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Rewards act as external motivators and can improve performance in various cognitive tasks. However, previous research demonstrated mixed findings regarding the effect of reward on working memory (WM) performance, and the question of whether reward enhances WM performance is arguable. It remains unclear how the effect of reward on WM can be influenced by various factors, such as types of reward and experimental paradigms. In this meta-analytic study, we systematically investigated the effect of reward on WM by analyzing data from 51 eligible studies involving a total of 1767 participants. Our results showed that reward robustly enhanced WM performance, with non-monetary rewards inducing more benefits than monetary rewards. This may be because, while both types of reward could induce extrinsic motivation, non-monetary rewards enhanced intrinsic motivation while monetary rewards reduced it. Notably, all three reward methods—reward binding, reward expectation, and subliminal reward—effectively improved WM performance, with the reward binding paradigm exhibiting the greatest effects. This finding suggests that the reward effect can be attributed to both increasing the total amount of WM resources and improving the flexibility of resource reallocation. Moreover, the type of WM, the experimental paradigms, and the outcome measures are three moderators that should be jointly considered when assessing the reward effects on WM. Overall, this meta-analytic study provides solid evidence that reward improves WM performance and reveals possible mechanisms underlying these improvements.

Industrial wastewater typically contains many organic and inorganic pollutants and is also contaminated by various microorganisms. Microbial species in industrial wastewater have not been extensively investigated. In this experiment, a Klebsiella pneumoniae strain was isolated for the first time from industrial wastewater containing a high concentration of sulfate and phosphate. Mass spectrometry, genetic analysis, and biochemical identification were conducted to understand the genetic and biochemical characteristics of this Klebsiella pneumoniae strain recovered from industrial wastewater. Growth experiments revealed that it exhibited an excellent growth rate in nutrient broth. Further analyses showed that the strain was sensitive to most antibiotics but resistant to chloramphenicol and nitrofurantoin. It also exhibited significant resistance to piperacillin/tazobactam and cefotaxime/clavulanic acid. Resistance gene experiments indicated the presence of gyrA, OqxB, and ParC genes associated with antibiotic resistance in the isolated Klebsiella pneumoniae strain. Proteomics uncovered the following three proteins related to drug resistance: the multi-drug resistant outer membrane protein MdtQ, the multi-drug resistant secretion protein, and the modulator of drug activity B, which are coexistent in Klebsiella pneumoniae. Proteomics and bioinformatics analyses further analyzed the protein composition and functional enrichment of Klebsiella pneumoniae. The isolation of Klebsiella pneumoniae from a high concentration in sulfate and phosphate industrial wastewater provides a new direction for further research on the characteristics and drug resistance traits of industrial wastewater microorganisms and the potential risks they may pose when released into the environment.

With the continuous expansion of the scale of power generated by grid-connected renewable energy, the form and operation characteristics of power grids have undergone significant changes, and the power electronic characteristics of power systems are prominent, resulting in the frequent occurrence of wide-band oscillation problems when renewable energy power generation equipment is connected to a power grid. Oscillation has the characteristics of nonlinearity and has an oscillation frequency ranging from a few hertz to several thousand hertz or more, which seriously threatens the stable operation of power grids. This paper summarizes the wide-band oscillation events that occur worldwide under the background of renewable energy access to power grids, classifies the wide-band oscillation events according to the distribution of oscillation frequency bands, and sorts out the characteristics of various oscillations. From the perspectives of the source side and network side, the existing oscillation suppression measures are classified and prospected.

With regard to the aims of achieving the “Dual Carbon” goal and addressing the significant greenhouse gas emissions caused by urban expansion, there has been a growing emphasis on spatial research and the prediction of urban carbon emissions. This article examines land use data from 2000 to 2020 and combines Grid and the PLUS model to predict carbon emissions in 2030 through a multi-scenario simulation. The research findings indicate the following: (1) Between 2000 and 2020, construction land increased by 95.83%, with carbon emissions also increasing. (2) By 2030, for the NDS (natural development scenario), carbon emissions are expected to peak at 6012.87 × 104 t. Regarding the ratio obtained through the EDS (economic development scenario), construction land is projected to grow to 3990.72 km2, with expected emissions of 6863.29 × 104 t. For the LCS (low-carbon scenario), the “carbon peak” is expected to be reached before 2030. (3) The intensity of carbon emissions decreases as the city size increases. (4) The shift of the center of carbon emission intensity and the center of construction land all indicate movement towards the southeast. Studying the trends of regional land use change and the patterns of land use carbon emissions is beneficial for optimizing the land use structure, thereby enabling us to achieve low-carbon emission reductions and sustainable urban development.

