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Psoriasis is a chronic, systemic immune-mediated skin disease. Although many new strategies for psoriasis treatment have been developed, there is great need in clinic for treating psoriasis. Gentiopicroside (GPS), derived from Gentiana manshurica Kitagawa, has multiple pharmacological activities including anti-inflammatory, anti-oxidative and antiviral activities. In this study, we investigated the potential effects of GPS in imiquimod (IMQ)-induced psoriasis mouse model and the underlying mechanisms. The mice were sensitized on their shaved back with IMQ cream for 7 days with or without topical application of 1% or 2% GPS cream. We showed that the application of GPS cream significantly ameliorated psoriasis-like skin lesions; GPS effect was better than that of calcipotriol. GPS rectified the immune cells infiltration and keratinocytes activation in the skin lesions, and significantly inhibited TNF-α/IFN-γ stimulated human keratinocyte (HaCaT) activation in vitro. Proteomic analysis from keratinocytes with and without GPS treatment prompted that GPS regulated the Keap1-Nrf2 pathway, which was the most important pathway in regulating oxidative stress and inflammation. We demonstrated that GPS regulated the protein expression of p62 and Keap1, induced Nrf2 nuclear translocation followed by transcription of Nrf2 downstream antioxidant genes in HaCaT cells. Furthermore, the antioxidant effects of GPS were abolished in Nrf2-/-keratinocytes. Simultaneously, Nrf2-/-mice showed increased psoriasiform symptoms with a diminished protective effect in response to GPS treatment. Collectively, the study discloses that GPS inhibits keratinocyte activation and ameliorates psoriasis-like skin lesions in an Nrf2-dependent manner.

Liver cirrhosis is prognostically associated with poor life expectancy owing to subsequent liver failure. Thus, understanding liver regeneration processes during cirrhotic injury is highly important. This study explored the role of macrophage heterogeneity in liver regeneration following splenectomy. We collected detailed clinical information from 54 patients with decompensated cirrhosis before and after splenectomy. Obvious liver regeneration was observed after splenectomy in cirrhotic patients. Single-cell RNA sequencing (scRNA-seq) was performed on three paired liver tissues from patients before and after surgery to explore the immune microenvironment map and the characteristics of liver regeneration-associated macrophages (RAMs). scRNA-seq analysis revealed that the composition of hepatic immune cells changed after splenectomy; among these changes, the proportion of CD300E+RAMs significantly increased after surgery, and high expression levels of functional genes associated with cell proliferation promoted liver regeneration. Moreover, a mouse model of carbon tetrachloride-induced cirrhosis and a coculture system consisting of primary bone marrow-derived macrophages and hepatocytes were established for validation. We observed a similar phenomenon of liver regeneration in cirrhotic mice and further confirmed that CD300E+monocyte-derived macrophages facilitated hepatocyte NAD+synthesis via the secretion of NAMPT, which subsequently promoted hepatocyte proliferation. This study characterized the hepatic immune microenvironment in patients with cirrhosis following splenectomy. Our findings demonstrated that CD300E+macrophages play a crucial role in remodeling the hepatic immune microenvironment after splenectomy, thereby promoting liver regeneration in patients with decompensated cirrhosis. CD300E+macrophages are anticipated to emerge as a novel therapeutic strategy for the treatment of liver cirrhosis.

Prognostics and health management (PHM) has garnered significant attention in industrial fields, particularly due to its successful application in managing battery degradation. However, current approaches are inadequate in addressing multiple thresholds, including both theoretical formulation and practical computational complexity. These limitations hinder the development and implementation of threshold-varying assessments, thereby impeding the advancement of PHM application. This article investigates prognostic applications with different failure thresholds and highlights the importance of failure threshold selection. In addition, theoretical evaluation and analysis are provided for multiple threshold settings, encompassing both discrete and continuous series. This introduces a novel technical domain for prognostic applications. The effectiveness of threshold-varying assessment is verified with several different approaches on real battery degradation experiments. Furthermore, we demonstrate the practical significance of threshold-varying assessments in enabling on-demand scheduling for maintenance or replacement of spare parts. Most importantly, to meet the real-time requirements of practical prognostic applications, this article also discusses the computational complexity of threshold-varying assessment and finds an applicable solution for this common difficulty.

The assumption of transversely isotropic media with a vertical symmetry axis vertically transverse isotropic (VTI) has been widely utilized in the exploration of shale reservoirs, with its reflection response and pre-stack inversion attracting significant attention from geophysicists. However, accurately estimating anisotropic parameters using existing approximate reflection coefficient formulas for VTI media remains challenging. In theory, inversions based on exact reflection coefficients for VTI media could overcome these limitations. Nevertheless, such inversions present a highly nonlinear problem. The simultaneous inversion of five parameters further exacerbates the ill-posed nature of the inversion, making it difficult for conventional linearized algorithms to handle effectively. To address these challenges, this article proposes a nonlinear pre-stack inversion scheme based on the exact reflection coefficient equations for VTI media. Specifically, we establish an inversion objective function within a Bayesian framework and introduce a heuristic Newton-Raphson-based optimization (NRBO) algorithm for solving the nonlinear objective function. This approach mitigates the inaccuracies introduced by conventional linearized algorithms, enhancing the precision of the inversion results. Tests on 1-D single-well synthetic data, 2-D Hess VTI model synthetic data, and field data demonstrate that the proposed inversion scheme can stably and effectively estimate both elastic and anisotropic parameters simultaneously. Moreover, the inversion accuracy of the proposed approach surpasses that of inversions based on an approximate formula.

