Theoretically,blue phosphorescent materials are capable of achieving 100%internal quantum effi-ciency.Nevertheless,the mutual constraints among efficiency,color purity,and stability remain one of the key bottlenecks i...Theoretically,blue phosphorescent materials are capable of achieving 100%internal quantum effi-ciency.Nevertheless,the mutual constraints among efficiency,color purity,and stability remain one of the key bottlenecks in the industrialization of organic light-emitting diodes(OLEDs).In addition,the design and application of host materials also exert a significant impact on the overall performance of blue light-emitting de-vices.To address this issue,this study constructs a series of host materials with high triplet energy levels by designing different connection modes,based on 9-phenylcarbazole and benzimidazole units.Through a combi-nation of theoretical and experimental approaches,the correlation between the chemical structure and perfor-mance has been unraveled.It is found that the designed and synthesized blue phosphorescent bipolar host ma-terials based on different biphenyl linking sites,i.e.,9-(3'-(1-phenyl-1H-benzo[d]imidazol-2-yl)-[1,1'-bi-phenyl]-3-yl)-9H-carbazole(mCzmBI),9-(2'-(1-phenyl-1H-benzo[d]imidazol-2-yl)-[1,1'-biphenyl]-3-yl)-9H-carbazole(mCzoBI)and 9-(3'-(1-phenyl-1H-benzo[d]imidazol-2-yl)-[1,1'-biphenyl]-2-yl)-9H-carbazole(oCzmBI).The three compounds have a similar triplet energy level of 2.70 eV,accompanied with the glass transition temperatures of 92℃,103℃,and 93℃respectively.mCzmBI,mCzoBI and oCzmBI are regioiso-mers,but differ in the linking sites of carbazole and benzimidazole on the biphenyl linker.This difference in linking positions enables effective regulation of the host materials’properties.Constructed with the blue phos-phorescent material bis(4,6-difluorophenylpyridinato-N,C2)picolinatoiridium(Ⅲ)(FIrpic)as the vip,the influence of the three hosts on device performance is clarified.Overall,the device using mCzmBI,a host linked by biphenyl at double meta-positions,achieved a maximum current efficiency of 24.9 cd·A^(-1)and a max-imum external quantum efficiency exceeding 12.8%,it also demonstrates low efficiency roll-off under highbrightness conditions.This work offers an effective strategy to the development of high-efficiency blue phospho-rescent hosts.展开更多
Two tetrasubstituted carbazole derivatives TBICz and TOXDCz have been designed and synthesized,which possess the twist skeletons and exhibit excellent thermal and morphological stabilities.Utilizing these novel compou...Two tetrasubstituted carbazole derivatives TBICz and TOXDCz have been designed and synthesized,which possess the twist skeletons and exhibit excellent thermal and morphological stabilities.Utilizing these novel compounds as host material,high efficiency solution-processed green phosphorescent organic light-emitting diodes(PhOLEDs)have been achieved.The high triplet energies of TBICz and TOXDCz ensure efficient energy transfer from the host to the phosphor and triplet exciton confinement on the phosphor.Solution-processable green phospho⁃rescent devices employing Ir(ppy)3 as vip and the two tetrasubstituted carbazole derivatives as hosts exhibit high ef⁃ficiencies.The best EL performance is achieved for the TBICz-based device,with a maximum current efficiency of 27.3 cd/A,a maximum power efficiency of 15.9 lm/W,and a maximum external quantum efficiency of 7.8%,which provides more host material options for solution-processed OLEDs.展开更多
The semi-hydrogenation of alkynes to alkenes is of great significance in the industrial production of pharmaceutical and fine chemicals.Electrochemical semi-hydrogenation(ECSH)has emerged as a promising alternative to...The semi-hydrogenation of alkynes to alkenes is of great significance in the industrial production of pharmaceutical and fine chemicals.Electrochemical semi-hydrogenation(ECSH)has emerged as a promising alternative to conventional thermochemical hydrogenation.However,its practical application is hindered by low reaction rate and competing hydrogen evolution reaction(HER).In this work,the controllable incorporation of sulfur into the lattice of Pd nanostructures is proposed to develop disordered and electron-deficient Pd-based nanosheets on Ni foam and enhance their ECSH performance of alkynes.Mechanistic investigations demonstrate that the electronic and geometric structures of Pd sites are optimized by lattice sulfur,which tunes the competitive adsorption of H*and alkynes,inherently inhibits the H*coupling and weakens alkene adsorption,thereby promotes the semi-hydrogenation of alkynes and prevents the over-hydrogenation of alkenes.The optimized Pd-based nanosheets exhibit efficient electrocatalytic semi-hydrogenation performance in an H-cell,achieving 97%alkene selectivity,94%Faradaic efficiency,and a reaction rate of 303.7μmol mgcatal.^(-1) h^(-1) using 4-methoxyphenylacetylene as the model substrate.Even in a membrane electrode assembly(MEA)configuration,the optimized Pd-based nanosheets achieves a single-cycle alkyne conversion of 96%and an alkene selectivity of 97%,with continuous production of alkene at a rate of 1901.1μmol mgcatal.^(-1) h^(-1).The potential-and time-independent selectivity,good substrate universality with excellent tolerance to active groups(C–Br/Cl/C]O,etc.)further highlight the potential of this strategy for advanced catalysts design and green chemistry.展开更多
