Search

Mg3Sb2-based thermoelectric materials are characterized by their economic efficiency, nontoxicity, and environmental friendliness and represent a highly promising and eco-friendly functional material for midtemperature applications. To achieve a higher thermoelectric performance, we introduced two compounds, LaCl3and CeCl3, into Mg3.2Sb1.5Bi0.5under the guidance of first-principles calculations. The Mg3.2Sb1.5Bi0.5+ 0.03CeCl3sample reached a maximum ZT value of approximately 1.6 at 723 K. The calculations indicate that two n-type dopants, LaCl3and CeCl3, can adequately improve the band structure of Mg3Sb2, and the introduction of Cl atoms will also lead to lattice distortion and reduce the lattice thermal conductivity (κL). Experimental results demonstrate that the introduction of Cl atoms efficiently reduces the thermal conductivity while improving the electrical transport properties. Specifically, the Mg3.2Sb1.5Bi0.5+ 0.03CeCl3sample achieved an exceptionally low κLof 0.3 W m–1K–1at 723 K, thereby validating the effectiveness of LaCl3and CeCl3doping. This work provides valuable insights into achieving thermoelectric decoupling in Mg3Sb2-based thermoelectric materials.

Automatic modulation recognition (AMR) has significant applications in communication system optimization, spectrum management, signal identification and classification, and security and protection. However, most of the existing AMR models are too large in terms of parameter number and computational complexity. Therefore, this letter proposes a multi-channel deepthwise separable convolution and gated recurrent unit network (MSGNet), which has a better recognition performance in the case of a lower number of parameters. MSGNet uses light weight modules: deepthwise separable convolution and gated recurrent unit, from the three input data channels to enter successively separated amplitude/phase samples, in-phase/quadrature-phase samples, and the real/imaginary samples obtained by Fourier transform of the signals, from which to obtain the signal space, time and frequency features. To speed up the training of the model and prevent overfitting, the batch normalization layer and dropout algorithm are added to the MSGNet model. Simulation results on the benchmark dataset indicate that the proposed model has a low number of parameters and computational complexity and a high level of recognition accuracy.

Broad learning system (BLS), which emerges as a lightweight network paradigm, has recently attracted great attention for recognition problems due to its good balance between efficiency and accuracy. However, the supervision mechanism in BLS and its variants generally relies on the strict binary label matrix, which imposes limitations on approximation and fails to adequately align with the data distribution. To address this issue, in this article, two novel flexible label-induced BLS models with the manifold manner are proposed, whose notable characteristics are as follows. First, two proposed label relaxation strategies can both enlarge the margins between different categories and simultaneously enhance the diversity within labels. Second, the integration of manifold geometrical criterion enables the models to capture local feature structures, ensuring the obtained flexible labels align better with the similarity between samples. Third, the proposed models can be optimized efficiently with the alternating direction method of multipliers. Each iteration benefits from a closed-form solution, facilitating the optimization process. Extensive experiments and thorough theoretical analysis are intended to show the advantages of our proposed models compared to other state-of-the-art recognition algorithms.

Constrained multiobjective optimization problems with deceptive constraints (DCMOPs) are a kind of complex optimization problems and have received some attention. For DCMOPs, the closer a solution is to the feasible region, the larger its constraint value. Moreover, multiple local infeasible regions will have different minimal constraint values according to their distances to feasible regions. Therefore, most of the existing algorithms are easy to fall into local regions, and even cannot find any feasible solution. To address DCMOPs, this article proposes a new evolutionary multitasking algorithm with a cooperative multistep mutation strategy. In this algorithm, the DCMOP is transformed into a multitasking optimization problem, in which the main task is the original DCMOP and the created auxiliary task aims to provide effective help for solving the main task. Specially, the designed cooperative multistep mutation strategy contains two contributions to solve deceptive constraints. First, a multistep mechanism is proposed, in which the individuals will use multiple different steps to generate the multiple offspring solutions along one direction, so as to expand search range to find feasible regions. Second, a cooperative mechanism between the two tasks is proposed, in which the main purpose is to provide effective and stable search directions. To be specific, an opposite solution generation method is utilized to generate the opposite solution of auxiliary population in the search space, and the direction from the auxiliary population to the main population will be formed. Combined with these two mechanisms, the proposed cooperative multistep mutation strategy can effectively improve the population diversity along the promising and stable search directions. In the experiments, the proposed algorithm is tested on the two benchmark DCMOPs, which contain objective space constraints and decision space constraints respectively. The results show the effectiveness and superiority of the proposed algorithm over the latest compared algorithms.

This study examines the hot deformation behavior of Mo-14Re alloy at various true strains (15%, 35%, 65%) and strain rates (0.01 s−1, 10 s−1) at a temperature of 1400K. The findings indicate that dynamic recovery (DRV) and dynamic recrystallization (DRX) occur concomitantly as strain increases at a low strain rate of 0.01 s−1, with DRV being the predominant softening mechanism. At a strain of 65%, DRX emerges as the primary softening process. Conversely, under high strain rates of 10 s−1, DRX is inhibited, and the Mo-14Re alloy experiences work hardening due to an increase in dislocation density. Microscopic analysis shows that the high-density dislocations facilitate the continued nucleation and growth of recrystallized grains at low strain rates. At high strain rates, tangled dislocations hinder dislocation motion and recrystallization. Regarding texture evolution, stronger {100}//CD and weaker {111}//CD fiber texture is observed at low strain rates of 0.01 s−1, while stronger {111}//CD and weaker {100}//CD fiber texture forms at high strain rates of 10 s−1, with enhanced texture intensity. Mechanistic analysis confirms the activation of the {110}<111>, {112}<111>, and {123}<111> dislocation slip systems at elevated temperatures, with the {123}<111> system being the most dominant.

