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This paper analytically explores a supply chain where a manufacturer sells products through a retail platform that can operate under either a reselling or agency selling model, corresponding to Scenarios R and A. The model incorporates consumer valuation discounts based on the platform’s retail channel and sensitivity to carbon emissions. The paper first examines the manufacturer’s investment decision in green technology to reduce carbon emissions and then analyzes the platform’s adoption of blockchain technology to disclose information about the manufacturer’s actions. The analysis shows that the manufacturer consistently prefers to invest in green technology over taking no action in both scenarios. However, the platform’s decision to adopt blockchain depends on consumer acceptance of the product: blockchain is deployed when consumer acceptance is high, and not when it is low. The study further identifies two key factors – consumer acceptance and environmental sensitivity – that critically influence the supply chain members’ choice of operational model. Specifically, as consumer acceptance of the product increases, both the manufacturer’s and platform’s preferences shift from Mode RN/RT (Reselling, No blockchain/Retail, No blockchain) to Mode RB (Reselling, Blockchain) and Mode RT (Agency, Blockchain) to Mode AB (Agency, Blockchain). Conversely, as consumer environmental sensitivity increases, preferences shift in the opposite direction, from Mode RB (Reselling, Blockchain) and Mode AB (Agency, Blockchain) to Mode RN/RT (Reselling, No blockchain) and Mode AN/AT (Agency, No blockchain). These findings highlight the interconnectedness of consumer attitudes toward sustainability and the operational decisions of supply chain members.

• A new 3D Eulerian porous media model was established for CO 2 absorption in RPBs. • Flow and mass transfer characteristics within the RPB packing are effectively analyzed. • Mass transfer in RPB is quantitatively evaluated under diverse operational conditions. • The correlation to predict overall gas phase mass transfer coefficient was developed. Rotating Packed Bed (RPB), as a representative equipment of hyper-gravity technology, is widely used in process intensification of various reactions and separations. This paper constructs a three-dimensional CFD model of RPB by employing the porous media Eulerian two-fluid method, coupling mass transfer, heat transfer, and chemical reaction models. The CFD model successfully simulated the CO 2 absorption process by MEA solution within the RPB, with the simulation results aligning well with both experimental and calculation data. The CFD model predicts the overall gas phase mass transfer coefficient (K G a) range of 1.876 to 3.029 s−1, while experimental data fall within the range of 1.7 to 2.4 s−1, with deviations ranging from 1.70 % to 26.2 %. Detailed distributions of flow and mass transfer parameters within the packing were obtained, and a quantitative analysis was conducted on the impact of different operating parameters on mass transfer and decarbonization performance. The K G a and CO 2 removal rate first increase (400 ∼ 1500 rpm) and then stabilize (1500 ∼ 2500 rpm) with the increase of rotational speed. The correlation to predict overall gas phase mass transfer coefficient was developed, and the calculated values are in agreement with the simulated values with deviations within ± 26 %. This work provides a novel and practical approach to designing and optimizing processes for RPB in engineering applications. [ABSTRACT FROM AUTHOR]

Physical spatiotemporal characteristics of cellular cortex dominate cell functions and even determine cell fate. The cellular cortex is able to reorganize to a dynamic steady status with changed stiffnesses once stimulated, and thus alter the physiological and pathological activities of almost all types of cells. TGF-β2, a potent pleiotropic growth factor, plays important roles in cartilage development, endochondral ossification, and cartilage diseases. However, it is not yet known whether TGF-β2 would alter the physical spatiotemporal characteristics of the cell cortex such as cortex stiffness, thereby affecting the function of chondrocytes. In this study, we investigated the influence of TGF-β2 on cellular cortex stiffness of chondrocytes and the underlying mechanism. We firstly detected TGF-β2-induced changes in cytoskeleton and focal adhesion plaque, which were closely related to cellular cortex stiffness. We then characterized the landscape of nanoscale cortex stiffness in individual chondrocytes induced by TGF-β2 via atomic force microscopy. By using inhibitors, latrunculin A and blebbistatin, we verified the importance of cytoskeleton-focal adhesion plaque axis on cellular cortex stiffness of chondrocytes induced by TGF-β2. We finally elucidated that TGF-β2 enhanced the phosphorylation of Smad3 and facilitated the nuclear accumulation of p-Smad3. The p-Smad3 aggregated in the nuclei enhanced the cytoskeleton and focal adhesion plaque at transcriptional level, thereby mediating changes in cell cortex stiffness. Taken together, these results provide an understanding about the role of TGF-β2 on physical spatiotemporal properties of cell cortex in chondrocytes, and might provide cues for interpretation of cartilage development and interventions to cartilage diseases.

Epstein–Barr nuclear antigen 1 (EBNA1), a sequence-specific DNA binding protein of Epstein–Barr virus (EBV), is essential for viral genome replication and maintenance and is therefore an attractive target for the therapeutic intervention of EBV-associated cancers. Several EBNA1-specific inhibitors have demonstrated the ability to block EBNA1 function in vitro, but practical delivery strategies for these inhibitors in vivo are still lacking. Here, we report an intelligent hierarchical targeting theranostic nanosystem (denoted as mZGOCS@MnO2–P5) that integrates an azide (N3) terminal dual-targeting peptide (N3–P5), a tumor microenvironment-responsive degradable MnO2nanosheet, and a mesoporous ZnGa2O4:Cr3+, Sn4+near-infrared persistent luminescence (NIR-PL) nanosphere (mZGOCS). In our design, mZGOCS@MnO2–P5 enables primarily targeting of the EBV-specific oncoprotein LMP1 (an EBV-encoded transmembrane protein) via the LMP1 targeting motif within P5. Once internalized into cells, the MnO2nanosheet would be degraded in the acidic and reducing tumor microenvironment, simultaneously releasing P5 and recovering the NIR-PL of ZnGa2O4:Cr3+, Sn4+initially quenched by the MnO2nanosheet, thereby providing an autofluorescence interference-free NIR-PL imaging signal for monitoring the delivery efficacy of P5. The released P5 can secondarily target EBNA1 via the EBNA1 binding motif, blocking its function and thus inhibiting the growth of EBV-positive tumors. The feasibility of our developed hierarchical targeting theranostic nanosystem is well demonstrated both in vitro and in vivo, highlighting the huge translational potential of mZGOCS@MnO2–P5 in EBV-associated cancer therapy.

