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Ensemble learning models have been widely used for wind power forecasting to facilitate efficient dispatching of power systems. However, traditional ensemble methods cannot always function well due to insufficient accuracy and diversity of base learners, ignorance of ensemble pruning, as well as the lack of adaptation capability. Therefore, a novel probabilistic wind power forecasting method is proposed based on selective ensemble of finite mixture Gaussian process regression models (SEFMGPR). First, a set of diverse local Gaussian process regression (GPR) models are constructed through multimodal perturbation mechanism, i.e., perturbing the training data and input attributes simultaneously. Then, a set of finite mixture GPR models (FMGPR) is built by integrating local GPR models through finite mixture mechanism (FMM). Next, the highly influential FMGPR models are selected using genetic algorithm (GA) based ensemble pruning. When a new test sample comes, the component predictions from the selected FMGPR models are adaptively combined by using FMM again and the probabilistic prediction results of the SEFMGPR model are obtained. Besides, an incremental adaptation mechanism is used to alleviate performance degradation of SEFMGPR. The application results from a real wind farm dataset show that SEFMGPR outperforms the traditional global and ensemble wind power prediction methods, and can maintain high prediction accuracy by effectively handling time-varying changes of wind power data.

Carbonaceous materials present the promising anode candidates for sodium-ion batteries (SIBs) and potassium-ion batteries (PIBs), however, the poor rate performance and low capacity due to limited interlayer spacing greatly impact their practical application. Herein, we report soybean roots-derived mesoporous hard carbon constructed by N, P co-doping (NPDCs) with enlarged interlayer distance and more active sites for high-performance SIBs and PIBs. As a result, the optimized NPDC achieves an excellent cyclic stability (197 mA g−1 after 2000 cycles at 2.0 A g−1) for SIBs, superior rate capability (a high reversible capacity of 179 mAh g−1 at 5.0 A g−1) for PIBs. Kinetic and first-principles calculation studies further demonstrate the dual-doping improves the Na (K) ions diffusion and adsorption as well as electrons transport. This work provides a facile and sustainable strategy to realize advanced carbon-based materials with heteratoms doping in application for energy storage devices.

Due to the sodium abundance and availability, sodium-ion batteries (SIBs) have the potential to meet the worldwide growing demand of electrical energy storage. P2-type sodium transition-metal layer oxides with a high energy density are considered as the most promising cathode materials for SIBs. We present here a detailed study of the enhanced rate capability and cyclic stability of the Ti-doped Na0.67Ni0.33Mn0.67O2 cathode material. The combined analysis of ex-situ X-ray absorption fine structure (XAFS) spectroscopy, aberration-corrected high resolution transmission electron microscopy (AB-HRTEM) and X-ray diffraction (XRD) show that the strong Ti–O bond in the transition metal layers stabilizes the local structure, destroy the Na+-vacancy ordering and arrest the irreversible multiphase transformation that occurs during the intercalation/deintercalation process. Actually, Na0.67Ni0.33Mn0.52Ti0.15O2 exhibits a reversible capacity of 89.6 mA h g−1 even at 5 C, an excellent cyclability with 88.78 % capacity retention after 200 cycles at 0.5 C. This study provides a better understanding in optimization of the design of high-energy cathode materials based on titanium doped layered oxides for SIBs.

The SnSe nanoparticles encapsulated in the carbon nanofibers (SnSe@C) with microrod morphology and core–shell structure are prepared by electrospinning and annealing process, and investigated as anode materials for sodium ion batteries. Benefiting from this unique structure, the SnSe@C can deliver a reversible capacity of 283.8 mAh g−1 after 500 cycles at a high current density of 1.0 A g−1. The sodium ion storage mechanisms of SnSe are further characterized by ex-situ X-ray diffraction, high-resolution transmission electron microscope and selected area electron diffraction measurements. Besides, the excellent electrochemical performance of the electrodes is investigated by pseudocapacitance and in situ electrochemical impedance spectroscopy measurements. This work may provide a new avenue for synthesis of metal selenides with core–shell structure and a good idea for studying the kinetics process.

This brief presents a low voltage folded-switching ultra-wideband down-conversion mixer in TSMC 0.18 $\mu \text{m}$ SiGe BiCMOS technology. The transconductance stage uses BJT (HBT) transistors to improve the conversion gain. The switching stage uses a CMOS inverter that is switched to reduce the supply voltage and the dc power consumption. The design technology for the mixer combines the advantages of BJT (HBT) and CMOS. The results show a conversion gain of 8.8~ 17.1 dB, ranging from 3 GHz to 11 GHz, a single-ended noise figure from 12.8 dB to 16.56 dB, an input return loss of less than −8 dB and LO-to-RF isolation that is better than −32 dB. The IIP3 is 0 dBm at 3 GHz. The total dc power consumption from a 1V supply voltage with output buffers is 3.45 mW.

The low efficiency and instability of electron emission from two-dimensional (2D) materials such as WS2 and NiS hamper application to vacuum microelectronic devices. Although nickel-based alloys have many favorable physicochemical properties suitable for nanoelectronics, electron field emission (FE) from heterostructures comprising tungsten-nickel sulfide alloys and graphite carbon (WNi–S/C) has been seldom studied because it is difficult to fabricate the nanostructures in situ. In this work, field emitters composed of the WNi–S/C nanobelt composite are produced on Si microchannel plates (WNi–S/C@Si) hydrothermally. The structure has a porous and vertically aligned 3D morphology in addition to excellent adhesion strength with the substrate. Geometrical modulation of the WNi–S/C@Si is performed by changing the hydrothermal reaction temperature and an ultralow turn-on electric field of 0.51 V μm−1 for a current density 0.1 mA cm−2, threshold electric field of 0.65 V μm−1 for a current density of 1 mA cm−2, and stability of 97.1% for 8 h are accomplished. The related mechanisms are investigated and discussed. Moreover, finite element simulation shows that not only the shape of the nanostructures but also the high aspect plays a key role in reducing screen effects. These outstanding properties suggest large potential in high-performance FE and vacuum nanodevices.

