Problem In Applications Research Articles

Enzymatic degradation of polybutylene succinate by recombinant cutinase cloned from Paraphoma chrysanthemicola.

Polybutylene succinate (PBS), a biodegradable plastic, can be used as an alternative to traditional plastics to effectively solve the growing plastic pollution. Although PBS is theoretically completely biodegradable, slow degradation remains a problem in practical applications, leading to the possibility of environmental pollution. In this study, after the PBS degradation ability of the fungus Paraphoma chrysanthemicola was determined, a P. chrysanthemicola cutinase (PCC) gene was cloned and expressed in Pichia pastoris and its PBS degradation ability was further characterized. With a molecular weight of approximately 20kDa, PCC showed good PBS degradation activity at pH 6.0-8.0 and 20-40°C. Metal ions have different effects on PCC activity. Specifically, Ca2+, Zn2+, and Co2+ promoted enzyme activity, whereas Cu2+, Fe2+, and Ni2+ inhibited enzyme activity. The weight loss of the PBS films was greater than 50% after 60h of PCC treatment, and scanning electron microscopy revealed the appearance of cracks on the surface of the PBS films during the degradation process, which deepened with the progression of degradation time. This PBS degradation by PCC occurs via surface erosion, with the resulting degradation products being mainly 1,4-succinic acid and succinic acid butanediyl ester. This study provides a preliminary elucidation of the enzymatic mechanisms involved in PBS degradation by PCC and offers insights into the development of more effective biotechnological approaches to address the environmental challenges associated with plastic waste.

DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules

Modern generative models based on deep learning have made it possible to design millions of hypothetical materials. To screen these candidate materials and identify promising new materials, we need fast and accurate models to predict material properties. Graphical neural networks (GNNs) have become a current research focus due to their ability to directly act on the graphical representation of molecules and materials, enabling comprehensive capture of important information and showing excellent performance in predicting material properties. Nevertheless, GNNs still face several key problems in practical applications: First, although existing nested graph network strategies increase critical structural information such as bond angles, they significantly increase the number of trainable parameters in the model, resulting in a increase in training costs; Second, extending GNN models to broader domains such as molecules, crystalline materials, and catalysis, as well as adapting to small data sets, remains a challenge. Finally, the scalability of GNN models is limited by the over-smoothing problem. To address these issues, we propose the DenseGNN model, which combines Dense Connectivity Network (DCN), hierarchical node-edge-graph residual networks (HRN), and Local Structure Order Parameters Embedding (LOPE) strategies to create a universal, scalable, and efficient GNN model. We have achieved state-of-the-art performance (SOAT) on several datasets, including JARVIS-DFT, Materials Project, QM9, Lipop, FreeSolv, ESOL, and OC22, demonstrating the generality and scalability of our approach. By merging DCN and LOPE strategies into GNN models in computing, crystal materials, and molecules, we have improved the performance of models such as GIN, Schnet, and Hamnet on materials datasets such as Matbench. The LOPE strategy optimizes the embedding representation of atoms and allows our model to train efficiently with a minimal level of edge connections. This substantially reduces computational costs and shortens the time required to train large GNNs while maintaining accuracy. Our technique not only supports building deeper GNNs and avoids performance penalties experienced by other models, but is also applicable to a variety of applications that require large deep learning models. Furthermore, our study demonstrates that by using structural embeddings from pre-trained models, our model not only outperforms other GNNs in distinguishing crystal structures but also approaches the standard X-ray diffraction (XRD) method.

