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The multi-cationic site occupation control of rare earth doped phosphor materials is one of the key factors in achieving single-phase full-spectrum emission. In this work, the site distribution of Eu2+in Ca9NaGd0.667(PO4)7(CNGP):x%Eu2+phosphors with multi-cationic sites was determined. Under 385 nm excitation, CNGP:x%Eu2+samples exhibited a cyan emission composed of three sub-Gaussian components. The CNGP:1.5%Eu2+phosphor was structurally modified through the partial substitution of Mg2+for Ca2+according to the crystallographic site engineering theory, and corresponding luminescent properties had been adjusted. When the concentration of Mg2+changed, the Ca9–yMgyNaGd0.667(PO4)7:1.5%Eu2+(CNGPE:yMg2+) phosphors exhibited a tunable emission from cyan to orange and achieved white light emission at y= 0.6. Based on the change of the crystal field environment, the mechanism of Mg2+substitution affecting the luminescence characteristics of CNGP:x%Eu2+phosphors was researched in detail. The light-emitting diode device consisted of a CNGPE:0.6Mg2+phosphor and a 385 nm chip and had a high color rendering index (CRI = 91.9), low correlated color temperature (CCT = 4852 K), and excellent color stability. The optical performance of this device makes it promising to be used in full spectrum lighting.

• Profuse lamellar-type microstructures were found in the solution-treated Sm(Co bal Fe 0.31 Cu 0.07 Zr 0.025) 7.7 magnets. Within the lamellar-type microstructure, there are three microregions exhibiting an obvious difference in composition and 2:17R ordering degree. • TEM images indicate the 1:3R phase may have precipitated and then re-dissolved during the solution treatment. • A long-time solution treatment under appropriate temperature could enhance the compositional uniformity and promote the transformation from 2:17R phase to 1:7H phase. The solution-treated Fe-rich 2:17-type Sm-Co-based magnets generally contain an inhomogeneous microstructure, which hinders the development of the complete cellular structure required for high-performance magnets. Here, the microstructure evolution of Sm(Co bal Fe 0.31 Cu 0.07 Zr 0.025) 7.7 magnet during the solution treatment is systematically investigated. Unlike the normal Fe-medium magnets, it was interestingly found that the initial-stage solution-treated Sm(Co bal Fe 0.31 Cu 0.07 Zr 0.025) 7.7 magnet possesses a profuse lamellar-type microstructure. Within the lamellar-type microstructure, there are three microregions exhibiting an obvious difference in composition and 2:17R ordering degree. Further, various 1:3R phases precipitated within the lamellar-type microstructure. With the disappearance of lamellar-type microstructure, the 1:7H phase increased substantially, and the distribution of defect-aggregated cell boundaries became uniform, promoting the homogeneous precipitation of 1:5H cell boundary phases during aging. The current study reveals a typical microstructural evolution in Fe-rich 2:17-type Sm-Co-based magnets during solution treatment. [Display omitted] [ABSTRACT FROM AUTHOR]

The chemical absorption method using MEA has been one of the most effective CO2capture technologies, but the problems of low process efficiency and poor economics limit its application. In this work, the rapid phase change absorbent composed of alcohol ether solvent and potassium amino acid salt (AaK) solution was investigated, and the effects of different AaKs and ethers on the phase change behavior, phase separation mechanism, and CO2absorption performance of the absorbent were analyzed. The results showed that when the carbon chain of the linear AaK was longer, the solubility in water was larger, and the lower liquid phase (LP) volume, phase separation time, AaK enrichment in the LP, and CO2molar loading were larger. When the alkyl group of the branched-chain AaK was larger, the solubility in water was smaller, and the gas–liquid mass transfer, enrichment effect, and absorption rate were smaller. When the ether bonds of ether solvent were greater, the solubility of organic salts and competition for water were stronger; thus, the LP volume was smaller. Moreover, the absorption rate and CO2loading of AaK systems were affected by the spatial structure effect of amino and carboxyl groups and the viscosity effect of solvent before absorption.

Broadband photodetectors that can decipher the wavelength (λ) and intensity (I) of an unknown incident light are urgently demanded. Photothermoelectric (PTE) detectors can achieve ultrabroadband photodetection surpassing the bandgap limitation; however, their practical application is severely hampered by the lack of deciphering strategy. In this work, we report a variable elimination method to decipher λ and Iof the incident lights based on an integrated Ag2Se film-based PTE detector. Nanostructured Ag2Se films with controlled thickness are synthesized using an ion sputtering of Ag and a room-temperature selenization method and then assembled into a detector. Under identical illumination, Ag2Se films of different thicknesses produce varying output photothermal voltages, influenced by factors including λ. By establishing a direct relationship between the photothermal voltage and the absorption of Ag2Se films of varied thickness, we successfully eliminate variables independent of λ, thus determining λ. Subsequently, Iis determined by the calibrated responsivity relationship using obtained λ. Our PTE detector achieves a broadband spectrum from 400 to 950 nm and high accuracy, with deviations as low as ∼2.63 and ∼0.53% for deciphered λ and I, respectively. This method allows for self-powered broadband decipherable photodetection without a complex device architecture or computational assistance, which could boost the research enthusiasm and promote the commercialization of PTE broadband detectors.