An increasing number of cloud providers now offer mobile edge computing (MEC) services for their customers to support task offloading. This is undertaken to reduce latency associated with forwarding data from IoT devices owned by customers to cloud platforms. However, two challenges remain in existing MEC scenarios: (i) the coverage of MEC services is limited; (ii) there is limited ability to develop an audit trail about which MEC service providers have processed a user's data. A new architecture for automatically offloading user tasks in MEC scenarios is proposed that addresses the two challenges above. The architecture makes use of drones to dynamically cache data generated from IoT devices and forward this data to MEC servers that participate in a private blockchain network. Our simulated experiments demonstrate the flexibility of the task offloading process through the proposed architecture, which can provide greater visibility of MEC service providers involved in processing users' data.

Convolutional neural networks (CNN) have achieved excellent performance for the hyperspectral image (HSI) classification problem due to better extracting spectral and spatial information. However, CNN can only perform convolution calculations on Euclidean datasets. To solve this problem, recently, the graph convolutional neural network (GCN) is proposed, which can be applied to the semisupervised HSI classification problem. However, the GCN is a direct push learning method, which requires all nodes to participate in the training process to get the node embedding. This may bring great computational cost for the hyperspectral data with a large number of pixels. Therefore, in this article, we propose an inductive learning method to solve the problem. It constructs the graph by sampling and aggregating (GraphSAGE) feature from a node's local neighborhood. This could greatly reduce the space complexity. Moreover, to enhance the classification performance, we also construct the graph using spectral and spatial information (spectra-spatial GraphSAGE). Experiments on several hyperspectral image datasets show that the proposed method can achieve better classification performance compared with state-of-the-art HSI classification methods.

Poor electrical conductivity and large volume changes are two disadvantages of metal selenides. In this work, MnSe nanoparticles encapsulated into N-carbon fibers are designed and prepared using electrospinning and calcination methods. When calcined at 700 °C, the composite has a high reversible capacity of 684 mAh g−1 after 100 cycles at 0.1 A g−1, owing to the improved conductivity and protection provided by carbon fibers. Furthermore, the in situ electrochemical impedance measurements reveal the process kinetics of the composites. This work will provide the basis for the further development of the application of metal selenides in the field of flexible electrodes.

Solid oxide electrolysis cell (SOEC) is a promising electrochemical device with high efficiency for energy conversion and storage. In this work, La1.5Sr0.5Ni1-xMnxO4+δ (LSNMx) (x = 0, 0.1, 0.25, 0.5, 0.75) Ruddlesden-Popper oxides are synthesized via the conventional solid-state method and evaluated as potential air electrodes for SOEC. The effects of substituting Mn for Ni on the crystal structure, oxygen content, thermal expansion behavior, electrical conductivity, and electrochemical performance are comprehensively investigated. XRD results show that the solid-solution concentration of Mn in LSNMx cannot exceed 0.5. The X-ray photoelectron spectroscopy of O1s suggest that Mn doping increases the concentration of oxygen vacancy. The polarization resistance (Rp) of LSNMx (x = 0, 0.1, 0.25, 0.5) exhibits a significantly decreasing tendency with increasing Mn content. LSNM0.5 shows the lowest Rp value of 0.488 Ω cm2 at 800 °C and is reduced by as much as 81.1% in comparison with that of the LSN electrode. A current density of 500 mA cm−2 at 1.4 V is obtained for the LSNM0.5 air electrode with 70% CO2 + 30% CO feed gas on the fuel electrode in a fuel electrode-supported single cell at 800 °C. This current density shows an increase of approximately 85.2% compared with that of the un-doped LSN electrode. The stability test of the half-cell shows that the LSNM0.5 air electrode is relatively stable after anodic polarization at 500 mA cm−2 and 800 °C in air for 145 h. Thus, the Mn-doped LSNM0.5 Ruddlesden-Popper material is a promising air electrode for SOEC.

This study presents an ultra-wideband up-conversion mixer chip that operates within the frequency range of 2–12 GHz using TSMC 0.18 μm complementary metal oxide semiconductor (CMOS) technology. The architecture is based on that of a Gilbert cell mixer. The design of the transconductance stage is based on an inverting amplifier. Two series resonant inductors are used to increase the conversion gain of the mixer, and a transformer that consists of two self-made inductors reduces the spatial requirement of the chip. This up-mixer achieves a 12.48 dB conversion gain and features an excellent gain flatness value of ±1.2 dB. The local oscillator (LO)-to-radio-frequency isolation is 44–20 dB, the input third-order intercept point (IIP3) is −6.8 to −8 dBm and the DC power consumption is 5.084 mW for a supply voltage of 1 V. The total size of the chip for the up-mixer is 1.2 × 1.2 mm2, including the pad frames.