Low-temperature direct ammonia fuel cell (DAFC) stands out as a more secure technology than the hydrogen fuel cell system, while there is still a lack of elegant bottom-up synthesis procedures for efficient ammonia oxidation reaction (AOR) electrocatalysts. The widely accepted d-band center, even with consideration of the d-band width, usually fails to describe variations in AOR reactivity in many practical conditions, and a more accurate activity descriptor is necessary for a less empirical synthesis path. Herein, the upper d-band edge, εu, derived from the d-band model, is identified as an effective descriptor for accurately establishing the descriptor–activity relationship. Using the PtCo alloy with varying atomic composition as an example, the εuvalue succeeds in reflecting the corresponding trends of AOR activity, showing striking linear correlation with a coefficient of determination (R2) as high as 0.90. The effectiveness of the established descriptor–activity relationship is verified experimentally. The optimum electrocatalyst delivers an excellent peak current density of 74.04 A g–1at 5 mV s–1, and the assembled DAFC generates a high power density, outperforming the majority of the extensively reported systems. This work brings fundamental insights into the relationship between chemical reactivity and electronic structure and benefits rational optimization of AOR electrocatalyst for next-generation low-temperature DAFC.

Diquat (DQ), a contact herbicide extensively utilized in both agricultural and nonagricultural domains, exhibits a high correlation with neuronal disorders. Nevertheless, the toxicity and underlying mechanisms associated with exposure to environmental concentrations of DQ remain ambiguous. Here, we report dose-dependent cellular neurotoxicity of DQ in Caenorhabditis elegans. First, DQ significantly compromised the development and brood size of worms, shortened the lifespan, and caused epidermal abnormalities. An unbiased transcriptomic analysis disclosed several pathways related to cell death and peroxisome homeostasis underlying this organismal-level toxicity. Moreover, exposure of DQ to C. elegansled to a notable increase of embryonic cell death. Concurrently, DQ exposure specifically caused the loss of dopamine neurons but not two other types of neurons in adulthood, which is in accordance with DQ-induced muscle-related defects such as pharyngeal pumping, body bends, and head thrashes. Mechanistically, DQ exposure induces the generation of reactive oxygen species (ROS) and enhances glutathione-related ROS scavenging pathway. Protein levels and activities of mitochondrial electron transport chain complexes were specifically impaired in the DQ-treated worms. Collectively, this study suggests an ROS-mediated cell death pathway involving the neuronal and behavioral toxicity of DQ, which offers a novel mitochondria-related perspective to elucidate the general toxicity caused by a widely distributed herbicide, DQ, at near-environment concentrations.

Nanometer-thick ultrathin coatings with superior mechanical strength and desirable lubricating and antifouling performance are critical for the miniaturization of implantable medical devices. However, integrating these properties at the nanoscale remains challenging due to the inherent trade-off between mechanical strength and hydration as well as limitations in coating thickness. In this work, we address these challenges by employing dual-function metal coordination to construct a ∼25 nm thick bilayer structure. Contact mechanics and interfacial molecular force measurements confirm the dual role of vanadium (VIII) ions in forming this bilayer: VIIIions bridge the ligand sites to reinforce the protein bottom layer, and simultaneously anchor the end blocks of the designed ABA triblock hydrophilic polymers to form a hydrated, looping top layer. This VIII-enabled structure demonstrates remarkable load-bearing capacity and lubricating performance (i.e., friction coefficient μ on the order of 10–3over 100 cycles under ∼10 MPa), while it also exhibits excellent resistance to biofouling in complex biological fluids. This work presents a useful strategy for integrating seemingly incompatible properties into ultrathin coatings, offering the potential for customizing multifunctional surfaces for micro-devices/machines toward bioengineering applications.

Methane, a powerful greenhouse gas, significantly influences climate change. Estimating methane emission fluxes is critical for implementing emission reduction strategies, and plumes segmentation is an important technique for accurate quantification. However, existing methane plumes segmentation studies focus on pixel-level localization of spatial and spectral features, which makes it difficult to capture global contextual information of remote sensing images. To tackle these challenges, we propose a novel frequency and spatial domain injection network (FSDINet) for methane plumes segmentation. FSDINet introduces three blocks that process spectral features utilizing the frequency domain to capture global contextual information and supplement local information with pixel values in the spatial domain, thus allowing for precise localization and segmentation. We conduct extensive experiments on hyperspectral and multispectral datasets. On the Airborne Visible InfraRed Imaging Spectrometer—Next Generation (AVIRIS-NG) hyperspectral dataset, the majority of our model evaluation scores exceed those of existing methods in a range of conditions, including both strong and weak plumes, with the highest precision and intersection over union (IOU) of 87.75% and 65.56%, respectively. On the WorldView-3 multispectral dataset, precision and IOU improved up to 8.76% and 4.04% over methods utilizing spatial and spectral features. Furthermore, the zero-shot experiments on the Earth Surface Mineral Dust Source Investigation (EMIT) dataset confirm the robust generalization capacity of our model when confronted with different sensor data.

