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With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assisted MEC. Specifically, the joint UAV deployment and task offloading problem is first modeled as a large-scale multiobjective optimization problem with the purpose of minimizing energy consumption while improving user satisfaction. Then, a large-scale UAV deployment and task offloading multiobjective optimization method based on the evolutionary algorithm, called LDOMO, is designed to address the above formulated problem. In LDOMO, a CSO-based evolutionary strategy and a MLP-based evolutionary strategy are proposed to explore solution spaces with different features for accelerating convergence and maintaining the diversity of the population, and two local search optimizers are designed to improve the quality of the solution. Finally, simulation results show that our proposed LDOMO outperforms several representative multiobjective evolutionary algorithms.

Tungsten is a very important refractory metal in the fields of nuclear fusion demonstration reactor, spallation neutron source, and semiconductor. The expected properties of tungsten plates can be obtained by rolling and deforming. The DEFORM software was used to simulate the rolling of tungsten plates in large size in this paper, and the influence of rolling mode on damage factor (damage value) and strain was analyzed. The results show that, the serious damage area for one-way rolling is mainly distributed at the side edge of the billets, which is easy to produce cracks; the serious damage area for cross rolling is uniformly distributed around the billets. The strain curves of cross rolling in the rolling direction may reach a “maximum value” and then descend to a “minimum value”, and the difference of strain between the two maximum value is smaller in cross rolling than that in one-way rolling.

This study examines the impact of financial and industrial agglomeration on regional economic development through a descriptive analysis. By establishing an evaluation index system for financial agglomeration effects and constructing a spatial measurement model, we empirically investigate the dynamics between financial accumulation and economic growth. Our Analysis of 37 Chinese cities reveals that Moran’s I, indicating the spatial correlation of economic growth, varies between 0.300 and 0.410, suggesting a strengthening spatial correlation over time. These findings highlight the significance of financial agglomeration in regional economic development and suggest avenues for economic structure optimization.

The 5W2H management model, rooted in World War II, stands out for its simplicity and effectiveness in enterprise management, offering considerable advantages in strategy planning and execution. This study focuses on the model’s role in enhancing strategic economic management within businesses, combining qualitative and quantitative analyses of various enterprises’ strategic management frameworks. Implementing the 5W2H model led to notable enhancements in strategic definition, implementation efficiency, and tangible achievements. Specifically, businesses observed a 20% improvement in achieving strategic objectives, a 15% faster strategy implementation, and a 25% increase in strategic engagement among employees. Additionally, the model proved instrumental in improving internal communication, fostering better teamwork, and optimizing the allocation of resources. Our findings affirm the significant value of the 5W2H model in refining corporate strategy formulation and execution, highlighting its essential place in modern enterprise management practices.

With the help of cooperative game theory, this study constructs an integrated demand response economic game scheduling model for carbon trading, aiming to maximize collective and individual benefits. The model effectively reduces the system operating cost by adjusting the electricity price to incentivize PDR-VGU output. The impacts of price demand and integrated flexible operation demand on the coordination degree of carbon trading and the optimized dispatch results under different scenarios are analyzed in depth. It is found that when considering the carbon trading mechanism, the system operating cost and carbon emissions are reduced by RMB 2,129 and 9.63 tons, and RMB 2,350 and 11.96 tons, respectively, showing a win-win situation in terms of economy and environmental protection. In addition, the energy time-shift strategy implemented in the carbon capture power plant system effectively balances the peak-to-valley difference of thermal power output, further reducing the cost.

The wheel reducer is one of important transmission structures of heavy or new energy vehicles for increased torque and reduces speed. The gearbox mainly carries the internal load from gear transmission and the external load from frame connection. The reasonable design and strength performance of the gearbox are important guarantees for the smooth operation of the wheel reducer. Based on the three-dimensional geometric structure of the gearbox and the finite element analysis method, a structural strength analysis method integrating "model-strength evaluation-running" is established and the structural strength of the gearbox is investigated comprehensively. The results show that the box under the load and strength evaluation criteria of combined calculation has sufficient design strength and stiffness, which can support the smooth operation of the infield transmission system and resist the deformation caused by external load. The distributed contact area of the joint surface of gearboxes is ensured by the sufficient distribution and strength of bolts, and no oil leakage occurs in the gearbox. The operational experiment result of gearboxes is in agreement with the above theoretical analysis. The proposed structural strength analysis method provides a technical basis for the structural design of the gearbox.

