We present a novel method for scale-invariant 3D face recognition by integrating computer-generated holography with the Mellin transform.This approach leverages the scale-invariance property of the Mellin transform to...We present a novel method for scale-invariant 3D face recognition by integrating computer-generated holography with the Mellin transform.This approach leverages the scale-invariance property of the Mellin transform to address challenges related to variations in 3D facial sizes during recognition.By applying the Mellin transform to computer-generated holograms and performing correlation between them,which,to the best of our knowledge,is being done for the first time,we have developed a robust recognition framework capable of managing significant scale variations without compromising recognition accuracy.Digital holograms of 3D faces are generated from a face database,and the Mellin transform is employed to enable robust recognition across scale factors ranging from 0.4 to 2.0.Within this range,the method achieves 100%recognition accuracy,as confirmed by both simulation-based and hybrid optical/digital experimental validations.Numerical calculations demonstrate that our method significantly enhances the accuracy and reliability of 3D face recognition,as evidenced by the sharp correlation peaks and higher peak-to-noise ratio(PNR)values than that of using conventional holograms without the Mellin transform.Additionally,the hybrid optical/digital joint transform correlation hardware further validates the method's effectiveness,demonstrating its capability to accurately identify and distinguish 3D faces at various scales.This work provides a promising solution for advanced biometric systems,especially for those which require 3D scale-invariant recognition.展开更多
This review provides an insightful and comprehensive exploration of the emerging 2D material borophene,both pristine and modified,emphasizing its unique attributes and potential for sustainable applications.Borophene...This review provides an insightful and comprehensive exploration of the emerging 2D material borophene,both pristine and modified,emphasizing its unique attributes and potential for sustainable applications.Borophene’s distinctive properties include its anisotropic crystal structures that contribute to its exceptional mechanical and electronic properties.The material exhibits superior electrical and thermal conductivity,surpassing many other 2D materials.Borophene’s unique atomic spin arrangements further diversify its potential application for magnetism.Surface and interface engineering,through doping,functionalization,and synthesis of hybridized and nanocomposite borophene-based systems,is crucial for tailoring borophene’s properties to specific applications.This review aims to address this knowledge gap through a comprehensive and critical analysis of different synthetic and functionalisation methods,to enhance surface reactivity by increasing active sites through doping and surface modifications.These approaches optimize diffusion pathways improving accessibility for catalytic reactions,and tailor the electronic density to tune the optical and electronic behavior.Key applications explored include energy systems(batteries,supercapacitors,and hydrogen storage),catalysis for hydrogen and oxygen evolution reactions,sensors,and optoelectronics for advanced photonic devices.The key to all these applications relies on strategies to introduce heteroatoms for tuning electronic and catalytic properties,employ chemical modifications to enhance stability and leverage borophene’s conductivity and reactivity for advanced photonics.Finally,the review addresses challenges and proposes solutions such as encapsulation,functionalization,and integration with composites to mitigate oxidation sensitivity and overcome scalability barriers,enabling sustainable,commercial-scale applications.展开更多
Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unu...Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.展开更多
Electrochemical carbon dioxide reduction reaction(CO_(2)RR)provides a promising way to convert CO_(2)to chemicals.The multicarbon(C_(2+))products,especially ethylene,are of great interest due to their versatile indust...Electrochemical carbon dioxide reduction reaction(CO_(2)RR)provides a promising way to convert CO_(2)to chemicals.The multicarbon(C_(2+))products,especially ethylene,are of great interest due to their versatile industrial applications.However,selectively reducing CO_(2)to ethylene is still challenging as the additional energy required for the C–C coupling step results in large overpotential and many competing products.Nonetheless,mechanistic understanding of the key steps and preferred reaction pathways/conditions,as well as rational design of novel catalysts for ethylene production have been regarded as promising approaches to achieving the highly efficient and selective CO_(2)RR.In this review,we first illustrate the key steps for CO_(2)RR to ethylene(e.g.,CO_(2)adsorption/activation,formation of~*CO intermediate,C–C coupling step),offering mechanistic understanding of CO_(2)RR conversion to ethylene.Then the alternative reaction pathways and conditions for the formation of ethylene and competitive products(C_1 and other C_(2+)products)are investigated,guiding the further design and development of preferred conditions for ethylene generation.Engineering strategies of Cu-based catalysts for CO_(2)RR-ethylene are further summarized,and the correlations of reaction mechanism/pathways,engineering strategies and selectivity are elaborated.Finally,major challenges and perspectives in the research area of CO_(2)RR are proposed for future development and practical applications.展开更多
DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in...DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.展开更多
The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial ma...The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.展开更多
