Chemical synthesis is essential in industries such as petrochemicals, fine chemicals, and pharmaceuticals, driving economic and social development. The increasing demand for new molecules and materials calls for novel...Chemical synthesis is essential in industries such as petrochemicals, fine chemicals, and pharmaceuticals, driving economic and social development. The increasing demand for new molecules and materials calls for novel chemical reactions;however, manual experimental screening is time-consuming. Artificial intelligence (AI) offers a promising solution by leveraging large-scale experimental data to model chemical reactions, although challenges such as the lack of standardization and predictability in chemical synthesis hinder AI applications. Additionally, the multi-scale nature of chemical reactions, along with complex multiphase processes, further complicates the task. Recent advances in microchemical systems, particularly continuous flow methods using microreactors, provide precise control over reaction conditions, enhancing reproducibility and enabling high-throughput experimentation. These systems minimize transport-related inconsistencies and facilitate scalable industrial applications. This review systematically explores recent developments in intelligent synthesis based on microchemical systems, focusing on reaction system design, synthesis robots, closed-loop optimization, and high-throughput experimentation, while identifying key areas for future research.展开更多
Background:Scutellaria baicalensis Georgi is a medicinal plant prized for its bioactive flavonoid derivatives.Flavonoid O-methyltransferases(OMTs)in this species play a vital role in enhancing these compounds’pharmac...Background:Scutellaria baicalensis Georgi is a medicinal plant prized for its bioactive flavonoid derivatives.Flavonoid O-methyltransferases(OMTs)in this species play a vital role in enhancing these compounds’pharmacological activities,including their antioxidant,anti-inflammatory,and anticancer effects.However,a comprehensive genomic overview of the OMT gene family in S.baicalensis is lacking.Methods:This study conducted a genome-wide identification of the OMT gene family in S.baicalensis using bioinformatics approaches.The identified genes were characterized through phylogenetic,physicochemical,and structural analyses.Furthermore,the response of methoxylated flavonoids and key SbOMT genes to drought stress was investigated.Results:A total of 54 SbOMTs were identified and classified into 9 CCoAOMT and 45 COMT subfamily members.These proteins,with lengths from 129 to 695 amino acids and molecular weights from 14.42 to 76.94 kDa,were predominantly acidic.Subcellular localization predicted 43% to be cytoplasmic.Structurally,the CCoAOMT subfamily was more conserved than the COMT subfamily.Promoter analysis revealed hormone-and stress-responsive cis-elements.Under drought stress,the root content of methoxylated flavonoids(wogonin,wogonoside,and oroxylin A)decreased initially and then increased.The expression of SbOMT06,SbOMT41,SbOMT27,and SbOMT29 was positively correlated with this accumulation,suggesting their involvement in biosynthesis.Conclusion:This study provides foundational insights into the SbOMT gene family,revealing key candidates likely involved in methoxyflavonoid biosynthesis.The findings advance our understanding of the molecular mechanisms in S.baicalensis and offer valuable resources for future metabolic engineering and pathway optimization efforts.展开更多
Multimodal spatiotemporal data from smart city consumer electronics present critical challenges including cross-modal temporal misalignment,unreliable data quality,limited joint modeling of spatial and temporal depend...Multimodal spatiotemporal data from smart city consumer electronics present critical challenges including cross-modal temporal misalignment,unreliable data quality,limited joint modeling of spatial and temporal dependencies,and weak resilience to adversarial updates.To address these limitations,EdgeST-Fusion is introduced as a cross-modal federated graph transformer framework for context-aware smart city analytics.The architecture integrates cross-modal embedding networks for modality alignment,graph transformer encoders for spatial dependency modeling,temporal self-attention for dynamic pattern learning,and adaptive anomaly detection to ensure data quality and security during aggregation.A privacy-preserving federated learning protocol with differential privacy guarantees enables collaborative model training without centralizing sensitive data.The framework employs data-quality-aware weighted aggregation to enhance robustness against noisy and malicious client updates.Experimental evaluation on the GeoLife,PeMS-Bay,and SmartHome+datasets demonstrates that EdgeST-Fusion achieves 21.8%improvement in prediction accuracy,35.7%reduction in communication overhead,and 29.4%enhancement in security resilience compared to recent baselines.Real-world deployment across three smart city testbeds validates practical viability with 90.0%average accuracy and sub-250 ms inference latency.The proposed framework remains feasible for deployment on heterogeneous and resource-constrained consumer electronics devices whilemaintaining strong privacy guarantees and scalability for large-scale urban environments.展开更多
To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determ...To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determining the position of the grasping information.These results can then be used to guide the visually impaired or to execute grasping tasks with a robotic arm.In this paper,we collected,annotated,and published the benchmark TQUGraspingObject dataset for testing,validation,and evaluation of deep learning(DL)models for detecting,recognizing,and localizing grasping objects in 2D and 3D space,especially 3D point cloud data.Our dataset is collected in a shared room,with common everyday objects placed on the tabletop in jumbled positions by Intel RealSense D435(IR-D435).This dataset includes more than 63k RGB-D pairs and related data such as normalized 3D object point cloud,3D object point cloud segmented,coordinate system normalizationmatrix,3D object point cloud normalized,and hand pose for grasping each object.At the same time,we also conducted experiments on fourDL networks with the best performance:SSD-MobileNetV3,ResNet50-Transformer,ResNet101-Transformer,and YOLOv12.The results present that YOLOv12 has the most suitable results in detecting and recognizing objects in images.All data,annotations,toolkit,source code,point cloud data,and results are publicly available on our project website:https://github.com/HuaTThanhIT2327Tqu/datasetv2.展开更多
