Acute ischemic stroke is a clinical emergency and a condition with high morbidity,mortality,and disability.Accurate predictive,diagnostic,and prognostic biomarkers and effective therapeutic targets for acute ischemic ...Acute ischemic stroke is a clinical emergency and a condition with high morbidity,mortality,and disability.Accurate predictive,diagnostic,and prognostic biomarkers and effective therapeutic targets for acute ischemic stroke remain undetermined.With innovations in high-throughput gene sequencing analysis,many aberrantly expressed non-coding RNAs(ncRNAs)in the brain and peripheral blood after acute ischemic stroke have been found in clinical samples and experimental models.Differentially expressed ncRNAs in the post-stroke brain were demonstrated to play vital roles in pathological processes,leading to neuroprotection or deterioration,thus ncRNAs can serve as therapeutic targets in acute ischemic stroke.Moreover,distinctly expressed ncRNAs in the peripheral blood can be used as biomarkers for acute ischemic stroke prediction,diagnosis,and prognosis.In particular,ncRNAs in peripheral immune cells were recently shown to be involved in the peripheral and brain immune response after acute ischemic stroke.In this review,we consolidate the latest progress of research into the roles of ncRNAs(microRNAs,long ncRNAs,and circular RNAs)in the pathological processes of acute ischemic stroke–induced brain damage,as well as the potential of these ncRNAs to act as biomarkers for acute ischemic stroke prediction,diagnosis,and prognosis.Findings from this review will provide novel ideas for the clinical application of ncRNAs in acute ischemic stroke.展开更多
Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowad...Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.展开更多
This investigation focuses on the utilization of cucurbit[6]uril(Q[6])as the host compound for the development of long-lasting afterglow materials.By strategically manipulating the outer surface interactions of Q[6],c...This investigation focuses on the utilization of cucurbit[6]uril(Q[6])as the host compound for the development of long-lasting afterglow materials.By strategically manipulating the outer surface interactions of Q[6],classical aggregation-caused quenching(ACQ)compounds such as fluorescein sodium(FluNa)and calcein sodium(CalNa)were transformed into afterglow materials with varying colors and durations upon exposure to ultraviolet light.This transformation was facilitated through a host-vip doping method combined with coordination with metal ions.Even at a reduced doping concentration of 5×10^(-5)wt%,the materials exhibit remarkable afterglow properties,lasting up to 2 s,with a phosphorescence lifetime of up to 150 ms.Moreover,by adjusting the concentration of the vip compound,the persistent luminescence color of the materials could be easily transitioned from orange to yellow and subsequently to green.These findings suggest that the developed afterglow materials hold significant potential for multilevel anti-counterfeiting and information encryption applications when exposed to ultraviolet light.The supramolecular assembly strategy,which relies on the outer surface interactions of cucurbit[n]uril,offers a simpler and more efficient approach to crafting multi-color luminescent materials.Additionally,this method opens avenues for enhancing the application potential of aggregation-caused quenching(ACQ)compounds in various technological domains.展开更多
Alzheimer's disease,a progressively degenerative neurological disorder,is the most common cause of dementia in the elderly.While its precise etiology remains unclear,researchers have identified diverse pathologica...Alzheimer's disease,a progressively degenerative neurological disorder,is the most common cause of dementia in the elderly.While its precise etiology remains unclear,researchers have identified diverse pathological characteristics and molecular pathways associated with its progression.Advances in scientific research have increasingly highlighted the crucial role of non-coding RNAs in the progression of Alzheimer's disease.These non-coding RNAs regulate several biological processes critical to the advancement of the disease,offering promising potential as therapeutic targets and diagnostic biomarkers.Therefore,this review aims to investigate the underlying mechanisms of Alzheimer's disease onset,with a particular focus on microRNAs,long non-coding RNAs,and circular RNAs associated with the disease.The review elucidates the potential pathogenic processes of Alzheimer's disease and provides a detailed description of the synthesis mechanisms of the three