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To ensure safer drilling and to prevent kicks and blowouts, Managed Pressure Drilling (MPD) technology has been proposed. However, conventional MPD necessitates manual operation of the choke within the choke manifolds by engineers, a task heavily reliant on their experience. This paper proposes a PID controller tuned using the Dung Beetle Optimizer (DBO) and details several laboratory experiments on automatic choke control utilising DBO-PID alongside a modified hydraulic control box. The experimental results demonstrate that DBO-PID can quickly and stably control the surface back pressure (SBP) to the desired level. Following extensive laboratory experiments validation, a field experiment implementing DBO-PID with a hydraulic control box was conducted on an offshore platform. The field experiment results indicate that DBO-PID is 29 % faster than manual control by an engineer in stabilising the SBP, with an overshoot of less than 4 % when nearing the target SBP. Additionally, during the control processes for both constant and variably targeted SBP, pressure fluctuation was 72 % and 63 % less, respectively, compared to manual operation. Both laboratory and field experiments have conclusively demonstrated that the automatic choke control system, comprising DBO-PID and a hydraulic control box, can efficiently manage SBP, thereby preventing kicks and blowouts. Furthermore, this system could replace manual operations in MPD, enhancing the safety of oil and gas resource development, protecting engineers' lives, and safeguarding the natural environment.

Complex knowledge graph question answering (KGQA) aims at answering natural language questions by entities retrieving from a knowledge graph (KG). Recently, the relation path-based models have shown the unique advantage for complex KGQA. However, these existing models ignore the dependency between different relation paths, which leads to aimless reasoning over the KG. To resolve this issue, we propose the question-directed reasoning with relation-aware graph attention network (QRGAT) that encodes the reasoning process as a reasoning graph. The relation-aware GAT can recognize neighbor entities along with the corresponding relations for each entity. With the relation-aware GAT stacked in multiple layers, it can collaboratively capture the dependency of different relation paths for each entity. The question-directed reasoning utilizes the information learned by the relation-aware GAT to solve the aimless reasoning on the KG by constructing a reasoning graph. Extensive experiments demonstrate that our QRGAT outperforms the baseline models on both popular datasets WebQuestionsSP and ComplexWebQuestions. Compared with the strong GNN-based baseline NSM$_{+h}$, our QRGAT achieves the performance improvements of 2.3% on WebQuestionsSP and 3.6% on ComplexWebQuestions by the metric Hits@1.

Objective: There is a lack of published evidence on factors associated with adherence (maintenance of cumulative vaccination) to seasonal influenza vaccination (SIV) in Manitoba, Canada. We sought to assess the associations. Methods: A cohort study utilizing Manitoba administrative health databases. Participants received SIV in 2010/11 influenza season, remained registered Manitoba residents and received at least one SIV during the 2011/12‒2019/20 seasons. We dichotomized adherence into “more adherent” (6‒9 SIVs) and “less adherent” (1‒5 SIVs) and used multivariable adjusted generalized estimating equation logistic regression models to assess association between adherence and socioeconomic, health-related, and primary care physician (PCP) characteristics, stratified by age group (< 5, 5‒17, 18‒44, 45‒64, ≥ 65) and sex. Results are adjusted odds ratios with 95% confidence intervals. Results: There were 152,493 participants. Males had lower odds of being more adherent except among ≥ 65-year-olds (1.03, 95% CI 1.01‒1.05). Compared with the lowest income quintile, those in higher income quintiles had higher odds of being more adherent. The odds mostly increased with increase in income quintile. Those with more contact with their PCP/hospitalization one year prior had higher odds of being more adherent. The odds increased with increased contact among those 18‒44, 45‒64 and ≥ 65 years old. Those who had PCP with more years of practice had higher odds of being more adherent. The odds increased as years of practice increased. These observations were mostly consistent irrespective of sex. Conclusion: Female gender, having higher income, having more contact with the health system, and having an experienced PCP may determine increased adherence to SIV in Manitoba. These findings require attention.

