Search Results (24)

Shape Memory Alloy (SMA) actuators are pivotal in modern engineering due to their unique thermomechanical properties, but their inherent non-linearities, hysteresis, and temperature sensitivity pose significant control challenges. This systematic review evaluates artificial intelligence (AI)-based control methodologies to address these limitations, analyzing their efficacy in enhancing precision, adaptability, and reliability for SMA and Magnetic SMA (MSMA) systems. A PRISMA-guided literature review (2003–2025) identified 24 studies, which were categorized by control architectures (hybrid AI-linear, pure AI, adaptive, and model predictive control) and evaluated through quantitative metrics, including Root Mean Square Error (RMSE%) and a weighted scoring system for experimental rigor. Results revealed hybrid AI-linear controllers as the dominant approach (36%), with online-trained neural networks achieving superior accuracy (+2.4%) over offline methods. Feedforward neural networks outperformed recurrent architectures (+3.1%), while Model Predictive Control (MPC) excelled for SMA actuators (+5.8% accuracy) but underperformed for MSMAs (−7.7%). Sensorless strategies proved advantageous for MSMAs (+5.0%), leveraging intrinsic material properties like electrical resistance for state estimation. The analysis underscores AI’s capacity to mitigate hysteresis and non-linear dynamics, though material-specific optimization is critical: SMA systems favor dynamic control and MPC, whereas MSMAs benefit from sensorless AI and pure neural networks. Challenges persist in computational demands for online training and reinforcement learning’s exploration–exploitation trade-offs. Future research should prioritize adaptive algorithms for fatigue compensation, lightweight AI models for embedded deployment, and standardized benchmarking to bridge material-specific performance gaps. This synthesis establishes AI as a transformative paradigm for SMA actuation, enabling precise control in aerospace, biomedical, and soft robotics applications. Full article

The contemporary challenge in remote sensing lies in the precise retrieval of increasingly abundant and high-resolution remotely sensed images (RS image) stored in expansive data warehouses. The heightened spatial and spectral resolutions, coupled with accelerated image acquisition rates, necessitate advanced tools for effective data management, retrieval, and exploitation. The classification of large-sized images at the pixel level generates substantial data, escalating the workload and search space for similarity measurement. Semantic-based image retrieval remains an open problem due to limitations in current artificial intelligence techniques. Furthermore, on-board storage constraints compel the application of numerous compression algorithms to reduce storage space, intensifying the difficulty of retrieving substantial, sensitive, and target-specific data. This research proposes an innovative hybrid approach to enhance the retrieval of remotely sensed images. The approach leverages multilevel classification and multiscale feature extraction strategies to enhance performance. The retrieval system comprises two primary phases: database building and retrieval. Initially, the proposed Multiscale Multiangle Mean-shift with Breaking Ties (MSMA-MSBT) algorithm selects informative unlabeled samples for hyperspectral and synthetic aperture radar images through an active learning strategy. Addressing the scaling and rotation variations in image capture, a flexible and dynamic algorithm, modified Deep Image Registration using Dynamic Inlier (IRDI), is introduced for image registration. Given the complexity of remote sensing images, feature extraction occurs at two levels. Low-level features are extracted using the modified Multiscale Multiangle Completed Local Binary Pattern (MSMA-CLBP) algorithm to capture local contexture features, while high-level features are obtained through a hybrid CNN structure combining pretrained networks (Alexnet, Caffenet, VGG-S, VGG-M, VGG-F, VGG-VDD-16, VGG-VDD-19) and a fully connected dense network. Fusion of low- and high-level features facilitates final class distinction, with soft thresholding mitigating misclassification issues. A region-based similarity measurement enhances matching percentages. Results, evaluated on high-resolution remote sensing datasets, demonstrate the effectiveness of the proposed method, outperforming traditional algorithms with an average accuracy of 86.66%. The hybrid retrieval system exhibits substantial improvements in classification accuracy, similarity measurement, and computational efficiency compared to state-of-the-art scene classification and retrieval methods. Full article