Small-target detection in sea clutter has always been difficult due to the low signal clutter ratio (SCR). Feature-based detection methods have emerged as a significant area of research in recent years, where pattern recognition methods involve the use of similarity measures. The Gaussian kernel function used in traditional spectral clustering algorithms performs well when handling nonlinear data, however, it may not effectively capture the relationships between sparsely distributed samples. Therefore, this article proposes a novel method for small-target detection in sea clutter: spectral clustering based on neighborhood density similarity measure (SC-ND) to address the aforementioned issues. It incorporates neighborhood density into the Gaussian kernel function and then uses label information to guide the construction of the similarity matrix, at which point a false alarm controllable is achieved. The similarity matrix is then spectrally decomposed to obtain a new representation of the samples, which are subsequently classified using an improved k-means algorithm. Validated on the IPIX 1993 radar dataset, the proposed method achieves a detection probability of 71.1% with an observation time of 1.024 s and a false alarm rate of 0.001. The results are better than fractal-based detector, tri-feature-based detector, and feature-based detector using three TF features.

This article presents a wideband millimeter-wave (mmWave) phased array with a large beam-scanning angle for the communication of unmanned aerial vehicle (UAV). The proposed array is composed of 8 shorted patch antenna elements. The shorted patch operates in TM1/2,1 mode. A T-shaped slot with dual resonance modes is etched on the radiating shorted patch. By combining the hybrid operating modes together, a wideband performance is realized. The beamwidth of the shorted patch can be increased by coupling with neighboring elements. The inherent wide-beam characteristic of the slot is fully utilized. Thus, the antenna element achieves a wide beamwidth of about 140° in a broad frequency band. A wide-angle beam-scanning phased array is achieved using wide beamwidth of the proposed elements. The measured result demonstrates that the proposed array can cover a beam-scanning angle of more than ±75° in a broad operating frequency band from 24 to 30 GHz. To validates its effectiveness for UAV communication, the proposed array is also simulated and measured on the model of an UAV.

This letter has developed an electrical circuit analogy-based maximum latency calculation (MLC) method of the Internet data center (IDC) in power-communication network. Firstly, by analogy with the circuit model, the basic concepts to describe information flow are defined, including information current, information resistance, information conductivity, and information voltage. Based on these concepts, the information processing model considering both channel blocking and user priority is established. By analogy with the electrical circuit, the information flow calculation laws are introduced to calculate the maximum latency of IDCs. Verification results show that the maximum latency of IDCs in power-communication network can be accurately calculated by the proposed MLC method.

The spatial-temporal distribution (STD) of electric vehicle (EV) charging load is significantly influenced by EV users’ route choice and charging behaviors. In a congested traffic network that accommodates gasoline vehicles and EVs, users’ route choice mutually influences each other, resulting in a dynamic user equilibrium (DUE). Due to the limited battery capacity and insufficient charging facilities, fast charging during trips and slow charging at trip destinations may be co-existed for EV users to complete their daily trip-chain demand. This paper introduces a trip-chain-based DUE (TC-DUE) model to comprehensively analyze EV users’ route choice, fast and slow charging behaviors throughout their entire trip chains, and to derive the STD of EV fast and slow charging load. In the TC-DUE model, each user makes route choice for each trip in the trip chain to maximize his trip chain utility. Route choice serves as the foundation for employing a traffic-charge microsimulation (TCM) to simulate each user’s driving, parking, fast, and slow charging behaviors. The STD of fast and slow charging load, and the assessment of traffic congestion are derived by the TCM. An iterative solution procedure via route choice and TCM is adopted for the TC-DUE model. The iterations stop until a TC-DUE is reached where no user can improve his trip chain utility by charging his route choice unilaterally. Finally, we apply the TC-DUE model to the road network of Berlin, Germany, comprising 11,345 nodes and 24,335 links. The results indicate that the faster routes and earlier charging behaviors are attained when considering the traffic congestion caused by the participation of other users, which leads to a more balanced spatial distribution of fast charging load, an earlier peak hour and a higher peak value for both fast and slow charging load.

The neural Lyapunov method is a promising tool for the transient stability analysis (TSA) of power systems. However, existing variations suffer from falsification difficulties and inconsistent domain of attraction (DOA) estimations. In this study, we propose i) for the former, a neural Lyapunov function with a novel trial solution structure to reduce the complexity of the falsified function; and ii) for the latter, a TSA framework based on a multi-model ensemble, capable of incorporating various types of candidate Lyapunov functions. The multi-model ensemble framework provides an effective solution to reduce the conservativeness of a single neural Lyapunov function for system DOA estimation, which has the potential to expand the isoenergetic surface defined by the LaSalle lemma. The proposed method was established and evaluated on a virtual synchronous generator model considering voltage dynamics, where the internal voltage is treated as a state variable rather than a parameter. Case studies suggested that the proposed method can effectively deal with strongly coupled dynamical systems with significant time-scale differences in the changes of the state variables. Moreover, compared with other Lyapunov methods, it can less conservatively characterize the stable region.