The multi-compartment vehicle routing problem (MCVRP) has been applied in fuel or food delivery, waste collection, and livestock transportation. Ant colony optimization algorithm (ACO) has been recognized as an efficient method to solve the VRP and its variants. In this paper, an improved hybrid ant colony optimization algorithm (IHACO) is proposed to minimize the total mileage of the MCVRP. First, a probabilistic model is designed to guide the algorithm search towards high-quality regions or solutions by considering both similar blocks of customers and customer permutations. Then, a heuristic rule is presented to generate initial individuals to initialize the probabilistic model, which can drive the search to the high-quality regions faster. Moreover, a new local search using the geometry optimization is developed to execute exploitation from the promising regions. Finally, two types of variable neighborhood descent (VND) techniques based on the speed-up search strategy and the first move strategy are devised to further enhance the local exploitation ability. Comparative numerical experiments with other algorithms and statistical analyses are carried out, and the results show that IHACO can achieve better solutions.

The score functions are often used to rank the interval-valued intuitionistic fuzzy sets (IVIFSs) in multiattribute decision making (MADM). The purpose of this paper is to develop an information-based score function of the IVIFS and apply it to MADM. Considering the information amount, the reliability, the certainty information, and the relative closeness degree, we propose an information-based score function of the IVIFS. Comparing the information-based score function with existing ranking methods, we find that the information-based score function can overcome the drawbacks of the existing ranking methods and can rank the IVIFSs well. Three illustrative examples of MADM with linear programming are examined to demonstrate the applicability and feasibility of the information-based score function. It is shown that the information-based score function is well defined and can be applied to MADM.

The work aim to the reaction mechanism of laser engraving polycarbonate (PC)-based films. The calculation of infrared-laser acting on the PC films, was carried out by using one-dimensional heat conduction model. The power density of laser device required for laser engraving polycarbonate-based films was determined to be 4.86~7.73×104 W/cm2. Characterized by the FTIR spectra, SEM, DSC and TGA, the reaction mechanism was studied. The results show that the light-sensitive particles in the PC films absorb the laser energy with wavelength of 1064nm. After reaching the temperature of 509°C, the main chain of PC molecule opened due to the breakage of C-O bond in PC molecular terminal group and C-O bond between PC monomers. As the energy continues to be absorbed, the temperature reach to 610°C, the decomposition products decomposed into carbon black, which can form a black carbon spot.

Transition metal phosphides (TMPs)/carbonaceous matrices have gradually attracted attention in the field of energy storage. In this study, we presented nickel phosphide (Ni2P) nanoparticles anchored to nitrogen-doped carbon porous spheres (Ni2P/NC) by using metal-organic framework-Ni as the template. The comprehensive encapsulation architecture provides closer contact among the Ni2P nanoparticles and greatly improves the structural integrity as well as the electronic conductivity, resulting in excellent lithium storage performance. The reversible specific capacity of 286.4 mA h g−1 has been obtained even at a high current density of 3.0 A g−1 and 450.4 mA h g−1 is obtained after 800 cycles at 0.5 A g−1. Furthermore, full batteries based on LiNi1/3Co1/3Mn1/3O2||Ni2P/NC exhibit both good rate capability and cycling life. This study provides a powerful and in-depth insight on new advanced electrodes in high-performance energy storage devices.

Pancreatic ductal adenocarcinoma (PDAC) is radioresistant. Due to their strong X-ray absorption capacity, gold nanoparticles (AuNPs) have been used as radiosensitizers for cancer therapeutics. Herein, we describe a novel conjugate complex consisting of a peptide for targeting plectin-1 (PTP) specifically expressed on the PDAC cell membrane and AuNPs, termed AuNP-PTP, to be used for PDAC radiotherapy in vitro and in vivo. Previous studies revealed that compared with unmodified AuNPs, AuNP-PTP along with relevant low-energy X-ray irradiation of 6 MV at a dose of 2 Gy (RF) increased the targeting efficiency and induced apoptosis in treated PANC-1 cells and tumours. Importantly, extensive histopathological examination did not reveal evidence of acute or chronic injury in mice due to AuNPs or AuNP-PTP for up to six weeks despite the presence of X-ray exposure. The delicate AuNP-PTP hybrid provides a novel strategy to enhance radiotherapy efficiency in PDAC treatment.

Hyperspectral unmixing is an important technique for remote sensing image analysis. Among various unmixing techniques, nonnegative matrix factorization (NMF) shows unique advantage in providing a unified solution with well physical interpretation. In order to explore the geometric information of the hyperspectral data, graph regularization is often used to improve the NMF unmixing performance. It groups neighboring pixels, uses groups as graph vertices, and then assigns weights to connected vertices. The construction of neighborhood and the weights are normally determined by k -nearest neighbors or heat kernel in a deterministic process, which do not fully reveal the structural relationships among data. In this article, we introduce an adaptive graph to regularize a multilayer NMF (AGMLNMF) model for hyperspectral unmixing. In AGMLNMF, a graph is constructed based on the probabilities between neighbors. This enables the optimal neighborhood be automatically determined. Moreover, the weights of the graph are assigned based on the relationships among neighbors, which reflects the intrinsic structure of the complex data. Experiments on both synthetic and real datasets show that this method has outperformed several state-of-the-art unmixing approaches.

A facile method is designed to synthesize CeO2 nanoparticles embedded one dimensional N doped carbon matrix by the electrospinning and calcination process. The electrochemical tests reveal that the CeO2@NC composite delivers excellent electrochemical performance with good cycling stability and rate capability. CeO2@NC composite exhibits a high reversible capacity of 724.8 mAh g−1 after 100 cycles at a current density of 100 mA g−1, owing to that the one dimensional N doped carbon matrix can effectively improve the electron conductivity of composite and accelerate the ion transfer with short diffusion length. This work can be extended to construct one dimensional other metal oxides/carbon composite and applied in a new generation of anode materials.