In this paper,we design a total infrared(IR)absorber based on a dispersive band structure of two-dimensional(2D)multiwall carbon nanotube(MWCNTs)square array working from near IR(NIR)to mid IR(MIR)regime.The absorptio...In this paper,we design a total infrared(IR)absorber based on a dispersive band structure of two-dimensional(2D)multiwall carbon nanotube(MWCNTs)square array working from near IR(NIR)to mid IR(MIR)regime.The absorption characteristics have been investigated by the 2D finite-difference time domain(FDTD)method in square lattice photonic crystal(PC)of the multipole Drude-Lorentz model inserted to the dispersive dielectric function of MWCNTs.Dispersive photonic band structure and scattering parameters for the wide range of lattice constants from 15 nm to 3500 nm with various filling ratios have been calculated.The results show that for large lattice constant(>2000 nm),the Bragg gap moves to the IR regime and leads to MWCNTs arrays acting as a total absorber.For a structure with lattice constant of 3500 nm and filling factor of 12%,an enhanced absorption coefficient up to 99%is achieved in the range of 0.35 eV(λ=3.5μm)nominated in the MIR regime.Also,the absorption spectrum peak can be tuned in the range of 0.27—0.38 eV(λ=4.59—3.26μm)with a changing filling factor.Our results and methodology can be used to design new MWCNTs based photonic devices for applications like night-vision,thermal detector,and total IR absorbers.展开更多
Alzheimer’s disease (AD) is a brain disorder that eventually causes memory loss and the ability to perform simple cognitive functions;research efforts within pharmaceuticals and other medical treatments have minimal ...Alzheimer’s disease (AD) is a brain disorder that eventually causes memory loss and the ability to perform simple cognitive functions;research efforts within pharmaceuticals and other medical treatments have minimal impact on the disease. Our preliminary biological studies showed that Repeated Electromagnetic Field Stimulation (REFMS) applying an EM frequency of 64 MHz and a specific absorption rate (SAR) of 0.4 - 0.9 W/kg decrease the level of amyloid-β peptides (Aβ), which is the most likely etiology of AD. This study emphasizes uniform E/H field and SAR distribution with adequate penetration depth penetration through multiple human head layers driven with low input power for safety treatments. In this work, we performed numerical modeling and computer simulations of a portable Meander Line antenna (MLA) to achieve the required EMF parameters to treat AD. The MLA device features a low cost, small size, wide bandwidth, and the ability to integrate into a portable system. This study utilized a High-Frequency Simulation System (HFSS) in the design of the MLA with the desired characteristics suited for AD treatment in humans. The team designed a 24-turn antenna with a 60 cm length and 25 cm width and achieved the required resonant frequency of 64 MHz. Here we used two numerical human head phantoms to test the antenna, the MIDA and spherical head phantom with six and seven tissue layers, respectively. The antenna was fed from a 50-Watt input source to obtain the SAR of 0.6 W/kg requirement in the center of the simulated brain tissue layer. We found that the E/H field and SAR distribution produced was not homogeneous;there were areas of high SAR values close to the antenna transmitter, also areas of low SAR value far away from the antenna. This paper details the antenna parameters, the scattering parameters response, the efficiency response, and the E and H field distribution;we presented the computer simulation results and discussed future work for a practical model.展开更多
Today, mammography is the best method for early detection of breast cancer. Radiologists failed to detect evident cancerous signs in approximately 20% of false negative mammograms. False negatives have been identified...Today, mammography is the best method for early detection of breast cancer. Radiologists failed to detect evident cancerous signs in approximately 20% of false negative mammograms. False negatives have been identified as the inability of the radiologist to detect the abnormalities due to several reasons such as poor image quality, image noise, or eye fatigue. This paper presents a framework for a computer aided detection system that integrates Principal Component Analysis (PCA), Fisher Linear Discriminant (FLD), and Nearest Neighbor Classifier (KNN) algorithms for the detection of abnormalities in mammograms. Using normal and abnormal mammograms from the MIAS database, the integrated algorithm achieved 93.06% classification accuracy. Also in this paper, we present an analysis of the integrated algorithm’s parameters and suggest selection criteria.展开更多
Human body with curved and soft interfaces requests advanced flexible materials and structures for the interaction with organs and signal collection from targets in applications such as bioengineering and diagnostic d...Human body with curved and soft interfaces requests advanced flexible materials and structures for the interaction with organs and signal collection from targets in applications such as bioengineering and diagnostic devices.Among them,it is highly demanded to achieve creative design in flexible materials and structures with great stretchable capability for required applications.To this end,both inorganic and organic materials could be adopted and designed with assembly and self-assembly methods for flexible electronics and electrodes.Soft or flexible materials and structures inspired by nature can lead to highly conformal contacts between devices and the human body.These approaches hold great potential for applications in flexible electronics,medical imaging technology and portable disease diagnostics.Novel strategy on related sensors/actuator and energy storage/generation devices could overcome certain limitations on flexible materials engineering and thus advance the field as well.All these methods would deliver a profound impact to our future intelligent society.展开更多
Single cylindrical submicron pores in PMMA polymer membranes are micropatterned by electron beam lithography and integrated into all PMMA-based electrophoretic flow detector systems. Pore dimensions are 450 nm in diam...Single cylindrical submicron pores in PMMA polymer membranes are micropatterned by electron beam lithography and integrated into all PMMA-based electrophoretic flow detector systems. Pore dimensions are 450 nm in diameter and 1 μm in length. The pores are electrically characterized in aqueous KCl electrolyte, exhibiting a stable time-independent ionic current through the pore with a noise level of less than 1% of the open-pore current. The current-voltage curves are linear and scale with electrolyte concentration. The negative surface charge of the membrane over-proportionally decreases pore conductance at low electrolyte concentrations (≤0.1 M) that are still beyond those typically applied in biological experiments. Pores do not exhibit rectification of current flowing through them, allowing for operation with either polarity. To allow for detection of yet much smaller particles, the described PMMA-based system also was successfully equipped with pores of 1.5 nm instead of 450 nm in diameter. This was achieved by introducing naturally occurring biological protein pores of α-hemolysin on a lipid bilayer into the prepatterned PMMA membrane of an assembled PMMA-based electrophoretic flow detector system. Characteristics of translocation events of single-stranded linear plasmid DNA molecules through the pores were recorded, and ionic current deductions during biomolecule translocation were clear and distinguished. Based on the presented submicron scale open pore ionic current transport properties, as well as the observed passage of DNA molecules through protein pores inserted into PMMA membranes, our current research proposes that all PMMA electrophoretic flow detectors exhibit an excellent potential for future use as biomedical resistive-pulse sensors, as long as pore dimensions match those of biomolecules to be detected.展开更多