This letter proposes a novel hybrid automatic repeat request with chase combining assisted sparse code multiple access (HARQ-CC-SCMA) scheme. Depending on whether the same superimposed packet is retransmitted, synchronous and asynchronous modes are considered for retransmissions. Moreover, a factor graph aggregation (FGA) method is used for multi-user detection. Specifically, a large-scale factor graph is constructed by combining all the received superimposed signals and message passing algorithm (MPA) is applied to calculate log-likelihood ratio (LLR). Monte Carlo simulations are preformed to show that FGA surpasses bit-level combining (BLC) and HARQ with incremental redundancy (HARQ-IR) in synchronous mode. Moreover, FGA performs better than BLC at high signal-to-noise ratio (SNR) region in asynchronous mode. However, FGA in asynchronous mode is worse than BLC at low SNR, because significant error propagation is induced by the presence of failed messages after the maximum allowable HARQ rounds.

To accommodate the evolving demands of unmanned operations, the future sixth-generation (6G) network will support not only communication links but also sensing-communication-computing-control $(\mathrm {SC}^{3})$ loops. In each $\mathrm {SC}^{3}$ cycle, the sensor uploads sensing data to the computing center, and the computing center calculates the control command and sends it to the actuator to take action. To maintain the task-level connections between the sensor-to-computing-center link and the computing-center-to-actuator link, we propose to treat the sensor and actuator as a virtual user. In this way, the two communication links of the $\mathrm {SC}^{3}$ loop become the uplink and downlink (UL&DL) of the virtual user. Based on the virtual user, we propose a task-oriented UL&DL optimization scheme. This scheme jointly optimizes UL&DL transmit power, time, bandwidth, and CPU frequency to minimize the control linear quadratic regulator (LQR) cost. We decouple the complex problem into a convex UL&DL bandwidth allocation problem with the closed-form solution for the optimal time allocation. Simulation results demonstrate that the proposed scheme achieves a task-level balance between the UL&DL, surpassing conventional communication schemes that optimize each link separately.

The paucity of essential thrombocythemia (ET) and prefibrotic primary myelofibrosis (pre-PMF) in individuals younger than 18 years highlights several unresolved issues in diagnosis, clinical outcomes, and treatment strategies. To address these knowledge gaps, we analyzed a large bidirectional cohort consisting of childhood and adolescent ET (CAA-ET, n= 156) and pre-PMF (CAA-preMF, n= 13), as well as adult ET (n= 349). We introduced immunophenotypic abnormalities as novel clonal markers in CAA-ET and CAA-preMF, establishing a comprehensive method for clonal marker detection that integrated driver and non-driver mutations, positive endogenous erythroid colony formation, immunophenotypic abnormalities, and chromosomal aberrations. Next-generation sequencing revealed distinct mutational profiles between CAA-ET and adult ET, along with different age-related trends in the distribution of driver mutations. Venous thrombosis was more prevalent in CAA-ET, with JAK2 V617F emerging as a potential risk factor (P= 0.018). Immunophenotypic abnormalities were identified as risk factors for disease progression (P= 0.027). Significant differences between expected and actual treatment practices were identified. Compared to CAA-ET, CAA-preMF demonstrated poorer progression-free survival (P< 0.001) and faster disease progression (P= 0.019). This study provides a critical foundation for refining diagnostic, prognostic, and therapeutic approaches for CAA-ET and CAA-preMF.

The application of InGaN based red LEDs in Micro LED display has been severely limited due to their low luminous efficiency and poor wavelength stability. In this work, we demonstrate InGaN based red LEDs with improved wavelength stability on SiC substrates. The key of our method is to introduce a graphene intermediate layer between the epitaxial LED structure and SiC substrate. Another important process is to modulate the growth behavior and stress state of the GaN film and LED structure on graphene. This is achieved by optimizing the nitrogen-plasma pre-treatment time of graphene. As a consequence, InGaN based red LEDs with small wavelength shift with the change of driving current are obtained. The wavelength shift is 8 nm as the forward driving current density increase from 1 to 10 A/cm2, which is significantly lower than the 25 nm of the reference red LEDs directly grown on SiC substrates. In addition, we analyse the mechanism responds to the improvements of wavelength stability for the red LEDs grown on graphene/SiC. This work provides a feasible approach for enhancing the wavelength stability of InGaN based red LEDs.

This article focuses on the Byzantine-resilient distributed filtering problem for a class of stochastic nonlinear systems (SNSs) over sensor networks. The phenomenon of measurement censoring, which is depicted by a certain Tobit model with both left-censoring and right-censoring thresholds, is considered for the sensor outputs. To save limited network resources, the Round-Robin scheduling mechanism (RRSM) is deployed to govern data transmissions. A Byzantine attack model is introduced with which the Byzantine nodes manipulate the measurement signals arbitrarily before transmitting them to the neighbors. In this research, we endeavor to propose a Byzantine-resilient distributed filtering scheme that ensures an upper bound of filtering error variance and the stochastic boundedness of the resultant filtering error system. Such an upper bound is first calculated in terms of the solutions to coupled difference equations and then minimized by designing suitable filter parameters. Subsequently, the stochastic boundedness analysis issue is addressed for the filtering error dynamics. Finally, the validity of the obtained theoretical results is illustrated by a numerical example.

Hydroformylation is an important reaction for aldehyde synthesis and requires ethylene and syngas (CO + H2) with the feedstocks. While these feedstocks are typically synthesized separately, it would be highly desirable to directly convert abundant ethane into ethylene and syngas in a single step. In this work, we loaded B2O3onto perovskite La0.8Sr0.2FeO3(LSF) and formed a core–shell structured catalyst, with B2O3and Sr3B2O6being the shell and LSF being the core. The shell structure can substantially inhibit deep oxidation of ethane into CO2and the optimized La0.8Sr0.2FeO3@20B2O3can achieve 69.2% ethane conversion and 88.8% ethylene + syngas selectivity at 700 °C with good stability, with a ratio of CO/H2close to 1 : 1. The effects of reaction temperature, space velocity, B2O3loading, and La/Sr were also investigated. This catalyst marks a promising progress for the direct oxidation of ethane and has good potential for industrial applications.