Stagnation remains a persistent challenge in optimization with metaheuristic algorithms (MAs), often leading to premature convergence and inefficient use of the remaining evaluation budget. This study introduces $MsMA$, a novel meta-level strategy that externally monitors MAs to detect stagnation and adaptively partitions computational resources. When stagnation occurs, $MsMA$ divides the optimization run into partitions, restarting the MA for each partition with function evaluations guided by solution history, enhancing efficiency without modifying the MA’s internal logic, unlike algorithm-specific stagnation controls. The experimental results on the CEC’24 benchmark suite, which includes 29 diverse test functions, and on a real-world Load Flow Analysis (LFA) optimization problem demonstrate that MsMA consistently enhances the performance of all tested algorithms. In particular, Self-Adapting Differential Evolution (jDE), Manta Ray Foraging Optimization (MRFO), and the Coral Reefs Optimization Algorithm (CRO) showed significant improvements when paired with MsMA. Although MRFO originally performed poorly on the CEC’24 suite, it achieved the best performance on the LFA problem when used with MsMA. Additionally, the combination of MsMA with Long-Term Memory Assistance (LTMA), a lookup-based approach that eliminates redundant evaluations, resulted in further performance gains and highlighted the potential of layered meta-strategies. This meta-level strategy pairing provides a versatile foundation for the development of stagnation-aware optimization techniques. Full article

Steganography, a technique for concealing information, often faces challenges such as low decoding accuracy and inadequate extraction of edge and global features. To overcome these limitations, we propose a dual-stream U-Net framework with integrated edge enhancement for image steganography. Our main contributions include the adoption of a dual-stream U-Net structure in the encoder, integrating an edge-enhancement stream with the InceptionDMK module for multi-scale edge detail extraction, and incorporating a multi-scale median attention (MSMA) module into the original input stream to enhance feature representation. This dual-stream design promotes deep feature fusion, thereby improving the edge details and embedding capacity of stego images. Moreover, an iterative optimization strategy is employed to progressively refine the selection of cover images and the embedding process, achieving enhanced stego quality and decoding performance. Experiments show that our method produces high-quality stego images across multiple public datasets, achieving near-100% decoding accuracy. It also surpasses existing methods in visual quality metrics like PSNR and SSIM. This framework offers a promising approach for enhancing steganographic security in real-world applications such as secure communication and data protection. Full article

Magnetic shape memory alloy-based actuators (MSMA-BAs) have extensive applications in the field of micro-nano positioning technology. However, complex hysteresis seriously affects its performance. To describe the hysteresis of MSMA-BA, this study proposes integrating a hysteresis operator and the rate-of-change function of the input signal into the least squares support vector machine (LS-SVM) framework to construct a rate-dependent dynamic hysteresis model for MSMA-BAs. The hysteresis operator converts the multi-valued mapping of hysteresis into a one-to-one mapping, while the rate-of-change function of the input signal captures the rate dependence of the hysteresis, thereby enhancing the model’s ability to describe complex hysteresis. In addition, with the powerful nonlinear fitting capability and good generalization of LS-SVM, the dynamic performance of the proposed model is effectively improved. Experimental results show that the proposed model accurately describes the hysteresis of MSMA-BA. 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

In order to further improve performance of the Slime Mould Algorithm, the Enhanced Multi-Strategy Slime Mould Algorithm (EMSMA) is proposed in this paper. There are three main modifications to SMA. Firstly, a leader covariance learning strategy is proposed to replace the anisotropic search operator in SMA to ensure that the agents can evolve in a better direction during the optimization process. Secondly, the best agent is further modified with an improved non-monopoly search mechanism to boost the algorithm’s exploitation and exploration capabilities. Finally, a random differential restart mechanism is developed to assist SMA in escaping from local optimality and increasing population diversity when it is stalled. The impacts of three strategies are discussed, and the performance of EMSMA is evaluated on the CEC2017 suite and CEC2022 test suite. The numerical and statistical results show that EMSMA has excellent performance on both test suites and is superior to the SMA variants such as DTSMA, ISMA, AOSMA, LSMA, ESMA, and MSMA in terms of convergence accuracy, convergence speed, and stability. Full article