Chemical etching on the surface of glass is an essential program to improve its anti-reflective properties of glass. Developing a model for chemical etching kinetics is crucial for improving and refining the etching process. In this study, we investigated the impact of reaction temperature, reaction time, the viscosity and additives of the chemical etching solution on the kinetics of chemical etching by experiment. Random forest was trained using 400 chemical etching reaction rates under different operating conditions. Base on machine learning model training, the random forest demonstrated strong predictive capability with an R2exceeding 0.9. Additionally, the impacts of chemical etching kinetics were analyzed and the machine learning model was evaluated by etching experiments. The relative importance of chemical etching kinetics conditions was reaction time > the viscosity of solution > the amount of thickener added > reaction temperature > the amount of sodium gluconate added > the amount of water reducer added. Finally, a high-accuracy chemical etching kinetics model was established.

Background:Acute respiratory distress syndrome (ARDS) is a serious pathological process with high mortality. Ferroptosis is pivotal in sepsis, whose regulatory mechanisms in sepsis-induced ARDS remains unknown. We aimed to determine key ferroptosis-related genes in septic ARDS and investigate therapeutic traditional Chinese medicine. Method:Sepsis-induced ARDS dataset obtained from Gene Expression Omnibus was analyzed to identify ferroptosis-related differentially expressed genes. Enrichment analysis and protein-protein interaction network construction were performed to identify hub genes. Immune cells infiltration was analyzed and competitive endogenous RNA network was constructed. The diagnostic value of hub genes in septic ARDS was analyzed and the occurrence of ferroptosis and the expression of hub genes were detected. Traditional Chinese medicine targeting hub genes was predicted viaSymMap database and was verified. Results:Sixteen ferroptosis-related differentially expressed genes were obtained, among which the top four genes (IL1B, TXN, MAPK3, HSPB1) were selected as hub genes, which may be potential diagnostic markers of septic ARDS. Immunoassay showed that sepsis-induced ARDS and hub genes were closely related to immune cells. The competitive endogenous RNA network showed 26 microRNAs and 38 long noncoding RNA. Ferroptosis occurred and the expressions of IL1B, MAPK3, and TXNwere increased in septic ARDS mice and LPS-challenged human pulmonary alveolar epithelial cells. Sea buckthorn alleviated septic lung injury and affected hub genes expression. Conclusions:Ferroptosis-related genes of IL1B, MAPK3, and TXNserve as potential diagnostic genes for sepsis-induced ARDS. Sea buckthorn may be therapeutic medication for ARDS. This study provides a new direction for septic ARDS treatment.

• A CALPHAD-aided strategy is implemented to design a high-performance Ni 45.9 Fe 23 Cr 23 V 4 Nb 3 Mo 1 B 0.1 (at.%) medium entropy alloy strengthened by D0 22 nanoparticles. • The newly designed alloy exhibits excellent mechanical properties at both room and intermediate temperatures, with ultimate tensile strength and fracture elongation of ∼845 MPa and 27.5 % at 700 °C, respectively, and ∼589 MPa and 24.3 % at 800 °C, respectively. • The superior combination of strength and ductility over a wide temperature range is primarily attributed to the high-density interactions of planar dislocation configurations (mainly stacking faults) and the shearable D0 22 nanoparticles. • Anomalous deformation twinning at 800 °C can be activated due to the localized stress concentration, the locally low stacking-fault energy, and especially the high diffusion rate at grain boundaries. Precipitation-strengthened medium/high-entropy alloys (MEAs/HEAs) have great potential for high-temperature applications. In this study, we designed a novel Ni 45.9 Fe 23 Cr 23 V 4 Nb 3 Mo 1 B 0.1 (at.%) MEA alloy, hardened by the D0 22 (Ni, Fe, Cr) 3 (Nb, V)-type nanoprecipitates, with an excellent strength-ductility combination from room to elevated temperatures. Specifically, the tensile strengths, at 700 and 800 °C, could be maintained as high as 845 and 589 MPa, respectively; meanwhile, elongations at all testing temperatures exceeded 25 % without any intermediate-temperature embrittlement. The temperature-dependent deformation mechanisms were unraveled using multi-scale characterizations, which involved profound slip planarities, such as stacking fault (SF) networks and deformation twins (DTs). Furthermore, the critical resolved shear stress (CRSS) to initiate SFs in both face-centered cubic (FCC) and D0 22 phases was evaluated, and the possible reasons for the origin of anomalous DTs at 800 °C were discussed in detail. The main findings demonstrate that the shearable D0 22 nanoparticles can provide the FCC matrix with considerable dislocation storage capacity, reinforcing strain hardening at ambient and intermediate temperatures. This work provides fundamental insights into the controllable design and deformation mechanisms of high-performance D0 22 -strengthened MEAs/HEAs. [Display omitted] [ABSTRACT FROM AUTHOR]

