Flash droughts(FDs)develop quickly and can rapidly deplete soil moisture,posing significant threats to agriculture and pastoral systems.To investigate the spatiotemporal characteristics and development mechanisms of F...Flash droughts(FDs)develop quickly and can rapidly deplete soil moisture,posing significant threats to agriculture and pastoral systems.To investigate the spatiotemporal characteristics and development mechanisms of FDs in Inner Mongolia,China,and to assess the roles of key meteorological drivers in driving soil moisture variability,FD events were identified using root-zone soil moisture data during the growing seasons from 1982 to 2022.The results indicate the presence of five FD hotspot regions,located in the southern Alxa Plateau,the Hetao Plain in Bayannur,the northwestern Xilingol Plain,the western Liaohe River Plain,and the northern Da Hinggan Ling.Over 41 years,FDs occurred on average 7.44 events across the study area,with a mean duration of 9.17 pentads(1 pentad equals 5 days).The duration exhibited a significant increasing trend of 0.39 pentads/10 years.FD onsets primarily lasted for 2-3 pentads.During the FD development phase,precipitation and evapotranspiration decreased while temperature,potential evapotranspiration,incoming solar radiation,and vapor pressure deficit increased.The dominant meteorological drivers of FD development exhibited notable spatial heterogeneity across hotspot regions,and vapor pressure deficit consistently was the most influential factor.These findings improve the understanding of climate drivers at different stages of FD development and provide scientific support for early warning and prevention of droughts in Inner Mongolia.展开更多
Energy consumption(EC)is a core factor in maintaining sustainable development;little is known about the drivers of temporal-spatial EC changes and the corresponding inequality from the perspective of government,thereb...Energy consumption(EC)is a core factor in maintaining sustainable development;little is known about the drivers of temporal-spatial EC changes and the corresponding inequality from the perspective of government,thereby weakening the policy implications for energy conservation and emission reduction.To fill this gap,this study uses spatial-temporal logarithmic mean Divisia index models and extended Theil index inequality models to investigate the drivers of EC changes and inequality,considering the scale and structure of governmental environmental expenditure(EG)across 30 Chinese provinces from 2007 to 2021.The findings reveal that:First,EG acts as a positive driver of total EC,while industrial investment efficiency and fiscal expenditure pressure exert negative effects.Second,disparities in EG functions act as a negative driver,accounting for a 4.56%decrease in average interprovincial EC gaps.Third,China’s EC inequality demonstrated an overall upward trajectory from 0.074 to 0.093 over the period,mainly driven by positive contributions from inequalities in EG and the fiscal expenditure structure.This study highlights the importance of optimizing the government expenditure structure and scale in formulating policies for sustainable EC.展开更多
To clarify the thermal evolution characteristics of organic matter in the ZizhongWeiyuan area in Sichuan Basin,solid bitumen reflectance of the Lower Cambrian Qiongzhusi Formation(QFm)shale was measured by Raman Spect...To clarify the thermal evolution characteristics of organic matter in the ZizhongWeiyuan area in Sichuan Basin,solid bitumen reflectance of the Lower Cambrian Qiongzhusi Formation(QFm)shale was measured by Raman Spectroscopy(RS)method.Constrained by vitrinite reflectance(Ro)data,burial and thermal evolution histories of QFm shale were reconstructed through basin numerical simulation technology.The evolution model of and critical period of organic matter was determined,and its dominant drivers were analyzed.The results show that the asphalt Raman vitrinite reflectance(_(Rmc)Ro)ranges from 3.21%to 4.15%.Thermal maturity within the trough follows a southern part>central part>northern part trend.Thermal maturity is moderate within the paleo-uplift,whereas organic matter outside the paleo-uplift has undergone graphitization.Two types of thermal evolution imprints were established:a continuous heating type and a stop heating type of Silurian–Permian.Sedimentary burial,paleogeomorphology,tectonic movement and Emeishan mantle plume are the dominant drivers of multi-stage thermal imprints of the QFm shale.The three factors are coupled with each other.The Late Caledonian and Late Indosinian are the key periods of organic matter thermal evolution.The Leshan-Longnüsi paleo-uplift weakens the thermal effect of the Permian Emeishan mantle plume.The current thermal evolution pattern of the QFm is mainly determined by the continuous subsidence of the Triassic–Cretaceous.Stop heating model of Silurian–Permian locks the maturity of organic matter in the gold window,thus controlling the enrichment of QFm shale gas.It provides new insights for shale gas migration,enrichment and effective exploration and development of shale gas in the Lower Paleozoic QFm.展开更多
Matter and Radiation at Extremes(MRE),is committed to the publication of original and impactful research and review papers that address extreme states of matter and radiation,and the associated science and technology ...Matter and Radiation at Extremes(MRE),is committed to the publication of original and impactful research and review papers that address extreme states of matter and radiation,and the associated science and technology that are employed to produce and diagnose these conditions in the laboratory.Drivers,targets and diagnostics are included along with related numerical simulation and computational methods.It aims to provide a peer-reviewed platform for the international physics community and promote worldwide dissemination of the latest and impactful research in related fields.展开更多
