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The complexity of forged turbine blade machining requirements brings challenges to point cloud registration in automated hole positioning. In this paper, a weighted point cloud registration method based on directional distance function is proposed, which converts various machining requirements into adaptive weight coefficients. In addition, a coarse registration method using the global bounding box information is proposed, which fuses the multi-feature parameters into the objective function, forming a mutual feedback mechanism between the point cloud segmentation and the coarse registration, and realizing the coarse registration under two-way high-quality point clouds. Combining the two methods, a hole positioning scheme of forged turbine blade based on robot arm is developed. In the test of two typical turbine blades, the maximum homogenization improvement of the blade body machining allowance reaches 26.9 %, and the maximum improvement of the qualified rate of the machining allowance reaches 11.6 %. The proportion exceeding the lower limit of the allowable machining allowance is reduced by 19.8 % at most, and the average optimization range of about 10 % is reached when dealing with unqualified blades, which is very close to the allowable machining allowance value. The average error of blade reference hole positioning is 0.420 μm, and the maximum error is 1.652 μm, which is two orders of magnitude lower than the allowable machining accuracy. The proposed method can provide reliable data support for automatic machining of robotic arm.

Integrating semiconductor nanowires with silicon platforms can combine the advantages of both materials to provide new functionality compared with each type of material. However, the direct localized growth of semiconductor nanowires on silicon platforms still remains a challenge. Herein, an atmospheric pressure microplasma jet, based on the integration of microplasma with the 3D printing technique, was applied for the synthesis of gold nanoparticles (AuNPs). The obtained AuNPs were then used as catalysts for the physical vapor deposition (PVD) growth of cadmium sulfide nanowires (CdS NWs). The results showed that AuNPs can be directly fabricated on the surface of silicon wafers in one step without chemical reducing agents. They also demonstrated good catalytic activity in the growth of CdS NWs via a typical vapor–liquid–solid (VLS) mechanism, where CdS NWs of straight sidewalls with a AuNP at their tips were formed. Complementary characterization reveals that CdS NWs possess crystalline structure, with the atomic ratio of Cd/S close to 1:1 stoichiometry. The energy band gap was estimated to be 2.5 eV from the photoluminescence spectrum. In addition, the influence of process parameters like the Au3+concentration and plasma processing time on the products was studied. Both increase of the Au3+concentration and plasma processing time led to the formation of high-density AuNPs and CdS NWs. With the rise of the Au3+concentration, the average diameter of the AuNPs gradually increased from 13.17 nm at 0.02 mM to 17.0 nm at 0.05 mM and finally 20.44 nm at 0.1 mM. This also led to the formation of CdS NWs of large diameters. This work offers a simple, direct, and green way for localized fabrication of metal catalysts on target substrates for the growth of NWs and has unprecedented potential in various fields like catalysis, surface modification, and additive manufacturing.

Studying frailty is crucial for enhancing the health and quality of life among older adults, refining healthcare delivery methods, and tackling the obstacles linked to an aging demographic. Approaches to frailty modeling often utilise simple analytic techniques rather than available advanced machine learning methods, which may be sub-optimal. There is no large-scale systematic review on applications of machine learning methods on frailty modeling. In this study we explore the use of machine learning methods to predict or classify frailty in older persons in routinely collected data. We reviewed 181 research articles, and categorised analytic methods into three categories: generalised linear models, survival models, and non-linear models. These methods have a moderate agreement with existing frailty scores and predictive validity for adverse outcomes. Limited evidence suggests that non-linear methods outperform generalised linear methods. The top-three predictor/input variables are specific diagnosis or groups of diagnoses, functional performance (e.g., ADLs), and impaired cognition. Mortality, hospital admissions and prolonged hospital stay are the mainly predicted outcomes. Most studies utilise classical machine learning methods with cross-sectional data. Longitudinal data collected by wearable sensors have been used for frailty modeling. We also discuss the opportunities to use more advanced machine learning methods with high dimensional longitudinal data for more personalised and accessible frailty tools.

The lattice strain is considered critical for determining the catalytic activity of metal catalysts such as Pt. However, the dynamic manipulation of Pt lattice strain under reaction conditions to directly examine its catalytic effect has not been achieved. In this study, we realized the reversible manipulation of Pt electrocatalytic properties under electrochemical conditions by tuning the Pt lattice strain with the temperature-controlled Martensite-Austenite phase transition of the shape memory NiTi alloy substrate. This enabled establishing a straightforward relationship between the strain-induced electronic structure transformation and electrocatalytic activity leaping. The binding strength between Pt and surface oxygen species was confirmed as the critical factor that dominated the hydrogen evolution and the oxygen reduction activities. In contrast, the oxygen evolution activity was barely affected by NiTi substrate phase transition.

Purpose: The High Energy Photon Source (HEPS) is designed to be one of the world’s brightest synchrotron light sources. In this paper, we provide an overview of the initial commissioning process of the HEPS storage ring, from the start of commissioning to the achievement of the first beam storage. We introduce the possible challenges that may arise during this critical phase of commissioning and detail the efforts made to achieve this important milestone in the HEPS storage ring commissioning. Methods: The commissioning process began with transportation through the high-energy transfer line BR (Booster-to-Ring), followed by beam injection and the first-turn transportation. This was followed by iterative trajectory correction and optimization, primarily utilizing a self-developed trajectory correction program based on the independently developed high-level application framework, Pyapas. Through iterative manual adjustment of the variable parameters, we successfully advanced the beam further. Subsequently, the RF cavities and sextupole magnets were gradually powered on. Through multi-turn trajectory correction and parameters optimization in a larger variable space, beam storage in the HEPS storage ring was achieved. Results: On July 23, the first beam injection and the first-turn beam transportation were achieved within a few hours of starting the storage ring commissioning. By July 29, the beam circulation exceeded 10 turns, and by August 4, the beam circulated in the storage ring more than 1000 turns. On August 6, the first beam storage in the HEPS storage ring was achieved. Furthermore, the storage ring beam current reached approximately 60 microamperes on the same day. Conclusion: The successful beam storage is a significant milestone in the HEPS storage ring commissioning and a solid step toward the completion of the HEPS construction. It is hoped that the process to reach this achievement, as presented in this paper, will provide a useful reference for the commissioning of similar facilities both domestically and internationally.

