Target Entrapment Based on Adaptive Transformation of Gene Regulatory Networks

The complexity of unknown scenarios and the dynamics involved in target entrapment make designing control strategies for swarm robots a formidable task,which in turn impacts their efficiency in complex and dynamic settings.To address these challenges,this paper introduces an adaptive swarm robot entrapment control model grounded in the transformation of gene regulatory networks(AT-GRN).This innovative model enables swarm robots to dynamically adjust entrap-ment strategies by assessing current environmental conditions via real-time sensory data.Further-more,an improved motion control model for swarm robots is designed to dynamically shape the for-mation generated by the AT-GRN.Through two sets of rigorous experimental environments,the proposed model significantly enhances the trapping performance of swarm robots in complex envi-ronments,demonstrating remarkable adaptability and stability.

Data analysis guidelines for single‑cell RNA‑seq in biomedical studies and clinical applications

The application of single-cell RNA sequencing(scRNA-seq)in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies.With the expansion of capacity for high-throughput scRNA-seq,including clinical samples,the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field.Here,we review the workflow for typical scRNA-seq data analysis,covering raw data processing and quality control,basic data analysis applicable for almost all scRNA-seq data sets,and advanced data analysis that should be tailored to specific scientific questions.While summarizing the current methods for each analysis step,we also provide an online repository of software and wrapped-up scripts to support the implementation.Recommendations and caveats are pointed out for some specific analysis tasks and approaches.We hope this resource will be helpful to researchers engaging with scRNA-seq,in particular for emerging clinical applications.

Superhydrophobic Surface-Assisted Preparation of Microspheres and Supraparticles and Their Applications

Superhydrophobic surface(SHS) has been well developed, as SHS renders the property of minimizing the water/solid contact interface. Water droplets deposited onto SHS with contact angles exceeding 150°, allow them to retain spherical shapes, and the low adhesion of SHS facilitates easy droplet collection when tilting the substrate. These characteristics make SHS suitable for a wide range of applications. One particularly promising application is the fabrication of microsphere and supraparticle materials. SHS offers a distinct advantage as a universal platform capable of providing customized services for a variety of microspheres and supraparticles. In this review, an overview of the strategies for fabricating microspheres and supraparticles with the aid of SHS, including cross-linking process, polymer melting,and droplet template evaporation methods, is first presented. Then, the applications of microspheres and supraparticles formed onto SHS are discussed in detail, for example, fabricating photonic devices with controllable structures and tunable structural colors, acting as catalysts with emerging or synergetic properties, being integrated into the biomedical field to construct the devices with different medicinal purposes, being utilized for inducing protein crystallization and detecting trace amounts of analytes. Finally,the perspective on future developments involved with this research field is given, along with some obstacles and opportunities.

Regulating intramolecular hydrogen bonds of p-phenylenediimidazole-based small-molecule compounds towards the enhanced lithium storage capacity

The use of redox-active organic electrode materials in energy storage is restricted due to their inferior solvent resistance,abysmal conductivity,and the resultant low practical capacity.To address these issues,a class of bipolar p-phenylenediimidazole-based small-molecule compounds are designed and fabricated.Theπ-conjugated backbone of these small molecules allows for electron delocalization on a big conjugation plane,endowing them with good conductivity and reaction reversibility.Furthermore,when the para-positions of phenylene are occupied by hydroxyl groups,as-formed intramolecular hydrogen bonds(N-H...O)between phenolic hydroxyl groups and the–NH groups of imidazole rings further enhance the structural planarity,resulting in higherπ-conjugation degree and better conductivity,and thus higher utilization of active sites and electrode capacity,proved by both experimental results and theoretical calculations.The optimized composite electrode DBNQ@rGO-45 shows a high specific capacity(∼308 mA h g^(−1)at 100 mA g^(−1))and a long cycling stability(112.9 mA h g^(−1)after 6000 cycles at 2000 mA g^(−1)).The significantly better electrochemical properties for hydroxyl group-containing compounds than those without hydroxyl groups attributed to intramolecular hydrogen bond-induced conjugation enhancement will inspire the structure design of organic electrodes for better energy storage.