In this article, we propose a robust high-order control barrier functions (HoCBFs)-based optimal control method for nonlinear systems with state constraints to achieve safety-stability perspectives. First, a kind of HoCBFs is presented for constrained nonlinear systems to address state constraints with high relative degrees. Second, the robustness property of the HoCBFs is analyzed based on the asymptotic stability of the forward invariant set. Specifically, a robust HoCBFs-based Lyapunov function is constructed to prove the uniform asymptotic stability of the set associated with the HoCBFs. In this way, a new sufficient condition is obtained for the stability analysis of the forward invariant set by using the inequalities of high-order derivatives of Lyapunov function. Third, a robust HoCBFs-based optimal control scheme is proposed for the constrained nonlinear system to achieve the safety-stability perspectives of constraints satisfaction and system stabilization, where the robust HoCBFs are combined with control Lyapunov functions (CLFs) to satisfy the small control property (SCP) in solving a quadratic program (QP). Furthermore, the proposed optimal control scheme is shown to be Lipschitz continuous and has no initial condition restrictions. Finally, two examples are presented to demonstrate the control performance of the proposed scheme. Note to Practitioners—The motivation of this article is that constraints exist widely in actual control systems, and the lack of constraint satisfaction in control systems may inevitably lead to safety defects, which usually degrade the control performances or even damage the entire system. In this article, a robust HoCBFs-based optimal control scheme is proposed for constrained nonlinear systems. The theoretical derivation demonstrates that the proposed control scheme can achieve safety-stability perspectives, which ensure system stabilization and task-oriented performance without violating the state constraints. The satisfactory control performances of the simulation on a constrained robotic manipulator show the potential practical application on a real robotic system.

As a continuation of our efforts to develop new agrochemicals with typical architecture and efficient bioactivity from plant natural products, natural neolignan honokiol was used as a lead compound to prepare novel analogs bearing the core 2-aminobenzoxazole scaffold. Their insecticidal potency against two representative agricultural pests, Plutella xylostellaLinnaeus and Mythimna separata(Walker), were evaluated in vivo. The pesticide bioassay results revealed that compounds 7″a, 9, 10d, and 10jexhibited prominent larvicidal activity against the larvae of P. xylostella(LC50= 7.95, 11.85, 15.51, and 12.06 μg/mL, respectively), superior to the precursor honokiol (LC50= 43.35 μg/mL) and two botanical insecticides, toosendanin (LC50= 26.20 μg/mL) and rotenone (LC50= 23.65 μg/mL). Compounds 7d, 10d, and 10jdisplayed a more pronounced nonchoice antifeedant effect (AFC50= 9.48, 9.14, and 12.41 μg/mL, respectively) than honokiol (AFC50= 54.81 μg/mL) on P. xylostella. Moreover, compounds 7b, 7″a, 9, 10d, 10f, and 10jshowed better growth inhibitory activity against M. separata(LC50= 0.36, 0.34, 0.28, 0.16, 0.26, and 0.11 mg/mL, respectively) than honokiol, toosendanin, and rotenone (LC50= 1.48, 0.53, and 0.46 mg/mL, respectively). A potted plant assay under greenhouse conditions illustrated that compounds 10dand 10jcontinued to provide good control efficacy against P. xylostellaand an apparent protective effect on plants. Further cytotoxicity assay revealed that the aforementioned potent compounds showed relatively moderate toxicity and a good safety profile for non-target mammalian cells. Overall, the current work provides valuable insight into the agrochemical innovation of honokiol-derived analogs for use as natural-inspired pesticides in agricultural pest management.

The reconfigurable intelligent surface (RIS) has received a lot of interest because of its capacity to reconfigure the wireless communication environment in a cost- and energy-efficient way. However, the realistic power consumption modeling and measurement validation of RIS has received far too little attention. Therefore, in this work, we model the power consumption of RIS and conduct measurement validations using various RISs to fill this vacancy. Firstly, we propose a practical power consumption model of RIS. The RIS hardware is divided into three basic parts: the FPGA control board, the drive circuits, and the RIS unit cells. The power consumption of the first two parts is modeled as $P_{\text {static}}$ and that of the last part is modeled as $P_{\text {units}}$ . Expressions of $P_{\text {static}}$ and $P_{\text {units}}$ vary amongst different types of RISs. Secondly, we conduct measurements on various RISs to validate the proposed model. Five different RISs including the PIN diode, varactor diode, and RF switch types are measured, and measurement results validate the generality and applicability of the proposed power consumption model of RIS. Finally, we summarize the measurement results and discuss the approaches to achieve the low-power-consumption design of RIS-assisted wireless communication systems.

This article proposes a unified analytical model (AM) for the armature magnetic field (AMF) of slotless permanent magnet (PM) motors with equidirectional toroidal winding (ETW). The AM is based on the magnetic field generated by the active side conductors of ETW single-phase coils. It not only can clarify the operating mechanism of ETW AMF in the theory and quantitatively analyze its AMFs and inductance, but also reveals the characteristics that distinguish ETW inductance analysis from other windings. In addition, it can serve as the cornerstone for analyzing the AMF of other windings. First, the structural parameters unified principle of different slotless PM motors with ETW is proposed. Second, based on the principle, the unified AM of AMF generated by the active side conductors and inductance of ETW single-phase coils are established and verified by the finite-element model. Finally, a prototype of a dual rotor slotless axial flux PM motor with ETW is manufactured, and the armature magnetic density waveforms under single-phase and three-phase excitation conditions and the inductance of ETW are tested, which verifies the accuracy of the proposed AM.

Byline: Xiaoshuai. Zhang, Jing. Liang, Zhaohui. Du, Qi. Xie, Ting. Li, Fang. Tang Purpose: Spindle cell carcinoma (SpCC) is a relatively rare tumor with an unfavorable prognosis. This study aimed [...]

Byline: Hairong. Liu, Xin. Xu, Jing. Liang, Jun. Wang, Yan. Li Gut microbiota dysbiosis is involved in intestinal diseases. The resident microorganisms in the digestive tract contribute to maintenance of [...]