With the combination of the N-heterocyclic carbene-PdCl2-1-methylimidazole complex and Cu2O, we succeeded in the first example of double direct C–H bond arylation reactions between thiophenes and aryl chlorides, giving the desired 2,5-diarylated thiophenes in moderate to high yields under suitable conditions, consistent with the density functional theory calculations.

Co-pyrolysis may enhance the energy efficiency of anaerobic digestion waste (biogas residues), while simultaneously facilitating the clean utilization of coal. A thermogravimetric analyzer (TG), TG-FTIR-MS, and infrared heating technique were employed to investigate the rapid co-pyrolysis properties of long-flame coal (LFC) and biogas residues (BR) in this study. Meanwhile, the effects of temperature on the co-pyrolysis product distributions and compositions were studied in reactors that were heated with electric heating and rapid infrared heating. The co-pyrolysis of LFC and BR had the potential to decrease greenhouse gas emissions, as indicated by the TG-FTIR/MS findings. The co-pyrolysis kinetics were calculated using two models (KAS and FWO); the average Eαvalues are 176.09 and 187.26 kJ/mol, respectively. Tar yields increased initially in IH (electric heating) and EH (infrared heating) reactors, before declining as the temperature rose. The tar yields are greatest for EH at 500 °C (16.12 wt%) and IH at 600 °C (16.02 wt%). Tar yields generated via infrared heating were greatly higher than those generated by EH at 600–700 ℃. IH has a higher tar yield due to the fact that there are significant synergistic effects between coal and lignin, protein, and lipid at high temperatures. The GC-MS results indicate that IH has promoted the aromatization and ketonization reactions. Moreover, more monocyclic aromatic and bicyclic aromatic products are generated by infrared heating, suggesting an indication of tar quality improvement. An elevated co-pyrolysis temperature results in a greater degree of graphitization in char.

The aim of this paper is to enhance the performance of an integrated sensing and communications (ISAC) system in the multiuser multiple-input multiple-output (MIMO) downlink in which a two-dimensional (2D) fluid antenna system (FAS) with multiple activated ports is employed at the base station (BS) to maximize the sum-rate of the downlink users subject to a sensing constraint. The unique feature of this setup is that the locations of the antenna ports at the FAS can be optimized jointly with the precoding design to achieve a higher sum-rate. The required optimization problem is however NP-hard. To overcome this, we start by considering the perfect channel state information (CSI) scenario where all the port CSI is available. Deep reinforcement learning is utilized to build an end-to-end learning framework for the joint optimization problem. In particular, by fixing the activated ports, we adopt a primal-dual based learning algorithm to design a constraint-aware neural network for optimizing the ISAC precoder. Then, by using the neural precoding network to calculate the reward, we adopt the deep reinforcement learning algorithm to design the port selection and precoder jointly. An advantage actor and critic (A2C) algorithm is proposed to train the policy, in which the actor network uses the pointer network to learn the stochastic policy and the critic network adopts the Long Short-Term Memory (LSTM) encoder architecture to learn the expected reward from the observations. Afterwards, the partial CSI case is addressed, where we propose a masked autoencoder (MAE) induced channel extrapolation for predicting all the CSI to facilitate the joint design. Simulation results demonstrate the promising performance of using FAS for multiuser MIMO and also validate the proposed learning-based scheme.

Dynamic pruning is an effective model compression method to reduce the computational cost of networks. However, existing dynamic pruning methods are limited to pruning along a single dimension (channel, spatial, or depth), which cannot maximally excavate the redundancy of the network. Meanwhile, most of the current state-of-the-arts usually implement dynamic pruning via masked-out partial channels and pixels for training while failing to accelerate the inference speed. To tackle these limitations, we propose a novel fuzzy-based multidimensional dynamic pruning paradigm to dynamically compress neural networks along both the channel and spatial dimensions. Specifically, we design a multidimensional fuzzy-mask block to simultaneously learn which spatial positions or channels are redundant and need to be pruned. Then, the Gumbel-Softmax trick combined with a sparsity loss is introduced to train these mask modules in an end-to-end manner. During the testing stage, we convert features and convolution kernels into two matrices, respectively, and then implement sparse convolution through matrix multiplication to accelerate the network inference. Extensive experiments demonstrate that our method outperforms existing methods in terms of accuracy and computational cost. For instance, on the CIFAR-10 dataset, our method prunes 68% FLOPs of ResNet-56 with only a 0.07% Top-1 accuracy drop.

Cooperative perception as an emerging application of LiDAR-driven signal processing in driving scenarios has received widespread attention in recent years. Despite impressive advancements in previous works through sophisticated strategies, challenges remain due to inevitable data sparsity and localization errors. To this end, we propose a Spatio-Temporal Cooperative Perception (STCP) framework to address the above issues. Our novelties derive from two core components. A multi-scale temporal integration module is introduced to aggregate historical clues from the ego agent for mitigating data sparsity interference. In addition, we design a spatial cooperation transformer to perform pragmatic cooperation and eliminate the feature misalignment from collaborators due to localization errors. Extensive experiments are conducted on real-world and simulated multi-agent 3D object detection datasets. Quantitative analyses show that our framework outperforms existing methods on DAIR-V2X and V2X-Sim datasets with significant gains of 2.16% and 2.98% regarding AP@0.5.