Objective To explore the effect of ophiopogonin B(OP-B) on the biological behavior of esophageal squamous cell carcinoma cell line(EC9706) and its mechanism related to the regulation of Down syndrome cell adhesion molecule antisense 1 (DSCAM-AS1) and miR-199a-3p expression. Methods EC9706 cells were divided into control group, experiment groups with low, medium and high dosage of OP-B, si-NC group, si-DSCAM-AS1 group and (pcDNA-DSCAM-AS1+OP-B) group. Colony formation and CCK-8 method were used to detect EC9706 proliferation. Flow cytometry and scratch healing test were used to analyze the apoptosis rate and migration of EC9706 cells. RT-qPCR was performed to detect the expression of DSCAM-AS1 and miR-199a-3p. The targeting relationship between DSCAM-AS1 and miR-199a-3p was verified using dual luciferase reporter gene experiment. Results OP-B reduced EC9706 viability, colony formation counting, migration distance and DSCAM-AS1 expression (P＜0.05), increased cell apoptosis rate and miR-199a-3p expression (P＜0.05). DSCAM-AS1 interacted directly with miR-199a-3p. After interference to DSCAM-AS1 expression, the cell viability, colony formation and migration distance of EC9706 cells were significantly reduced(P＜0.05), while apoptosis rates were significantly increased(P＜0.05). Over-expression of DSCAM-AS1 antagonized the effects of OP-B on the proliferation, apoptosis and migration of EC9706 cells(P＜0.05). Conclusions OP-B inhibits proliferation and migration of esophageal squamous cell carcinoma cells and induces cell apoptosis. The mechanism is potentially explained by down-regulating DSCAM-AS1/miR-199a-3p pathway.

Alkylphenols are a kind of compounds produced by alkylation of phenolic compounds. Compared with other non-hydrocarbon compounds, alkylphenols are more soluble in water. In this paper, through three groups of oil-water distribution experiments at different temperatures (25 ℃, 45 ℃ and 65 ℃), different brine concentrations (4000 mg/L, 6000 mg/L and 8000 mg/L) and different crude oil types (X37, X45 and X61), the distribution characteristics of alkylphenols between oil and water were studied, and the variation law of alkylphenol distribution coefficient was determined. The results show that the oil-water partition coefficient increases with the increase of alkyl molecular weight of homologues; The oil-water partition coefficient of alkylphenols decreases with the increase of temperature, and alkyl homologues with higher molecular weights are more sensitive to temperature; The partition coefficient of alkylphenol in oil-water system increases with the increase of water salinity. According to the experimental results, temperature and salinity of water phase will cause the difference of oil-water partition coefficient of alkylphenol, and the influence of temperature is greater than that of salinity. The distribution behavior of alkylphenols between oil and water can provide more reference for the secondary migration of crude oil and the direction of water drive front in reservoir development.

As far as we know, it is usually difficult for the accuracy of similar cases obtained by the existing case recommendation methods to meet the needs of judges, and thus the effect of auxiliary judgment is limited. Therefore, in this paper a novel context-aware similar case matching and recommendation model (CASCMR). In order to achieve end-to-end efficient text matching and recommendation was proposed. The model uses a multi-semantic document expression framework to realize the pre-calculation and storage of text vectors, so as to reduce the matching time and improve the efficiency. Specifically, in order to model the long legal text, CASCMR uses BERT for encoding since its attention mechanism can capture the long-term dependency well. At the same time, the global and local information of the legal text as captured by Bi-LSTM and CNN, respectively, is considered to be helpful to improve the representation of the text, as well as the prediction performance of the model. The proposed model was then applied to the similar case matching task of CAIL2019-SCM, and its accuracy is higher than that of the state-of-the-art method.