Strabismus Detection Based on Uncertainty Estimation and Knowledge Distillation

Strabismus significantly impacts human health as a prevalent ophthalmic condition.Early detection of strabismus is crucial for effective treatment and prognosis.Traditional deep learning models for strabismus detection often fail to estimate prediction certainty precisely.This paper employed a Bayesian deep learning algorithm with knowledge distillation,improving the model's performance and uncertainty estimation ability.Trained on 6807 images from two tertiary hospitals,the model showed significantly higher diagnostic accuracy than traditional deep-learning models.Experimental results revealed that knowledge distillation enhanced the Bayesian model’s performance and uncertainty estimation ability.These findings underscore the combined benefits of using Bayesian deep learning algorithms and knowledge distillation,which improve the reliability and accuracy of strabismus diagnostic predictions.

An organic visible-photocatalytic-adsorbence mechanism to high-efficient removal of heavy metal antimony ions

Purification of emerging heavy metal antimony contaminated water based on advanced ingenious strategies.An activated modified coconut shell charcoal(CSC)was synthesized and evaluated as a substrate-supported loaded organic photovoltaic material,PM6:PYIT:PM6-b-PYIT,to prepare a surprisingly highly efficient,stable,environmentally friendly,and recyclable organic photocatalyst(CSC–N–P.P.P),which showed excellent effects on the simultaneous removal of Sb(Ⅲ)and Sb(Ⅴ).The removal efficiency of CSC-N-P.P.P on Sb(Ⅲ)and Sb(Ⅴ)reached an amazing 99.9%in quite a short duration of 15 min.At the same time,under ppb level and indoor visible light(~1 W m^(2)),it can be treated to meet the drinking water standards set by the European Union and the U.S.National Environmental Protection Agency in 5 min,and even after 25 cycles of recycling,the efficiency is still maintained at about 80%,in addition to the removal of As(Ⅲ),Cd(Ⅱ),Cr(Ⅵ),and Pb(Ⅱ)can also be realized.The catalyst not only solves the problems of low reuse rate,difficult structure adjustment and high energy consumption of traditional photocatalysts but also has strong applicability and practical significance.The pioneering approach provides a much-needed solution strategy for removing highly toxic heavy metal antimony pollution from the environment.

Chelation of lithium ion with crown ether for eliminating adverse effects caused by Li-TFSI/tBP doping system in Spiro-OMeTAD

Lithium bis(trifluoromethanesulfonyl)imide(Li-TFSI)/4-tert-butylpyridine(tBP)is a classic doping system for the hole transport material Spiro-OMeTAD in typical n-i-p structure perovskite solar cells(PSCs),but this system will cause many problems such as high hygroscopicity,Li+migration,pinholes and so on,which hinder PSC from maintaining high efficiency and stability for long-term.In this work,an effective strategy is demonstrated to improve the performance and stability of PSC by replacing t BP with 12-crown-4.The chelation of 12-crown-4 with Li+not only improves the doping effect of Li-TFSI,but also perfectly solves the problems caused by the Li-TFSI/tBP system.The PSC based on this strategy achieved a champion power conversion efficiency(PCE)over 21%,which is significantly better than the pristine device(19.37%).More importantly,the without encapsulated device based on Li-TFSI/12-crown-4 still maintains 87%of the initial PCE even after 60 days exposure in air,while the pristine device only maintains 22%of the initial PCE under the same aging conditions.This strategy paves a novel way for constructing efficient and stable PSCs.

Au nanoclusters anchored on TiO_(2) nanosheets for high-efficiency electroreduction of nitrate to ammonia

Electrocatalytic nitrate reduction reaction(NO_(3)RR)offers a unique rationale for green NH_(3) synthesis,yet the lack of high-efficiency NO_(3)RR catalysts remains a great challenge.In this work,we show that Au nanoclusters anchored on TiO_(2) nanosheets can efficiently catalyze the conversion of NO_(3)RR-to-NH_(3) under ambient conditions,achieving a maximal Faradic efficiency of 91%,a peak yield rate of 1923μg·h^(-1)·mgcat.-1,and high durability over 10 consecutive cycles,all of which are comparable to the recently reported metrics(including transition metal and noble metal-based catalysts)and exceed those of pristine TiO_(2).Moreover,a galvanic Zn-nitrate battery using the catalyst as the cathode was proposed,which shows a power density of 3.62 mW·cm^(-2) and a yield rate of 452μg·h^(-1)·mgcat.-1.Theoretical simulations further indicate that the atomically dispersed Au clusters can promote the adsorption and activation of NO_(3)-species,and reduce the NO_(3)RR-to-NH_(3) barrier,thus leading to an accelerated cathodic reaction.This work highlights the importance of metal clusters for the NH_(3) electrosynthesis and nitrate removal.