Due to the widespread usage of social media in our recent daily lifestyles,sentiment analysis becomes an important field in pattern recognition and Natural Language Processing(NLP).In this field,users’feedback data o...Due to the widespread usage of social media in our recent daily lifestyles,sentiment analysis becomes an important field in pattern recognition and Natural Language Processing(NLP).In this field,users’feedback data on a specific issue are evaluated and analyzed.Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research.Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature.Emotions describe a state of mind of distinct behaviors,feelings,thoughts and experiences.The main objective of this paper is to propose a new model named BERT-CNN to detect emotions from text.This model is formed by a combination of the Bidirectional Encoder Representations from Transformer(BERT)and the Convolutional Neural networks(CNN)for textual classification.This model embraces the BERT to train the word semantic representation language model.According to the word context,the semantic vector is dynamically generated and then placed into the CNN to predict the output.Results of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR datasets.The BERTCNN model achieves an accuracy of 94.7%and an F1-score of 94%for semeval2019 task3 dataset and an accuracy of 75.8%and an F1-score of 76%for ISEAR dataset.展开更多
Iron-based anodes for lithium-ion batteries(LIBs)with higher theoretical capacity,natural abundance and cheapness have received considerable attention,but they still suffer from the fast capacity fading.To address thi...Iron-based anodes for lithium-ion batteries(LIBs)with higher theoretical capacity,natural abundance and cheapness have received considerable attention,but they still suffer from the fast capacity fading.To address this issue,we report a facile synthesis of plate-like carbonsupported Fe_(3)C nanoparticles through chemical blowing/carbonization under calcination.The ultrafine Fe_(3)C nanoparticles are prone to be oxidized when exposing in air;thus,Fe_(3)C/C with mild oxidization and the fully oxidized product of Fe_(2)O_(3)/C are successfully prepared by controlling the oxidization condition.When applied as an anode material in LIB,the Fe_(3)C/C electrode demonstrates excellent cycle stability(826 mAh·g^(-1)after 120 cycles under 500 mA·g^(-1))and rate performance(410.6 mAh·g^(-1) under 2 A·g^(-1)),compared with the Fe_(2)O_(3)/C counterpart.The enhanced electrochemical performance can be ascribed to the synergetic effect of the Fe_(3)C with mild oxidation and the unique hierarchical structure of plate-like carbon decorated with Fe_(3)C catalyst.More importantly,this work may offer new approaches to synthesize other transition metal(e.g.,Co,Ni)-based anode material by replacing the precursor ingredient.展开更多
This study seeks to establish techniques of architecture planning for living robots and human together. This experiment is elemental step to search relations between a small mobile robot and human. This experiment is ...This study seeks to establish techniques of architecture planning for living robots and human together. This experiment is elemental step to search relations between a small mobile robot and human. This experiment is ＂How can a small mobile robot approach to individuals distances of foreigners （non-Japanese） up to the point where foreigners （non-Japanese） do not feel like approaching any more＂. It focuses on a small mobile robot and identifies distances at which the robot can approach to individuals, particularly adult foreign male. This paper clarifies how the individual distances change by the robot＇s speed and angle of approach varies.展开更多
Recently, Japan has experienced a low birthrate and an aging population. The development of communication robots, such as cleaning and a care-giver robot, has been progressing. Care-giver robots provide daily assistan...Recently, Japan has experienced a low birthrate and an aging population. The development of communication robots, such as cleaning and a care-giver robot, has been progressing. Care-giver robots provide daily assistance, including contacting emergency services. This study is part of the ＂study on planning techniques of living space in harmony with robots＂, and focused on the elderly. Minimum distance was the subjects felt ＂I do not want any more approached＂. Subjects were 21 elderly persons （eight males and thirteen females）, aged 66-86 years. The experimental room was an assembly room in a public accommodation （14 m× 6.5 m）. The small mobile robot used in this experiment was external form dimensions of 120 mm （W）× 130 mm （D）× 70 mm （H）, In this experiment, considering the personal space as the small mobile robot is watching robot without support function for person. The robot moved toward standing or sitting subjects at constant velocities from a distance 5 m apart. Research factors are 5 angles （0°, 45°, 90°, 135° and 180°） and 2 speeds （0.08 m/s and 0.24 m/s）.展开更多