Byline: Jun. Wang, Jing. Liang, Mingxin. He, Qi. Xie, Qingming. Wu, Guanxin. Shen, Baoli. Zhu, Jun. Yu, Li. Yu, Xiaohua. Tan, Lanlan. Wei, Jun. Ren, Youyong. Lv, Lijuan. Deng, Qian. [...]

Feature selection in machine learning as a key data processing technique has two conflicting goals: 1) minimizing the classification error rate and 2) minimizing the number of features selected. However, most of the existing multiobjective feature selection methods face the problems of easily falling into local optima and slow convergence by virtue of their problem characteristics, such as partially conflicting objectives and highly discontinuous Pareto fronts. To solve these problems, this article proposes a multiform optimization framework to solve a multiobjective feature selection task together with several auxiliary single-objective feature selection tasks in a multitask environment. The proposed framework uses the problem-solving experience of single-objective tasks to assist the multiobjective feature selection task in exploring more promising regions and accelerating the convergence speed. Specifically, a knowledge transfer strategy based on the search experience of different tasks is developed to accomplish multiform optimization. In addition, a diversity enhancement mechanism is presented to improve search ability in promising decision space areas by considering historical information about the population. In most cases, the experiment results on 27 datasets demonstrate that the proposed technique can uncover more diversified feature subsets on the Pareto front in less time than existing state-of-the-art methods.

Dynamic multiobjective optimization problems (DMOPs) are widely encountered in real-world applications and have received considerable attention in recent years. During the process of solving DMOPs, tracking the constantly changing Pareto optimal set (POS) quickly is the main task, and numerous methods have been proposed to achieve this task from different perspectives. However, how to improve the evolution of static optimizers via using historical information from past environments gets little attention. In fact, with adequate historical evolution information to learn, the trend of population evolution in the historical environments is promising to guide the future evolution and thus enhance the search ability. Therefore, in this article, a historical evolution learning-based framework is proposed to assist the static optimizers in fully using the historical evolution direction and POSs distribution. Specifically, two new models are designed with a purpose of generating offsprings and environmental selection, respectively. For enhancing the contribution of offsprings, a direction guidance model is developed to guide the offsprings according to the direction tendency of historical evolution. Furthermore, to improve the robustness of static optimizers and avoid the disturbance caused by misleading solutions, a manifold revise model is proposed to produce promising solutions via consulting historical POSs distribution. The proposed framework employs these two models collaboratively, and it is flexible and readily to be embedded into various dynamic response mechanisms and static optimizers in coping with DMOPs. The superiority of the framework has been comprehensively demonstrated by experimental comparison results on a variety of benchmark test problems.

Evolutionary constrained multiobjective optimization has received extensive attention and research in the past two decades, and a lot of benchmarks have been proposed to test the effect of the constrained multiobjective evolutionary algorithms (CMOEAs). Especially, the constraint functions are highly correlated with the objective values, which makes the features of constraints too monotonic and differ from the properties of the real-world problems. Accordingly, previous CMOEAs cannot solve real-world problems well, which generally involve decision space constraints with multimodal/nonlinear features. Therefore, we propose a new benchmark framework and design a suite of new test functions with scalable high-dimensional decision space constraints. To be specific, different high-dimensional constraint functions and mixed linkages in variables are considered to be close to realistic features. In this framework, several parameter interfaces are provided, so that users can easily adjust the parameters to obtain the variant functions and test the generalization performance of the algorithms. Different types of existing CMOEAs are employed to test the use of the proposed test functions, and the results show that they are easy to fall into local feasible regions. Therefore, we improve one evolutionary multitasking-based CMOEA to better handle these problems, in which a new search algorithm is designed to enhance the search abilities of populations. Compared with the existing CMOEAs, the proposed CMOEA presents better performance.

To study temperature and rate-dependent behaviors of molybdenum-rhenium (Mo–Re) alloys, compressive tests at variable temperatures and strain rates were conducted. After the same machining and thermal annealing processes, fully recrystallization happens in Mo and Mo–Re alloys with low Re content (5 wt %), while recovery occurs in Mo–Re alloys with high Re content (14 wt% and 42 wt %). The strength increases as Re content rises at all temperatures (300 °C, 600 °C and 1200 °C), meaning sufficient strengthening effect of Re. The rate-dependent behaviors, however, show different trends between Mo and Mo–Re alloys. Positive strain rate sensitivity is detected in Mo and low Re Mo–Re alloys in all temperatures, which is consistent to other research. While, near zero strain rate sensitivity is found in high Re Mo–Re alloys at 600 °C. Such an unusual phenomenon may be contributed to high stored dislocation density during mechanical processes. The stored dislocations are activated at a proper temperature to accommodate plastic flow, which weakens the influence of strain rate on strength.

Background: The objective of this study was to evaluate the comparative efficacy of current interventions for the prevention of retinopathy of prematurity (ROP) in premature infants. Methods: A network meta-analysis (NMA) was performed. We searched PubMed, Web of Science, Scopus, Embase, and the Cochrane Library for relevant studies from their inception to May 5, 2022. Publications were eligible for our study if they were randomized controlled trials (RCTs) involving preterm infants at &lt;37 weeks of gestational age and reported the incidence of any-stage ROP treated with the interventions of interest. The overall effect was pooled using the random effects model. Results: We identified 106 RCTs (involving 23894 participants). This NMA showed that vitamin A supplementation markedly reduced the incidence of ROP, in comparison with placebo (odds ratio [OR] = 0.59, 95% credible interval [95% CrI] 0.33, 0.85), fish oil-based lipid emulsion (OR = 0.57, 95% CrI 0.24, 0.90), early erythropoietin (OR = 0.51, 95% CrI 0.34, 0.98), probiotics (OR = 0.48, 95% CrI 0.32, 0.97), and human milk (OR = 0.50, 95% CrI 0.21, 0.78). Vitamin A supplementation has the highest probability of being the best option for reducing the ROP risk compared with the other 20 interventions based on its surface under the cumulative ranking curve (SUCRA) value (SUCRA = 92.50%, 95% CrI 0.71, 1.00). Conclusions: Our findings suggest that among 21 interventions, vitamin A supplementation might be the best method of preventing ROP. This NMA offers an important resource for further efforts to develop preventive strategies for ROP.

