Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progr...Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progressive Layered U-Net(PLU-Net),designed to improve brain tumor segmentation accuracy from Magnetic Resonance Imaging(MRI)scans.The PLU-Net extends the standard U-Net architecture by incorporating progressive layering,attention mechanisms,and multi-scale data augmentation.The progressive layering involves a cascaded structure that refines segmentation masks across multiple stages,allowing the model to capture features at different scales and resolutions.Attention gates within the convolutional layers selectively focus on relevant features while suppressing irrelevant ones,enhancing the model's ability to delineate tumor boundaries.Additionally,multi-scale data augmentation techniques increase the diversity of training data and boost the model's generalization capabilities.Evaluated on the BraTS 2021 dataset,the PLU-Net achieved state-of-the-art performance with a dice coefficient of 0.91,specificity of 0.92,sensitivity of 0.89,Hausdorff95 of 2.5,outperforming other modified U-Net architectures in segmentation accuracy.These results underscore the effectiveness of the PLU-Net in improving brain tumor segmentation from MRI scans,supporting clinicians in early diagnosis,treatment planning,and the development of new therapies.展开更多
The global population is increasing,compelling a greater food supply for survival and agricultural activity to support economic development.On the other hand,traditional farm machinery and activities result in the ove...The global population is increasing,compelling a greater food supply for survival and agricultural activity to support economic development.On the other hand,traditional farm machinery and activities result in the overuse of fertilizers,irrigation water,and land,thereby undermining environmental sustainability.The current study aims to present advanced ground robots as effective solutions for autonomous operations,enhancing efficiency,productivity,and revenues in agriculture while consuming fewer resources and preserving the environment.In this regard,an overview of diverse imaging sensors and navigation technologies for ground robots is provided as key components that assist in automation and autonomy.Recent trends adopted for deploying ground robots while integrating the internet-of-things(IoT),artificial intelligence(AI),cloud computing,edge computing,collaborative robotics,and energy and resource-efficient systems are elucidated,driving smart and sustainable agriculture.Moreover,state-of-the-art applications of ground robots in three agricultural branches are explored.Three case studies from Ireland are presented as evidence of the transformation of traditional agriculture.Some limitations that necessitates future considerations are highlighted.The current study signifies the importance of employing ground robots to leap from conventional agricultural practices to precision and sustainable operations.展开更多
Unmanned Aerial Vehicles(UAVs)integrated with Wireless Sensor Networks(WSNs)present a transformative approach to environmental monitoring by enabling real-time,low power,wide-area,and high-resolution data collection.T...Unmanned Aerial Vehicles(UAVs)integrated with Wireless Sensor Networks(WSNs)present a transformative approach to environmental monitoring by enabling real-time,low power,wide-area,and high-resolution data collection.This paper proposes a UAV-based WSN framework designed for efficient ecological data acquisition,including parameters such as temperature,humidity,various gases,detection of motion of a material,and safety features.The system leverages UAVs for dynamic deployment and data retrieval from distributed sensor nodes in remote or inaccessible areas,reducing the reliance on fixed infrastructure.Long Range Communication(LoRa)technology is also integrated with a WSN to enhance network coverage and adaptability issues.The proposed system covers vast areas through LoRa communication ensuring minimal energy consumption and cost-effective sensing capabilities.Field tests and simulation findings show how well the system captures spatiotemporal environmental fluctuations,making it an invaluable tool for monitoring climate change,ecological research,and disaster response.展开更多
Swarm intelligence,derived from the collective behaviour of biological entities,is a novel methodology for overseeing satellite constellations within decentralized control systems.Conventional centralized control syst...Swarm intelligence,derived from the collective behaviour of biological entities,is a novel methodology for overseeing satellite constellations within decentralized control systems.Conventional centralized control systems in satellite constellations encounter constraints in scalability,resilience,and fault tolerance,particularly in extensive constellations.This research examines the use of swarm-based multi-agent systems and distributed algorithms for efficient communication,collision avoidance,and collaborative task execution in satellite constellations.We provide a comprehensive study of current swarm control algorithms,their relevance to satellite systems,and identify areas requiring further research.Principal subjects encompass decentralized decision-making,self-organization,adaptive communication protocols,and collision-free trajectory planning.The review article examines implementation obstacles,contrasts swarm algorithms with traditional approaches,and delineates potential research avenues to improve the autonomy and efficiency of satellite constellations.The detailed Literature and comparative analysis within this manuscript demonstrate the prospective advantages of swarm intelligence in satellite systems,providing insights for academic researchers and industry personnel alike.展开更多