Fabrications of Graphene Oxide and Silver Nanowire Hybrid Nanocomposites for Li-Metal Anodes with Dendrite Suppression

Lithium (Li) metal is the most promising anode material for Li-metal batteries due to its high theoretical capacity (3,860 mAh g-1) and low electrochemical potential (-3.04 V vs. the standard hydrogen electrode). However, many of previously studies for Li-metal batteries utilized a relatively thick ~ 50 μm Li metal foils, which lowers overall energy densities of the cells. Therefore, there have been growing research to reduce the thickness of the Li-metal anode to improve the energy density of batteries. Typical anode current collector of a copper foil show low affinity of Li metals with great initial deposition overpotentials when Li-metals are eletrodeposited. Such lithiophobicity accelerate the problems of Li dendritic growth with reduction in cycle life, which has been critical problems in practical applications of pristine Cu current collectors for Li-metal batteries. Therefore, various types of surface modification methods have been reported so far, such as coatings of carbon materials such as GO or metal-based materials such as Ag, Au. However, only carbon or metal modifications have been suffering from the relatively low lithiophilicity of the carbons and pulverizations of metals during cyclings by alloying and dealloying with Li-metals, respectively. Carbon and metal hybrid composites were also studied to mitigate these problems, but there still large rooms for further improvements showing unstable cycle performances at high C-rates.Herein, we report the fabrication of lithiophilic graphene oxide (GO) and silver nanowire (AgNW) nanocomposite anodes by Layer-by-Layer (LbL) assembly methods for uniform Li depositions. GO provides a fast Li ion movement channel with high ionic conductivity, and AgNW shows low nucleation overpotential behavior due to the formation of an alloy with Li. Therefore, we alternately stack these two materials using the LbL assembly method to induce a synergistic effect of their advantages. The resulting GO and AgNW nanocomposite films show uniformly deposited morphology after Li depositions and low nucleation overpotential compared to copper foils when pre-deposited at capacity of 4 mAh cm-2. The symmetric cells with the GO-AgNW composite electrodes with 4 mAh cm-2 of Li pre-depositions show stable behavior for 400 cycles with an overpotential of 10 mV at 1 mA cm-2 and 1 mAh cm-2. In addition, the full cells using LFP cathode materials operate more than 400 cycles at 1 C-rate and 200 cycles even at high 3 C-rate, showing longer cycle life and capacity retention than copper foil. These LbL self-assembled anodes provides a facile route to prepare lithiophillic anode coatings that can be advantageous for the preparations of high energy density Li-metal batteries.

The Logic and Application of Greedy Algorithms

Abstract. Being easy to implement and offering faster solutions, the greedy algorithms have widely been used in solving combinatorial optimization problems in computer science and many practical applications, such as resource allocation and data compression. This paper aims at analyzing the definitions, theory, and application of greedy algorithms in computer science as well as their logic and efficacy in numerous situations. In this analysis, concepts such as the greedy choice property and optimal substructure are examined as essential to understanding how greedy algorithms operate. The provided analysis mainly assumes prior knowledge of concepts and problems like the Fractional Knapsack problem and the Shortest Path problem solution as well as some practical problems, for example, resource allocation and the exact coin change problem. Analytical data and real applications are used to analyze the performance of greedy algorithms vis-a-vis other categories, such as the dynamic programming paradigm. The results reveal that greedy algorithms have the potential for great efficiency while also being restricted by certain parameters. They are ideal in the following aspects pertaining to computational aspects, that is, speed, ease of implementation, and applicability to large datasets. But they do not necessarily promise strong optimality in certain situations. The conclusion notes that greedy algorithms deserve further applications when the specified conditions are met and states that the effectiveness of these conditions needs to be studied.

A network coding transmission strategy for preventing eavesdropping attacks

For secure network coding to prevent eavesdropping attacks, a general design is used that puts restrictions on the eavesdropping channel set or encryption technology is embedded, which can cause problems in practical applications. Based on random linear network coding technology, in this paper, a transmission strategy to prevent eavesdropping attacks is proposed for single-source multicast networks, without either precondition of restricting the eavesdropping channel set or embedding encryption technology. The data transmission process is divided into three phases, and the global encoding vectors are separated from the encoded data on the channel. The proposed strategy can enable the sink to decode successfully, making it very difficult for eavesdroppers to meet the conditions for successful decoding by randomly selecting the eavesdropping channel set without obtaining network topology knowledge. The feasibility of the proposed method is strictly demonstrated using layered network technology, and its security is analyzed. The simulation results confirm the validity of the theoretical analysis.