The process of urbanization has deepened the contradiction between ecosystem services’ supply and demand, resulting in a significant risk to ecological security. Thus, it is imperative to conduct an analysis of the correlation between ecosystem services’ supply and demand to achieve sustainable urban growth. This study evaluated the supply, demand, coordination index, and matching types of ecosystem services’ supply and demand in 2000, 2010, and 2020 based on multisource data in Changchun City. The results showed that ecosystem services’ supply decreased overall, while their demand continued to increase from 2000 to 2020, together with their spatial heterogeneity. The regions characterized by a low supply of and high demand for ecosystem services mostly encompassed central urban regions that have undergone a substantial level of socioeconomic advancement. Conversely, the regions characterized by a high supply and low demand were primarily hilly regions with a sparse population that were situated at higher altitudes. There has been slight incoordination between ecosystem services’ supply and demand in Changchun. In the future, it is imperative for sustainable urban development strategies to protect cultivated and ecological lands, extensively enhance the benefits of the lands, and facilitate the coordinated development of cities, agriculture, and ecology.

Mulching and nitrogen (N) fertilization are the main drivers for sustainable crop production. The sole use of nitrogen fertilizer threatened both the physiology and production of maize in rain-fed areas. Therefore, we proposed that wheat straw mulching with N fertilization would increase maize yield by improving soil fertility, physiology, and nitrogen use efficiency. A two-year field study evaluated the effects of CK (control), N (nitrogen application at 172 kg ha−1), HS (half wheat straw mulch, 2500 kg ha−1), HS+N (half wheat straw, 2500 kg ha−1 plus 172 kg N ha−1), FS (full wheat straw, 5000 kg ha−1), and FS+N (full wheat straw, 5000 kg ha−1 plus 172 kg N ha−1) on maize growth, physiology, and biochemistry. Compared with the control, the FS+N treatment resulted in the increase of 56% photosynthetic efficiency, 9.6% nitrogen use efficiency, 60% nitrogen uptake, 80% soluble sugar, 59% starches, 48% biomass, and 29% grain yield of maize. In addition, the FS+N regime increased 47%, 42%, and 106% of soil organic carbon and available P and N content in comparison with the control. Maize grain and biomass yields were positively correlated with N uptake, photosynthesis, soil organic carbon, and soil available N and P contents. Conclusively, the use of wheat straw at 5000 kg ha−1, along with 172 kg N ha−1, is a promising option for building a sustainable wheat–maize cropping system to achieve optimal crop yield and improved plant and soil health in a semi-arid region of China.

The reluctance machine is a potential candidate for electrical vehicle propulsion because of its reliable structure, low cost, flexible flux regulation ability, and wide speed range. However, the torque density is unsatisfactory because of the poor excitation ability and low stator core utilization factor. To solve this problem, in this paper, a novel hybrid reluctance machine (HRM) with the skewed permanent magnet (PM) and the zero-sequence current is proposed for electric vehicles. The skewed PM has two magnetomotive force (MMF) components with different functions. The radial MMF component provides extra torque by the flux modulation effect. The tangential MMF component can generate a constant biased field in the stator core to relieve the saturation caused by the zero-sequence current and thus improve the utilization factor of the stator core. Therefore, torque improvement and the relief of stator core saturation can be simultaneously achieved by the skewed PM. In this paper, the machine structure and principle of the proposed machine are introduced. And ultimately, the machine’s electromagnetic performances are evaluated under different PM magnetization directions and zero-sequence current angles by using finite element analysis (FEA).