This brief proposes a frequency domain model for injection-locked LC oscillators by introducing the impulse sensitivity function (ISF) and injection factor, analyzes the effect of the injection pulse width and injection position on oscillator performance based on this model, and derives an equation that can guide the design and optimization of oscillators. The model fills the gap in the field of frequency domain models for injection-locked LC oscillators. Based on the above model, a clock generator circuit structure operating at 14 GHz, combining a phase-locked loop with an injection-locked oscillator, is designed and measured based on a 65-nm cmos process. The circuit achieves better phase noise performance by optimizing the two parameters, which verifies the accuracy of the proposed model.

The rapid development of information and communication technologies has facilitated machining condition monitoring toward a data-driven paradigm, of which the Industrial Internet of Things (IIoT) serves as the fundamental basis to acquire data from physical equipment with sensing technologies as well as to learn the relationship between the system condition and the collected condition monitoring data. However, most data-driven methods suffer from using a single-domain space, ignoring the importance of the learned features, and failing to incorporate the handcrafted features assisted by domain knowledge. To solve these limitations, a novel deep learning approach is proposed for machining condition monitoring in the IIoT environment, which consists of three phases, including: 1) the unsupervised parallel feature extraction; 2) adaptive feature importance weighting; and 3) hybrid feature fusion. First, separate sparse autoencoders are utilized to conduct the unsupervised parallel feature extraction, which enables to learn abstract feature representation from multiple domain spaces simultaneously. Then, an attention module is designed for the adaptive feature importance weighting, which can assign higher weights to those critical features accordingly. Moreover, a hybrid feature fusion is deployed to complement the automatic feature learning and further yield better model performance by fusing the handcrafted features assisted by domain knowledge. Finally, a real-life case study and extensive experiments have been conducted to show the effectiveness and superiority of the proposed approach.

Global navigation satellite system (GNSS) technique has irreplaceable advantages in the continuous monitoring of surface deformation. Reducing noise to improve the signal-to-noise ratio (SNR) and extract the concerned signals is of great significance. As an improved algorithm of empirical mode decomposition (EMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm has better signal processing ability. Using the CEEMDAN algorithm, the height time series of 29 GNSS stations in Chinese mainland were analyzed, and good denoising effects and extraction from periodic signals were achieved. The numerical results showed that the annual signal obtained with the CEEMDAN algorithm was significantly based on Lomb_Scargle spectrum analysis, and large differences in the long-term signals were found between the stations at different locations in Chinese mainland. With respect to data denoising, compared with the EMD and wavelet denoising algorithms, the CEEMDAN algorithm respectively improved the SNR by 29.35% and 36.54%, increased the correlation coefficient by 8.67% and 11.96%, and reduced root mean square error (RMSE) by 44.68% and 43.48%, indicating that the CEEMDAN algorithm had better denoising behavior than the other two algorithms. In addition, the results demonstrated that different denoising methods had little influence on estimating the annual vertical deformation velocity. The extraction of periodic signals showed that more components were retained by using the CEEMDAN algorithm than the EMD algorithm, which indicated that the CEEMDAN algorithm had advantages over frequency aliasing. In conclusion, the CEEMDAN algorithm was recommended for processing the GNSS height time series to analyze the vertical deformation due to its excellent features of denoising and the extraction of periodic signals.

In the intensive care unit, sepsis is a prevalent clinical syndrome (i.e. the final pathway to death from most infections). Peripheral blood gene expression profiling is becoming more and more accepted as a potential diagnostic or prognostic tool. This work aimed to recognize genes related to sepsis, providing potential translational therapeutic targets. RNA sequencing was performed on peripheral blood mononuclear cells from 20 healthy control subjects and 51 sepsis patients. Weighted gene coexpression network analysis was employed to pick out sepsis-related and immunocyte-related gene modules. Genes in the yellow module are primarily involved in excessive inflammation and immune suppression. STRING and Cytoscape were combined to identify ACTG1and IQGAP1as hub genes with highest connective degree, and prognostic predication value of ACTG1was confirmed. Both univariate and multivariate logistic regression analyses were carried out. ACTG1messenger RNA expression was increased in animal and in cell-related sepsis models. Small interfering RNA revealed decreasing ACTG1can reduce the in vitro sepsis model apoptosis. We have authenticated ACTG1as a reliable signature of a poor outcome of sepsis and promising therapeutic targets for sepsis.The risk of death during sepsis increased with higher plasma ACTG1-induced apoptosis.