Byline: Ting. Li, Qi. Xie, Junwei. Li, Zhen. Li, Junjuan. Xiao, Mingguo. Liu, Jun. Wang, Yan. Li, Fang. Tang, Jing. Liang Background: Spindle cell carcinoma (SpCC) is a rare tumor [...]

ABSTRACTDeep learning is a powerful branch of machine learning, which presents a promising new approach for diagnose diseases. However, the deep learning for detecting anterior cruciate ligament still limits to the evaluation of whether there are injuries. The accuracy of the deep learning model is not high, and the parameters are complex. In this study, we have developed a deep learning model based on ResNet-18 to detect ACL conditions. The results suggest that there is no significant difference between our proposed model and two orthopaedic surgeons and radiologists in diagnosing ACL conditions.

Composting has become the most economical and environmentally friendly way to solve the problem of consuming huge amounts of urban green waste. However, the microbial succession and metabolic types of carbon conversion in green waste composting are less studied. The results of this study showed that grass clippings (GC) decomposed more readily than branches and fallen leaves (BL). The pH and electrical conductivity (EC) of GC and BL composts were 6.71–7.61 and 6.89–8.35, and 4.22–6.43 mS/cm and 1.31–2.31 mS/cm, respectively. By the end of composting, the total organic carbon (TOC), cellulose, and hemicellulose contents of GC and BL composts decreased by 31.13% and 23.80%, 39.44% and 34.15%, and 29.27% and 18.26%, respectively. The C/N ratios of the GC and BL composts decreased by 17.09% and 24.34%, respectively. The humification rate, degree of polymerization, and humification of GC and BL composts increased 283.21%, 859.91%, 1302.58%, and by 415.6%, 24.8%, and 513.6%, respectively. TOC, cellulose, and hemicellulose were more readily degraded in the GC compost than they were in the BL compost. The dominant bacteria in both types of green waste were Firmicutes, Actinobacteriota, Proteobacteria, Chloroflexi, and Myxococcota, with a total relative abundance of > 80%. Machine learning showed that among the environmental factors affecting carbon conversion, EC, pH, and temperature had the highest importance in terms of the decrease in TOC. “Global and overview maps” and “carbohydrate metabolism,” which is directly related to carbon conversion, were the two most important metabolic functions involved in compost decomposition. FAPROTAX predictions indicated that chemoheterotrophy, aerobic chemoheterotrophy, and xylanolysis were the most important metabolic processes involved in carbon conversion. This study provides a potential theoretical basis for improving the efficiency of green waste composting. Overall, GC has less cellulose and lignin, is more easily degraded, and is more efficient in carbon conversion. BL is more difficult to degrade, so BL compost produces more cellulolysis metabolism to degrade organic matter, along with bacterial succession associated with degradation.

In practical engineering applications, many problems involve high computational costs in evaluating the objective function during optimization. Traditional optimization algorithms may require a large number of evaluations to find the optimal solution, which leads to large consumption of computational resources. In recent years, surrogate-assisted evolutionary algorithms (SAEAs) have received increasing attention in solving computationally expensive optimization problems (EOPs). This paper provides a review of research on surrogate-assisted evolutionary algorithms. Firstly, it introduces the characteristics and challenges of expensive optimization problems. Secondly, it introduces the framework of SAEAs and the representative single-objective and multi-objective expensive optimization algorithms. Then, it presents methods for surrogate model construction and model management strategy, summarizes relevant literature, and analyzes the characteristics of different methods. Finally, it concludes existing challenges and future research directions in this topic. Through a comprehensive review and analysis of surrogate-assisted evolutionary algorithms, this paper provides essential references and guidance for further research.

With the advent of foundation models like ChatGPT, educators are excited about the transformative role that artificial intelligence (AI) might play in propelling the next education revolution. The developing speed and the profound impact of foundation models in various industries force us to think deeply about the changes they will make to education, a domain that is critically important for the future of humans. In this article, we discuss the strengths of foundation models, such as personalized learning, education inequality, and reasoning capabilities, as well as the development of agent architecture tailored for education, which integrates AI agents with pedagogical frameworks to create adaptive learning environments. Furthermore, we highlight the risks and opportunities of AI overreliance and creativity. Finally, we envision a future where foundation models in education harmonize human and AI capabilities, fostering a dynamic, inclusive, and adaptive educational ecosystem.

Infrared small target detection plays a crucial role in various domains like early warning, national defense, and monitoring. Although existing detection methods have achieved some good results, they only rely on the original size and information of small targets for detection and are faced with challenges, such as the small size and obscure feature information of small targets. To overcome these limitations, this article introduces a coarse-to-fine detection network named MSMA-Net. This network initially determines the rough location of targets through a coarse preliminary screening, aiming to reduce false alarms and improve computational efficiency. Simultaneously, to improve the discriminability of the features and enhance the spatial details and resolution of the targets, the network utilizes multiscale super-resolution to transform low-resolution feature maps into high-resolution representations, gradually refining and strengthening the feature representation. Finally, the network employs a multilevel feature fusion attention mechanism to facilitate effective information transmission and fusion in multiscale and multilevel feature representations. This attention mechanism enhances the accuracy as well as robustness of object detection, ultimately obtaining accurate detection results. Extensive experimental results demonstrate that compared with existing detection methods, our approach can effectively suppress false alarms and get better performance even when the target has a small size and obscure feature information.