aforementioned non-coding RNAs.It comprehensively summarizes the various non-coding RNAs that have been identified to play key regulatory roles in Alzheimer's disease,as well as how these noncoding RNAs influence the disease's progression by regulating gene expression and protein functions.For example,miR-9 targets the UBE4B gene,promoting autophagy-mediated degradation of Tau protein,thereby reducing Tau accumulation and delaying Alzheimer's disease progression.Conversely,the long non-coding RNA BACE1-AS stabilizes BACE1 mRNA,promoting the generation of amyloid-βand accelerating Alzheimer's disease development.Additionally,circular RNAs play significant roles in regulating neuroinflammatory responses.By integrating insights from these regulatory mechanisms,there is potential to discover new therapeutic targets and potential biomarkers for early detection and management of Alzheimer's disease.This review aims to enhance the understanding of the relationship between Alzheimer's disease and non-coding RNAs,potentially paving the way for early detection and novel treatment strategies.展开更多
Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiv...Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.展开更多
The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic developm...The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic development.This study provides valuable insights into optimizing wireless communication,paving the way for a more connected and productive future in the mining industry.The IoT revolution is advancing across industries,but harsh geometric environments,including open-pit mines,pose unique challenges for reliable communication.The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency(RF)protocols such as Bluetooth,Wi-Fi,GSM/GPRS,Narrow Band(NB)-IoT,SigFox,ZigBee,and Long Range Wireless Area Network(LoRaWAN).This study addresses the optimization of network implementations by comparing two leading free-spreading IoT-based RF protocols such as ZigBee and LoRaWAN.Intensive field tests are conducted in various opencast mines to investigate coverage potential and signal attenuation.ZigBee is tested in the Tadicherla open-cast coal mine in India.Similarly,LoRaWAN field tests are conducted at one of the associated cement companies(ACC)in the limestone mine in Bargarh,India,covering both Indoor-toOutdoor(I2O)and Outdoor-to-Outdoor(O2O)environments.A robust framework of path-loss models,referred to as Free space,Egli,Okumura-Hata,Cost231-Hata and Ericsson models,combined with key performance metrics,is employed to evaluate the patterns of signal attenuation.Extensive field testing and careful data analysis revealed that the Egli model is the most consistent path-loss model for the ZigBee protocol in an I2O environment,with a coefficient of determination(R^(2))of 0.907,balanced error metrics such as Normalized Root Mean Square Error(NRMSE)of 0.030,Mean Square Error(MSE)of 4.950,Mean Absolute Percentage Error(MAPE)of 0.249 and Scatter Index(SI)of 2.723.In the O2O scenario,the Ericsson model showed superior performance,with the highest R^(2)value of 0.959,supported by strong correlation metrics:NRMSE of 0.026,MSE of 8.685,MAPE of 0.685,Mean Absolute Deviation(MAD)of 20.839 and SI of 2.194.For the LoRaWAN protocol,the Cost-231 model achieved the highest R^(2)value of 0.921 in the I2O scenario,complemented by the lowest metrics:NRMSE of 0.018,MSE of 1.324,MAPE of 0.217,MAD of 9.218 and SI of 1.238.In the O2O environment,the Okumura-Hata model achieved the highest R^(2)value of 0.978,indicating a strong fit with metrics NRMSE of 0.047,MSE of 27.807,MAPE of 27.494,MAD of 37.287 and SI of 3.927.This advancement in reliable communication networks promises to transform the opencast landscape into networked signal attenuation.These results support decision-making for mining needs and ensure reliable communications even in the face of formidable obstacles.展开更多
Metalens technology has been applied extensively in miniaturized and integrated infrared imaging systems.However,due to the high phase dispersion of unit structures,metalens often exhibits chromatic aberration,making ...Metalens technology has been applied extensively in miniaturized and integrated infrared imaging systems.However,due to the high phase dispersion of unit structures,metalens often exhibits chromatic aberration,making broadband achromatic infrared imaging challenging to achieve.In this paper,six different unit structures based on chalcogenide glass are constructed,and their phase-dispersion parameters are analyzed to establish a database.On this basis,using chromatic aberration compensation and parameterized adjoint topology optimization,a broadband achromatic metalens with a numerical aperture of 0.5 is designed by arranging these six unit structures in the far-infrared band.Simulation results show that the metalens achieves near diffraction-limited focusing within the operating wavelength range of 9−11μm,demonstrating the good performance of achromatic aberration with flat focusing efficiency of 54%−58%across all wavelengths.展开更多
