The increasing focus on electrocatalysis for sustainable hydrogen(H_(2))production has prompted significant interest in MXenes,a class of two-dimensional(2D)materials comprising metal carbides,carbonitrides,and nitrid...The increasing focus on electrocatalysis for sustainable hydrogen(H_(2))production has prompted significant interest in MXenes,a class of two-dimensional(2D)materials comprising metal carbides,carbonitrides,and nitrides.These materials exhibit intriguing chemical and physical properties,including excellent electrical conductivity and a large surface area,making them attractive candidates for the hydrogen evolution reaction(HER).This scientific review explores recent advancements in MXene-based electrocatalysts for HER kinetics.It discusses various compositions,functionalities,and explicit design principles while providing a comprehensive overview of synthesis methods,exceptional properties,and electro-catalytic approaches for H_(2) production via electrochemical reactions.Furthermore,challenges and future prospects in designing MXenes-based electrocatalysts with enhanced kinetics are highlighted,emphasizing the potential of incorporating different metals to expand the scope of electrochemical reactions.This review suggests possible efforts for developing advanced MXenes-based electrocatalysts,particularly for efficient H_(2) generation through electrochemical water-splitting reactions..展开更多
Ultra-wideband(UWB)is highly preferred for short distance communication.As a result of this significance,this project targets the design of a compact UWB antennas.This paper describes a printed UWB rhombusshaped anten...Ultra-wideband(UWB)is highly preferred for short distance communication.As a result of this significance,this project targets the design of a compact UWB antennas.This paper describes a printed UWB rhombusshaped antenna with a partial ground plane.To achieve wideband response,two stubs and a notch are incorporated at both sides of the rhombus design and ground plane respectively.To excite the antenna,a simple microstrip feed line is employed.The suggested antenna is built on a 1.6 mm thick FR4 substrate.The proposed design is very compact with overall electrical size of 0.18λ×0.25λ(14×18 mm2).The rhombus shaped antenna covers frequency ranging from 3.5 to 11 GHz with 7.5 GHz impedance bandwidth.The proposed design simulated and measured bandwidths are 83.33%and 80%,respectively.Radiation pattern in terms of E-field and H-field are discussed at 4,5.5 and 10 GHz respectively.The proposed design has 65%radiation efficiency and 1.5 dBi peak gain.The proposed design is simulated in CST(Computer Simulation Technology)simulator and the simulated design is fabricated for the measured results.The simulated and measured findings are in close resemblance.The obtained results confirm the application of the proposed design for the ultra-wide band applications.展开更多
Medical Imaging Segmentation is an essential technique for modern medical applications.It is the foundation of many aspects of clinical diagnosis,oncology,and computer-integrated surgical intervention.Although signifi...Medical Imaging Segmentation is an essential technique for modern medical applications.It is the foundation of many aspects of clinical diagnosis,oncology,and computer-integrated surgical intervention.Although significant successes have been achieved in the segmentation of medical images,DL(deep learning)approaches.Manual delineation of OARs(organs at risk)is vastly dominant but it is prone to errors given the complex irregularities in shape,low texture diversity between tissues and adjacent blood area,patientwide location of organisms,and weak soft tissue contrast across adjacent organs in CT images.Till now several models have been implemented onmulti organs segmentation but not caters to the problemof imbalanced classes some organs have relatively small pixels as compared to others.To segment OARs in thoracic CT images,we proposed the model based on the encoder-decoder approach using transfer learning with the efficientnetB7 DL model.We have built a fully connected CNN(Convolutional Neural network)having 5 layers of encoding and 5 layers of decoding with efficientnetB7 specifically to tackle imbalance class pixels in an accurate way for the segmentation of OARs.Proposed methodology achieves 0.93405 IOU score,0.95138 F1 score and class-wise dice score for esophagus 0.92466,trachea 0.94257,heart 0.95038,aorta 0.9351 and background 0.99891.The results showed that our proposed framework can be segmented organs accurately.展开更多