To address the challenges of complexity,power consumption,and cost constraints in traditional display driver integrated circuits(DDICs)caused by external NOR Flash and SRAM,this work proposes an embedded resistive ran...To address the challenges of complexity,power consumption,and cost constraints in traditional display driver integrated circuits(DDICs)caused by external NOR Flash and SRAM,this work proposes an embedded resistive random-access memory(RRAM)integration solution based on a 40 nm high-voltage CMOS logic platform.Targeting the yield fluctuations and stability challenges during RRAM mass production,systematic process optimizations are implemented to achieve synergistic improvements in RRAM performance and yield.Through modifications to the film sputtering and pre-deposition treatment,the withinwafer resistance uniformity(RSU)of the oxygen-deficient layer(ODL)thin film is improved from 11%to 8%,while inter-wafer process stability variation reduces from 23%to below 6%.Consequently,the yield of 8 Mb RRAM embedded mass production products increases from 87%to 98.5%.In terms of device performance,the RRAM demonstrates a fast 4.8 ns read speed,exceptional read disturb immunity of 3×10^(8) cycles at 95℃,10^(3) write/erase endurance cycles for the 1 Mb cells,and data retention of 12.5 years at 125℃.Post high-temperature operating life(HTOL)testing exhibits stable high/low resistance window.This study provides process optimization strategies and a reliability assurance framework for the mass production of highly integrated,low-power embedded RRAM display driver IC.展开更多
Greenhouse gas(GHG)emissions from China’s food system are a major environmental concern;however,studies quantifying their drivers and future projections remain limited.This study uses structural decomposition analysi...Greenhouse gas(GHG)emissions from China’s food system are a major environmental concern;however,studies quantifying their drivers and future projections remain limited.This study uses structural decomposition analysis and growth curve models to assess food-related GHG trends from 1961 to 2020,identify key drivers and their contributions,and project emissions for 2050 under six economic and population scenarios.It also proposes reduction pathways to help China achieve its 2060 carbon neutrality goal.Animal and plant foods are categorized into 14 groups based on the similarity of their emission coefficients.China’s total food related GHG emissions rose tenfold,from 351.7 to 3719.8 million tons CO_(2)-equivalent(CO_(2)e)/year,between 1961 and 2020.Per-capita emissions increased from 532.1 to 2584.4 kg CO_(2)e/year.Emissions from plant based foods grew from 435.0 to 824.6 kg CO_(2)e/year,while animal-based emissions surged from 97.1 to 1759.8 kg CO_(2)e/year,with animal products contributing more owing to their higher emission coefficients.Key drivers include rising food intake,increasing demand for animal-based foods(especially red meat),and population growth.Scenario analyses predict that emissions will peak at 3826.2 million tons CO_(2)e/year in 2031(low economy-low population)and 3971.0 million tons CO_(2)e/year in 2039(high economy-medium population).Compared with Australian,Indian,and Japanese diets,Chinese diets exhibit lower per-capita emissions than Australia and India but have higher emissions than in Japan.Adhering to China’s national dietary guidelines could reduce Chinese per-capita food-related GHGs by 31.5%,and optimized diets could lower them by 45.3%.This study provides valuable insights for Chinese policymakers to reduce food-related GHG emissions,refine national dietary guidelines,and raise public awareness regarding the food system’s environmental impact,thus encouraging people to follow sustainable diets.展开更多
Accurately recognizing driver distraction is critical for preventing traffic accidents,yet current detection models face two persistent challenges.First,distractions are often fine-grained,involving subtle cues such a...Accurately recognizing driver distraction is critical for preventing traffic accidents,yet current detection models face two persistent challenges.First,distractions are often fine-grained,involving subtle cues such as brief eye closures or partial yawns,which are easily missed by conventional detectors.Second,in real-world scenarios,drivers frequently exhibit overlapping behaviors,such as simultaneously holding a cup,closing their eyes,and yawning,leading tomultiple detection boxes and degradedmodel performance.Existing approaches fail to robustly address these complexities,resulting in limited reliability in safety critical applications.To overcome these pain points,we propose YOLO-Drive,a novel framework that enhances YOLO-based driver monitoring with EfficientViM and Polarized Spectral–Spatial Attention(PSSA)modules.Efficient ViMprovides lightweight yet powerful global–local feature extraction,enabling accurate recognition of subtle driver states.PSSA further amplifies discriminative features across spatial and spectral domains,ensuring robust separation of concurrent distraction cues.By explicitly modeling fine-grained and overlapping behaviors,our approach delivers significant improvements in both precision and robustness.Extensive experiments on benchmark driver distraction datasets demonstrate that YOLO-Drive consistently out-performs stateof-the-art models,achieving higher detection accuracy while maintaining real-time efficiency.These results validate YOLO-Drive as a practical and reliable solution for advanced driver monitoring systems,addressing long-standing challenges of subtle cue recognition and multi-cue distraction detection.展开更多
Accurate detection of driver fatigue is essential for improving road safety.This study investigates the effectiveness of using multimodal physiological signals for fatigue detection while incorporating uncertainty qua...Accurate detection of driver fatigue is essential for improving road safety.This study investigates the effectiveness of using multimodal physiological signals for fatigue detection while incorporating uncertainty quantification to enhance the reliability of predictions.Physiological signals,including Electrocardiogram(ECG),Galvanic Skin Response(GSR),and Electroencephalogram(EEG),were transformed into image representations and analyzed using pretrained deep neu-ral networks.The extracted features were classified through a feedforward neural network,and prediction reliability was assessed using uncertainty quantification techniques such as Monte Carlo Dropout(MCD),model ensembles,and combined approaches.Evaluation metrics included standard measures(sensitivity,specificity,precision,and accuracy)along with uncertainty-aware metrics such as uncertainty sensitivity and uncertainty precision.Across all evaluations,ECG-based models consistently demonstrated strong performance.The findings indicate that combining multimodal physi-ological signals,Transfer Learning(TL),and uncertainty quantification can significantly improve both the accuracy and trustworthiness of fatigue detection systems.This approach supports the development of more reliable driver assistance technologies aimed at preventing fatigue-related accidents.展开更多