This paper seeks to envision future architectural planning as it relates to living with robots and to clarify the research theme for it. Living with robots is no longer a fantasy seen just on TV or in movies. The conc...This paper seeks to envision future architectural planning as it relates to living with robots and to clarify the research theme for it. Living with robots is no longer a fantasy seen just on TV or in movies. The concept of the intelligent space emerged from the field of robotics in the mid-1990s. The idea of the intelligent space is that the robot will support the architecture and users of the space by keeping in close contact with architectural space equipped with intelligent technologies. The benefit of the intelligent space is that the robot can be smaller and less intelligent because the robot will be assisted by advanced technologies embedded in the architectural space, such as sensors and actuators. Therefore, the robot can consequently assist user＇s activities with delicate care. This paper describes the vision and possibility for architectural planning as it relates to live with robots who can support the inhabitants＇ lives. This paper introduces the study of the relationship between humans and moving robot in architectural space, especially support region for humans by desktop mobile robot.展开更多
Recently, the demand of services for architectural space is more increasing due to the diversification and complication of people＇s needs. In this research, it is assumed that the robot will support people＇s various...Recently, the demand of services for architectural space is more increasing due to the diversification and complication of people＇s needs. In this research, it is assumed that the robot will support people＇s various needs and people＇s lives in the near future. In this study, the authors investigated the relationship between a robot and a person in quasi space close to actual life space. The authors carried out an experiment to clarify the feelings of worriment due to a robot in the living room in the case of Japanese males. This research aims to establish architecture techniques for robots and humans living together. In this experiment, the robot approached a male sitting on a sofa in quasi space. At different distances between the robot and the male, the subject evaluated his feelings of worriment from ＂no worriment＂ to ＂worriment＂ in five steps from one to five. The robot used in this experiment is middle-sized （380 mm （L） × 380 mm （W） × 350 mm （H））. In this experiment, it is considered that the robot asks for help outside when an emergency occurs. In this experiment, the authors had three situations for the subject： watching television, reading a magazine and just sitting.展开更多
Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking an...Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking and detecting the spread of fake news in Arabic becomes critical.Several artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake news.Thus,fake news in Arabic is identified by utilizing AI approaches.This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic corpus.The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.展开更多
In this work, machining test was carried out in various machining conditions using ultrasonic vibration capable CNC machine. For work material, alumina ceramic (Al2O3) was used while for tool material diamond electrop...In this work, machining test was carried out in various machining conditions using ultrasonic vibration capable CNC machine. For work material, alumina ceramic (Al2O3) was used while for tool material diamond electroplated grinding wheel was used. To evaluate ultrasonic vibration effect, grinding test was performed with and without ultrasonic vibration in same machining condition. In ultrasonic mode, ultrasonic vibration of 20 kHz was generated by HSK 63 ultrasonic actuator. On the other hand, grinding forces were measured by KISTLER dynamometer. And an optimal sampling rate for grinding force measurement was obtained by a signal processing and frequency analysis. The surface roughness of the ceramic was also measured by using stylus type surface roughness instrument and atomic force microscope (AFM). Besides, the scanning electron microscope (SEM) was used for observation of surface integrality.展开更多