This paper mainly studies the well-posedness of steady incompressible impinging jet flow problem through a 3D axisymmetric finitely long nozzle.This problem originates from the physical phenomena encountered in practi...This paper mainly studies the well-posedness of steady incompressible impinging jet flow problem through a 3D axisymmetric finitely long nozzle.This problem originates from the physical phenomena encountered in practical engineering fields,such as in short take-off and vertical landing(STOVL)aircraft.Nowadays many intricate phenomena associated with impinging jet flows remain inadequately elucidated,which limits the ability to optimize aircraft design.Given a boundary condition in the inlet,the impinging jet problem is transformed into a Bernoulli-type free boundary problem according to the stream function.Then the variational method is used to study the corresponding variational problem with one parameter,thereby the wellposedness is established.The main conclusion is as follows.For a 3D axisymmetric finitely long nozzle and an infinitely long vertical wall,given an axial velocity in the inlet of nozzle,there exists a unique smooth incom‑pressible impinging jet flow such that the free boundary initiates smoothly at the endpoint of the nozzle and extends to infinity along the vertical wall at far fields.The key point is to investigate the regularity of the corner where the nozzle and the vertical axis intersect.展开更多
Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
My favourite pet is a lovely dog.I bought him last year in the pet market.He has long ears,a long body and brown fur.He is very small.He weights①about one kilogram.He is very active.He wants to catch everything.Every...My favourite pet is a lovely dog.I bought him last year in the pet market.He has long ears,a long body and brown fur.He is very small.He weights①about one kilogram.He is very active.He wants to catch everything.Every day he runs here and there.But if you want him to do something,he will be a very good boy.展开更多
The diagnosis and treatment processes for long COVID have been relatively slow to develop for several reasons,such as a lack of consensus about its definition among medical professionals and researchers,and restricted...The diagnosis and treatment processes for long COVID have been relatively slow to develop for several reasons,such as a lack of consensus about its definition among medical professionals and researchers,and restricted access to knowledge from the relevant pandemics of the past.Legacies of viral pandemics unfortunately do not include sufficient research on how the acute symptoms of infection evolved into chronic health conditions.More so,the idea of surviving a viral pandemic with long-lasting symptoms is not new,yet it is curiously disjointed as a medical concept throughout documented histories on the subject.Individuals with long-term conditions that are rooted in acute viral infections,such as COVID-19,require a coordinated system of care that includes comprehensive rehabilitation.This commentary will discuss the philosophical underpinnings of the historical scarcity of documented incidences of individuals with chronic virus symptoms and the need for a shift in post-viral infection treatment approaches.展开更多
The Carboniferous,an important coal-forming period in geological history,was characterized by extensive vegetation and high oxygen levels.Evidence suggests frequent wildfires took place during this time,especially in ...The Carboniferous,an important coal-forming period in geological history,was characterized by extensive vegetation and high oxygen levels.Evidence suggests frequent wildfires took place during this time,especially in peatlands.However,the control mechanisms for changes in wildfire activity in peatlands during this period remain unclear.In this study,evidence from the Gzhelian in the Ordos Basin,such as the inertinite/vitrinite(Ⅰ/Ⅴ)ratio,indicated varying wildfire frequencies.Climate indicators(CaO/MgO and CaO/MgO·Al_(2)O_(3))revealed that high-frequency wildfires mainly occurred in warm and humid climates.Based on former age constraints,we deduced that orbital cycles(long eccentricity)controlled the climate influence on peatland wildfires during the Gzhelian.Higher eccentricity brought more sunshine and rainfall,creating warmer,wetter peatlands conducive to vegetation growth,which increased fuel loads and led to more wildfires.Global Gzhelian wildfire records show that wildfires occurred mainly in tropical regions with abundant vegetation,reinforcing the idea that fuel loads drove fire activity.While wildfires can release mercury(Hg),the frequent volcanic activity during this period likely contributed significantly to Hg enrichment.展开更多
