This study developed a novel heterogeneous Vis-Photo+Fenton-like system by integrating visible-light-responsive Co_(3)O_(4)/TiO_(2) photocatalysis with peroxymonosulfate(PMS)activation for efficient atrazine(ATZ)degra...This study developed a novel heterogeneous Vis-Photo+Fenton-like system by integrating visible-light-responsive Co_(3)O_(4)/TiO_(2) photocatalysis with peroxymonosulfate(PMS)activation for efficient atrazine(ATZ)degradation.The synergistic process achieved complete ATZ removal within 60 min under near-neutral pH(6.9),outperform-ing individual Fenton-like(39%)and photocatalytic(24%)processes.Key factors influencing the degradation efficiency included light sources(UV>visible),pH(optimal at 6.9),catalyst dosage(0.01 g Co_(3)O_(4)/TiO_(2)),and PMS:ATZ molar ratio(1:2).The system exhibited a synergistic coefficient of 5.03(degradation)and 1.97(miner-alization),attributed to enhanced radical generation and accelerated Co^(3+)/Co^(2+)redox cycling through photoin-duced electron transfer.Intermediate analysis revealed dealkylation,dechlorination,and oxidation pathways,with reduced toxicity of by-products(e.g.,CEAT,CIAT)confirmed by ecotoxicity assessments.The mineralization efficiency(Vis-Photo+Fenton-like)reached 83.1%,significantly higher than that of standalone processes(Fenton-like:43.2%;photocatalysis:30.5%).The catalyst demonstrated excellent stability(nearly 90%recov-ery,<1μg/L Co leaching)and practical applicability.This study provides an efficient,sludge-free,and solar-compatible strategy for eliminating persistent herbicides in water treatment.展开更多
Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Num...Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.展开更多
What does the impact of migration mean in a social context?This article aims to see migrants by taking their stories into account.Through the individual stories,the readers can see the way in which a bigger picture is...What does the impact of migration mean in a social context?This article aims to see migrants by taking their stories into account.Through the individual stories,the readers can see the way in which a bigger picture is emerged in terms of community,society and nation.By doing so,the authors want to show the way in which migrants integrate into a society,including both migrants having a residence permit and those who are undocumented,as these two groups of people do differ greatly.The cultural clash does mean the inner struggle and the outer struggle that a society has to confront with.展开更多
The photon region surrounding a black hole is crucial for distant observers to receive the emitted spectrum from its vicinity.This paper investigates the optical features of a regular spinning antide Sitter(AdS)black ...The photon region surrounding a black hole is crucial for distant observers to receive the emitted spectrum from its vicinity.This paper investigates the optical features of a regular spinning antide Sitter(AdS)black hole.These kinds of black holes hold deviation parameter k,and the cosmological constant A including their mass M and spin a.The cosmological parameter depends on the curvature radius by A=-3/l~2.We investigate the structure of geodesics for unstable circular orbits of photons as observed by an observer at specific Boyer-Lindquist coordinates(r_(O),v_(O))in the region between the outer and cosmological horizon,so-called the domain of outer communication.Our investigations include the analysis of three observables from its shadow plot:the black hole shadow radius(R_(s)),the distortion of the black hole(δ_(s)),and shadow area A.With the help of these observables,we calculate the angular diameter of the apparent size of the shadow.The shadows cast by spinning regular spacetimes are smaller compared to those produced by rotating black holes in both general relativity and regular spacetimes.We also calculate the rate at which energy is emitted from the black hole.展开更多
As quantum computing continues to advance,traditional cryptographic methods are increasingly challenged,particularly when it comes to securing critical systems like Supervisory Control andData Acquisition(SCADA)system...As quantum computing continues to advance,traditional cryptographic methods are increasingly challenged,particularly when it comes to securing critical systems like Supervisory Control andData Acquisition(SCADA)systems.These systems are essential for monitoring and controlling industrial operations,making their security paramount.A key threat arises from Shor’s algorithm,a powerful quantum computing tool that can compromise current hash functions,leading to significant concerns about data integrity and confidentiality.To tackle these issues,this article introduces a novel Quantum-Resistant Hash Algorithm(QRHA)known as the Modular Hash Learning Algorithm(MHLA).This algorithm is meticulously crafted to withstand potential quantum attacks by incorporating advanced mathematical and algorithmic techniques,enhancing its overall security framework.Our research delves into the effectiveness ofMHLA in defending against both traditional and quantum-based threats,with a particular emphasis on its resilience to Shor’s algorithm.The findings from our study demonstrate that MHLA significantly enhances the security of SCADA systems in the context of quantum technology.By ensuring that sensitive data remains protected and