Ecosystems along the eastern margin of the Qinghai-Tibet Plateau(EQTP)are highly fragile and extremely sensitive to climate change and human disturbances.To quantitatively assess climate-induced ecosystem responses,th...Ecosystems along the eastern margin of the Qinghai-Tibet Plateau(EQTP)are highly fragile and extremely sensitive to climate change and human disturbances.To quantitatively assess climate-induced ecosystem responses,this study proposes a Climate-Induced Productivity Index(CIPI)based on the Super Slack-Based Measure(Super-SBM)model using remote sensing data from 2001 to 2020.The results reveal persistently low CIPI values(0.47-0.53)across major ecosystem types,indicating widespread vulnerability to climatic variability.Among these ecosystems,forests exhibit the highest CIPI(0.55),followed by shrublands(0.54),croplands(0.53),grasslands(0.51),and barelands(0.43).The Theil index analysis further demonstrates significant intra-group disparities,suggesting that extreme climatic events amplify CIPI heterogeneity.Moreover,the dominant environmental drivers differ among ecosystem types:the Palmer Drought Severity Index(PDSI)primarily constrains grassland productivity,solar radiation(SRAD)strongly influences shrub and cropland systems,whereas subsurface factors exert greater control in forested regions.This study provides a quantitative framework for evaluating climate-ecosystem interactions and offers a scientific basis for long-term ecological monitoring and security planning across the EQTP.展开更多
The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We r...The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We retrieved the start of spring phenology(SOS)of eight forest communities from the MODIS products and adopted it as an indicator for spring phenology.Trend analysis,partial correlation analysis,and GeoDetector were employed to reveal the spatio-temporal patterns and climatic drivers of SOS.The results indicated that the SOS presented an advance trend from 2001 to 2020,with a mean rate of−0.473 d yr^(−1).The SOS of most forests correlated negatively with air temperature(TEMP)and positively with precipitation(PRE),suggesting that rising TEMP and increasing PRE in spring would forward and delay SOS,respectively.The dominant factors influencing the sensitivity of SOS to climatic variables were altitude,forest type,and latitude,while the effects of slope and aspect were relatively minor.The response of SOS to climatic factors varied significantly in space and among forest communities,partly due to the influence of altitude,slope,and aspect.展开更多
This article introduces a novel 20 V radiation-hardened high-voltage metal-oxide-semiconductor field-effect transistor(MOSFET)driver with an optimized input circuit and a drain-surrounding-source(DSS)structure.The inp...This article introduces a novel 20 V radiation-hardened high-voltage metal-oxide-semiconductor field-effect transistor(MOSFET)driver with an optimized input circuit and a drain-surrounding-source(DSS)structure.The input circuit of a conventional inverter consists of a thick-gate-oxide n-type MOSFET(NMOS).These conventional drivers can tolerate a total ionizing dose(TID)of up to 100 krad(Si).In contrast,the proposed comparator input circuit uses both a thick-gate-oxide p-type MOSFET(PMOS)and thin-gate-oxide NMOS to offer a high input voltage and higher TID tolerance.Because the thick-gate-oxide PMOS and thin-gate-oxide NMOS collectively provide better TID tolerance than the thick-gate-oxide NMOS,the circuit exhibits enhanced TID tolerance of>300 krad(Si).Simulations and experimental date indicate that the DSS structure reduces the probability of unwanted parasitic bipolar junction transistor activation,yielding a better single-event effect tolerance of over 81.8 MeVcm^(2)mg^(-1).The innovative strategy proposed in this study involves circuit and layout design optimization,and does not require any specialized process flow.Hence,the proposed circuit can be manufactured using common commercial 0.35μm BCD processes.展开更多
Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportatio...Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.展开更多
The emergence of smart grids in India is propelled by an intricate interaction of market dynamics,regulatory structures,and stakeholder obligations.This study analyzes the primary factors that are driving the widespre...The emergence of smart grids in India is propelled by an intricate interaction of market dynamics,regulatory structures,and stakeholder obligations.This study analyzes the primary factors that are driving the widespread use of smart grid technologies and outlines the specific roles and obligations of different stakeholders,such as government entities,utility companies,technology suppliers,and consumers.Government activities and regulations are crucial in facilitating the implementation of smart grid technology by offering financial incentives,regulatory assistance,and strategic guidance.Utility firms have the responsibility of implementing and integrating smart grid infrastructure,with an emphasis on improving the dependability of the grid,minimizing losses in transmission and distribution,and integrating renewable energy sources.Technology companies offer the essential hardware and software solutions,which stimulate creativity and enhance efficiency.Consumers actively engage in the energy ecosystem by participating in demand response,implementing energy saving measures,and adopting distributed energy resources like solar panels and electric vehicles.This study examines the difficulties and possibilities in India’s smart grid industry,highlighting the importance of cooperation among stakeholders to build a strong,effective,and environmentally friendly energy future.展开更多
BACKGROUND Through deeper understanding of targetable driver mutations in non-small-cell lung cancer(NSCLC)over the past years,some patients with driver mutations have benefited from the targeted molecular therapies.A...BACKGROUND Through deeper understanding of targetable driver mutations in non-small-cell lung cancer(NSCLC)over the past years,some patients with driver mutations have benefited from the targeted molecular therapies.Although the anaplastic lymphoma kinase and BRAF mutations are not frequent subtypes in NSCLC,the availability of several targeted-drugs has been confirmed through a series of clinical trials.But little is clear about the proper strategy in rare BRAF G469A mutation,not to mention co-exhibition of anaplastic lymphoma kinase and BRAF G469A mutations,which is extremely rare in NSCLC.CASE SUMMARY We present a patient to stage IVA lung adenocarcinoma with coexisting echinoderm microtubule associated protein like-4 rearrangement and BRAF G469A mutation.She received several targeted drugs with unintended resistance and suffered from unbearable adverse events.CONCLUSION Due to the rarity of co-mutations,the case not only enriches the limited literature on NSCLC harbouring BRAF G469A and echinoderm microtubule associated protein like-4 mutations,but also suggests the efficacy and safety of specific multiple-drug therapy in such patients.展开更多