Currently, more and more studies suggested that reductionism was lack of holistic and integrative view of biological processes, leading to limited understanding of complex systems like microbiota and the associated diseases. In fact, microbes are rarely present in individuals but normally live in complex multispecies communities. With the recent development of a variety of metaomics techniques, microbes could be dissected dynamically in both temporal and spatial scales. Therefore, in-depth understanding of human microbiome from different aspects such as genomes, transcriptomes, proteomes, and metabolomes could provide novel insights into their functional roles, which also holds the potential in making them diagnostic biomarkers in many human diseases, though there is still a huge gap to fill for the purpose. In this mini-review, we went through the frontlines of the metaomics techniques and explored their potential applications in clinical diagnoses of human diseases, e.g., infectious diseases, through which we concluded that novel diagnostic methods based on human microbiomes shall be achieved in the near future, while the limitations of these techniques such as standard procedures and computational challenges for rapid and accurate analysis of metaomics data in clinical settings were also examined.

The prime objective of the cloud data storage process is to make the service, irrespective of being infinitely extensible, a more reliable storage and low-cost model that also encourages different data storage types. Owing to the storage process, it must satisfy the cloud users’ prerequisites. Nevertheless, storing massive amounts of data becomes critical as this affectsthe data quality or integrity. Hence, this poses various challenges for existing methodologies. An efficient, reliable cloud storage model is proposed using a hybrid heuristic approach to overcome the challenges. The prime intention of the proposed system is to store the data effectively in the cloud environment by resolving two constraints, which are general and specific (structural). The cloud data were initially gathered and used to analyze the storage performance. Since the data were extensive, different datasets and storage devices were considered. Every piece of data was specified by its corresponding features, whereas the devices were characterized by the hardware or software components. Subsequently, the objective function was formulated using the network’s structural and general constraints. The structural constraints were determined by the interactions between the devices and data instances in the cloud. Then, the general constraints regarding the data allocation rules and device capacity were defined. To mitigate the constraints, the components were optimized using the Hybrid Pelican–Billiards Optimization Algorithm (HP-BOA) to store the cloud data. Finally, the performance was validated, and the results were analyzed and compared against existing approaches. Thus, the proposed model exhibited the desired results for storing cloud data appropriately.

Objective: We aimed to investigate the effect and mechanisms of action of two drug pairs [Huangqi-Chuanxiong and Sanleng-Ezhu Herb (HCSE)] on the treatment of ischemic stroke.Materials and methods: We mined the current literature related to ischemic stroke and formulated a new formulation of Chinese herbs. Then, we identified the main candidate target genes of the new formulation by network pharmacology. Next, we performed enrichment analysis of the target genes to identify the potential mechanism of action of the new formulation in the treatment of ischemic stroke. Next, we experimentally validated the mechanism of action of the new formulation against ischemic stroke. Infarct volume and neurological deficits were evaluated by 2,3,5-triphenyltetrazolium (TTC) staining and Longa’s score, respectively. The predicted pathways of signal-related proteins were detected by western blotting.Results: We mined the current literature and identified a new formulation of Chinese herbs for the treatment of ischemic stroke. The formulation included Huangqi, Chuanxiong, Sanleng and Ezhu. Next, we used network pharmacological analysis to identify 23 active compounds and 327 target genes for the new formulation. The key target genes were MAPK3, MAPK1, HSP90AA1, STAT3, PIK3R1, PIK3CA and AKT1. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed significant enrichment of the PI3K/AKT and MAPK/ERK signaling pathways. By performing experiments, we found that the new formulation reduced the infarct volume of middle cerebral artery occlusion (MCAO) induced mice and activated the PI3K/AKT and MAPK/ERK signaling pathways. These findings confirmed that the new formulation has a significant protective effect against ischemic stroke injury by activating the PI3K/AKT and MAPK/ERK signaling pathways.Conclusion: We identified a new treatment formulation for ischemic stroke by data mining and network pharmacological target prediction. The beneficial effects of the new formulation act by regulating multiple target genes and pathways. The mechanism of action of the new formulation may be related to the AKT and ERK signaling pathways. Our findings provide a theoretical basis for the effects of the new formulation on ischemic stroke injury.

To reduce the delamination of mango juice beverage during processing and storage，improve product stability and shelf life quality，the single factor experiment，sensory evaluation and stability analysis were carried out to study the stability of five kinds of hydrophilic-colloid，such as xanthan gum，pectin，soybean polysaccharide，sodium carboxymethyl cellulose (CMC-Na)，gellan gum when used in mango juice drink.Xanthan gum and CMC-Na were further selected for the optimized combination experiment.The results showed that the stability of 0.7 g/kg xanthan gum and 1.8 g/kg CMC-Na was the optimum addition.