A method for balancing thermal and electrical packaging requirements for gallium nitride (GaN) high power amplifier (HPA) modules is presented. The goal is to find a design approach that minimizes the junction tempera...A method for balancing thermal and electrical packaging requirements for gallium nitride (GaN) high power amplifier (HPA) modules is presented. The goal is to find a design approach that minimizes the junction temperature of the GaN so that it is reliable and has interconnects that meet electrical performance requirements. One benefit of GaN is that it can simultaneously achieve high power density and operate at microwave and millimeter-wave frequencies. However, the power density can be so high that the necessary thermal solutions can have negative impact on electrical performance. This is especially a concern for the electrical interconnects required for the input/ output ports on high power amplifier devices. This is because the signal interconnects must operate at GHz frequencies, which means that special care must be taken to avoid problems such as undesired signal coupling and ground path inductance. Therefore, this work focuses on GaN packaging and its integration into a module. The results show that an optimum thickness for the GaN heat spreader exits for thermal performance but the electrical design is impacted negatively if the optimum thermal design is chosen. Therefore, a balanced design is chosen which meets overall system level requirements.展开更多
During the prodromal stage of Alzheimer’s disease (AD), neurodegenerative changes can be identified by measuring volumetric loss in AD-prone brain regions on MRI. Cognitive assessments that are sensitive enough to me...During the prodromal stage of Alzheimer’s disease (AD), neurodegenerative changes can be identified by measuring volumetric loss in AD-prone brain regions on MRI. Cognitive assessments that are sensitive enough to measure the early brain-behavior manifestations of AD and that correlate with biomarkers of neurodegeneration are needed to identify and monitor individuals at risk for dementia. Weak sensitivity to early cognitive change has been a major limitation of traditional cognitive assessments. In this study, we focused on expanding our previous work by determining whether a digitized cognitive stress test, the Loewenstein-Acevedo Scales for Semantic Interference and Learning, Brief Computerized Version (LASSI-BC) could differentiate between Cognitively Unimpaired (CU) and amnestic Mild Cognitive Impairment (aMCI) groups. A second focus was to correlate LASSI-BC performance to volumetric reductions in AD-prone brain regions. Data was gathered from 111 older adults who were comprehensively evaluated and administered the LASSI-BC. Eighty-seven of these participants (51 CU;36 aMCI) underwent MR imaging. The volumes of 12 AD-prone brain regions were related to LASSI-BC and other memory tests correcting for False Discovery Rate (FDR). Results indicated that, even after adjusting for initial learning ability, the failure to recover from proactive semantic interference (frPSI) on the LASSI-BC differentiated between CU and aMCI groups. An optimal combination of frPSI and initial learning strength on the LASSI-BC yielded an area under the ROC curve of 0.876 (76.1% sensitivity, 82.7% specificity). Further, frPSI on the LASSI-BC was associated with volumetric reductions in the hippocampus, amygdala, inferior temporal lobes, precuneus, and posterior cingulate.展开更多
With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet coverage.Meanwhile,the civil aviation communications have i...With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet coverage.Meanwhile,the civil aviation communications have increased dramatically,especially for providing airborne Internet services.However,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service management.Firstly,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight management.Secondly,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service continuity.Finally,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated problem.Simulation results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.展开更多
Artificial Intelligence(AI)has shown the power to enhance the functionality of sensors and enable intelligent human‐machine interfaces through machine learning‐based data analysis.However,the good performance of AI ...Artificial Intelligence(AI)has shown the power to enhance the functionality of sensors and enable intelligent human‐machine interfaces through machine learning‐based data analysis.However,the good performance of AI is always accompanied by a large amount of data and high computational complexity.Though cloud computing appears to be the right solution to this issue with the advent of the 5G era,a certain intelligence of the edge terminal is also important to make the entire integrated intelligent system more efficient.The current development of microelectronic,wearable,AI,and neuromorphic technologies pave the way to realize advanced edge computing by integrating silicon‐based high‐computing‐power neuromorphic chips with anthropomorphic wearable sensory devices and show the potential to achieve human‐like self‐sustainable decentralized intelligence to enable the next‐generation of AI.Hence,in this review,we systematically introduce the related progress in terms of wearable electronics that can mimic the biological features of humans'sensory systems and the development of neuromorphic/in‐sensor computing technologies.Discussion on implementing the integrated human‐like perception and sensation system with silicone‐based computing chips and non‐silicone‐based wearable functional units and our perspectives are also provided.展开更多