Although pore intrusion can cause defects in the selective layer, it can improve the interface adhesion between the selective layer and the support layer. Consequently, a novel strategy for a controlled pore intrusion depth has been proposed for the first time. In this work, pore intrusion was regulated by the addition of glycerol, DI water, or paraffin oil as a prewetting agent to a support to prepare poly(dimethylsiloxane) (PDMS)/polysulfone (PSF) composite membranes. We found that the pore intrusion depth was proportional to the curing rate of PDMS. The prewetting agent demonstrated a greater affinity for the support, and a high boiling point was beneficial in alleviating pore intrusion. The properties of the support could affect the gas permeance. The PDMS/PSF composite membrane with different pore intrusion depths could be prepared by changing the extraction time of the support. This strategy would provide a reference for studies on defect-free PDMS composite membranes.

BRCA1, a breast cancer susceptibility gene, has emerged as a central mediator that brings together multiple signaling complexes in response to DNA damage. The A, B, and C complexes of BRCA1, which are formed based on their phosphorylation-dependent interactions with the BRCA1-C-terminal domains, contribute to the roles of BRCA1 in DNA repair and cell cycle checkpoint control. However, their functions in DNA damage response remain to be fully appreciated. Specifically, there has been no systematic investigation of the roles of BRCA1-A, -B, and -C complexes in the regulation of BRCA1 localization and functions, in part because of cellular lethality associated with loss of CtIP protein, which is an essential component in BRCA1-C complex. To systematically investigate the functions of these complexes in DNA damage response, we depleted a key component in each of these complexes. We used the degradation tag system to inducibly deplete endogenous CtIP and obtained a series of RAP80/FANCJ/CtIP single-, double-, and triple-knockout cells. We showed that loss of BRCA1-B/FANCJ and BRCA1-C/CtIP, but not BRCA1-A/RAP80, resulted in reduced cell proliferation and increased sensitivity to DNA damage. BRCA1-C/CtIP and BRCA1-A/RAP80 were involved in BRCA1 recruitment to sites of DNA damage. However, BRCA1-A/RAP80 was not essential for damage-induced BRCA1 localization. Instead, RAP80/H2AX and CtIP have redundant roles in BRCA1 recruitment. Altogether, our systematic analysis uncovers functional differences between BRCA1-A, -B, and -C complexes and provides new insights into the roles of these BRCA1-associated protein complexes in DNA damage response and DNA repair.

Probabilistic bits (p-bits) with thermal- and spin torque-induced nondeterministic magnetization switching are promising candidates for performing probabilistic computing. Previously reported spin torque p-bits include volatile low-energy barrier nanomagnets (LBNMs) with spontaneously fluctuating magnetizations and initialization-necessary nonvolatile magnets. However, initialization-free nonvolatile spin torque p-bits are still lacking. Here, we demonstrate moderately thermal stable spin–orbit torque (SOT) p-bits with non-consecutively deposited Pt//Pt/Co/Pt stacks. Backhopping-like (BH) magnetization switching with a wide range current-tunable probability of final up and down magnetization states from 0% to 100% was achieved, regardless of the initial magnetization state, which was attributed to the interplay of SOT and thermal contributions. Integer factorization using such BH-SOT p-bits in zero magnetic field was demonstrated at times that are significantly shorter than those of existing nonvolatile STT or volatile LBNMs p-bits. Our realization of initialization-free and magnetic field-free moderately thermally stable BH-SOT p-bits opens up a new perspective for probabilistic spintronic applications.

A flexible arc-shaped micro-Fiber Bragg Grating (mFBG) array three-dimensional tactile sensor for fingertip signal detection and human pulse monitoring is presented. It is based on a three mFBGs array which is embedded in an arc-shaped poly (dimethylsiloxane) (PDMS) elastomer, which can effectively discriminate the normal force, left force, and right force by monitoring the reflected intensity variation of the three mFBGs. Different from the traditional FBG sensors, this sensor measures force by detecting changes in light intensity, effectively avoiding the wavelength cross-sensitivity impact of temperature variations on the sensor performance. This design strategy simplifies the sensor structure, reduces the system complexity and signal interrogation cost, and enhances reliability and practicality. Through systematic experiments, we successfully validated the sensor’s superior performance, achieving a minimum detection force of 0.01 N and providing robust data support for practical applications. In addition, the sensor has been used to monitor human pulse accurately. The successful fabrication and experimental validation of this sensor lay a foundation for its widespread application in fields such as robot perception and human vital signal detection.

Using recycled hardened cement powder (RHCP) as a substitute for cement clinker presents an opportunity for CO2reduction and solid waste utilization in the cement industry. This work proposes a promising mineralization method of RHCP to obtain carbonated RHCP (CRHCP), and the mineralization reaction kinetics and corresponding microscopic mechanism of the whole process are analyzed. Results show that by prewetting RHCP before mineralization, the homogeneous liquid film is formed on its surface, which can accelerate the diffusion and dissolution of CO2, making the mineralization reaction rate conform to the ideal diffusion control. Based on this method, the CO2uptake of CRHCP at the ambient temperature, CO2concentration of 20%, CO2partial pressure of 0.1 MPa, w/s of 0.25, and reaction time of 24 h reaches the optimal value of 29.75%, and CRHCP is a core–shell structure of silica gel-coated CaCO3. Moreover, compared with PC, the blended cement system with 20% CRHCP achieves a higher compressive strength, especially the 3 day early strength reaching 36.58 MPa (increased by 20.00%). This is attributed to the positive effect of CaCO3and amorphous silica gel in CRHCP, resulting in the continuous generation of hydration products and the gradual development of strength in the blended cement system with CRHCP.