Implicit modular coupled heat transfer analysis for functionally graded materials using the SVC-FMC method

Functionally graded materials have found extensive applications in the aerospace industry and solar energy systems due to their excellent performance in enhancing heat transfer/insulation. Developing corresponding theoretical frameworks for the coupled radiation and conduction heat transfer (CRCHT) is crucial for unlocking their full potential and expanding their applicability in various high-performance and critical applications. Considering the limitations of existing frameworks in addressing CRCHT problems in practical applications, such as the explicit iteration procedure and the linearization assumption, a fully implicit framework as the source value caching function Monte Carlo (SVC-FMC) method is proposed based on the null collision reverse Monte Carlo and the Green's Function Markov Superposition Monte Carlo, which are suitable for solving the radiative transfer equation and the energy equation within non-uniform media. The accuracy of the proposed method is validated with different heat transfer systems. It was observed that the maximum root mean square error during the verification process did not exceed 0.08, thereby demonstrating the efficacy of SVC-FMC in analyzing CRCHT processes involving gradient materials. Results reveal that the material properties that transcend the functional distribution form will augment the strengthening effect of heat transfer/insulation. The scattering characteristics significantly influence the strengthening effect caused by refractive index distribution.

Enhancement of interlayer exchange coupling via intercalation in 2D magnetic bilayers: towards high Curie temperature.

Two-dimensional magnetic materials are considered as promising candidates for developing next-generation spintronic devices by providing the possibility of scaling down to nanometers. However, a low Curie temperature is a crucial problem for practical applications, being intimately related to weak interlayer exchange coupling. Here, by using density functional theory calculations, we show that interlayer exchange coupling can be enhanced by intercalating 3d transition metals (Sc to Zn) into a bilayer of CrI3 and NiI2. It is found that intercalated Ni and Cr atoms exhibit strong antiferromagnetic coupling with the CrI3 and NiI2 host layers, respectively. This enhances the ferromagnetic interlayer exchange coupling between the host layers by many folds compared to pristine CrI3 and NiI2 bilayers. Moreover, both intercalated compounds show out-of-plane magnetic anisotropy with half metallic nature, which makes them ideal candidates for spintronics applications. Thereby our work provides a rational approach to raise the Curie temperature of non-metallic two-dimensional magnets by intercalation.

High entropy alloy nanoparticles decorated carbon-based electrode as interfacial Li-ion localized accelerators for anode-free lithium metal batteries

Lithium metal is deemed as the main representative of the material for next-generation energy storage cells. Nevertheless, the problems of lithium dendrite growth and anode electrode volume expansion have become difficult problems in practical applications. Here, we introduced lithophilic CuInNiSnCd HEA-NPs on the surface of three-dimensional (3D) carbon hosts through a high-temperature redox strategy (HEA/CF). The application of the HEA/CF 3D framework can effectively decrease the non-uniform current density of the anode electrode, thereby slowing down the volume expansion of lithium metal during cycling. The introduction of CuInNiSnCd HEA-NPs improves the lithophilic of the carbon substrate and reduces the nucleation potential of lithium. Meanwhile, their evolution process was monitored in situ by the dynamic electrochemical impedance spectroscopy (DEIS) with relaxation time (DRT) distribution analysis, and the introduction of the HEA was beneficial to improving Li+ migration kinetics. Consequently, the symmetric battery with Li@HEA/CF anode has an excellent cycle life of over 3000 h (10 mA cm−2/10 mAh cm−2). Furthermore, the anode-free HEA/CF assembled full cell has excellent rate performance with a stable CE of more than 99.2 % after 160 cycles at 1C. This research contributes to the use of high-entropy materials in advanced energy storage batteries and provides valuable guidance for enhancing the performance of lithium batteries.