Coupling Co-Ni phosphides for energy-saving alkaline seawater splitting

The coupling of energy-saving small molecule conversion reactions and hydrogen evolution reaction(HER)in seawater electrolytes can reduce the energy consumption of seawater electrolysis and mitigate chlorine corrosion issues.However,the fabrication of efficient multifunctional catalysts for this promising technology is of great challenge.Herein,a heterostructured catalyst comprising CoP and Ni_(2)P on nickel foam(CoP/Ni_(2)P@NF)is reported for hydrazine oxidation(HzOR)-assisted alkaline seawater splitting.The coupling of CoP and Ni_(2)P optimizes the electronic structure of the active sites and endows excellent electrocatalytic performance for HzOR and HER.Impressively,the two-electrode HzOR-assisted alkaline seawater splitting(OHzS)cell based on the CoP/Ni_(2)P@NF required only 0.108 V to deliver 100 mA·cm^(−2),much lower than 1.695 V for alkaline seawater electrolysis cells.Moreover,the OHzS cell exhibits satisfactory stability over 48 h at a high current density of 500 mA·cm^(−2).Furthermore,the CoP/Ni_(2)P@NF heterostructured catalyst also efficiently catalyzed glucose oxidation,methanol oxidation,and urea oxidation in alkaline seawater electrolytes.This work paves a path for high-performance heterostructured catalyst preparation for energy-saving seawater electrolysis for H_(2) production.

High-Throughput Growth of Hexagonal Boron Nitride Film Using Porous-Structure Isolation Layer

Chemical vapor deposition is considered as the most hopeful method for the synthesis of large-area high-quality hexagonal boron nitride on the substrate of catalytic metal. However, the size the hexagonal boron nitride films are limited to the size of growth chamber, which indicates a lower production efficiency. In this paper, the utilization efficiency of growth chamber is highly improved by alternately stacking multiple pieces of Cu foils and carbon fiber surface felt with porous structure. Uniform and continuous hexagonal boron nitride films are prepared on Cu foils through chemical vapor deposition utilizing ammonia borane as the precursor. This work develops a simple and practicable method for high-throughput preparation of hexagonal boron nitride films, which could contribute to the industrial application of hexagonal boron nitride. .

Wafer-level substrate-free YIG single crystal film for a broadband tunable terahertz isolator

Yttrium iron garnet(YIG)is a promising material for various terahertz applications due to its special optical properties.At present,a high-quality YIG wafer is the desire of terahertz communities and it is still challenging to prepare substrate-free YIG single crystal films.In this work,we prepared wafer-level substrate-free La:YIG single crystal films,for the first time,to our knowledge.Terahertz optical and magneto-optical properties of La:YIG films were characterized by terahertz time domain spectroscopy(THz-TDS).Results show that the as-prepared La:YIG film has an insertion loss of less than 3 dB and a low absorption coefficient of less than10 cm-1below 1.6 THz.Benefitting from the thickness of the substrate-free YIG films and low insertion loss,their terahertz properties could be further manipulated by simply using a wafer-stacking technique.When four La:YIG films were stacked,there was an insertion loss of less than 10 dB in the range of 0.1-1.2 THz.The Faraday rotation angle of the four-layer-stacked La:YIG films reached 19°,and the isolation could reach17 dB.By further increasing the stacking number to eight pieces,a remarkable Faraday rotation angle of45°was achieved with an isolation of 23 dB,which is important for practical application in the THz band.This material may provide a milestone opportunity to make various non-reciprocal devices,such as isolators and phase shifters.