The in-plane optical anisotropy(IPOA) of c-plane In Ga N/Ga N quantum disks(Qdisks) in nanowires grown on MoS_(2)/Mo and Ti/Mo substrates is investigated using reflectance difference spectroscopy(RDS) at room temperat...The in-plane optical anisotropy(IPOA) of c-plane In Ga N/Ga N quantum disks(Qdisks) in nanowires grown on MoS_(2)/Mo and Ti/Mo substrates is investigated using reflectance difference spectroscopy(RDS) at room temperature. A large IPOA related to defect or impurity states is observed. The IPOA of samples grown on MoS_(2)/Mo is approximately one order of magnitude larger than that of samples grown on Ti/Mo substrates. Numerical calculations based on the envelope function approximation have been performed to analyze the origin of the IPOA. It is found that the IPOA primarily results from the segregation of indium atoms in the In Ga N/Ga N Qdisks. This work highlights the significant influence of substrate materials on the IPOA of semiconductor heterostructures.展开更多
Artificial light-harvesting systems(LHSs) have drawn increasing research interest in recent times due to the energy crisis worldwide. Concurrently, macrocycle-based host–vip interactions have played an important ro...Artificial light-harvesting systems(LHSs) have drawn increasing research interest in recent times due to the energy crisis worldwide. Concurrently, macrocycle-based host–vip interactions have played an important role in the development of supramolecular chemistry. In recent years, studies towards artificial LHSs driven by macrocycle-based host–vip interactions are gradually being disclosed. In this mini-review, we briefly introduce the burgeoning progress of artificial LHSs driven by host–vip interactions. We believe that an increasing number of reports of artificial LHSs driven by host–vip interactions will appear in the near future and will provide a viable alternative for the future production of renewable energy.展开更多
A stochastic resource allocation model, based on the principles of Markov decision processes(MDPs), is proposed in this paper. In particular, a general-purpose framework is developed, which takes into account resource...A stochastic resource allocation model, based on the principles of Markov decision processes(MDPs), is proposed in this paper. In particular, a general-purpose framework is developed, which takes into account resource requests for both instant and future needs. The considered framework can handle two types of reservations(i.e., specified and unspecified time interval reservation requests), and implement an overbooking business strategy to further increase business revenues. The resulting dynamic pricing problems can be regarded as sequential decision-making problems under uncertainty, which is solved by means of stochastic dynamic programming(DP) based algorithms. In this regard, Bellman’s backward principle of optimality is exploited in order to provide all the implementation mechanisms for the proposed reservation pricing algorithm. The curse of dimensionality, as the inevitable issue of the DP both for instant resource requests and future resource reservations,occurs. In particular, an approximate dynamic programming(ADP) technique based on linear function approximations is applied to solve such scalability issues. Several examples are provided to show the effectiveness of the proposed approach.展开更多
Pillar[n]arenes are a new kind of supramolecular macrocyclic hosts which have developed rapidly due to their unique topology and high functionality, giving rise to many applications in the construction of interesting ...Pillar[n]arenes are a new kind of supramolecular macrocyclic hosts which have developed rapidly due to their unique topology and high functionality, giving rise to many applications in the construction of interesting and functional materials. Among them, water-soluble pillar[n]arenes bearing triethylene oxide (TEO) chains have drawn increasing research interest due to their advantageous properties. In this review, we summarized the recent progress of dynamic materials fabricated from water soluble pillar[n]arenes bearing TEO groups, including thermo responsive materials with lower critical solution temperature (LCST) behavior, cyclic host liquids, and smart windows. It is anticipated that more and more ‘smart' supramolecular materials based on modified pillar[n]arenes will be developed in this burgeoning area of research.展开更多