1 On December 26,2024,a remarkable act of heroism took place at Poospatuck Creek.Kayla Masotto,a 28⁃year⁃old resident of Mastic,Long Island,stepped into the role of a life⁃saver when she witnessed a man fall through t...1 On December 26,2024,a remarkable act of heroism took place at Poospatuck Creek.Kayla Masotto,a 28⁃year⁃old resident of Mastic,Long Island,stepped into the role of a life⁃saver when she witnessed a man fall through the ice on the lake.2 At about 6 pm,Kayla was washing vegetables in the kitchen when her attention was suddenly caught by a boy skating alone on the lake.In a split second,she witnessed the ice beneath him break,and the boy fell into the water.Without any hesitation,Kayla immediately grabbed her paddleboard(桨叶式冲浪板)and ran across her backyard,reaching the edge of the lake in seconds.展开更多
The work deals with cellulose paper filled with nanocellulose and SrAl_(2)O_(4):Eu,Dy oxide phosphor.It was found that both nanocellulose and oxide improve the tensile strength of the composites obtained.The samples w...The work deals with cellulose paper filled with nanocellulose and SrAl_(2)O_(4):Eu,Dy oxide phosphor.It was found that both nanocellulose and oxide improve the tensile strength of the composites obtained.The samples with the oxide demonstrate a long-lasting photoluminescence(PL)under sunlight and ultra-violet(UV)illumination.Room-temperature the PL spectra reveal a wide multicomponent band spreading over all the visible spectral regions.The short-wavelength part of the band is ascribed to the cellulose-related luminescence,while the long-wavelength PL component with maxima near 540 nm corresponds to the luminescence of the SrAl_(2)O_(4):Eu,Dy phosphor.The dependency of the PL intensity on oxide concentration suggests the reabsorption of cellulose emission by the oxide and vice versa.The study of the dielectric properties of composite papers shows the presence of dielectric relaxations at low temperatures(T~−50℃).Similar cellulose materials to those studied can be considered as alternatives for artificial petroleum-based polymers.Low cost,eco-friendliness,biocompatibility,and the simplicity of recycling are among the main advantages of these materials.They are produced from the cellulose which is one of the most abundant renewable materials in nature.The data on the mechanical,dielectric,and optical properties indicate that the papers studied can be used in flexible lighting devices,WLEDs,coating,markers,labels,etc.展开更多
Li-air batteries have attracted widespread attention due to their high theoretical energy density.However,safety and environmental challenges are significant in Li-air batteries based on organic liquid electrolytes,as...Li-air batteries have attracted widespread attention due to their high theoretical energy density.However,safety and environmental challenges are significant in Li-air batteries based on organic liquid electrolytes,as they are exposed to air.In this study,we synthesized a composite electrolyte membrane filled with garnet material using a casting method and successfully applied it to Li-air batteries.The polymer electrolyte composed of poly(vinylidene fluoride-hexafluoropropylene)(PVDF-HFP)filled with garnet LLZTO as an active filler(15-PHL CPE),which exhibited excellent flexibility,wide electrochemical window,high ion conductivity and good thermal stability.Furthermore,symmetric Li‖15-PHL CPE‖Li cells stably operate over 3500h at 0.1 mA cm^(-2)and 25℃.The assembled 15-PHL CPE-based Li-air battery reaches a stable cycling performance of 97 cycles at 200 mA g^(-1)with a well-maintained potential gap of1.93 V,which demonstrates promising application in Li-air batteries.展开更多
基金supported by the National Natural Science Foundation of China,Nos.82301486(to SL)and 82071325(to FY)Medjaden Academy&Research Foundation for Young Scientists,No.MJR202310040(to SL)+2 种基金Nanjing Medical University Science and Technique Development,No.NMUB20220060(to SL)Medical Scientific Research Project of Jiangsu Commission of Health,No.ZDA2020019(to JZ)Health China Buchang Zhiyuan Public Welfare Project for Heart and Brain Health,No.HIGHER202102(to QD).