High-sensitivity magnetic field measurements are crucial in modern geoscience exploration. This study introduces an innovative free precession Bell-Bloom atomic magnetometer (FPBBAM) that exhibits a wide range of applications in geomagnetic monitoring and seismic disaster early warning due to its outstanding measurement sensitivity and low environmental requirements. The article details this magnetometer’s design principles, performance characteristics, and practical applications. It also explores how its internal mechanisms, particularly the behavior of the alkali-metal atoms (AMAs), contribute to geoscience and remote sensing applications. Extensive testing revealed that the magnetometer’s noise tolerance in a geomagnetic background exceeds $300\mathrm {pT/}(\mathrm {Hz})^{1/2}$ . However, the noise sensitivity surpasses $70~\mathrm {pT/}(\mathrm {Hz})^{1/2}$ in a shielded indoor environment. In a magnetic measurement environment with a background noise of 350 nT, the magnetic field measurement results’ root mean square error (RMSE) is better than 77.6 nT. Moreover, the magnetometer demonstrated the ability to operate stably over an extended period under challenging working conditions. This study validates the device’s real-world geoscience applications by monitoring urban spatial magnetic fields.

Calcium nitrate (Ca(NO3)2) waste slag is accumulated during the production process of nitrophosphate fertilizer, which would cause harm to the environment and human. To realize high value-added and non-toxic conversion of Ca(NO3)2waste slag, we propose a facile precipitation-transformation method to synthesize aragonite nanowhiskers with high aspect ratio in the absence of the organic additives. In this process, vaterite is preferentially synthesized from Ca(NO3)2waste slag and ammonium carbonate, and then transformed to the aragonite nanowhiskers as slow-released calcium and carbonate sources in the presence of Mg2 +. The synthesized aragonite nanowhiskers under optimizing conditions exhibit the diameter of 300 nm and the aspect ratio of about 50, which was higher than that reported in most literatures. The aragonite nanowhiskers are applied to the uranium extraction, and presented qmaxof 476.19 mg/g in the simulated uranium solution. In natural seawater, it shows high Eof 86.54 % and an excellent selectivity. The aragonite nanowhiskers featuring high aspect ratio could provide more adsorption sites for uranium due to the unique arrangement of Ca2+and CO32−in aragonite crystal. This work gives a simple recovery route of Ca(NO3)2waste slag to synthesize aragonite nanowhiskers, and offers an ideal adsorbent candidate for actual uranium extraction.

Mantle properties and metasomatic processes, together with interlayer interactions, in orogenic belts are still under debate. Post-collisional magmatic rocks could provide secondhand constraints on these issues. We have conducted an integrated geochronological, geochemical and isotopic study on post-collisional Zhanwa dolerites, the Dashui granodiorite–monzonite–monzogabbro complex and the Jiuzigou pyroxenite–syenite complex at West Qinling. The Zhanwa dolerites, being tholeiitic with flat trace element patterns, were mixtures of melts from asthenospheric and oceanic slab-fluid-metasomatized lithospheric mantle. The Dashui monzonite–monzogabbro suite is ultrapotassic with strongly right-inclined trace element patterns and evolved isotopic composition with (87Sr/86Sr)i = 0.7066–0.7068, ε Nd(t) = −4.56 to −4.35, ε Hf(t) = −3.14 to −2.35 and (206Pb/204Pb)i = 17.99–18.12. It was derived from melting of a mantle source metasomatized by continent-crustal materials. In contrast, the Dashui granodiorites are shoshonitic–calc-alkaline with more evolved isotopic compositions with (87Sr/86Sr)i, ε Nd(t), ε Hf(t), and (206Pb/204Pb)i of 0.7074–0.7091, −7.56 to −5.51, −5.61 to −3.53 and 18.18–18.46, respectively. They represent hybrids of mantle-derived K-rich and crustal-derived felsic melts. The Jiuzigou complex has extremely high trace element contents. Its mantle source was metasomatized by both oceanic crustal fluids and sedimentary melts. Generation of these rocks (c. 235–223 Ma) not only recorded multiple mantle metasomatism, but also captured collective asthenospheric and lithospheric magmatic responses in a post-collisional setting. [ABSTRACT FROM AUTHOR]

Polymer aerogels, with their porous and lightweight features, excel in applications such as energy storage, absorption, and thermal insulation, making them a sought-after new material. However, the covalent cross-linking networks of current polymer aerogels result in unsustainable manufacturing and processing practices, persistently depleting our finite natural resources and causing significant global environmental impacts. Herein, we have constructed a high-performance dynamic covalent cross-linking aerogel network using biobased materials, with its structure and green sustainability akin to those of plants in nature. Abundant reversible cross-linking points endow the aerogel with ultrafast degradation capabilities, enabling allow for closed-loop chemical monomer recovery and reprocessing. Furthermore, utilizing the highly active reversible network, net-zero emission material reuse and reprocessing can be achieved. Additionally, the controlled dynamic aerogel network features a multilevel roughness nanostructured surface similar to lotus leaf and a biomimetic pore structure, contributing to significant anisotropy. The distinctive structure and composition endow the dynamic aerogel with high compressive strength (2.2 MPa) vertically, low thermal conductivity (0.0257 W/(m·K)) horizontally, and outstanding fire resistance (LOI is as high as 36%). Notably, the aerogel demonstrates the highest hydrophobicity among polyimine materials, with a contact angle of 154°. Furthermore, those dynamic aerogels have excellent performance in a variety of potential applications such as oil–water separation, directional transport, and phase change energy storage, and it is anticipated that these applications will greatly benefit from systematic upgrades in recyclability and reprocessing.