OpenStreetMap(OSM)data are widely used but their reliability is still variable.Many contributors to OSM have not been trained in geography or surveying and consequently their contributions,including geometry and attri...OpenStreetMap(OSM)data are widely used but their reliability is still variable.Many contributors to OSM have not been trained in geography or surveying and consequently their contributions,including geometry and attribute data inserts,deletions,and updates,can be inaccurate,incomplete,inconsistent,or vague.There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data.Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs.This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users.The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature.Using such rules,some sets of potential bugs and errors can be identified and stored for further investigations.展开更多
As an established spatial analytical tool,Geographically Weighted Regression(GWR)has been applied across a variety of disciplines.However,its usage can be challenging for large datasets,which are increasingly prevalen...As an established spatial analytical tool,Geographically Weighted Regression(GWR)has been applied across a variety of disciplines.However,its usage can be challenging for large datasets,which are increasingly prevalent in today’s digital world.In this study,we propose two high-performance R solutions for GWR via Multi-core Parallel(MP)and Compute Unified Device Architecture(CUDA)techniques,respectively GWR-MP and GWR-CUDA.We compared GWR-MP and GWR-CUDA with three existing solutions available in Geographically Weighted Models(GWmodel),Multi-scale GWR(MGWR)and Fast GWR(FastGWR).Results showed that all five solutions perform differently across varying sample sizes,with no single solution a clear winner in terms of computational efficiency.Specifically,solutions given in GWmodel and MGWR provided acceptable computational costs for GWR studies with a relatively small sample size.For a large sample size,GWR-MP and FastGWR provided coherent solutions on a Personal Computer(PC)with a common multi-core configuration,GWR-MP provided more efficient computing capacity for each core or thread than FastGWR.For cases when the sample size was very large,and for these cases only,GWR-CUDA provided the most efficient solution,but should note its I/O cost with small samples.In summary,GWR-MP and GWR-CUDA provided complementary high-performance R solutions to existing ones,where for certain data-rich GWR studies,they should be preferred.展开更多
Public cloud computing provides a variety of services to consumersvia high-speed internet. The consumer can access these services anytimeand anywhere on a balanced service cost. Many traditional authenticationprotocol...Public cloud computing provides a variety of services to consumersvia high-speed internet. The consumer can access these services anytimeand anywhere on a balanced service cost. Many traditional authenticationprotocols are proposed to secure public cloud computing. However, therapid development of high-speed internet and organizations’ race to developquantum computers is a nightmare for existing authentication schemes. Thesetraditional authentication protocols are based on factorization or discretelogarithm problems. As a result, traditional authentication protocols arevulnerable in the quantum computing era. Therefore, in this article, we haveproposed an authentication protocol based on the lattice technique for publiccloud computing to resist quantum attacks and prevent all known traditionalsecurity attacks. The proposed lattice-based authentication protocolis provably secure under the Real-Or-Random (ROR) model. At the sametime, the result obtained during the experiments proved that our protocol islightweight compared to the existing lattice-based authentication protocols,as listed in the performance analysis section. The comparative analysis showsthat the protocol is suitable for practical implementation in a quantum-basedenvironment.展开更多
Electricity theft is one of the major issues in developing countries which is affecting their economy badly.Especially with the introduction of emerging technologies,this issue became more complicated.Though many new ...Electricity theft is one of the major issues in developing countries which is affecting their economy badly.Especially with the introduction of emerging technologies,this issue became more complicated.Though many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)techniques.Here,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations.Three levels of taxonomy are presented to classify state-of-the-art ETD techniques.Different types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from literature.The challenges of different ETD techniques and their mitigation are suggested for future work.It is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft tracking.This can help in the prevention of energy theft,in the future,as well as for ETD.展开更多
Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both cus...Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare infrastructures.In addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,transmitting.Therefore,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled domains.In this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT infrastructures.Sliding window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission activity.Additionally,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s body.In contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of accuracy.Moreover,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the IoT.The proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information perspectives.Simulation and analytical results have proved that the proposed privacy-preserving information fusion technique for wearable devices is an ideal solution for resource-constrained infrastructures such as IoT and the Internet ofMedical Things,where both device privacy and information integrity are important.Finally,the proposed hybrid approach is proven against well-known intruder attacks,especially those related to the privacy of the respective device in IoT infrastructures.展开更多
Fluorescent materials that respond to multiple stimuli have broad applications ranging from sensing and bioimaging to information encryption.Herein,we report the design and synthesis of a single-fluorophorebased amphi...Fluorescent materials that respond to multiple stimuli have broad applications ranging from sensing and bioimaging to information encryption.Herein,we report the design and synthesis of a single-fluorophorebased amphiphile DCSO,which shows temperature-,solvent-,humidity-,and radiation-dependent fluorescence.DCSO consists of a dicyanostilbene(DCS)group as a rigid hydrophobic core with oligo(ethylene glycol)(OEG)chains at both ends as a flexible hydrophilic periphery.The DCS group acts as a highly efficient fluorophore,while the OEG chain endows the molecule with thermo-responsiveness.Fluorescent colors can vary from blue to green to yellow in response to external stimuli.On the basis of light radiation,we demonstrate that this system can be applied to time-dependent information encryption,in which the correct information can only be read at a specific time under irradiation.This work further demonstrates the usefulness and application of single-fluorophore-based luminescent materials with multiple stimuli-responsive functions.展开更多
Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep lea...Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.展开更多
Molecular nanotubes are nanoscale organic materials with tubular architecture,that show potential applications in molecular recognition/separation,cross-membrane transportation,catalysis in confined spaces,and nanoele...Molecular nanotubes are nanoscale organic materials with tubular architecture,that show potential applications in molecular recognition/separation,cross-membrane transportation,catalysis in confined spaces,and nanoelectronics[1].However,the precise construction of molecular nanotubes with well-defined cavity sizes and shapes is non-trivial.展开更多
The current energy crisis is a worldwide problem and the search for clean and economic energy production is a global challenge.In this context,the exploitation and utilization of solar energy is becoming more and more...The current energy crisis is a worldwide problem and the search for clean and economic energy production is a global challenge.In this context,the exploitation and utilization of solar energy is becoming more and more popular.Photosynthesis in nature sets an outstanding example to harvest,transfer and store solar energy[1].In most of the photosynthetic organisms such as purple photosynthetic bacteria,the rigid protein scaffolds serve as key elements to bind pigments and control their excitation energy transfer.Sequential energy transfer is especially intriguing in this field to make full use of full-band solar energy.To mimic natural photosynthesis[2].展开更多
Education is the base of the survival and growth of any state,but due to resource scarcity,students,particularly at the university level,are forced into a difficult situation.Scholarships are the most significant fina...Education is the base of the survival and growth of any state,but due to resource scarcity,students,particularly at the university level,are forced into a difficult situation.Scholarships are the most significant financial aid mechanisms developed to overcome such obstacles and assist the students in continuing with their higher studies.In this study,the convoluted situation of scholarship eligibility criteria,including parental income,responsibilities,and academic achievements,is addressed.In an attempt to maximize the scholarship selection process,numerous machine learning algorithms,including Support Vector Machines,Neural Networks,K-Nearest Neighbors,and the C4.5 algorithm,were applied.The C4.5 algorithm,owing to its efficiency in the prediction of scholarship beneficiaries based on extraneous factors,was capable of predicting a phenomenal 95.62%of predictions using extensive data of a well-esteemed government sector university from Pakistan.This percentage is 4%and 15%better than the remainder of the methods tested,and it depicts the extent of the potential for the technique to enhance the scholarship selection process.The Decision Support Systems(DSS)would not only save the administrative cost but would also create a fair and transparent process in place.In a world where accessibility to education is the key,this research provides data-oriented consolidation to ensure that deserving students are helped and allowed to get the financial assistance that they need to reach higher studies and bridge the gap between the demands of the day and the institutions of intellect.展开更多