文摘Acute ischemic stroke is a clinical emergency and a condition with high morbidity,mortality,and disability.Accurate predictive,diagnostic,and prognostic biomarkers and effective therapeutic targets for acute ischemic stroke remain undetermined.With innovations in high-throughput gene sequencing analysis,many aberrantly expressed non-coding RNAs(ncRNAs)in the brain and peripheral blood after acute ischemic stroke have been found in clinical samples and experimental models.Differentially expressed ncRNAs in the post-stroke brain were demonstrated to play vital roles in pathological processes,leading to neuroprotection or deterioration,thus ncRNAs can serve as therapeutic targets in acute ischemic stroke.Moreover,distinctly expressed ncRNAs in the peripheral blood can be used as biomarkers for acute ischemic stroke prediction,diagnosis,and prognosis.In particular,ncRNAs in peripheral immune cells were recently shown to be involved in the peripheral and brain immune response after acute ischemic stroke.In this review,we consolidate the latest progress of research into the roles of ncRNAs(microRNAs,long ncRNAs,and circular RNAs)in the pathological processes of acute ischemic stroke–induced brain damage,as well as the potential of these ncRNAs to act as biomarkers for acute ischemic stroke prediction,diagnosis,and prognosis.Findings from this review will provide novel ideas for the clinical application of ncRNAs in acute ischemic stroke.
基金supported by the National Key Research and Development Program of China(No.2021YFA0715900).
文摘Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.
基金support of the National Natural Science Foundation of China(No.22361011)Guizhou Provincial Science and Technology Projects(No.ZK[2023]General 040)the Guizhou Provincial Key Laboratory Platform Project(No.ZSYS[2025]008)。
文摘This investigation focuses on the utilization of cucurbit[6]uril(Q[6])as the host compound for the development of long-lasting afterglow materials.By strategically manipulating the outer surface interactions of Q[6],classical aggregation-caused quenching(ACQ)compounds such as fluorescein sodium(FluNa)and calcein sodium(CalNa)were transformed into afterglow materials with varying colors and durations upon exposure to ultraviolet light.This transformation was facilitated through a host-vip doping method combined with coordination with metal ions.Even at a reduced doping concentration of 5×10^(-5)wt%,the materials exhibit remarkable afterglow properties,lasting up to 2 s,with a phosphorescence lifetime of up to 150 ms.Moreover,by adjusting the concentration of the vip compound,the persistent luminescence color of the materials could be easily transitioned from orange to yellow and subsequently to green.These findings suggest that the developed afterglow materials hold significant potential for multilevel anti-counterfeiting and information encryption applications when exposed to ultraviolet light.The supramolecular assembly strategy,which relies on the outer surface interactions of cucurbit[n]uril,offers a simpler and more efficient approach to crafting multi-color luminescent materials.Additionally,this method opens avenues for enhancing the application potential of aggregation-caused quenching(ACQ)compounds in various technological domains.