Efficient and reliable clustering mechanisms play a crucial role in enhancing the energy management efficiency of underwater wireless sensor networks (UWSNs). In recent years, game theory has been widely applied in clustering mechanisms for its ability to provide theoretical support in optimizing strategies. However, existing game theory-based clustering mechanisms only analyze the current cooperation and competition relationships of nodes in a single dimension, which limits the efficient energy utilization of the network. To address these limitations, this article proposes an adaptive energy-efficient clustering mechanism for UWSNs based on multidimensional game theory (MDGTC). During the candidate cluster head (C-CH) nodes selection, MDGTC enhances the opportunity of the potential optimal CH node to act as C-CH again by establishing a multidimensional clustering game model. Subsequently, an adaptive CH competition mechanism is introduced to further optimize the CH selection strategy by considering the energy and energy consumption status of local nodes and global networks. In addition, by combining a hierarchical architecture and a hybrid CH rotation mechanism, the stability of the proposed model is ensured, leading to a more balanced energy consumption among network nodes. In conclusion, MDGTC offers an effective distributed energy management architecture for UWSNs. The simulation results show that the MDGTC can achieve efficient energy utilization and prolong the network lifetime significantly.

Magnetic fluid, a novel functional material with unique properties, is gradually demonstrating its advantages in various fields, including sensors. Here, a novel magnetic fluid inclinometer was proposed, mainly consisting of magnetic fluid, a mass block containing (coating) a magnetic layer, and external permanent magnets. Within the nonuniform magnetic field generated by the permanent magnets, as the mass block approaches, it experiences increasing forces from the magnetic fluid. External tilt variations are reflected in the mass block’s displacement, detected through a circuit similar to a differential transformer, thereby outputting external tilt angles. The magnetic layer arranged on the mass block is a prominent feature of the structure. Theoretical analysis simplifies the mass block’s force based on magnetic field distribution and indicates linear output within a small range (approximately one-third of the movable range). Simulation tests reveal that the magnetic layer increases circuit output by about 15 times. The structure displays excellent response speed and repeatability, achieving an effective linear range exceeding 30°, an average sensitivity of approximately 5 mV/°, and stability within 0.1–0.2 s. Experimental validation confirms the magnetic layer’s efficacy, demonstrating a current range from 6° to 90° with an average sensitivity of 3 mV/°.

The received signal strength (RSS) measurement is widely adopted in wireless positioning due to its simplicity and accessibility. This article utilizes the Boolean line model (BLM) to characterize the RSS distribution considering both line-of-sight (LOS) and non-LOS (NLOS) conditions, where the reflectors are modeled as line segments with random lengths and inclination angles. We begin by calculating the probability of establishing a specular reflection link for a single reflector with a given transmitter–receiver pair. By leveraging this probability, we compute the distribution of RSS for a single NLOS link. By convolving the RSS distributions from the LOS and multiple NLOS paths, the RSS distribution in mixed LOS and NLOS conditions is derived. Theoretical developments are validated by comparing simulations and the experimental data collected from real-world scenarios.

Mainlobe jamming has emerged as a serious threat to the radar and the carrier frequency agility of the radar provides a promising way to address this challenge. In this article, the electromagnetic confrontation between a smart mainlobe jammer and a frequency agile radar is investigated from a game theory perspective. The interaction process between them typically includes multiple pulses, in which the radar determines its carrier frequency sequence, and the jammer synchronously chooses the jamming mode. This bilateral process is modeled as a two-player zero-sum extensive-form game with simultaneous moves. To solve the game, the backward induction (BI) algorithm is introduced to find the subgame perfect Nash equilibrium (SPNE). Although the BI algorithm can exactly find the SPNE, its computational cost grows exponentially with the number of pulses, making it impractical. Therefore, a fast algorithm called the sequential induction (SI) algorithm is proposed to obtain an approximation NE, whose computational complexity is linear with respect to the number of pulses. In addition, the obtained radar's SEQ strategy with history-independent and reuse characteristics is reliable and efficient in a game with a changing number of stages. Simulation results demonstrate the effectiveness of the proposed SI algorithm for approximately finding the NE.

Molecular communication (MC) is considered to be one of the most feasible communication paradigms for constructing nanoscale networks within the human body, where chemical signals play significant roles. Compared with the traditional wireless communication method with electromagnetic signals, the mechanism of MC is credited to its biocompatibility. However, signal-dependent counting noise, as a type of nonstationary noise, greatly affects the detection performance of MC signals. To counteract its effect in the MC systems, we deploy the variational mode decomposition (VMD) method to recover the waveform of the original MC signals. To optimize the denoising performance, the root mean squared error (RMSE) and signal-to-noise ratio (SNR) are used as the metrics for comparison. Finally, numerical results demonstrate the effectiveness of the VMD method for MC signals under various modulation orders, achieving significant noise reduction compared with other classical methods.

