Schizophrenia is a complex psychiatric disorder marked by positive and negative symptoms,leading to mood disturbances,cognitive impairments,and social withdrawal.While anti-psychotic medications remain the cornerstone...Schizophrenia is a complex psychiatric disorder marked by positive and negative symptoms,leading to mood disturbances,cognitive impairments,and social withdrawal.While anti-psychotic medications remain the cornerstone of treatment,they often fail to fully address certain symptoms.Additionally,treatment-resistant schizophrenia,affecting 30%-40%of patients,remains a substantial clinical challenge.Positive,negative symptoms and cognitive impairments have been linked to disruptions in the glutamatergic,serotonin,GABAergic,and muscarinic pathways in the brain.Recent advances using genome-wide association study and other approaches have uncovered a significant number of new schizophrenia risk genes that uncovered new,and reinforced prior,concepts on the genetic and neurological underpinnings of schizophrenia,including abnormalities in synaptic function,immune processes,and lipid metabolism.Concurrently,new therapeutics targeting different modalities,which are expected to address some of the limitations of anti-psychotic drugs currently being offered to patients,are currently being evaluated.Collectively,these efforts provide new momentum for the next phase of schizophrenia research and treatment.展开更多
Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Lever...Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Leveraging IoVtechnologies,operational data fromcore vehicle components can be collected and analyzed to construct fault diagnosis models,thereby enhancing vehicle safety.However,automakers often struggle to acquire sufficient fault data to support effective model training.To address this challenge,a robust and efficient federated learning method(REFL)is constructed for machinery fault diagnosis in collaborative IoV,which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally.In the REFL,the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness.Moreover,the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios.The proposed REFL is evaluated on non-independent and identically distributed(non-IID)real-world machinery fault dataset.Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis.展开更多
In this paper, a high calcium high sulfate ash as the main material, adding fly ash, lime, cement, gypsum and some modifiers to prepare autoclaved aerated concrete. The products complies with the technical requirement...In this paper, a high calcium high sulfate ash as the main material, adding fly ash, lime, cement, gypsum and some modifiers to prepare autoclaved aerated concrete. The products complies with the technical requirements of GB/T11968-2006. This paper also studies the influence of the physical methods and water ratio on autoclaved aerated concrete by high calcium high sulfate ash aerated concrete. The best ratio of water and Grinding time were found in practice study.展开更多
The study of metamaterials is among the most important and attractive topics of the electromagnetic field theory and applications in the past 15 years.Much effort has been devoted to scientific research into the new p...The study of metamaterials is among the most important and attractive topics of the electromagnetic field theory and applications in the past 15 years.Much effort has been devoted to scientific research into the new physical phenomena with great progress.This paper presents the thoughts about the applications of metamaterials in innovative antenna designs from an engineering perspective.The new understanding of metamaterials offers us great possibility to translate the physical concepts of metamaterials in laboratories to innovative antenna designs in practical engineering applications.The technologies have been successfully developed,significantly improving key performances of antennas at microwave and millimeter-wave bands.The recently invented metamaterial-based antennas demonstrate not only wide operating bandwidth,high antenna efficiency,high gain,but also significantly reduced volume with simple mechanical structures.展开更多
The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide...The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide Band(UWB)technology has emerged as a promising candidate for addressing this need,offering high precision,immunity to multipath interference,and robust performance in challenging environments.In this comprehensive survey,we systematically explore UWB-based localization for mobile autonomous machines,spanning from fundamental principles to future trends.To the best of our knowledge,this review paper stands as the pioneer in systematically dissecting the algorithms of UWB-based localization for mobile autonomous machines,covering a spectrum from bottom-ranging schemes to advanced sensor fusion,error mitigation,and optimization techniques.By synthesizing existing knowledge,evaluating current methodologies,and highlighting future trends,this review aims to catalyze progress and innovation in the field,unlocking new opportunities for mobile autonomous machine applications across diverse industries and domains.Thus,it serves as a valuable resource for researchers,practitioners,and stakeholders interested in advancing the state-of-the-art UWB-based localization for mobile autonomous machines.展开更多
Refractory high-entropy alloys(RHEAs)are promising for high-temperature applications due to their ex-ceptional mechanical properties at high temperatures.However,limited studies on their high-temperature fatigue behav...Refractory high-entropy alloys(RHEAs)are promising for high-temperature applications due to their ex-ceptional mechanical properties at high temperatures.However,limited studies on their high-temperature fatigue behavior hinder further development.This study systematically investigates the low-cycle fatigue(LCF)behavior of HfNbTiZr RHEA at room temperature(25℃)and elevated temperatures(350,450,and 600℃)through a combination of experimental analyses and dislocation-based damage-coupled crystal plasticity finite element(CPFE)simulations,to unveil the effects of creep damage on LCF behavior at varying temperatures.The results indicate that the LCF life dramatically decreases at an increased tem-perature,shifting from transgranular fatigue damage at lower temperatures(25-350℃)to a dual damage mechanism involving both intergranular fatigue and creep damage at higher temperatures(450-600℃).At 600℃,creep damage notably contributes to the accumulation of geometrically necessary dislocations(GNDs),crack initiation,and propagation at grain boundaries,and thus accelerates LCF failure.Compara-tive CPFE simulations reveal that creep damage significantly contributes to cyclic softening and reduction in elastic modulus,which also amplifies the strain localization under the LCF loading.The contribution of creep damage to the total stored energy density(SED)representing the overall damage increases with temperatures,accounting for 11%at 600℃.Additionally,CPFE simulations indicate that the creep dam-age notably influences the magnitude of GND density localized at grain boundaries.This study provides critical insights into the fatigue damage mechanisms of RHEAs,offering valuable guidance for their ap-plication in high temperatures.展开更多