Pulse-density modulation (PDM) can achieve high efficiency in high-frequency dc–dc converters by implementing both zero-voltage-switching (ZVS) and zero-current-switching (ZCS). However, the discrete feature of pulse density, defined as the ratio of transferring energy switching cycles $P$ to total switching cycles $N$, limits the voltage regulation flexibility. This article proposes a continuous pulse-density modulation (CPDM) for bidirectional series-resonant converter (BSRC). Compared to conventional PDM methods, this approach integrates a hybrid modulation comprising pulsewidth modulation and phase-shift modulation into one of the energy transfer switching cycles. The duty cycle is regulated to achieve continuous voltage regulation. The phase shift is controlled to ensure that the resonant current reaches zero at the end of the switching cycle, thereby enabling both ZVS and ZCS to be achieved during the remaining switching cycles. In addition, the converter achieves perfect load-independent output voltage and high-efficiency features, making it well-suited for a wide range of dc–dc converter applications. Finally, a 500 W BSRC prototype is constructed to verify the proposed CPDM. A high efficiency of 95% is achieved across the entire load range, along with continuous voltage regulation capability and load-independent characteristics.

An increasing number of field robots would be used for mission-critical tasks in remote or post-disaster areas. Due to the limited individual abilities, these robots usually require an edge information hub (EIH), with not only communication but also sensing and computing functions. Such EIH could be deployed on a flexibly-dispatched unmanned aerial vehicle (UAV). Different from traditional aerial base stations or mobile edge computing (MEC), the EIH would direct the operations of robots via sensing-communication-computing-control ( $\textbf {SC}^{3}$ ) closed-loop orchestration. This paper aims to optimize the closed-loop control performance of multiple $\textbf {SC}^{3}$ loops, with constraints on satellite-backhaul rate, computing capability, and on-board energy. Specifically, the linear quadratic regulator (LQR) control cost is used to measure the closed-loop utility, and a sum LQR cost minimization problem is formulated to jointly optimize the splitting of sensor data and allocation of communication and computing resources. We first derive the optimal splitting ratio of sensor data, and then recast the problem to a more tractable form. An iterative algorithm is finally proposed to provide a sub-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm. We also uncover the influence of $\textbf {SC}^{3}$ parameters on closed-loop controls, highlighting more systematic understanding.

Quaternion adaptive filters (QAFs) are extensively used in processing three- or four-dimensional signals effectively. However, their performance can significantly deteriorate or even diverge when system inputs and outputs are contaminated by complex noises. Therefore, this letter addresses the issue of parameter estimation in the quaternion errors-in-variables (QEIV) in asymmetric noise. First, a novel robust criterion, called improved quaternion minimum error entropy criterion with fiducial points (IQMEEF), is constructed. Then, a minimum total quaternion error entropy algorithm with fiducial points (MTQEEF) is proposed by integrating the IQMEEF criterion with the total least squares (TLS) method, leveraging stochastic gradient and quaternion generalized Hamilton-real (GHR) calculus theory. Finally, simulations validate the superior performance of MTQEEF in the QEIV model under asymmetric noise environments.

The flexible soft open point (SOP) connected to active distribution networks (ADNs) offers a promising manner of improving voltage and VAR control (VVC) by providing flexible power regulation. Due to the expansion of interconnective networks, the centralized optimization method has recently faced various challenges. This paper thus proposes a novel distributed coordinated reactive power optimization strategy for SOP-based multiregional ADNs based on local model decoupling and iterative interactions. By applying the alternating direction method of multipliers (ADMM), the centralized VVC optimization problem is divided into several subproblems, allowing each area to optimize its local subproblem in a fully distributed manner. Multiple resources are thereby coordinated by the VVC, including both discrete and continuous devices. To ensure computability of both integer and non-convex problems, the relaxation iteration and successive linear approximation methods are nested to the ADMM framework, within this approach to allow ready solution to the distributed VVC optimization problem to be generated using a relaxation iterative algorithm, which significantly improves algorithm convergence and computational efficiency. The effectiveness of the proposed method is demonstrated in this work using a modified IEEE standard interconnection system.

The electrical collector system (ECS) plays a crucial role in determining the performance of offshore wind farms (OWFs). Existing research has predominantly restricted ECS cable layouts to conventional radial or ring structures and employed graph theory heuristics for solutions. However, both economic efficiency and reliability of the OWFs heavily depend on their ECS structure, and the optimal ECS cable layout often deviates from typical configurations. In this context, this paper introduces a novel reliability-based ECS cable layout planning method for large-scale OWFs, employing a two-stage stochastic programming approach to address uncertainties of wind power and contingencies. To enhance reliability, the model incorporates optimal post-fault network reconfiguration strategies by adjusting wind turbine power supply paths through link cables. To tackle computation challenges arising from numerous contingency scenarios, a customized progressive contingency incorporation (CPCI) framework is developed to solve the model with higher efficiency by iteratively identifying non-trivial scenarios and solving the simplified problems. The convergence and optimality are theoretically proven. Numerical tests on several real-world OWFs validate the necessity of fully optimizing ECS structures and demonstrate the efficiency of the CPCI algorithm.