The main aim of this mini-review is to illustrate strategies and industrial applications based on inteins (INTErnal proteINS), which belong to a class of autocatalytic enzymes that are able to perform a catalytic reac...The main aim of this mini-review is to illustrate strategies and industrial applications based on inteins (INTErnal proteINS), which belong to a class of autocatalytic enzymes that are able to perform a catalytic reaction on a single substrate. However, since practical applications of inteins are strongly guided by a detailed understanding of their biological mechanisms and functions, the first part of this review will thus briefly discuss the physiological roles of inteins, describing what is currently known about their mechanisms of action. In the second part, specific biotechnological applications of inteins will be outlined (i.e. their use for (i) the purification of recombinant proteins, (ii) the cyclization of proteins and (iii) the production of seleno-proteins), paying attention to both potential strengths and weaknesses of this technology.展开更多
文摘Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progressive Layered U-Net(PLU-Net),designed to improve brain tumor segmentation accuracy from Magnetic Resonance Imaging(MRI)scans.The PLU-Net extends the standard U-Net architecture by incorporating progressive layering,attention mechanisms,and multi-scale data augmentation.The progressive layering involves a cascaded structure that refines segmentation masks across multiple stages,allowing the model to capture features at different scales and resolutions.Attention gates within the convolutional layers selectively focus on relevant features while suppressing irrelevant ones,enhancing the model's ability to delineate tumor boundaries.Additionally,multi-scale data augmentation techniques increase the diversity of training data and boost the model's generalization capabilities.Evaluated on the BraTS 2021 dataset,the PLU-Net achieved state-of-the-art performance with a dice coefficient of 0.91,specificity of 0.92,sensitivity of 0.89,Hausdorff95 of 2.5,outperforming other modified U-Net architectures in segmentation accuracy.These results underscore the effectiveness of the PLU-Net in improving brain tumor segmentation from MRI scans,supporting clinicians in early diagnosis,treatment planning,and the development of new therapies.
文摘The global population is increasing,compelling a greater food supply for survival and agricultural activity to support economic development.On the other hand,traditional farm machinery and activities result in the overuse of fertilizers,irrigation water,and land,thereby undermining environmental sustainability.The current study aims to present advanced ground robots as effective solutions for autonomous operations,enhancing efficiency,productivity,and revenues in agriculture while consuming fewer resources and preserving the environment.In this regard,an overview of diverse imaging sensors and navigation technologies for ground robots is provided as key components that assist in automation and autonomy.Recent trends adopted for deploying ground robots while integrating the internet-of-things(IoT),artificial intelligence(AI),cloud computing,edge computing,collaborative robotics,and energy and resource-efficient systems are elucidated,driving smart and sustainable agriculture.Moreover,state-of-the-art applications of ground robots in three agricultural branches are explored.Three case studies from Ireland are presented as evidence of the transformation of traditional agriculture.Some limitations that necessitates future considerations are highlighted.The current study signifies the importance of employing ground robots to leap from conventional agricultural practices to precision and sustainable operations.
文摘Unmanned Aerial Vehicles(UAVs)integrated with Wireless Sensor Networks(WSNs)present a transformative approach to environmental monitoring by enabling real-time,low power,wide-area,and high-resolution data collection.This paper proposes a UAV-based WSN framework designed for efficient ecological data acquisition,including parameters such as temperature,humidity,various gases,detection of motion of a material,and safety features.The system leverages UAVs for dynamic deployment and data retrieval from distributed sensor nodes in remote or inaccessible areas,reducing the reliance on fixed infrastructure.Long Range Communication(LoRa)technology is also integrated with a WSN to enhance network coverage and adaptability issues.The proposed system covers vast areas through LoRa communication ensuring minimal energy consumption and cost-effective sensing capabilities.Field tests and simulation findings show how well the system captures spatiotemporal environmental fluctuations,making it an invaluable tool for monitoring climate change,ecological research,and disaster response.