Tumors are defined by uncontrolled cell proliferation(Hariharan and Bilder,2006).Benign tumors are typically slow-growing and localized,while malignant ones are invasive and aggressive.The nuclear receptor Eip75B(E75)...Tumors are defined by uncontrolled cell proliferation(Hariharan and Bilder,2006).Benign tumors are typically slow-growing and localized,while malignant ones are invasive and aggressive.The nuclear receptor Eip75B(E75),a heme-binding protein responsive to ecdysone signaling,encodes three major isoforms,E75A,E75B,and E75C(Bialecki et al.,2002),among them,only E75A and E75C contain zinc finger domains that enable DNA binding.展开更多
Conformational entropy,one of the central concepts of polymer physics,is the key to revealing physical characteristics of polymers.Despite an increased repertoire of conformational-entropy effects in the structural fo...Conformational entropy,one of the central concepts of polymer physics,is the key to revealing physical characteristics of polymers.Despite an increased repertoire of conformational-entropy effects in the structural formation,transition,and properties of polymer systems,the physical origin of conformational entropy remains less understood compared to interaction energy and other types of entropy.This review seeks to provide a conceptual framework unveiling several principles and rules of conformational entropy in governing the structures and properties of polymers,from the perspective of fundamental physics and statistical mechanics.First,we focus on the fundamentals of entropy in thermodynamics,leading to the theoretical basis for the elucidation of conformational entropy.Second,we delineate the physical nature of statistics and dissipation of conformational entropy and its essential dependence on the environmental heat bath.Next,we explore the principles of conformational entropy in driving the ordering transitions of various systems of polymers and their nanocomposites,elucidating the emergent and collective behaviors as well as the interplay between energetic interactions and entropy.Moreover,we demonstrate how the concept of conformational entropy is generalized to the biological systems and other soft matters.Finally,we discuss future directions to signify this framework originated from polymers.展开更多
Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e....Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.展开更多
The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution l...The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution laws and fracture network characteristics of shale gas infill wells.A mechanism model of CN platform logging data and geomechanical parameters is established to simulate the influence of parent well’s production on the geostress in the infill well area.It is suggested that with the increase of production time,normal fault stress state and horizontal stress deflection will occur.The smaller the parent well spacing and the longer the production time,the earlier the normal fault stress state appears and the larger the range.Based on the model,the fracture network morphology and construction parameters of infill wells are optimized.parentparentparentparent The results indicate that:1:A well spacing of 500 m achieves a Pareto optimum between“full reserve coverage”and“stress barrier”;2:A parent well recovery degree of 30%corresponds to the critical point of stress reversal,where the lateral deflection rate of the infill fracture is less than 8%and the SRV loss is minimized;3:6-cluster intensive completion with twice the liquid intensity increases the fracture complexity index by 1.7 times,enhances well group EUR by 15.4%,and reduces single-well cost by 22%.This research fills the theoretical gap in the collaborative optimization of“multi-parameter,multi-objective and multi-constraint”and provide parameter optimization basis for shale gas infill well development in China and help to improve the development efficiency and economic benefits.展开更多
Objective:To investigate the effects and potential mechanisms of action of Panax notoginseng(Burk)F.H.Chen(P.notoginseng,San Qi)flowers in type 2 diabetes mellitus(T2DM)using network pharmacology,in vivo experiments,a...Objective:To investigate the effects and potential mechanisms of action of Panax notoginseng(Burk)F.H.Chen(P.notoginseng,San Qi)flowers in type 2 diabetes mellitus(T2DM)using network pharmacology,in vivo experiments,and RNA sequencing(RNA-seq).Methods:Network pharmacology analysis was performed to identify and correlate the drug targets of flower buds of P.notoginseng(PNF)with T2DM disease targets and to predict the key targets and pathways involved in the therapeutic effects of PNF in T2DM.In vivo experiments were conducted to assess the effects of PNF on glucose and lipid metabolism in mice with T2DM.RNA-seq was performed,and the results were integrated with network pharmacology data to assess the therapeutic mechanisms of PNF in T2DM.The results from transcriptomics and network pharmacology were validated using real-time polymerase chain reaction.Results:A total of 27 intersecting targets were identified by overlapping 35 drug targets with T2DM targets.Further topological analysis using the Centiscape 2.2 tool revealed five core targets,including signal transducer and activator of transcription 3(STAT3).Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis indicated that the JAK/STAT signaling pathway is a key mechanism underlying the therapeutic effects of PNF in T2DM.In vivo experiments confirmed that PNF effectively regulates glycolipid metabolism in a mouse model of diabetes.KEGG pathway enrichment analysis of RNA-seq data highlighted the JAK2/STAT3 and PI3K/AKT pathway as a potential mechanism.PNF high-dose(PNFH)increased the gene expression levels of PIK3R1 and AKT2,decreased the expression of PCK1,JAK2,and STAT3,and showed a trend toward increasing INSR expression without reaching statistical significance.Conclusion:PNF improves glycolipid metabolism disorders in T2DM,potentially by modulating the JAK2/STAT3 and PI3K/AKT signaling pathway.展开更多
We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parame...We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parameter E(z)from the DESI BAO Alcock-Paczynski(AP)data using Gaussian process to perform the null test.We find strong evidence of accelerated expansion from the DESI BAO AP data.By reconstructing the deceleration parameter q(z) from the DESI BAO AP data,we find that accelerated expansion persisted until z■0.7 with a 99.7%confidence level.Additionally,to provide insights into the Hubble tension problem,we propose combining the reconstructed E(z) with D_(H)/r_(d) data to derive a model-independent result r_(d)h=99.8±3.1 Mpc.This result is consistent with measurements from cosmic microwave background(CMB)anisotropies using the ΛCDM model.We also propose a model-independent method for reconstructing the comoving angular diameter distance D_(M)(z) from the distance modulus μ,using SNe Ia data and combining this result with DESI BAO data of D_(M)/r_(d) to constrain the value of r_(d).We find that the value of r_(d),derived from this model-independent method,is smaller than that obtained from CMB measurements,with a significant discrepancy of at least 4.17σ.All the conclusions drawn in this paper are independent of cosmological models and gravitational theories.展开更多