Rapeseed protein, as a valuable plant protein, holds great potential as a natural emulsifier for construction of food‐grade high internal phase emulsions (HIPEs). In this work, rapeseed protein, obtained through salt extraction combined with ultrafiltration, was employed as a sole stabilizer to formulate algae oil‐based HIPEs. The effects of protein concentration and pH changes on the physicochemical properties of HIPEs are systematically evaluated. The results show that a protein concentration of 0.5 wt% is sufficient to form stable and self‐supporting HIPEs. As the protein concentration increases, the droplet size of HIPEs gradually decreases, leading to a more robust structure and enhanced stability. Compared to neutral conditions (pH 7.0), the HIPEs under acidic pH 3.5 exhibit more densely packed emulsion droplets with smaller size and more uniform distribution, contributing to superior mechanical properties (higher G′ and yield stress) as well as preferable thixotropic and creep recovery behaviors, which thereby improve their physical stability during storage, thermal processing, and freeze‐thaw cycles. Furthermore, the rapeseed protein‐stabilized HIPEs inhibit the oxidation of algae oil, especially at pH 3.5. The results of oral lubrication indicate that the reduction in the friction coefficient is mainly associated with an increase in protein concentration, with minor effect from pH variation. These findings suggest that rapeseed protein is an effective emulsifier for preparing stable and processable HIPEs, especially under acidic conditions, which have great potential for applications in semi‐solid emulsion foods or edible oil structuring. Rapeseed protein, as a valuable plant protein, holds the great potential as a natural emulsifier for construction of food‐grade high internal phase emulsions (HIPEs). In this work, rapeseed protein, obtained through salt extraction combined with ultrafiltration, was employed as a sole stabilizer to formulate algae oil‐based HIPEs.

We study capacity-approaching irregular repeat accumulate (IRA) codes over integer rings $\mathbb {Z}_{2^{m}}$ for $2^{m}$ -PAM signaling, $m=1,2,\cdots $ . Such codes belong to the ensemble of lattice codes. First, the effect of zero-divisors on the iterative belief-propagation (BP) decoding is analyzed. We show that the symmetric Gaussian approximation is invalid for existing IRA codes over integer rings. Then we propose a new doubly IRA (D-IRA) ring code structure, which consists of irregular multiplier distribution and irregular node-degree distribution. Such structure can restore the symmetry and optimize the BP decoding threshold. Numerical result demonstrates the improved performance of D-IRA codes for AWGN channel. Also, it is demonstrated that the lattice based multiple-access scheme with D-IRA code performs within 3 dB the capacity limit.

Object: Transformer-based neural networks have been applied to the electroencephalography (EEG) decoding for motor imagery (MI). However, most networks focus on applying the self-attention mechanism to extract global temporal information, while the cross-frequency coupling features between different frequencies have been neglected. Additionally, effectively integrating different neural networks poses challenges for the advanced design of decoding algorithms. Methods: This study proposes a novel end-to-end Multi-Scale Vision Transformer Neural Network (MSVTNet) for MI-EEG classification. MSVTNet first extracts local spatio-temporal features at different filtered scales through convolutional neural networks (CNNs). Then, these features are concatenated along the feature dimension to form local multi-scale spatio-temporal feature tokens. Finally, Transformers are utilized to capture cross-scale interaction information and global temporal correlations, providing more distinguishable feature embeddings for classification. Moreover, auxiliary branch loss is leveraged for intermediate supervision to ensure the effective integration of CNNs and Transformers. Results: The performance of MSVTNet was assessed through subject-dependent (session-dependent and session-independent) and subject-independent experiments on three MI datasets, i.e., the BCI competition IV 2a, 2b and OpenBMI datasets. The experimental results demonstrate that MSVTNet achieves state-of-the-art performance in all analyses. Conclusion: MSVTNet shows superiority and robustness in enhancing MI decoding performance.

The issue of gas enrichment in the tectonic zone of coal seams, which poses significant threats to coal mine safety and leads to gas disasters, is primarily addressed through borehole extraction. The quality of this extraction is notably influenced by air leakage and sealing technologies used during drilling. To tackle the problem of gas leakage in directional long boreholes within the floor, a composite fissure leakage model for the surrounding rock and borehole is developed. This model inverts the characteristics of coal seam thickness variations and abnormal gas enrichment through heterogeneous geostatistical modeling. It enables the quantitative characterization of the air leakage mechanism in directional long boreholes of the bottom plate and proposes an effective sealing process. Due to damage and disturbance, fissures and irregular plastic zones form around the excavated roadway, which are major contributors to air leakage. Over time, the air content in the coal and rock seams gradually increases while the gas content and concentration decrease continuously. Compared to the polyurethane "two plugs and one injection" sealing process, the multi-stage grouting and sealing process provides a more stable gas extraction concentration and flow rate in the sealed drill holes. This process effectively seals the fissure zones of the roadway, reduces air leakage from the boreholes, and improves gas extraction concentration.