We have designed,assembled,and tested a 4-MA,60-ns fast linear transformer driver(LTD),which is the first operating generator featuring multiple LTD modules connected in parallel.The LTD-based accelerator comprises si...We have designed,assembled,and tested a 4-MA,60-ns fast linear transformer driver(LTD),which is the first operating generator featuring multiple LTD modules connected in parallel.The LTD-based accelerator comprises six modules in parallel,each of which has ten-stage cavities stacked in series.The six LTD modules are connected to a water tank of diameter 6 m via a 3-m-long impedance-matched deionized waterinsulated coaxial transmission line.In the water tank,the electrical pulses are transmitted down by six horizontal tri-plate transmission lines.A 2.1-m-diameter two-level vacuum insulator stack is utilized to separate the deionized water region from the vacuum region.In the vacuum,the currents are further transported downstream by a two-level magnetically insulated transmission-line and then converged through four post-hole convolutes.Plasma radiation loads or bremsstrahlung electron beam diodes serve as loads that are expected to generate intense soft X rays or warm X rays.The machine is 3.2 m in height and 22 m in outer diameter,including support systems such as a high-voltage charge supply,magnetic core reset system,trigger system,and support platform for inner stalk installation and maintenance.A total of 1440 individual±100-kV multi-gap spark switches and 2880 individual 100-kV capacitors are employed in the accelerator.A total of 12 fiberoptic laser-controlled trigger generators combining photoconductive and traditional gas spark switch technologies are used to realize the synchronous discharge of the more than 1000 gas switches.At an LTD charge voltage of±85 kV,the accelerator stores an initial energy of about 300 kJ and is expected to deliver a current of 3–5 MA into various loads.To date,the LTD facility has shot into a thick-walled aluminum liner load and a reflex triode load.With a thick-walled aluminum liner of inductance 1.81 nH,a current with peak up to 4.1 MA and rise time(10%–90%)of about 60 ns has been achieved.The current transport efficiency from the insulator stack to the liner load approaches 100%during peak times.The LTD accelerator has been used to drive reflex triode loads generating warm X rays with high energy fluence and large radiation area.It has been demonstrated that this LTD is a promising and high-efficiency prime pulsed power source suitable for use in constructing the next generation of large-scale accelerators with currents of tens of megaamperes.展开更多
DRIVEN by advancements in artificial intelligence technologies such as deep learning,core intelligent driving technologies like advanced driver assistance systems(ADAS)have made significant advances.Some advanced ADAS...DRIVEN by advancements in artificial intelligence technologies such as deep learning,core intelligent driving technologies like advanced driver assistance systems(ADAS)have made significant advances.Some advanced ADAS systems,particularly in highway scenarios,have reached or even surpassed human drivers in terms of precision and reliability[1].This mainstream development path is based on a replacement paradigm,whose central goal is to relieve human drivers of monotonous,repetitive tasks such as highway commuting,maximizing traffic efficiency and safety[2].This paradigm aims to replace error-prone human operators with a tireless,consistent machine intelligence.展开更多
When you go somewhere,do you like to be the driver or a passenger?When you are the driver,you are in control.You can go fast or slow.You can pick the route.When and where do you stop?You decide.You enjoy the feeling o...When you go somewhere,do you like to be the driver or a passenger?When you are the driver,you are in control.You can go fast or slow.You can pick the route.When and where do you stop?You decide.You enjoy the feeling of driving.Ifs fun!展开更多
Rideshare apps make getting around town easy and quick.With just a few taps on your phone,you can get a ride to your destination.How does the app work?It will show you a route and the cost.Then the app will find a dri...Rideshare apps make getting around town easy and quick.With just a few taps on your phone,you can get a ride to your destination.How does the app work?It will show you a route and the cost.Then the app will find a driver for you.展开更多
1 There was a taxi driver who was angry and wanted to finish his shift as soon as possible.When he arrived to pick up his last passenger of the day,he honked the horn(按喇叭),but no one came out of the apartment.Minut...1 There was a taxi driver who was angry and wanted to finish his shift as soon as possible.When he arrived to pick up his last passenger of the day,he honked the horn(按喇叭),but no one came out of the apartment.Minutes later,he honked again,but still there was no reaction.展开更多
To date,many previous studies have been proposed for driver authentication;however,these solutions have many shortcomings and are still far from practical for real-world applications.In this paper,we tackle the shortc...To date,many previous studies have been proposed for driver authentication;however,these solutions have many shortcomings and are still far from practical for real-world applications.In this paper,we tackle the shortcomings of the existing solutions and reach toward proposing a lightweight and practical authentication system,dubbed DriveMe,for identifying drivers on cars.Our novelty aspects are 1⃝Lightweight scheme that depends only on a single sensor data(i.e.,pressure readings)attached to the driver’s seat and belt.2⃝Practical evaluation in which one-class authentication models are trained from only the owner users and tested using data collected from both owners and attackers.3⃝Rapid Authentication to quickly identify drivers’identities using a few pressure samples collected within short durations(1,2,3,5,or 10 s).4⃝Realistic experiments where the sensory data is collected from real experiments rather than computer simulation tools.We conducted real experiments and collected about 13,200 samples and 22,800 samples of belt-only and seat-only datasets from all 12 users under different settings.To evaluate system effectiveness,we implemented extensive evaluation scenarios using four one-class detectors One-Class Support Vector Machine(OCSVM),Local Outlier Factor(LOF),Isolation Forest(IF),and Elliptic Envelope(EE),three dataset types(belt-only,seat-only,and fusion),and four different dataset sizes.Our average experimental results show that the system can authenticate the driver with an F1 score of 93.1%for seat-based data using OCSVM classifier,an F1 score of 98.53%for fusion-based data using LOF classifier,an F1 score of 91.65%for fusion-based data using IF classifier,and an F1 score of 95.79%for fusion-based data using EE classifier.展开更多