The purpose of this paper is to propose the novel generalized exponential intuitionistic fuzzy entropy (GIFE) and generalized exponential interval valued intuitionistic fuzzy entropy (GIVIFE) with interval area. First, we propose a novel GIFE. Then we compare the new GIFE with the existing intuitionistic fuzzy entropy (IFE) measures. Second, we define the interval area and the new axioms for the interval valued intuitionistic fuzzy entropy (IVIFE). Third, according to the newly defined axioms for the IVIFE, we use the interval area to construct the new GIVIFE. Finally, the advantages of the new generalized entropy measures are compared with the existing IVIFE measures by some examples. The two novel generalized exponential entropy measures can distinguish the special cases well. We have the conclusion that the two novel generalized entropy measures are reasonable and more flexible than the existing entropy.

The operation safety of high-speed railways has received great concern globally in recent years. The power supply of the electronic monitoring devices alongside high-speed railway tunnels and their maintenance are becoming increasingly important. In this paper, a portable renewable wind energy harvesting system integrated with an S-rotor and H-rotor has been designed to provide electricity to the monitoring sensors alongside railway tunnels in remote areas with shortages in the electricity supply. The proposed renewable wind energy harvesting system can harvest the wind energy inside the tunnel and convert it into electricity for self-powered applications. The renewable wind energy harvesting system scheme mainly consists of three components: a portable wind harvester mechanism, a generator module and a power storage module. The S-rotor and H-rotor are integrated as the wind harvester mechanisms in this system. The S-rotor harvests natural wind energy, and the H-rotor harvests piston wind energy when a high-speed train passes through the tunnel. Meanwhile, the S-rotor and H-rotor rotate the generator module separately through the one-way bearing. Finally, the supercapacitors in the power storage module are used to store the electrical energy. Experiments were conducted to study the performance of the system. A maximum electrical power output of 107.76 mW and a maximum efficiency of 23.2% are demonstrated in the test. The experimental data indicate that the wind energy harvesting system is beneficial and is a promising method to generate energy used for renewable self-powered applications in high-speed railway tunnels.

Just-in-time (JIT) learning has been widely used for data-driven soft sensor modeling. However, traditional JIT soft sensors do not always function well when applied to complex industrial processes...

Transition metal phosphides (TMPs) have attracted much considerable interest for electrochemical energy storage, due to their high theoretical capacity and earth-abundant. In this work, we report ultrafine carbon-coated bimetal phosphide nanoparticles embedded into the nitrogen-doped graphene network (NGN) through a solution-phase self-assembly strategy. The unique carbon-coated Ni1.4Co0.6P nanoparticles and strongly coupled with NGN can not only provide more active sites for lithium-ions reaction and enhance the conductivity of electrode, but also restrain the volume expansion during the charge/discharge process. When evaluated as anode material for lithium-ion batteries, the as-prepared hybrid electrode exhibits high specific capacities (1320 mAh g−1 at 120 mA g−1), superior rate capability (227 mAh g−1 at a current density of 3000 mA g−1) and excellent cycling stability (350 mAh g−1 at 1200 mA g−1 after 1000 cycles). This work can be extended to develop advanced electrode materials for next-generation energy storage systems.

The large volume change and low electronic conductivity are the two main drawbacks for metal carbonates as promising anode materials of lithium-ion batteries. Metal doping is one of the most effective methods to address these issues. Herein, cadmium-doped ferrous carbonate Fe1–xCdxCO3 (x = 0, 0.2, 0.4, 0.6, 0.8, and 1) composites have been synthesized by a simple hydrothermal method for the first time. The morphologies of FeCO3 are evolved from microspheres to the agglomerated nanoparticles and then nanocubes with the increase of Cd doping. Fe0.8Cd0.2CO3 microspheres deliver higher capacity and cycling stability in Fe1–xCdxCO3 composites and better electrochemical performance than the corresponding 0.8FeCO3 + 0.2CdCO3 mixtures owing to an inner synergistic effect between Cd and Fe atoms in Fe1–xCdxCO3. The density functional theory calculation and electrochemical impedance spectroscopy results indicate that Fe0.8Cd0.2CO3 can maintain the similar electronic structure with good electric conductivity to FeCO...

To observe the effect of electroacupuncture (EA) on nuclear factor kappa B (NF-κB) and nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome in uterine tissues of rats with primary dysmenorrhea (PD), thus to explore the possible mechanism of EA for PD. Fifty female Sprague-Dawley (SD) rats were randomly divided into a normal group, a model group, an EA at non-acupoint group, an EA at acupoint group and a Western medicine group, with 10 rats in each group. Except for the normal group, rats in the other four groups were treated with estradiol benzoate combined with oxytocin for 11 d to establish PD rat models. From day 1 of the modeling, rats in the normal group and the model group were only properly grasped without any intervention; Guanyuan (CV 4) and Sanyinjiao (SP 6) were selected for EA treatment in the EA at acupoint group; rats in the EA at non-acupoint group were treated with EA at 5 mm away from the acupoints selected above; rats in the Western medicine group were treated with ibuprofen via gavage. Rats in each group were treated for 10-day successively. On the 11th day, except for the normal group, rats in the other groups were intraperitoneally injected with oxytocin (2 U/rat), and the writhing number within 30 min in each group was compared; the pathological changes in rat uteruses were observed by hematoxylin-eosin (HE) staining, and the pathological damage scores were evaluated. Protein expression levels of NF-κB p65, phospho-NF-κB p65, NLRP3, cysteine aspastic acid-specific protease 1 (caspase-1), interleukin (IL)-1β and IL-18 were detected by Western blot. Compared with the normal group, the writhing number increased significantly (P 0.05) in the Western medicine group; compared with the Western medicine group, the phospho-NF-κB p65/NF-κB p65 ratio, also the NLRP3, IL-1β and IL-18 protein levels of the uterine tissues decreased significantly in the EA at acupoint group (all P 0.05); there were no significant differences between the EA at non-acupoint group and the model group in any indicators (all P>0.05). EA at acupoints significantly improves the pain and pathological damages of PD rats. The mechanism may be related to the reduced uterine inflammation via inhibiting NF-κB phosphorylation and NLRP3 activation in uteruses of PD rats.