文摘Swarm intelligence,derived from the collective behaviour of biological entities,is a novel methodology for overseeing satellite constellations within decentralized control systems.Conventional centralized control systems in satellite constellations encounter constraints in scalability,resilience,and fault tolerance,particularly in extensive constellations.This research examines the use of swarm-based multi-agent systems and distributed algorithms for efficient communication,collision avoidance,and collaborative task execution in satellite constellations.We provide a comprehensive study of current swarm control algorithms,their relevance to satellite systems,and identify areas requiring further research.Principal subjects encompass decentralized decision-making,self-organization,adaptive communication protocols,and collision-free trajectory planning.The review article examines implementation obstacles,contrasts swarm algorithms with traditional approaches,and delineates potential research avenues to improve the autonomy and efficiency of satellite constellations.The detailed Literature and comparative analysis within this manuscript demonstrate the prospective advantages of swarm intelligence in satellite systems,providing insights for academic researchers and industry personnel alike.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 62074036, 61674038, and 11574302)Foreign Cooperation Project of Fujian Province (Grant No. 2023I0005)+2 种基金Open Research Fund Program of the State Key Laboratory of Low-Dimensional Quantum Physics (Grant No. KF202108)the National Key Research and Development Program (Grant No. 2016YFB0402303)the Foundation of Fujian Provincial Department of Industry and Information Technology of China (Grant No. 82318075)。
文摘The in-plane optical anisotropy(IPOA) of c-plane In Ga N/Ga N quantum disks(Qdisks) in nanowires grown on MoS_(2)/Mo and Ti/Mo substrates is investigated using reflectance difference spectroscopy(RDS) at room temperature. A large IPOA related to defect or impurity states is observed. The IPOA of samples grown on MoS_(2)/Mo is approximately one order of magnitude larger than that of samples grown on Ti/Mo substrates. Numerical calculations based on the envelope function approximation have been performed to analyze the origin of the IPOA. It is found that the IPOA primarily results from the segregation of indium atoms in the In Ga N/Ga N Qdisks. This work highlights the significant influence of substrate materials on the IPOA of semiconductor heterostructures.
基金financial support of the National Natural Science Foundation of China (No. 21702020)
文摘Artificial light-harvesting systems(LHSs) have drawn increasing research interest in recent times due to the energy crisis worldwide. Concurrently, macrocycle-based host–vip interactions have played an important role in the development of supramolecular chemistry. In recent years, studies towards artificial LHSs driven by macrocycle-based host–vip interactions are gradually being disclosed. In this mini-review, we briefly introduce the burgeoning progress of artificial LHSs driven by host–vip interactions. We believe that an increasing number of reports of artificial LHSs driven by host–vip interactions will appear in the near future and will provide a viable alternative for the future production of renewable energy.
文摘A stochastic resource allocation model, based on the principles of Markov decision processes(MDPs), is proposed in this paper. In particular, a general-purpose framework is developed, which takes into account resource requests for both instant and future needs. The considered framework can handle two types of reservations(i.e., specified and unspecified time interval reservation requests), and implement an overbooking business strategy to further increase business revenues. The resulting dynamic pricing problems can be regarded as sequential decision-making problems under uncertainty, which is solved by means of stochastic dynamic programming(DP) based algorithms. In this regard, Bellman’s backward principle of optimality is exploited in order to provide all the implementation mechanisms for the proposed reservation pricing algorithm. The curse of dimensionality, as the inevitable issue of the DP both for instant resource requests and future resource reservations,occurs. In particular, an approximate dynamic programming(ADP) technique based on linear function approximations is applied to solve such scalability issues. Several examples are provided to show the effectiveness of the proposed approach.
基金financial support from the National Natural Science Foundation of China(No. 21702020)and Maynooth University
文摘Pillar[n]arenes are a new kind of supramolecular macrocyclic hosts which have developed rapidly due to their unique topology and high functionality, giving rise to many applications in the construction of interesting and functional materials. Among them, water-soluble pillar[n]arenes bearing triethylene oxide (TEO) chains have drawn increasing research interest due to their advantageous properties. In this review, we summarized the recent progress of dynamic materials fabricated from water soluble pillar[n]arenes bearing TEO groups, including thermo responsive materials with lower critical solution temperature (LCST) behavior, cyclic host liquids, and smart windows. It is anticipated that more and more ‘smart' supramolecular materials based on modified pillar[n]arenes will be developed in this burgeoning area of research.
基金This research was supported financially by EU FP7 Marie Curie Initial Training Network MULTI-POS(Multi-technology Positioning Professionals)[grant number 316528].