An Undersampling Method Approaching the Ideal Classification Boundary for Imbalance Problems

Data imbalance is a common problem in most practical classification applications of machine learning, and it may lead to classification results that are biased towards the majority class if not dealt with properly. An effective means of solving this problem is undersampling in the borderline area; however, it is difficult to find the area that fits the classification boundary. In this paper, we present a novel undersampling framework, whereby the clustering of samples in the majority class is conducted and segmentation is then performed in the boundary area according to the clusters obtained; this enables a better shape that fits the classification boundary to be obtained via the performance of random sampling in the borderline area of these segments. In addition, we hypothesize that there exists an optimal number of classifiers to be integrated into the method of ensemble learning that utilizes multiple classifiers that have been obtained via sampling to promote the algorithm. After passing the hypothesis test, we apply the improved algorithm to the newly developed method. The experimental results show that the proposed method works well.

Macrocyclic thiol ligand additive engineering for stable and efficient perovskite solar cells

Perovskite solar cells have a rapid development speed and excellent prospects, but still face performance and stability problems for practical applications. Excessive Pb2+ defects often accompany the preparation of perovskite, which greatly affect the performance and stability of perovskite solar cells. Herein, a macrocyclic thiol ligand (DXPSH) is introduced as an additive for organic amine salt. It effectively reduces the defect density of perovskite, induces better crystal orientation of perovskite, and improves carrier lifetime. As a result, the optimized DXPSH-modified PSCs achieves a maximum power conversion efficiency of 24.03 % and good long-term stability in ambient air.

Improved marine predators algorithm for engineering design optimization problems

The Marine Predator Algorithm (MPA) has unique advantages as an important branch of population-based algorithms. However, it emerges more disadvantages gradually, such as traps to local optima, insufficient diversity, and premature convergence, when dealing with complex problems in practical industrial engineering design applications. In response to these limitations, this paper proposes a novel Improved Marine Predator Algorithm (IMPA). By introducing an adaptive weight adjustment strategy and a dynamic social learning mechanism, this study significantly improves the encounter frequency and efficiency between predators and preys in marine ecosystems. The performance of the IMPA was evaluated through benchmark functions, CEC2021 suite problems, and engineering design problems, including welded beam design, tension/compression spring design, pressure vessel design, and three-bar design. The results indicate that the IMPA has achieved significant success in the optimization process over other methods, exhibiting excellent performance in both solving optimal parameter solutions and optimizing objective function values. The IMPA performs well in terms of accuracy and robustness, which also proves its efficiency in successfully solving complex industrial engineering design problems.

Progress and Perspective for "Green" Strategies of Catalytic Plastics Conversion into Fuels by Regulating Half-Reactions.

Nowadays, plastic waste threatens public health and the natural ecosystems of our lives. It is highly beneficial to recycle plastic waste in order to maximize the reuse of its contained carbon sources for the development of other valuable products. Unfortunately, traditional techniques usually require significant energy consumption and result in the generation of hazardous waste. Herein, the up-to-date developments on the "green" strategies under mild conditions including electrocatalysis, photocatalysis, and photoelectrocatalysis of plastic wastes are presented. During the oxidation of plastics in these "green" strategies, corresponding reduction reactions usually exist, which affect the property of catalytic plastics conversion. Particularly, we mainly focus on how to design the corresponding half reactions, such as the water reduction, carbon dioxide reduction, and nitrate reduction. Finally, we provide forward-looking insight into the enhancement of these "green" strategies, the extension of more half reactions into other organic catalysis, a comprehensive exploration of the underlying mechanisms through in situ studies and theoretical analysis and the problems for practical applications that needs to be solved.