confidential,MHLA not only fortifies individual systems but also contributes to the broader efforts of safeguarding industrial and infrastructure control systems against future quantumthreats.Our evaluation demonstrates that MHLA improves security by 38%against quantumattack simulations compared to traditional hash functionswhilemaintaining a computational efficiency ofO(m⋅n⋅k+v+n).The algorithm achieved a 98%success rate in detecting data tampering during integrity testing.These findings underline MHLA’s effectiveness in enhancing SCADA system security amidst evolving quantum technologies.This research represents a crucial step toward developing more secure cryptographic systems that can adapt to the rapidly changing technological landscape,ultimately ensuring the reliability and integrity of critical infrastructure in an era where quantum computing poses a growing risk.展开更多
Cardiovascular diseases(CVDs)remain one of the foremost causes of death globally;hence,the need for several must-have,advanced automated diagnostic solutions towards early detection and intervention.Traditional auscul...Cardiovascular diseases(CVDs)remain one of the foremost causes of death globally;hence,the need for several must-have,advanced automated diagnostic solutions towards early detection and intervention.Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability.To counter this limitation,this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat.We employ FastAI vision-learner-based convolutional neural networks(CNNs)that include ResNet,DenseNet,VGG,ConvNeXt,SqueezeNet,and AlexNet to classify heart sound recordings.Instead of raw waveform analysis,the proposed approach transforms preprocessed cardiovascular audio signals into spectrograms,which are suited for capturing temporal and frequency-wise patterns.The models are trained on the PASCAL Cardiovascular Challenge dataset while taking into consideration the recording variations,noise levels,and acoustic distortions.To demonstrate generalization,external validation using Google’s Audio set Heartbeat Sound dataset was performed using a dataset rich in cardiovascular sounds.Comparative analysis revealed that DenseNet-201,ConvNext Large,and ResNet-152 could deliver superior performance to the other architectures,achieving an accuracy of 81.50%,a precision of 85.50%,and an F1-score of 84.50%.In the process,we performed statistical significance testing,such as the Wilcoxon signed-rank test,to validate performance improvements over traditional classification methods.Beyond the technical contributions,the research underscores clinical integration,outlining a pathway in which the proposed system can augment conventional electronic stethoscopes and telemedicine platforms in the AI-assisted diagnostic workflows.We also discuss in detail issues of computational efficiency,model interpretability,and ethical considerations,particularly concerning algorithmic bias stemming from imbalanced datasets and the need for real-time processing in clinical settings.The study describes a scalable,automated system combining deep learning,feature extraction using spectrograms,and external validation that can assist healthcare providers in the early and accurate detection of cardiovascular disease.AI-driven solutions can be viable in improving access,reducing delays in diagnosis,and ultimately even the continued global burden of heart disease.展开更多
The integration of the Internet of Things(IoT)into healthcare systems improves patient care,boosts operational efficiency,and contributes to cost-effective healthcare delivery.However,overcoming several associated cha...The integration of the Internet of Things(IoT)into healthcare systems improves patient care,boosts operational efficiency,and contributes to cost-effective healthcare delivery.However,overcoming several associated challenges,such as data security,interoperability,and ethical concerns,is crucial to realizing the full potential of IoT in healthcare.Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems.In this context,this paper presents a novelmethod for healthcare data privacy analysis.The technique is based on the identification of anomalies in cloud-based Internet of Things(IoT)networks,and it is optimized using explainable artificial intelligence.For anomaly detection,the Radial Boltzmann Gaussian Temporal Fuzzy Network(RBGTFN)is used in the process of doing information privacy analysis for healthcare data.Remora Colony SwarmOptimization is then used to carry out the optimization of the network.The performance of the model in identifying anomalies across a variety of healthcare data is evaluated by an experimental study.This evaluation suggested that themodel measures the accuracy,precision,latency,Quality of Service(QoS),and scalability of themodel.A remarkable 95%precision,93%latency,89%quality of service,98%detection accuracy,and 96%scalability were obtained by the suggested model,as shown by the subsequent findings.展开更多