Marine infrastructure is increasingly vulnerable to harsh environmental conditions that accelerate the degradation of traditional materials such as Portland cement concrete and carbon steel.This review systematically ...Marine infrastructure is increasingly vulnerable to harsh environmental conditions that accelerate the degradation of traditional materials such as Portland cement concrete and carbon steel.This review systematically investigates recent advancements in sustainable alternatives,including geopolymer concrete,engineered innovacementitious composites(ECC),bio-concrete,fiber-reinforced polymers(FRPs),and bamboo,stainless steel,and steel-CFRP hybrid bars.Each material is evaluated based on marine durability,mechanical performance,environmental impact,and cost feasibility using life cycle assessment,durability modelling,and a multi-criteria decisionsupport framework.The results reveal that geopolymer concrete and FRP reinforcement’s exhibit superior corrosion resistance and environmental benefits,while ECC and steel-CFRP composites offer structural resilience with moderate environmental trade-offs.However,challenges remain in long-term performance validation,standardization,and market integration.The review concludes that a combined approach involving innovative materials,computational tools,and sustainability assessment is essential for advancing marine infrastructure.Outlook recommendations include focused field studies,development of regulatory guidelines,and interdisciplinary collaboration to drive the practical adoption of eco-efficient materials in coastal and offshore construction.展开更多
Metal oxohydroxides(MOOH) are widely accepted as the true active species for oxygen evolution reaction(OER).However,the MOOH converted from precatalysts usually exhibits better catalytic performance than those directl...Metal oxohydroxides(MOOH) are widely accepted as the true active species for oxygen evolution reaction(OER).However,the MOOH converted from precatalysts usually exhibits better catalytic performance than those directly synthesized.The underlying structural reason for this phenomenon remains controversial.In this work,CoOOH and Co(OH)2with similar morphology are employed as model catalysts to investigate the origin of in-situ converted catalyst s high activity,as Co(OH)2can be fully converted to CoOOH during OER.In-situ Raman,electron paramagnetic resonance,HR-TEM,and X-ray spectroscopic studies reveal that O vacancies in the CoOOH converted from Co(OH)2play a key role in its higher intrinsic activity towards OER than directly synthesized CoOOH.Furthermore,theoretical calculations and electrochemical methods indicate that O vacancies in CoOOH affect the interaction between Co-O bond,downshift the d-band center of Co,further weaken the adsorption of OH*,and finally facilitate the OER process over CoOOH.This work not only provides a deep understanding of pre-catalyst's high OER activity by taking Co(OH)2as an example but also deliver insights into the activation process of other electrochemic al oxidation reactions.展开更多
Ocean Renewable Energy(ORE)systems—comprising wind,wave,tidal,and ocean thermal energy—are increasingly seen as viable alternatives to fossil fuels.However,their integration into the power grid is hindered by enviro...Ocean Renewable Energy(ORE)systems—comprising wind,wave,tidal,and ocean thermal energy—are increasingly seen as viable alternatives to fossil fuels.However,their integration into the power grid is hindered by environmental sensitivity,dynamic ocean conditions,and high maintenance demands.Artificial Intelligence(AI)offers promising solutions to these challenges by enabling intelligent,adaptive,and resilient energy systems.This review explores AI applications in ORE,focusing on three critical domains:optimization,forecasting,and control.Optimization techniques,including Genetic Algorithms(GA)and Swarm Intelligence(SI),are employed to enhance device efficiency,improve energy capture,optimize farm layouts,reduce environmental impacts,and lower installation costs.Forecasting uses Machine Learning(ML)and Deep Learning(DL)models to predict wave height,tidal flow,and energy output,aiding in grid integration and energy scheduling.In control systems,AI approaches like Reinforcement Learning(RL)and Fuzzy Logic ensure real-time responsiveness and predictive maintenance,improving system reliability in dynamic marine environments.Emerging technologies such as Edge AI enable decentralized computation for real-time decision-making,while Digital Twin frameworks simulate and predict system performance before deployment.Explainable AI(XAI)is also discussed to ensure transparent and trustworthy decision-making.Ethical and regulatory concerns are acknowledged to ensure responsible AI integration in ocean settings.Overall this review offers a comprehensive synthesis of how AI enhances the performance,efficiency,and scalability of ORE systems.It serves as a valuable resource for researchers,policymakers,and industry professionals seeking to advance clean,smart,and sustainable ocean energy solutions.展开更多
High entropy alloys(HEAs),particularly CoCrNiFeMn system,have emerged as a transformative class of high-performance alloys due to their exceptional mechanical and functional properties.However,traditional manufacturin...High entropy alloys(HEAs),particularly CoCrNiFeMn system,have emerged as a transformative class of high-performance alloys due to their exceptional mechanical and functional properties.However,traditional manufacturing methods for HEAs are limited by inefficiencies and high costs,restricting their widespread applications.Additive manufacturing(AM),specifically laser powder bed fusion(LPBF),offers a promising alternative by enabling the fabrication of HEAs with unique microstructures and enhanced properties.This study investigates the thermal stability and mechanical performance of LPBF-printed CoCrNiFeMn HEA across a wide temperature range.The as-built LPBF HEA with a hierarchically heterogeneous microstructure,featured by columnar grains and ultrafine dislocation cellular structure,demonstrates exceptional thermal stability,with minimal hardness reduction and no apparent recrystallisation even after prolonged exposure to high temperatures(up to 1373 K),in stark contrast to the significant property degradation observed in conventionally processed HEAs.This stability is attributed to the unique dislocation cellular structures and the intrinsic thermal self-stabilizing effects induced by the LPBF process and the inhibition of recrystallisation due to the low stored energy and columnar grain morphology.The LPBF-fabricated HEA also exhibits outstanding strength-ductility synergy across a broad temperature spectrum,with cryogenic deformation enhancing both strength and ductility due to the activation of deformation twinning.At elevated temperatures,the alloy undergoes a slight reduction in strength but retains good ductility,except at 873 K,where a sharp decline in ductility is observed likely due to grain boundary decohesion and porosity-related crack initiation manifested by the cleavage fracture surface and the cracks at grain boundaries.These findings provide new insights into the temperature-dependent mechanical behavior of AM HEAs,highlight the critical role of dislocation cellular structures in achieving superior thermal and mechanical performance,and underscore the potential of additively manufactured HEAs with tailored microstructures for extreme environments.展开更多
Moldy core is a serious internal defect in pears.Since there is no significant difference in appearance between the healthy pears and those with mild moldy core,it is still a great challenge for the early detection of...Moldy core is a serious internal defect in pears.Since there is no significant difference in appearance between the healthy pears and those with mild moldy core,it is still a great challenge for the early detection of moldy pear core.This study transformed the vibration acoustic signals(VA signal)of pears into recurrence plots and Markov transition field to enable image-based classification of moldy cores.In addition to traditional machine-learning baselines(Random Forest and k-Nearest Neighbors)trained on LBP-extracted texture features from RP/MTF,the deep models were constructed and compared,which include ResNet-101,DenseNet-121,SqueezeNet,Vision Transformer(ViT),and an improved SqueezeNet(ISqueezeNet).Hyperparameters were tuned via Bayesian optimization over optimizer type,learning rate,batch size,and L2 weight decay,yielding model-specific optimal settings.Under these configurations,the ISqueezeNet achieved the highest test accuracy of 93.05%,with class-wise accuracies of 89.28%(healthy),96.15%(slight),and 94.44%(moderate and severe).Comparisons with lightweight networks(MobileNetV1 and ShuffleNetV2)further showed that ISqueezeNet attains superior accuracy with favorable parameter efficiency and inference speed.Grad-CAM visualizations confirmed that the model focuses on lesionrelevant regions,supporting interpretability and practical reliability.These results indicate that the proposed approach is promising for early,nondestructive detection of moldy pear cores.展开更多