基金the financial support from the Sunway University International Research Network Grant Scheme(STR-IRNGSSET-GAMRG-01-2022)the Universiti Kebangsaan Malaysia Grant(GUP-2022-080)。
文摘The increasing focus on electrocatalysis for sustainable hydrogen(H_(2))production has prompted significant interest in MXenes,a class of two-dimensional(2D)materials comprising metal carbides,carbonitrides,and nitrides.These materials exhibit intriguing chemical and physical properties,including excellent electrical conductivity and a large surface area,making them attractive candidates for the hydrogen evolution reaction(HER).This scientific review explores recent advancements in MXene-based electrocatalysts for HER kinetics.It discusses various compositions,functionalities,and explicit design principles while providing a comprehensive overview of synthesis methods,exceptional properties,and electro-catalytic approaches for H_(2) production via electrochemical reactions.Furthermore,challenges and future prospects in designing MXenes-based electrocatalysts with enhanced kinetics are highlighted,emphasizing the potential of incorporating different metals to expand the scope of electrochemical reactions.This review suggests possible efforts for developing advanced MXenes-based electrocatalysts,particularly for efficient H_(2) generation through electrochemical water-splitting reactions..
基金Taif University Researchers are supporting project number(TURSP-2020/216),Taif University,Taif,Saudi Arabia.
文摘Ultra-wideband(UWB)is highly preferred for short distance communication.As a result of this significance,this project targets the design of a compact UWB antennas.This paper describes a printed UWB rhombusshaped antenna with a partial ground plane.To achieve wideband response,two stubs and a notch are incorporated at both sides of the rhombus design and ground plane respectively.To excite the antenna,a simple microstrip feed line is employed.The suggested antenna is built on a 1.6 mm thick FR4 substrate.The proposed design is very compact with overall electrical size of 0.18λ×0.25λ(14×18 mm2).The rhombus shaped antenna covers frequency ranging from 3.5 to 11 GHz with 7.5 GHz impedance bandwidth.The proposed design simulated and measured bandwidths are 83.33%and 80%,respectively.Radiation pattern in terms of E-field and H-field are discussed at 4,5.5 and 10 GHz respectively.The proposed design has 65%radiation efficiency and 1.5 dBi peak gain.The proposed design is simulated in CST(Computer Simulation Technology)simulator and the simulated design is fabricated for the measured results.The simulated and measured findings are in close resemblance.The obtained results confirm the application of the proposed design for the ultra-wide band applications.
基金Taif University Researchers Supporting Project number(TURSP-2020/98),Taif University,Taif,Saudi Arabia.
文摘Medical Imaging Segmentation is an essential technique for modern medical applications.It is the foundation of many aspects of clinical diagnosis,oncology,and computer-integrated surgical intervention.Although significant successes have been achieved in the segmentation of medical images,DL(deep learning)approaches.Manual delineation of OARs(organs at risk)is vastly dominant but it is prone to errors given the complex irregularities in shape,low texture diversity between tissues and adjacent blood area,patientwide location of organisms,and weak soft tissue contrast across adjacent organs in CT images.Till now several models have been implemented onmulti organs segmentation but not caters to the problemof imbalanced classes some organs have relatively small pixels as compared to others.To segment OARs in thoracic CT images,we proposed the model based on the encoder-decoder approach using transfer learning with the efficientnetB7 DL model.We have built a fully connected CNN(Convolutional Neural network)having 5 layers of encoding and 5 layers of decoding with efficientnetB7 specifically to tackle imbalance class pixels in an accurate way for the segmentation of OARs.Proposed methodology achieves 0.93405 IOU score,0.95138 F1 score and class-wise dice score for esophagus 0.92466,trachea 0.94257,heart 0.95038,aorta 0.9351 and background 0.99891.The results showed that our proposed framework can be segmented organs accurately.