Multihop question answering from knowledge bases (KBQA) is a hot research topic in natural language processing. Recently, the graph neural network-based (GNN-based) methods have achieved promising results as the KB can be organized as a knowledge graph (KG). However, they often suffered from the sparsity of the KG which was detrimental to the structure encoding and reasoning capabilities of GNN. Specifically, a KG is a sparse graph linked by directed relations and previous studies have paid scant attention to the directional characteristic of relations in the KG, limiting the patterns of relation path that GNN-based approaches could resolve. This study proposes a bidirectional recurrent GNN (BRGNN) to tackle these difficulties. To model the bidirectional information of relations, all adjacent relations of an entity are grouped by their directions, and they are separately aggregated into the entity representation in outward and inward directions. For the reasoning process, BRGNN simultaneously considers the neighbor relations in both directions to cover more patterns of relation paths and improve the recall of answers. Extensive experiments on three benchmarks: WebQuestionsSP, ComplexWebQuestions, and MetaQA, verify that BRGNN can answer more questions by taking into account the directional information, and it is competitive to all state-of-the-art approaches.

Through the intelligent vehicles trip data collection, processing and analysis, so as to predict the vehicles trip destination, this technology can improve the user’s driving experience and the city traffic conditions. The vehicles dispatching system can also judge the real-time road conditions according to the prediction results and plan a more reasonable and efficient driving route, which is of great significance to urban traffic planning and urban construction planning. However, the amount of information in the vehicles data is less, which cannot meet the training needs of some artificial intelligence models. In addition, due to communication technology and other issues, there is a certain degree of deviation between the GPS data of the vehicles and the real data. The previous vehicles destination prediction model did not well eliminate the negative impact of this deviation. In addition, the previous model linearly adds the characteristics of each vehicles’s trip data, which cannot reflect the complex rules of the vehicles’s trip destination. Therefore, to address the above-mentioned drawbacks, we propose a novel vehicle trip destination prediction method named Hybrid Trip Destination Prediction Model of Vehicle Based on Autoencoder and High-Order Interaction Features (HAHIF). The HAHIF model extracts robust hidden features using the autoencoder model and considers the second-order association between them using a factorization machine to improve its superiority and effectiveness. Compared with mainstream benchmarks, the HAHIF model has an MSE value of 0.096, an RMSE value of 0.427, and a MAE value of 0.203 on the public dataset, both of which are the first place, which verifies that the HAHIF model has good predictive ability.

Significant racial/ethnic inequities in the uptake of differentiated influenza vaccines (DIVs) have been previously reported, though less is known about regional disparities. We conducted a retrospective longitudinal study (2014/15–2017/18 influenza seasons) among privately insured adults aged 65 + years in the US. The exposure was the beneficiary’s area of residence (US Census Bureau division) and the outcome was the type of influenza vaccine: differentiated (high-dose [HDV], adjuvanted, recombinant, and cell-based) versus conventional standard-dose egg-based. Multilevel logistic regression modeling, guided by a causal diagram, was used to assess the influence of socio-demographics, medical, healthcare utilization, community, and vaccinator characteristics in confounding or mediating regional disparities. Among those vaccinated in physician offices, beneficiaries in the East North Central region were twice as likely to receive a DIV vs those in the South Atlantic, whereas those in the East and West South Central were least likely. Disparities became more pronounced in models adjusted for individual and community characteristics, suggesting that crude uptake estimates understate the true magnitude of disparities. A vaccinator’s previous HDV use was most influential in explaining regional differences. Similar but less pronounced patterns emerged for vaccinations in pharmacies/facilities. Regional disparities remained even in fully adjusted models, pointing to currently poorly understood factors that may include quality of healthcare, client health literacy and engagement, and other political and cultural factors.