文摘Alzheimer's disease,a progressively degenerative neurological disorder,is the most common cause of dementia in the elderly.While its precise etiology remains unclear,researchers have identified diverse pathological characteristics and molecular pathways associated with its progression.Advances in scientific research have increasingly highlighted the crucial role of non-coding RNAs in the progression of Alzheimer's disease.These non-coding RNAs regulate several biological processes critical to the advancement of the disease,offering promising potential as therapeutic targets and diagnostic biomarkers.Therefore,this review aims to investigate the underlying mechanisms of Alzheimer's disease onset,with a particular focus on microRNAs,long non-coding RNAs,and circular RNAs associated with the disease.The review elucidates the potential pathogenic processes of Alzheimer's disease and provides a detailed description of the synthesis mechanisms of the three aforementioned non-coding RNAs.It comprehensively summarizes the various non-coding RNAs that have been identified to play key regulatory roles in Alzheimer's disease,as well as how these noncoding RNAs influence the disease's progression by regulating gene expression and protein functions.For example,miR-9 targets the UBE4B gene,promoting autophagy-mediated degradation of Tau protein,thereby reducing Tau accumulation and delaying Alzheimer's disease progression.Conversely,the long non-coding RNA BACE1-AS stabilizes BACE1 mRNA,promoting the generation of amyloid-βand accelerating Alzheimer's disease development.Additionally,circular RNAs play significant roles in regulating neuroinflammatory responses.By integrating insights from these regulatory mechanisms,there is potential to discover new therapeutic targets and potential biomarkers for early detection and management of Alzheimer's disease.This review aims to enhance the understanding of the relationship between Alzheimer's disease and non-coding RNAs,potentially paving the way for early detection and novel treatment strategies.
基金founded by the Open Project Program of Anhui Province Key Laboratory of Metallurgical Engineering and Resources Recycling(Anhui University of Technology)(No.SKF21-06)Research Fund for Young Teachers of Anhui University of Technology in 2020(No.QZ202001).
文摘Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.
文摘The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic development.This study provides valuable insights into optimizing wireless communication,paving the way for a more connected and productive future in the mining industry.The IoT revolution is advancing across industries,but harsh geometric environments,including open-pit mines,pose unique challenges for reliable communication.The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency(RF)protocols such as Bluetooth,Wi-Fi,GSM/GPRS,Narrow Band(NB)-IoT,SigFox,ZigBee,and Long Range Wireless Area Network(LoRaWAN).This study addresses the optimization of network implementations by comparing two leading free-spreading IoT-based RF protocols such as ZigBee and LoRaWAN.Intensive field tests are conducted in various opencast mines to investigate coverage potential and signal attenuation.ZigBee is tested in the Tadicherla open-cast coal mine in India.Similarly,LoRaWAN field tests are conducted at one of the associated cement companies(ACC)in the limestone mine in Bargarh,India,covering both Indoor-toOutdoor(I2O)and Outdoor-to-Outdoor(O2O)environments.A robust framework of path-loss models,referred to as Free space,Egli,Okumura-Hata,Cost231-Hata and Ericsson models,combined with key performance metrics,is employed to evaluate the patterns of signal attenuation.Extensive field testing and careful data analysis revealed that the Egli model is the most consistent path-loss model for the ZigBee protocol in an I2O environment,with a coefficient of determination(R^(2))of 0.907,balanced error metrics such as Normalized Root Mean Square Error(NRMSE)of 0.030,Mean Square Error(MSE)of 4.950,Mean Absolute Percentage Error(MAPE)of 0.249 and Scatter Index(SI)of 2.723.In the O2O scenario,the Ericsson model showed superior performance,with the highest R^(2)value of 0.959,supported by strong correlation metrics:NRMSE of 0.026,MSE of 8.685,MAPE of 0.685,Mean Absolute Deviation(MAD)of 20.839 and SI of 2.194.For the LoRaWAN protocol,the Cost-231 model achieved the highest R^(2)value of 0.921 in the I2O scenario,complemented by the lowest metrics:NRMSE of 0.018,MSE of 1.324,MAPE of 0.217,MAD of 9.218 and SI of 1.238.In the O2O environment,the Okumura-Hata model achieved the highest R^(2)value of 0.978,indicating a strong fit with metrics NRMSE of 0.047,MSE of 27.807,MAPE of 27.494,MAD of 37.287 and SI of 3.927.This advancement in reliable communication networks promises to transform the opencast landscape into networked signal attenuation.These results support decision-making for mining needs and ensure reliable communications even in the face of formidable obstacles.