Surface-supported clusters forming by aggregation of excessive adatoms could be the main defects of 2D materials after chemical vapor deposition.They will significantly impact the electronic/magnetic properties.Moreov...Surface-supported clusters forming by aggregation of excessive adatoms could be the main defects of 2D materials after chemical vapor deposition.They will significantly impact the electronic/magnetic properties.Moreover,surface supported atoms are also widely explored for high active and selecting catalysts.Severe deformation,even dipping into the surface,of these clusters can be expected because of the very active edge of clusters and strong interaction between supported clusters and surfaces.However,most models of these clusters are supposed to simply float on the top of the surface because ab initio simulations cannot afford the complex reconstructions.Here,we develop an accurate graph neural network machine learning potential(MLP)from ab initio data by active learning architecture through fine-tuning pre-trained models,and then employ the MLP into Monte Carlo to explore the structural evolutions of Mo and S clusters(1-8 atoms)on perfect and various defective MoS2 monolayers.Interestingly,Mo clusters can always sink and embed themselves into MoS2 layers.In contrast,S clusters float on perfect surfaces.On the defective surface,a few S atoms will fill the vacancy and rest S clusters float on the top.Such significant structural reconstructions should be carefully taken into account.展开更多
Paired electrolysis of waste feedstocks holds an energy-efficient alternative for chemical production;however,the sluggish anodic oxidation limited the total efficiency under larger current density.Herein,we construct...Paired electrolysis of waste feedstocks holds an energy-efficient alternative for chemical production;however,the sluggish anodic oxidation limited the total efficiency under larger current density.Herein,we constructed ultralow-coordinated Ni species with Ni–O coordination number of∼3 via a hydrothermal synthesis-sulfidation-annealing process and electrochemical activation and demonstrated the vital role in accelerating the proton deintercalation and reactive oxygen intermediate·OH formation during electro-reforming polyethylene terephthalate hydrolysate(POR).The target catalyst NiCoSx/NF afforded a high formate productivity of 7.4 mmol cm^(−2)h^(−1)at∼600 mA cm^(−2)with a formate Faradic efficiency(FE_(formate))of 92.4%in POR and maintained a FE_(formate)of∼90%for 100 h at 2 A in a membrane electrode assembly electrolyzer.Coupling POR on NiCoSx/NF with carbon dioxide reduction reaction on oxygen vacancies enriched Vo-BiSnO reached effective concurrent formate production with 172.7%of FE_(formate)at 500 mA cm^(−2)and long-term stability.Such excellent performance shows the great prospect of electrocatalyst design by regulating the local metal environment.展开更多
Recent advances in additive manufacturing have enabled the construction of metallic lattice structures with tailored mechanical and functional properties.One potential application of metallic lattice struc-tures is in...Recent advances in additive manufacturing have enabled the construction of metallic lattice structures with tailored mechanical and functional properties.One potential application of metallic lattice struc-tures is in the impact load mitigation where an external kinetic energy is absorbed by the deformation/crushing of lattice cells.This has motivated a growing number of experimental and numerical studies,recently,on the crushing behavior of additively produced lattice structures.The present study overviews the dynamic and quasi-static crushing behavior of additively produced Ti64,316L,and AlSiMg alloy lattice structures.The first part of the study summarizes the main features of two most commonly used additive processing techniques for lattice structures,namely selective-laser-melt(SLM)and electro-beam-melt(EBM),along with a description of commonly observed process induced defects.In the second part,the deformation and strain rate sensitivities of the selected alloy lattices are outlined together with the most widely used dynamic test methods,followed by a part on the observed micro-structures of the SLM and EBM-processed Ti64,316L and AlSiMg alloys.Finally,the experimental and numerical studies on the quasi-static and dynamic compression behavior of the additively processed Ti64,316L,and AlSiMg alloy lattices are reviewed.The results of the experimental and numerical studies of the dynamic properties of various types of lattices,including graded,non-uniform strut size,hollow,non-uniform cell size,and bio-inspired,were tabulated together with the used dynamic testing methods.The dynamic tests have been noted to be mostly conducted in compression Split Hopkinson Pressure Bar(SHPB)or Taylor-and direct-impact tests using the SHPB set-up,in all of which relatively small-size test specimens were tested.The test specimen size effect on the compression behavior of the lattices was further emphasized.It has also been shown that the lattices of Ti64 and AlSiMg alloys are relatively brittle as compared with the lattices of 316L alloy.Finally,the challenges associated with modelling lattice structures were explained and the micro tension tests and multi-scale modeling techniques combining microstructural characteristics with macroscopic lattice dynamics were recommended to improve the accuracy of the numerical simulations of the dynamic compression deformations of metallic lattice structures.展开更多
With growing concerns for global warming and environmental issues,the research community has contributed significantly to green technology in the area of material science through the development of natural fiber-reinf...With growing concerns for global warming and environmental issues,the research community has contributed significantly to green technology in the area of material science through the development of natural fiber-reinforced polymer composites(NFRPC).Polymers serve as the matrix in NFRPC,while natural fibers serve as the reinforcing materials.Demand for high-performing materials made with natural resources is growing continuously.Natural fiber-reinforced polymer composites are sustainable biocomposites fabricated with natural fibers embedded with a polymer matrix.They offer a wide range of advantages,including a low weight-to-strength ratio,high flexural strength,damping properties,and resistance to corrosion,wear,and impact.Understanding the basic properties,characteristics,and processing techniques for natural fibers is important to consider their use as raw materials for high-quality biocomposite.Natural fibers come with low density and a high strengthto-weight ratio,allowing them to be a potential reinforcement for low-weight composites.This article attempts to present a comprehensive review of the available natural fibers,their classification,types,structures,physical properties,characteristics,and mechanical properties.Natural fibers are hydrophilic in nature and require physical and chemical treatment prior to their application as reinforcing material.This review will also cover the required physical and chemical treatments of natural fibers for fabricating biocomposites.展开更多