Cobalt sulfides (including CoS, CoS2, Co3S4, Co9S8 and nonstoichiometric Co1−xS) are deemed to a kind of novel anode material for lithium ion batteries due to their large specific capacities. However, cobalt sulfides suffer from poor cycling stability with the large volume expansion. In this work, we designed and synthesized a novel Co-metal organic frameworks (MOFs) material by using carbazolyl carboxylates ligand H4DCDC. The Co1−xS nanoparticles embedded in N-doped carbon sheets (Co1−xS/NCS) composites are obtained via vulcanizing the Co-MOFs precursor. As a result, the Co1−xS/NCS exhibits outstanding cycling stability and maintains a high capacity of 796.3 mA h g−1 at the current density of 200 mA g−1 after 100 cycles. The N-doped carbon sheets can not only enhance the conductivity of materials but also buffer the volume expansion during charge-discharge process.

In this work, the effect of Ga substitution on the crystal structure, phase transitions, magnetocaloric effect, and exchange bias properties, was systematically investigated in Ni43Mn46Sn11−xGax alloys. With increasing Ga content, the Curie temperature of austenite slightly decreases, while the martensitic transformation temperature shifts to a higher temperature and finally disappears, due to the emergence of γ phase. Large low-field magnetic entropy changes around the martensitic transformation were observed as the transition from a paramagnetic martensitic phase to a ferromagnetic austenitic phase took place. Ni43Mn46Sn11 has a reentrant-spin-glass ground state, and Ga doping can enhance the antiferromagnetic interaction arising from lattice contraction, leading to close of intermediate ferromagnetic windows and establishment of canonical spin-glass for x > 2. Prominently, the zero-field-cooled exchange was achieved in the spin-glass region with 5 ⩽ x ⩽ 10 .

Just-in-time (JIT) learning based soft sensors have been widely used for predicting product quality variables of nonlinear processes. They dynamically build online local models by selecting the samples most relevant to the query data from a historical database whenever an estimate is requested. However, building high-performance JIT soft sensors remains challenging due to difficulties defining similarity measures and the selection of input variables for facilitating efficient relevant sample selection and model building. In this study, we propose a novel soft sensing framework, referred to as JIT learning with variable selection and weighting (JIT-VSW). In this framework, a mixture weighted similarity (MWS) measure is defined by combining multiple weighted Euclidean distance (WED) based similarity measures. The MWS measure enables variable weighting embedded in WED measures to account for the relevance between input and output variables and facilitates the handling of highly complex process characteristics through the mixture-type similarity measure. Meanwhile, a wrapper optimization approach using evolutional algorithms is proposed for input variable selection. Further, the selection of input variables and the determination of MWS parameters, i.e., weights assigned to input variables and mixture coefficients of WED similarity measures, are formulated as a mixed integer optimization problem and solved simultaneously by using the mixed integer genetic algorithms (MIGA). The effectiveness and superiority of JIT-VSW are verified through three real-world applications.

The interesting phenomena induced by the interaction between local moments and itinerant electrons in 4f compounds have attracted extensive attention for many years. Here, we report the synthesis of a new 4f compound, i.e. NdPd1-δBi2 (δ∼0.23) which is superconducting with a transition temperature Tc around 3.2 K. A comprehensive investigation of the thermodynamic properties suggests NdPd1-δBi2 is also an anti-ferromagnet with the Neel temperature TN ∼4 K. Moreover, its high-temperature paramagnetic state is characterized by an enhanced heat capacity with a Sommerfeld coefficient γ over 300 mJ mol−1 K−2, which presumably results from Nd3+ crystal-electric-field splitting rather than the Kondo effect. In the superconducting state, the suppression of superconductivity with field increasing for H ||c is faster in comparison with H || ab, resulting in a moderate upper critical field anisotropy with Γ = H c 2 ( 0 K , ‖ a b ) / H c 2 ( 0 K , ‖ c ) ∼2.6. Collectively, NdPd1-δBi2 may serve as an intriguing system to study the interplay among magnetic order, Kondo effect, and superconductivity in 4f compounds.

High-performance transition metal carbonates are considered as promising electrode materials in lithium-ion batteries. However, the poor conductivity limits their further application. Herein, graphene modified CdCO3 cubic particles composite (CdCO3@rGO) are prepared using a facile hydrothermal method and their electrochemical performance are discussed. CdCO3@rGO can maintain a reversible capacity of 423.1 mAh g−1 at 200 mA g−1 after 100 cycles while CdCO3 only delivers a capacity of 142.0 mAh g−1. The excellent cycling stability and rate capability of CdCO3@rGO can be attributed to graphene modification, which not only decreases the particle size with large surface area but also improves the electronic conductivity and effectively buffers volume changes during the lithium insertion/extraction process.

Safety is a stumbling block to the applications of the lithium-ion batteries in electric vehicles, especially under the abuse condition of overcharge. Much research on preventing overcharge is bein...