•The electrochemical mechanism of zinc-based chalcogenides in PIBs was concluded.•Pointed out structural design strategies of zinc-based chalcogenides for PIBs.•Summarized the recent advance of zinc-based chalcogenides for PIBs.•The challenges and perspectives of zinc-based chalcogenides in PIBs are provided.

On the power supply side, renewable energy (RE) is an important substitute to traditional energy, the effective utilization of which has become one of the major challenges in risk-constrained power system operations. This paper proposes a risk-based power dispatching strategy considering the demand response (DR) and RE utilization in the stochastic optimal scheduling of parallel manufacturing process (PMP) in industrial manufacturing enterprises (IME). First, the specific production behavior model of PMP is formulated to characterize the flexibility of power demand. Then, a two-step strategic model is proposed to comprehensively quantify multiple factors in the optimal scheduling of DR in PMP loads considering risk-based power system dispatch, thermal generators, wind power integration. Case studies are based on the modified IEEE 24-bus power system, which verify the effectiveness of the proposed strategy in optimally coordinating IME assets with generation resources for promoting the RE utilization, as well as the impacts of power transmission risk on decision performance.

Joint action by strong winds and high-intensive rainfall leads to serious soil erosion problems in large sediment generating and coarse sediment class areas of the middle Yellow River. Investigating the variation of trend and alternating wind and rainfall energy is an important prerequisite for controlling regional sediment transport problems. We calculated the wind and rainfall erosion energies by using the aerodynamic energy and rainfall kinetic energy formulas. The Mann-Kendall (MK) trend test and Theil-Sen approach were used to analyze the spatial-temporal variation and alternation of joint erosion energy during the last 40 years (1979–2018). The results show the following. 1) Wind erosion energy increased and rainfall erosion energy decreased. However, the former is larger than the latter. 2) The dominant erosive energy varies for different seasons. Wind erosive energy dominates in spring, and rainfall erosive energy dominates in summer. 3) There is a regular wind-rainfall-wind alternation of erosive energy. The wind-rainfall energies were alternately distributed in May–July and the rainfall-wind energies were alternately distributed in September–December. Furthermore, the dominant time of rainfall erosive energy decreased when wind erosive energy increased. The results can help decision makers to develop soil erosion control strategies to reduce soil erosion when it occurs in wind and water staggered areas.

Polyolefin elastomer (POE) has very weak crystalline ability, consequently, applying the conventional preparative temperature‐rising elution fractionation (P‐TREF) to separate is challenging. Here, a unique, home‐built P‐TREF apparatus with an extensive range of temperatures from −80°C to 150°C is applied to first fractionate POE depending on its crystallizability. The main fractions are eluted at 0°C, 8°C, 15°C, 20°C, 25°C, 30°C, and 35°C. The corresponding weight percentages of fractions are 8.31, 13.38, 15.59, 12.05, 13.39, 17.30, and 10.53 wt%, respectively. The chain structures of fractions are further analyzed by high‐temperature gel permeation chromatography (HT‐GPC), 13C‐nuclear magnetic resonance spectroscopy (13C‐NMR), differential scanning calorimetry (DSC), and successive self‐nucleation and annealing (SSA). The crystallinity of the fraction grows continually as the elution temperature rises. The 1‐octene comonomer concentrations within the fractions decreases from 13.8 to 7.9 mol% when the elution temperature rises from −10°C to 40°C. These findings enable for the detailed recognition of the chain microstructure of POE resin and the extension of the TREF approach to POE resins. This lays the groundwork for fundamental studies and practical uses in industry.

This paper studies a lattice-based broadcasting (LBC) scheme for a single-cell downlink system. The base station (BS) has N antennas and the K user-equipments (UEs) have a single-antenna. The system is referred to as massive-user MIMO (mU-MIMO) when K is very large. At the transmitter side, the BS encodes the messages for the UEs with state-of-the-art channel codes and q-PAM modulation. We suggest a codeword-level precoding, which transforms the K UEs’ coded sequences by an invertible integer matrix $\mathbf {A}^{-1}$ over $\mathbb {Z}_{q}$ , resulting in K codeword-level precoded (CLP) streams. Then, the CLP streams undergo a linear signal-level precoding (SLP) with regularized integer-forcing, and the resultant signals are transmitted via the N antennas of BS. The operations of CLP and SLP are both linear. At the receiver side, each UE attempts to compute an integer-combination (ICB) of the K CLP streams w.r.t. the integer matrix A, which turns out to be its desired message thanks to the CLP. For computing the ICB, we put forth new soft detection algorithms, which efficiently calculate the a posteriori probability of ICB over the lattice. The complexity is shown to be (much) less than $O(Kq)$ , which is suited for large values of K. Further, we develop a particle swarm based approach that jointly optimizes A and the power allocation matrix. Numerical results demonstrate dramatically enhanced performance over baseline schemes including regularized zero-forcing and vector perturbation precoding, for both slow and fast fading channels.