Plasmonic nanoantennas provide unique opportunities for precise control of light–matter coupling in surface-enhanced infrared absorption(SEIRA)spectroscopy,but most of the resonant systems realized so far suffer from...Plasmonic nanoantennas provide unique opportunities for precise control of light–matter coupling in surface-enhanced infrared absorption(SEIRA)spectroscopy,but most of the resonant systems realized so far suffer from the obstacles of low sensitivity,narrow bandwidth,and asymmetric Fano resonance perturbations.Here,we demonstrated an overcoupled resonator with a high plasmon-molecule coupling coefficient(μ)(OC-Hμresonator)by precisely controlling the radiation loss channel,the resonator-oscillator coupling channel,and the frequency detuning channel.We observed a strong dependence of the sensing performance on the coupling state,and demonstrated that OC-Hμresonator has excellent sensing properties of ultra-sensitive(7.25%nm^(−1)),ultra-broadband(3–10μm),and immune asymmetric Fano lineshapes.These characteristics represent a breakthrough in SEIRA technology and lay the foundation for specific recognition of biomolecules,trace detection,and protein secondary structure analysis using a single array(array size is 100×100μm^(2)).In addition,with the assistance of machine learning,mixture classification,concentration prediction and spectral reconstruction were achieved with the highest accuracy of 100%.Finally,we demonstrated the potential of OC-Hμresonator for SARS-CoV-2 detection.These findings will promote the wider application of SEIRA technology,while providing new ideas for other enhanced spectroscopy technologies,quantum photonics and studying light–matter interactions.展开更多
In this paper the operation of a three level H-bridge converter as well as its parallel operations is analyzed and simulated on the computer. Based on the simulation results the operating behavior between （a） a thre...In this paper the operation of a three level H-bridge converter as well as its parallel operations is analyzed and simulated on the computer. Based on the simulation results the operating behavior between （a） a three level H-bridge neutral point clamped convener, （b） a three level back-to-back H-bridge neutral point clamped convener, （c） two three level H-bridge neutral point clamped converters parallel connected is being compared. From the simulation results it is obvious that in the first two cases the ripples, the distortion in primary and secondary winding currents, and the power factor are quite satisfactory and almost identical to each other. In the third case as compared with the first two, it is observed that current harmonics with higher amplitude appear in the primary winding of the transformer.展开更多
Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the...Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.展开更多
Cartilage and facial muscle tissue provide basic yet vital functions for homeostasis throughout the body, making human survival and function highly dependent upon these somatic components. When cartilage and facial mu...Cartilage and facial muscle tissue provide basic yet vital functions for homeostasis throughout the body, making human survival and function highly dependent upon these somatic components. When cartilage and facial muscle tissues are harmed or completely destroyed due to disease, trauma, or any other degenerative process, homeostasis and basic body functions consequently become negatively affected. Although most cartilage and cells can regenerate themselves after any form of the aforementioned degenerative disease or trauma, the highly specific characteristics of facial muscles and the specific structures of the cells and tissues required for the proper function cannot be exactly replicated by the body itself. Thus, some form of cartilage and bone tissue engineering is necessary for proper regeneration and function. The use of progenitor cells for this purpose would be very beneficial due to their highly adaptable capabilities, as well as their ability to utilize a high diffusion rate, making them ideal for the specific nature and functions of cartilage and facial muscle tissue. Going along with this, once the progenitor cells are obtained, applying them to a scaffold within the oral cavity in the affected location allows them to adapt to the environment and create cartilage or facial muscle tissue that is specific to the form and function of the area. The principal function of the cartilage and tissue is vascularization, which requires a specific form that allows them to aid the proper flow of bodily functions related to the oral cavity such as oxygen flow and removal of waste. Facial muscle is also very thin, making its reproduction much more possible. Taking all these into consideration, this review aims to highlight and expand upon the primary benefits of the cartilage and facial muscle tissue engineering and regeneration, focusing on how these processes are performed outside of and within the body.展开更多
Electrical energy is essential for modern society to sustain economic growths.The soaring demand for the electrical energy,together with an awareness of the environmental impact of fossil fuels,has been driving a shif...Electrical energy is essential for modern society to sustain economic growths.The soaring demand for the electrical energy,together with an awareness of the environmental impact of fossil fuels,has been driving a shift towards the utilization of solar energy.However,traditional solar energy solutions often require extensive spaces for a panel installation,limiting their practicality in a dense urban environment.To overcome the spatial constraint,researchers have developed transparent photovoltaics(TPV),enabling windows and facades in vehicles and buildings to generate electric energy.Current TPV advancements are focused on improving both transparency and power output to rival commercially available silicon solar panels.In this review,we first briefly introduce wavelength-and non-wavelengthselective strategies to achieve transparency.Figures of merit and theoretical limits of TPVs are discussed to comprehensively understand the status of current TPV technology.Then we highlight recent progress in different types of TPVs,with a particular focus on solution-processed thin-film photovoltaics(PVs),including colloidal quantum dot PVs,metal halide perovskite PVs and organic PVs.The applications of TPVs are also reviewed,with emphasis on agrivoltaics,smart windows and facades.Finally,current challenges and future opportunities in TPV research are pointed out.展开更多
基金financial supports from the National Natural Science Foundation of China(Grant No.6227511362405124).