To address the difficulty of early fault diagnosis of rolling bearings, this paper proposes a rolling bearing diagnosis method by combining the attention entropy and adaptive deep kernel extreme learning machine (ADKELM). Firstly, the wavelet threshold denoising method is employed to eliminate the noise in the vibration signal. Then, the empirical wavelet transform (EWT) is utilized to decompose the denoised signal, and extract the attention entropy of the intrinsic mode function (IMF) as the feature vector. Next, the hyperparameters of the deep kernel extreme learning machine (DKELM) are optimized using the marine predators algorithm (MPA), so as to achieve the adaptive changes in the DKELM parameters. By analyzing the fault diagnosis performances of the ADKELM model with different kernel functions and hidden layers, the optimal ADKELM model is determined. Compared with conventional machine learning models such as extreme learning machine (ELM), back propagation neural network (BPNN) and probabilistic neural network (PNN), the high efficiency of the method proposed in this paper is verified.

The deep integration of power grids and communication networks is the basis for realizing the complete observability and controllability of power grids. The communication node or link is always built according to the physical nodes. This step is alternatively known as “designing with the same power tower”. However, the communication networks do not form a “one-to-one correspondence” relationship with the power physical network. The existing theory cannot be applied to guide the practical power grid planning. In this paper, a local evolution model of a communication network based on the physical power grid topology is proposed in terms of reconnection probabilities. Firstly, the construction and upgrading of information nodes and links are modeled by the reconnection probabilities. Then, the power flow entropy is employed to identify whether the power cyber-physical system (CPS) is at the self-organized state, indicating the high probability of cascading failures. In addition, on the basis of the cascading failure propagation model of the partially dependent power CPS, operation reliabilities of the power CPS are compared with different reconnection probabilities using the cumulative probability of load loss as the reliable index. In the end, a practical provincial power grid is analyzed as an example. It is shown that the ability of the power CPS to resist cascading failures can be improved by the local growth evolution model of the communication networks. The ability is greater when the probability of reconnection is p = 0.06. By updating or constructing new links, the change in power flow entropy can be effectively reduced.

A theoretical model for studying the temperature properties of singlemode-multimode-singlemode (SMS) fiber structure fabricated by absorptive multimode fiber (MMF) cladding is established. Moreover, an SMS-based temperature sensor is fabricated and experimentally demonstrated. Experimental results show that the dip wavelength of the transmission spectrum changes linearly with temperature, which is in good agreement with the simulated results obtained by using the model. Further, a comprehensive study of temperature characteristics affected by the thermo-optic effect, thermal expansion effect, and thermal effect of absorption characteristics is performed for SMS fiber optic structures with different refractive indexes, thermo-optic coefficients, and absorption properties of MMF cladding, MMF core diameters, and thermal expansion coefficients of packaging shell. According to the obtained rules, investigations are carried out into the thermal response of an SMS fiber structure resulting from combined thermal effects for temperature performance optimization. Excellent temperature stability with a temperature sensitivity of 0 pm/°C or good temperature sensitivity of −441.58 pm/°C is achieved accordingly.

In order to accurately diagnose the fault type of power transformer, this paper proposes a transformer fault diagnosis method based on the combination of time-shift multiscale bubble entropy (TSMBE) and stochastic configuration network (SCN). Firstly, bubble entropy is introduced to overcome the shortcomings of traditional entropy models that rely too heavily on hyperparameters. Secondly, on the basis of bubble entropy, a tool for measuring signal complexity, TSMBE, is proposed. Then, the TSMBE of the transformer vibration signal is extracted as a fault feature. Finally, the fault feature is inputted into the stochastic configuration network model to achieve an accurate identification of different transformer state signals. The proposed method was applied to real power transformer fault cases, and the research results showed that TSMBE-SCN achieved 99.01%, 99.1%, 99.11%, 99.11%, 99.14% and 99.02% of the diagnostic rates under different folding numbers, respectively, compared with conventional diagnostic models MBE-SCN, TSMSE-SCN, MSE-SCN, TSMDE-SCN and MDE-SCN. This comparison shows that TSMBE-SCN has a strong competitive advantage, which verifies that the proposed method has a good diagnostic effect. This study provides a new method for power transformer fault diagnosis, which has good reference value.