As one of the key components of supercapacitors (SCs), separators can directly affect the energy density, output power, and safety stability of SCs. However, it is still a challenge to prepare separators that simultaneously combine large pore size, ultrathin thickness, and excellent mechanical properties. Herein, a 5 μm ultrathin separator with a three-dimensional (3D) porous nanofiber skeleton dotted by fumed Al2O3nanoparticles has been developed using biaxial stretching. The unique structure of the 3D porous nanofiber skeleton ensures a mechanical strength up to 40 MPa, while the fumed Al2O3nanoparticles dotted on the 3D skeleton and the incorporation of the annealing process achieve a large average pore size of 130.8 nm, thus harmoniously resolving the contradiction between strength and large average pore size for ultrathin composite separators. The ultrathin thickness greatly shortens the ion transmission channel and effectively reduces ion transmission resistance. Moreover, the fumed Al2O3nanoparticles exposed on the surface of the 3D porous nanofiber skeleton enhance the wettability of the electrolyte as well as the thermal stability of the separator, achieving a low bulk resistance of 0.3 Ω and zero shrinkage at 130 °C. Due to the unique structure, UAPFS7 offers a better overall performance compared to commercial separators. These findings indicate that the developed separators exhibit excellent comprehensive performance and have the potential to promote the large-scale application of next-generation energy storage devices.

For the aim of achieving the carbon-free energy scenario, green hydrogen (H2) with non-CO2emission and high energy density is regarded as a potential alternative to traditional fossil fuels. Over the last decades, significant breakthroughs have been realized on the alkaline hydrogen evolution reaction (HER), which is a fundamental advancement and efficient process to generate high-purity H2in the laboratory. Based on this, the development of the practical industry-oriented anion exchange membrane water electrolyzer (AEMWE) is on the rise, showing competitiveness with the incumbent megawatt-scale H2production technologies. Still, great challenges lie in exploring the electrocatalysts with remarkable activity and stability for alkaline HER, as well as bridging the gap of performance difference between the three-electrode cell and AEMWE devices. In this perspective, we systematically discuss the in-depth mechanisms for activating alkaline HER electrocatalysts, including electronic modification, defect construction, morphology control, synergistic function, field effect, etc. In addition, the current status of AEMWE is reviewed, and the underlying bottlenecks that impede the application of HER electrocatalysts in AEMWE are summarized. Finally, we share our thoughts regarding the future development directions of electrocatalysts toward both alkaline HER and AEMWE, in the hope of advancing the commercialization of water electrolysis technology for green H2production.

Nitrogen (N) and phosphorous (P) fertilizer management is an effective approach to improve grasslands and feed quality following grassland degradation; however, it is still unclear how N and P fertilizer management affects soil nematodes and how this leads to changes in vegetation production. This study investigated the responses of vegetation and soil nematode communities to a 9-year fertilization (N and P) experiment in an alpine grassland in the Qinghai–Tibetan Plateau, China. The results indicated that N fertilizer significantly increased the abundance of plant parasites, whereas P fertilizer had a notably positive effect on fungivorous nematodes compared to nitrogen fertilization. The abundance of fungivorous nematodes increased following P fertilization, but the increase was not statistically significant. N fertilizer significantly reduced bacterivorous carbon biomass and the metabolic footprint of bacterivorous nematodes, while the suppressive effect of P fertilizer and N + P fertilizer on the carbon flux of bacterial channels was significantly greater than that of N fertilization alone. The functional metabolic footprints of N and P fertilizers were significantly lower than those of the unfertilized plot (CK). N fertilizer is detrimental to the increase in vegetation production following grazing prohibition, as it is mediated by the increase of plant parasite nematodes. P fertilizer, by influencing the abundance of bacterivorous and fungivorous nematodes, benefits the production of grassland ecosystems. This is because P fertilizer inhibits carbon from entering the soil food web, thereby maintaining the carbon content in above-ground biomass. Therefore, a certain level of P addition positively contributes to the recovery of above-ground productivity.

Exceptionally preserved complex burrows are described from the lower Silurian of South China, and they are identified as Asterosoma ludwigaeSchlirf, 2000, which is characterized by straight or curved bulbs that bud from an axial, vertical or horizontal, cylindrical burrow following a dichotomous or fan-like pattern. Striae and concentric lamination of burrow fills indicate that the special mining behaviour of the tracemaker reflects appendages that imprinted the burrow wall during their activities, implying that polychaetes or holothuroids could be strong candidates for the tracemaker of A. ludwigae. Abundant organic-matter-rich peloids and muddy matrix in burrow fills may consist of tracemaker's secretions or excrement. Abundant extracellular polymeric substance and clay minerals in filling sediment indicate the possible presence of microbes, implying that Asterosoma is not a simple fodinichnion, but rather created a beneficial symbiosis between tracemakers and microbes in burrows. Phanerozoic spatiotemporal distributions indicate that Asterosoma was distributed in broad habitats from deep sea to delta, but showed a strong preference for the shallow sea. Compilation of new ichnofossils and previously reported ichnotaxa from South China reveals that ichnospecies richness, burrow size and complexity all increased significantly during the late Aeronian, indicating a full recovery after the late Ordovician mass extinction. [ABSTRACT FROM AUTHOR]