A stator interturn short-circuit (ITSC) is a common motor fault, which endangers the entire power system. The ITSC fault disrupts the three-phase balance of the motor windings, resulting in a change in the voltage distribution. The leakage current of the motor is an important indicator for assessing the status of the motor, which is determined by the voltage and the equivalent impedance of the winding to ground. This article theoretically analyzes the change of the leakage current before and after the ITSC, identifying some characteristic frequency components of the leakage current that can be used to diagnose the ITSC fault. Besides, a numerical simulation model is built to calculate the voltage distribution of the winding-to-ground with and without ITSC. Finally, the experimental platform is conducted, and experiments are carried out to verify the above analysis. The experimental results demonstrate significant alterations in the characteristic values associated with slight ITSC, which are independent of the operating condition.

Surface electromyography (sEMG)-based hand gesture recognition has garnered widespread attention in rehabilitation robotics due to its noninvasive measurement and intuitive motion decoding. However, affected by various factors such as individual differences, achieving cross-user adaptability and long-term reliability for sEMG classification poses a significant challenge. Existing domain adaptation (DA) methods primarily focus on global distribution alignment to mitigate statistical distribution discrepancies across domains, yielding significant achievements. Nevertheless, these methods often overlook fine-grained category-level subdomain distribution alignment, leading to discriminative structure confusion and subdomain misalignment, which hinder cross-subject and cross-session gesture recognition. To tackle these issues, this article proposes a plug-and-play subdomain adaptation method called PPSDA to enhance the classification performance and generalization ability for gesture recognition across domains. Specifically, handcrafted features are extracted and utilized for source domain supervised training to preserve discriminative structures. Subsequently, source and target domains co-training is performed, wherein the local maximum mean discrepancy (LMMD) is minimized to capture fine-grained information on relevant subdomains for subdomain distribution alignment. To validate the performance of the proposed PPSDA, we recruited 12 healthy subjects and developed an sEMG-driven exoskeleton rehabilitation glove for cross-subject and cross-session evaluations. Extensive experimental results demonstrate the effectiveness and superiority of the proposed PPSDA over existing DA approaches.

We report the successful synthesis of a highly ordered TiO2nanotube array coated with a thin film of C3N4nanoparticles using a straightforward hydrothermal-calcination approach. The resulting g-C3N4/TiO2-NTAs (CN/TONTs) nanohybrids exhibit significantly enhanced photoelectrochemical (PEC) photodegradation and gas-sensing capabilities when irradiated with UV–visible light. The PEC activity of these nanohybrids is notably superior to that of their semiconductor counterparts, primarily due to the synergistic coupling of strong heterojunction interfaces and abundant surface defects. Furthermore, nanosecond time-resolved transient photoluminescence and time-resolved fluorescence spectroscopy analyses suggest a coherent interfacial charge transfer mechanism, which elucidates the dominant dynamic processes within the CN/TONTs nanostructure. The optimized CN/TONTs-5 nanocomposite was observed to achieve a remarkable 98.3% photodegradation rate of methyl orange under UV–visible light excitation, which was found to outperform CN/TONTs-2 and CN/TONTs-8 by factors of 1.36 and 1.12, respectively. Furthermore, CN/TONTs-5 displays enhanced gas-sensing capabilities, exhibiting heightened sensitivity and accelerated response kinetics in comparison to CN/TONTs-2 and CN/TONTs-8.

Encapsulating smart nano-containers loaded with corrosion inhibitors into coating is a promising approach to functionalize micro-arc oxidation (MAO) coating. The encapsulation strategy of smart containers has a great influence on the microstructure, corrosion resistance and self-healing performance of the smart MAO coating. In order to provide a comprehensive understanding, two kinds of MAO coating encapsulated smart containers (HNT-8HQ), MAO-HNT-8HQ (1S) coating (one-step preparation in an MAO electrolyte containing smart nano-containers) and MAO + HNT-8HQ (2S) coating (pre-prepared MAO coating through an embedding nano-container processing) were prepared on AZ31 magnesium alloy. The incorporated HNT-8HQ in electrolyte can effectively reduce coating porosity and increase coating thickness. Both smart MAO coatings show considerable improvements in the corrosion resistance and a certain self-healing capacity. The post-embedding treated coating (MAO + HNT-8HQ (2S)) has better long-term durability and self-healing performance than one-step preparation coating (MAO-HNT-8HQ (1S)). The low-frequency impedance modulus (|Z|ƒ=0.01 Hz) of MAO + HNT-8HQ (2S) coating is 1.33 times that of MAO-HNT-8HQ (1S) coating after immersion in 3.5 wt% NaCl solution for 168 h. The MAO + HNT-8HQ (2S) coating has higher impedance values than MAO-HNT-8HQ (1S) coating during the entire self-healing process. The low-frequency impedance modulus of scratched MAO + HNT-8HQ (2S) coating is 3.33 times that of scratch MAO-HNT-8HQ (1S) coating after a 72 h self-healing process.

Cisplatin-based chemoresistance is a major obstacle for the treatment breast cancer (BC) including Triple-negative breast cancer (TNBC). SIRT7 is reportedly involved in the progression of BC, the underlining mechanism in Cisplatin-based chemoresistance in BC remains unclear. This work is to elucidate effects of SIRT7 on cisplatin resistance in breast cancer regulated by miR-152-3p.