Interferometry is a crucial investigative technique used across diverse fields to achieve highprecision measurements.It works by analyzing the phase difference between two interfering waves,which results from variatio...Interferometry is a crucial investigative technique used across diverse fields to achieve highprecision measurements.It works by analyzing the phase difference between two interfering waves,which results from variations in optical path lengths within an interferometer.We introduce a novel method for directly measuring changes in the phase difference within an optical interferometer,importantly,with the added advantage of a controllable enhancement factor.This approach is achieved through a two-step process:first,the optical phase difference is encoded into a sub-GHz radiofrequency(RF)signal using microwave-photonic manipulation;then,RF interferometry-assisted phase amplification is implemented at the destructive interference point.In our experiments,we demonstrate a phase sensitivity of 2.14 rad∕nm operating at 140 MHz using a miniature in-fiber Fabry-Pérot interferometer for sub-nanometer displacement sensing,which reveals a sensitivity magnification factor of 258.6.With further refinement,we anticipate that even higher enhancement factors can be achieved,paving the way for the development of cost-effective,ultrasensitive interferometry-based instruments for high-precision optical measurements.展开更多
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev...Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.展开更多
The study of target proteins is crucial for understanding molecular interactions and developing analytical platforms,therapeutic agents and functional tools.Herein,we present a novel nanoplatform activated by near-inf...The study of target proteins is crucial for understanding molecular interactions and developing analytical platforms,therapeutic agents and functional tools.Herein,we present a novel nanoplatform activated by near-infrared(NIR) light for triple-modal proteins study,which enabling target protein labeling,enrichment and visualization.Azido-naphthalimide-coated upconversion nanoparticles(UCNPs) serve as NIR light-responsive nanoplatforms,showing promising applications in studying interactions between various bioactive molecules and proteins in living systems.Under NIR light irradiation,azido-naphthalimides are activated by ultraviolet(UV) and blue light emitted from UCNPs and the resulting amino-naphthalimides intermediate not only crosslink nearby target proteins but also enable imaging performance.We demonstrate that this nanoplatform is capable of selective protein labeling and imaging in complex protein environments,achieving specific labeling and imaging of both intracellular and extracellular proteins in mammalian cells as well as bacteria.Furthermore,in vivo protein labeling has been achieved using this novel NIR light-activatable nanoplatform.This technique will open new avenues for discoveries and mechanistic interrogation in chemical biology.展开更多
Corn starch(CS)is a renewable,biodegradable polysaccharide valued for its film-forming ability,yet native CS films exhibit lowmechanical strength,highwater sensitivity,and limited thermal stability.This study improves...Corn starch(CS)is a renewable,biodegradable polysaccharide valued for its film-forming ability,yet native CS films exhibit lowmechanical strength,highwater sensitivity,and limited thermal stability.This study improves CS-based films by blending with poly(vinyl alcohol)(PVA)or glycerol(GLY)and using citric acid(CA)as a green,non-toxic cross-linker.Composite films were prepared by casting CS–PVA or CS-GLY with CA at 0%-0.20%(w/w of starch).The influence of CA on physicochemical,mechanical,optical,thermal,and water barrier properties was evaluated.CA crosslinking markedly enhanced the tensile strength,water resistance,and thermal stability of CS-PVA films while increasing transparency in CS–GLY films.At 0.20%CA,the composite achieved 34.99MPa tensile strength,reducedwater vapor permeability,andminimized water uptake.FTIR confirmed ester bond formation between CAand hydroxyl groups of CS,PVA,and GLY,whereas thermal analysis showed higher decomposition temperatures and lower weight loss in crosslinked films.Increasing CA levels also decreased opacity and improved light transmittance,indicating greater homogeneity and reduced crystallinity.This dual-polymer matrix combined with a natural crosslinking strategy provides a sustainable route to high-performance,biodegradable CS-based packaging materials.展开更多
The Stern-Gerlach(SG)experiment is a fundamental experiment for revealing the existence of“spin”.In this experiment,beams of silver atoms are sent through inhomogeneous magnetic fields to observe their deflection.Th...The Stern-Gerlach(SG)experiment is a fundamental experiment for revealing the existence of“spin”.In this experiment,beams of silver atoms are sent through inhomogeneous magnetic fields to observe their deflection.Thus,the conventional SG experiment can be viewed as a magnetic-type spin effect.In this work,we successfully generalize the SG effect from magnetic-type to electric-type by solving Dirac's equation with a potential barrier,revealing an extraordinary spin effect.Beams of Dirac particles can be regarded as matter waves.Based on Dirac's equation,we obtain the explicit forms of the incident,reflected,and transmitted waves.The electric-type SG effect shows that the reflected and transmitted waves can exhibit notable spatial shifts,which depend on the spin direction and the incident angle of the wave.The electrictype SG effect has potential applications for separating Dirac particles with different spin directions and for estimating the spin direction of Dirac particles.Some discussions related to the interaction between spin and the electric field are also presented.展开更多
Methanol,a crucial C1 intermediate,bridges traditional fossil-based chemical processes with emerging sustainable catalytic technologies by serving as both a versatile hydrogenation product from CO/CO_(2)and an active ...Methanol,a crucial C1 intermediate,bridges traditional fossil-based chemical processes with emerging sustainable catalytic technologies by serving as both a versatile hydrogenation product from CO/CO_(2)and an active intermediate for hydrocarbon synthesis.Despite significant progress in methanol-to-hydrocarbon(MTH)conversion,a comprehensive understanding of reaction mechanisms remains essential to enhance catalyst design and industrial applicability.This review critically synthesizes recent advances in mechanistic insights related to methanol conversion and methanol-mediated catalytic processes.Firstly,we systematically outline key reaction pathways involved in initial carbon–carbon(C–C)bond formation through direct and indirect mechanisms,emphasizing significant breakthroughs from spectroscopic analyses and theoretical calculations.Subsequently,we highlight the autocatalytic characteristics and dual-cycle mechanisms underlying MTH processes,critically evaluating the roles of zeolite structures,pore sizes,topology,and acidity in governing product selectivity and catalyst stability.Additionally,we discuss cutting-edge developments in tandem catalytic systems employing methanol as a pivotal intermediate for CO_(x)hydrogenation,emphasizing the transferable mechanistic principles and catalytic insights.Finally,we identify future research directions,including elucidating precise hydrocarbon pool(HCP)intermediates,optimizing zeolite structures through computational-guided design,and developing robust catalytic systems leveraging advanced characterization methods and artificial intelligence.By integrating multidisciplinary approaches from catalytic science,materials engineering,and reaction engineering,this review provides actionable guidance towards rational design and optimization of advanced catalytic systems for efficient methanol conversion processes.展开更多