In Malaysia, the Stormwater Management Manual for Malaysia (Manual Saliran Mesra Alam or MSMA) was introduced to manage stormwater and solve water-related problems. However, massive development caused the conventional stormwater system to be unable to cater to the excessive runoff and led to flooding, also affecting residential areas. This shows that there is an urgent requirement for a sustainable stormwater management practice (SSMP) in residential areas. This study is conducted to evaluate stormwater practitioners’ opinions on the proposed SSMPs, including green roofs, rain gardens/bioretention systems, and porous pavements, based on Strength, Weakness, Opportunity, and Threat (SWOT) factors through surveys and correlation analysis. The questionnaire was distributed to 14 branches of the Department of Irrigation and Drainage (DID), 14 branches of the City Council, and 28 selected private engineering companies. In total, 118 respondents were targeted to obtain their perspectives on the SWOT factors for each selected SSMP according to the Likert scale. The survey showed that the respondents agreed with most of the SWOT factors on the selected SSMPs. The results of the distributed questionnaire were used as the data for the correlation analysis. The analysis indicated that green roofs, rain gardens/bioretention systems, and porous pavements have a strong positive relationship, with a p-value of less than 0.05 for the Strength and Opportunity factors, and a weak positive relationship, with a p-value of more than 0.05 for the Weakness and Threat factors. This shows that the proposed SSMPs are suitable to implement in residential areas. Observations were conducted to obtain the residents’ opinions on the performance of stormwater management in their residential areas and to evaluate the suitability of the proposed SSMPs to be implemented in the observed areas. Based on the observations, it can be concluded that only rain gardens/bioretention systems and porous pavements are suitable when compared to green roofs. An interview session was conducted with practitioners in stormwater management to gain their opinions on the studies and the proposed SSMPs. The interviewees agreed with the issues and that the SSMPs should be implemented in landed residential areas. Full article

We present the design, simulation, and characterization of a magnetic shape-memory alloy (MSMA) film actuator that transitions from bistable switching to resonant self-actuation when subjected to a stationary heat source. The actuator design comprises two Ni-Mn-Ga films of 10 µm thickness integrated at the front on either side of an elastic cantilever that moves freely between two heatable miniature permanent magnets and, thus, forms a bistable microswitch. Switching between the two states is induced by selectively heating the MSMA films above their Curie temperature Tc. When continuously heating the permanent magnets above Tc, the MSMA film actuator exhibits an oscillatory motion in between the magnets with large oscillation stroke in the frequency range of 50–60 Hz due to resonant self-actuation. A lumped-element model (LEM) is introduced to describe the coupled thermo-magnetic and magneto-mechanical performance of the actuator. We demonstrate that this performance can be used for the thermomagnetic energy generation of low-grade waste heat (T < 150 °C) with a high power output per footprint in the order of 2.3 µW/cm². Full article

Consumer knowledge of the goods produced or processed by the numerous suppliers and processors is still relatively low due to the growing complexity of the structure of pharmaceutical supply chains. Information asymmetry in the pharmaceutical sector has an effect on welfare, sustainability, and health. (1) Background: In this respect, we wanted to develop a productive structure for a pharmaceutical supply chain that satisfies the consumer information needs and fosters consumer confidence in the pharmacy goods they buy. By using blockchain technology, the main goals were to develop and implement a pharmaceutical supply chain. (2) Objectives: The main objectives of this work were to leverage an Ethereum platform for the development of a tractability system in a pharmaceutical supply chain environment and to analyze the efficiency of MSMAChain with respect to the cost and execution of transactions based on our designed smart contracts. (3) Results: This research looked into a variety of issues related to the value, viability, and effects of blockchain technology for use in supply chain applications. The methods and creations in this environment were monitored and researched. It is vital to identify a number of crucial subjects including future research areas, in order to achieve the widespread acceptance of the supply chain traceability provided by blockchain technology. (4) Conclusions: MSMAChain, an Ethereum blockchain-based approach, leverages smart contracts and decentralized off-chain storage for efficient product traceability in terms of the cost and execution of transaction for a health care supply chain. Full article

Modifier Adaptation (MA) is a method of real-time optimization (RTO) which modifies a single model to match the first order properties of the plant. Known uncertainties in the parameters of this model are discarded in favor of real-time measurements, but they can be used to quantify the mismatch between the plant and model. Using multi-model methods increases the computation time, but can improve rate of convergence of the RTO scheme. This article proposes a framework, known as multiple solution modifier adaptation (MSMA), which produces several models which are all modified in the same way as standard MA, each producing a potential solution to be applied to the plant. From this framework, three recommended schemes are proposed on how to select the operating point to be applied to the plant: (1) Selecting the solution based off the modifiers; (2) Selecting the mean solution from convex models; (3) Selecting the closest solution to the current operating point. Each of these methods have different advantages, including limiting the increase in computational complexity and improving the model adequacy conditions of the scheme. These recommended schemes are shown on three different case studies of varying complexity with all three schemes showing improvements over standard MA. Full article