Casing is inevitably damaged during perforation operation, and the perforation parameters will have a significant impact on the degree of casing damage, which will directly affect the stability of casing structure in the process of hydraulic fracturing. A three-dimensional numerical model, which was composed of perforating charge, casing–cement sheath, and formation and consistent with the engineering practice, was established, considering the dynamic characteristics of materials in the process of explosion impact. The numerical simulation results were verified by physical model tests and showed that the stress superposition between adjacent perforations increases the damage degree of casing. Perforation density and phase angle directly affect the range of stress superposition area, with the increase of perforation density or decrease of phase angle, the damage area and damage coefficient of casing are increasing. Based on the results of numerical simulation, an innovative perforation parameter optimization method considering casing yield strength was proposed, which could effectively reduce the stress superposition area between adjacent perforations and keep casing damage within a reasonable range. Finally, perforation parameter optimization based on real wells was carried out, and analysis results showed that the casing stress was lower than the yield strength after parameter optimization, which could ensure the stability of the casing. The research results can provide an important reference for the optimization of perforation design and field operation.

Knowledge of wellbore multiphase flow characteristics is important for early gas invasion identification and treatment, especially in oil-based drilling fluid. The wellbore multiphase flow law in downhole dual-gradient drilling (DDGD) is more complex than that in conventional single-gradient drilling. There was no relevant literature report at present. In this paper, considering the synergistic feedback relationship of multiphase flow, interphase mass transfer, and wellbore temperature, a fully transient wellbore multiphase flow model was established based on oil-based drilling fluid in deepwater DDGD. The model also coupled the variable temperature–mass flows caused by the dynamic transfer of hollow glass balls and formation gas. The bubble interface mass transfer theory was adopted to describe the interphase mass transfer rate. The model was verified through the experimental measurement data. The calculated results indicated that the dynamic transfer of hollow glass balls varied the wellbore pressure gradient and flow rate in DDGD, resulting in a sudden decrease in gas fraction and gas solubility along the flow direction at the separator. Additionally, the transient interphase mass transfer and wellbore temperature had large impact on the wellbore multiphase flow behaviors. Moreover, the model based on bubble interface mass transfer theory could more accurately characterize the development of the wellbore multiphase flow. This research could provide a certain theoretical basis for the detection and treatment of gas invasion in deepwater DDGD.

A two-dimensional Ni(II) coordination polymer (NiCP) of the formula {[NiL(terephthalate)(H2O)2]·2H2O}n(L = bis(1-(pyridin-4-ylmethyl)-benzimidazol-2-ylmethyl)ether), has been obtained from a solvothermal reaction, and characterized by single-crystal X-ray diffraction, elemental analysis, and IR and UV/Vis spectra. The coordinated terephthalate anions and the L ligands connect the Ni(II) ions in two directions, resulting in the construction of a corrugated layered structure. The electrochemical properties of a NiCP-CPE composite electrode supported by this coordination polymer were studied. For the electrocatalytic hydrogen evolution reaction, the required overpotential of this electrode (NiCP-CPE) is −521 mV when the current density reaches 10 mA cm−2. Compared with the solid carbon paste electrode (sCPE, −976 mV), the smaller overpotential proves effective electrocatalysis of the coordination polymer of the hydrogen evolution reaction. The doped electrode also exhibits high-efficiency in the electrochemical sensing of l-ascorbic acid in water, showing a detection limit of 0.28 μM in a linear range of 0.4–4000 μM.