The sustainability of methane catalytic decomposition is significantly enhanced by the production of high-quality value-added carbon products such as carbon nanotubes(CNTs).Understanding the production yields and prop...The sustainability of methane catalytic decomposition is significantly enhanced by the production of high-quality value-added carbon products such as carbon nanotubes(CNTs).Understanding the production yields and properties of CNTs is crucial for improving process feasibility and sustainability.This study employs machine learning technique to develop and analyze predictive models for the carbon yield and mean diameter of CNTs produced through methane catalytic decomposition.Utilizing comprehensive datasets from various experimental studies,the models incorporate variables related to catalyst composition,catalyst preparation,and operational parameters.Both models achieved high predictive accuracy,with R^(2)values exceeding 0.90.Notably,the reduction time during catalyst preparation was found to critically influence carbon yield,evidenced by a permutation importance value of 39.62%.Additionally,the use of Mo as a catalytic metal was observed to significantly reduce the diameter of produced CNTs.These findings highlight the need for future machine learning and simulation studies to include catalyst reduction parameters,thereby enhancing predictive accuracy and deepening process insights.This research provides strategic guidance for optimizing methane catalytic decomposition to produce enhanced CNTs,aligning with sustainability goals.展开更多
The synergy of single atoms(SAs)and nanoparticles(NPs)has demonstrated great potential in promoting the electrocatalytic carbon dioxide reduction reaction(CO_(2)RR);however,the rationalization of the SAs/NPs proportio...The synergy of single atoms(SAs)and nanoparticles(NPs)has demonstrated great potential in promoting the electrocatalytic carbon dioxide reduction reaction(CO_(2)RR);however,the rationalization of the SAs/NPs proportion remains one challenge for the catalyst design.Herein,a Ni2+-loaded porous poly(ionic liquids)(PIL)precursor synthesized through the free radical self-polymerization of the ionic liquid monomer,1-allyl-3-vinylimidazolium chloride,was pyrolyzed to prepare the Ni,N co-doped carbon materials,in which the proportion of Ni SAs and NPs could be facilely modulated by controlling the annealing temperature.The catalyst Ni-NC-1000 with a moderate proportion of Ni SAs and NPs exhibited high efficiency in the electrocatalytic conversion of CO_(2)into CO.Operando Ni K-edge X-ray absorption near-edge structure(XANES)spectra and theoretical calculations were conducted to gain insight into the synergy of Ni SAs and NPs.The charge transfer from Ni NPs to the surrounding carbon layer and then to the Ni SAs resulted in the electron-enriched Ni SAs active sites.In the electroreduction of CO_(2),the coexistence of Ni SAs and NPs strengthened the CO_(2)activation and the affinity towards the key intermediate of*COOH,lowering the free energy for the potential-determining*CO_(2)→*COOH step,and therefore promoted the catalysis efficiency.展开更多
The NIST Cybersecurity Framework (NIST CSF) serves as a voluntary guideline aimed at helping organizations, tiny and medium-sized enterprises (SMEs), and critical infrastructure operators, effectively manage cyber ris...The NIST Cybersecurity Framework (NIST CSF) serves as a voluntary guideline aimed at helping organizations, tiny and medium-sized enterprises (SMEs), and critical infrastructure operators, effectively manage cyber risks. Although comprehensive, the complexity of the NIST CSF can be overwhelming, especially for those lacking extensive cybersecurity resources. Current implementation tools often cater to larger companies, neglecting the specific needs of SMEs, which can be vulnerable to cyber threats. To address this gap, our research proposes a user-friendly, open-source web platform designed to simplify the implementation of the NIST CSF. This platform enables organizations to assess their risk exposure and continuously monitor their cybersecurity maturity through tailored recommendations based on their unique profiles. Our methodology includes a literature review of existing tools and standards, followed by a description of the platform’s design and architecture. Initial tests with SMEs in Burkina Faso reveal a concerning cybersecurity maturity level, indicating the urgent need for improved strategies based on our findings. By offering an intuitive interface and cross-platform accessibility, this solution aims to empower organizations to enhance their cybersecurity resilience in an evolving threat landscape. The article concludes with discussions on the practical implications and future enhancements of the tool.展开更多
Photocatalytic membranes hold significant potential for promoting pollutant degradation and reducing membrane fouling in filtration systems.Although extensive research has been conducted on the independent design of p...Photocatalytic membranes hold significant potential for promoting pollutant degradation and reducing membrane fouling in filtration systems.Although extensive research has been conducted on the independent design of photocatalysts or membrane materials to improve their catalytic and filtration performance,the complex structures and interface mechanisms,as well as insufficient light utilization,are still often overlooked,limiting the overall performance improvement of photocatalytic membranes.This work provides an overview of enhancement strategies involving restricted area effects,external fields,such as mechanical,magnetic,thermal,and electrical fields,as well as coupling techniques with advanced oxidation processes(e.g.,O_(3),Fenton,and persulfate oxidation)for dual enhancement of photocatalysts and membranes.In addition,the synthesis method of photocatalytic membranes and the influence of factors,such as light source type,frequency,and relative position on photocatalytic membrane performance were also studied.Finally,economic feasibility and pollutant removal performance were further evaluated to determine the promising enhancement strategies,paving the way for more efficient and scalable applications of photocatalytic membranes.展开更多
CrCoNi medium entropy alloy(MEA)fabricated by laser powder bed fusion(LPBF)benefits from its distinctive hierarchical microstructure and has great potential as a structural material.However,while the intriguing chemic...CrCoNi medium entropy alloy(MEA)fabricated by laser powder bed fusion(LPBF)benefits from its distinctive hierarchical microstructure and has great potential as a structural material.However,while the intriguing chemical short-range order(CSRO)widely exists in high/medium entropy alloys,its formation in the LPBF-built samples still lacks enough understanding.In this study,we verified its existence by fine transmission electron microscopy characterizations and utilized hybrid Monte Carlo/molecular dynamics simulations to investigate the features and effects of CSRO in LPBF-built CrCoNi MEA(AM model).Results showed that the CSRO fraction and the stacking fault energy of the AM model lie between those of the well-annealed and random solid solution counterparts.Among these models,the AM model exhibited the best strain hardening ability due to its highest capability to generate and store sessile dislocations.The results agreed well with existing data and provide guidance to the future development of LPBF-built CrCoNi MEA.展开更多
基金supported by the Ministry of Health National Medical Research Council (to JL)the National University of Singapore (to JJEC)
文摘Schizophrenia is a complex psychiatric disorder marked by positive and negative symptoms,leading to mood disturbances,cognitive impairments,and social withdrawal.While anti-psychotic medications remain the cornerstone of treatment,they often fail to fully address certain symptoms.Additionally,treatment-resistant schizophrenia,affecting 30%-40%of patients,remains a substantial clinical challenge.Positive,negative symptoms and cognitive impairments have been linked to disruptions in the glutamatergic,serotonin,GABAergic,and muscarinic pathways in the brain.Recent advances using genome-wide association study and other approaches have uncovered a significant number of new schizophrenia risk genes that uncovered new,and reinforced prior,concepts on the genetic and neurological underpinnings of schizophrenia,including abnormalities in synaptic function,immune processes,and lipid metabolism.Concurrently,new therapeutics targeting different modalities,which are expected to address some of the limitations of anti-psychotic drugs currently being offered to patients,are currently being evaluated.Collectively,these efforts provide new momentum for the next phase of schizophrenia research and treatment.