基金funded by the Inner Mongolia Autonomous Region Natural Science Foundation Youth Fund Project(Grants No.2024QN04020)A Science and technology program of Inner Mongolia Autonomous Region(Grants No.2022YFDZ0027)。
文摘Flash droughts(FDs)develop quickly and can rapidly deplete soil moisture,posing significant threats to agriculture and pastoral systems.To investigate the spatiotemporal characteristics and development mechanisms of FDs in Inner Mongolia,China,and to assess the roles of key meteorological drivers in driving soil moisture variability,FD events were identified using root-zone soil moisture data during the growing seasons from 1982 to 2022.The results indicate the presence of five FD hotspot regions,located in the southern Alxa Plateau,the Hetao Plain in Bayannur,the northwestern Xilingol Plain,the western Liaohe River Plain,and the northern Da Hinggan Ling.Over 41 years,FDs occurred on average 7.44 events across the study area,with a mean duration of 9.17 pentads(1 pentad equals 5 days).The duration exhibited a significant increasing trend of 0.39 pentads/10 years.FD onsets primarily lasted for 2-3 pentads.During the FD development phase,precipitation and evapotranspiration decreased while temperature,potential evapotranspiration,incoming solar radiation,and vapor pressure deficit increased.The dominant meteorological drivers of FD development exhibited notable spatial heterogeneity across hotspot regions,and vapor pressure deficit consistently was the most influential factor.These findings improve the understanding of climate drivers at different stages of FD development and provide scientific support for early warning and prevention of droughts in Inner Mongolia.
基金supported by the Humanities and Social Sciences Youth Foundation,Ministry of Education of China[Grant No.24YJC630248]Sichuan Office of Philosophy and Social Science,China[Grant No.SCJJ24ND299].
文摘Energy consumption(EC)is a core factor in maintaining sustainable development;little is known about the drivers of temporal-spatial EC changes and the corresponding inequality from the perspective of government,thereby weakening the policy implications for energy conservation and emission reduction.To fill this gap,this study uses spatial-temporal logarithmic mean Divisia index models and extended Theil index inequality models to investigate the drivers of EC changes and inequality,considering the scale and structure of governmental environmental expenditure(EG)across 30 Chinese provinces from 2007 to 2021.The findings reveal that:First,EG acts as a positive driver of total EC,while industrial investment efficiency and fiscal expenditure pressure exert negative effects.Second,disparities in EG functions act as a negative driver,accounting for a 4.56%decrease in average interprovincial EC gaps.Third,China’s EC inequality demonstrated an overall upward trajectory from 0.074 to 0.093 over the period,mainly driven by positive contributions from inequalities in EG and the fiscal expenditure structure.This study highlights the importance of optimizing the government expenditure structure and scale in formulating policies for sustainable EC.
基金funded by the Innovative Research Group Project of the National Natural Science Foundation of China(Nos.U24A20592 and 42272137)Guizhou Province Science and Technology Innovation Talent Team,Construction of the Science and Technology Innovation Talent Team for the Evaluation and Development of Unconventional Natural Gas Resources in Complex Structural Areas(No.Qian Ke He Platform Talent-CXTD[2023]013)。
文摘To clarify the thermal evolution characteristics of organic matter in the ZizhongWeiyuan area in Sichuan Basin,solid bitumen reflectance of the Lower Cambrian Qiongzhusi Formation(QFm)shale was measured by Raman Spectroscopy(RS)method.Constrained by vitrinite reflectance(Ro)data,burial and thermal evolution histories of QFm shale were reconstructed through basin numerical simulation technology.The evolution model of and critical period of organic matter was determined,and its dominant drivers were analyzed.The results show that the asphalt Raman vitrinite reflectance(_(Rmc)Ro)ranges from 3.21%to 4.15%.Thermal maturity within the trough follows a southern part>central part>northern part trend.Thermal maturity is moderate within the paleo-uplift,whereas organic matter outside the paleo-uplift has undergone graphitization.Two types of thermal evolution imprints were established:a continuous heating type and a stop heating type of Silurian–Permian.Sedimentary burial,paleogeomorphology,tectonic movement and Emeishan mantle plume are the dominant drivers of multi-stage thermal imprints of the QFm shale.The three factors are coupled with each other.The Late Caledonian and Late Indosinian are the key periods of organic matter thermal evolution.The Leshan-Longnüsi paleo-uplift weakens the thermal effect of the Permian Emeishan mantle plume.The current thermal evolution pattern of the QFm is mainly determined by the continuous subsidence of the Triassic–Cretaceous.Stop heating model of Silurian–Permian locks the maturity of organic matter in the gold window,thus controlling the enrichment of QFm shale gas.It provides new insights for shale gas migration,enrichment and effective exploration and development of shale gas in the Lower Paleozoic QFm.
文摘Matter and Radiation at Extremes(MRE),is committed to the publication of original and impactful research and review papers that address extreme states of matter and radiation,and the associated science and technology that are employed to produce and diagnose these conditions in the laboratory.Drivers,targets and diagnostics are included along with related numerical simulation and computational methods.It aims to provide a peer-reviewed platform for the international physics community and promote worldwide dissemination of the latest and impactful research in related fields.