Floating photovoltaic(FPV)technology is emerging as a highly promising approach to accelerate decarbonization of the global economy,due to its higher power generation efficiency and lower land occupation.With the rapi...Floating photovoltaic(FPV)technology is emerging as a highly promising approach to accelerate decarbonization of the global economy,due to its higher power generation efficiency and lower land occupation.With the rapid development of FPV technology,the mechanical performance degradation of key components caused by the harsh marine environment has become a pressing issue,as it significantly contributes to failure behavior observed in FPV systems.A comprehensive compilation of the mechanical performance of key components in FPV systems is also currently unavailable.Here,the mechanical behavior of each structural component in FPV systems under harsh marine environments is systematically reviewed.It further emphasizes the synergistic effects of mechanical performance degradation among different components on the overall system.The drop-off rate(v)of normalized elongation at break(EAB)of polymer under the synergistic effect of various environmental factors increases from 7.5×10^(−4)h^(−1)to 21.8×10^(−4)h^(−1)compared with the single environmental stress.Moreover,the development of novel materials and innovative mechanical structures applied in FPV systems to enhance mechanical performance is discussed.The novel flexible PV modules applied in FPV systems minimize the loads acting on the mooring lines by 80%and increase power generation by 5%.Notably,this paper provides a theoretical foundation for developing standards of FPV systems,especially the establishment of standards related to the synergistic effects of the mechanical performance degradation of different key components on FPV systems.展开更多
The lunar magma ocean hypothesis suggests that the primordial KREEP(an acronym of potassium(K),rare earth element(REE),and phosphorus(P))was the final product of fractional crystallization.However,the primordial KREEP...The lunar magma ocean hypothesis suggests that the primordial KREEP(an acronym of potassium(K),rare earth element(REE),and phosphorus(P))was the final product of fractional crystallization.However,the primordial KREEP(a.k.a.urKREEP)has never been identified in previous lunar samples or meteorites.The Moon is the focus of many countries’and agencies’space exploration plans,and with the advancement of technology,crewed missions have been proposed.We propose two candidate landing sites,located respectively in the northwest(9.5°W,0.9°S)and southeast(11.1°W,6.2°S)of Lalande crater(8.6°W,4.5°S),for future crewed missions,with the primary goal of sampling the speculated urKREEP.Both sites are situated on the Th-(a critical marker of KREEP)and silica-rich Lalande ejecta in the Mare Insularum and Mare Nubium,respectively.Their geolocations at the low latitude on the lunar nearside,the flat surface,and the low rock abundance suggest the sites are safe for landing and meet the needs of real-time Earth-Moon communication.The astronauts could perform many extravehicular activities,such as collecting KREEP-rich samples,screening clast samples,and drilling regolith cores,to gather a variety of samples,such as Lalande ejecta,basalts,Copernicus ejecta,and regolith.The returned samples are valuable to explore the speculated urKREEP,to reveal the relationship between heat-producing elements and volcanism,to refine the lunar cratering chronology function,and to investigate volatiles in the regolith.展开更多
Lymphomas represent one of the most common malignant diseases in young men and an important issue is how treatments will affect their reproductive health.It has been hypothesized that chemotherapies,similarly to envir...Lymphomas represent one of the most common malignant diseases in young men and an important issue is how treatments will affect their reproductive health.It has been hypothesized that chemotherapies,similarly to environmental chemicals,may alter the spermatogenic epigenome.Here,we report the genomic and epigenomic profiling of the sperm DNA from a 31-year-old Hodgkin lymphoma patient who faced recurrent spontaneous miscarriages in his couple 11-26 months after receiving chemotherapy with adriamycin,bleomycin,vinblastine,and dacarbazine(ABVD).In order to capture the potential deleterious impact of the ABVD treatment on mutational and methylation changes,we compared sperm DNA before and 26 months after chemotherapy with whole-genome sequencing(WGS)and reduced representation bisulfite sequencing(RRBS).The WGS analysis identified 403 variants following ABVD treatment,including 28 linked to genes crucial for embryogenesis.However,none were found in coding regions,indicating no impact of chemotherapy on protein function.The RRBS analysis identified 99high-quality differentially methylated regions(hqDMRs)for which methylation status changed upon chemotherapy.Those hqDRMs were associated with 87 differentially methylated genes,among which 14 are known to be important or expressed during embryo development.While no variants were detected in coding regions,promoter regions of several genes potentially important for embryo development contained variants or displayed an altered methylated status.These might in turn modify the corresponding gene expression and thus affect their function during key stages of embryogenesis,leading to potential developmental disorders or miscarriages.展开更多
基金supported by the Financial Supports of the National Natural Science Foundation of China(Nos.51508056,52370030 and 42007352)the Chongqing Postgraduate Joint Training Base Project(No.JDLHPYJD2022005)the special fund of Henan Key Labora-tory of Water Pollution Control and Rehabilitation Technology(No.CJSZ2024001).