Superconductivity in topological materials, either at ambient or extreme conditions, has continued to intrigue scientists as a promising candidate for realizing topological superconductivity, a platform to host the long-sought Majorana fermions in condensed matter. The recent discovery of extremely large magnetoresistance (XMR) in the rare-earth monopnictides opens a new avenue to search for topologically nontrivial states therein, although contrasting opinions argue that it is the carrier compensation effect that is responsible for the observed large, nonsaturating magnetoresistance. Here we study the quantum oscillations and pressure-induced superconductivity in the topologically nontrivial candidate YBi. While the magnetotransport and quantum oscillations do reveal nearly compensated charge carriers, first-principles calculations clearly show that the electronic surface states manifest topologically nontrivial features. Upon applying external hydrostatic pressures, the magnetoresistance is found to decrease and at $P\ensuremath{\sim}2.5$ GPa, superconductivity emerges. There exists, however, a regime where XMR and superconductivity coexist in the phase diagram. YBi may therefore represent a rare system for studying the interplay between XMR, topological states, and superconductivity.

Niobium oxide has been extensively investigated as a promising anode material owing to its special structure and excellent electrochemical performance. Herein, Multiwalled carbon nanotube (MWCNT)-modified Nb2O5 is prepared by using a simple hydrothermal method. The morphologies tests show a uniform particle size of Nb2O5 and the average distribution of MWCNTs between nanoparticles. The electronic and ion conductivity of Nb2O5 can be improved by the MWCNT modification. The fabricated Nb2O5-MWCNT composites deliver superior electrochemical performance than the pristine Nb2O5 and MWCNT. Ex situ tests reveal that the capacity of Nb2O5 is mainly provided by the Nb4+/Nb5+ electrochemical couple.

Aiming at reducing the total energy consumption of three stages processing-transportation-assembly in the assembly manufacturing industry, a three-stage multiobjective integrated scheduling problem with job batch transportation considering the energy consumption (3sMISP_JBTEC) is proposed, and a comprehensive energy consumption model of multistage of 3sMISP_JBTEC with an improved turn off/on strategy in the processing stage and considering speed in the transportation stage is formulated. Then, a hybrid estimation of distribution algorithm with variable neighborhood search (HEDA_VNS) is developed to solve the scheduling problem. In the HEDA_VNS, the reasonable coding/decoding rules and speed scheduling scheme (SSS) are designed. Moreover, two local search strategies are designed to further enhance the performance of HEDA_VNS. Among them, three types of neighborhood search strategies are devised in Local Search I to improve the search efficiency while retaining the structure of the original high-quality solution. A variable neighborhood hybrid operation based on the speed scheduling set is designed in Local Search II to further improve the quality of the solution while balancing the optimization goals. Finally, simulations and comparisons show the efficiency of the proposed HEDA_VNS.

Rare-earth monopnictides are predicted to be nontrivial semimetal candidates and show pressure-induced superconductivity. Here, we grow LuBi single crystal and study the magnetization, transport behaviors and electronic band structures to reveal its topological semimetal feature and superconductivity under pressure. At 0 GPa, the quantum oscillations indicate that there are several topologically nontrivial carrier pockets around the Fermi level, among which the hole ones are isotropic in shape, while the electron ones are anisotropic and responsible for the angular magnetoresistance. Upon compression, the superconductivity emerges in the titled compound, showing a similar pressure dependence as that observed in LaBi. Our calculation suggests that the electronic band structures are robust at low- and high-pressure respectively and thus the topological features are always preserved. Besides, the nearly pressure-independent density of state in LuBi indicates that the conventional electron-phonon coupling appears to play a minor role in the superconductivity.

Aiming at the multi-objective flow shop scheduling problem with limited buffers, a hybrid pathfinder algorithm (HPFA) is presented to minimize the electricity cost and the maximum tardiness. Firstly, the position vector of individuals in PFA is converted to job permutations by the largest-order-value (LOV) rule. Secondly, a heuristic operation named NEH is added to improve the quality of the initial solution. Thirdly, after pathfinder location update, a variable neighborhood search (VNS), which is designed based on three sub-regions, is applied to enrich the search efficiency and to avoid premature convergence. Simulation results and comparisons with the INSGA-II based on benchmarks demonstrate the effectiveness and efficiency of the proposed HPFA.

Hyperspectral unmixing is one of the most important procedures for remote sensing image processing. The multilayer non-negative matrix factorization (MLNMF)-based method has been widely used for hyperspectral unmixing due to its good performance for highly mixed data with multiple-decomposition structure. However, few works consider the spatial information in the image, which may enhance the performance. In order to solve this issue, we propose a homogeneous region regularized multilayer non-negative matrix factorization (HR-MLNMF) method for hyperspectral unmixing. In HR-MLNMF, the spatial information, depicted by the homogeneous region, is applied to regularize MLNMF, which could enhance the smoothness of each homogeneous spatial field to achieve better performance. Experiments on both synthetic and real datasets have validated the effectiveness of our method and shown that it has outperformed several state-of-the-art approaches of hyperspectral unmixing.

In this paper, a bandwidth controllable filtering waveguide is proposed. The filtering waveguide consists of a common rectangular waveguide and metasurface-based metallic bars. Inside the operational frequency band, the metallic bars forming a metasurface can behave as perfect electric conductor (PEC), which generates a pass-band for signal transmission, while outside the band of interest, the metallic bars act as perfect magnetic conductor (PMC) and block the transmission of undesired signals, where a stop-band is generated. After integrating the pass-band and stop-band features into the waveguide, a customized waveguide with filtering response is realized. Moreover, the pass-band and stop-band of the filtering waveguide can be flexibly and easily adjusted to meet different requirements with low insertion loss.

