addition, an adiabatic temperature change of 0.6 K was obtained for the prepared porous alloy at a strain of 1.2% at about 150 MPa. This work confirms that the introduction of porous structures into polycrystalline Ni-Mn-Ga SMAs is an effective way to reduce costs and improve performance, and provides opportunities for engineering applications. Full article

Shape memory alloys (SMAs) are becoming a more important factor in actuation technology. Due to their unique features, they have the potential to save weight and installation space as well as reduce energy consumption. The system integration of the generally small-diameter NiTi wires is an important cornerstone for the emerging technology. Crimping, a common method for the mechanical and electrical connection of SMA wires, has several drawbacks when it comes to miniaturization and high-force outputs. For high-force applications, for example, multiple SMA wires in parallel are needed to keep actuation frequencies high while scaling up the actuation force. To meet these challenges, the proposed study deals with the development of a resistance-welding process for manufacturing NiTi wire bundles. The wires are welded to a sheet metal substrate, resulting in promising functional properties and high joint strengths. The welding process benefits from low costs, easy-to-control parameters and good automation potential. A method for evaluating the resistance-welding process parameters is presented. With these parameters in place, a manufacturing process for bundled wire actuators is discussed and implemented. The welded joints are examined by peel tests, microscopy and fatigue experiments. The performance of the manufactured bundle actuators is demonstrated by comparison to a single wire with the same accumulated cross-sectional area. Full article

The evaluation of the analytical capabilities of a novel disposable flow cell for spectroscopic bioprocess monitoring is presented. The flow cell is presterilized and can be connected to any kind of bioreactor by weldable tube connections. It is clamped into a reusable holder, which is equipped with SMA-terminated optical fibers or an integrated light source and detection unit. This modular construction enables spectroscopic techniques like UV-Vis spectroscopy or turbidity measurements by scattered light for modern disposable bioreactors. A NIR scattering module was used for biomass monitoring in different cultivations. A high-cell-density fed-batch cultivation with Komagataella phaffii and a continuous perfusion cultivation with a CHO DG44 cell line were conducted. A high correlation between the sensor signal and biomass or viable cell count was observed. Furthermore, the sensor shows high sensitivity during low turbidity states, as well as a high dynamic range to monitor high turbidity values without saturation effects. In addition to upstream processing, the sensor system was used to monitor the purification process of a monoclonal antibody. The absorption module enables simple and cost-efficient monitoring of downstream processing and quality control measurements. Recorded absorption spectra can be used for antibody aggregate detection, due to an increase in overall optical density. Full article

Stone matrix asphalt and asphalt concrete mixture with 13.2 mm nominal maximum aggregate size (named SMA13 and AC13, respectively) are widely used in the surface course of asphalt pavement in China. Generally, the pavement performance of SMA13 is superior to that of AC13, while the cost of the former is significantly higher than that of the latter. The objective of this paper was to develop a new hot mix asphalt (named SMAC13) whose performance and cost are between SMA13 and AC13. A boundary sieve size (BSS) of 2.36 mm was selected between fine and coarse aggregates. Based on the union set of aggregate gradation ranges of SMA13 and AC13, the family of gradation curves in the forms of S shapes were designed in terms of the BSS passing rate. According to the evaluation of the skeleton interlock of coarse aggregate of the gradation curve family, the aggregate gradation range of SMAC13 was determined. Also, the performance of three kinds of asphalt mixtures were compared through laboratory tests. The results indicated that SMA13 shows the best rutting resistance, followed by SMAC13 then AC13, while in terms of low-temperature performance in resistance to cracking, the sequence is SMAC13, AC13, and SMA13. The sequence of water stability is AC13, SMAC13, and SMA13. Furthermore, the cost of SMAC13 is 25% less than that of SMA13. Therefore, SMAC13 can be used as an alternative for the surface course of asphalt pavement in terms of performance and cost. Full article

This work introduces a neuromorphic sensor (NS) based on force-sensing resistors (FSR) and spiking neurons for robotic systems. The proposed sensor integrates the FSR in the schematic of the spiking neuron in order to make the sensor generate spikes with a frequency that depends on the applied force. The performance of the proposed sensor is evaluated in the control of a SMA-actuated robotic finger by monitoring the force during a steady state when the finger pushes on a tweezer. For comparison purposes, we performed a similar evaluation when the SNN received input from a widely used compression load cell (CLC). The results show that the proposed FSR-based neuromorphic sensor has very good sensitivity to low forces and the function between the spiking rate and the applied force is continuous, with good variation range. However, when compared to the CLC, the response of the NS follows a logarithmic-like function with improved sensitivity for small forces. In addition, the power consumption of NS is 128 µW that is 270 times lower than that of the CLC which needs 3.5 mW to operate. These characteristics make the neuromorphic sensor with FSR suitable for bioinspired control of humanoid robotics, representing a low-power and low-cost alternative to the widely used sensors. Full article

Low-alloy steel is widely employed in the aviation industry for its exceptional mechanical properties. These materials are frequently used in critical structural components such as aircraft landing gear and engine mounts, where a high strength-to-weight ratio is crucial for optimal performance. However, the mechanical properties of low-alloy steel are influenced by various components and their compositions, making identification and prediction challenging. Accurately predicting these mechanical properties can significantly reduce the development time of new alloy steel, lower production costs, and offer valuable insights for design analysis. support vector regression (SVR) is known for its superior learning and generalization capabilities. However, optimizing SVR performance can be challenging due to the significant impact of the penalty factor and kernel parameters. To address this issue, a hybrid method called SMA-SVR is proposed, which combines the Slime Mould Algorithm (SMA) with SVR. This hybrid approach aims to efficiently and accurately predict two crucial mechanical parameters of low-alloy steel: tensile strength and 0.2% proof stress. Detailed descriptions of the modeling processes and principles that are involved in the hybrid method are provided. Furthermore, three other popular hybrid models for comparison are introduced. To evaluate the performance of these models, four statistical measures are utilized: Mean Absolute Error, Root Mean Square Error, R-Squared, and computational time. Using data from the NIMS database and from material tests conducted on a universal testing machine, experiments were carried out to compare the performance of these models. The results indicate that SMA-SVR outperforms the other methods in terms of accuracy and computational efficiency. Full article

In the realm of power systems, short-term electric load forecasting is pivotal for ensuring supply–demand balance, optimizing generation planning, reducing operational costs, and maintaining grid stability. Short-term load curves are characteristically coarse, revealing high-frequency data upon decomposition that exhibit pronounced non-linearity and significant noise, complicating efforts to enhance forecasting precision. To address these challenges, this study introduces an innovative model. This model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to bifurcate the original load data into low- and high-frequency components. For the smoother low-frequency data, a temporal convolutional network (TCN) is utilized, whereas the high-frequency components, which encapsulate detailed load history information yet suffer from a lower fitting accuracy, are processed using an enhanced soft thresholding TCN (SF-TCN) optimized with the slime mould algorithm (SMA). Experimental tests of this methodology on load forecasts for the forthcoming 24 h across all seasons have demonstrated its superior forecasting accuracy compared to that of non-decomposed models, such as support vector regression (SVR), recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network-LSTM (CNN-LSTM), TCN, Informer, and decomposed models, including CEEMDAN-TCN and CEEMDAN-TCN-SMA. Full article