Oral cancer is the most commonly occurring‘head and neck cancers’across the globe.Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public.Since earlier identification of ...Oral cancer is the most commonly occurring‘head and neck cancers’across the globe.Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public.Since earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this regard.In this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques.The proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data pre-processing.Followed by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep features.Moreover,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and classification.Furthermore,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several aspects.The extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.展开更多
Arabic is the world’s first language,categorized by its rich and complicated grammatical formats.Furthermore,the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for ver...Arabic is the world’s first language,categorized by its rich and complicated grammatical formats.Furthermore,the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns.The Arabic language consists of distinct variations utilized in a community and particular situations.Social media sites are a medium for expressing opinions and social phenomena like racism,hatred,offensive language,and all kinds of verbal violence.Such conduct does not impact particular nations,communities,or groups only,extending beyond such areas into people’s everyday lives.This study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection(IALODL-OHSD)on Arabic Cross-Corpora.The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media.In the IALODL-OHSD model,a threestage process is performed,namely pre-processing,word embedding,and classification.Primarily,data pre-processing is performed to transform the Arabic social media text into a useful format.In addition,the word2vec word embedding process is utilized to produce word embeddings.The attentionbased cascaded long short-term memory(ACLSTM)model is utilized for the classification process.Finally,the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results.To illustrate a brief result analysis of the IALODL-OHSD model,a detailed set of simulations were performed.The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.展开更多
Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and things.In recent times,IoE gained significant attention among entrepreneurs,individual...Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and things.In recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected entities.On the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful data.Security and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection Systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System(IMVO-DLIDS)for IoT environment.The presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT environment.The proposed IMVO-DLIDS model follows a three-stage process.At first,data pre-processing is performed to convert the actual data into useful format.In addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature subsets.Finally,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for classification.Here,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU model.The experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct measures.An extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.展开更多
Malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and cybercrime.Such malicious software programs come under different classificat...Malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and cybercrime.Such malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,etc.Ransomware is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of money.To prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning(ML)algorithm.This study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection(LBAAA-OMLMD)approach in Computer Networks.The presented LBAAA-OMLMDmodelmainly aims to detect and classify the existence of ransomware and goodware in the network.To accomplish this,the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality problems.Besides,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is applied.The FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier results.The experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset,and the outcomes are inspected in distinct measures.The comprehensive comparative examination demonstrated the betterment of the LBAAAOMLMD model over recent algorithms.展开更多
Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19)...Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.展开更多
Sleep plays a vital role in optimum working of the brain and the body.Numerous people suffer from sleep-oriented illnesses like apnea,insomnia,etc.Sleep stage classification is a primary process in the quantitative ex...Sleep plays a vital role in optimum working of the brain and the body.Numerous people suffer from sleep-oriented illnesses like apnea,insomnia,etc.Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording.Sleep stage scoring is mainly based on experts’knowledge which is laborious and time consuming.Hence,it can be essential to design automated sleep stage classification model using machine learning(ML)and deep learning(DL)approaches.In this view,this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification(CMVODL-SSC)model using Electroencephalogram(EEG)signals.The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals.Primarily,data pre-processing is performed to convert the actual data into useful format.Besides,a cascaded long short term memory(CLSTM)model is employed to perform classification process.At last,the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model.In order to report the enhancements of the CMVODL-SSC model,a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%.展开更多