基金supported in part by National key R&D projects(2024YFB4207203)National Natural Science Foundation of China(52401376)+3 种基金the Zhejiang Provincial Natural Science Foundation of China under Grant(No.LTGG24F030004)Hangzhou Key Scientific Research Plan Project(2024SZD1A24)“Pioneer”and“Leading Goose”R&DProgramof Zhejiang(2024C03254,2023C03154)Jiangxi Provincial Gan-Po Elite Support Program(Major Academic and Technical Leaders Cultivation Project,20243BCE51180).
文摘Recently,Internet ofThings(IoT)has been increasingly integrated into the automotive sector,enabling the development of diverse applications such as the Internet of Vehicles(IoV)and intelligent connected vehicles.Leveraging IoVtechnologies,operational data fromcore vehicle components can be collected and analyzed to construct fault diagnosis models,thereby enhancing vehicle safety.However,automakers often struggle to acquire sufficient fault data to support effective model training.To address this challenge,a robust and efficient federated learning method(REFL)is constructed for machinery fault diagnosis in collaborative IoV,which can organize multiple companies to collaboratively develop a comprehensive fault diagnosis model while keeping their data locally.In the REFL,the gradient-based adversary algorithm is first introduced to the fault diagnosis field to enhance the deep learning model robustness.Moreover,the adaptive gradient processing process is designed to improve the model training speed and ensure the model accuracy under unbalance data scenarios.The proposed REFL is evaluated on non-independent and identically distributed(non-IID)real-world machinery fault dataset.Experiment results demonstrate that the REFL can achieve better performance than traditional learning methods and are promising for real industrial fault diagnosis.
文摘In this paper, a high calcium high sulfate ash as the main material, adding fly ash, lime, cement, gypsum and some modifiers to prepare autoclaved aerated concrete. The products complies with the technical requirements of GB/T11968-2006. This paper also studies the influence of the physical methods and water ratio on autoclaved aerated concrete by high calcium high sulfate ash aerated concrete. The best ratio of water and Grinding time were found in practice study.
文摘The study of metamaterials is among the most important and attractive topics of the electromagnetic field theory and applications in the past 15 years.Much effort has been devoted to scientific research into the new physical phenomena with great progress.This paper presents the thoughts about the applications of metamaterials in innovative antenna designs from an engineering perspective.The new understanding of metamaterials offers us great possibility to translate the physical concepts of metamaterials in laboratories to innovative antenna designs in practical engineering applications.The technologies have been successfully developed,significantly improving key performances of antennas at microwave and millimeter-wave bands.The recently invented metamaterial-based antennas demonstrate not only wide operating bandwidth,high antenna efficiency,high gain,but also significantly reduced volume with simple mechanical structures.
文摘The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide Band(UWB)technology has emerged as a promising candidate for addressing this need,offering high precision,immunity to multipath interference,and robust performance in challenging environments.In this comprehensive survey,we systematically explore UWB-based localization for mobile autonomous machines,spanning from fundamental principles to future trends.To the best of our knowledge,this review paper stands as the pioneer in systematically dissecting the algorithms of UWB-based localization for mobile autonomous machines,covering a spectrum from bottom-ranging schemes to advanced sensor fusion,error mitigation,and optimization techniques.By synthesizing existing knowledge,evaluating current methodologies,and highlighting future trends,this review aims to catalyze progress and innovation in the field,unlocking new opportunities for mobile autonomous machine applications across diverse industries and domains.Thus,it serves as a valuable resource for researchers,practitioners,and stakeholders interested in advancing the state-of-the-art UWB-based localization for mobile autonomous machines.
基金National Science Foundation of China(Nos.52401212 and52401214)the National Science Foundation of Jiangsu Province(No.BK20241020)+1 种基金the Avi-ation Foundation(No.2023Z0530S6004)the Jiangsu Province University Collaborative Innovation Centre(High-Tech Ships)Pro-gram(No.XTCX202401).
文摘Refractory high-entropy alloys(RHEAs)are promising for high-temperature applications due to their ex-ceptional mechanical properties at high temperatures.However,limited studies on their high-temperature fatigue behavior hinder further development.This study systematically investigates the low-cycle fatigue(LCF)behavior of HfNbTiZr RHEA at room temperature(25℃)and elevated temperatures(350,450,and 600℃)through a combination of experimental analyses and dislocation-based damage-coupled crystal plasticity finite element(CPFE)simulations,to unveil the effects of creep damage on LCF behavior at varying temperatures.The results indicate that the LCF life dramatically decreases at an increased tem-perature,shifting from transgranular fatigue damage at lower temperatures(25-350℃)to a dual damage mechanism involving both intergranular fatigue and creep damage at higher temperatures(450-600℃).At 600℃,creep damage notably contributes to the accumulation of geometrically necessary dislocations(GNDs),crack initiation,and propagation at grain boundaries,and thus accelerates LCF failure.Compara-tive CPFE simulations reveal that creep damage significantly contributes to cyclic softening and reduction in elastic modulus,which also amplifies the strain localization under the LCF loading.The contribution of creep damage to the total stored energy density(SED)representing the overall damage increases with temperatures,accounting for 11%at 600℃.Additionally,CPFE simulations indicate that the creep dam-age notably influences the magnitude of GND density localized at grain boundaries.This study provides critical insights into the fatigue damage mechanisms of RHEAs,offering valuable guidance for their ap-plication in high temperatures.
文摘Marine infrastructure is increasingly vulnerable to harsh environmental conditions that accelerate the degradation of traditional materials such as Portland cement concrete and carbon steel.This review systematically investigates recent advancements in sustainable alternatives,including geopolymer concrete,engineered innovacementitious composites(ECC),bio-concrete,fiber-reinforced polymers(FRPs),and bamboo,stainless steel,and steel-CFRP hybrid bars.Each material is evaluated based on marine durability,mechanical performance,environmental impact,and cost feasibility using life cycle assessment,durability modelling,and a multi-criteria decisionsupport framework.The results reveal that geopolymer concrete and FRP reinforcement’s exhibit superior corrosion resistance and environmental benefits,while ECC and steel-CFRP composites offer structural resilience with moderate environmental trade-offs.However,challenges remain in long-term performance validation,standardization,and market integration.The review concludes that a combined approach involving innovative materials,computational tools,and sustainability assessment is essential for advancing marine infrastructure.Outlook recommendations include focused field studies,development of regulatory guidelines,and interdisciplinary collaboration to drive the practical adoption of eco-efficient materials in coastal and offshore construction.