In this paper, a metal-strip integrated filtering waveguide is proposed. The overall structure consists of a traditional rectangular waveguide and a metal-strip surface which is loaded at the bottom wall of the waveguide. The customized surface can be considered as a meta-surface, when the surface acts as a PEC, the filtering waveguide works at pass-band. When the surface plays a role as a PMC outside the interested frequency band, a stop-band can be created. The proposed filtering waveguide operates in Ku-band with pass-band of 12 GHz~15.1 GHz and stopband of 15.8 GHz~17.4 GHz. Simulation results show that the reflection coefficient of the waveguide in the passband is lower than -20 dB, and the propagation coefficient in the stopband is lower than -60 dB.

An efficient approach to reduce antenna radar cross section (RCS) is proposed in this paper. A wideband Vivaldi antenna is adopted for demonstration, the shape of which is reformed by the differential evolution algorithm (DEA) under co-optimization of Python and HFSS. By utilizing this simple and efficient design approach, no additional structure is required for RCS reduction while other electromagnetic performance of the antenna can be well maintained. Results show that the designed Vivaldi antenna achieves a good RCS reduction from 4 GHz to 9 GHz, validating the possibility and feasibility of this method for further radar target application.

Synergic effect of electronic correlation and spin-orbit coupling is an emerging topic in topological materials. Central to this rapidly developing area are the prototypes of strongly correlated heavy-fermion systems. Recently, some Ce-based compounds are proposed to host intriguing topological nature, among which the electronic properties of CeSb are still under debate. In this paper, we report a comprehensive study combining magnetic and electronic transport measurements, and electronic band structure calculations of this compound to identify its topological nature. Quantum oscillations are clearly observed in both magnetization and magnetoresistance at high fields, from which one pocket with a nontrivial Berry phase is recognized. Angular-dependent magnetoresistance shows that this pocket is elongated in nature and corresponds to the electron pocket as observed in LaBi. Nontrivial electronic structure of CeSb is further confirmed by first-principle calculations, which arises from spin splitting in the fully polarized ferromagnetic state. These features indicate that magnetic-field can induce nontrivial topological electronic states in this prototypical Kondo semimetal.

Rare-earth monopnictides display rich physical behaviors, featuring most notably spin and orbital orders in their ground state. Here, we grow ErBi single crystal and study its magnetic, thermal, and electrical properties. An analysis of the magnetic entropy and magnetization indicates that the weak magnetic anisotropy in ErBi possibly derives from the mixing effect, namely the anisotropic ground state of $\mathrm{E}{\mathrm{r}}^{3+}(4{f}^{11})$ mingles with the isotropic excited state through exchange interaction. At low temperature, an extremely large magnetoresistance $(\ensuremath{\sim}{10}^{4}%)$ with a parabolic magnetic-field dependence is observed, which can be ascribed to the nearly perfect electron-hole compensation and ultrahigh carrier mobility. When the magnetic field is rotated in the $ab$ $(ac)$ plane and the current flows in the $b$ axis, the angular magnetoresistance in ErBi shows a twofold (fourfold) symmetry. Similar case has been observed in LaBi where the anisotropic Fermi surface dominates the low-temperature transport. Our theoretical calculation suggests that near the Fermi level ErBi shares similarity with LaBi in the electronic band structures. These findings indicate that the angular magnetoresistance of ErBi could be mainly determined by its anisotropic Fermi surface topology. Besides, contributions from several other possibilities, including the spin-dependent scattering, spin-orbit scattering, and demagnetization correlation to the angular magnetoresistance of ErBi are also discussed.

The lack of online sensors for Mooney viscosity measurement has posed significant challenges for enabling efficient monitoring, control, and optimization of industrial rubber mixing process. To obtain real-time and accurate estimations of Mooney viscosity, a novel soft sensor method, referred to as multimodal perturbation- (MP-) based ensemble just-in-time learning Gaussian process regression (MP-EJITGPR), is proposed by exploiting ensemble JIT learning. This method employs perturbations on similarity measure and input variables for generating the diversity of JIT learners. Furthermore, a set of accurate and diverse JIT learners are built through an evolutionary multiobjective optimization by balancing the accuracy and diversity objectives explicitly. Moreover, all base JIT learners are combined adaptively using a finite mixture mechanism. The proposed method is applied to an industrial rubber mixing process for Mooney viscosity prediction, and the experimental results demonstrate its effectiveness and superiority over traditional soft sensor methods.

Transitional metal phosphides (TMPs) have been attracted much attention as potential anode materials for lithium-ion batteries, due to their high theoretical capacity, good thermal stability and low cost. In this work, a rational design of multiwalled carbon nanotubes (MCNTs) modified porous CoP/carbon composite (CoP/C@MCNTs) has been realized using metal–organic framework as templates. The CoP/C@MCNTs hybrids with small CoP nanoparticles embed into the three-dimensional carbon skeleton are well incorporated with MCNTs. Owing to their unique architecture, the as-prepared CoP/C@MCNTs composite exhibits promising lithium storage performance with a reversible capacity of 547.5 mAh g−1 at a current density of 500 mA g−1 after 200 cycles and a discharge capacity of 290.2 mAh g−1 can still be maintained at a high current density of 3000 mA g−1. We believe that the interesting strategy for synthetic method in this work can open up a way to design other TMPs-based anode materials.

In this work, the evolution of magnetic ground state and exchange bias (EB) with Al substitution in Ni50Mn37Sb13-xAlx Heusler alloys was investigated in detail. Al doping reduces the cell volume, enhancing antiferromagnetic interactions between Mn atoms and changing the ground state from “reentrant” spin glass (RSG) to spin glass. The conventional EB increases greatly from 314 Oe for x = 0 to 6682 Oe for x = 10. More interestingly, the zero-field-cooled EB begans to emerge at x = 6 where RSG is surppressed and reaches a maximum value at x = 8. These results imply that tuning cell volume provides an effective way to adjust the strength of exchange interaction and further modulate EB in Ni-Mn-based Heusler alloys.