In recent years, the utilization of In-Band Full-Duplex (IBFD) technology has gained considerable attention as an effective solution to address limited spectrum resources. However, achieving an IBFD system necessitates the utilization of both passive suppression and active cancellation techniques to completely eliminate strong self-interference (SI) signals in the local receiver. This research paper aims to tackle this challenge by introducing the Multi-Domain Intelligent Cancellation (MDIC) method, which integrates analog-domain and digital-domain cancellation techniques through comprehensive nonlinear system modeling. Moreover, this study presents an extensive analysis of the underlying principles of MDIC, while evaluating the limits of SI suppression performance under the assumption of perfect channel estimation and no phase noise. The channel parameter estimation is conducted using the time-domain least squares method, without considering the effects of Gaussian white noise and phase noise. To enhance the performance of SI cancellation (SIC), a deep learning network (DLN) is introduced in the digital domain. Simulation results demonstrate that the proposed MDIC method achieves a substantial SIC performance of approximately 60 dB, which closely approaches the theoretical limit. Furthermore, experimental data indicates that the proposed deep learning approach exhibits superior SIC performance and broader adaptability to broadband signals compared to traditional methods.

In the context of label-efficient learning on video data, the distillation method and the structural design of the teacher-student architecture have a significant impact on knowledge distillation. However, the relationship between these factors has been overlooked in previous research. To address this gap, we propose a new weakly supervised learning framework for knowledge distillation in video classification that is designed to improve the efficiency and accuracy of the student model. Our approach leverages the concept of substage-based learning to distill knowledge based on the combination of student substages and the correlation of corresponding substages. We also employ the progressive cascade training method to address the accuracy loss caused by the large capacity gap between the teacher and the student. Additionally, we propose a pseudo-label optimization strategy to improve the initial data label. To optimize the loss functions of different distillation substages during the training process, we introduce a new loss method based on feature distribution. We conduct extensive experiments on both real and simulated data sets, demonstrating that our proposed approach outperforms existing distillation methods in terms of knowledge distillation for video classification tasks. Our proposed substage-based distillation approach has the potential to inform future research on label-efficient learning for video data.

The problems of low computational efficiency and checkerboard phenomenon in optimizing steel joints with the bi-directional evolutionary structural optimization (BESO) method are intractable, which usually leads to failure to achieve optimized solutions even after spending long calculation time. This paper proposes an improved algorithm based on the BESO theory for steel joints (SJ-BESO) by utilizing a sensitivity filtering radius to weight the sensitivity values of each element. And a sensitivity threshold segment is proposed to determine the deletion or addition of elements. Additionally, a convergence parameter is developed to ensure the stability of the optimization calculation. Several real-word examples of steel joints are provided to illustrate the proposed method. The results show that the improved BESO algorithm not only reduces the number of iterations, but also produces ideal structures with good stiffness. The 3D printing manufacturing of the example model verifies the manufacturability of the obtained designs and enhances its intuitive understanding. The SJ-BESO is applicable and promising for the topology optimization of steel joints.

Due to the open accessibility of wireless networks with severe privacy risks, covert communication has gained increasing attention, where the effective range is limited by the low transmit power. Fortunately, employing intelligent reflecting surface (IRS) as a relay has become an appealing solution to extend the range of covert communication. To this end, we investigate the covert communication in which the ground transmitter secretly delivers information to the full-duplex receiver through a two-way IRS, avoiding detection by the warden. Then, the error detection probability is determined with an optimal threshold at a warden, which is the worst case for covert transmission. Moreover, we analyze the closed-form expression of outage probability. To improve the covertness, artificial noise is generated by the receiver to interfere with the adversarial monitoring. Thus, considering the optimal prior probability, we maximize the expected error detection probability of warden subject to the covertness constraint. Specifically, we alternately optimize the prior probability and the transmit power of artificial noise while satisfying the outage probability and covertness requirement. Numerical results demonstrate the effectiveness of the proposed scheme for covert communications via the two-way IRS.

Electrocatalysis is generally confined to dynamic liquid–solid and gas–solid interfaces and is rarely applicable in solid-state reactions. Here, we report a paradigm shift strategy to exploit electrocatalysis to accelerate solid-state reactions in the context of lithium-ion batteries (LIBs). We employ heteroatom doping, specifically boron for silicon and sulfur for phosphorus, to catalyze electrochemical Li-alloying reactions in solid-state electrode materials. The preferential cleavage of polar dopant-host chemical bonds upon lithiation triggers chemical bond breaking of the host material. This solid-state catalysis, distinct from liquid and gas phases, requires a critical doping concentration for optimal performance. Beyond a critical concentration of ∼1 atom %, boron and sulfur doping drastically reduces activation energies and accelerates redox kinetics during lithiation/delithiation processes, leading to markedly enhanced rate performance in boron-doped silicon and sulfur-doped black/red phosphorus anode. Notably, a sulfur-doped black phosphorus anode coupled with a lithium cobalt oxide cathode achieves an ultrafast-charging battery, recharging 80% energy of a battery in 302 Wh kg–1in 9 min, surpassing the thus far reported LIBs.

Hybrid copper(I) halides have garnered a significant amount of attention as potential substitutes in luminescence and scintillation applications. Herein, we report the discovery and crystal growth of new zero-dimensional compounds, (C2H8N)3Cu2I5and (C2H8N)4Cu2Br6. The bromide and iodide have a triclinic structure with space group P1̅ and an orthorhombic structure with space group Pnma, respectively. (C2H8N)3Cu2I5exhibits cyan emission peaking at 504 nm with a photoluminescence quantum yield (PLQY) of 34.79%, while (C2H8N)4Cu2Br6shows yellowish-green emission peaking at 537 nm with a PLQY of 38.45%. The temperature-dependent photoluminescence data of both compounds were fitted to theoretical models, revealing that nonradiative intermediate states significantly affect thermal quenching and antiquenching. Electron–phonon interactions, the origin of emission line width broadening and peak shifting, were also investigated via fittings. The scintillation properties of (C2H8N)3Cu2I5were evaluated, and an X-ray imaging device was successfully fabricated using (C2H8N)3Cu2I5. This work demonstrates the potentiality of copper halides in lighting and X-ray imaging applications.