With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous conn...With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications.The difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing topology.In the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same time.Typically,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing system.With this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)technique.The presented COAER-UAVC technique establishes effective routes for communication between the UAVs.It is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from predators.Besides,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system.The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.展开更多
文摘Theoretically,blue phosphorescent materials are capable of achieving 100%internal quantum effi-ciency.Nevertheless,the mutual constraints among efficiency,color purity,and stability remain one of the key bottlenecks in the industrialization of organic light-emitting diodes(OLEDs).In addition,the design and application of host materials also exert a significant impact on the overall performance of blue light-emitting de-vices.To address this issue,this study constructs a series of host materials with high triplet energy levels by designing different connection modes,based on 9-phenylcarbazole and benzimidazole units.Through a combi-nation of theoretical and experimental approaches,the correlation between the chemical structure and perfor-mance has been unraveled.It is found that the designed and synthesized blue phosphorescent bipolar host ma-terials based on different biphenyl linking sites,i.e.,9-(3'-(1-phenyl-1H-benzo[d]imidazol-2-yl)-[1,1'-bi-phenyl]-3-yl)-9H-carbazole(mCzmBI),9-(2'-(1-phenyl-1H-benzo[d]imidazol-2-yl)-[1,1'-biphenyl]-3-yl)-9H-carbazole(mCzoBI)and 9-(3'-(1-phenyl-1H-benzo[d]imidazol-2-yl)-[1,1'-biphenyl]-2-yl)-9H-carbazole(oCzmBI).The three compounds have a similar triplet energy level of 2.70 eV,accompanied with the glass transition temperatures of 92℃,103℃,and 93℃respectively.mCzmBI,mCzoBI and oCzmBI are regioiso-mers,but differ in the linking sites of carbazole and benzimidazole on the biphenyl linker.This difference in linking positions enables effective regulation of the host materials’properties.Constructed with the blue phos-phorescent material bis(4,6-difluorophenylpyridinato-N,C2)picolinatoiridium(Ⅲ)(FIrpic)as the vip,the influence of the three hosts on device performance is clarified.Overall,the device using mCzmBI,a host linked by biphenyl at double meta-positions,achieved a maximum current efficiency of 24.9 cd·A^(-1)and a max-imum external quantum efficiency exceeding 12.8%,it also demonstrates low efficiency roll-off under highbrightness conditions.This work offers an effective strategy to the development of high-efficiency blue phospho-rescent hosts.
文摘Two tetrasubstituted carbazole derivatives TBICz and TOXDCz have been designed and synthesized,which possess the twist skeletons and exhibit excellent thermal and morphological stabilities.Utilizing these novel compounds as host material,high efficiency solution-processed green phosphorescent organic light-emitting diodes(PhOLEDs)have been achieved.The high triplet energies of TBICz and TOXDCz ensure efficient energy transfer from the host to the phosphor and triplet exciton confinement on the phosphor.Solution-processable green phospho⁃rescent devices employing Ir(ppy)3 as vip and the two tetrasubstituted carbazole derivatives as hosts exhibit high ef⁃ficiencies.The best EL performance is achieved for the TBICz-based device,with a maximum current efficiency of 27.3 cd/A,a maximum power efficiency of 15.9 lm/W,and a maximum external quantum efficiency of 7.8%,which provides more host material options for solution-processed OLEDs.
基金financially supported by the National Natural Science Foundation of China(51701127,92163209,12264053)Shenzhen Fundamental Research Program(JCYJ20220811170904003,JCYJ20210324094000001)+6 种基金Shenzhen Peacock Plan(20180703896C)Shenzhen Key Laboratory of 2D Metamaterials for Information Technology(ZDSYS201707271014468)the research projects of Guangdong Provincial Education Office(2024KCXTD064)ZJUHIC start-up fund(02090200-K02013002)Beijing Natural Science Foundation(JQ22004)the Natural Science Foundation of Hangzhou(2024SZRYBB020001)the Scientific Research and Innovation Project of Postgraduate Students in the Academic Degree of Yunnan University(KC-23234366).