Soft network metamaterials are widely used in fields such as flexible electronics, tissue engineering, and biomedicine due to their superior properties including low density, high stretchability, and high breathability. However, the prediction and customization of the nonlinear mechanical behavior of soft network metamaterials remain a challenging problem. In this study, a family of hydrogel-based network metamaterials with biological tissue-like mechanical properties are developed based on a machine learning-driven optimization design method. Numerical and experimental results explain the relationship between the mechanical properties of the designed metamaterials and their microstructural features and stretching ratios. The results indicate that the hydrogel-based network metamaterials exhibit J-shaped stress-deformation (σ–λ) behavior similar to biological tissues. This phenomenon arises from the transition of the deformation mode of metamaterials from bending-dominated to stretching-dominated as the stretching ratio increases. Based on the proposed design scheme, the Poisson’s ratio of metamaterials can be adjusted within a remarkably wide range of −1.06 to 1.34. Furthermore, through optimizing the design parameters of the metamaterial, the customization of network metamaterials with biological tissue-like zero Poisson’s ratio behavior and stress response is achieved. The potential applications of hydrogel-based network metamaterials are demonstrated through artificial skin and LED integrated device. This research offers novel insights into predicting, designing, and fabricating the mechanical behavior of soft network metamaterials.

Charge-coupled device (CCD) sensors are the primary imaging sensors in scientific astronomical cameras, known for their exceptional channel uniformity. However, the unavoidable baseline drift during alternating current (ac) coupling leads to inconsistencies among pixels in the row direction, especially during high-speed readouts. Standard corrections are insufficient, posing challenges to astronomical observations. The digital correlated double sampling (DCDS) technology enables digital signal processing (DSP) for CCD readout. A real-time digital baseline restoration method was developed and implemented in a field-programmable gate array (FPGA). This method overcomes traditional limitations and can eliminate baseline drift error effectively. Furthermore, the performance of this method in terms of readout noise, channel gain, and nonlinearity was tested, confirming its potential for compensating for baseline drift error in scientific applications.

A novel method for improving the quality of arbitrary periodic waveforms is proposed via injection-locking of an optoelectronic oscillator (OEO). A low-quality arbitrary periodic signal with its frequency spectrum discretized by its repetition frequency is injected into the OEO system. When its repetition frequency equals an integral multiple of the free spectral range (FSR) of the oscillation loop, the injection-pulling mechanism triggers mode-locking in the frequency domain. Consequently, each oscillation mode synchronizes with the relative discretized frequency component of the injected periodic signal, and the gain accumulation for each frequency mode is improved through the pulling mechanism of the injection-locking process. In a proof-of-concept experiment, this approach successfully generates linearly frequency-modulated waveforms (LFMWs) with different bandwidths, a three-level frequency hopping (FH) microwave waveform covering an FH range of 400 MHz, and a 100-Mb/s phase-coded microwave waveform, respectively. This utilization of the injection-locking mechanism within a low phase noise OEO significantly enhances the spectral purity of the produced arbitrary periodic microwave waveforms.

This paper addresses the challenge of distributed adaptive platooning control of connected vehicles with randomly switching topologies. A linearized longitudinal vehicle platoon dynamic model is investigated, which simultaneously considers the bounded external disturbances and leader control input. By representing the switching topologies model in terms of a continuous-time Markov process, a distributed adaptive control scheme is developed such that follower vehicles can synchronize their velocities and accelerations with the leader while preserving the intended distance. The outstanding feature of the proposed distributed adaptive control scheme is that it avoids acquiring global information associated with communication topology. Besides, the design procedure is scalable since the control gain can be derived by only solving a linear matrix inequality offline related to the vehicle dynamics parameters, which means that the method remains applicable when the platoon size changes. Finally, the efficacy of theoretical findings is substantiated through the utilization of simulations.

For the mechanical DC vacuum circuit breaker, the velocity characteristic of the actuating mechanism will directly influence the breaking capacity and reliability of the circuit breaker. In this paper, a 10 kV/5 kA DC vacuum circuit breaker is taken as the research object to establish a post-arc sheath development model, and the post-arc breakdown endurance performance under the field-circuitry coupling effect of the electromechanical system is obtained. A non-intrusive polynomial chaos expansion model is established according to the relationship between the velocity of the actuating mechanism and the breakdown endurance performance, and the probability distribution of breakdown under the uncertainty of the velocity of the mechanism in the electromechanical coupling system is analyzed.

The main factors affecting the fault interruption capability of an active DC vacuum circuit breaker are the structural parameters of the contact surface of the vacuum interrupter, the gap distance at current zero point and the parameters of the commutation circuitry. This paper takes 10 kV/5 kA DC vacuum circuit breaker as the research object, establishes the sheath development model under the coupling of multiple fields and circuitry, and parameterizes the post-arc dielectric recovery strength under the influence of electromechanical parameters. Moreover, the criterion for post-arc re-breakdown is proposed and the limit of breaking current threshold is obtained under the influence of multiple factors.