文摘OpenStreetMap(OSM)data are widely used but their reliability is still variable.Many contributors to OSM have not been trained in geography or surveying and consequently their contributions,including geometry and attribute data inserts,deletions,and updates,can be inaccurate,incomplete,inconsistent,or vague.There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data.Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs.This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users.The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature.Using such rules,some sets of potential bugs and errors can be identified and stored for further investigations.
基金supported by National Key Research and Development Program of China[grant num-ber 2021YFB3900904]the National Natural Science Foundation of China[grant numbers 42071368,U2033216,41871287].
文摘As an established spatial analytical tool,Geographically Weighted Regression(GWR)has been applied across a variety of disciplines.However,its usage can be challenging for large datasets,which are increasingly prevalent in today’s digital world.In this study,we propose two high-performance R solutions for GWR via Multi-core Parallel(MP)and Compute Unified Device Architecture(CUDA)techniques,respectively GWR-MP and GWR-CUDA.We compared GWR-MP and GWR-CUDA with three existing solutions available in Geographically Weighted Models(GWmodel),Multi-scale GWR(MGWR)and Fast GWR(FastGWR).Results showed that all five solutions perform differently across varying sample sizes,with no single solution a clear winner in terms of computational efficiency.Specifically,solutions given in GWmodel and MGWR provided acceptable computational costs for GWR studies with a relatively small sample size.For a large sample size,GWR-MP and FastGWR provided coherent solutions on a Personal Computer(PC)with a common multi-core configuration,GWR-MP provided more efficient computing capacity for each core or thread than FastGWR.For cases when the sample size was very large,and for these cases only,GWR-CUDA provided the most efficient solution,but should note its I/O cost with small samples.In summary,GWR-MP and GWR-CUDA provided complementary high-performance R solutions to existing ones,where for certain data-rich GWR studies,they should be preferred.
基金Korean Government (Ministry of Science and ICT)through the National Research Foundation of Korea (NRF)Grant 2021R1A2C1010481.
文摘Public cloud computing provides a variety of services to consumersvia high-speed internet. The consumer can access these services anytimeand anywhere on a balanced service cost. Many traditional authenticationprotocols are proposed to secure public cloud computing. However, therapid development of high-speed internet and organizations’ race to developquantum computers is a nightmare for existing authentication schemes. Thesetraditional authentication protocols are based on factorization or discretelogarithm problems. As a result, traditional authentication protocols arevulnerable in the quantum computing era. Therefore, in this article, we haveproposed an authentication protocol based on the lattice technique for publiccloud computing to resist quantum attacks and prevent all known traditionalsecurity attacks. The proposed lattice-based authentication protocolis provably secure under the Real-Or-Random (ROR) model. At the sametime, the result obtained during the experiments proved that our protocol islightweight compared to the existing lattice-based authentication protocols,as listed in the performance analysis section. The comparative analysis showsthat the protocol is suitable for practical implementation in a quantum-basedenvironment.
基金supported by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sk?odowska-Curie Grant Agreement(801522)Science Foundation Ireland and co-funded by the European Regional Development Fund through the ADAPT Centre for Digital Content Technology(13/RC/2106_P2)。
文摘Electricity theft is one of the major issues in developing countries which is affecting their economy badly.Especially with the introduction of emerging technologies,this issue became more complicated.Though many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)techniques.Here,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations.Three levels of taxonomy are presented to classify state-of-the-art ETD techniques.Different types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from literature.The challenges of different ETD techniques and their mitigation are suggested for future work.It is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft tracking.This can help in the prevention of energy theft,in the future,as well as for ETD.
基金Ministry of Higher Education of Malaysia under the Research GrantLRGS/1/2019/UKM-UKM/5/2 and Princess Nourah bint Abdulrahman University for financing this researcher through Supporting Project Number(PNURSP2024R235),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare infrastructures.In addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,transmitting.Therefore,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled domains.In this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT infrastructures.Sliding window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission activity.Additionally,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s body.In contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of accuracy.Moreover,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the IoT.The proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information perspectives.Simulation and analytical results have proved that the proposed privacy-preserving information fusion technique for wearable devices is an ideal solution for resource-constrained infrastructures such as IoT and the Internet ofMedical Things,where both device privacy and information integrity are important.Finally,the proposed hybrid approach is proven against well-known intruder attacks,especially those related to the privacy of the respective device in IoT infrastructures.