Point-of-care diagnostics and inline quantitative phase imaging(QPI)drive the demand for portable,ultra-miniaturized,and robust optical imaging and metrology systems.We propose and demonstrate a wavefront sensor integ...Point-of-care diagnostics and inline quantitative phase imaging(QPI)drive the demand for portable,ultra-miniaturized,and robust optical imaging and metrology systems.We propose and demonstrate a wavefront sensor integrated into a photonic integrated circuit,enabling single-shot optical phase retrieval.We implemented an integrated wavefront sensor array with a spatial resolution of 17μm and a numerical aperture of 0.1.Furthermore,we experimentally demonstrated the reconstruction of wavefronts defined by Zernike polynomials,specifically the first 14 terms(Z_(1)to Z_(14)),achieving an average root mean square error below 0.07.This advancement paves the way for fully integrated,portable,and robust optical imaging systems,facilitating integrated wavefront sensors in demanding applications such as point-of-care diagnostics,endoscopy,in situ QPI,and inline surface profile measurement.展开更多
Conductive elastomers combining micromechanical sensitivity,lightweight adaptability,and environmental sustainability are critically needed for advanced flexible electronics requiring precise responsiveness and long-t...Conductive elastomers combining micromechanical sensitivity,lightweight adaptability,and environmental sustainability are critically needed for advanced flexible electronics requiring precise responsiveness and long-term wearability;however,the integration of these properties remains a significant challenge.Here,we present a biomass-derived conductive elastomer featuring a rationally engineered dynamic crosslinked network integrated with a tunable microporous architecture.This structural design imparts pronounced micromechanical sensitivity,an ultralow density(~0.25 g cm^(−3)),and superior mechanical compliance for adaptive deformation.Moreover,the unique micro-spring effect derived from the porous architecture ensures exceptional stretchability(>500%elongation at break)and superior resilience,delivering immediate and stable electrical response under both subtle(<1%)and large(>200%)mechanical stimuli.Intrinsic dynamic interactions endow the elastomer with efficient room temperature self-healing and complete recyclability without compromising performance.First-principles simulations clarify the mechanisms behind micropore formation and the resulting functionality.Beyond its facile and mild fabrication process,this work establishes a scalable route toward high-performance,sustainable conductive elastomers tailored for next-generation soft electronics.展开更多
The increasing need for efficient,sustainable,and environmentally friendly adsorbent materials has driven interest in bio-based alternatives.Conventional silica-based adsorbents,while effective,are often brittle and e...The increasing need for efficient,sustainable,and environmentally friendly adsorbent materials has driven interest in bio-based alternatives.Conventional silica-based adsorbents,while effective,are often brittle and energy-intensive to produce.In contrast,wood offers a renewable and low-energy option with natural porosity suitable for adsorption.This study investigated the fabrication of wood sponge from tropical balsa(Ochroma bicolor)and pulai(Alstonia scholaris)wood through a dual-stage delignification process as a novel bio-based adsorbent.The process involved alkaline treatment using sodium sulfite(Na_(2)SO_(3))and sodium hydroxide(NaOH)at 100℃for 8,9,and 10 h,followed by bleaching process using hydrogen peroxide(H_(2)O_(2))at 100℃for 1,2,and 3 h.The treated samples were then rinsed to neutral pH,frozen at−20℃for 24 h,and freeze-dried at−50℃for 48 h.The results revealed a notable reduction in density and specific gravity,accompanied by increased weight loss and pore diameter size with prolonged delignification process.Optical changes showed increased translucency and layered structures,particularly in balsa wood.FTIR analysis confirmed a reduction in lignin and hemicellulose content,validating the chemical modification within the treated samples.The resulting wood sponges exhibited good porosity and adsorption capacity,ranging from 1.3 to 5.7 g/g.The optimal treatment—10 h of alkaline delignification followed by 3 h of H_(2)O_(2)bleaching—demonstrated the highest performance,highlighting the potential of tropical wood species as efficient,biodegradable,and eco-friendly adsorbent materials.展开更多
The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expo...The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expose them to increasingly sophisticated and persistentmalware attacks.These adaptive and stealthy threats can evade conventional detection,establish remote control,propagate across devices,exfiltrate sensitive data,and compromise network integrity.This study presents a Software-Defined Internet of Things(SD-IoT)control-plane-based,AI-driven framework that integrates Gated Recurrent Units(GRU)and Long Short-TermMemory(LSTM)networks for efficient detection of evolving multi-vector,malware-driven botnet attacks.The proposed CUDA-enabled hybrid deep learning(DL)framework performs centralized real-time detection without adding computational overhead to IoT nodes.A feature selection strategy combining variable clustering,attribute evaluation,one-R attribute evaluation,correlation analysis,and principal component analysis(PCA)enhances detection accuracy and reduces complexity.The framework is rigorously evaluated using the N_BaIoT dataset under k-fold cross-validation.Experimental results achieve 99.96%detection accuracy,a false positive rate(FPR)of 0.0035%,and a detection latency of 0.18 ms,confirming its high efficiency and scalability.The findings demonstrate the framework’s potential as a robust and intelligent security solution for next-generation IoT ecosystems.展开更多
基金supported by the National Natural Science Foundation of China(22378227)Shijiazhuang Science and Technology Bureau(231790163A).