文摘This study developed a novel heterogeneous Vis-Photo+Fenton-like system by integrating visible-light-responsive Co_(3)O_(4)/TiO_(2) photocatalysis with peroxymonosulfate(PMS)activation for efficient atrazine(ATZ)degradation.The synergistic process achieved complete ATZ removal within 60 min under near-neutral pH(6.9),outperform-ing individual Fenton-like(39%)and photocatalytic(24%)processes.Key factors influencing the degradation efficiency included light sources(UV>visible),pH(optimal at 6.9),catalyst dosage(0.01 g Co_(3)O_(4)/TiO_(2)),and PMS:ATZ molar ratio(1:2).The system exhibited a synergistic coefficient of 5.03(degradation)and 1.97(miner-alization),attributed to enhanced radical generation and accelerated Co^(3+)/Co^(2+)redox cycling through photoin-duced electron transfer.Intermediate analysis revealed dealkylation,dechlorination,and oxidation pathways,with reduced toxicity of by-products(e.g.,CEAT,CIAT)confirmed by ecotoxicity assessments.The mineralization efficiency(Vis-Photo+Fenton-like)reached 83.1%,significantly higher than that of standalone processes(Fenton-like:43.2%;photocatalysis:30.5%).The catalyst demonstrated excellent stability(nearly 90%recov-ery,<1μg/L Co leaching)and practical applicability.This study provides an efficient,sludge-free,and solar-compatible strategy for eliminating persistent herbicides in water treatment.
文摘Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.
文摘What does the impact of migration mean in a social context?This article aims to see migrants by taking their stories into account.Through the individual stories,the readers can see the way in which a bigger picture is emerged in terms of community,society and nation.By doing so,the authors want to show the way in which migrants integrate into a society,including both migrants having a residence permit and those who are undocumented,as these two groups of people do differ greatly.The cultural clash does mean the inner struggle and the outer struggle that a society has to confront with.
基金supported by the National Postdoctoral Fellowship of the Science and Engineering Research Board(SERB),Department of Science and Technology(DST),Government of India,File No.PDF/2021/003491。
文摘The photon region surrounding a black hole is crucial for distant observers to receive the emitted spectrum from its vicinity.This paper investigates the optical features of a regular spinning antide Sitter(AdS)black hole.These kinds of black holes hold deviation parameter k,and the cosmological constant A including their mass M and spin a.The cosmological parameter depends on the curvature radius by A=-3/l~2.We investigate the structure of geodesics for unstable circular orbits of photons as observed by an observer at specific Boyer-Lindquist coordinates(r_(O),v_(O))in the region between the outer and cosmological horizon,so-called the domain of outer communication.Our investigations include the analysis of three observables from its shadow plot:the black hole shadow radius(R_(s)),the distortion of the black hole(δ_(s)),and shadow area A.With the help of these observables,we calculate the angular diameter of the apparent size of the shadow.The shadows cast by spinning regular spacetimes are smaller compared to those produced by rotating black holes in both general relativity and regular spacetimes.We also calculate the rate at which energy is emitted from the black hole.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R343),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabiathe Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia for funding this research work through the project number NBU-FFR-2025-1092-10.