基金financially supported by the financial support from Natural Science Foundation of China(No.22209129)the High-Level Innovation and Entrepreneurship Talent Project of Qinchuangyuan(No.QCYRCXM-2022-123)+3 种基金the Innovation Capability Support Program of Shaanxi(No.2023-CXTD-26)the financial support from the"Young Talent Support Plan''of Xi'an Jiaotong University(No.HG6J024)the financial support from China Postdoctoral Science Foundation 2024M752560Postdoctoral Fellowship Program of CPSF under Grant Number GZB20230574
文摘Metal oxohydroxides(MOOH) are widely accepted as the true active species for oxygen evolution reaction(OER).However,the MOOH converted from precatalysts usually exhibits better catalytic performance than those directly synthesized.The underlying structural reason for this phenomenon remains controversial.In this work,CoOOH and Co(OH)2with similar morphology are employed as model catalysts to investigate the origin of in-situ converted catalyst s high activity,as Co(OH)2can be fully converted to CoOOH during OER.In-situ Raman,electron paramagnetic resonance,HR-TEM,and X-ray spectroscopic studies reveal that O vacancies in the CoOOH converted from Co(OH)2play a key role in its higher intrinsic activity towards OER than directly synthesized CoOOH.Furthermore,theoretical calculations and electrochemical methods indicate that O vacancies in CoOOH affect the interaction between Co-O bond,downshift the d-band center of Co,further weaken the adsorption of OH*,and finally facilitate the OER process over CoOOH.This work not only provides a deep understanding of pre-catalyst's high OER activity by taking Co(OH)2as an example but also deliver insights into the activation process of other electrochemic al oxidation reactions.
文摘Ocean Renewable Energy(ORE)systems—comprising wind,wave,tidal,and ocean thermal energy—are increasingly seen as viable alternatives to fossil fuels.However,their integration into the power grid is hindered by environmental sensitivity,dynamic ocean conditions,and high maintenance demands.Artificial Intelligence(AI)offers promising solutions to these challenges by enabling intelligent,adaptive,and resilient energy systems.This review explores AI applications in ORE,focusing on three critical domains:optimization,forecasting,and control.Optimization techniques,including Genetic Algorithms(GA)and Swarm Intelligence(SI),are employed to enhance device efficiency,improve energy capture,optimize farm layouts,reduce environmental impacts,and lower installation costs.Forecasting uses Machine Learning(ML)and Deep Learning(DL)models to predict wave height,tidal flow,and energy output,aiding in grid integration and energy scheduling.In control systems,AI approaches like Reinforcement Learning(RL)and Fuzzy Logic ensure real-time responsiveness and predictive maintenance,improving system reliability in dynamic marine environments.Emerging technologies such as Edge AI enable decentralized computation for real-time decision-making,while Digital Twin frameworks simulate and predict system performance before deployment.Explainable AI(XAI)is also discussed to ensure transparent and trustworthy decision-making.Ethical and regulatory concerns are acknowledged to ensure responsible AI integration in ocean settings.Overall this review offers a comprehensive synthesis of how AI enhances the performance,efficiency,and scalability of ORE systems.It serves as a valuable resource for researchers,policymakers,and industry professionals seeking to advance clean,smart,and sustainable ocean energy solutions.
基金support from the Australian Centre for Microscopy and Microanalysis(ACMM)as well as the Microscopy Australian node at the University of Sydneysupport from the Australian Research Council under DP23010228,from The University of Sydney under the Robinson Fellowship Scheme and from The University of Sydney Nano Institute under the Kickstarter Funding and Student Ambassador Scholarshipsupport from the National Natural Science Foundation of China(Grant number 52274381).
文摘High entropy alloys(HEAs),particularly CoCrNiFeMn system,have emerged as a transformative class of high-performance alloys due to their exceptional mechanical and functional properties.However,traditional manufacturing methods for HEAs are limited by inefficiencies and high costs,restricting their widespread applications.Additive manufacturing(AM),specifically laser powder bed fusion(LPBF),offers a promising alternative by enabling the fabrication of HEAs with unique microstructures and enhanced properties.This study investigates the thermal stability and mechanical performance of LPBF-printed CoCrNiFeMn HEA across a wide temperature range.The as-built LPBF HEA with a hierarchically heterogeneous microstructure,featured by columnar grains and ultrafine dislocation cellular structure,demonstrates exceptional thermal stability,with minimal hardness reduction and no apparent recrystallisation even after prolonged exposure to high temperatures(up to 1373 K),in stark contrast to the significant property degradation observed in conventionally processed HEAs.This stability is attributed to the unique dislocation cellular structures and the intrinsic thermal self-stabilizing effects induced by the LPBF process and the inhibition of recrystallisation due to the low stored energy and columnar grain morphology.The LPBF-fabricated HEA also exhibits outstanding strength-ductility synergy across a broad temperature spectrum,with cryogenic deformation enhancing both strength and ductility due to the activation of deformation twinning.At elevated temperatures,the alloy undergoes a slight reduction in strength but retains good ductility,except at 873 K,where a sharp decline in ductility is observed likely due to grain boundary decohesion and porosity-related crack initiation manifested by the cleavage fracture surface and the cracks at grain boundaries.These findings provide new insights into the temperature-dependent mechanical behavior of AM HEAs,highlight the critical role of dislocation cellular structures in achieving superior thermal and mechanical performance,and underscore the potential of additively manufactured HEAs with tailored microstructures for extreme environments.
基金Key R&D Projects in Shandong Province(Grant No.2022TZXD007)PhD Start-up Fund of University of Jinan(Grant No.XBS2494).