Preparation of meter-scale Cu foils with decimeter grains and the use for the synthesis of graphene films

Chemical vapor deposition(CVD)is the most promising method for the preparation of high-quality and large-area graphene films,especially the epitaxial growth of graphene on large-area single-crystal Cu foils.While single-crystal Cu foils are normally achieved by thermally annealing the commercial poly-crystalline Cu foils,their size and therefore the size of graphene films grown on them are limited to the size of the reaction chamber.We report a simple and feasible method to prepare large-area Cu foils with decimeter grains by thermally annealing the rolled-up Cu foils,where the Cu layers are separated by thin porous carbon fiber cloths.The carbon fiber cloths prevent Cu layers from sticking to each other at high temperatures while do not block the gas transportation.In such a way,the utilization efficiency of the reaction chamber is significantly improved,e.g.,0.2 m×(1e2)m Cu foils can be processed even in a 5 cm diameter quartz tube chamber.High-quality graphene films grown on such Cu foils are then demon-strated.This method may be suitable for the annealing of other metal foils to enlarge grain size and the synthesis of other two-dimensional materials on them such as h-BN.

A deep Q-learning model for sequential task offloading in edge AI systems

Currently,edge Artificial Intelligence(AI)systems have significantly facilitated the functionalities of intelligent devices such as smartphones and smart cars,and supported diverse applications and services.This fundamental supports come from continuous data analysis and computation over these devices.Considering the resource constraints of terminal devices,multi-layer edge artificial intelligence systems improve the overall computing power of the system by scheduling computing tasks to edge and cloud servers for execution.Previous efforts tend to ignore the nature of strong pipelined characteristics of processing tasks in edge AI systems,such as the encryption,decryption and consensus algorithm supporting the implementation of Blockchain techniques.Therefore,this paper proposes a new pipelined task scheduling algorithm(referred to as PTS-RDQN),which utilizes the system representation ability of deep reinforcement learning and integrates multiple dimensional information to achieve global task scheduling.Specifically,a co-optimization strategy based on Rainbow Deep Q-Learning(RainbowDQN)is proposed to allocate computation tasks for mobile devices,edge and cloud servers,which is able to comprehensively consider the balance of task turnaround time,link quality,and other factors,thus effectively improving system performance and user experience.In addition,a task scheduling strategy based on PTS-RDQN is proposed,which is capable of realizing dynamic task allocation according to device load.The results based on many simulation experiments show that the proposed method can effectively improve the resource utilization,and provide an effective task scheduling strategy for the edge computing system with cloud-edge-end architecture.

Safeguarding cross-silo federated learning with local differential privacy

Federated Learning(FL)is a new computing paradigm in privacy-preserving Machine Learning(ML),where the ML model is trained in a decentralized manner by the clients,preventing the server from directly accessing privacy-sensitive data from the clients.Unfortunately,recent advances have shown potential risks for user-level privacy breaches under the cross-silo FL framework.In this paper,we propose addressing the issue by using a three-plane framework to secure the cross-silo FL,taking advantage of the Local Differential Privacy(LDP)mechanism.The key insight here is that LDP can provide strong data privacy protection while still retaining user data statistics to preserve its high utility.Experimental results on three real-world datasets demonstrate the effectiveness of our framework.

Enhancing the robustness of object detection via 6G vehicular edge computing

Academic and industrial communities have been paying significant attention to the 6th Generation (6G) wireless communication systems after the commercial deployment of 5G cellular communications. Among the emerging technologies, Vehicular Edge Computing (VEC) can provide essential assurance for the robustness of Artificial Intelligence (AI) algorithms to be used in the 6G systems. Therefore, in this paper, a strategy for enhancing the robustness of AI model deployment using 6G-VEC is proposed, taking the object detection task as an example. This strategy includes two stages: model stabilization and model adaptation. In the former, the state-of-the-art methods are appended to the model to improve its robustness. In the latter, two targeted compression methods are implemented, namely model parameter pruning and knowledge distillation, which result in a trade-off between model performance and runtime resources. Numerical results indicate that the proposed strategy can be smoothly deployed in the onboard edge terminals, where the introduced trade-off outperforms the other strategies available.