Uncertainty exists about whether lowering systolic blood pressure to less than 120 mm Hg is superior to that of less than 140 mm Hg, particularly in patients with diabetes and patients with previous stroke.

Nickel-catalyzed hydrocyanation of 1,3-butadiene with hydrogen cyanide gas is the predominant method for the synthesis of adiponitrile, which is an important precursor for polymer production. However, the use of fossil-derived alkenes raises environmental concerns, and hydrogen cyanide is highly volatile and extremely toxic. Herein, we report the use of biomass-derived 1,4-butanediol, as well as other primary alcohols, for photochemical synthesis of linear and branched nitriles and dinitriles, including adiponitrile, with 1,4-dicyanobenzene as the CN source. This mild, sustainable method does not require hydrogen cyanide gas or an air- or moisture-sensitive metal catalyst and is applicable for the production of dinitriles as precursors of diamines, which have potential utility for the development of novel polyamides.

Electroreduction of nitrate (NO3RR) to ammonia in membraneless electrolyzers is of great significance for reducing the cost and saving energy consumption. However, severe chemical crossover with side reactions makes it challenging to achieve ideal electrolysis. Herein, we propose a general strategy for efficient membraneless ammonia synthesis by screening NO3RR catalysts with inferior oxygen reduction activity and matching the counter electrode (CE) with good oxygen evolution activity while blocking anodic ammonia oxidation. Consequently, screening the available Co–Co system, the membraneless NO3–-to-NH3conversion performance was significantly higher than H-type cells using costly proton-exchange membranes. At 200 mA cm–2, the full-cell voltage of the membraneless system (∼2.5 V) is 4 V lower than that of the membrane system (∼6.5 V), and the savings are 61.4 kW h (or 56.9%) per 1 kg NH3produced. A well-designed pulse process, inducing reversible surface reconstruction that in situ generates and restores the active Co(III) species at the working electrode and forms favorable Co3O4/CoOOH at the CE, further significantly improves NO3–-to-NH3conversion and blocks side reactions. A maximum NH3yield rate of 1500.9 μmol cm–2h–1was achieved at −0.9 V (Faraday efficiency 92.6%). This pulse-coupled membraneless strategy provides new insights into design complex electrochemical synthesis.

Polyurethane foams are widely used in daily life due to their superior performance. However, it is very challenging to recycle and reuse waste polyurethane foams since the three‐dimensional crosslinking network structure makes them non‐melting and insoluble. In this work, a low‐cost, green, room‐temperature solid‐state shear milling method is proposed to break the cross‐linking bonds of waste polyurethane foam (crosslinking density decreases from 0.768 to 0.489 mol dm−3). The recycled polyurethane (RPU) powders regain their thermoplastic processability. Recycled waste polyester (PET) fibers are modified through plasma treatment and used as reinforcing fillers for the reprocessed RPU elastomer. Compared to original PET fibers, modified polyester (m‐PET) fibers construct strong interfacial interactions and physical interlocks with RPU matrix, leading to significantly enhanced mechanical properties. Molecular dynamics studies demonstrate that RPU/m‐PET composites have higher cohesive energy density and lower free volume, and significantly improved compatibility, which greatly promotes stress dissipation upon stretching. This study provides a feasible strategy for the high‐value recycling of waste polymer materials.

Radiosensitizers play a pivotal role in enhancing radiotherapy (RT). One of the challenges in RT is the limited accumulation of nanoradiosensitizers and the difficulty in activating antitumor immunity. Herein, a smart strategy was used to achieve in situaggregation of nanomanganese adjuvants (MnAuNP-C&B) to enhance RT-induced antitumor immunity. The aggregated MnAuNP-C&B system overcomes the shortcomings of small-sized nanoparticles that easily flow back into blood vessels and diffuse into surrounding tissues, and it also prolongs the retention time of nanomanganese within cancer cells and tumors. The MnAuNP-C&B system significantly enhances the radiosensitization effect in RT. Additionally, the pH-responsive disassembly of MnAuNP-C&B triggers the release of Mn2+, further promoting RT-induced activation of the STING pathway and eliciting robust antitumor immunity. Overall, our study presents a smart strategy wherein in situaggregation of nanomanganese effectively inhibits tumor growth through radiosensitization and the activation of antitumor immunity.

Oral health is the basis of human health, and almost everyone has been affected by oral diseases. Among them, endodontic disease is one of the most common oral diseases. Limited by the characteristics of oral biomaterials, clinical methods for endodontic disease treatment still face large challenges in terms of reliability and stability. The hydrogel is a kind of good biomaterial with an adjustable 3D network structure, excellent mechanical properties, and biocompatibility and is widely used in the basic and clinical research of endodontic disease. This Review discusses the recent advances in functional hydrogels for dental hard tissue and endodontic disease treatment. The emphasis is on the working principles and therapeutic effects of treating different diseases with functional hydrogels. Finally, the challenges and opportunities of hydrogels in oral clinical applications are discussed and proposed. Some viewpoints about the possible development direction of functional hydrogels for oral health in the future are also put forward. Through systematic analysis and conclusion of the recent advances in functional hydrogels for dental hard tissue and endodontic disease treatment, this Review may provide significant guidance and inspiration for oral disease and health in the future.