文摘To address the challenges of complexity,power consumption,and cost constraints in traditional display driver integrated circuits(DDICs)caused by external NOR Flash and SRAM,this work proposes an embedded resistive random-access memory(RRAM)integration solution based on a 40 nm high-voltage CMOS logic platform.Targeting the yield fluctuations and stability challenges during RRAM mass production,systematic process optimizations are implemented to achieve synergistic improvements in RRAM performance and yield.Through modifications to the film sputtering and pre-deposition treatment,the withinwafer resistance uniformity(RSU)of the oxygen-deficient layer(ODL)thin film is improved from 11%to 8%,while inter-wafer process stability variation reduces from 23%to below 6%.Consequently,the yield of 8 Mb RRAM embedded mass production products increases from 87%to 98.5%.In terms of device performance,the RRAM demonstrates a fast 4.8 ns read speed,exceptional read disturb immunity of 3×10^(8) cycles at 95℃,10^(3) write/erase endurance cycles for the 1 Mb cells,and data retention of 12.5 years at 125℃.Post high-temperature operating life(HTOL)testing exhibits stable high/low resistance window.This study provides process optimization strategies and a reliability assurance framework for the mass production of highly integrated,low-power embedded RRAM display driver IC.
基金funded by the General Program of the National Natural Science Foundation of China[Grant No.42171300]the Strategic Research Program of the National Natural Science Foundation of China[Grant No.42542001]+1 种基金Post-funded Project of National Social Science Fund of China[Grant No.25FJYB015]Special Project of Strategic Research and Decision Support System of the Chinese Academy of Sciences[Grant No.GHJ-ZLZX-2025-48].
文摘Greenhouse gas(GHG)emissions from China’s food system are a major environmental concern;however,studies quantifying their drivers and future projections remain limited.This study uses structural decomposition analysis and growth curve models to assess food-related GHG trends from 1961 to 2020,identify key drivers and their contributions,and project emissions for 2050 under six economic and population scenarios.It also proposes reduction pathways to help China achieve its 2060 carbon neutrality goal.Animal and plant foods are categorized into 14 groups based on the similarity of their emission coefficients.China’s total food related GHG emissions rose tenfold,from 351.7 to 3719.8 million tons CO_(2)-equivalent(CO_(2)e)/year,between 1961 and 2020.Per-capita emissions increased from 532.1 to 2584.4 kg CO_(2)e/year.Emissions from plant based foods grew from 435.0 to 824.6 kg CO_(2)e/year,while animal-based emissions surged from 97.1 to 1759.8 kg CO_(2)e/year,with animal products contributing more owing to their higher emission coefficients.Key drivers include rising food intake,increasing demand for animal-based foods(especially red meat),and population growth.Scenario analyses predict that emissions will peak at 3826.2 million tons CO_(2)e/year in 2031(low economy-low population)and 3971.0 million tons CO_(2)e/year in 2039(high economy-medium population).Compared with Australian,Indian,and Japanese diets,Chinese diets exhibit lower per-capita emissions than Australia and India but have higher emissions than in Japan.Adhering to China’s national dietary guidelines could reduce Chinese per-capita food-related GHGs by 31.5%,and optimized diets could lower them by 45.3%.This study provides valuable insights for Chinese policymakers to reduce food-related GHG emissions,refine national dietary guidelines,and raise public awareness regarding the food system’s environmental impact,thus encouraging people to follow sustainable diets.
基金funded by the Guangzhou Development Zone Science and Technology Project(2023GH02)the University of Macao(MYRG2022-00271-FST)research grants by the Science and Technology Development Fund of Macao(0032/2022/A)and(0019/2025/RIB1).
文摘Accurately recognizing driver distraction is critical for preventing traffic accidents,yet current detection models face two persistent challenges.First,distractions are often fine-grained,involving subtle cues such as brief eye closures or partial yawns,which are easily missed by conventional detectors.Second,in real-world scenarios,drivers frequently exhibit overlapping behaviors,such as simultaneously holding a cup,closing their eyes,and yawning,leading tomultiple detection boxes and degradedmodel performance.Existing approaches fail to robustly address these complexities,resulting in limited reliability in safety critical applications.To overcome these pain points,we propose YOLO-Drive,a novel framework that enhances YOLO-based driver monitoring with EfficientViM and Polarized Spectral–Spatial Attention(PSSA)modules.Efficient ViMprovides lightweight yet powerful global–local feature extraction,enabling accurate recognition of subtle driver states.PSSA further amplifies discriminative features across spatial and spectral domains,ensuring robust separation of concurrent distraction cues.By explicitly modeling fine-grained and overlapping behaviors,our approach delivers significant improvements in both precision and robustness.Extensive experiments on benchmark driver distraction datasets demonstrate that YOLO-Drive consistently out-performs stateof-the-art models,achieving higher detection accuracy while maintaining real-time efficiency.These results validate YOLO-Drive as a practical and reliable solution for advanced driver monitoring systems,addressing long-standing challenges of subtle cue recognition and multi-cue distraction detection.
基金the Australian Research Council Discovery Projects funding scheme(DP190102181,DP210101465).
文摘Accurate detection of driver fatigue is essential for improving road safety.This study investigates the effectiveness of using multimodal physiological signals for fatigue detection while incorporating uncertainty quantification to enhance the reliability of predictions.Physiological signals,including Electrocardiogram(ECG),Galvanic Skin Response(GSR),and Electroencephalogram(EEG),were transformed into image representations and analyzed using pretrained deep neu-ral networks.The extracted features were classified through a feedforward neural network,and prediction reliability was assessed using uncertainty quantification techniques such as Monte Carlo Dropout(MCD),model ensembles,and combined approaches.Evaluation metrics included standard measures(sensitivity,specificity,precision,and accuracy)along with uncertainty-aware metrics such as uncertainty sensitivity and uncertainty precision.Across all evaluations,ECG-based models consistently demonstrated strong performance.The findings indicate that combining multimodal physi-ological signals,Transfer Learning(TL),and uncertainty quantification can significantly improve both the accuracy and trustworthiness of fatigue detection systems.This approach supports the development of more reliable driver assistance technologies aimed at preventing fatigue-related accidents.