In view of the temperature control system with Programmable Logic Controller (PLC) as the core, the main problems focus on the inability to realize wireless communication, remote downloading, poor temperature stability and poor anti-interference ability. A multi-channel Proportional Integral Differential (PID) temperature control system based on PLC and Internet of Things (IOT) is designed. It can not only adjust the temperature in real time. But also, the thirdparty cloud platform can be used through Message Queuing Telemetry Transport (MQTT) protocol, wireless data monitoring and remote control can also be carried out, and PLC programs can be downloaded remotely. Compared with the traditional wired monitoring system, it has great advantages and brings unprecedented convenience to the maintenance of the equipments. Based on the prototype, the multi-channel temperature stability and anti-interference tests, and wireless communication monitoring and alarm tests are carried out. The results show that the actual temperature of the system can change with the set value by PID control algorithm with the multi-channel heaters, and the temperature stability can reach ± 0.1 °C in steady state. In addition, real-time data such as temperature, voltage and current can be obtained through the cloud platform monitoring interface. And after triggering the alarm, the mobile devices (such as mobile phones) receive messages in real time.

Objective: The potential association between Hashimoto’s thyroiditis (HT) and papillary thyroid carcinoma (PTC) has been studied from different perspectives for many years. This study was aimed to evaluate the impact of HT on the clinical outcomes of PTC patients after radioactive iodine (RAI) therapy. Material and methods: We conducted a retrospective study on consecutive patients with PTC who underwent RAI therapy from April 2017 to May 2020. The diagnosis of HT was based on pathological examination, and patients were divided into the HT and non-HT group. Distributions of age, gender, ultrasound features, papillary variants, extrathyroidal extension, and other histopathological characteristics were observed. Propensity score matching (PSM) was used to compare the clinical features and outcomes between the two groups at 1 and 3-year follow-up. Results: In total, 782 patients with PTC were enrolled (570 women, 212 men). HT was presented in 130 (16.6%) patients, and was associated with younger age, smaller primary tumors, less extrathyroidal extension, and less lymph node metastasis at presentation. On review of the images, only calcification and blood flow distribution were significantly different among the US features (P&lt; 0.05). At the end of follow-up (three years), the responses to RAI therapy were significantly different between the two groups (ER: 76.9% vs 64.9%; IDR:11.5% vs 17.2%; BIR: 4.6% vs 10.7%; SIR: 6.9% vs 7.2%, P= 0.03). Patients with HT had less frequently evidence of disease (11.6% vs 17.9%). When compared with the matched groups, 123 pairs of patients were successfully matched, PTC patients with HT were found to have a better response to RAI therapy. Conclusions: PTC patients with HT had less aggressive characteristics at presentation. Importantly, the presence of HT not only had a significant association with the outcome, but was also protective from the risk of recurrence.

Ambient concentrations are commonly used as proxies for personal PM2.5exposure in epidemiological studies, despite indoor settings being the places where people spend most of their time. In a panel study of 110 nonsmoking, healthy college students in Lhasa, Tibet, indoor PM2.5was monitored using calibrated low-cost sensors for two multiweek periods, in over 40 dormitories where participants resided. We also repeatedly measured fractional exhaled nitric oxide (FeNO), an acute respiratory inflammation biomarker, for each participant. Time-averaged indoor PM2.5concentrations in individual dormitories ranged from 3.2 to 30 μg/m3in the summer and from 3.6 to 57 μg/m3in the fall, in most cases exceeding the outdoor level (4.3 and 4.9 μg/m3, respectively). The hourly mean indoor PM2.5concentrations displayed a clear trimodal diel pattern, with peaks coincident with periods of increased activities. Further questionnaire-based analysis suggests that incense burning and smoking contributed to elevated levels of indoor PM2.5. Overnight PM2.5levels in the dormitories were significantly associated with increased FeNO the following morning, with the effects attenuated as the hourly lag increased. In contrast, inconclusive associations were observed for ambient PM2.5. The results demonstrate that disregarding indoor exposure can result in biased estimates of acute health effects of PM2.5in low PM2.5areas.

To achieve high accuracy and effectiveness in sensing and modulating neural activity, efficient charge-transfer biointerfaces and a high spatiotemporal resolution are required. Ultrathin bioelectrode arrays exhibiting mechanical compliance with biological tissues offer such biointerfaces. However, their thinness often leads to a lack of mechano-electrical stability or sufficiently high electrochemical capacitance, thus deteriorating their overall performance. Here, we report ultrathin (∼115 nm) bioelectrode arrays that simultaneously enable ultraconformability, mechano-electrical stability and high electrochemical performance. These arrays show high opto-electrical conductivity (2060 S cm–1@88% transparency), mechanical stretchability (110% strain), and excellent electrochemical properties (24.5 mC cm–2charge storage capacity and 3.5 times lower interfacial impedance than commercial electrodes). The improved mechano-electrical and electrochemical performance is attributed to the synergistic interactions within the poly(3,4-ethylenedioxythiophene) sulfonate (PEDOT:PSS)/graphene oxide (GO) interpenetrating network (PGIN), where π–π and hydrogen bonding interactions improve conductive pathways between PEDOT chains and enhance the charge-transfer mobility. This ultrathin bioelectrode is compatible with photolithography processing and provides spatiotemporally precise signal mapping capabilities for sensing and modulating neuromuscular activity. By capturing weak multichannel facial electromyography signals and applying machine learning algorithms, we achieve high accuracy in silent speech recognition. Moreover, the high transparency of the bioelectrode allows simultaneous recording of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals, facilitating dual-mode brain activity analysis with both high temporal and high spatial resolution.