Space-air-ground integrated communication networks and artificial intelligence (AI) play critical roles in future 6G wireless communication. The Power Internet of Things (PIoT) assisted by the space-air-ground integrated communication network, that is, space-air-ground integrated PIoT (SAG-PIoT), has emerged as a promising solution to provide seamless communication and computing services for PIoT devices with stringent QoS requirements. Cloud-edge-device collaboration is leveraged to enable intelligent resource management in SAG-PIoT. This article proposes an AI-based cloud-edge-device collaboration framework for SAG-PIoT to cope with multi-dimensional resource heterogeneity and network dynamics. Specifically, the SAG-PIoT application scenarios and research challenges are first introduced. Then we develop a hybrid and hierarchical cloud-edge-device collaboration architecture to adapt with different scenarios and domains, followed by the detailed implementation procedures. Next, a queue-aware deep actor-critic (Q-DAC)-based task offloading algorithm is proposed to facilitate decision making optimization under incomplete information. A case study is provided to validate the energy consumption and end-to-end queuing delay performance of Q-DAC.

Cloud-edge-end collaboration enables harmonious and efficient resource allocation for the Power Internet of Things (PIoT). However, the security and complexity issues of computation offloading evolve into the main obstacles. In this article, we first propose a blockchain and AI-based secure cloud-edge-end collaboration PIoT (BASE-PIoT) architecture to ensure data security and intelligent computation offloading. Its advantages in flexible resource allocation, secure data sharing, and differentiated service guarantee are elaborated. Then the adaptability of three typical blockchains with PIoT is analyzed, and some typical application scenes of BASE-PIoT including computation offloading, energy scheduling, and access authentication are illustrated. Finally, we propose a blockchain-empowered federated deep actor-critic-based task offloading algorithm to address the secure and low-latency computation offloading problem. The coupling between the long-term security constraint and short-term queuing delay optimization is decoupled by using Lyapunov optimization. Numerical results verify its excellent performance in total queuing delay and consensus delay.

In this work, a new type of zinc(II) coordination polymer {[Zn(HIDC)(BBM)0.5]·H2O}n(Zn-CP) was synthesized using 4,5-imidazoledicarboxylic acid (H3IDC) and 2,2-(1,4-butanediyl)bis-1,3-benzimidazole (BBM) under hydrothermal conditions. Its structure has been characterized by infrared spectroscopy, elemental analysis and single crystal X-ray diffraction analysis. The Zn(II) ion is linked by the HIDC2−ligand to form a zigzag chain by chelating and bridging, and then linked by BBM to form a layered network structure. Adjacent layers are further connected by hydrogen bond interaction to form a 3-D supramolecular framework. The solid-state fluorescence performance of Zn-CP shows that compared with free H3IDC ligand, its fluorescence intensity is significantly enhanced.

The exponential growth of digital image data has given rise to the need of efficient content management and retrieval tools. Currently, there is a lack of tools for processing the collected unlabeled data in a schematic manner. K-means is one of the most widely used clustering methods and has been applied in a variety of fields, one of them being image sorting. Although a useful tool for image management, the K-means method is heavily influenced by initializations, the most important one being the need to know the number of clusters a priori. A number of different methods have been proposed for identifying the correct number of clusters for K-means, one of them being the variance ratio criterion (VRC). Despite its popularity, the VRC method comes with two very important shortcomings: it only yields good results when the data dimensionality is low and it does not scale well for a high number of clusters, making it very difficult to use in computer vision applications. We propose an extension to the VRC method that works for increased cluster number and high-dimensionality data sets and therefore is fit for image data sets.

Biomass-derived carbon materials prepared via pyrolysis from natural wood structures show potential for a storage application. Natural wood is composed of multiple carbon sources, including lignin, hemicellulose, and cellulose, which influence the formation and microstructure of pyrolysis carbon. However, the mechanism is not fully understood. In this work, vast lignin is selectively consumed via biodegradation with fungi from basswood. The results demonstrate that the as-prepared carbon material has a short-range ordered graphitic structure after thermal treatment. The improved graphitization degree of carbon suggests that cellulose is beneficial to graphite formation during pyrolysis. The elevated graphitization degree helps to improve the charge transfer and the thermodynamic stability of the electrode reaction. As a proof of concept, the obtained carbon current collector as a sodium-metal anode can undergo cycling at an areal capacity of 10 mAh cm–2for over 4500 h and yield an excellent Coulombic efficiency of >99.5%.