文摘Moldy core is a serious internal defect in pears.Since there is no significant difference in appearance between the healthy pears and those with mild moldy core,it is still a great challenge for the early detection of moldy pear core.This study transformed the vibration acoustic signals(VA signal)of pears into recurrence plots and Markov transition field to enable image-based classification of moldy cores.In addition to traditional machine-learning baselines(Random Forest and k-Nearest Neighbors)trained on LBP-extracted texture features from RP/MTF,the deep models were constructed and compared,which include ResNet-101,DenseNet-121,SqueezeNet,Vision Transformer(ViT),and an improved SqueezeNet(ISqueezeNet).Hyperparameters were tuned via Bayesian optimization over optimizer type,learning rate,batch size,and L2 weight decay,yielding model-specific optimal settings.Under these configurations,the ISqueezeNet achieved the highest test accuracy of 93.05%,with class-wise accuracies of 89.28%(healthy),96.15%(slight),and 94.44%(moderate and severe).Comparisons with lightweight networks(MobileNetV1 and ShuffleNetV2)further showed that ISqueezeNet attains superior accuracy with favorable parameter efficiency and inference speed.Grad-CAM visualizations confirmed that the model focuses on lesionrelevant regions,supporting interpretability and practical reliability.These results indicate that the proposed approach is promising for early,nondestructive detection of moldy pear cores.
基金supported by the National Natural Science Foundation of China(Grant No.12374253,12074053,12004064)J.G.thanks the Foreign talents project(G2022127004L),The authors also acknowledge computer support from the Shanghai Supercomputer Center,the DUT Supercomputing Center,and the Tianhe supercomputer of Tianjin Center.
文摘Surface-supported clusters forming by aggregation of excessive adatoms could be the main defects of 2D materials after chemical vapor deposition.They will significantly impact the electronic/magnetic properties.Moreover,surface supported atoms are also widely explored for high active and selecting catalysts.Severe deformation,even dipping into the surface,of these clusters can be expected because of the very active edge of clusters and strong interaction between supported clusters and surfaces.However,most models of these clusters are supposed to simply float on the top of the surface because ab initio simulations cannot afford the complex reconstructions.Here,we develop an accurate graph neural network machine learning potential(MLP)from ab initio data by active learning architecture through fine-tuning pre-trained models,and then employ the MLP into Monte Carlo to explore the structural evolutions of Mo and S clusters(1-8 atoms)on perfect and various defective MoS2 monolayers.Interestingly,Mo clusters can always sink and embed themselves into MoS2 layers.In contrast,S clusters float on perfect surfaces.On the defective surface,a few S atoms will fill the vacancy and rest S clusters float on the top.Such significant structural reconstructions should be carefully taken into account.
基金We highly thank the funding from the National Natural Science Foundation of China(grants 22222806,22178162,22072065,and 22408170)the Distinguished Youth Foundation of Jiangsu Province(BK20220053)+2 种基金the National Key Research and Development Program of China(2024YFE0206900)the Six Talent Peaks Project in Jiangsu Province(grant JNHB-035)Agency for Science,Technology and Research(A*STAR)through Low Carbon Energy Research Finding Initiative(LCERFI01-0033|U2102d2006).
文摘Paired electrolysis of waste feedstocks holds an energy-efficient alternative for chemical production;however,the sluggish anodic oxidation limited the total efficiency under larger current density.Herein,we constructed ultralow-coordinated Ni species with Ni–O coordination number of∼3 via a hydrothermal synthesis-sulfidation-annealing process and electrochemical activation and demonstrated the vital role in accelerating the proton deintercalation and reactive oxygen intermediate·OH formation during electro-reforming polyethylene terephthalate hydrolysate(POR).The target catalyst NiCoSx/NF afforded a high formate productivity of 7.4 mmol cm^(−2)h^(−1)at∼600 mA cm^(−2)with a formate Faradic efficiency(FE_(formate))of 92.4%in POR and maintained a FE_(formate)of∼90%for 100 h at 2 A in a membrane electrode assembly electrolyzer.Coupling POR on NiCoSx/NF with carbon dioxide reduction reaction on oxygen vacancies enriched Vo-BiSnO reached effective concurrent formate production with 172.7%of FE_(formate)at 500 mA cm^(−2)and long-term stability.Such excellent performance shows the great prospect of electrocatalyst design by regulating the local metal environment.
基金the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 101034425 for the project titled A2M2TECHThe Scientific and Technological Research Council of Türkiye (TUBITAK) with grant No 120C158 for the same A2M2TECH project under the TUBITAK's 2236/B program
文摘Recent advances in additive manufacturing have enabled the construction of metallic lattice structures with tailored mechanical and functional properties.One potential application of metallic lattice struc-tures is in the impact load mitigation where an external kinetic energy is absorbed by the deformation/crushing of lattice cells.This has motivated a growing number of experimental and numerical studies,recently,on the crushing behavior of additively produced lattice structures.The present study overviews the dynamic and quasi-static crushing behavior of additively produced Ti64,316L,and AlSiMg alloy lattice structures.The first part of the study summarizes the main features of two most commonly used additive processing techniques for lattice structures,namely selective-laser-melt(SLM)and electro-beam-melt(EBM),along with a description of commonly observed process induced defects.In the second part,the deformation and strain rate sensitivities of the selected alloy lattices are outlined together with the most widely used dynamic test methods,followed by a part on the observed micro-structures of the SLM and EBM-processed Ti64,316L and AlSiMg alloys.Finally,the experimental and numerical studies on the quasi-static and dynamic compression behavior of the additively processed Ti64,316L,and AlSiMg alloy lattices are reviewed.The results of the experimental and numerical studies of the dynamic properties of various types of lattices,including graded,non-uniform strut size,hollow,non-uniform cell size,and bio-inspired,were tabulated together with the used dynamic testing methods.The dynamic tests have been noted to be mostly conducted in compression Split Hopkinson Pressure Bar(SHPB)or Taylor-and direct-impact tests using the SHPB set-up,in all of which relatively small-size test specimens were tested.The test specimen size effect on the compression behavior of the lattices was further emphasized.It has also been shown that the lattices of Ti64 and AlSiMg alloys are relatively brittle as compared with the lattices of 316L alloy.Finally,the challenges associated with modelling lattice structures were explained and the micro tension tests and multi-scale modeling techniques combining microstructural characteristics with macroscopic lattice dynamics were recommended to improve the accuracy of the numerical simulations of the dynamic compression deformations of metallic lattice structures.
文摘With growing concerns for global warming and environmental issues,the research community has contributed significantly to green technology in the area of material science through the development of natural fiber-reinforced polymer composites(NFRPC).Polymers serve as the matrix in NFRPC,while natural fibers serve as the reinforcing materials.Demand for high-performing materials made with natural resources is growing continuously.Natural fiber-reinforced polymer composites are sustainable biocomposites fabricated with natural fibers embedded with a polymer matrix.They offer a wide range of advantages,including a low weight-to-strength ratio,high flexural strength,damping properties,and resistance to corrosion,wear,and impact.Understanding the basic properties,characteristics,and processing techniques for natural fibers is important to consider their use as raw materials for high-quality biocomposite.Natural fibers come with low density and a high strengthto-weight ratio,allowing them to be a potential reinforcement for low-weight composites.This article attempts to present a comprehensive review of the available natural fibers,their classification,types,structures,physical properties,characteristics,and mechanical properties.Natural fibers are hydrophilic in nature and require physical and chemical treatment prior to their application as reinforcing material.This review will also cover the required physical and chemical treatments of natural fibers for fabricating biocomposites.