AbstractObjectivesTo systematically evaluate the effects of telehealth interventions on the caregiver burden and mental health of caregivers for people with dementia (PWD).MethodRelevant randomized controlled trials (RCTs) of telehealth interventions on caregivers were extracted from nine electronic databases (PubMed, The Cochrane Library, Web of Science, Embase, CINAHL, SinoMed, CNKI, WanFang, and VIP). The retrieval time was from inception to 26 July 2023.ResultsTwenty-two articles with 2132 subjects were included in the final analysis. The meta-analysis demonstrated that telehealth interventions exerted a significant effect in reducing caregiver burden (SMD: −0.14, 95 % CI: −0.25, −0.02, p = 0.02), depression (SMD = −0.17; 95%CI: −0.27, −0.07, p < 0.001) and stress (SMD = −0.20, 95%CI: −0.37, −0.04, p = 0.01). However, no statistically significant effect was observed on anxiety (SMD = −0.12, 95%CI: −0.27, 0.03, p = 0.12). Moreover, subgroup analysis showed that tailored interventions were associated with more evident reductions in depression (SMD = −0.26; 95%CI: −0.40, −0.13, p < 0.001) than standardized interventions (SMD = −0.08; 95%CI: −0.22, 0.06, p = 0.25). In addition, telehealth was effective in relieving depression in Internet-based (SMD = −0.17, 95%CI: −0.30, −0.03, p = 0.01) and Telephone-based group (SMD = −0.18, 95%CI: −0.34, −0.02, p = 0.03), while there was no significant difference in the Internet and Telephone-based group (SMD = −0.18, 95%CI: −0.54, 0.18, p = 0.32).ConclusionTelehealth could effectively reduce the burden and relieve the depression and stress of caregivers of PWD, while its effect on anxiety requires further research. Overall, telehealth has potential benefits in dementia care.

A double network organohydrogel based on DMSO/H2O dual solvent was constructed, which can improve the water retention performance, restrain the anode/cathode side reaction of the aqueous zinc ion batteries (AZIBs), and improve the cycle stability.

Graphical Abstract:

Telomere dysfunction is intricately linked to the aging process and stands out as a prominent cancer hallmark. Here we demonstrate that telomerase activity is differentially regulated in cancer and normal cells depending on the expression status of fructose-1,6-bisphosphatase 1 (FBP1). In FBP1-expressing cells, FBP1 directly interacts with and dephosphorylates telomerase reverse transcriptase (TERT) at Ser227. Dephosphorylated TERT fails to translocate into the nucleus, leading to the inhibition of telomerase activity, reduction in telomere lengths, enhanced senescence and suppressed tumor cell proliferation and growth in mice. Lipid nanoparticle-mediated delivery of FBP1mRNA inhibits liver tumor growth. Additionally, FBP1 expression levels inversely correlate with TERT pSer227 levels in renal and hepatocellular carcinoma specimens and with poor prognosis of the patients. These findings demonstrate that FBP1 governs cell immortality through its protein phosphatase activity and uncover a unique telomerase regulation in tumor cells attributed to the downregulation or deficiency of FBP1 expression.

Developing highly efficient electrocatalysts for organic electrosynthesis such as the nucleophile oxidation reaction (NOR) is of great significance for renewable energy utilization. However, the kinetic understanding of nickel-based catalysts in NOR still needs to be clarified. Herein, we synthesized several metal ions-doped Ni(OH)2and studied the mechanism of metal ions doping in enhancing the electrocatalytic NOR performance. Ethanol oxidation reaction (EOR) was used as a model reaction and a series of electrochemical measurements for the reaction order, electron transfer process, and metal redox kinetics of Ni(II)/Ni(III) were combined with the electronic structural characterization and theoretical calculation. The rate-determining step (RDS) of EOR was confirmed as the oxidation process of Ni(II)–OH to Ni(III)–O with one electron transfer; meanwhile, the kinetic mechanism of metal ions doping was unveiled: (1) decreasing the oxidation potential of Ni(II)–OH to Ni(III)–O was achieved by enhancing the interaction between the adjacent Ni and O and lowering the O–H bond dissociation energy barrier; (2) accelerating the cycling kinetics of Ni(II)/Ni(III) redox by bimetallic synergy improved the electronic transfer ability of materials. Our study is valuable in deeply understanding the kinetic mechanism of the synergetic electrochemical-chemically involved reaction processes in NOR.

Negative ions are crucial in various scientific and technological fields, such as antimatter research, interstellar chemistry, accelerator-based radionuclide dating, fusion injection for controlled nuclear fusion, and neurological disease treatment. Thus, producing a large number of negative ions is highly important. Herein, we present the first theoretical evidence of a large fraction of negative-ion formation during the grazing scattering of carbon atoms from a CsI(100) surface. The conversion efficiency of the negative ions reaches ≥86% within a projectile energy range of Ep∈ [5,10] keV. These remarkable results are attributed to large anion and cation polarizations, which lead to an evidently large Mott–Littleton polarization interaction. This interaction lowers the energy defect of valence band electron capture to below 1.8 eV near the surface anion sites, drastically increasing the electron-capture probability. The destruction of negative ions occurs owing to affinity electron detachment via the Coulomb barrier tunneling to the vacuum level during the interactions with surface anion sites along their trajectories. Our results indicate that this collision system can be used to design next-generation negative carbon ion sources for studying the isotope shift of electron affinity and provide insights into electron correlation effects and molecular-level irradiation pathological studies, especially for promoting nerve tissue repair and regeneration through Ep∈ [5,10] keV negative carbon ion irradiation.