In order to solve the problem of bag expanding of braised brisket with potato，the microorganism of expanding bag was separated，purified，identified and gas production characteristics were studied.Seven dominant strains were obtained.Morphology observation，16S rRNA sequence and MALDI-TOF MS were combined to identify the obtained strains.As a consequence，strain 001 and 002 were identified as Bacillus subtilis，strain 003 and 004 were Staphylococcus epidermidis，strain 005 and 006 were Bacillus licheniformis，strain 007 were the member from Bacillus coagulans.When those strains were innoculated into the braised brisket with potato，obvious gas generation was observed for strain 001，002，005，and 006 which was the main reason for the bag expansion of braised brisket with potato.

Numerous experiments have proved that microRNAs (miRNAs) could be used as diagnostic biomarkers for many complex diseases. Thus, it is conceivable that predicting the unobserved associations between miRNAs and diseases is extremely significant for the medical field. Here, based on heterogeneous networks built on the information of known miRNA–disease associations, miRNA function similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases, we developed a computing model of biased random walk with restart on multilayer heterogeneous networks for miRNA–disease association prediction (BRWRMHMDA) through enforcing degree-based biased random walk with restart (BRWR). Assessment results reflected that an AUC of 0.8310 was gained in local leave-one-out cross-validation (LOOCV), which proved the calculation algorithm’s good performance. Besides, we carried out BRWRMHMDA to prioritize candidate miRNAs for esophageal neoplasms based on HMDD v2.0. We further prioritize candidate miRNAs for breast neoplasms based on HMDD v1.0. The local LOOCV results and performance analysis of the case study all showed that the proposed model has good and stable performance.

Targeting microRNAs (miRNAs) with drug small molecules (SMs) is a new treatment method for many human complex diseases. Unsurprisingly, identification of potential miRNA-SM associations is helpful for pharmaceutical engineering and disease therapy in the field of medical research. In this paper, we developed a novel computational model of HeteSim-based inference for SM-miRNA Association prediction (HSSMMA) by implementing a path-based measurement method of HeteSim on a heterogeneous network combined with known miRNA-SM associations, integrated miRNA similarity, and integrated SM similarity. Through considering paths from an SM to a miRNA in the heterogeneous network, the model can capture the semantics information under each path and predict potential miRNA-SM associations based on all the considered paths. We performed global, miRNA-fixed local and SM-fixed local leave one out cross validation (LOOCV) as well as 5-fold cross validation based on the dataset of known miRNA-SM associations to evaluate the prediction performance of our approach. The results showed that HSSMMA gained the corresponding areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.9913, 0.9902, 0.7989, and 0.9910 ± 0.0004 based on dataset 1 and AUCs of 0.7401, 0.8466, 0.6149, and 0.7451 ± 0.0054 based on dataset 2, respectively. In case studies, 2 of the top 10 and 13 of the top 50 predicted potential miRNA-SM associations were confirmed by published literature. We further implemented case studies to test whether HSSMMA was effective for new SMs without any known related miRNAs. The results from cross validation and case studies showed that HSSMMA could be a useful prediction tool for the identification of potential miRNA-SM associations. Keywords: microRNA, small molecule, association prediction, HeteSim algorithm, heterogeneous network

Detecting the transportation mode of an individual's everyday travel provides useful information in urban design, real-time journey planning, and activity monitoring. In existing systems, the accelerometer and GPS are predominant signal sources which quickly drain the limited battery life of the wearable devices. In this paper, we present an alternative approach for fine-grained transportation mode detection using kinetic energy harvester (KEH). We demonstrate the feasibility of using the output signal from the KEH device as the information source to achieve transportation mode detection. The proposed system is motivated by the fact that different transportation modalities produce distinctive motion patterns which are expected to leave distinctive patterns for context detection. To achieve fine-grained transportation mode detection, we design a transportation detection framework based on attention-based Long Short Term Memory (LSTM). We evaluate our approach using 38.6 hours of transportation data, which is collected from a total of six volunteers in three months' time using our prototype. The evaluation results show that our approach is able to reach an overall accuracy of over 97% to detect fine-grained transportation modalities. In addition, our measurements show that the power consumption of the sampling KEH signal is only 460uW which significantly outperforms the existing transportation mode detection systems.