文摘We present a novel method for scale-invariant 3D face recognition by integrating computer-generated holography with the Mellin transform.This approach leverages the scale-invariance property of the Mellin transform to address challenges related to variations in 3D facial sizes during recognition.By applying the Mellin transform to computer-generated holograms and performing correlation between them,which,to the best of our knowledge,is being done for the first time,we have developed a robust recognition framework capable of managing significant scale variations without compromising recognition accuracy.Digital holograms of 3D faces are generated from a face database,and the Mellin transform is employed to enable robust recognition across scale factors ranging from 0.4 to 2.0.Within this range,the method achieves 100%recognition accuracy,as confirmed by both simulation-based and hybrid optical/digital experimental validations.Numerical calculations demonstrate that our method significantly enhances the accuracy and reliability of 3D face recognition,as evidenced by the sharp correlation peaks and higher peak-to-noise ratio(PNR)values than that of using conventional holograms without the Mellin transform.Additionally,the hybrid optical/digital joint transform correlation hardware further validates the method's effectiveness,demonstrating its capability to accurately identify and distinguish 3D faces at various scales.This work provides a promising solution for advanced biometric systems,especially for those which require 3D scale-invariant recognition.
基金the Engineering and Physical Sciences Research Council(EPSRC)for funding the researchUK India Education Research Initiative(UKIERI)for funding support.
文摘This review provides an insightful and comprehensive exploration of the emerging 2D material borophene,both pristine and modified,emphasizing its unique attributes and potential for sustainable applications.Borophene’s distinctive properties include its anisotropic crystal structures that contribute to its exceptional mechanical and electronic properties.The material exhibits superior electrical and thermal conductivity,surpassing many other 2D materials.Borophene’s unique atomic spin arrangements further diversify its potential application for magnetism.Surface and interface engineering,through doping,functionalization,and synthesis of hybridized and nanocomposite borophene-based systems,is crucial for tailoring borophene’s properties to specific applications.This review aims to address this knowledge gap through a comprehensive and critical analysis of different synthetic and functionalisation methods,to enhance surface reactivity by increasing active sites through doping and surface modifications.These approaches optimize diffusion pathways improving accessibility for catalytic reactions,and tailor the electronic density to tune the optical and electronic behavior.Key applications explored include energy systems(batteries,supercapacitors,and hydrogen storage),catalysis for hydrogen and oxygen evolution reactions,sensors,and optoelectronics for advanced photonic devices.The key to all these applications relies on strategies to introduce heteroatoms for tuning electronic and catalytic properties,employ chemical modifications to enhance stability and leverage borophene’s conductivity and reactivity for advanced photonics.Finally,the review addresses challenges and proposes solutions such as encapsulation,functionalization,and integration with composites to mitigate oxidation sensitivity and overcome scalability barriers,enabling sustainable,commercial-scale applications.
文摘Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.
基金financially supported via Australian Research Council(FT180100705)the support by the National Natural Science Foundation of China(22209103)+3 种基金the support from UTS Chancellor's Research Fellowshipsthe support from Open Project of State Key Laboratory of Advanced Special Steel,the Shanghai Key Laboratory of Advanced Ferrometallurgy,Shanghai University(SKLASS 2021-**)Joint International Laboratory on Environmental and Energy Frontier MaterialsInnovation Research Team of High-Level Local Universities in Shanghai。
文摘Electrochemical carbon dioxide reduction reaction(CO_(2)RR)provides a promising way to convert CO_(2)to chemicals.The multicarbon(C_(2+))products,especially ethylene,are of great interest due to their versatile industrial applications.However,selectively reducing CO_(2)to ethylene is still challenging as the additional energy required for the C–C coupling step results in large overpotential and many competing products.Nonetheless,mechanistic understanding of the key steps and preferred reaction pathways/conditions,as well as rational design of novel catalysts for ethylene production have been regarded as promising approaches to achieving the highly efficient and selective CO_(2)RR.In this review,we first illustrate the key steps for CO_(2)RR to ethylene(e.g.,CO_(2)adsorption/activation,formation of~*CO intermediate,C–C coupling step),offering mechanistic understanding of CO_(2)RR conversion to ethylene.Then the alternative reaction pathways and conditions for the formation of ethylene and competitive products(C_1 and other C_(2+)products)are investigated,guiding the further design and development of preferred conditions for ethylene generation.Engineering strategies of Cu-based catalysts for CO_(2)RR-ethylene are further summarized,and the correlations of reaction mechanism/pathways,engineering strategies and selectivity are elaborated.Finally,major challenges and perspectives in the research area of CO_(2)RR are proposed for future development and practical applications.
基金the National Natural Science Foundation of China(62271485,61903363,U1811463,62103411,62203250)the Science and Technology Development Fund of Macao SAR(0093/2023/RIA2,0050/2020/A1)。
文摘DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.
文摘The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.
文摘In this paper,we design a total infrared(IR)absorber based on a dispersive band structure of two-dimensional(2D)multiwall carbon nanotube(MWCNTs)square array working from near IR(NIR)to mid IR(MIR)regime.The absorption characteristics have been investigated by the 2D finite-difference time domain(FDTD)method in square lattice photonic crystal(PC)of the multipole Drude-Lorentz model inserted to the dispersive dielectric function of MWCNTs.Dispersive photonic band structure and scattering parameters for the wide range of lattice constants from 15 nm to 3500 nm with various filling ratios have been calculated.The results show that for large lattice constant(>2000 nm),the Bragg gap moves to the IR regime and leads to MWCNTs arrays acting as a total absorber.For a structure with lattice constant of 3500 nm and filling factor of 12%,an enhanced absorption coefficient up to 99%is achieved in the range of 0.35 eV(λ=3.5μm)nominated in the MIR regime.Also,the absorption spectrum peak can be tuned in the range of 0.27—0.38 eV(λ=4.59—3.26μm)with a changing filling factor.Our results and methodology can be used to design new MWCNTs based photonic devices for applications like night-vision,thermal detector,and total IR absorbers.