Revealing plasticity-evolved behaviors of laser-arc hybrid welding welds is indispensable for controlling high-reliability Ti–6Al–4V welded joints used in marine equipment. The relationship between dual heat source coupling and the plasticity of welded joints in thick plate titanium alloy is analyzed in this research. The plasticity-enhancing mechanisms based on the action of the keyhole are also elucidated. The results underscore that due to the faster cooling rate, the keyhole effect weakens the texture strengths of the β and α′ phases by 65.8% and 70.3%, respectively, and improves the ductility of the keyhole-affected region by 20% compared to the keyhole-free region. Furthermore, the keyhole region facilitates the generation of small-sized brittle phase α′, an increase in the number of small-angle grain boundaries, and easily activated pyramidal-slip systems {10 1‾1}<112‾0>, implying improved grain deformability and plasticity. These results have provided valuable insights into the plasticity-enhancing behaviors of thick Ti–6Al–4V laser-arc hybrid welded joints, offering guidance for subsequently regulating the performance of welded joints to achieve superior plasticity welded joints.

Potential Natural Vegetation (PNV) represents the climax of vegetation succession in a natural environment, free from significant disturbances. The reconstruction of PNV is widely used to study climate-vegetation relationships and predict future vegetation distributions. However, fine-scale PNV maps with high accuracy are still rare in biodiversity hotspots due to the complexity of ecosystems and limited field observations. In this study, we mapped the spatiotemporal distribution of 16 PNV types using adequate field and literature data, and an improved Comprehensive and Sequential Classification System (CSCS) approach under current (2005–2016) and future (2021–2080) climate scenarios in Yunnan province, Southwest China. We found that 1) from T0 (2005–2016) to T3 (2021–2080), regions with cold alpine PNV types, such as mid-mountain humid evergreen broad-leaved forests (EBLF), are projected to experience more significant temperature increases compared to regions with warm PNV types, like tropic rainforests and monsoon rainforests. High-emission scenarios (SSP585) are expected to result in temperature increases approximately 2°C higher than low-emission scenarios (SSP126). Precipitation is projected to increase for water-deficient PNV types (e.g., monsoon rainforest and semi-humid EBLF) but decrease for humid PNV types (e.g., rainforest and mountain mossy EBLF). The SSP370 scenario predicts a slightly smaller increase in precipitation compared to other scenarios. 2) All PNV types are expected to shift to higher latitudes (by an average of 0.86°) and higher elevations (by an average of 454 m) by T3, based on their current niches. Alpine PNV types are more sensitive to climate change and are projected to shift more prominently than other types. For example, mountain mossy EBLF is expected to move 1.78° northward, while mid-mountain moist EBLF is projected to rise by 578 m. 3) Cold PNV types are likely to be replaced by warm types both in latitude and altitude. Semi-humid EBLF is projected to shrink the most, by 57,984 km2(51.5% of its present range), while monsoon EBLF is expected to expand the most, by 44,881 km2(64.7% of its present range). The suitable habitat for cold-temperate sclerophyllous EBLF and temperate shrublands may disappear entirely in Yunnan. Given the over-estimate of the projected PNV shift without accounting for the lag effects, these findings are still useful in planning future conservation and management efforts, which should prioritize PNV types experiencing drastic changes in temperature (e.g., mid-mountain moist EBLF), precipitation (e.g., mountain mossy EBLF), and distribution area (e.g., semi-humid EBLF and cold-temperate sclerophyllous EBLF).

A design method of generating multiple transmission zeros (TZs) is proposed by introducing feeding-forward transconductance amplifiers for the cascaded Q-enhanced reconfigurable bandpass filters (BPFs) in this article. With the proposed method, it can generate two TZs at the upper and lower stopbands for every two Q-enhancement resonant units, significantly improving the stopband suppression. Furthermore, by controlling the feeding-forward amplifiers, the position of the zero and the depth of the TZs can be flexibly controlled. Meanwhile, to improve the power consumption, linearity, and noise figure (NF), the guideline of distribution of the transmission poles (TPs) generated by the Q-enhancement units is discussed in detail. To demonstrate the proposed method, a reconfigurable on-chip Q-enhanced BPF was designed and measured based on a 55-nm CMOS process with a core area of $1.36\times 0.62$ mm. Multiple TZs were clearly observed in the filter and achieved a stopband suppression of more than 50 dBc up to 15 GHz. The bandwidth of the proposed filter can be tuned with a range covering from 0.2 to 1 GHz at a center frequency of 1.8 GHz, while the center frequency can be tuned from 1.68 to 1.94 GHz with a constant bandwidth of 600 MHz. The input 1-dB compression point (IP1dB) is better than -1 dBm for all tuning states, and the NF varies from 12.7 to 20.3 dB. The dynamic range (DR) can reach more than 165 dB and the total power consumption is less than 180 mW.