文摘As quantum computing continues to advance,traditional cryptographic methods are increasingly challenged,particularly when it comes to securing critical systems like Supervisory Control andData Acquisition(SCADA)systems.These systems are essential for monitoring and controlling industrial operations,making their security paramount.A key threat arises from Shor’s algorithm,a powerful quantum computing tool that can compromise current hash functions,leading to significant concerns about data integrity and confidentiality.To tackle these issues,this article introduces a novel Quantum-Resistant Hash Algorithm(QRHA)known as the Modular Hash Learning Algorithm(MHLA).This algorithm is meticulously crafted to withstand potential quantum attacks by incorporating advanced mathematical and algorithmic techniques,enhancing its overall security framework.Our research delves into the effectiveness ofMHLA in defending against both traditional and quantum-based threats,with a particular emphasis on its resilience to Shor’s algorithm.The findings from our study demonstrate that MHLA significantly enhances the security of SCADA systems in the context of quantum technology.By ensuring that sensitive data remains protected and confidential,MHLA not only fortifies individual systems but also contributes to the broader efforts of safeguarding industrial and infrastructure control systems against future quantumthreats.Our evaluation demonstrates that MHLA improves security by 38%against quantumattack simulations compared to traditional hash functionswhilemaintaining a computational efficiency ofO(m⋅n⋅k+v+n).The algorithm achieved a 98%success rate in detecting data tampering during integrity testing.These findings underline MHLA’s effectiveness in enhancing SCADA system security amidst evolving quantum technologies.This research represents a crucial step toward developing more secure cryptographic systems that can adapt to the rapidly changing technological landscape,ultimately ensuring the reliability and integrity of critical infrastructure in an era where quantum computing poses a growing risk.
基金funded by the deanship of scientific research(DSR),King Abdulaziz University,Jeddah,under grant No.(G-1436-611-309).
文摘Cardiovascular diseases(CVDs)remain one of the foremost causes of death globally;hence,the need for several must-have,advanced automated diagnostic solutions towards early detection and intervention.Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability.To counter this limitation,this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat.We employ FastAI vision-learner-based convolutional neural networks(CNNs)that include ResNet,DenseNet,VGG,ConvNeXt,SqueezeNet,and AlexNet to classify heart sound recordings.Instead of raw waveform analysis,the proposed approach transforms preprocessed cardiovascular audio signals into spectrograms,which are suited for capturing temporal and frequency-wise patterns.The models are trained on the PASCAL Cardiovascular Challenge dataset while taking into consideration the recording variations,noise levels,and acoustic distortions.To demonstrate generalization,external validation using Google’s Audio set Heartbeat Sound dataset was performed using a dataset rich in cardiovascular sounds.Comparative analysis revealed that DenseNet-201,ConvNext Large,and ResNet-152 could deliver superior performance to the other architectures,achieving an accuracy of 81.50%,a precision of 85.50%,and an F1-score of 84.50%.In the process,we performed statistical significance testing,such as the Wilcoxon signed-rank test,to validate performance improvements over traditional classification methods.Beyond the technical contributions,the research underscores clinical integration,outlining a pathway in which the proposed system can augment conventional electronic stethoscopes and telemedicine platforms in the AI-assisted diagnostic workflows.We also discuss in detail issues of computational efficiency,model interpretability,and ethical considerations,particularly concerning algorithmic bias stemming from imbalanced datasets and the need for real-time processing in clinical settings.The study describes a scalable,automated system combining deep learning,feature extraction using spectrograms,and external validation that can assist healthcare providers in the early and accurate detection of cardiovascular disease.AI-driven solutions can be viable in improving access,reducing delays in diagnosis,and ultimately even the continued global burden of heart disease.
基金funded by Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah under grant No.(RG-6-611-43)the authors,therefore,acknowledge with thanks DSR technical and financial support.