文摘Alzheimer’s disease (AD) is a brain disorder that eventually causes memory loss and the ability to perform simple cognitive functions;research efforts within pharmaceuticals and other medical treatments have minimal impact on the disease. Our preliminary biological studies showed that Repeated Electromagnetic Field Stimulation (REFMS) applying an EM frequency of 64 MHz and a specific absorption rate (SAR) of 0.4 - 0.9 W/kg decrease the level of amyloid-β peptides (Aβ), which is the most likely etiology of AD. This study emphasizes uniform E/H field and SAR distribution with adequate penetration depth penetration through multiple human head layers driven with low input power for safety treatments. In this work, we performed numerical modeling and computer simulations of a portable Meander Line antenna (MLA) to achieve the required EMF parameters to treat AD. The MLA device features a low cost, small size, wide bandwidth, and the ability to integrate into a portable system. This study utilized a High-Frequency Simulation System (HFSS) in the design of the MLA with the desired characteristics suited for AD treatment in humans. The team designed a 24-turn antenna with a 60 cm length and 25 cm width and achieved the required resonant frequency of 64 MHz. Here we used two numerical human head phantoms to test the antenna, the MIDA and spherical head phantom with six and seven tissue layers, respectively. The antenna was fed from a 50-Watt input source to obtain the SAR of 0.6 W/kg requirement in the center of the simulated brain tissue layer. We found that the E/H field and SAR distribution produced was not homogeneous;there were areas of high SAR values close to the antenna transmitter, also areas of low SAR value far away from the antenna. This paper details the antenna parameters, the scattering parameters response, the efficiency response, and the E and H field distribution;we presented the computer simulation results and discussed future work for a practical model.
文摘Today, mammography is the best method for early detection of breast cancer. Radiologists failed to detect evident cancerous signs in approximately 20% of false negative mammograms. False negatives have been identified as the inability of the radiologist to detect the abnormalities due to several reasons such as poor image quality, image noise, or eye fatigue. This paper presents a framework for a computer aided detection system that integrates Principal Component Analysis (PCA), Fisher Linear Discriminant (FLD), and Nearest Neighbor Classifier (KNN) algorithms for the detection of abnormalities in mammograms. Using normal and abnormal mammograms from the MIAS database, the integrated algorithm achieved 93.06% classification accuracy. Also in this paper, we present an analysis of the integrated algorithm’s parameters and suggest selection criteria.
文摘Human body with curved and soft interfaces requests advanced flexible materials and structures for the interaction with organs and signal collection from targets in applications such as bioengineering and diagnostic devices.Among them,it is highly demanded to achieve creative design in flexible materials and structures with great stretchable capability for required applications.To this end,both inorganic and organic materials could be adopted and designed with assembly and self-assembly methods for flexible electronics and electrodes.Soft or flexible materials and structures inspired by nature can lead to highly conformal contacts between devices and the human body.These approaches hold great potential for applications in flexible electronics,medical imaging technology and portable disease diagnostics.Novel strategy on related sensors/actuator and energy storage/generation devices could overcome certain limitations on flexible materials engineering and thus advance the field as well.All these methods would deliver a profound impact to our future intelligent society.
文摘Single cylindrical submicron pores in PMMA polymer membranes are micropatterned by electron beam lithography and integrated into all PMMA-based electrophoretic flow detector systems. Pore dimensions are 450 nm in diameter and 1 μm in length. The pores are electrically characterized in aqueous KCl electrolyte, exhibiting a stable time-independent ionic current through the pore with a noise level of less than 1% of the open-pore current. The current-voltage curves are linear and scale with electrolyte concentration. The negative surface charge of the membrane over-proportionally decreases pore conductance at low electrolyte concentrations (≤0.1 M) that are still beyond those typically applied in biological experiments. Pores do not exhibit rectification of current flowing through them, allowing for operation with either polarity. To allow for detection of yet much smaller particles, the described PMMA-based system also was successfully equipped with pores of 1.5 nm instead of 450 nm in diameter. This was achieved by introducing naturally occurring biological protein pores of α-hemolysin on a lipid bilayer into the prepatterned PMMA membrane of an assembled PMMA-based electrophoretic flow detector system. Characteristics of translocation events of single-stranded linear plasmid DNA molecules through the pores were recorded, and ionic current deductions during biomolecule translocation were clear and distinguished. Based on the presented submicron scale open pore ionic current transport properties, as well as the observed passage of DNA molecules through protein pores inserted into PMMA membranes, our current research proposes that all PMMA electrophoretic flow detectors exhibit an excellent potential for future use as biomedical resistive-pulse sensors, as long as pore dimensions match those of biomolecules to be detected.