Rethink Motion Information for Occluded Person Re-Identification

Person re-identification aims to identify the same pedestrians captured by various cameras from different viewpoints in multiple scenarios. Occlusion is the toughest problem for practical applications. In video-based ReID tasks, motion information can be easily obtained from sampled frames, and provide discriminative human part representations. However, most motion-based methodologies are designed for video frames which are not suitable for processing single static image input. In this paper, we propose a Motion-Aware Fusion (MAF) network, aiming to acquire motion information from static images in order to improve the performance of ReID tasks. Specifically, a visual adapter is introduced to enable visual feature extraction, either from image or video data. We design a motion consistency task to guide the motion-aware transformer to learn representative human-part motion information and greatly improve the learning quality of features of occluded pedestrians. Extensive experiments on popular holistic, occluded, and video datasets demonstrate the effectiveness of our proposed method. This method outperforms state-of-the-art approaches by improving the mean average precision (mAP) by 1.5% and rank-1 accuracy by 1.2% on the challenging Occluded-REID dataset. At the same time, it surpasses other methods on the MARS dataset with an improvement of 0.2% in mAP and 0.1% in rank-1 accuracy.

A Hydrogel Electrolyte with High Adaptability over a Wide Temperature Range and Mechanical Stress for Long-Life Flexible Zinc-Ion Batteries.

Flexible zinc-ion batteries have garnered significant attention in the realm of wearable technology. However, the instability of hydrogel electrolytes in a wide-temperature range and uncontrollable side reactions of the Zn electrode have become the main problems for practical applications. Herein, N,N-dimethylformamide (DMF) to design a binary solvent (H2O-DMF) is introduced and combined it with polyacrylamide (PAM) and ZnSO4 to synthesize a hydrogel electrolyte (denoted as PZD). The synergistic effect of DMF and PAM not only guides Zn2+ deposition on Zn(002) crystal plane and isolates H2O from the Zn anode, but also breaks the hydrogen bonding network between water to improve the wide-temperature range stability of hydrogel electrolytes. Consequently, the symmetric cell utilizing PZD can stably cycle over 5600 h at 0.5mA cm- 2@0.5 mAh cm-2. Furthermore, the Zn//PZD//MnO2 full cell exhibits favorable wide-temperature range adaptability (for 16000 cycles at 3 A g-1 under 25 °C, 750 cycles with 98 mAh g-1 at 0.1 A g-1 under -20 °C) and outstanding mechanical properties (for lighting up the LEDs under conditions of pressure, bending, cutting, and puncture). This work proposes a useful modification for designing a high-performance hydrogel electrolyte, which provides a reference for investigating the practical flexible aqueous batteries.

RFID tag recognition model for Internet of Things for training room management

With the rapid development of the Internet of Things and intelligent technology, the application of Radio Frequency Identification (RFID) technology in training room management is becoming increasingly widespread. An efficient and accurate RFID system can significantly improve the management efficiency and resource utilization of the training room, thereby improving teaching quality and reducing management costs. Although RFID technology has many advantages, there are still some problems in practical applications, such as label collision and recognition of unknown labels. These issues not only affect the performance of the system but may also cause interference with actual teaching and management. This study proposes a grouping-based bit arbitration query tree algorithm and anti-collision technology to solve label collisions and reduce label recognition time in the technology. A new unknown label recognition algorithm is also proposed to improve the recognition efficiency and accuracy of identifying new unknown labels. Related experiments have shown that the recognition accuracy of the algorithm designed this time is 95.86%. Compared with other algorithms, the number of idle time slots is the smallest. When the number of queries is 1000, the algorithm has 1842 queries, and the communication complexity is the best. When the number of unknown tags is 10,000, the actual accuracy rate is 95.642%. Compared with traditional recognition algorithms, the new unknown label recognition algorithm has a smaller frame length in the same label proportion and good recognition performance. On a theoretical level, the research content on RFID technology helps to improve and develop the basic theories of the Internet of Things and intelligent recognition technology and provides solutions and application technologies for equipment management and IoT applications in training rooms. On a practical level, the research results can provide specific guidance for the management of training rooms, help solve equipment management and safety maintenance problems in practical applications, and improve the management efficiency of training rooms.