A new one-dimensional Zn(II) coordination polymer, {[ZnCl2(BBM)]·CH3OH}n(2,2-(1,4-butanediyl)bis-1,3-benzimidazole [BBM]), has been obtained from the hydrothermal reaction of zinc chloride with the flexible bis-benzimidazole ligand BBM and characterized by single-crystal X-ray diffraction, elemental analysis, IR and UV–vis spectra. Structural analysis has revealed that the BBM ligand connects the Zn(II) atoms to form a square-wave chain, which is further extended into supramolecular layers through hydrogen bonds and π···π stacking interactions. Solid-state fluorescence investigations showed that the Zn(II) coordination polymer has an emission peak at 381 nm upon excitation at 330 nm, which is attributed to ligand-centered luminescence. It is only slightly red shifted as compared to the ligand but partially quenched due to the strong π···π stacking interactions.

A bicomponent anion exchange membrane (AEM) in the form of poly(vinylbenzyl chloride) (PVBC) cross-linked by polybenzimidazole (PBI) and quaternized by N-methylpiperidine (NMPD) is provided. On the one hand, the cross-linking between the benzimidazole moiety of PBI and chloromethyl group of PVBC increases the compatibility of two components without macroscopic phase separation. On the other hand, the difference between hydrophilic NMPD-functionalized PVBC and hydrophobic PBI leads to microphase separation and results in the construction of continuous ion transport channels in the membrane. The AEM with a PBI/PVBC weight ratio of 1/1 (PBI1-PVBC1-NMPD/OH) presents a hydroxide conductivity >80 mS cm–1at 80 °C. Moreover, the supporting effect of PBI as well as the cross-linking structure gives the membrane good mechanical properties and nearly temperature-independent water uptake (<50%) and swelling ratio (∼10%). After soaking in 1 M KOH at 60 °C for 336 h, ∼80% of hydroxide conductivity of PBI1-PVBC1-NMPD/OH is maintained. In addition, the feasibility of PBI1-PVBC1-NMPD/OH used in the AEM-based water electrolyzer is demonstrated.

Recently, the application of the proton exchange membrane fuel cells (PEMFCs) is extensively increasing as a popular renewable energy source. PEMFCs need low temperature for the operation along with high power density and easy implementation ability. These characteristics turned them into the most interesting type of fuel cells. However, PEMFCs need a technique for keeping them in a desired operating point. This is an essential item, especially during the current variation. In this study, a new configuration is proposed for designing an optimal PEMFC system. Two-phase interleaved boost DC/DC converter is also utilized for increasing the output voltage terminal. LQR optimal strategy is used for regulating the PEMFC. For improving the efficiency of the proposed LQR controller, a newly developed version of the whale optimization algorithm, called improved chaotic whale optimization algorithm is proposed. Simulation results of the proposed system are compared with different methods and the results showed that the proposed system has higher efficiency from the viewpoint of the current ripple and overshoot.

The propagation of a truncated Airy beam with spatial phase modulation (SPM) is investigated in Kerr nonlinearity with an optical lattice. Before the truncated Airy beam enters the optical lattice, a sinusoidal phase is introduced on the wave-front of the beam. The effect of the spatial phase modulation and optical lattice on propagation behavior is analyzed by direct numerical simulation. It is found that the propagation direction of a truncated Airy beam can be effectively controlled by adjusting the values of phase shift. The effects of optical amplitude, truncation factor, spatial modulation frequency, lattice period and lattice depth on the propagation are discussed in detail. By choosing a high modulation depth, the finite-energy Airy beam can be deflected with a large deflection angle in an optical lattice.