基金supported by the Agency for Science,Technology and Research(A*STAR),Singapore,under the project Methane Pyrolysis for Hydrogen and Carbon Nanotube Production via Novel Catalytic Membrane Reactor System(No.U2102d2011)。
文摘The sustainability of methane catalytic decomposition is significantly enhanced by the production of high-quality value-added carbon products such as carbon nanotubes(CNTs).Understanding the production yields and properties of CNTs is crucial for improving process feasibility and sustainability.This study employs machine learning technique to develop and analyze predictive models for the carbon yield and mean diameter of CNTs produced through methane catalytic decomposition.Utilizing comprehensive datasets from various experimental studies,the models incorporate variables related to catalyst composition,catalyst preparation,and operational parameters.Both models achieved high predictive accuracy,with R^(2)values exceeding 0.90.Notably,the reduction time during catalyst preparation was found to critically influence carbon yield,evidenced by a permutation importance value of 39.62%.Additionally,the use of Mo as a catalytic metal was observed to significantly reduce the diameter of produced CNTs.These findings highlight the need for future machine learning and simulation studies to include catalyst reduction parameters,thereby enhancing predictive accuracy and deepening process insights.This research provides strategic guidance for optimizing methane catalytic decomposition to produce enhanced CNTs,aligning with sustainability goals.
基金National Natural Science Foundation of China(grants 22072065,22178162,and 22222806)Distinguished Youth Foundation of Jiangsu Province(grant BK20220053)Six talent peaks project in Jiangsu Province(grant JNHB-035)。
文摘The synergy of single atoms(SAs)and nanoparticles(NPs)has demonstrated great potential in promoting the electrocatalytic carbon dioxide reduction reaction(CO_(2)RR);however,the rationalization of the SAs/NPs proportion remains one challenge for the catalyst design.Herein,a Ni2+-loaded porous poly(ionic liquids)(PIL)precursor synthesized through the free radical self-polymerization of the ionic liquid monomer,1-allyl-3-vinylimidazolium chloride,was pyrolyzed to prepare the Ni,N co-doped carbon materials,in which the proportion of Ni SAs and NPs could be facilely modulated by controlling the annealing temperature.The catalyst Ni-NC-1000 with a moderate proportion of Ni SAs and NPs exhibited high efficiency in the electrocatalytic conversion of CO_(2)into CO.Operando Ni K-edge X-ray absorption near-edge structure(XANES)spectra and theoretical calculations were conducted to gain insight into the synergy of Ni SAs and NPs.The charge transfer from Ni NPs to the surrounding carbon layer and then to the Ni SAs resulted in the electron-enriched Ni SAs active sites.In the electroreduction of CO_(2),the coexistence of Ni SAs and NPs strengthened the CO_(2)activation and the affinity towards the key intermediate of*COOH,lowering the free energy for the potential-determining*CO_(2)→*COOH step,and therefore promoted the catalysis efficiency.
文摘The NIST Cybersecurity Framework (NIST CSF) serves as a voluntary guideline aimed at helping organizations, tiny and medium-sized enterprises (SMEs), and critical infrastructure operators, effectively manage cyber risks. Although comprehensive, the complexity of the NIST CSF can be overwhelming, especially for those lacking extensive cybersecurity resources. Current implementation tools often cater to larger companies, neglecting the specific needs of SMEs, which can be vulnerable to cyber threats. To address this gap, our research proposes a user-friendly, open-source web platform designed to simplify the implementation of the NIST CSF. This platform enables organizations to assess their risk exposure and continuously monitor their cybersecurity maturity through tailored recommendations based on their unique profiles. Our methodology includes a literature review of existing tools and standards, followed by a description of the platform’s design and architecture. Initial tests with SMEs in Burkina Faso reveal a concerning cybersecurity maturity level, indicating the urgent need for improved strategies based on our findings. By offering an intuitive interface and cross-platform accessibility, this solution aims to empower organizations to enhance their cybersecurity resilience in an evolving threat landscape. The article concludes with discussions on the practical implications and future enhancements of the tool.
基金supported by the BRICS STI Framework Programme(No.52261145703)the Higher Education Discipline Innovation Project(National 111 Project,No.B16016)the Guangxi Key Research and Development Plan Project(AB24010117).
文摘Photocatalytic membranes hold significant potential for promoting pollutant degradation and reducing membrane fouling in filtration systems.Although extensive research has been conducted on the independent design of photocatalysts or membrane materials to improve their catalytic and filtration performance,the complex structures and interface mechanisms,as well as insufficient light utilization,are still often overlooked,limiting the overall performance improvement of photocatalytic membranes.This work provides an overview of enhancement strategies involving restricted area effects,external fields,such as mechanical,magnetic,thermal,and electrical fields,as well as coupling techniques with advanced oxidation processes(e.g.,O_(3),Fenton,and persulfate oxidation)for dual enhancement of photocatalysts and membranes.In addition,the synthesis method of photocatalytic membranes and the influence of factors,such as light source type,frequency,and relative position on photocatalytic membrane performance were also studied.Finally,economic feasibility and pollutant removal performance were further evaluated to determine the promising enhancement strategies,paving the way for more efficient and scalable applications of photocatalytic membranes.
基金financially supported by the National Key R&D Program of China(No.2022YFB4602102)the National Natural Science Foundation of China(Grant No.51971144)the Natural Science Foundation of Shanghai(Grant No.19ZR1425200)。
文摘CrCoNi medium entropy alloy(MEA)fabricated by laser powder bed fusion(LPBF)benefits from its distinctive hierarchical microstructure and has great potential as a structural material.However,while the intriguing chemical short-range order(CSRO)widely exists in high/medium entropy alloys,its formation in the LPBF-built samples still lacks enough understanding.In this study,we verified its existence by fine transmission electron microscopy characterizations and utilized hybrid Monte Carlo/molecular dynamics simulations to investigate the features and effects of CSRO in LPBF-built CrCoNi MEA(AM model).Results showed that the CSRO fraction and the stacking fault energy of the AM model lie between those of the well-annealed and random solid solution counterparts.Among these models,the AM model exhibited the best strain hardening ability due to its highest capability to generate and store sessile dislocations.The results agreed well with existing data and provide guidance to the future development of LPBF-built CrCoNi MEA.