This paper presents research on the application of magnetic shape memory alloys (MSMAs) in actuator design. MSMAs are a relatively new group of so-called smart materials that are distinguished by repeatable strains up to 6% and dynamics much better than that of thermally activated shape memory alloys (SMAs). The shape change mechanism in MSMAs is based on the rearrangement of martensite cells in the presence of an external magnetic field. In the first part of the article a review of the current state of MSMA actuator design is presented, followed by a description of the design, modelling and control of a newly proposed actuator. The developed actuator works with MSMA samples of 3 × 10 × 32 mm³, guaranteeing an available operating range of up to 1 mm, despite its great deformation range and dynamics. In the paper its dynamics model is proposed and its transfer function is derived. Moreover, the generalised Prandtl-Ishlinskii model of MSMA-actuator hysteresis is proposed. This model is then inverted and used in the control system for hysteresis compensation. A special test stand was designed and built to test the MSMA actuator with compensation. The step responses are recorded, showing that the compensated MSMA actuator exhibits the positioning accuracy as ±2 µm. As a result, the authors decided to apply a control system based on an inverse hysteresis model. The paper concludes with a summary of the research results, with theoretical analysis compared with the registered actuator characteristics. Full article

In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability. Full article

While there are many high profile Opuntioid cactus species invading rangeland environments in Australia, Cereus uruguayanus Ritt. ex Kiesl. has also naturalised and formed large and dense infestations at several locations. With no herbicides registered for control of C. uruguayanus in Australia, the primary aim of this study was to identify effective herbicides to control it using a range of techniques. This involved a large screening trial of twelve herbicides and four techniques, followed by a rate refinement trial for cut stump applications and another to test residual herbicides. Despite most treatments (except monosodium methylarsonate (MSMA)) taking a long time to kill plants, at least one effective herbicide was identified for basal bark (triclopyr/picloram), cut stump (aminopyralid/metsulfuron-methyl, glyphosate, metsulfuron-methyl, triclopyr/picloram, triclopyr/picloram/aminopyralid), stem injection (glyphosate, MSMA, triclopyr/picloram/aminopyralid) and foliar applications (aminopyralid/metsulfuron-methyl, MSMA, triclopyr, triclopyr/picloram/aminopyralid) due to their ability to kill both small and large plants. Ground application of residual herbicides was less conclusive with neither hexazinone nor tebuthiuron causing adequate mortality at the rates applied. This study has identified effective herbicides for the control of C. uruguayanus using several techniques, but further research is needed to refine herbicide rates and develop integrated management strategies for a range of situations and infestation sizes and densities. Full article

The synergistic control of resistance reduction and sealing poses challenges to enhancing the rapid dynamic response ability of servo hydraulic cylinders; the key to solving this problem is effectively controlling the sealing gap value. In this study, a micro-variation between the hydraulic cylinder and the piston based on the disadvantage of conventional seals, constant gap seals, and lip gap seals was constructed; MSMA assist support blocks were designed on the piston to form a gap seal strip; then, the sealing gap value could be changed by controlling the magnetic field intensity. Simultaneously, the effects of magnetic field strength, parts-manufacturing precision, temperature, and hysteresis on the micro-variation in the MSMA were analyzed, and effective solutions were proposed. Finally, experiments on the magnetic field, temperature, and hysteresis were conducted by the measurement system. The results showed that the variable value of the sealing gap with the MSMA is feasible under ideal conditions, and can effectively change the amount of MSMA expansion by controlling the magnetic field strength, temperature, preload, etc., and then change the amount of the sealing gap of the hydraulic cylinder. This is the key to achieving friction and sealing control, which plays a crucial and active role in improving the efficiency of hydraulic systems. However, the impact of hysteresis effects cannot be ignored, which will be the main problem to be solved in the future. Full article