With the increase in capacitor voltage transformer (CVT) operation life, CVT impedance changes, and the short-time switching of overhead lines, it is very easy to cause a transient oscillation accident in which a CVT participates, reduce the insulation level of a CVT, and even induce regional power grid oscillation and easily cause capacitor breakdown, after long-term operation with defects. In this paper, the whole power network, including the generator, power grid, and consumer, is modeled, and the theoretical model of the CVT ferro-resonance connected grid is built. The frequency spectrum analysis, and the measurements of resonance bus voltage and line voltage, were carried out to find the design method of a fast saturation damper (FSD) and its voltage–current characteristics that affect the vibration, based on theory and practice; this paper puts forward effective measures to reduce the resonance effect of a CVT in operation, and avoid the oscillation of the line and even the regional power grid system.

Owing to the stochastic states of power systems with large-scale renewable generation, the impact of high-voltage direct current (HVDC) systems on the stability of the power system should be examined in a probabilistic manner. A probabilistic small signal stability assessment methodology to select the best locations for multi-infeed high-voltage direct current systems in alternating current (AC) grids is proposed in this paper. The Latin hypercube sampling-based Monte Carlo simulation approach is taken to generate the stochastic operation scenarios of power systems with the consideration of several stochastic factors, i.e., load demand and power generation. The damping ratio of the critical oscillation modes and the controllability of power injection to oscillation modes are analyzed by the probabilistic small signal stability. A probabilistic index is proposed to select the best locations of high-voltage direct current systems for improving the damping of the oscillation modes. The proposed methodology is applied to an IEEE 39 bus system considering the stochastic load demand and power generation. The results of probabilistic small signal stability assessment and a time-domain simulation show that the installation of a high-voltage direct current system on the selected locations can effectively improve the system damping.

The development of multianalyte immunoassays with an emphasis on food safety has attracted increasing interest, due to its high target throughput, short detection time, reduced sample consumption, and low overall cost. In this study, a superior polyclonal antibody (pAb) against sulfonamides (SAs) was raised by using a bioconjugate of bovine serum albumin with a rationally designed hapten 4-[(4-aminophenyl) sulfonyl-amino]-2-methoxybenzoic acid (SA10-X). The results showed that the pAb could recognize 19 SAs with 50% inhibition (IC50) below 100 µg L−1 and a recognition profile for SAs containing, either a five-atom ring or a six-atom ring, with highly uniform affinity. A three-dimensional quantitative structure-activity relationship analysis indicated that the electrostatic features of SAs play a considerably important role, during recognition with pAb than stereochemical effects. Skimmed milk samples were directly diluted five times before analysis. After optimization, the limit of detection for sulfamonomethoxine, sulfamethoxazole, sulfaquinoxaline, sulfadimethoxine, and sulfamethazine were 1.00, 1.25, 2.95, 3.35, and 6.10 µg L−1, respectively. The average recoveries for these 5 SAs were 72.0⁻107.5% with coefficients of variation less than 14.1%. The established method, based on pAb, with broad specificity and uniform affinity, offered a simple, sensitive, and high-throughput screening tool for the detection of multi-SAs in milk samples.

This paper inspects the complementary force allocation control schemes for an integrated, dual-mover linear switched reluctance machine (LSRM). The performance of the total force is realized by the coordination of the two movers. First, the structure and characteristics of the LSRM are investigated. Then, a complimentary force allocation control scheme for the two movers is proposed. Next, three force allocation methods—constant proportion, constant proportion with a saturation interval and error compensation, and the variable proportion allocation strategies—are proposed and analyzed, respectively. Experimental results demonstrate that the complimentary force interaction between the two movers can effectively reduce the total amount of force ripples from each method. The results under the variable proportion method also show that dynamic error values falling into 0.044 mm and −0.04 mm under the unit ramp force reference can be achieved. With the sinusoidal force reference with an amplitude of 60 N and a frequency of 0.5 Hz, a dynamic force control precision of 0.062 N and 0.091 N can also be obtained.