A Petri net based framework is proposed for automatic high level video event description, recognition and reasoning purposes. In comparison with the existing approaches reported in the literature, our work is characterized with a number of novel features: (i) the high level video event modeling and recognition based on Petri net are fully automatic, which are not only capable of covering single video events but also multiple ones without limit; (ii) more variations of event paths can be found and modeled using the proposed algorithms; (iii) the recognition results are more accurate based on automatic built high level event models. Experimental results show that the proposed method outperforms the existing benchmark in terms of recognition precision and recall. Additional advantages can be achieved such that hidden variations of events hardly identified by humans can also be recognized.

Online sequential extreme learning machine (OS-ELM) and its variants provide a promising way to solve data stream problems, but most of them do not take the timeliness of the problems into account, which may degrade the performance of the model. The main reason is that these algorithms are unable to adapt to the latest data accordingly when the distribution of the data stream changes. To mitigate this limitation, the forgetting factor is introduced into the relevant models, which is used to balance the relative importance of past data and new data when necessary. However, there is no efficient way to set the forgetting factor properly so far. In this paper, we have developed a novel updating strategy for setting the forgetting factor and proposed a dynamic forgetting factor based OS-ELM algorithm (DOS-ELM). In the sequential learning phase of DOS-ELM, the forgetting factor can be adjusted dynamically according to the change degree of the model accuracy in each learning epoch. This updating process does not require setting any parameters artificially and thus greatly improves the flexibility of the model. The experimental results on ten classification problems, five regression problems, one time-series problem show that DOS-ELM can deal well with both stationary and non-stationary data stream problems. In addition, we have extended DOS-ELM to an online deep model named ML-DOS-ELM, which can handle more complex tasks such as the face recognition problem and the handwritten digit recognition problem. Our experimental evaluations show that both DOS-ELM and ML-DOS-ELM can achieve higher prediction accuracy compared to the other similar algorithms.

Recently, there are a number of particle swarm optimization algorithms (PSOs) proposed for tackling multi-objective optimization problems (MOPs). Most of multi-objective PSOs (MOPSOs) were designed to speed up their convergence, which have been validated when tackling various kinds of MOPs. However, they may face some challenges for tackling some complicated MOPs, such as the UF test problems with complicated Pareto-optimal sets, mainly due to their neglect on the diversity. To solve the above problem, a novel hybrid MOPSO (called HMOPSO-ARA) is suggested in this paper with an adaptive resource allocation strategy, which shows a superior performance over most MOPSOs. Using the decomposition approach in HMOPSO-ARA, MOPs are transferred into a set of subproblems, each of which is accordingly optimized by one particle using a novel velocity update approach with the strengthened search capability. Then, an adaptive resource allocation strategy is employed based on the relevant improvement on the aggregated function, which can reasonably assign the computational resource to the particles according to their performance, so as to accelerate the convergence speed to the true Pareto-optimal front. Moreover, a decomposition-based clonal selection strategy is further used to enhance our performance, where the cloning process is run on the external archive based on the relevant fitness improvement. The experiments validate the superiority of HMOPSO-ARA over four competitive MOPSOs (SMPSO, CMPSO, dMOPSO and AgMOPSO) and four competitive multi-objective evolutionary algorithms (MOEA/D-ARA, MOEA/D-DE MOEA/D-GRA and EF_PD) when tackling thirty-five test problems (DTLZ1-DTLZ9, WFG1-WFG9, UF1-UF10 and F1-F9), in terms of two widely used performance indicators.