基金National Key R&D Program of China,No.2022YFF1302401National Natural Science Foundation of China,No.42271007。
文摘Ecosystems along the eastern margin of the Qinghai-Tibet Plateau(EQTP)are highly fragile and extremely sensitive to climate change and human disturbances.To quantitatively assess climate-induced ecosystem responses,this study proposes a Climate-Induced Productivity Index(CIPI)based on the Super Slack-Based Measure(Super-SBM)model using remote sensing data from 2001 to 2020.The results reveal persistently low CIPI values(0.47-0.53)across major ecosystem types,indicating widespread vulnerability to climatic variability.Among these ecosystems,forests exhibit the highest CIPI(0.55),followed by shrublands(0.54),croplands(0.53),grasslands(0.51),and barelands(0.43).The Theil index analysis further demonstrates significant intra-group disparities,suggesting that extreme climatic events amplify CIPI heterogeneity.Moreover,the dominant environmental drivers differ among ecosystem types:the Palmer Drought Severity Index(PDSI)primarily constrains grassland productivity,solar radiation(SRAD)strongly influences shrub and cropland systems,whereas subsurface factors exert greater control in forested regions.This study provides a quantitative framework for evaluating climate-ecosystem interactions and offers a scientific basis for long-term ecological monitoring and security planning across the EQTP.
基金National Key Research and Development Program of China,No.2023YFE0208100,No.2021YFC3000201Natural Science Foundation of Henan Province,No.232300420165。
文摘The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We retrieved the start of spring phenology(SOS)of eight forest communities from the MODIS products and adopted it as an indicator for spring phenology.Trend analysis,partial correlation analysis,and GeoDetector were employed to reveal the spatio-temporal patterns and climatic drivers of SOS.The results indicated that the SOS presented an advance trend from 2001 to 2020,with a mean rate of−0.473 d yr^(−1).The SOS of most forests correlated negatively with air temperature(TEMP)and positively with precipitation(PRE),suggesting that rising TEMP and increasing PRE in spring would forward and delay SOS,respectively.The dominant factors influencing the sensitivity of SOS to climatic variables were altitude,forest type,and latitude,while the effects of slope and aspect were relatively minor.The response of SOS to climatic factors varied significantly in space and among forest communities,partly due to the influence of altitude,slope,and aspect.
基金supported by the National Natural Science Foundation of China(U2241221).
文摘This article introduces a novel 20 V radiation-hardened high-voltage metal-oxide-semiconductor field-effect transistor(MOSFET)driver with an optimized input circuit and a drain-surrounding-source(DSS)structure.The input circuit of a conventional inverter consists of a thick-gate-oxide n-type MOSFET(NMOS).These conventional drivers can tolerate a total ionizing dose(TID)of up to 100 krad(Si).In contrast,the proposed comparator input circuit uses both a thick-gate-oxide p-type MOSFET(PMOS)and thin-gate-oxide NMOS to offer a high input voltage and higher TID tolerance.Because the thick-gate-oxide PMOS and thin-gate-oxide NMOS collectively provide better TID tolerance than the thick-gate-oxide NMOS,the circuit exhibits enhanced TID tolerance of>300 krad(Si).Simulations and experimental date indicate that the DSS structure reduces the probability of unwanted parasitic bipolar junction transistor activation,yielding a better single-event effect tolerance of over 81.8 MeVcm^(2)mg^(-1).The innovative strategy proposed in this study involves circuit and layout design optimization,and does not require any specialized process flow.Hence,the proposed circuit can be manufactured using common commercial 0.35μm BCD processes.
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia through research group No.(RG-NBU-2022-1234).
文摘Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.
文摘The emergence of smart grids in India is propelled by an intricate interaction of market dynamics,regulatory structures,and stakeholder obligations.This study analyzes the primary factors that are driving the widespread use of smart grid technologies and outlines the specific roles and obligations of different stakeholders,such as government entities,utility companies,technology suppliers,and consumers.Government activities and regulations are crucial in facilitating the implementation of smart grid technology by offering financial incentives,regulatory assistance,and strategic guidance.Utility firms have the responsibility of implementing and integrating smart grid infrastructure,with an emphasis on improving the dependability of the grid,minimizing losses in transmission and distribution,and integrating renewable energy sources.Technology companies offer the essential hardware and software solutions,which stimulate creativity and enhance efficiency.Consumers actively engage in the energy ecosystem by participating in demand response,implementing energy saving measures,and adopting distributed energy resources like solar panels and electric vehicles.This study examines the difficulties and possibilities in India’s smart grid industry,highlighting the importance of cooperation among stakeholders to build a strong,effective,and environmentally friendly energy future.
基金Supported by the Medical Education Collaborative Innovation Fund of Jiangsu University,No.JDY2022015。
文摘BACKGROUND Through deeper understanding of targetable driver mutations in non-small-cell lung cancer(NSCLC)over the past years,some patients with driver mutations have benefited from the targeted molecular therapies.Although the anaplastic lymphoma kinase and BRAF mutations are not frequent subtypes in NSCLC,the availability of several targeted-drugs has been confirmed through a series of clinical trials.But little is clear about the proper strategy in rare BRAF G469A mutation,not to mention co-exhibition of anaplastic lymphoma kinase and BRAF G469A mutations,which is extremely rare in NSCLC.CASE SUMMARY We present a patient to stage IVA lung adenocarcinoma with coexisting echinoderm microtubule associated protein like-4 rearrangement and BRAF G469A mutation.She received several targeted drugs with unintended resistance and suffered from unbearable adverse events.CONCLUSION Due to the rarity of co-mutations,the case not only enriches the limited literature on NSCLC harbouring BRAF G469A and echinoderm microtubule associated protein like-4 mutations,but also suggests the efficacy and safety of specific multiple-drug therapy in such patients.