Reaction of 1,4-bis(benzo[d]oxazol-2-yl)butane (BBO) with [Ag(CH3CN)4(ClO4)] afforded a new binuclear silver(I) complex, with composition [Ag2(BBO)2(ClO4)2], characterized by elemental analysis, UV/Vis and IR spectroscopy, and single-crystal X-ray diffraction. The results show that the Ag(I) complex consists of a centrosymmetric dimetallacyclic structure assembled from two Ag(I) atoms and two bridging BBO ligands. The coordination environment of silver(I) complex can be described as distorted trigonal planar, with one oxygen atom from a perchlorate anion and two nitrogen atoms from two BBO ligands. The luminescence properties of the ligand and the Ag(I) complex were studied in the solid state. The emission peaks of the Ag(I) complex are attributed to ligand-centered transitions. There is no effect of the complexation except for a partial quenching. The cyclic voltammograms of the Ag(I) complex indicated an irreversible Ag+/Ag couple.

The gastric cancer grading of patients determines their clinical treatment plan. We use hyperspectral imaging (HSI) gastric cancer section data to automatically classify the three different cancer grades (low grade, intermediate grade, and high grade) and healthy tissue. This paper proposed the use of HSI data combined with a shallow residual network (SR-Net) as the classifier. We collected hyperspectral data from gastric sections of 30 participants, with the wavelength range of hyperspectral data being 374 nm to 990 nm. We compared the classification results between hyperspectral data and color images. The results show that using hyperspectral data and a SR-Net an average classification accuracy of 91.44% could be achieved, which is 13.87% higher than that of the color image. In addition, we applied a modified SR-Net incorporated direct down-sampling, asymmetric filters, and global average pooling to reduce the parameters and floating-point operations. Compared with the regular residual network with the same number of blocks, the floating-point operations of a SR-Net are one order of magnitude less. The experimental results show that hyperspectral data with a SR-Net can achieve cutting-edge performance with minimum computational cost and therefore have potential in the study of gastric cancer grading.

A novel model-free iterative learning control algorithm is proposed in this paper to improve both the robustness against output disturbances and the tracking performance in steady-state. For model-free ILC, several methods have been investigated, such as the time-reversal error filtering, the Model-Free Inversion-based Iterative Control (MFIIC), and the Non-Linear Inversion-based Iterative Control (NLIIC). However, the time-reversal error filtering has a conservative learning rate. Other two methods, although with much faster error convergence, have either a high noise sensitivity or a non-optimized steady-state. To improve the performance and robustness of model-free ILC, we apply the time-reversal based ILC and recursively accelerate its error convergence using the online identified learning filter. The effectiveness of the proposed algorithm has been validated by a numerical simulation. The proposed approach not only improves the transient response of the MFIIC, but achieves lower tracking error in steady-state compared to that of the NLIIC.

Red phosphorus (RP) as the anode material for the sodium-ion battery (SIB) possesses a high energy density, but the poor electronic conductivity and huge volume change during Na+insertion/extraction restrict its application. In this work, the edible fungus slag-derived porous carbon (PC) is adopted as a carbon matrix to combine with RP to form PC@RP composites through a facile vaporization–condensation approach. The conductive porous carbon architecture improves the transfer of electron and Na+in the composite. The robust carbon framework together with the chemical bonding between PC and RP effectively buffer the huge volumetric change of RP. As a result, the PC@RP composite material delivers a specific capacity of 655.1 mA h g–1at 0.1 A g–1with a capacity retention of 87% after 100 charging/discharging cycles. In particular, the full SIB assembled with P2–Na2/3Ni1/3Mn1/3Ti1/3O2as the cathode material and PC@RP as the anode material exhibits a specific capacity of 77.3 mA h g–1(based on the mass of cathode material) at 0.5 C, and 85% capacity is retained after 100 charging/discharging cycles.

Src homolog and collagen homolog (Shc) proteins have been identified as adapter proteins associated with cell surface receptors and have been shown to play important roles in signaling and disease. Shcbp1 acts as a Shc SH2-domain binding protein 1 and is involved in the regulation of signaling pathways, such as FGF, NF-ΚB, MAPK/ERK, PI3K/AKT, TGF-β1/Smad and β -catenin signaling. Shcbp1 participates in T cell development, the regulation of downstream signal transduction pathways, and cytokinesis during mitosis and meiosis. In addition, Shcbp1 has been demonstrated to correlate with Burkitt-like lymphoma, breast cancer, lung cancer, gliomas, synovial sarcoma, human hepatocellular carcinoma and other diseases. Shcbp1 may play an important role in tumorigenesis and progression. Accordingly, recent studies are reviewed herein to discuss and interpret the role of Shcbp1 in normal cell proliferation and differentiation, tumorigenesis and progression, as well as its interactions with proteins.