文摘The semi-hydrogenation of alkynes to alkenes is of great significance in the industrial production of pharmaceutical and fine chemicals.Electrochemical semi-hydrogenation(ECSH)has emerged as a promising alternative to conventional thermochemical hydrogenation.However,its practical application is hindered by low reaction rate and competing hydrogen evolution reaction(HER).In this work,the controllable incorporation of sulfur into the lattice of Pd nanostructures is proposed to develop disordered and electron-deficient Pd-based nanosheets on Ni foam and enhance their ECSH performance of alkynes.Mechanistic investigations demonstrate that the electronic and geometric structures of Pd sites are optimized by lattice sulfur,which tunes the competitive adsorption of H*and alkynes,inherently inhibits the H*coupling and weakens alkene adsorption,thereby promotes the semi-hydrogenation of alkynes and prevents the over-hydrogenation of alkenes.The optimized Pd-based nanosheets exhibit efficient electrocatalytic semi-hydrogenation performance in an H-cell,achieving 97%alkene selectivity,94%Faradaic efficiency,and a reaction rate of 303.7μmol mgcatal.^(-1) h^(-1) using 4-methoxyphenylacetylene as the model substrate.Even in a membrane electrode assembly(MEA)configuration,the optimized Pd-based nanosheets achieves a single-cycle alkyne conversion of 96%and an alkene selectivity of 97%,with continuous production of alkene at a rate of 1901.1μmol mgcatal.^(-1) h^(-1).The potential-and time-independent selectivity,good substrate universality with excellent tolerance to active groups(C–Br/Cl/C]O,etc.)further highlight the potential of this strategy for advanced catalysts design and green chemistry.
文摘Due to the widespread usage of social media in our recent daily lifestyles,sentiment analysis becomes an important field in pattern recognition and Natural Language Processing(NLP).In this field,users’feedback data on a specific issue are evaluated and analyzed.Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research.Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature.Emotions describe a state of mind of distinct behaviors,feelings,thoughts and experiences.The main objective of this paper is to propose a new model named BERT-CNN to detect emotions from text.This model is formed by a combination of the Bidirectional Encoder Representations from Transformer(BERT)and the Convolutional Neural networks(CNN)for textual classification.This model embraces the BERT to train the word semantic representation language model.According to the word context,the semantic vector is dynamically generated and then placed into the CNN to predict the output.Results of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR datasets.The BERTCNN model achieves an accuracy of 94.7%and an F1-score of 94%for semeval2019 task3 dataset and an accuracy of 75.8%and an F1-score of 76%for ISEAR dataset.
基金financially supported by State Grid Corporation of China(No.5202011600TY)。
文摘Iron-based anodes for lithium-ion batteries(LIBs)with higher theoretical capacity,natural abundance and cheapness have received considerable attention,but they still suffer from the fast capacity fading.To address this issue,we report a facile synthesis of plate-like carbonsupported Fe_(3)C nanoparticles through chemical blowing/carbonization under calcination.The ultrafine Fe_(3)C nanoparticles are prone to be oxidized when exposing in air;thus,Fe_(3)C/C with mild oxidization and the fully oxidized product of Fe_(2)O_(3)/C are successfully prepared by controlling the oxidization condition.When applied as an anode material in LIB,the Fe_(3)C/C electrode demonstrates excellent cycle stability(826 mAh·g^(-1)after 120 cycles under 500 mA·g^(-1))and rate performance(410.6 mAh·g^(-1) under 2 A·g^(-1)),compared with the Fe_(2)O_(3)/C counterpart.The enhanced electrochemical performance can be ascribed to the synergetic effect of the Fe_(3)C with mild oxidation and the unique hierarchical structure of plate-like carbon decorated with Fe_(3)C catalyst.More importantly,this work may offer new approaches to synthesize other transition metal(e.g.,Co,Ni)-based anode material by replacing the precursor ingredient.