文摘Chemical synthesis is essential in industries such as petrochemicals, fine chemicals, and pharmaceuticals, driving economic and social development. The increasing demand for new molecules and materials calls for novel chemical reactions;however, manual experimental screening is time-consuming. Artificial intelligence (AI) offers a promising solution by leveraging large-scale experimental data to model chemical reactions, although challenges such as the lack of standardization and predictability in chemical synthesis hinder AI applications. Additionally, the multi-scale nature of chemical reactions, along with complex multiphase processes, further complicates the task. Recent advances in microchemical systems, particularly continuous flow methods using microreactors, provide precise control over reaction conditions, enhancing reproducibility and enabling high-throughput experimentation. These systems minimize transport-related inconsistencies and facilitate scalable industrial applications. This review systematically explores recent developments in intelligent synthesis based on microchemical systems, focusing on reaction system design, synthesis robots, closed-loop optimization, and high-throughput experimentation, while identifying key areas for future research.
基金funded by the National Natural Science Foundation of China(82404814,82404863)Start-up Research Fund of Nanjing Agricultural University(130-804141)+1 种基金the National Administration of Traditional Chinese Medicine High-level Key Discipline Construction Project(zyyzdxk-2023293)Scientific research Project of Jiangsu Institute of Product Quality Supervision and Inspection(KJ2025008).
文摘Background:Scutellaria baicalensis Georgi is a medicinal plant prized for its bioactive flavonoid derivatives.Flavonoid O-methyltransferases(OMTs)in this species play a vital role in enhancing these compounds’pharmacological activities,including their antioxidant,anti-inflammatory,and anticancer effects.However,a comprehensive genomic overview of the OMT gene family in S.baicalensis is lacking.Methods:This study conducted a genome-wide identification of the OMT gene family in S.baicalensis using bioinformatics approaches.The identified genes were characterized through phylogenetic,physicochemical,and structural analyses.Furthermore,the response of methoxylated flavonoids and key SbOMT genes to drought stress was investigated.Results:A total of 54 SbOMTs were identified and classified into 9 CCoAOMT and 45 COMT subfamily members.These proteins,with lengths from 129 to 695 amino acids and molecular weights from 14.42 to 76.94 kDa,were predominantly acidic.Subcellular localization predicted 43% to be cytoplasmic.Structurally,the CCoAOMT subfamily was more conserved than the COMT subfamily.Promoter analysis revealed hormone-and stress-responsive cis-elements.Under drought stress,the root content of methoxylated flavonoids(wogonin,wogonoside,and oroxylin A)decreased initially and then increased.The expression of SbOMT06,SbOMT41,SbOMT27,and SbOMT29 was positively correlated with this accumulation,suggesting their involvement in biosynthesis.Conclusion:This study provides foundational insights into the SbOMT gene family,revealing key candidates likely involved in methoxyflavonoid biosynthesis.The findings advance our understanding of the molecular mechanisms in S.baicalensis and offer valuable resources for future metabolic engineering and pathway optimization efforts.
基金supported by the University of Tabuk,Saudi Arabia。
文摘Multimodal spatiotemporal data from smart city consumer electronics present critical challenges including cross-modal temporal misalignment,unreliable data quality,limited joint modeling of spatial and temporal dependencies,and weak resilience to adversarial updates.To address these limitations,EdgeST-Fusion is introduced as a cross-modal federated graph transformer framework for context-aware smart city analytics.The architecture integrates cross-modal embedding networks for modality alignment,graph transformer encoders for spatial dependency modeling,temporal self-attention for dynamic pattern learning,and adaptive anomaly detection to ensure data quality and security during aggregation.A privacy-preserving federated learning protocol with differential privacy guarantees enables collaborative model training without centralizing sensitive data.The framework employs data-quality-aware weighted aggregation to enhance robustness against noisy and malicious client updates.Experimental evaluation on the GeoLife,PeMS-Bay,and SmartHome+datasets demonstrates that EdgeST-Fusion achieves 21.8%improvement in prediction accuracy,35.7%reduction in communication overhead,and 29.4%enhancement in security resilience compared to recent baselines.Real-world deployment across three smart city testbeds validates practical viability with 90.0%average accuracy and sub-250 ms inference latency.The proposed framework remains feasible for deployment on heterogeneous and resource-constrained consumer electronics devices whilemaintaining strong privacy guarantees and scalability for large-scale urban environments.
文摘To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determining the position of the grasping information.These results can then be used to guide the visually impaired or to execute grasping tasks with a robotic arm.In this paper,we collected,annotated,and published the benchmark TQUGraspingObject dataset for testing,validation,and evaluation of deep learning(DL)models for detecting,recognizing,and localizing grasping objects in 2D and 3D space,especially 3D point cloud data.Our dataset is collected in a shared room,with common everyday objects placed on the tabletop in jumbled positions by Intel RealSense D435(IR-D435).This dataset includes more than 63k RGB-D pairs and related data such as normalized 3D object point cloud,3D object point cloud segmented,coordinate system normalizationmatrix,3D object point cloud normalized,and hand pose for grasping each object.At the same time,we also conducted experiments on fourDL networks with the best performance:SSD-MobileNetV3,ResNet50-Transformer,ResNet101-Transformer,and YOLOv12.The results present that YOLOv12 has the most suitable results in detecting and recognizing objects in images.All data,annotations,toolkit,source code,point cloud data,and results are publicly available on our project website:https://github.com/HuaTThanhIT2327Tqu/datasetv2.
基金supported by the National Natural Science Foundation of China(32170824 and 32322027 to X.M.)HRHI program of Westlake Laboratory of Life Sciences and Biomedicine(1011103360222B1 to X.M.)+1 种基金"Pioneer"and"Leading Goose"R&D Program of Zhejiang(2024SSYS0034)State Key Laboratory of Gene Expression.
文摘Tumors are defined by uncontrolled cell proliferation(Hariharan and Bilder,2006).Benign tumors are typically slow-growing and localized,while malignant ones are invasive and aggressive.The nuclear receptor Eip75B(E75),a heme-binding protein responsive to ecdysone signaling,encodes three major isoforms,E75A,E75B,and E75C(Bialecki et al.,2002),among them,only E75A and E75C contain zinc finger domains that enable DNA binding.
基金financially supported by the National Natural Science Foundation of China (Nos. 22533003 and 22025302)financial support from the Ministry of Science and Technology of China (No. 2022YFA1203203)State Key Laboratory of Chemical Engineering (No. SKL-ChE23T01).