Feature construction has shown promise in improving the accuracy of crop classification by constructing high-level features. However, current feature construction methods often rely on domain knowledge and have a limited interpretability of the solutions. To address this, this study proposes a new genetic programming (GP) approach to automatically evolve solutions with high interpretability that can construct high-level features for crop classification from hyperspectral images. A flexible representation of multiple trees is proposed in the proposed GP approach to construct various types of high-level features from the original ones, simultaneously. To improve the search ability, a new offspring generation method is developed to dynamically guide the evolution of the population while improving the diversity of the population. The new approach wraps with three classification algorithms, i.e., support vector machine (SVM), naive Bayes (NB), and k-nearest neighbor (KNN), for crop classification on three datasets with different difficulties and tasks. The results demonstrate that the features constructed by the new approach can effectively distinguish different crop categories. The new approach achieves better performance than the compared GP-based method, classic methods, and deep learning methods in crop classification using hyperspectral images. Importantly, the proposed approach shows the high interpretability of the constructed features.

Cracks have consistently been a significant challenge limiting the development of additive manufactured nickel-based superalloys. It is essential to investigate the location of cracks and their forming mechanism. This study extensively examines the impact of solidification process, microstructural evolution, and stress concentration on crack initiation during direct energy deposition (DED). The results emphasize that the crack formation is significantly related to large-angle grain boundaries, rapid cooling rates. Cracks caused by large-angle grain boundaries and a fast-cooling rate predominantly appear near the edge of the deposited samples. Liquation cracks are more likely to form near the top of the deposited sample, due to the presence of γ/γ′ eutectics. The secondary dendritic arm and the carbides in the interdendritic regions can obstruct liquid flow during the final stage of solidification, which results in the formation of solidification cracks and voids. This work paves the way to avoid cracks in nickel-based superalloys fabricated by DED, thereby enhancing the performance of superalloys.

A direct-drive wind-wave combined energy conversion (WWCEC) system that uses a linear-rotary generator (LRG) has aroused much interest recently. Thanks to the application of the LRG, this system has the advantages of high energy output, low non-working hours, and low development cost. This paper aims to investigate the suitable topology of LRG for the WWCEC system, and two types of permanent magnet (PM) LRGs (PMLRGs) are proposed and compared. One is a mover-PM machine (MPMLRG) which belongs to the PM synchronous generator, and the other one is a stator-PM machine (SPMLRG) which belongs to the flux-switching PM generator. The performance contrast between both PMLRGs is conducted, including no-load and on-load characteristics, in which the mutual effect and thermal analysis are involved. The assembly complexity and manufacturing cost are also analyzed. The comparison results indicate that the MPMLRG has superiority in output power, and the SPMLRG is easier to assemble. To validate our analysis, an MPMLRG prototype is processed and tested. Furthermore, the levelized cost of energy (LCOE) of the WWCEC system is estimated, underscoring its cost-effectiveness. Some suggestions for suitable LRG topologies for the WWCEC system are summarized in the outlook section.

Constrained multi-objective optimization problems (CMOPs) generally contain multiple constraints, which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions, thus they propose serious challenges for solvers. Among all constraints, some constraints are highly correlated with optimal feasible regions; thus they can provide effective help to find feasible Pareto front. However, most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints, and do not consider judging the relations among constraints and do not utilize the information from promising single constraints. Therefore, this paper attempts to identify promising single constraints and utilize them to help solve CMOPs. To be specific, a CMOP is transformed into a multi-tasking optimization problem, where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively. Besides, an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships. Moreover, an improved tentative method is designed to find and transfer useful knowledge among tasks. Experimental results on three benchmark test suites and 11 real-world problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.

The 4F2 cell architecture dynamic random access memory (DRAM) has emerged as a candidate for high-density future DRAM, meeting performance, power, area, and cost (PPAC) targets. This study proposes an improved parasitic capacitance-predictively aware design technology co-optimization (DTCO) flow that optimizes the bitline (BL) capacitance from structure and process perspectives, emphasizing manufacturability and scalability. A novel BL process flow is developed to optimize BL capacitance with scalability, utilizing a high-accuracy 3-D field solver for parasitic capacitance extraction of the vertical channel transistor (VCT) array. When air gap is used as the novel BL spacer, the BL capacitance decreases by 49.5%. In addition, we investigate and optimize the PPAC of the DRAM one transistor and one capacitor (1T1C) cell. The novel BL of 4F2 VCT-based 1T1C DRAM demonstrates a 66.6% reduction in BL dynamic power consumption during read/write operations, with 9.4% and 11.6% enhancement in read speed when reading data “1” and “0,” respectively. Moreover, a 58.8% reduction in cell array area and lower costs is yielded compared with the current VCT.

As a flexible type of energy transmission carrier, mobile energy storages usually are studied with a fixed driving speed, resulting in unsatisfactory system operation results. To address the problem, an optimal scheduling strategy of mobile energy storage capable of variable-speed energy transmission is proposed. Firstly, by analyzing the hydrogen-carrier vessel (HCV)’s variable speed-spatiotemporal characteristic mapping mechanism and the ability to supply hydrogen and electricity, an interisland energy variable-speed transmission model is developed. On this basis, an island energy microgrid (EMG) operation model compatible with variable-speed transmission energy is established, in order to reconcile HCV with hydrogen balance, power flow and frequency response. Additionally, considering the impact of uncertainties on the energy output, energy demand, frequency stability, and especially energy transmission sequencing, a stochastic dispatching strategy of the island energy microgrid groups (EMGG) is designed. This model is first linearized as a mixed-integer linear program (MILP) problem, and then is solved quickly by a two-layer algorithm, where the upper layer finely manages the possible optimal EMG power reserve that ensures the frequency security, and the lower layer optimally coordinates the inter-island variable-speed energy transmission and island EMG operation. Finally, case studies on Island groups in the South China Sea verify the effectiveness of the proposed strategy.