文摘This study seeks to establish techniques of architecture planning for living robots and human together. This experiment is elemental step to search relations between a small mobile robot and human. This experiment is ＂How can a small mobile robot approach to individuals distances of foreigners （non-Japanese） up to the point where foreigners （non-Japanese） do not feel like approaching any more＂. It focuses on a small mobile robot and identifies distances at which the robot can approach to individuals, particularly adult foreign male. This paper clarifies how the individual distances change by the robot＇s speed and angle of approach varies.
文摘Recently, Japan has experienced a low birthrate and an aging population. The development of communication robots, such as cleaning and a care-giver robot, has been progressing. Care-giver robots provide daily assistance, including contacting emergency services. This study is part of the ＂study on planning techniques of living space in harmony with robots＂, and focused on the elderly. Minimum distance was the subjects felt ＂I do not want any more approached＂. Subjects were 21 elderly persons （eight males and thirteen females）, aged 66-86 years. The experimental room was an assembly room in a public accommodation （14 m× 6.5 m）. The small mobile robot used in this experiment was external form dimensions of 120 mm （W）× 130 mm （D）× 70 mm （H）, In this experiment, considering the personal space as the small mobile robot is watching robot without support function for person. The robot moved toward standing or sitting subjects at constant velocities from a distance 5 m apart. Research factors are 5 angles （0°, 45°, 90°, 135° and 180°） and 2 speeds （0.08 m/s and 0.24 m/s）.
文摘This paper seeks to envision future architectural planning as it relates to living with robots and to clarify the research theme for it. Living with robots is no longer a fantasy seen just on TV or in movies. The concept of the intelligent space emerged from the field of robotics in the mid-1990s. The idea of the intelligent space is that the robot will support the architecture and users of the space by keeping in close contact with architectural space equipped with intelligent technologies. The benefit of the intelligent space is that the robot can be smaller and less intelligent because the robot will be assisted by advanced technologies embedded in the architectural space, such as sensors and actuators. Therefore, the robot can consequently assist user＇s activities with delicate care. This paper describes the vision and possibility for architectural planning as it relates to live with robots who can support the inhabitants＇ lives. This paper introduces the study of the relationship between humans and moving robot in architectural space, especially support region for humans by desktop mobile robot.
文摘Recently, the demand of services for architectural space is more increasing due to the diversification and complication of people＇s needs. In this research, it is assumed that the robot will support people＇s various needs and people＇s lives in the near future. In this study, the authors investigated the relationship between a robot and a person in quasi space close to actual life space. The authors carried out an experiment to clarify the feelings of worriment due to a robot in the living room in the case of Japanese males. This research aims to establish architecture techniques for robots and humans living together. In this experiment, the robot approached a male sitting on a sofa in quasi space. At different distances between the robot and the male, the subject evaluated his feelings of worriment from ＂no worriment＂ to ＂worriment＂ in five steps from one to five. The robot used in this experiment is middle-sized （380 mm （L） × 380 mm （W） × 350 mm （H））. In this experiment, it is considered that the robot asks for help outside when an emergency occurs. In this experiment, the authors had three situations for the subject： watching television, reading a magazine and just sitting.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under Grant Number(120/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR32).
文摘Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking and detecting the spread of fake news in Arabic becomes critical.Several artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake news.Thus,fake news in Arabic is identified by utilizing AI approaches.This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic corpus.The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.