Live video streaming has come to dominate today’s Internet traffic. Content Delivery Network (CDN) providers, responsible for hosting outsourced live streaming services, are now striving to ensure an enhanced quality of experience (QoE) to meet the ever-increasing user expectations. Existing congestion control (CC) schemes in the kernel, however, suffer from unsatisfactory performance for live video delivery due to disparities in traffic characteristics and differentiated optimization goals between generic traffic and live video traffic. In this paper, we propose XCC, a streaming context-aware CC approach that helps achieve better QoE for the live streaming services from CDN provider. The core of XCC is to adaptively coordinate the transmission strategy and frame rate through a cross-layer feedback framework, responding to the fluctuating traffic dynamics and network conditions in the short term. Further, XCC matches the long-term traffic characteristics (i.e., two-stage delivery mode) by employing a task-specific state transition mechanism as the underlying TCP. XCC has been implemented in the Linux kernel’s TCP stack and media engine and has been fully deployed in Alibaba Cloud’s production service. Evaluation in experimental environments and A/B testing serving tens of millions of sessions demonstrate that XCC is competitive in streaming delay against the most prevalent TCP in today’s Operating Systems, while reducing startup delay by 9.9%, stall time by 36.4%, and stall frequency by 42.5% on average in deployment.

Phase is of great significance in many fields, which can be used to carry important information. In a fiber optic sensing system, phase is crucial for extracting environment information along fiber link. Therefore, the accuracy of the phase resolving result significantly influences the performance of a fiber optic sensing system. There are some widely used phase demodulation method like 3 × 3 coupler demodulation method, in-phase quadrature (IQ) demodulation method and phase generated carrier (PGC) demodulation method, which usually have an imbalance error or distortion problem. In this paper, a novel phase resolving method is proposed and used in an interferometric fiber optic sensing system. Comparison between proposed method and commercial IQ demodulator is carried out. The proposed method is proved to have a better orthogonality performance. Furthermore, the proposed method has less bandwidth limits compared with a digital IQ demodulation method, giving it a broader application.

Demand-responsive transit (DRT) with smartphone-based applications is emerging as a flexible and sustainable mobility service, transforming urban transportation. Nevertheless, to satisfy the real-time and inconsistent demand, it is becoming increasingly important to capture the decision-making psychology of order cancellations. In this study, a two-phase optimization framework is presented in response to real-time disruptions, including order cancellations and the insertion of new real-time passengers. In contrast to random real-time demand, this paper is more concerned about the impacts of the feedback information on order cancellations. Bounded rationality is incorporated into the model to discuss the decision-making process of cancellation behaviors. With regard to the soft window, a compensation strategy is proposed to promote the profit while encouraging passengers for a long-term use. Additionally, solution algorithm based on variable neighborhood search (VNS) and rolling horizon is constructed to approach the Pareto solutions set. To testify the validity of the proposed algorithm, small-scale experiments in simplified Sioux Falls network are investigated for multiple runs. Meanwhile, a real-world case study in Beijing is explored to evaluate the system performance considering real-time disruptions. The results indicate that the dynamic DRT service can substantially improve the system profit but increase the penalty cost. The profit presents a significant improvement to 940 (renminbi) RMB as a result of the insert of real-time passengers. This study, therefore, not only provides a deeper insight into the analysis of passenger cancellation behavior but also contributes to construct a more flexible DRT service.

In this study, a series of dopant-free, low-cost hole-transporting materials (HTMs) based on triphenylamine-functionalized azadipyrromethene dyes 1–3(TPA-ADPs 1–3) were designed and synthesized. The properties of these new HTMs were investigated by optical spectroscopy, cyclic voltammetry, thermogravimetric analysis, differential scanning calorimetric, atomic force microscopy, and X-ray diffraction, as well as theoretical calculations. The results indicated that the TPA-ADPs 1–3presented well-matched energy levels with perovskite, higher hole mobility, as well as more effective defect passivation at the perovskite/HTM interface by the coordination interaction between the ADP moiety and the undercoordinated Pb2+. The n-i-p perovskite solar cells (PSCs) employing HTMs 1–3as well as doped Spiro-OMeTAD were fabricated and characterized. The TPA-ADP 1-based PSCs exhibited the best performance with a champion power conversion efficiency (PCE) of 22.13% and an fill factor of 0.81, which was superior to that of the devices based on the doped Spiro-OMeTAD. Long-term device performance studies indicated that the TPA-ADP 1-based PSCs maintained 80% of the initial PCE after 1800 h of storage in the ambient condition of 40–60% RH, which was also higher than the stability of doped Spiro-OMeTAD-based devices under the same conditions.

Climate warming has caused a widespread increase in extreme fire weather, making forest fires longer-lived and larger1–3. The average forest fire size in Canada, the USA and Australia has doubled or even tripled in recent decades4,5. In return, forest fires feed back to climate by modulating land–atmospheric carbon, nitrogen, aerosol, energy and water fluxes6–8. However, the surface climate impacts of increasingly large fires and their implications for land management remain to be established. Here we use satellite observations to show that in temperate and boreal forests in the Northern Hemisphere, fire size persistently amplified decade-long postfire land surface warming in summer per unit burnt area. Both warming and its amplification with fire size were found to diminish with an increasing abundance of broadleaf trees, consistent with their lower fire vulnerability compared with coniferous species9,10. Fire-size-enhanced warming may affect the success and composition of postfire stand regeneration11,12as well as permafrost degradation13, presenting previously overlooked, additional feedback effects to future climate and fire dynamics. Given the projected increase in fire size in northern forests14,15, climate-smart forestry should aim to mitigate the climate risks of large fires, possibly by increasing the share of broadleaf trees, where appropriate, and avoiding active pyrophytes.