Currently, scholars typically investigate the dynamics of two coupled partial differential systems. However, in practical engineering applications, there are often $N$ coupled partial differential systems involved. In this paper, a novel $N$ coupled hyperbolic-parabolic partial differential systems (HPPDS) is introduced. Firstly, the stabilization problem of the isolated HPPDS is addressed using Lyapunov functions and the backstepping method. Secondly, the synchronization problem of $N$ coupled HPPDS is considered in the ${L^{2}} \times {H^{1}}$ sense. The synchronization problem of the target system is transformed into the stability problem of the decoupled system. The definition of synchronization error is provided, and the synchronization error system is derived. Subsequently, a boundary controller is designed using the backstepping method to achieve stability of the error system, i.e., synchronization of the $N$ coupled HPPDS. Finally, two simulation examples are presented to demonstrate the validity of the obtained results.

Various SARS-CoV-2-related coronaviruses have been increasingly identified in pangolins, showing a potential threat to humans. Here we report the infectivity and pathogenicity of the SARS-CoV-2-related virus, PCoV-GX/P2V, which was isolated from a Malayan pangolin (Manis javanica). PCoV-GX/P2V could grow in human hepatoma, colorectal adenocarcinoma cells, and human primary nasal epithelial cells. It replicated more efficiently in cells expressing human angiotensin-converting enzyme 2 (hACE2) as SARS-CoV-2 did. After intranasal inoculation to the hACE2-transgenic mice, PCoV-GX/P2V not only replicated in nasal turbinate and lungs, but also caused interstitial pneumonia, characterized by infiltration of mixed inflammatory cells and multifocal alveolar hemorrhage. Existing population immunity established by SARS-CoV-2 infection and vaccination may not protect people from PCoV-GX/P2V infection. These findings further verify the hACE2 utility of PCoV-GX/P2V by in vivoexperiments using authentic viruses and highlight the importance for intensive surveillance to prevent possible cross-species transmission.

The incredible potentiality of reconfigurable intelligent surface (RIS) in addressing power supply and obstacle environment of Internet of Medical Things (IoMT) has been capturing our interest. Considering the nettlesome “double-fading” effect introduced by passive RIS, we investigate an active RIS-enhanced IoMT system in this article, where the wireless power transfer (WPT) from power station (PS) to IoMT devices and the wireless information transfer (WIT) from IoMT devices to the access point (AP) are both implemented with the assistance of active RIS. Aiming to maximize the sum throughput of the considered IoMT system, a joint design of time schedules and reflecting coefficient matrices of the active RIS is proposed. Trapped by the non-convex and obstinate optimization problem, we explore the semi-definite programming (SDP) relaxation and successive convex approximation (SCA) techniques based on alternating optimization (AO) algorithm. Simulation results verify our solution approach to the intractable optimization problem and showcase the boosted spectrum and energy efficiency of the active RIS-enhanced IoMT system.

In multimodal multiobjective optimization problems (MMOPs), there are several Pareto optimal solutions corresponding to the identical objective vector. This paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision variables. Due to the increase in the dimensions of decision variables in real-world MMOPs, it is difficult for current multimodal multiobjective optimization evolutionary algorithms (MMOEAs) to find multiple Pareto optimal solutions. The proposed algorithm adopts a dual-population framework and an improved environmental selection method. It utilizes a convergence archive to help the first population improve the quality of solutions. The improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first population. The combination of these two strategies helps to effectively balance and enhance convergence and diversity performance. In addition, to study the performance of the proposed algorithm, a novel set of multimodal multiobjective optimization test functions with extensible decision variables is designed. The proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.

The success of deep learning methods heavily relies on the availability of adequate samples. However, in the task of infrared small target detection (ISTD), the lack of high-quality training samples is a challenging problem due to the confidentiality of the application field and the difficulty of labeling. This limitation often leads to suboptimal detection performance of convolutional neural networks (CNNs). To address this challenge, we propose an adaptive labeling strategy and an ISTD network called the multi-criteria decision network (MCDNet) to achieve higher-quality sample labeling and more accurate detection results. In the adaptive labeling strategy, we propose a second-order differential autocorrelation method to determine the size of fuzzy edge targets accurately. In addition, we introduce local backgrounds to enhance the saliency information in the labels and improve the richness and contrast of training label content. To obtain accurate and robust detection results with limited target feature information, we design MCDNet. In particular, we propose a multi-criteria decision (MCD) method that can combine the CNN decisions and the infrared small target prior saliency decisions through weighted fusion, and set decision weights based on the importance of different decision criteria in the decision-making process. This method can integrate the advantages of both the CNN decisions and the prior saliency decisions, avoid the one-sidedness of a single criterion, and improve the reliability and stability of the decision-making. The experimental results indicate that our method has a higher accuracy compared to other contrastive methods.

Fog harvesting plays a pivotal role in harnessing atmospheric water resources and holds significant promise for alleviating global water scarcity. Nonetheless, enhancing harvesting efficiency remains a persistent challenge, especially concerning the rapid detachment of droplets from surfaces. In this study, we discovered that the trichomes of Sarracenianot only efficiently harvest and transport liquid but also quickly drain harvested liquid. We have elucidated the augmentation mechanism behind effective fog harvesting and drainage within the lid of Sarracenia. The trichomes facing the counterflow can enhance fog harvesting efficiency by 80% through air-flow-assisted spreading of liquid film. The wedge corner generated by the interface between hydrophilic and hydrophobic surfaces, coupled with the reduction of cross-sectional angles, diminishes the adhesive force of liquid droplets, fosters droplet spheroidization, and substantially facilitates droplet detachment. In addition, the quantitative detachment of droplets can be achieved by adjusting the cross-sectional angle and wetting gradient. This integrated structure combining efficient condensation and detachment has diverse applications in cooling towers and seawater desalination.