文摘Conformational entropy,one of the central concepts of polymer physics,is the key to revealing physical characteristics of polymers.Despite an increased repertoire of conformational-entropy effects in the structural formation,transition,and properties of polymer systems,the physical origin of conformational entropy remains less understood compared to interaction energy and other types of entropy.This review seeks to provide a conceptual framework unveiling several principles and rules of conformational entropy in governing the structures and properties of polymers,from the perspective of fundamental physics and statistical mechanics.First,we focus on the fundamentals of entropy in thermodynamics,leading to the theoretical basis for the elucidation of conformational entropy.Second,we delineate the physical nature of statistics and dissipation of conformational entropy and its essential dependence on the environmental heat bath.Next,we explore the principles of conformational entropy in driving the ordering transitions of various systems of polymers and their nanocomposites,elucidating the emergent and collective behaviors as well as the interplay between energetic interactions and entropy.Moreover,we demonstrate how the concept of conformational entropy is generalized to the biological systems and other soft matters.Finally,we discuss future directions to signify this framework originated from polymers.
文摘Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.
文摘The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution laws and fracture network characteristics of shale gas infill wells.A mechanism model of CN platform logging data and geomechanical parameters is established to simulate the influence of parent well’s production on the geostress in the infill well area.It is suggested that with the increase of production time,normal fault stress state and horizontal stress deflection will occur.The smaller the parent well spacing and the longer the production time,the earlier the normal fault stress state appears and the larger the range.Based on the model,the fracture network morphology and construction parameters of infill wells are optimized.parentparentparentparent The results indicate that:1:A well spacing of 500 m achieves a Pareto optimum between“full reserve coverage”and“stress barrier”;2:A parent well recovery degree of 30%corresponds to the critical point of stress reversal,where the lateral deflection rate of the infill fracture is less than 8%and the SRV loss is minimized;3:6-cluster intensive completion with twice the liquid intensity increases the fracture complexity index by 1.7 times,enhances well group EUR by 15.4%,and reduces single-well cost by 22%.This research fills the theoretical gap in the collaborative optimization of“multi-parameter,multi-objective and multi-constraint”and provide parameter optimization basis for shale gas infill well development in China and help to improve the development efficiency and economic benefits.
基金supported by the Creation and Talent Introduction Base of Prevention and Treatment of Diabetes and Its Complications withTraditional Chinese Medicine(B20055).
文摘Objective:To investigate the effects and potential mechanisms of action of Panax notoginseng(Burk)F.H.Chen(P.notoginseng,San Qi)flowers in type 2 diabetes mellitus(T2DM)using network pharmacology,in vivo experiments,and RNA sequencing(RNA-seq).Methods:Network pharmacology analysis was performed to identify and correlate the drug targets of flower buds of P.notoginseng(PNF)with T2DM disease targets and to predict the key targets and pathways involved in the therapeutic effects of PNF in T2DM.In vivo experiments were conducted to assess the effects of PNF on glucose and lipid metabolism in mice with T2DM.RNA-seq was performed,and the results were integrated with network pharmacology data to assess the therapeutic mechanisms of PNF in T2DM.The results from transcriptomics and network pharmacology were validated using real-time polymerase chain reaction.Results:A total of 27 intersecting targets were identified by overlapping 35 drug targets with T2DM targets.Further topological analysis using the Centiscape 2.2 tool revealed five core targets,including signal transducer and activator of transcription 3(STAT3).Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis indicated that the JAK/STAT signaling pathway is a key mechanism underlying the therapeutic effects of PNF in T2DM.In vivo experiments confirmed that PNF effectively regulates glycolipid metabolism in a mouse model of diabetes.KEGG pathway enrichment analysis of RNA-seq data highlighted the JAK2/STAT3 and PI3K/AKT pathway as a potential mechanism.PNF high-dose(PNFH)increased the gene expression levels of PIK3R1 and AKT2,decreased the expression of PCK1,JAK2,and STAT3,and showed a trend toward increasing INSR expression without reaching statistical significance.Conclusion:PNF improves glycolipid metabolism disorders in T2DM,potentially by modulating the JAK2/STAT3 and PI3K/AKT signaling pathway.
基金supported in part by the National Key Research and Development Program of China (Grant No.2020YFC2201504)the National Natural Science Foundation of China (Grant Nos.12588101 and 12535002)。
文摘We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parameter E(z)from the DESI BAO Alcock-Paczynski(AP)data using Gaussian process to perform the null test.We find strong evidence of accelerated expansion from the DESI BAO AP data.By reconstructing the deceleration parameter q(z) from the DESI BAO AP data,we find that accelerated expansion persisted until z■0.7 with a 99.7%confidence level.Additionally,to provide insights into the Hubble tension problem,we propose combining the reconstructed E(z) with D_(H)/r_(d) data to derive a model-independent result r_(d)h=99.8±3.1 Mpc.This result is consistent with measurements from cosmic microwave background(CMB)anisotropies using the ΛCDM model.We also propose a model-independent method for reconstructing the comoving angular diameter distance D_(M)(z) from the distance modulus μ,using SNe Ia data and combining this result with DESI BAO data of D_(M)/r_(d) to constrain the value of r_(d).We find that the value of r_(d),derived from this model-independent method,is smaller than that obtained from CMB measurements,with a significant discrepancy of at least 4.17σ.All the conclusions drawn in this paper are independent of cosmological models and gravitational theories.
基金support from the Roy A.Wilkens Professorship Endowment。
文摘Interferometry is a crucial investigative technique used across diverse fields to achieve highprecision measurements.It works by analyzing the phase difference between two interfering waves,which results from variations in optical path lengths within an interferometer.We introduce a novel method for directly measuring changes in the phase difference within an optical interferometer,importantly,with the added advantage of a controllable enhancement factor.This approach is achieved through a two-step process:first,the optical phase difference is encoded into a sub-GHz radiofrequency(RF)signal using microwave-photonic manipulation;then,RF interferometry-assisted phase amplification is implemented at the destructive interference point.In our experiments,we demonstrate a phase sensitivity of 2.14 rad∕nm operating at 140 MHz using a miniature in-fiber Fabry-Pérot interferometer for sub-nanometer displacement sensing,which reveals a sensitivity magnification factor of 258.6.With further refinement,we anticipate that even higher enhancement factors can be achieved,paving the way for the development of cost-effective,ultrasensitive interferometry-based instruments for high-precision optical measurements.