Fluoroacetate dehalogenases (FAcD), a homodimeric enzyme, catalyzes the conversion of fluoroacetic acid to glycolic acid (GoA). It has been proved that the enzyme has a half-of-the-site reactivity. Namely, its catalytic (C) subunit converts the first substrate to a covalent intermediate; then, the non-catalytic (NC) subunit binds a second substrate and promotes the conversion of the intermediate in the Csubunit into the final product. After the release of the product, the Csubunit becomes the NCsubunit, and the previous NCsubunit becomes the Csubunit. To elucidate the detailed mechanism behind this cooperative catalysis, we have conducted microsecond-scale MD simulations along the reaction pathway. The simulations indicate that the substrate in the NCsubunit induces W185 and Y141 adopting an open conformation in the Csubunit. The opening of W185 (C) facilitates the entry of catalytic water, enhancing the catalytic activity for product formation, while the opening of Y141 (C) creates an unfavorable environment for product binding, promoting its release. An interaction network analysis reveals that the substrate in the NCsubunit can induce conformational changes through a conserved water chain at the interface.

Background: The traditional scarf osteotomy (TSO) has limited ability to correct the first metatarsal pronation. A novel modification that we refer to as a “dovetailed notch scarf osteotomy” (DNSO) has been developed to enhance the ability to correct coronal plane pronation. The study aimed to observe and compare TSO to DNSO in the treatment of moderate to severe hallux valgus deformity.Methods: This retrospective study included 78 feet that had a TSO and 105 feet that had a DNSO. Minimum follow-up was 24 months. Weightbearing computed tomography (WBCT) and weightbearing anterior-posterior (AP) radiographs were taken preoperatively and at the last follow-up. We measured the intermetatarsal angle (IMA), hallux valgus angle, distal metatarsal articular surface angle on AP radiographs and first metatarsal coronal pronation angle (α angle), tibial sesamoid coronal grading, and first metatarsal length on WBCT. Clinical assessment was done using visual analog scale (VAS), American Orthopaedic Foot & Ankle Society (AOFAS) ankle-hindfoot scale, Foot and Ankle Ability Measure (FAAM), and the 36-Item Short Form Health Survey (SF-36). The occurrence of postoperative complications was also documented.Results: The DNSO group exhibited a significantly higher correction amount of α angle and IMA (14.3 ± 9.9 and 10.3 ± 4.6 degrees) than the TSO group (8.6 ± 5.9 and 5.4 ± 5.9 degrees) during the final follow-up assessment (P< .05).The DNSO group (10.1 [8.0-12.0] degrees and 4.8 [3.9-5.6] degrees) demonstrated significantly smaller α angle and IMA compared with the TSO group (4.8 [3.9-5.6] degrees and 9.5 [7.5-11.5] degrees) at 24 months postsurgery (P< .05). The postoperative FAAM activities of daily living and SF-36 physical functioning scores were significantly higher in the DNSO group (97.2 ± 3.3 and 95.7 ± 4.4 points) compared with the TSO group (92.3 ± 3.3 and 87.7 ± 8.7 points) (P< .05). Additionally, hallux varus occurred in 1 case in the DNSO group, whereas 4 cases were observed in the TSO group.Conclusion: Two osteotomy methods can effectively correct moderate to severe hallux valgus deformity. Compared with the TSO, the DNSO has stronger correction ability. The most crucial aspect lies in its controllability when correcting first metatarsal pronation and addressing IMA.Level of Evidence: Level III, retrospective comparative study.

This study aimed to determine the effectiveness of postoperative adjuvant lenvatinib + PD-1 blockade for patients with early-stage hepatocellular carcinoma (HCC) with microvascular invasion (MVI).

The operational efficiency error of a pumping station unit is affected by several factors, resulting in a large error between the theoretical and actual pumping efficiencies. To solve the problem of accurately simulating the operating efficiency of the pumping station unit, this study conducted research on efficiency simulation models of pumping based on data-driven from three aspects: ①model algorithm, ②feature input and ③response output. In this study, eight machine learning models were introduced to establish pumping unit efficiency simulation models and they were compared with the traditional polynomial regression models, and the models with better performance were selected. Then the experiment proposed using “upstream water level + downstream water level” (UWL + DWL) instead of the traditional “head” (H) as feature input to train the models, and discussed the effects on efficiency simulation accuracy with direct fitting efficiency and fitting power then calculate the efficiency through formula. An example analysis was carried out using the historical data of eight units at Pizhou Station and Suining 2nd Station on the East Line of the South-to-North Water Diversion Project. The experimental results show that ① among all methods, the models based on Gaussian Process Regression (GPR) has the most accurate in ERMS, EMA, R², and EMIindicators, R²is close to 0.92. ②After UWL + DWL was used to train the models, all the test set indicators of the GPR models improved, and the EMAindicator decreased from an average of 0.39–0.26 %. ③ The study found that the fitting efficiency directly is better than the calculation efficiency after fitting the power, but the R²of fitting the power is better than the efficiency, which may provide another idea for the optimal goal of economic operation of pumping stations. ④ On the whole, trainning the GPR models with UWL + DWL to simulate efficiency has the highest precision, for example the EMAand EMIindicators of No.4 unit in Pizhou station can be reduced from 16.49 % and 20.40–0.18 % and 1.55 % respectively. The research results have practical significance and can provide strong support for the economical operation of pump stations.