Regulator of chromosome condensation domain-containing protein 1 (RCCD1), previously reported as a partner of histone H3K36 demethylase KDM8 involved in chromosome segregation, has been identified as a potential driver for breast cancer in a recent transcriptome-wide association study. We report here that, unexpectedly, RCCD1 is also localized in mitochondria. We show that RCCD1 resides in the mitochondrial matrix, where it interacts with the mitochondrial contact site/cristae organizing system (MICOS) and mitochondrial DNA (mtDNA) to regulate mtDNA transcription, oxidative phosphorylation, and the production of reactive oxygen species. Interestingly, RCCD1 is upregulated under hypoxic conditions, leading to decreased generation of reactive oxygen species and alleviated apoptosis favoring cancer cell survival. We show that RCCD1 promotes breast cancer cell proliferation in vitro and accelerates breast tumor growth in vivo. Indeed, RCCD1 is overexpressed in breast carcinomas, and its level of expression is associated with aggressive breast cancer phenotypes and poor patient survival. Our study reveals an additional dimension of RCCD1 functionality in regulating mitochondrial homeostasis, whose dysregulation inflicts pathologic states such as breast cancer.

In this paper, we introduce an intelligent reflecting surface (IRS)-assisted wireless powered heterogeneous network (WPHN) consisting of two heterogeneous groups of devices. Specifically, one group of devices, i.e., energy-harvesting devices (EHDs), are charged by external energy supplies, while the other group of devices, i.e., non-energy-harvesting devices (NEHDs), are powered by internal energy supplies. An IRS aims to participate in the wireless energy transfer (WET) in downlink and the wireless information transfer (WIT) in the uplink. A sum throughput maximization problem is formulated subject to the constraints of individual energy consumption, transmission time scheduling, and IRS phase shifts. To cope with the non-convexity of the problem, we first derive the optimal IRS phase shifts of the uplink WIT independently. Next, the semi-definite programming (SDP) relaxation is adopted to recast this non-convex problem into the convex one, which can be numerically solved. Then, a novel low-complexity scheme is developed to gain more insights and mitigate the computational complexity induced by the SDP relaxation. In particular, the dual problem and Karush-Kuhn-Tucker conditions are first utilized to obtain the optimal transmission time scheduling. Then, we propose a method based on Riemannian manifold optimization to compute the optimal IRS phase shifts of the downlink WET in closed-form. Finally, simulation results are presented to verify the optimality of our proposed scheme, and highlight the benefits induced by the IRS to coordinate these heterogeneous devices.

Despite the success of meta-heuristics for many computationally hard optimization problems, the design of meta-heuristics is dependent on the problem domain and needs to be tailored by experts. In contrast, selection hyper-heuristics are emerging cross-domain search methodologies that perform the search over the space of a set of low-level heuristics. In this study, we introduce a novel selection hyper-heuristic to learn, select, and apply low-level heuristics to solve multiobjective optimization problems. The proposed selection hyper-heuristic is an iterative process that includes three main components: 1) the reward balance strategy; 2) the bandit-based learning strategy; and 3) the multistage selection strategy. The performance and behavior of the proposed selection hyper-heuristic are investigated on a range of benchmarks as well as several real-world problems. The empirical results demonstrate the effectiveness and the cross-domain ability of the proposed selection hyper-heuristic by comparing it with several state-of-the-art algorithms.

Ovarian mesenchymal cells (oMCs) constitute a distinct microenvironment that supports folliculogenesis under physiological conditions. Supplementation of exogenous non-ovarian mesenchymal-related cells has been reported to be an efficient approach to improve ovarian functions. However, the development and cellular and molecular characteristics of endogenous oMCs remain largely unexplored. In this study, we surveyed the single-cell transcriptomic landscape to dissect the cellular and molecular changes associated with the aging of oMCs in mice. Our results showed that the oMCs were composed of five ovarian differentiated MC (odMC) populations and one ovarian mesenchymal progenitor (oMP) cell population. These cells could differentiate into various odMCs via an oMP-derived route to construct the ovarian stroma structures. Comparative analysis revealed that ovarian aging was associated with decreased quantity of oMP cells and reduced quality of odMCs. Based on the findings of bioinformatics analysis, we designed different strategies involving supplementation with young oMCs to examine their effects on female fertility and health. Our functional investigations revealed that oMCs supplementation prior to ovarian senescence was the optimal method to improve female fertility and extend the reproductive lifespan of aged females in the long-term.

The key task in dynamic multiobjective optimization problems (DMOPs) is to find Pareto-optima closer to the true one as soon as possible once a new environment occurs. Previous dynamic multiobjective evolutionary algorithms (DMOEAs) normally focus on DMOPs with regular environmental changes, but neglect widespread random one, limiting their applications in real-world fields. To address this issue, a knowledge guided transfer strategy (KTS)-based DMOEA is proposed in this article. First, knowledge described as a two-tuple is extracted under each historical environment and preserved to a knowledge pool. Redundant knowledge is recognized and adaptively removed so as to guarantee the diversity of the pool. Second, a knowledge matching strategy is developed to re-evaluate the representative of each stored knowledge under a new environment, with the purpose of finding the most valuable one to promote positive knowledge transfer. Third, an improved knowledge transfer mechanism based on subspace alignment is introduced. By integrating it with the knowledge reuse mechanism, a hybrid transfer strategy is constructed to adaptively select the most suitable one in terms of the similarity degree of selected knowledge on the current environment, and then generate a new initial population. Experiments on 20 benchmark problems demonstrate that the KTS outperforms five state-of-the-art algorithms, achieving good versatility in solving DMOPs with both regular and random changes.