文摘A method for balancing thermal and electrical packaging requirements for gallium nitride (GaN) high power amplifier (HPA) modules is presented. The goal is to find a design approach that minimizes the junction temperature of the GaN so that it is reliable and has interconnects that meet electrical performance requirements. One benefit of GaN is that it can simultaneously achieve high power density and operate at microwave and millimeter-wave frequencies. However, the power density can be so high that the necessary thermal solutions can have negative impact on electrical performance. This is especially a concern for the electrical interconnects required for the input/ output ports on high power amplifier devices. This is because the signal interconnects must operate at GHz frequencies, which means that special care must be taken to avoid problems such as undesired signal coupling and ground path inductance. Therefore, this work focuses on GaN packaging and its integration into a module. The results show that an optimum thickness for the GaN heat spreader exits for thermal performance but the electrical design is impacted negatively if the optimum thermal design is chosen. Therefore, a balanced design is chosen which meets overall system level requirements.
文摘During the prodromal stage of Alzheimer’s disease (AD), neurodegenerative changes can be identified by measuring volumetric loss in AD-prone brain regions on MRI. Cognitive assessments that are sensitive enough to measure the early brain-behavior manifestations of AD and that correlate with biomarkers of neurodegeneration are needed to identify and monitor individuals at risk for dementia. Weak sensitivity to early cognitive change has been a major limitation of traditional cognitive assessments. In this study, we focused on expanding our previous work by determining whether a digitized cognitive stress test, the Loewenstein-Acevedo Scales for Semantic Interference and Learning, Brief Computerized Version (LASSI-BC) could differentiate between Cognitively Unimpaired (CU) and amnestic Mild Cognitive Impairment (aMCI) groups. A second focus was to correlate LASSI-BC performance to volumetric reductions in AD-prone brain regions. Data was gathered from 111 older adults who were comprehensively evaluated and administered the LASSI-BC. Eighty-seven of these participants (51 CU;36 aMCI) underwent MR imaging. The volumes of 12 AD-prone brain regions were related to LASSI-BC and other memory tests correcting for False Discovery Rate (FDR). Results indicated that, even after adjusting for initial learning ability, the failure to recover from proactive semantic interference (frPSI) on the LASSI-BC differentiated between CU and aMCI groups. An optimal combination of frPSI and initial learning strength on the LASSI-BC yielded an area under the ROC curve of 0.876 (76.1% sensitivity, 82.7% specificity). Further, frPSI on the LASSI-BC was associated with volumetric reductions in the hippocampus, amygdala, inferior temporal lobes, precuneus, and posterior cingulate.
基金supported in part by the National Key R&D Program of China under Grant 2020YFB1806104in part by Innovation and Entrepreneurship of Jiangsu Province High-level Talent Program+1 种基金in part by Natural Sciences and Engineering Research Council of Canada (NSERC)the support from Huawei
文摘With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet coverage.Meanwhile,the civil aviation communications have increased dramatically,especially for providing airborne Internet services.However,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service management.Firstly,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight management.Secondly,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service continuity.Finally,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated problem.Simulation results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.
基金supported by NRF‐CRP28‐2022‐0038“Integrating Wideband Tuneable Acoustic Filters on Silicon for High‐Speed Wireless Communication”(WBS:grant no.A‐8001503‐00‐00)National University of Singapore(NUS),Singapore,and RIE2025 IAF‐ICP under I2301E0027“Piezo Specialty Lab‐in‐Fab 2.0(LiF 2.0)-Enabling Unrivalled Power Efficient Transducers Beyond Material Limits”at National University of Singapore(NUS),Singapore.
文摘Artificial Intelligence(AI)has shown the power to enhance the functionality of sensors and enable intelligent human‐machine interfaces through machine learning‐based data analysis.However,the good performance of AI is always accompanied by a large amount of data and high computational complexity.Though cloud computing appears to be the right solution to this issue with the advent of the 5G era,a certain intelligence of the edge terminal is also important to make the entire integrated intelligent system more efficient.The current development of microelectronic,wearable,AI,and neuromorphic technologies pave the way to realize advanced edge computing by integrating silicon‐based high‐computing‐power neuromorphic chips with anthropomorphic wearable sensory devices and show the potential to achieve human‐like self‐sustainable decentralized intelligence to enable the next‐generation of AI.Hence,in this review,we systematically introduce the related progress in terms of wearable electronics that can mimic the biological features of humans'sensory systems and the development of neuromorphic/in‐sensor computing technologies.Discussion on implementing the integrated human‐like perception and sensation system with silicone‐based computing chips and non‐silicone‐based wearable functional units and our perspectives are also provided.
基金supported by A*STAR under the“Nanosystems at the Edge”program(Grant No.A18A4b0055)Ministry of Education(MOE)under the research grant of R-263-000-F18-112/A-0009520-01-00+1 种基金National Research Foundation Singapore grant CRP28-2022-0038the Reimagine Re-search Scheme(RRSC)Project(Grant A-0009037-02-00&A0009037-03-00)at National University of Singapore.