文摘Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.
基金supported by the National Natural Science Foundation of China (No.22007008)the LiaoNing Revitalization Talents Program (No.XLYC1907021)the Fundamental Research Funds for the Central Universities (Nos.DUT23YG120,DUT19RC(3)009)。
文摘The study of target proteins is crucial for understanding molecular interactions and developing analytical platforms,therapeutic agents and functional tools.Herein,we present a novel nanoplatform activated by near-infrared(NIR) light for triple-modal proteins study,which enabling target protein labeling,enrichment and visualization.Azido-naphthalimide-coated upconversion nanoparticles(UCNPs) serve as NIR light-responsive nanoplatforms,showing promising applications in studying interactions between various bioactive molecules and proteins in living systems.Under NIR light irradiation,azido-naphthalimides are activated by ultraviolet(UV) and blue light emitted from UCNPs and the resulting amino-naphthalimides intermediate not only crosslink nearby target proteins but also enable imaging performance.We demonstrate that this nanoplatform is capable of selective protein labeling and imaging in complex protein environments,achieving specific labeling and imaging of both intracellular and extracellular proteins in mammalian cells as well as bacteria.Furthermore,in vivo protein labeling has been achieved using this novel NIR light-activatable nanoplatform.This technique will open new avenues for discoveries and mechanistic interrogation in chemical biology.
基金supported through RIIM Competition funding from the Indonesia Endowment Fund for Education Agency,Ministry of Finance of the Republic of Indonesia and National Research and Innovation Agency of Indonesia according to the contract number:61/IV/KS/5/2023 and 2131/UN6.3.1/PT.00/2023.
文摘Corn starch(CS)is a renewable,biodegradable polysaccharide valued for its film-forming ability,yet native CS films exhibit lowmechanical strength,highwater sensitivity,and limited thermal stability.This study improves CS-based films by blending with poly(vinyl alcohol)(PVA)or glycerol(GLY)and using citric acid(CA)as a green,non-toxic cross-linker.Composite films were prepared by casting CS–PVA or CS-GLY with CA at 0%-0.20%(w/w of starch).The influence of CA on physicochemical,mechanical,optical,thermal,and water barrier properties was evaluated.CA crosslinking markedly enhanced the tensile strength,water resistance,and thermal stability of CS-PVA films while increasing transparency in CS–GLY films.At 0.20%CA,the composite achieved 34.99MPa tensile strength,reducedwater vapor permeability,andminimized water uptake.FTIR confirmed ester bond formation between CAand hydroxyl groups of CS,PVA,and GLY,whereas thermal analysis showed higher decomposition temperatures and lower weight loss in crosslinked films.Increasing CA levels also decreased opacity and improved light transmittance,indicating greater homogeneity and reduced crystallinity.This dual-polymer matrix combined with a natural crosslinking strategy provides a sustainable route to high-performance,biodegradable CS-based packaging materials.
基金supported by the Quantum Science and Technology-National Science and Technology Major Project of China(Grant No.2024ZD0301000)the National Natural Science Foundation of China(Grant No.12275136)。
文摘The Stern-Gerlach(SG)experiment is a fundamental experiment for revealing the existence of“spin”.In this experiment,beams of silver atoms are sent through inhomogeneous magnetic fields to observe their deflection.Thus,the conventional SG experiment can be viewed as a magnetic-type spin effect.In this work,we successfully generalize the SG effect from magnetic-type to electric-type by solving Dirac's equation with a potential barrier,revealing an extraordinary spin effect.Beams of Dirac particles can be regarded as matter waves.Based on Dirac's equation,we obtain the explicit forms of the incident,reflected,and transmitted waves.The electric-type SG effect shows that the reflected and transmitted waves can exhibit notable spatial shifts,which depend on the spin direction and the incident angle of the wave.The electrictype SG effect has potential applications for separating Dirac particles with different spin directions and for estimating the spin direction of Dirac particles.Some discussions related to the interaction between spin and the electric field are also presented.
基金the Inner Mongolia Natural Science Foundation(2023ZD05,2025JQ028,2025MS02001)the National Natural Science Foundation of China(22278238,22238004)+3 种基金the National Key Research and Development Program of China(2024YFE0211400)the Major Science and Technology Program of Inner Mongolia Autonomous Region(20212120326)the“Elite Talents Revitalize Inner Mongolia”Initiative–Tier-1 Talent Team(202410)the Ordos Science and Technology Breakthrough(JBGS2024003),and Ordos Laboratory for their financial support.
文摘Methanol,a crucial C1 intermediate,bridges traditional fossil-based chemical processes with emerging sustainable catalytic technologies by serving as both a versatile hydrogenation product from CO/CO_(2)and an active intermediate for hydrocarbon synthesis.Despite significant progress in methanol-to-hydrocarbon(MTH)conversion,a comprehensive understanding of reaction mechanisms remains essential to enhance catalyst design and industrial applicability.This review critically synthesizes recent advances in mechanistic insights related to methanol conversion and methanol-mediated catalytic processes.Firstly,we systematically outline key reaction pathways involved in initial carbon–carbon(C–C)bond formation through direct and indirect mechanisms,emphasizing significant breakthroughs from spectroscopic analyses and theoretical calculations.Subsequently,we highlight the autocatalytic characteristics and dual-cycle mechanisms underlying MTH processes,critically evaluating the roles of zeolite structures,pore sizes,topology,and acidity in governing product selectivity and catalyst stability.Additionally,we discuss cutting-edge developments in tandem catalytic systems employing methanol as a pivotal intermediate for CO_(x)hydrogenation,emphasizing the transferable mechanistic principles and catalytic insights.Finally,we identify future research directions,including elucidating precise hydrocarbon pool(HCP)intermediates,optimizing zeolite structures through computational-guided design,and developing robust catalytic systems leveraging advanced characterization methods and artificial intelligence.By integrating multidisciplinary approaches from catalytic science,materials engineering,and reaction engineering,this review provides actionable guidance towards rational design and optimization of advanced catalytic systems for efficient methanol conversion processes.