A systematic investigation on the structure, magnetism, magnetocaloric effect, and critical behaviors of multiferroic Lu2NiMnO6 synthesized by sol-gel method are reported. This compound crystallizes in a monoclinic structure with the space group P21/n. Upon decreasing temperature, a single magnetic transition from paramagnetic to ferromagnetic phase has been observed, around which considerable magnetic entropy changes can be obtained. Besides, evaluation of the critical exponents are performed using various techniques, like the modified Arrott plot, Kouvel-Fisher method and critical isotherm analysis based on the magnetic data around magnetic transition temperature. The obtained critical exponent values are found to be coincided and comparable with those predicted by the mean-field model, which are also confirmed by the single scaling equation: m = f±(h), where m and h are the renormalized magnetization and field, respectively by observation of isotherm M(H) curves below and above the critical temperatures collapsing into two independent universal branches. The exponents determined in this study are in good agreement with those calculated from the renormalization group approach for a three-dimensional system coupled with the attractive long-range interactions between spins that decay as J ( r ) ∼ r − ( 3 + σ ) with σ = 1.325.

The full duplex (FD) two-way relay channel (TWRC) has recently been shown to be a feasible way to improve the network throughput in practical communications. In this correspondence, we consider the convolutionally coded physical-layer network coding over the TWRC in which two sources work in the FD mode and there exists a direct link between them. Every source receives two packets, one packet from the other source and one network coded packet from the relay. A near-maximum-likelihood decoding algorithm for the sources is proposed. A salient feature of the proposed algorithm is that it exploits the code structure to mitigate the error propagation induced by the relay. Furthermore, we derive the near maximum-likelihood decoding bound on end-to-end BER for the system. Simulation results indicate that the performance of the proposed algorithm matches well the bound.

Ternary metal oxides have attracted much attention in energy storage fields. Herein, tantalum-based oxides MTaO4 (M = Al, Cr and Fe) are synthesized by a facile co-precipitation method, and their performance as lithium-ion battery anodes are evaluated. Among them, the FeTaO4 electrode presents superior electrochemical performance compared to the MTaO4 (M = Al and Cr) and a reversible capacity of more than 200 mAh g−1 can be maintained after 100 cycles, while a capacity of 53.6 and 128.9 mAh g−1 can be obtained at the same condition for AlTaO4 and CrTaO4, respectively. The explanation that FeTaO4 exhibits the excellent electrochemical performance in MTaO4 (M = Al, Cr and Fe) are further discussed.

In this study, a new pressurized weighted-sum-of-gray-gases (WSGG) model of a H2O/CO2 gas mixture under pressurized conditions was proposed to calculate the radiation heat transfer of various pressurized combustion equipment. The new WSGG model and its parameters, which are suitable for a pressure range of 1–30 bar, was developed based on the statistical narrowband (SNB) model of EM2C laboratory. Moreover, its temperature range is 500–2500 K, path-length range is 0.001–60 m, and the range of the molar ratio for H2O/CO2 is 0.125–4. Therefore, the new WSGG model is suitable for many fuels under pressurized conditions. The one-dimensional case results demonstrate that the new WSGG model can well predict the radiation heat transfer of the gas mixture under pressurized conditions compared with the benchmark model. In addition, the new model was used to study the effect of pressure on the radiation heat transfer of the gas mixture. The results show that increasing the pressure within a certain range could enhance the radiation heat transfer of the gas mixture, and this effect was also associated with the temperature, the molar ratio and the path-length. In conclusion, the new WSGG model can be applied to the calculation and research of radiation heat transfer in pressurized combustion chambers, such as pressurized boilers, gas turbines, internal combustion engines and aircraft engines.

Multi-sensor information fusion (IF) has attracted the attention of many researchers in different fields because it can improve modeling accuracy by integrating information gathered from multiple sensors. Traditionally, the inputs of the multi-sensor IF problem are mostly quantitative data. However, to provide a more comprehensive decision support, both quantitative data and experts’ domain knowledge should be synthesized in the IF process. Moreover, there may be insufficient data in many practical conditions, which would make many conventional approaches inapplicable. Because the belief rule base (BRB) has shown advantages in nonlinear modeling with insufficient data and experts’ domain knowledge, a BRB-IF model is proposed for the multi-sensor IF problem. To improve its efficiency, an optimization model and the corresponding optimization algorithm for BRB-IF are proposed. The particle swarm optimization algorithm and differential evolutionary algorithm are tested as the optimization engine to solve the optimization model with the operator recommendation strategy. The efficiency of the proposed BRB-IF is validated by a practical case study of threat level assessment, where a comparison between BRB-IF and the neural network is conducted.

The development of the stereolithography technique for the additive manufacturing of silica glass has brought revolutionary change in glass manufacturing. Here, we demonstrate the fabrication of 3D luminescent transparent glass parts manufactured by the stereolithographic technique together with solution impregnation and high temperature sintering. Prefabricated glass parts with nanopores were prepared by the stereolithography technique and debinded and pre-sintered at first. To functionalize the additive manufactured glass with photoluminescence, Eu3+, Tb3+ and Ce3+ ions were doped with a solution impregnation method and further sintered at high temperature. The photoluminescence from these rare earth ions in the blue, cyan and red spectral region can be facilely generated by illumination with a 254 nm UV lamp. Furthermore, we developed a space-selective doping method that enables the doping of different ions in different parts of a silica glass in a space-selective fashion, resulting in a multicolor luminescent glass object giving distinguishable luminescence from each part.