An innovative solution that overcomes the long-standing inherently low efficiency in N-heterocyclic carbene-catalyzed aerobic oxidation of aldehydes is reported. This solution included the design and synthesis of a novel polymerized catalyst and the utilization of a flow reactor. The unprecedentedly high efficiency achieved via this protocol makes it synthetically applicable. A total turnover number (TON) of 26,300 was achieved based on recycling experiments (runs). The highest TON in a single run could be up to 2475 with a turnover frequency (TOF) of 208 h–1, far superior to its traditional counterpart, in which a typical TON ranges from 20 to 100 with a TOF of less than 10 h–1. The catalyst has been recycled over 50 times and is still fully active. The success was attributed to the discovery of hidden reactivity, which was observed for the first time as an autoacceleration in the reaction rate during kinetic investigations. The research also provided concrete evidence supporting the conclusion that radical intermediates played crucial roles in the catalytic cycle by having a determinative impact on the overall reaction rate.

Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation types connecting two entities, is proposed to provide the meta-level explainable semantics for various HIN tasks. Traditionally, meta-paths are primarily used for schema-simple HINs, e.g., bibliographic networks with only a few entity types, where meta-paths are often enumerated with domain knowledge. However, the adoption of meta-paths for schema-complex HINs, such as knowledge bases (KBs) with hundreds of entity and relation types, has been limited due to the computational complexity associated with meta-path enumeration. Additionally, effectively assessing meta-paths requires enumerating relevant path instances, which adds further complexity to the meta-path learning process. To address these challenges, we propose SchemaWalk, an inductive meta-path learning framework for schema-complex HINs. We represent meta-paths with schema-level representations to support the learning of the scores of meta-paths for varying relations, mitigating the need of exhaustive path instance enumeration for each relation. Further, we design a reinforcement-learning based path-finding agent, which directly navigates the network schema (i.e., schema graph) to learn policies for establishing meta-paths with high coverage and confidence for multiple relations. Extensive experiments on real data sets demonstrate the effectiveness of our proposed paradigm.

Intrahepatic cholangiocarcinoma (ICC) ranks as the second most primary liver cancer that often goes unnoticed with a high mortality rate. Hepatectomy is the main treatment for ICC, but only 15% of patients are suitable for surgery. Despite advancements in therapeutic approaches, ICC has an unfavorable prognosis, largely due to lymph node metastasis (LNM) that is closely linked to the elevated recurrence rates. Consequently, the identification of precise and suitable techniques for the detection and staging of LNM assumes paramount importance for ICC therapy. While preoperative imaging plays a crucial role in ICC diagnosis, its efficacy in accurately diagnosing LNM remains unsatisfactory. The inclusion of lymph node dissection as part of the hepatectomy procedures is significant for the accurate pathological diagnosis of LNM, although it continues to be a topic of debate. The concept of sentinel lymph node in ICC has presented a novel and potentially valuable approach for diagnosing LNM. This review aims to explore the current state and prospects of LNM in ICC, offering a promising avenue for enhancing the clinical diagnosis and treatment of ICC to improve patient prognosis.

Extruded rice has increasingly gained popularity in the market due to its convenience and acceptable texture. The objective of this study was to understand how the physicochemical, thermal, and textural properties of the extruded rice affected its cooking properties and texture of the cooked one. It was found that air trapped in the grains during extrusion reduced the transparency of extruded rice. More air trapped in the grains reduced the true density of the extruded rice, which in turn decreased the hardness of extruded rice. A looser internal structure of extruded rice grain, as indicated by the lower true density, resulted in a faster hydration and shorter optimum cooking time. Extruded rices showed two thermal-transition peaks, with peak 1 from 93.3 °C to 112.8 °C and peak 2 from 107.5 °C to 132.5 °C. The increased hardness of extruded rice led to increases in its thermal-transition temperatures, longer optimum steaming time, and decreases in its water absorption and cooking loss, which resulted in an increase in the hardness and a reduction in the adhesiveness of the steamed one. This study provides insights into the key factors determining the eating quality of extruded rice, which is beneficial for food scientists in developing premium extruded rice.

BACKGROUND: Checkpoint inhibitor (CPI)-based therapy is recommended for first-line treatment of advanced/metastatic renal cell carcinoma (mRCC). Cabozantinib is a tyrosine kinase inhibitor (TKI) approved in the USA for treating mRCC, including after CPI-based therapy. However, data on the benefits of subsequent TKI therapy are limited.OBJECTIVE: To study the real-world use and outcomes of cabozantinib versus other TKIs after CPI-based therapy for mRCC.METHODS: This retrospective study used data from the US Oncology Network electronic health record database supplemented by chart review. Patients initiated TKI therapy between 2016 and 2021 after CPI-based therapy. The primary endpoint was real-world response rate in the first 6 months of treatment (RR-6m; physician assessment). Secondary endpoints included overall response rate (ORR), progression-free survival (PFS) and overall survival (OS). Covariates were adjusted by inverse probability of treatment weighting.RESULTS: Of 485 included patients, 331 received cabozantinib and 154 another TKI. Baseline characteristics were generally similar between arms. For cabozantinib versus other TKIs, adjusted RR-6m (available for 69.3% of patients) was 62.5% versus 46.0% (rate difference: superiority, 16.5% [95% CI: 7.8–25.1], p= 0.0002), adjusted ORR was 62.4% versus 49.4% (p= 0.0020), adjusted median OS was 19.2 versus 19.1 months (p= 0.7353) and adjusted median PFS was 7.9 versus 9.2 months (p= 0.8752).CONCLUSIONS: Cabozantinib following CPI-based therapy was effective for treating mRCC in the US real-world setting. Differences in adjusted RR-6m and ORR significantly favored cabozantinib versus other TKIs. The lack of OS difference may reflect differences in post-index therapy.