Electrophoretic displays (EPDs), also known as e-paper, offer a paper-like visual experience by reflecting ambient light, making them distinct from traditional LCD or LED displays. They are favored for their eye comfort, energy efficiency, and material flexibility, which make them appealing for a wide range of embedded devices, including eReaders, smartphones, tablets, and wearables. However, EPDs face a significant challenge: the necessity for a fast refresh rate (to maintain an acceptable display performance) introduces a pronounced ghosting effect. This effect results in noticeable color discrepancies between the displayed and source images, harming the user experience and hindering EPDs’ broader application in devices requiring dynamic content display. This article proposes a software-based solution to address the ghosting issue in EPDs. Our approach involves developing analytical models to predict the occurrence of ghosting effects and adjusting the source images to counteract the anticipated color deviations, which can reduce the perceivable ghosts on the display. Experimental evaluation conducted on real-world EPDs validates the effectiveness of our proposed approach in reducing the ghosting effect.

Network pruning is an effective technique that reduces the computational costs of networks while maintaining accuracy. However, pruning requires expert knowledge and hyperparameter tuning, such as determining the pruning rate for each layer. Automatic pruning methods address this challenge by proposing an effective training-free metric to quickly evaluate the pruned network without fine-tuning. However, most existing automatic pruning methods only investigate a certain pruning granularity, and it remains unclear whether metrics benefit automatic pruning at different granularities. Neural architecture search also studies training-free metrics to accelerate network generation. Nevertheless, whether they apply to pruning needs further investigation. In this study, we first systematically analyze various advanced training-free metrics for various granularities in pruning, and then we investigate the correlation between the training-free metric score and the after-fine-tuned model accuracy. Based on the analysis, we proposed FreePrune score, a more general metric compatible with all pruning granularities. Aiming at generating high-quality pruned networks and unleashing the power of FreePrune score, we further propose FreePrune, an automatic framework that can rapidly generate and evaluate the candidate networks, leading to a final pruned network with both high accuracy and pruning rate. Experiments show that our method achieves high correlation on various pruning granularities and comprehensively improves the accuracy.

Free-viewpoint videos offer audiences a more immersive and liberated way to watch sports. The rendering of sports scenes encompasses two essential elements: dynamic targets and static scenes. While much current research focuses on achieving high-quality rendering of human bodies, rendering large-scale sports scenes presents various challenges. Sports arenas are characterized by large spatial extents, restricted camera placement, uncontrollable lighting, weak textures, and repetitive patterns, all of which pose significant obstacles to achieving high-quality scene rendering. In this work, we propose a neural radiance field rendering method based on scene-prior geometric constraints. We introduce prior 3-D geometric dimensions and 2-D semantic masks, to derive high-precision ground plane depth maps from camera imaging parameters. This is a geometry-based method that does not rely on visual features, and thus, it is unaffected by insufficient textures, repetition, and reflections. Subsequently, we apply ground depth maps as geometric consistency constraints to optimize the neural rendering network, thereby reducing the impact of color inconsistencies across viewpoints. The visual sensor network we build can synchronously capture static fields and dynamic targets in sports scenes. Based on the visual sensor network, we collected multiviewpoint datasets of large-scale sports scenes at Invengo and Xidian Gymnasium for performance evaluation. Experimental results demonstrate that our method can generate high-precision and cross-viewpoint scale-consistent depth constraints and helps reduce holes and artifacts in synthesized views. Our method outperforms the state of the art (SOTA) for novel view rendering for challenging large-scale sports scenes.

Despite the fact that many distributed resource allocation (DRA) algorithms have been reported in the literature, it is still unknown how to allocate resources optimally over multiple interacting coalitions. One major challenge in solving such a problem is that the relevance of one coalition's decision to other coalitions' benefits may lead to conflicts of interest among these coalitions. Within this context, a new game model is formulated in the present note, termed as resource allocation game, where each coalition contains multiple agents cooperating to maximize the coalition-level benefit subject to an intracoalition resource constraint described by a coupled equality. Inspired by techniques such as variable replacement, gradient tracking, and leader-following consensus, a new DRA algorithm is developed. It is shown that the proposed algorithm converges linearly to the Nash equilibrium (NE) of the proposed game while satisfying the resource constraint during the whole NE-seeking process. Finally, the effectiveness of the proposed allocation algorithm is verified by numerical simulations.

Thermoplastic carbon fiber reinforced polyetheretherkrtone (CF/PEEK) and thermoset carbon fiber reinforced epoxy (CF/epoxy) composites are being widely applied in aviation and aerospace fields for their excellent performance. To compare the drilling characteristics of two typical carbon fiber reinforced composites under varying feed speeds, drilling experiments were carried out using three different special drills involving twist, brad, and dagger drills. The drilling performance of CF/epoxy and CF/PEEK composites was analyzed in terms of chip morphology, drilling temperature, thrust force, delamination damage, and surface morphology. The results show that CF/PEEK composites produced continuous chips, so that CF/PEEK composites generated higher drilling temperature and thrust force than that of CF/epoxy composites. CF/epoxy composites showed larger delamination damage and poorer machined surface than CF/PEEK composite due to its poor interlaminar toughness. Burrs produced agglomeration and crimping at the hole edges of the CF/PEEK composites due to PEEK resin is softened by heat, matrix plastic deformation. Brad drill revealed fewer burrs and merely a tearing damage at the exit. Dagger drill showed more burrs. The hole wall damage is minimal for brad drill. The results provide guidance for drilling of high quality thermoset and thermoplastic composites.