Detection of variety and wax bloom of Shaanxi plum during post-harvest handling

The detection of plum variety and wax bloom has extensive applications in the fields of fruit classification and fruit quality assessment. By automating the detection and identification of plum varieties and wax bloom, it is possible to enhance the efficiency and accuracy of variety identification and quality assessment, and reduce manual intervention and misjudgment, thereby improving the market competitiveness of fruits. Currently, many works focus on improving the detection performance of single attribute detection of plum varieties or wax bloom, and it is often necessary to use two models to detect the same plum variety and quality information separately, which leads to inefficient and resource-consuming problems in practical applications. To solve this problem and improve the efficiency of detection, a Multi-Label detection model based on YOLOv7 is proposed. Firstly, the double detection head structure is introduced to improve the prediction ability for two types of attribute features. Then, the loss function suitable for multi-attribute labels is improved, and two classification loss functions are used to optimize the prediction results of the two types of attribute labels, respectively. Finally, a multi-label non-maximum suppression algorithm is proposed to solve the problem of filtering redundant bounding boxes of multi-attribute labels. Experimental results on the plum image dataset show that the proposed Multi-Label YOLOv7 model achieves a mAP@0.5 of 96.2 %, a precision of 94.6 %, and a recall of 89.5 %. The experimental results show that the Multi-Label YOLOv7 model can effectively detect variety and wax bloom attributes and improve the efficiency of multi-attribute label detection. The code and dataset for this experiment can be found at https://github.com/hejinrong/Muti-Label-YOLOv7.

On phase recovery and preserving early reflections for deep-learning speech dereverberation.

In indoor environments, reverberation often distorts clean speech. Although deep learning-based speech dereverberation approaches have shown much better performance than traditional ones, the inferior speech quality of the dereverberated speech caused by magnitude distortion and limited phase recovery is still a serious problem for practical applications. This paper improves the performance of deep learning-based speech dereverberation from the perspectives of both network design and mapping target optimization. Specifically, on the one hand, a bifurcated-and-fusion network and its guidance loss functions were designed to help reduce the magnitude distortion while enhancing the phase recovery. On the other hand, the time boundary between the early and late reflections in the mapped speech was investigated, so as to make a balance between the reverberation tailing effect and the difficulty of magnitude/phase recovery. Mathematical derivations were provided to show the rationality of the specially designed loss functions. Geometric illustrations were given to explain the importance of preserving early reflections in reducing the difficulty of phase recovery. Ablation study results confirmed the validity of the proposed network topology and the importance of preserving 20 ms early reflections in the mapped speech. Objective and subjective test results showed that the proposed system outperformed other baselines in the speech dereverberation task.

Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.

Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer's disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation. This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation. A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods. Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.

Subject Review: Anomaly Detection in Cyber Security Using Convolution Neural Network

Security concerns are multiplying as a result of the rapid advancement of computer and communications technology. Cybersecurity is evolving into different types of techniques to reduce these concerns. Amongst these techniques is anomaly detection (AD). Anomaly identification (AI) is a major problem in many academic fields and practical applications. It has been presented that Convolution Neural Networks (CNNs) be used to identify anomalies from different type; however, there is currently no guide over which model to apply in a specific instance. In this study presented the most relevant Convolution Neural Networks (CNNs) as a key solution for anomaly detection (AD) in the literature, compares between anomaly detection (AD) techniques which current in the previous studies. In addition, the pros and cons of this technique are examined in variousapplication contexts, and their results are presented. Finally, this study offers a number of recommendations for future studies that will assist readers in their subsequent efforts in this field.

On the performance of standard nature-inspired algorithms in solving non-stationary optimization problems

Non-stationary optimization problems are a very important class of problems in many practical applications. These problems are characterized by objective functions and constraints that change with time or environmental conditions, so the optimization solution also needs to be dynamically adjusted accordingly. Many algorithms from the field of evolutionary and biology inspired computation are known as an effective approach for dealing with hard optimization problems in changing environments, that is the result of modelling of self-organized systems in nature and evolution in the biology. Natural systems always exist in the changing environments. This article aims to compare the performance of three common nature-inspired techniques, namely genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and differential evolution (DE) in their standard implementation when solving non-stationary optimization problems, so as to provide a reference and rationale for subsequent selection of appropriate algorithms and improvements.