Icing, a common natural phenomenon, always originates from a molecule. Molecular simulation is crucial for understanding the relevant process but still faces a great challenge in obtaining a uniform and accurate description of ice and liquid water with limited model parameters. Here, we propose a series–parallel machine learning (ML) approach consisting of a classification back-propagation neural network (BPNN), parallel regression BPNNs, and a genetic algorithm to establish conventional TIP5P-BG and temperature-dependent TIP5P-BGT models. The established water models exhibit a comprehensive balance among the crucial physical properties (melting point, density, vaporization enthalpy, self-diffusion coefficient, and viscosity) with mean absolute percentage errors of 2.65 and 2.40%, respectively, and excellent predictive performance on the related properties of liquid water. For ice, the simulation results on the critical nucleus size and growth rate are in good accordance with experiments. This work offers a powerful molecular model for phase transition and icing in nanoconfinement and a construction strategy for a complex molecular model in the extreme case.

Peritoneal metastasis is one of the most common risk factors contributing to the poor prognosis of gastric cancer. We previously reported that extracellular vesicles from gastric cancer cells could facilitate peritoneal metastasis. However, their impact on gastric cancer-induced peritoneal metastasis under hypoxic conditions remains unclear. This study aims to elucidate how hypoxia-resistant gastric cancer cell-derived extracellular vesicles affect the peritoneal metastasis of normoxic gastric cancer cells. Proteomic analysis revealed elevated levels of Caveolin1 and Laminin β2 in hypoxia-resistant gastric cancer cells and their corresponding extracellular vesicles. Importantly, Caveolin1 was found to play a central role in mediating Laminin β2 sorting into extracellular vesicles derived from hypoxia-resistant gastric cancer cells, and subsequently, extracellular vesicle-associated Laminin β2 promoted peritoneal metastasis in normoxic gastric cancer cells by activating the AKT pathway. Further investigation confirmed that Caveolin1 activation by Rho-related Coiled-coil kinase 1-mediated phosphorylation of Y14 residue is a key factor facilitating Laminin β2 sorting into extracellular vesicles. Moreover, Y14 phosphorylated- Caveolin1 enhanced Laminin β2 sorting by activating Rab11. Finally, our study demonstrated that a combined assessment of plasma extracellular vesicle-associated Caveolin1 and extracellular vesicle-associated Laminin β2 could provide an accurate predictive tool for peritoneal metastasis occurrence in gastric cancer.

Accurately distinguishing tumor cells from normal cells is a key issue in tumor diagnosis, evaluation, and treatment. Fluorescence-based immunohistochemistry as the standard method faces the inherent challenges of the heterogeneity of tumor cells and the lack of big data analysis of probing images. Here, we have demonstrated a machine learning-driven imaging method for rapid pathological diagnosis of five types of cancers (breast, colon, liver, lung, and stomach) using a perovskite nanocrystal probe. After conducting the bioanalysis of survivin expression in five different cancers, high-efficiency perovskite nanocrystal probes modified with the survivin antibody can recognize the cancer tissue section at the single cell level. The tumor to normal (T/N) ratio is 10.3-fold higher than that of a conventional fluorescent probe, which can successfully differentiate between tumors and adjacent normal tissues within 10 min. The features of the fluorescence intensity and pathological texture morphology have been extracted and analyzed from 1000 fluorescence images by machine learning. The final integrated decision model makes the area under the receiver operating characteristic curve (area under the curve) value of machine learning classification of breast, colon, liver, lung, and stomach above 90% while predicting the tumor organ of 92% of positive patients. This method demonstrates a high T/N ratio probe in the precise diagnosis of multiple cancers, which will be good for improving the accuracy of surgical resection and reducing cancer mortality.

The aggregation behavior of two amphiphilic hyperbranched copolymers of poly[oligo(ethylene glycol) methacrylate-co-lauryl methacrylate] (H-[P(OEGMA-co-LMA)]) at the air/water interface was investigated by using the Langmuir film balance technique and atomic force microscopy (AFM). At the air/water interface, H-[P(OEGMA-co-LMA)] copolymers spontaneously form the ultrafine network-like monolayer structures of micelles; each micelle consists of a tiny hydrophobic core of one or two carbon backbones and lauryl side groups and a short hydrophilic shell of oligo(ethylene glycol) (OEG) side groups, and the micellar cores are connected by the branching agent ethylene glycol dimethacrylate (EGDMA). These ultrafine micellar structures are successfully revealed by our relative aggregation number method presented in this work, which is based on our previous relative mass method and methylene number method. The surface pressure–molecular area isotherms of POEGMA29%-PLMA71% (weight percent) and POEGMA69%-PLMA31% are condensed and expanded, respectively, because the density/number of OEG side groups in the former shells is smaller than that in the latter case. Upon monolayer compression, the isotherms of the former are classified into regions I–IV, whereas those of the latter are classified into regions II and III based on their different variation trends of surface pressure. Subphase pH has little influence on the isotherms of the two copolymers because the stretching degrees of hydrophilic OEG side groups in the shells are probably limited by the connected cores, which is different from the large effects in our previous block copolymers containing POEGMA or poly[oligo(ethylene glycol) acrylate] blocks. Under neutral and alkaline conditions, in region III, the mean molecular area (mmA) values of the isotherms of the two copolymers at 20 °C are smaller than those at 10 °C due to the collapse of the OEG side groups above 15 °C. Furthermore, the isotherms of POEGMA69%-PLMA31% move to larger mmAvalues at 30 °C due to the increased thermal mobility and stretching degrees of more OEG side groups.