Tertiary alkylamines are privileged structural motifs widely present in natural products, pharmaceutical agents, and bioactive molecules, and their efficient synthesis has been a longstanding goal in organic chemistry. The functionalization of α-amino radicals derived from abundant precursors represents an emerging approach to accessing alkylamines, but application of this strategy to obtain tertiary alkylamines remains challenging. Here, we show that dual photoredox/nickel catalysis enables aminoalkylation of organohalides (sp2- and sp3-hybridized) in combination with secondary alkylamines and aldehydes. The multicomponent process proceeds through selective generation of α-amino radicals from the reduction of in situ-generated iminium ions by photoredox catalysis, followed by nickel-catalyzed cross-coupling to build a wide array of functionally diverse tertiary alkylamines. This strategy could also be extended to unprecedented four-component reactions and their asymmetric variants to deliver enantioenriched α-aryl-substituted γ-amino acid derivatives. Taken together, this work offers a streamlined synthetic route to aliphatic tertiary amines.

• A Co 42 Cr 20 Ni 30 Ti 4 Al 4 quinary medium entropy alloy (MEA) with excellent mechanical properties over a wide temperature ranging from 77 to 873 K. • Post heat treatment leads to refinement of grains and heterogeneous precipitation of L1 2 phase. • Stacking faults mediated deformation behavior of the SLMed Co 42 Cr 20 Ni 30 Ti 4 Al 4 MEA. Modern engineering has long been in demand for high-performance additive manufactured materials for harsh working conditions. The idea of high entropy alloy (HEA), medium entropy alloy (MEA), and multi-principal-element alloy (MPEA) provides a new way for alloy design. In this work, we develop a Co 42 Cr 20 Ni 30 Ti 4 Al 4 quinary MEA which exhibits a superiority of mechanical properties over a wide temperature ranging from 77 to 873 K via selective laser melting (SLM) and post-heat treatment. The present MEA achieves an excellent ultimate tensile strength (UTS) of 1586 MPa with a total elongation (TE) of 22.7 % at 298 K, a UTS of 1944 MPa with a TE of 22.6 % at 77 K, and a UTS of 1147 MPa with a TE of 9.1 % at 873 K. The excellent mechanical properties stem from the microstructures composed of partially refined grains and heterogeneously precipitated L1 2 phase due to the concurrence of recrystallization and precipitation. The grain boundary hardening, precipitation hardening, and dislocation hardening contribute to the high YS at 298 and 77 K. Interactions of nano-spaced stacking faults (SFs) including SFs networks, Lomer–Cottrell locks (L–C locks), and anti-phase boundaries (APBs) induced by the shearing of L1 2 phase are responsible for the high strain hardening rate and plasticity at 77 K. Our work provides a new insight for the incorporation of precipitation hardening and additive manufacturing technology, paving the avenue for the development of high-performance structural materials. [Display omitted] [ABSTRACT FROM AUTHOR]

Novel sigma-2 (σ2) receptor ligands with benzimidazolone and 5,6-dimethoxyisoindoline as pharmacophores were designed and synthesized. Compound 4exhibited low nanomolar affinity for the σ2receptors (Ki(σ2) = 2.30 nM) and high subtype selectivity (Ki(σ1)/Ki(σ2) > 1500). Radioligand [18F]4was prepared in radiochemical yields of 18 ± 7%, with >99% radiochemical purity and molar activity of 244 ± 136 GBq/μmol. Biodistribution and blocking studies in mice and small animal PET/CT imaging in rats indicated highly specific binding of [18F]4in organs known to express the σ2receptors. Small animal PET/CT imaging with [18F]4showed clear visualization of the tumors in subcutaneous A549 lung cancer and U87MG glioma xenografts, and intracranial orthotopic U87MG glioma models. Co-administration of CM398 with [18F]4significantly reduced activity uptake in the tumors, indicating that [18F]4specifically binds to the σ2receptors expressed in A549 and U87MG xenografts.

Immunotherapy has revolutionized the cancer treatment paradigm, yet efficient immunotherapeutic responses against immune-cold/desert cancers remain challenging. Herein, we report that photoactivatable nanoagonists yield a potent antitumor synergy of photoimmunotherapy against pancreatic ductal adenocarcinoma (PDAC). The nanoagonist was fabricated by assembling an amphiphilic boron dipyrromethene-derived polymer conjugated with a Toll-like receptor agonist via a photocleavable linker and stimulator of interferon genes agonist. The nanoagonist enables light-induced generation of reactive oxygen species and on-demand release of the agonists to yield synergistic photoimmunotherapy. The produced tumor antigens promote dendritic cell maturation, which is further amplified by these agonists for eliciting adaptive immunity, accompanied by apparently abscopal and long-term memory effects. The nanoagonist further alleviates the fibrosis of tumor stroma and the immunosuppressive microenvironment, leading to the deep infiltrations of clinically used therapeutics and immune cells to yield preferable combinational treatments against PDAC models. These results provide valuable insights into activatable nanoparticles for cancer therapy against immune-desert cancers.