文摘Metalens technology has been applied extensively in miniaturized and integrated infrared imaging systems.However,due to the high phase dispersion of unit structures,metalens often exhibits chromatic aberration,making broadband achromatic infrared imaging challenging to achieve.In this paper,six different unit structures based on chalcogenide glass are constructed,and their phase-dispersion parameters are analyzed to establish a database.On this basis,using chromatic aberration compensation and parameterized adjoint topology optimization,a broadband achromatic metalens with a numerical aperture of 0.5 is designed by arranging these six unit structures in the far-infrared band.Simulation results show that the metalens achieves near diffraction-limited focusing within the operating wavelength range of 9−11μm,demonstrating the good performance of achromatic aberration with flat focusing efficiency of 54%−58%across all wavelengths.
文摘This paper mainly studies the well-posedness of steady incompressible impinging jet flow problem through a 3D axisymmetric finitely long nozzle.This problem originates from the physical phenomena encountered in practical engineering fields,such as in short take-off and vertical landing(STOVL)aircraft.Nowadays many intricate phenomena associated with impinging jet flows remain inadequately elucidated,which limits the ability to optimize aircraft design.Given a boundary condition in the inlet,the impinging jet problem is transformed into a Bernoulli-type free boundary problem according to the stream function.Then the variational method is used to study the corresponding variational problem with one parameter,thereby the wellposedness is established.The main conclusion is as follows.For a 3D axisymmetric finitely long nozzle and an infinitely long vertical wall,given an axial velocity in the inlet of nozzle,there exists a unique smooth incom‑pressible impinging jet flow such that the free boundary initiates smoothly at the endpoint of the nozzle and extends to infinity along the vertical wall at far fields.The key point is to investigate the regularity of the corner where the nozzle and the vertical axis intersect.
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
文摘My favourite pet is a lovely dog.I bought him last year in the pet market.He has long ears,a long body and brown fur.He is very small.He weights①about one kilogram.He is very active.He wants to catch everything.Every day he runs here and there.But if you want him to do something,he will be a very good boy.
文摘The diagnosis and treatment processes for long COVID have been relatively slow to develop for several reasons,such as a lack of consensus about its definition among medical professionals and researchers,and restricted access to knowledge from the relevant pandemics of the past.Legacies of viral pandemics unfortunately do not include sufficient research on how the acute symptoms of infection evolved into chronic health conditions.More so,the idea of surviving a viral pandemic with long-lasting symptoms is not new,yet it is curiously disjointed as a medical concept throughout documented histories on the subject.Individuals with long-term conditions that are rooted in acute viral infections,such as COVID-19,require a coordinated system of care that includes comprehensive rehabilitation.This commentary will discuss the philosophical underpinnings of the historical scarcity of documented incidences of individuals with chronic virus symptoms and the need for a shift in post-viral infection treatment approaches.
基金financially supported by the National Natural Science Foundation of China (Grant Nos.42472166, U24A20595, 42102127, 41972170)the Natural Science Foundation of Shandong Province (Grant No. ZR2021QD087)+2 种基金the Shandong Provincial Postdoctoral Science Foundation (SDCX-ZG-202203053)the Shandong University of Science and Technology (Grant No. 2018TDJH101)the Deep-Time Digital Earth program (DDE) for their support of this work
文摘The Carboniferous,an important coal-forming period in geological history,was characterized by extensive vegetation and high oxygen levels.Evidence suggests frequent wildfires took place during this time,especially in peatlands.However,the control mechanisms for changes in wildfire activity in peatlands during this period remain unclear.In this study,evidence from the Gzhelian in the Ordos Basin,such as the inertinite/vitrinite(Ⅰ/Ⅴ)ratio,indicated varying wildfire frequencies.Climate indicators(CaO/MgO and CaO/MgO·Al_(2)O_(3))revealed that high-frequency wildfires mainly occurred in warm and humid climates.Based on former age constraints,we deduced that orbital cycles(long eccentricity)controlled the climate influence on peatland wildfires during the Gzhelian.Higher eccentricity brought more sunshine and rainfall,creating warmer,wetter peatlands conducive to vegetation growth,which increased fuel loads and led to more wildfires.Global Gzhelian wildfire records show that wildfires occurred mainly in tropical regions with abundant vegetation,reinforcing the idea that fuel loads drove fire activity.While wildfires can release mercury(Hg),the frequent volcanic activity during this period likely contributed significantly to Hg enrichment.