Dynamic multiobjective optimization problems (DMOPs) are characterized by their multiple objectives, constraints, and parameters that may change over time. The challenge in solving DMOPs is how to track the varying Pareto-optimal solution sets quickly and accurately. Therefore, an interindividual correlation and dimension-based dual-learning method is proposed in this article. Two learning strategies, decomposition-based interindividual correlation transfer learning (DICTL) and dimension-wise learning (DL), are developed to, respectively, generate one-half of the initial population in the new environment. More specifically, DICTL learns the interindividual correlation from the final population of the adjacent environment and then transfers it to the new environment, aiming to maintain the diversity and distribution of the predicted population. While DL extracts the changing pattern of dynamic environments from the high-quality solutions of historical environments in the perspective of variable dimension, trying to improve the quality of the population and accelerate the convergence. The designed two learning strategies (DICTL&DL) work complementarily and collaboratively to make the algorithm adapt to dynamic environments better and faster. Comprehensive experiments have been conducted by comparing the proposed method with four state-of-the-art algorithms on 14 benchmark problems. The results demonstrate the superiority of the proposed method.

Byline: Hui. Xu, Wenqiao. Sun, Yongmei. Kong, Yahan. Huang, Zhigang. Wei, Licheng. Zhang, Jing. Liang, Xin. Ye Increasing evidence support that microwave ablation (MWA) induces spontaneous abscopal regression of the [...]

Mg3Sb2-based thermoelectric materials can convert heat and electricity into each other, making them a promising class of environmentally friendly materials. Further improving the electrical performance while effectively reducing the thermal conductivity is a crucial issue. In this paper, under the guidance of the oneness principle calculation, we designed a thermoelectric Zintl phase based on Mg3.2Sb1.5Bi0.5doped with Tb and Er. Calculation results show that using Tb and Er as cationic site dopants effectively improves the electrical properties and reduces the lattice thermal conductivity. Experimental results confirmed the effectiveness of codoping and effectively enhanced thermoelectric performance. The most immense ZTvalue obtained by the Mg3.185Tb0.01Er0.005Sb1.5Bi0.5sample was 1.71. In addition, the average Young’s modulus of the Mg3.185Tb0.01Er0.005Sb1.5Bi0.5sample is 51.85 GPa, and the Vickers hardness is 0.99 GPa. Under the same test environment, the material was subjected to 12 cycles in the temperature range of 323–723 K, and the average power factor error range was 1.8% to 2.1%, which is of practical significance for its application in actual device scenarios.

The wrinkles on graphene oxide (GO) membranes have unique properties; however, they interfere with the mass transfer of interlayer channels, posing a major challenge in the development of wrinkle-free GO membranes with smooth channels. In this study, the wrinkles on GO were flattened using vortex shear to tightly stack them into ultraflat GO membranes with Newton’s ring interference pattern, causing hydrolysis of the lipid bonds in the wrinkles and an increase in the number of oxygen-containing groups. With increasing flatness, the interlayer spacing of the GO membranes decreased, improving the stability of the interlayer structure, the flow resistance of water through the ultraflat interlayer decreased, and the water flux increased 3-fold. Importantly, the selectivity for K+/Mg2+reached approximately 379.17 in a real salt lake. A novel concept is proposed for the development of new membrane preparation methods. Our findings provide insights into the use of vortex shearing to flatten GO.

By selecting a small subset of relevant features, feature selection can reduce the dimensionality of the problem while maintaining or increasing the discriminating ability of the data. However, many existing feature selection approaches ignore the fact that there are multiple optimal solutions to a feature selection problem. Multiple feature subsets with different features selected can achieve very similar or the same classification accuracy. To search for multiple optimal feature subsets, a niching-based differential evolution (DE) method with duplication analysis is proposed. In the proposed method, the duplicated feature subsets in the population are modified by the proposed subset repairing scheme which can produce unique feature subsets. Second, the mutation operator in DE is improved, which uses both the niche and global information to produce promising feature subsets. Third, a new selection method considering the diversity among feature subsets is adopted to form a new population for the next-generation. In the experiments, the proposed method is compared with seven evolutionary feature selection algorithms and two typical feature selection methods on 18 datasets. The results show that the proposed algorithm achieves higher classification accuracy than the compared methods on most of the used datasets. Furthermore, the proposed method can find different feature subsets with very similar or the same classification accuracy.

Fitness landscape analysis devotes to characterizing different properties of optimization problems, such as evolvability, sharpness, and neutrality. Although several landscape features have been proposed, only a few of them can be used in practice as predictors of algorithm performance. In this study, the keenness (KEEs) is proposed to characterize the sharpness of the fitness landscape for continuous optimization problems and predict the performance of the differential evolution algorithm. Specifically, a mirror simple random walk algorithm is designed to construct the relevance between the front and back search points in the sampling. The fitness value of each point is replaced by the specific integer. The values in the set of integers with the same circumstance are computed as the feature scalar using the cumulative calculation mechanism. The results of experimental studies in various functions demonstrate the superiority of KEEsin terms of accuracy, reliability, and coverage of samples. Moreover, KEEshas shown excellent practicability in the application of differential evolution algorithm performance prediction for continuous optimization problems. Thus, KEEsis a new landscape feature for fitness landscape analysis of continuous optimization problems and algorithm performance prediction within limited prior knowledge of the unknown problem.

When solving dynamic multiobjective optimization problems (DMOPs) by evolutionary algorithms, the historical moving directions of some special points along the Pareto front, such as the center and knee points, are widely employed to predict the Pareto-optimal solutions (POSs). However, special points may be impacted by certain individuals with a large direction deviation, and thus, mislead the tracking of dynamic POS. To solve this issue, a correlation-guided layered prediction approach for solving DMOPs is proposed in this article, where multiple prediction models are integrated by considering the correlation of individuals’ moving directions. To be specific, the population is clustered into three subpopulations (i.e., high, mid, and low correlation) by correlation analysis to perform different prediction behaviors. The high correlation subpopulation aims to predict the moving direction via a linear prediction model. The mid correlation subpopulation is devoted to predicting the manifold change of POS by self-adaptively using the direction and length correction models. The diversity preservation is considered by the low correlation subpopulation. While the three subpopulations focus on different optimization tasks, they also cooperate to track the dynamic POS. The comprehensive experimental results on a variety of benchmark test problems demonstrate the superiority of the proposed approach, as compared with some state-of-the-art prediction-based dynamic multiobjective algorithms.