This correspondence studies slotted ALOHA (SA) operated with $q$-ary compute-forward (CF) for grant-free random access. At the transmitters, a practical $q$-ary channel code and repetition SA (RSA) protocol are utilized, where users send replicas of their coded-modulated packets in randomly selected slots. At the receiver, practical $q$-ary linear physical-layer network coding is employed to compute and stores a number of network-coding (NC) message streams per slot. Upon collecting a sufficient amount of NC message streams from a number of consecutive slots, the receiver carries out generalized matrix inversion (GMI) to recover the users' messages. We demonstrate that the developed scheme exhibits dramatically increased throughput by as much as 80%. Furthermore, thanks to GMI, there is no need to wait till the end of the frame for decoding, leading to slashed processing delay in RSA.

As environmental pollution becomes increasingly serious and industrial energy consumption continuously rises, an intelligent and efficient industrial energy management policy is urgently needed to reduce costs and maximize the benefits of industrial energy systems. However, modern industrial energy systems are characterized by hybrid industrial equipment actions, diverse objectives, and highly intermittent and stochastically distributed renewable energy sources. Therefore, efficient operation and control are difficult. This article presents a novel, model-free energy management policy using a hybrid action deep reinforcement learning algorithm for energy scheduling of industrial equipments operating in various modes. Specifically, the interaction process between the industrial energy management center and each equipment is modeled as a Markov decision process that minimizes the daily operating cost of the energy system and maximizes the revenue of the production equipment. Then, a double parameterized deep Q-networks that does not require an explicit environmental model is developed to learn the hybrid action signals using actor and critic networks, in which the double Q value mechanism avoids value overestimation and improves the algorithm efficiency. In addition, the policy gradient of the proposed algorithm is derived and its convergence proof is discussed. Finally, numerical studies are conducted using real-world data to evaluate algorithm performance and verify its effectiveness.

A $Q$ -enhanced CMOS tunable bandpass filter (BPF) with ultra-narrow bandwidth and ultralow dc power consumption for radio frequency identification (RFID) devices is proposed using 55-nm bulk CMOS technology in this article. The proposed filter is constructed by a capacitively coupled LC resonator (LCC), which consists of a 16-nH on-chip integrated inductor, fixed capacitors, and tunable varactors. nMOS cross-coupled transistors are used to compensate for the loss of the resonator and improve the loaded $Q$ of the resonator. To achieve low power consumption, an inductor with an ultralarge value is exploited to reduce the loss of the resonator. Besides, an improved dual varactor inverse (DVI) and degenerated resistors at the source of the cross-coupled transistors with linearity and resistance adjusting, respectively, are utilized to optimize the linearity under center frequency tuning. Besides, the active circuits, the fixed capacitance, and varactors are placed inside the inductor to improve the integration. With all the proposed techniques, the presented BPF achieves a 935–953-MHz center frequency tuning range with a constant narrow bandwidth of 180 kHz, a low power consumption of $85~\mu \text{W}$ , and a core area of $0.43\times0.43$ mm2, demonstrating a high-performance tunable on-chip filter using bulk CMOS technology for low-power and narrowband Internet of Things (NBIoT) applications.

This article focuses on distributed nonconvex optimization by exchanging information between agents to minimize the average of local nonconvex cost functions. The communication channel between agents is normally constrained by limited bandwidth, and the gradient information is typically unavailable. To overcome these limitations, we propose a quantized distributed zeroth-order algorithm, which integrates the deterministic gradient estimator, the standard uniform quantizer, and the distributed gradient tracking algorithm. We establish linear convergence to a global optimal point for the proposed algorithm by assuming Polyak–Łojasiewicz condition for the global cost function and smoothness condition for the local cost functions. Moreover, the proposed algorithm maintains linear convergence at low-data rates with a proper selection of algorithm parameters. Numerical simulations validate the theoretical results.

Objective: Lobula giant motion detectors (LGMDs) in locusts effectively predict collisions and trigger avoidance, with potential applications in autonomous driving and UAVs. Research on LGMD characteristics splits into two views: one focusing on the presynaptic visual pathway, the other on the postsynaptic LGMD neurons. Both perspectives have support, leading to two computational models, but they lack a biophysical description of the individual LGMD neuron behavior. This paper aims to mimic and explain LGMD behavior based on fractional spiking neurons (FSNs) and construct a biomimetic visual model for the LGMD compatible with these characteristics. Methods: We implement the visual model using an event camera to simulate photoreceptors and follow the ON/OFF visual pathway, incorporating lateral inhibition to mimic the LGMD system from the bottom up. Second, most computational models of motion perception use only the dendrites within the LGMD neurons as the ideal pathway for linear summation, ignoring dendritic effects inducing neuronal properties. Thus, we introduce FSN circuits by altering dendritic morphological parameters to simulate multi-scale spike frequency adaptation (SFA) observed in LGMDs. Additionally, we add one more circuit of dendritic trees into the FSNs to be compatible with the postsynaptic feed-forward inhibition (FFI) in LGMD neurons, providing a novel explanatory and predictive model. Results: We test that the event-driven biomimetic visual model can achieve collision detection and looming selection in different complex scenes, especially fast-moving objects.