基金supported by the National Natural Science Foundation of China(No.21702020)partially supported by the Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study(No.SN-ZJU-SIAS-006).
文摘Fluorescent materials that respond to multiple stimuli have broad applications ranging from sensing and bioimaging to information encryption.Herein,we report the design and synthesis of a single-fluorophorebased amphiphile DCSO,which shows temperature-,solvent-,humidity-,and radiation-dependent fluorescence.DCSO consists of a dicyanostilbene(DCS)group as a rigid hydrophobic core with oligo(ethylene glycol)(OEG)chains at both ends as a flexible hydrophilic periphery.The DCS group acts as a highly efficient fluorophore,while the OEG chain endows the molecule with thermo-responsiveness.Fluorescent colors can vary from blue to green to yellow in response to external stimuli.On the basis of light radiation,we demonstrate that this system can be applied to time-dependent information encryption,in which the correct information can only be read at a specific time under irradiation.This work further demonstrates the usefulness and application of single-fluorophore-based luminescent materials with multiple stimuli-responsive functions.
文摘Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.
文摘Molecular nanotubes are nanoscale organic materials with tubular architecture,that show potential applications in molecular recognition/separation,cross-membrane transportation,catalysis in confined spaces,and nanoelectronics[1].However,the precise construction of molecular nanotubes with well-defined cavity sizes and shapes is non-trivial.
文摘The current energy crisis is a worldwide problem and the search for clean and economic energy production is a global challenge.In this context,the exploitation and utilization of solar energy is becoming more and more popular.Photosynthesis in nature sets an outstanding example to harvest,transfer and store solar energy[1].In most of the photosynthetic organisms such as purple photosynthetic bacteria,the rigid protein scaffolds serve as key elements to bind pigments and control their excitation energy transfer.Sequential energy transfer is especially intriguing in this field to make full use of full-band solar energy.To mimic natural photosynthesis[2].
文摘Education is the base of the survival and growth of any state,but due to resource scarcity,students,particularly at the university level,are forced into a difficult situation.Scholarships are the most significant financial aid mechanisms developed to overcome such obstacles and assist the students in continuing with their higher studies.In this study,the convoluted situation of scholarship eligibility criteria,including parental income,responsibilities,and academic achievements,is addressed.In an attempt to maximize the scholarship selection process,numerous machine learning algorithms,including Support Vector Machines,Neural Networks,K-Nearest Neighbors,and the C4.5 algorithm,were applied.The C4.5 algorithm,owing to its efficiency in the prediction of scholarship beneficiaries based on extraneous factors,was capable of predicting a phenomenal 95.62%of predictions using extensive data of a well-esteemed government sector university from Pakistan.This percentage is 4%and 15%better than the remainder of the methods tested,and it depicts the extent of the potential for the technique to enhance the scholarship selection process.The Decision Support Systems(DSS)would not only save the administrative cost but would also create a fair and transparent process in place.In a world where accessibility to education is the key,this research provides data-oriented consolidation to ensure that deserving students are helped and allowed to get the financial assistance that they need to reach higher studies and bridge the gap between the demands of the day and the institutions of intellect.
基金MM would like to thank the Science Foundation Ireland(SFI)for a postgraduate scholarship received via a President of Ireland Young Researcher Award(PIYRA)bestowed on NMGS acknowledges the award of a Future Fellowship from the Australian Research Council.
文摘The main aim of this mini-review is to illustrate strategies and industrial applications based on inteins (INTErnal proteINS), which belong to a class of autocatalytic enzymes that are able to perform a catalytic reaction on a single substrate. However, since practical applications of inteins are strongly guided by a detailed understanding of their biological mechanisms and functions, the first part of this review will thus briefly discuss the physiological roles of inteins, describing what is currently known about their mechanisms of action. In the second part, specific biotechnological applications of inteins will be outlined (i.e. their use for (i) the purification of recombinant proteins, (ii) the cyclization of proteins and (iii) the production of seleno-proteins), paying attention to both potential strengths and weaknesses of this technology.