With the development of science and biotechnology, many evidences show that ncRNAs play an important role in the development of important biological processes, especially in chromatin modification, cell differentiation and proliferation, RNA progressing, human diseases, etc. Moreover, lncRNAs account for the majority of ncRNAs, and the functions of lncRNAs are expressed by the related RNA-binding proteins. It is well known that the experimental verification of lncRNA-protein relationships is a waste of time and expensive. So many time-saving and inexpensive computational methods are proposed to uncover potential lncRNA-protein interactions. In this work, we propose a novel computational method to predict the potential lncRNA-protein interactions with the bipartite network projection recommended algorithm (LPI-BNPRA). Our approach is a semi-supervised method based on the lncRNA similarity matrix, protein similarity matrix, and lncRNA-protein interaction matrix. Compared with three previous methods under the leave-one-out cross-validation, our model has a more high-confidence result with the AUC value of 0.8754 and the AUPR value of 0.6283. We also do case studies by the Mus musculus dataset to further reflect the reliability of our approach. This suggests that LPI-BNPRA will be a reliable computational method to uncover lncRNA-protein interactions in biomedical research. Keywords: lncRNA, protein, lncRNA-protein interaction prediction, semi-supervised method, recommended algorithm

Abstract Recently, many biological experiments have indicated that microRNAs (miRNAs) are a newly discovered small molecule (SM) drug targets that play an important role in the development and progression of human complex diseases. More and more computational models have been developed to identify potential associations between SMs and target miRNAs, which would be a great help for disease therapy and clinical applications for known drugs in the field of medical research. In this study, we proposed a computational model of triple layer heterogeneous network based small molecule–MiRNA association prediction (TLHNSMMA) to uncover potential SM–miRNA associations by integrating integrated SM similarity, integrated miRNA similarity, integrated disease similarity, experimentally verified SM–miRNA associations and miRNA–disease associations into a heterogeneous graph. To evaluate the performance of TLHNSMMA, we implemented global and two types of local leave-one-out cross validation as well as fivefold cross validation to compare TLHNSMMA with one previous classical computational model (SMiR-NBI). As a result, for Dataset 1, TLHNSMMA obtained the AUCs of 0.9859, 0.9845, 0.7645 and 0.9851 ± 0.0012, respectively; for Dataset 2, the AUCs are in turn 0.8149, 0.8244, 0.6057 and 0.8168 ± 0.0022. As the result of case studies shown, among the top 10, 20 and 50 potential SM-related miRNAs, there were 2, 7 and 14 SM–miRNA associations confirmed by experiments, respectively. Therefore, TLHNSMMA could be effectively applied to the prediction of SM–miRNA associations.

With increasingly fierce competition for jobs, the pressures on people have risen in recent years, leading to lifestyle and diet disorders that result in significantly higher risks of cardiovascular disease. Hypertension is one of the common chronic cardiovascular diseases; however, mainstream blood pressure measurement devices are relatively heavy. When multiple measurements are required, the user experience and the measurement results may be unsatisfactory. In this paper, we describe the design of a signal collection module that collects pulse waves and electrocardiograph (ECG) signals. The collected signals are input into a signal processing module to filter the noise and amplify the useful physiological signals. Then, we use a wavelet transform to eliminate baseline drift noise and detect the feature points of the pulse waves and ECG signals. We propose the concept of detecting the wave shape associated with an instance, an approach that minimizes the impact of atypical pulse waves on blood pressure measurements. Finally, we propose an improved method for measuring blood pressure based on pulse wave velocity that improves the accuracy of blood pressure measurements by 58%. Moreover, the results meet the american medical instrument promotion association standards, which demonstrate the feasibility of our measurement system.

Differential evolution (DE) is a simple and effective evolutionary algorithm that can be used to solve various optimization problems. In general, the population of DE tends to fall into stagnation or premature convergence so that it is unable to converge to the global optimum. To solve this issue, this paper proposes a tracking mechanism (TM) to promote population convergence when the population falls into stagnation and a backtracking mechanism (BTM) to re-enhance the population diversity when the population traps into the state of premature convergence. More specifically, when the population falls into stagnation, the TM is triggered so that the individuals who fall into the stagnant situation will evolve toward the excellent individuals in the population to promote population convergence. When the population goes into the premature convergence status, the BTM is activated so that the premature individuals go back to one of the previous statuses so as to restore the population diversity. The TM and BTM work together as a general framework and they are embedded into six classic DEs and nine state-of-the-art DE variants. The experimental results on 30 CEC2014 test functions demonstrate that the TM and BTM are able to effectively overcome the issues of stagnation and premature convergence, respectively, and therefore, enhance the performance of the DE significantly. Moreover, the experimental results also verify that the TM works together with the BTM as a general framework is better than other similar general frameworks.