文摘1 On December 26,2024,a remarkable act of heroism took place at Poospatuck Creek.Kayla Masotto,a 28⁃year⁃old resident of Mastic,Long Island,stepped into the role of a life⁃saver when she witnessed a man fall through the ice on the lake.2 At about 6 pm,Kayla was washing vegetables in the kitchen when her attention was suddenly caught by a boy skating alone on the lake.In a split second,she witnessed the ice beneath him break,and the boy fell into the water.Without any hesitation,Kayla immediately grabbed her paddleboard(桨叶式冲浪板)and ran across her backyard,reaching the edge of the lake in seconds.
基金financed by the National Research Foundation of Ukraine(Project No.2022.01/0168).
文摘The work deals with cellulose paper filled with nanocellulose and SrAl_(2)O_(4):Eu,Dy oxide phosphor.It was found that both nanocellulose and oxide improve the tensile strength of the composites obtained.The samples with the oxide demonstrate a long-lasting photoluminescence(PL)under sunlight and ultra-violet(UV)illumination.Room-temperature the PL spectra reveal a wide multicomponent band spreading over all the visible spectral regions.The short-wavelength part of the band is ascribed to the cellulose-related luminescence,while the long-wavelength PL component with maxima near 540 nm corresponds to the luminescence of the SrAl_(2)O_(4):Eu,Dy phosphor.The dependency of the PL intensity on oxide concentration suggests the reabsorption of cellulose emission by the oxide and vice versa.The study of the dielectric properties of composite papers shows the presence of dielectric relaxations at low temperatures(T~−50℃).Similar cellulose materials to those studied can be considered as alternatives for artificial petroleum-based polymers.Low cost,eco-friendliness,biocompatibility,and the simplicity of recycling are among the main advantages of these materials.They are produced from the cellulose which is one of the most abundant renewable materials in nature.The data on the mechanical,dielectric,and optical properties indicate that the papers studied can be used in flexible lighting devices,WLEDs,coating,markers,labels,etc.
基金supported by the National Natural Science Foundation of China(U2330124,22411560292,U20A2072,52072352,21875226)the Foundation for the Youth S&T Innovation Team of Sichuan Province(2020JDTD0035)Sichuan Science and Technology Program(2023ZYD0026)。
文摘Li-air batteries have attracted widespread attention due to their high theoretical energy density.However,safety and environmental challenges are significant in Li-air batteries based on organic liquid electrolytes,as they are exposed to air.In this study,we synthesized a composite electrolyte membrane filled with garnet material using a casting method and successfully applied it to Li-air batteries.The polymer electrolyte composed of poly(vinylidene fluoride-hexafluoropropylene)(PVDF-HFP)filled with garnet LLZTO as an active filler(15-PHL CPE),which exhibited excellent flexibility,wide electrochemical window,high ion conductivity and good thermal stability.Furthermore,symmetric Li‖15-PHL CPE‖Li cells stably operate over 3500h at 0.1 mA cm^(-2)and 25℃.The assembled 15-PHL CPE-based Li-air battery reaches a stable cycling performance of 97 cycles at 200 mA g^(-1)with a well-maintained potential gap of1.93 V,which demonstrates promising application in Li-air batteries.