基金supported by the National Natural Science Foundation of China(Grant Nos.12027811 and 51790524).
文摘We have designed,assembled,and tested a 4-MA,60-ns fast linear transformer driver(LTD),which is the first operating generator featuring multiple LTD modules connected in parallel.The LTD-based accelerator comprises six modules in parallel,each of which has ten-stage cavities stacked in series.The six LTD modules are connected to a water tank of diameter 6 m via a 3-m-long impedance-matched deionized waterinsulated coaxial transmission line.In the water tank,the electrical pulses are transmitted down by six horizontal tri-plate transmission lines.A 2.1-m-diameter two-level vacuum insulator stack is utilized to separate the deionized water region from the vacuum region.In the vacuum,the currents are further transported downstream by a two-level magnetically insulated transmission-line and then converged through four post-hole convolutes.Plasma radiation loads or bremsstrahlung electron beam diodes serve as loads that are expected to generate intense soft X rays or warm X rays.The machine is 3.2 m in height and 22 m in outer diameter,including support systems such as a high-voltage charge supply,magnetic core reset system,trigger system,and support platform for inner stalk installation and maintenance.A total of 1440 individual±100-kV multi-gap spark switches and 2880 individual 100-kV capacitors are employed in the accelerator.A total of 12 fiberoptic laser-controlled trigger generators combining photoconductive and traditional gas spark switch technologies are used to realize the synchronous discharge of the more than 1000 gas switches.At an LTD charge voltage of±85 kV,the accelerator stores an initial energy of about 300 kJ and is expected to deliver a current of 3–5 MA into various loads.To date,the LTD facility has shot into a thick-walled aluminum liner load and a reflex triode load.With a thick-walled aluminum liner of inductance 1.81 nH,a current with peak up to 4.1 MA and rise time(10%–90%)of about 60 ns has been achieved.The current transport efficiency from the insulator stack to the liner load approaches 100%during peak times.The LTD accelerator has been used to drive reflex triode loads generating warm X rays with high energy fluence and large radiation area.It has been demonstrated that this LTD is a promising and high-efficiency prime pulsed power source suitable for use in constructing the next generation of large-scale accelerators with currents of tens of megaamperes.
基金supported in part by the Science and Technology Development Fund,Macao Special Administrative Region(SAR)(0145/2023/RIA3)in part by the DeSciCPI Project from the Obuda University,Hungary.
文摘DRIVEN by advancements in artificial intelligence technologies such as deep learning,core intelligent driving technologies like advanced driver assistance systems(ADAS)have made significant advances.Some advanced ADAS systems,particularly in highway scenarios,have reached or even surpassed human drivers in terms of precision and reliability[1].This mainstream development path is based on a replacement paradigm,whose central goal is to relieve human drivers of monotonous,repetitive tasks such as highway commuting,maximizing traffic efficiency and safety[2].This paradigm aims to replace error-prone human operators with a tireless,consistent machine intelligence.
文摘When you go somewhere,do you like to be the driver or a passenger?When you are the driver,you are in control.You can go fast or slow.You can pick the route.When and where do you stop?You decide.You enjoy the feeling of driving.Ifs fun!
文摘Rideshare apps make getting around town easy and quick.With just a few taps on your phone,you can get a ride to your destination.How does the app work?It will show you a route and the cost.Then the app will find a driver for you.
文摘1 There was a taxi driver who was angry and wanted to finish his shift as soon as possible.When he arrived to pick up his last passenger of the day,he honked the horn(按喇叭),but no one came out of the apartment.Minutes later,he honked again,but still there was no reaction.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(1ITP)(Project Nos.RS-2024-00438551,30%,2022-11220701,30%,2021-0-01816,30%)the National Research Foundation of Korea(NRF)grant funded by the Korean Government(Project No.RS2023-00208460,10%).
文摘To date,many previous studies have been proposed for driver authentication;however,these solutions have many shortcomings and are still far from practical for real-world applications.In this paper,we tackle the shortcomings of the existing solutions and reach toward proposing a lightweight and practical authentication system,dubbed DriveMe,for identifying drivers on cars.Our novelty aspects are 1⃝Lightweight scheme that depends only on a single sensor data(i.e.,pressure readings)attached to the driver’s seat and belt.2⃝Practical evaluation in which one-class authentication models are trained from only the owner users and tested using data collected from both owners and attackers.3⃝Rapid Authentication to quickly identify drivers’identities using a few pressure samples collected within short durations(1,2,3,5,or 10 s).4⃝Realistic experiments where the sensory data is collected from real experiments rather than computer simulation tools.We conducted real experiments and collected about 13,200 samples and 22,800 samples of belt-only and seat-only datasets from all 12 users under different settings.To evaluate system effectiveness,we implemented extensive evaluation scenarios using four one-class detectors One-Class Support Vector Machine(OCSVM),Local Outlier Factor(LOF),Isolation Forest(IF),and Elliptic Envelope(EE),three dataset types(belt-only,seat-only,and fusion),and four different dataset sizes.Our average experimental results show that the system can authenticate the driver with an F1 score of 93.1%for seat-based data using OCSVM classifier,an F1 score of 98.53%for fusion-based data using LOF classifier,an F1 score of 91.65%for fusion-based data using IF classifier,and an F1 score of 95.79%for fusion-based data using EE classifier.