文摘In this work, machining test was carried out in various machining conditions using ultrasonic vibration capable CNC machine. For work material, alumina ceramic (Al2O3) was used while for tool material diamond electroplated grinding wheel was used. To evaluate ultrasonic vibration effect, grinding test was performed with and without ultrasonic vibration in same machining condition. In ultrasonic mode, ultrasonic vibration of 20 kHz was generated by HSK 63 ultrasonic actuator. On the other hand, grinding forces were measured by KISTLER dynamometer. And an optimal sampling rate for grinding force measurement was obtained by a signal processing and frequency analysis. The surface roughness of the ceramic was also measured by using stylus type surface roughness instrument and atomic force microscope (AFM). Besides, the scanning electron microscope (SEM) was used for observation of surface integrality.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 1/322/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R161)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR06).
文摘Oral cancer is the most commonly occurring‘head and neck cancers’across the globe.Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public.Since earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this regard.In this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques.The proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data pre-processing.Followed by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep features.Moreover,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and classification.Furthermore,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several aspects.The extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R263)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR43.
文摘Arabic is the world’s first language,categorized by its rich and complicated grammatical formats.Furthermore,the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns.The Arabic language consists of distinct variations utilized in a community and particular situations.Social media sites are a medium for expressing opinions and social phenomena like racism,hatred,offensive language,and all kinds of verbal violence.Such conduct does not impact particular nations,communities,or groups only,extending beyond such areas into people’s everyday lives.This study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection(IALODL-OHSD)on Arabic Cross-Corpora.The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media.In the IALODL-OHSD model,a threestage process is performed,namely pre-processing,word embedding,and classification.Primarily,data pre-processing is performed to transform the Arabic social media text into a useful format.In addition,the word2vec word embedding process is utilized to produce word embeddings.The attentionbased cascaded long short-term memory(ACLSTM)model is utilized for the classification process.Finally,the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results.To illustrate a brief result analysis of the IALODL-OHSD model,a detailed set of simulations were performed.The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(46/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R140)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4210118DSR13).
文摘Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and things.In recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected entities.On the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful data.Security and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection Systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System(IMVO-DLIDS)for IoT environment.The presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT environment.The proposed IMVO-DLIDS model follows a three-stage process.At first,data pre-processing is performed to convert the actual data into useful format.In addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature subsets.Finally,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for classification.Here,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU model.The experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct measures.An extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R319)PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4310373DSR34The authors are thankful to the Deanship of Scientific Research at Najran University for funding thiswork under theResearch Groups Funding program Grant Code(NU/RG/SERC/11/4).
文摘Malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and cybercrime.Such malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,etc.Ransomware is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of money.To prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning(ML)algorithm.This study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection(LBAAA-OMLMD)approach in Computer Networks.The presented LBAAA-OMLMDmodelmainly aims to detect and classify the existence of ransomware and goodware in the network.To accomplish this,the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality problems.Besides,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is applied.The FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier results.The experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset,and the outcomes are inspected in distinct measures.The comprehensive comparative examination demonstrated the betterment of the LBAAAOMLMD model over recent algorithms.
基金The authors thank the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under grant number(120/43)Princess Nourah bint Abdulrahman UniversityResearchers Supporting Project number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research atUmmAl-Qura University for supporting this work by Grant Code:(22UQU4331004DSR06).
文摘Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/158/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R235)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4340237DSR10).
文摘Sleep plays a vital role in optimum working of the brain and the body.Numerous people suffer from sleep-oriented illnesses like apnea,insomnia,etc.Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording.Sleep stage scoring is mainly based on experts’knowledge which is laborious and time consuming.Hence,it can be essential to design automated sleep stage classification model using machine learning(ML)and deep learning(DL)approaches.In this view,this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification(CMVODL-SSC)model using Electroencephalogram(EEG)signals.The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals.Primarily,data pre-processing is performed to convert the actual data into useful format.Besides,a cascaded long short term memory(CLSTM)model is employed to perform classification process.At last,the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model.In order to report the enhancements of the CMVODL-SSC model,a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(235/44)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R114)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR71)This study is supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1444).
文摘With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications.The difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing topology.In the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same time.Typically,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing system.With this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)technique.The presented COAER-UAVC technique establishes effective routes for communication between the UAVs.It is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from predators.Besides,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system.The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.