基金supported by the National Natural Science Foundation of China(Grant Nos.52175509 and 52450158)the National Key Research and Development Program of China(Grant No.2023YFF1500900)+2 种基金the Shenzhen Fundamental Research Program(Grant No.JCYJ20220818100412027)the Guangdong-Hong Kong Technology Cooperation Funding Scheme Category C Platform(Grant No.SGDX20230116093543005)the Innovation Project of Optics Valley Laboratory(Grant No.OVL2023PY003)。
文摘Point-of-care diagnostics and inline quantitative phase imaging(QPI)drive the demand for portable,ultra-miniaturized,and robust optical imaging and metrology systems.We propose and demonstrate a wavefront sensor integrated into a photonic integrated circuit,enabling single-shot optical phase retrieval.We implemented an integrated wavefront sensor array with a spatial resolution of 17μm and a numerical aperture of 0.1.Furthermore,we experimentally demonstrated the reconstruction of wavefronts defined by Zernike polynomials,specifically the first 14 terms(Z_(1)to Z_(14)),achieving an average root mean square error below 0.07.This advancement paves the way for fully integrated,portable,and robust optical imaging systems,facilitating integrated wavefront sensors in demanding applications such as point-of-care diagnostics,endoscopy,in situ QPI,and inline surface profile measurement.
基金supported by National Natural Science Foundation of China(No.52103044)Double First-Class Initiative University of Science and Technology of China(KY2400000037)the Young Talent Programme(GG2400007009).
文摘Conductive elastomers combining micromechanical sensitivity,lightweight adaptability,and environmental sustainability are critically needed for advanced flexible electronics requiring precise responsiveness and long-term wearability;however,the integration of these properties remains a significant challenge.Here,we present a biomass-derived conductive elastomer featuring a rationally engineered dynamic crosslinked network integrated with a tunable microporous architecture.This structural design imparts pronounced micromechanical sensitivity,an ultralow density(~0.25 g cm^(−3)),and superior mechanical compliance for adaptive deformation.Moreover,the unique micro-spring effect derived from the porous architecture ensures exceptional stretchability(>500%elongation at break)and superior resilience,delivering immediate and stable electrical response under both subtle(<1%)and large(>200%)mechanical stimuli.Intrinsic dynamic interactions endow the elastomer with efficient room temperature self-healing and complete recyclability without compromising performance.First-principles simulations clarify the mechanisms behind micropore formation and the resulting functionality.Beyond its facile and mild fabrication process,this work establishes a scalable route toward high-performance,sustainable conductive elastomers tailored for next-generation soft electronics.
基金This work was supported by Riset dan Inovasi untuk Indonesia Maju(RIIM)Kompetisi scheme(Grant number:48/II.7/HK/2025)RP ORNM 2025,National Collaborative Research/RiNa(No.499/2023)Penelitian Dosen Pemula grant from the Ministry of Higher Education,Science and Technology,Republic of Indonesia(1483az/IT9.2.1/PT.01.03/2025).
文摘The increasing need for efficient,sustainable,and environmentally friendly adsorbent materials has driven interest in bio-based alternatives.Conventional silica-based adsorbents,while effective,are often brittle and energy-intensive to produce.In contrast,wood offers a renewable and low-energy option with natural porosity suitable for adsorption.This study investigated the fabrication of wood sponge from tropical balsa(Ochroma bicolor)and pulai(Alstonia scholaris)wood through a dual-stage delignification process as a novel bio-based adsorbent.The process involved alkaline treatment using sodium sulfite(Na_(2)SO_(3))and sodium hydroxide(NaOH)at 100℃for 8,9,and 10 h,followed by bleaching process using hydrogen peroxide(H_(2)O_(2))at 100℃for 1,2,and 3 h.The treated samples were then rinsed to neutral pH,frozen at−20℃for 24 h,and freeze-dried at−50℃for 48 h.The results revealed a notable reduction in density and specific gravity,accompanied by increased weight loss and pore diameter size with prolonged delignification process.Optical changes showed increased translucency and layered structures,particularly in balsa wood.FTIR analysis confirmed a reduction in lignin and hemicellulose content,validating the chemical modification within the treated samples.The resulting wood sponges exhibited good porosity and adsorption capacity,ranging from 1.3 to 5.7 g/g.The optimal treatment—10 h of alkaline delignification followed by 3 h of H_(2)O_(2)bleaching—demonstrated the highest performance,highlighting the potential of tropical wood species as efficient,biodegradable,and eco-friendly adsorbent materials.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting ProjectNumber(PNURSP2025R97),PrincessNourah bint AbdulrahmanUniversity,Riyadh,Saudi Arabia.
文摘The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expose them to increasingly sophisticated and persistentmalware attacks.These adaptive and stealthy threats can evade conventional detection,establish remote control,propagate across devices,exfiltrate sensitive data,and compromise network integrity.This study presents a Software-Defined Internet of Things(SD-IoT)control-plane-based,AI-driven framework that integrates Gated Recurrent Units(GRU)and Long Short-TermMemory(LSTM)networks for efficient detection of evolving multi-vector,malware-driven botnet attacks.The proposed CUDA-enabled hybrid deep learning(DL)framework performs centralized real-time detection without adding computational overhead to IoT nodes.A feature selection strategy combining variable clustering,attribute evaluation,one-R attribute evaluation,correlation analysis,and principal component analysis(PCA)enhances detection accuracy and reduces complexity.The framework is rigorously evaluated using the N_BaIoT dataset under k-fold cross-validation.Experimental results achieve 99.96%detection accuracy,a false positive rate(FPR)of 0.0035%,and a detection latency of 0.18 ms,confirming its high efficiency and scalability.The findings demonstrate the framework’s potential as a robust and intelligent security solution for next-generation IoT ecosystems.