NiS_(2)/FeS Heterostructured Nanoflowers for High-Performance Sodium Storage

Transition metal sulfides demonstrate attractive potential for sodium storage owing to their high theoretical specific capacity and high reserve.However,the low conductivity and volume expansion deteriorate their high-rate performance and cycling stability.In this work,we construct NiS_(2)/FeS heterostructure by growing Ni-based layered double hydroxide nanosheets on Fe-based Prussian Blue nanocrystals followed by gaseous sulfurization,giving rise to flower-like NiS_(2)/FeS nanoparticles.The as-prepared nanocomposite exhibits good rate performance of 156 mAh g^(−1) at 50 A g^(-1) and long cycle life of 606 mAh g^(−1) at 5 A g^(−1) after 1,000 cycles,which are superior to the heterostructure-free counterpart of NiS_(2) and FeS.Density functional theory calculation further verifies that the enhanced electrochemical performance of NiS_(2)/FeS is due to the existence of interface derived from the heterostructure.

Engineering active sites of cathodic materials for high-performance Zn-nitrogen batteries

As an ideal carbon-free energy carrier,ammonia plays an indispensable role in modern society.The conventional industrial synthesis of NH3 by the Haber-Bosch technique under harsh reaction conditions results in serious energy consumption and environmental pollution.Therefore,it is essential to develop NH3 synthesis tactics under benign conditions.Electrochemical synthesis of NH_(3) has the advantages of mild reaction conditions and environmental friendliness,and has become a hotspot for research in recent years.It has been reported that zinc-nitrogen batteries(ZNBs),such as Zn-N_(2),Zn-NO,Zn-NO_(3)^(-),and Zn-NO_(2)^(-)batteries,can not only reduce nitrogenous species to ammonia but also have concomitant power output.However,the common drawbacks of these battery systems are unsatisfactory power density and ammonia production.In this review,the latest progress of ZNBs including the reaction mechanism of the battery and reactor design principles is systematically summarized.Subsequently,active site engineering of cathode catalysts is discussed,including vacancy defects,chemical doping,and heterostructure engineering.Finally,some insights are provided to improve the performance of ZNBs from a practical perspective of view.

Flexible bioelectronic innovation for personalized health management

1|INTRODUCTION With the rapid developments in intelligent medical care and interdisciplinary science,innovative flexible bioelectronics(FBEs)are emerging for monitoring health,diagnosing and treating diseases,and even cancer therapy.FBEs subvert physical form and break through the bottleneck of traditional rigid electronics.

In-situ construction of Li4Ti5O12/rutile TiO2 heterostructured nanorods for robust and high-power lithium storage

Li4Ti5O12 is considered as a safe and stable anode material for high-power lithium-ion batteries due to its“zero-strain”characteristic during the charge/discharge.However,the intrinsically low electronic conductivity leads to a deterioration in highrate performance,impeding its intensive application.Herein,the Li4Ti5O12/rutile TiO2(LTO/RT)heterostructured nanorods with tunable oxide phases have been in-situ fabricated by annealing the electrospun nanofiber precursor.By constructing such a heterostructured interface,the as-prepared sample delivers a high capacity of 160.3 mAh·g–1 at 1 C after 200 cycles,125.5 mAh·g–1 at 10 C after 500 cycles and a superior capacity retention of 90.3%after 1,000 cycles at 30 C,outperforming the heterostructure-free counterparts of pure LTO,RT and the commercial LTO product.Density Functional Theory calculation suggests a possible synergistic effect of the LTO/RT interface that would improve the electronic conductivity and Li-ion diffusion.

Pseudo-break imaging of carbon nanotubes for determining elastic bending energies

One-dimensional(1D)nanomaterials easily bend due to perturbations from their surroundings or their own behaviors.This phenomenon not only impacts the performances of various devices but has also been employed to develop a variety of new functional devices,in which the bending energies of the nanomaterials determine the device performances.However,measuring the energies of such nanomaterials is extremely difficult.Herein,pseudo-break imaging of 1D nanomaterials has been proposed and realized on individual carbon nanotubes(CNTs),in which a CNT appears to break and has a fracture but is actually intact.This imaging approach provides the values of the bending energies of the CNTs with an accuracy of 1–50 eV.Furthermore,this imaging approach can manipulate the bending shapes and energies of CNTs.This work presents a protocol for bending analysis and manipulation,which are vital to fundamental and applied studies of 1D nanomaterials.