Recently, algorithm-hardware (HW) co-exploration for neural networks (NNs) has become the key to obtaining high-quality solutions. However, previous efforts for field-programmable gate arrays (FPGAs) focus on neural architecture search (NAS) while lacking HW architecture search (HAS), thus limiting the full potential of co-design. Although expanding the scope of HAS offers performance potential, the exponentially increased joint search space presents a formidable challenge. To address this, we propose a deep and efficient framework NAF, which jointly searches for Networks and Accelerators for FPGAs in a balanced co-search space. First, we adjust the NAS space and then introduce a block-level bitwidth search on the software side. Meanwhile, we design a HW-friendly quantization algorithm to facilitate HW efficiency and accuracy. Second, we design a dataflow-configurable HW unit with computation and memory access optimizations for quantized multiplication. Based on this, we incorporate critical heterogeneous multicore architecture exploration on the HW side. Third, to enable rapid HW feedback in the enlarged HAS space, we perform resource and performance modeling and design a fast HW generation algorithm based on the genetic algorithm. Specifically, we apply optimization techniques, like mapping space pruning, greedy bandwidth allocation, and coarse-grained search, to speed up this process. We validate NAF in edge and cloud scenarios. Experimental results show that NAF efficiently explores a significantly larger joint space and provides high-quality solutions. Compared with previous state-of-the-art co-design works, the searched convolutional neural network-accelerator pairs improve the throughput by $2.07\times \sim 7.10\times $ and energy efficiency by $1.41\times \sim 2.27\times $ under similar accuracy on the ImageNet dataset.

A long-standing topic in artificial intelligence is the effective recognition of patterns from noisy images. In this regard, the recent data-driven paradigm considers 1) improving the representation robustness by adding noisy samples in training phase (i.e., data augmentation) or 2) pre-processing the noisy image by learning to solve the inverse problem (i.e., image denoising). However, such methods generally exhibit inefficient process and unstable result, limiting their practical applications. In this paper, we explore a non-learning paradigm that aims to derive robust representation directly from noisy images, without the denoising as pre-processing. Here, the noise-robust representation is designed as Fractional-order Moments in Radon space (FMR), with also beneficial properties of orthogonality and rotation invariance. Unlike earlier integer-order methods, our work is a more generic design taking such classical methods as special cases, and the introduced fractional-order parameter offers time-frequency analysis capability that is not available in classical methods. Formally, both implicit and explicit paths for constructing the FMR are discussed in detail. Extensive simulation experiments and robust visual applications are provided to demonstrate the uniqueness and usefulness of our FMR, especially for noise robustness, rotation invariance, and time-frequency discriminability.

ABSTRACTWith the rapid advancements in technology, digital media have become widely integrated into people’s lives. In oriental countries, individuals frequently visit temples for fortune-telling and blessing-praying during the ritual process. A new interactive multimedia system based on computer vision and multi-sensing techniques for carrying out the traditional ritual process in the temple is proposed, which contributes to cultural preservation. The three major ritual steps, namely, ‘incense waving,’ ‘moon-block casting,’ and ‘fortune-telling poem drawing,’ were implemented by multimedia-related hardware and algorithms, and the system was exhibited for field testing by 405 participants. Interviews with experts and a questionnaire survey of the participants’ opinions were conducted to evaluate the effectiveness of the system using statistical software packages, yielding the following findings: (1) the proposed system can bring the participants to experience the ritual process effectively; (2) the proposed three major ritual steps not only attracts more participations but also promotes people’s learning of the ritual process; (3) the system attracts participants by integrating multimedia effects with good usability; and (4) the uses of computer vision and multi-sensing techniques bring the users new experiences of the fortune-telling application.

Enantioselective three-component difunctionalization of alkenes with boron reagents represents an attractive strategy for assembling three-dimensional chiral organoboron compounds. However, regio- and enantiocontrol comprise the pivot challenges in these transformations, which predominantly require the use of activated conjugated alkenes. Herein, by utilizing various carbonyl directing groups, including amides, sulfinamides, ketones, and esters, we succeed in realizing a nickel-catalyzed 1,2-borylalkynylation of unactivated alkenes to enable the simultaneous incorporation of a boron entity and an sp-fragment across the double bond. The products contain boryl, alkynyl, and carbonyl functional groups with orthogonal synthetic reactivities, offering three handles for further derivatization to access valuable intermediates. The utility of this ligand-enabled asymmetric protocol has been highlighted through the late-stage decoration of drug-relevant molecules.

Inorganic supramolecular compounds are the emergent class of infrared (IR) nonlinear optical (NLO) materials. However, the reported inorganic supramolecular IR NLO pnictides are still scarce. In this work, a new inorganic supramolecular IR NLO phosphide, EuSi7P10, has been synthesized using the metal salt flux method. The structure of EuSi7P10features an anionic host framework containing the oriented [Si7P16] dual-T2 supertetrahedra with the guest Eu2+cations filling in the intervals. Additionally, EuSi7P10exhibits strong phase-matched (PM) second-harmonic generation (SHG) (4.0 × AgGaS2), large birefringence (0.087 @2050 nm), and wide infrared transparency. This study highlights the potential of inorganic supramolecular pnictides for exploring high-performance IR NLO crystals.