Compressive sensing theory, capturing fewer data points without compromising essential information, holds significant promise for high-sampling-rate applications, particularly in wind turbine monitoring. The primary strategy within current compressive sensing fault diagnosis involves designing a dictionary closely aligned with fault characteristics and utilizing atomic decomposition to match feature waveforms. However, these methods exhibit diminished performance in scenarios where fault features are weak, and noise interference is pronounced. We exploit a novel fault diagnosis method within the compressive sensing framework to address this challenge. In this approach, noise-robust statistics, specifically the cyclic spectrum, are estimated using the modified strip spectral correlation analysis (SSCA) algorithm. The crucial demodulation step in the SSCA algorithm entails spectral reconstruction by solving a sparse representation optimization problem. The computational complexity of this method is comparable to the original algorithm, while retaining its noise immunity performance. Through numerical and engineering data analysis, it is demonstrated that similar feature detection results can be attained in compressed samples compared with conventional Nyquist sampling.

This work presents a novel high-performance and high-safe magnesium-sodium hybrid ion batteries (MSHBs) system that has a large potential for future energy storage applications.

Decorating defective NiFe LDH nanosheets with single-atom Ru leads to strong metal-support interactions (SMSI), resulting in asymmetric redox chemistry, improving the catalytic activity and stability for water oxidation.

Covalent organic frameworks (COFs) linked by imine bonds have attracted significant attention, primarily due to the accessibility of monomers and the facile synthesis of highly crystalline materials. However, the reversible nature of imine bonds raises stability concerns for these materials in practical applications. Therefore, there is a pressing need for the development of imine-linked COFs that can combine high crystallinity, good stability, and functionality. Here, by adopting a two-in-one molecular design strategy, we designed an organic building block containing both acetal and amino connecting groups, featuring a pyridine functional site, and with side chains consisting of alkoxyl groups. Following a facile synthesis, these design elements were combined in a confined channel (0.85 nm), leading to two-dimensional (2D) COFs (COF-LIFM4 and COF-LIFM5) with good crystallinity and stability in both strong acid and base conditions. Moreover, COF-LIFM5 exhibited favorable CO2affinity and appropriate hydrophobicity, making it a promising modifier for improving mass transfer in the CO2reduction reaction (CO2RR). As a result, Cu/COF-LIFM5 showcased an enhanced C2+product selectivity in CO2RR, with a Faradaic efficiency exceeding 80% at a current density of 500 mA cm–2in a flow cell.

The recent surge of interest in polaritons has prompted fundamental questions about the role of dark states in strong light-matter coupling phenomena. Here, we systematically vary the relative number of dark states by controlling the number of stacked CdSe nanoplatelets confined in a Fabry–Pérot cavity. We find the emission spectrum to change significantly with an increasing number of nanoplatelets, with a gradual shift of the dominant emission intensity from the lower polariton branch to a manifold of dark states. Through accompanying calculations based on a kinetic model, this shift is rationalized by an entropic trapping of excitations by the dark state manifold, while a weak dark state dispersion due to local disorder explains their nonzero emission. Our results point toward the relevance of the dark state concentration to the optical and dynamical properties of cavity-embedded quantum emitters with ramifications for Bose–Einstein condensate formation, polariton lasing, polariton-based quantum transduction schemes, and polariton chemistry.

Circular dichroism (CD) spectroscopy has been extensively utilized for detecting and distinguishing the chirality of diverse substances and structures. However, CD spectroscopy is inherently weak and conventionally associated with chiral sensing, thus constraining its range of applications. Here, we report a DNA-origami-empowered metasurface sensing platform through the collaborative effect of metasurfaces and DNA origami, enabling achiral/slightly chiral sensing with high sensitivity via the enhanced ΔCD. An anapole metasurface, boasting over 60 times the average optical chirality enhancement, was elaborately designed to synergize with reconfigurable DNA origami. We experimentally demonstrated the detection of achiral/slightly chiral DNA linker strands via the enhanced ΔCD of the proposed platform, whose sensitivity was a 10-fold enhancement compared with the platform without metasurfaces. Our work presents a high-sensitivity platform for achiral/slightly chiral sensing through chiral spectroscopy, expanding the capabilities of chiral spectroscopy and inspiring the integration of multifunctional artificial nanostructures across diverse domains.

The periodic power fluctuation of low-frequency pulsed power load leads to disturbance to the power supply system. Pulsed power suppression circuit is necessary to balance the input and output power, and maintain an unpulsed input power of the power system. In this article, a pulsed power suppression circuit, which is inserted between the input dc bus and the energy storage capacitors, is proposed based on ripple voltage compensation method. The input and output power are decoupled by the energy storage capacitors and the suppression circuit. Thus, constant input power/current is achieved. The voltage difference between the fluctuating capacitor voltage and the dc bus voltage is compensated by the suppression circuit. Instantaneous power of the pulsed load is directly supplied by the energy storage capacitors, while the dc bus provides average power to the load. Only a small ratio of the average load power is processed by the suppression circuit. Therefore, the power rating and loss of the suppression circuit are reduced significantly in comparison with the state of the art. The implementation circuit and control scheme of the proposed pulse power suppression method are presented. Design guidelines and examples are conducted as well. Experimental results and comparative study are provided to verify the effectiveness and feasibility of the proposed solutions.

The efficiency and power density of ultralow-voltage (&lt;1 V) high-current output dc transformers (DCXs) are limited by the losses and footprint area of the high-current windings. High-frequency current cancellation and power switches and high-current winding heterogeneous integration methods are proposed to reduce the length, area, and losses of high-current windings. With the proposed current-cancellation-based heterogeneous integration method, most of the high-current printed circuit board (PCB) windings of the transformer are replaced by power switches and capacitors, and almost zero PCB winding is achieved for the high-current secondary windings of the DCX. As a result, both power density and efficiency are improved significantly. A 36-V-to-0.75-V/150-A output DCX with a 48:1 turns-ratio transformer is designed and implemented with the four-layer PCB. A peak efficiency of 94.1% is achieved, while the power density of the high-frequency transformer in the DCX is 0.66 A/mm2.