This study proposes an optimal model to design and simulate the proton exchange membrane fuel cell (PEMFC) systems. The purpose of this paper is to present an improved version of seagull optimization algorithm for optimal parameter identification of the PEMFC stacks. The new algorithm uses the Lévy flight mechanism to give faster convergence rates. The sum of the squared error between the empirical values and achieved optimal model is analyzed based on two empirical PEMFC models including BCS 500-W and NedStack PS6. This analysis is performed to show the potential of the presented method by considering different conditions. Simulation results are compared with several optimization algorithms and show the algorithm’s superiority in terms of the solutions quality and the convergence speed.

AbstractWe assessed Clostridioides difficiletoxin testing and positivity for all patients in Manitoba hospitals during June 2016–November 2018. The testing rate was 30 per 10,000 patient bed days (95% confidence interval [CI], 30–31) and the incidence rate was 3.5 per 10,000 patient bed days (95% CI, 3.3–3.7). The context of testing is essential to the interpretation of data among jurisdictions.

A route to enantiopure (O-methyl)6-2,6-helic[6]arenes (+)-P-2and (−)-M-2has been provided. By the reaction of enantiopure triptycene precursors (+)-1and (−)-1in refluxed o-DCB for 12 h in the presence of catalytic amount of FeCl3, and then followed by treatment of the obtained oligomers under the same conditions, (+)-P-2and (−)-M-2could be obtained in 51% and 53% total yield, respectively. It was also found that racemic and enantiopure (O-methyl)6-2,6-helic[6]arenes could be easily brominated by Br2to give the corresponding hexabromo-substituted helic[6]arene derivatives rac-4, (+)-P-4, and (−)-M-4in high yields. The crystal structure of (+)-P-4further confirmed the absolute configuration of the helic[6]arenes and their derivatives. Moreover, a series of hexaaryl-substituted helic[6]arene derivatives 5a–fwith deepened cavities could be conveniently synthesized in 55–71% yield by Suzuki–Miyaura coupling reactions of 4and arylboronic acids. rac-5acould encapsulate chloroform and exhibit self-sorting stacking in solid state. Enantiopure (+)-P-5a–fand (−)-M-5a–fshowed mirror images in their CD spectra.

Particle swarm optimizer (PSO) is an effective tool for solving many optimization problems. However, it may easily get trapped into local optimumwhen solving complex multimodal nonseparable problems. This paper presents a novel algorithm called distributed learning particle swarm optimizer (DLPSO) to solve multimodal nonseparable problems. The strategy for DLPSO is to extract good vector information from local vectors which are distributed around the search space and then to form a new vector which can jump out of local optima and will be optimized further. Experimental studies on a set of test functions show that DLPSO exhibits better performance in solving optimization problems with few interactions between variables than several other peer algorithms.

Currently, typical challenges that logistics industry faces include the exploding logistics (including reverse logistics) tasks, the lack of real-time and accurate logistics information, and demands towards sustainable logistics. Therefore, it is difficult for logistic companies to achieve highly-efficient and sustainable reverse logistics. This paper adopts a bottom-up logistics strategy that aims to achieve the real-time information-driven dynamic optimisation distribution for logistics tasks. Under this strategic framework, an IoT-enabled real-time information sensing model is designed to sense and capture the real-time data of logistics resources, which are shared among companies after the value-added processes. Real-time information-driven dynamic optimisation for logistics tasks is proposed to optimise the configuration of logistics resources, reduce logistics cost, energy consumption and the distribution distance, and alleviate the environmental pollution. The objective of this research is to develop an innovative logistics distribution model for sustainable logistics.