文摘Plasmonic nanoantennas provide unique opportunities for precise control of light–matter coupling in surface-enhanced infrared absorption(SEIRA)spectroscopy,but most of the resonant systems realized so far suffer from the obstacles of low sensitivity,narrow bandwidth,and asymmetric Fano resonance perturbations.Here,we demonstrated an overcoupled resonator with a high plasmon-molecule coupling coefficient(μ)(OC-Hμresonator)by precisely controlling the radiation loss channel,the resonator-oscillator coupling channel,and the frequency detuning channel.We observed a strong dependence of the sensing performance on the coupling state,and demonstrated that OC-Hμresonator has excellent sensing properties of ultra-sensitive(7.25%nm^(−1)),ultra-broadband(3–10μm),and immune asymmetric Fano lineshapes.These characteristics represent a breakthrough in SEIRA technology and lay the foundation for specific recognition of biomolecules,trace detection,and protein secondary structure analysis using a single array(array size is 100×100μm^(2)).In addition,with the assistance of machine learning,mixture classification,concentration prediction and spectral reconstruction were achieved with the highest accuracy of 100%.Finally,we demonstrated the potential of OC-Hμresonator for SARS-CoV-2 detection.These findings will promote the wider application of SEIRA technology,while providing new ideas for other enhanced spectroscopy technologies,quantum photonics and studying light–matter interactions.
文摘In this paper the operation of a three level H-bridge converter as well as its parallel operations is analyzed and simulated on the computer. Based on the simulation results the operating behavior between （a） a three level H-bridge neutral point clamped convener, （b） a three level back-to-back H-bridge neutral point clamped convener, （c） two three level H-bridge neutral point clamped converters parallel connected is being compared. From the simulation results it is obvious that in the first two cases the ripples, the distortion in primary and secondary winding currents, and the power factor are quite satisfactory and almost identical to each other. In the third case as compared with the first two, it is observed that current harmonics with higher amplitude appear in the primary winding of the transformer.
基金supported in part by National Institute of Health(NIH),USA(Grant Nos.:R01GM126189,R01AI164266,and R35GM148196)the National Science Foundation,USA(Grant Nos.DMS2052983,DMS-1761320,and IIS-1900473)+3 种基金National Aero-nautics and Space Administration(NASA),USA(Grant No.:80NSSC21M0023)Michigan State University(MSU)Foundation,USA,Bristol-Myers Squibb(Grant No.:65109)USA,and Pfizer,USAsupported by the National Natural Science Foundation of China(Grant Nos.:11971367,12271416,and 11972266).
文摘Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.
基金Acknowledgements The authors would like to thank the financial supports from Delta Dental, Osteo Science Foundation （Peter Geistlich Award）, Marquette Innovation Fund, AFOSR （FA9550-12-1-0225） and NSF （EEC-1160483, ECCS-1351533 and CMMI-1363485）.
文摘Cartilage and facial muscle tissue provide basic yet vital functions for homeostasis throughout the body, making human survival and function highly dependent upon these somatic components. When cartilage and facial muscle tissues are harmed or completely destroyed due to disease, trauma, or any other degenerative process, homeostasis and basic body functions consequently become negatively affected. Although most cartilage and cells can regenerate themselves after any form of the aforementioned degenerative disease or trauma, the highly specific characteristics of facial muscles and the specific structures of the cells and tissues required for the proper function cannot be exactly replicated by the body itself. Thus, some form of cartilage and bone tissue engineering is necessary for proper regeneration and function. The use of progenitor cells for this purpose would be very beneficial due to their highly adaptable capabilities, as well as their ability to utilize a high diffusion rate, making them ideal for the specific nature and functions of cartilage and facial muscle tissue. Going along with this, once the progenitor cells are obtained, applying them to a scaffold within the oral cavity in the affected location allows them to adapt to the environment and create cartilage or facial muscle tissue that is specific to the form and function of the area. The principal function of the cartilage and tissue is vascularization, which requires a specific form that allows them to aid the proper flow of bodily functions related to the oral cavity such as oxygen flow and removal of waste. Facial muscle is also very thin, making its reproduction much more possible. Taking all these into consideration, this review aims to highlight and expand upon the primary benefits of the cartilage and facial muscle tissue engineering and regeneration, focusing on how these processes are performed outside of and within the body.
基金supported by the National Natural Science Foundation of China(Grant number W2432035)financial support from the EPSRC SWIMS(EP/V039717/1)+3 种基金Royal Society(RGS\R1\221009 and IEC\NSFC\211201)Leverhulme Trust(RPG-2022-263)Ser Cymru programme–Enhancing Competitiveness Equipment Awards 2022-23(MA/VG/2715/22-PN66)the financial support from Kingdom of Saudi Arabia Ministry of Higher Education.
文摘Electrical energy is essential for modern society to sustain economic growths.The soaring demand for the electrical energy,together with an awareness of the environmental impact of fossil fuels,has been driving a shift towards the utilization of solar energy.However,traditional solar energy solutions often require extensive spaces for a panel installation,limiting their practicality in a dense urban environment.To overcome the spatial constraint,researchers have developed transparent photovoltaics(TPV),enabling windows and facades in vehicles and buildings to generate electric energy.Current TPV advancements are focused on improving both transparency and power output to rival commercially available silicon solar panels.In this review,we first briefly introduce wavelength-and non-wavelengthselective strategies to achieve transparency.Figures of merit and theoretical limits of TPVs are discussed to comprehensively understand the status of current TPV technology.Then we highlight recent progress in different types of TPVs,with a particular focus on solution-processed thin-film photovoltaics(PVs),including colloidal quantum dot PVs,metal halide perovskite PVs and organic PVs.The applications of TPVs are also reviewed,with emphasis on agrivoltaics,smart windows and facades.Finally,current challenges and future opportunities in TPV research are pointed out.