文摘The integration of the Internet of Things(IoT)into healthcare systems improves patient care,boosts operational efficiency,and contributes to cost-effective healthcare delivery.However,overcoming several associated challenges,such as data security,interoperability,and ethical concerns,is crucial to realizing the full potential of IoT in healthcare.Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems.In this context,this paper presents a novelmethod for healthcare data privacy analysis.The technique is based on the identification of anomalies in cloud-based Internet of Things(IoT)networks,and it is optimized using explainable artificial intelligence.For anomaly detection,the Radial Boltzmann Gaussian Temporal Fuzzy Network(RBGTFN)is used in the process of doing information privacy analysis for healthcare data.Remora Colony SwarmOptimization is then used to carry out the optimization of the network.The performance of the model in identifying anomalies across a variety of healthcare data is evaluated by an experimental study.This evaluation suggested that themodel measures the accuracy,precision,latency,Quality of Service(QoS),and scalability of themodel.A remarkable 95%precision,93%latency,89%quality of service,98%detection accuracy,and 96%scalability were obtained by the suggested model,as shown by the subsequent findings.
基金supported by the National Key R&D Pro-gram of China(Grant No.2023YFE0114600)The National Natural Science Foundation of China(NSFC)(Grant No.52477029)Joint Laboratory of China-Morocco Green Energy and Advanced Materials,The Youth Innovation Team of Shaanxi Universities and The Xi’an City Science and Technology Project(No.23GXFW0070)。
文摘Floating photovoltaic(FPV)technology is emerging as a highly promising approach to accelerate decarbonization of the global economy,due to its higher power generation efficiency and lower land occupation.With the rapid development of FPV technology,the mechanical performance degradation of key components caused by the harsh marine environment has become a pressing issue,as it significantly contributes to failure behavior observed in FPV systems.A comprehensive compilation of the mechanical performance of key components in FPV systems is also currently unavailable.Here,the mechanical behavior of each structural component in FPV systems under harsh marine environments is systematically reviewed.It further emphasizes the synergistic effects of mechanical performance degradation among different components on the overall system.The drop-off rate(v)of normalized elongation at break(EAB)of polymer under the synergistic effect of various environmental factors increases from 7.5×10^(−4)h^(−1)to 21.8×10^(−4)h^(−1)compared with the single environmental stress.Moreover,the development of novel materials and innovative mechanical structures applied in FPV systems to enhance mechanical performance is discussed.The novel flexible PV modules applied in FPV systems minimize the loads acting on the mooring lines by 80%and increase power generation by 5%.Notably,this paper provides a theoretical foundation for developing standards of FPV systems,especially the establishment of standards related to the synergistic effects of the mechanical performance degradation of different key components on FPV systems.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFF0503104)the National Natural Science Foundation of China(Grant Nos.42241111,62227901,and 42441826)+1 种基金the Macao Young Scholars Program(Grant No.AM201902)the Key Research Program of the Institute of Geology and Geophysics,Chinese Academy of Sciences(Grant No.IGGCAS-202401).
文摘The lunar magma ocean hypothesis suggests that the primordial KREEP(an acronym of potassium(K),rare earth element(REE),and phosphorus(P))was the final product of fractional crystallization.However,the primordial KREEP(a.k.a.urKREEP)has never been identified in previous lunar samples or meteorites.The Moon is the focus of many countries’and agencies’space exploration plans,and with the advancement of technology,crewed missions have been proposed.We propose two candidate landing sites,located respectively in the northwest(9.5°W,0.9°S)and southeast(11.1°W,6.2°S)of Lalande crater(8.6°W,4.5°S),for future crewed missions,with the primary goal of sampling the speculated urKREEP.Both sites are situated on the Th-(a critical marker of KREEP)and silica-rich Lalande ejecta in the Mare Insularum and Mare Nubium,respectively.Their geolocations at the low latitude on the lunar nearside,the flat surface,and the low rock abundance suggest the sites are safe for landing and meet the needs of real-time Earth-Moon communication.The astronauts could perform many extravehicular activities,such as collecting KREEP-rich samples,screening clast samples,and drilling regolith cores,to gather a variety of samples,such as Lalande ejecta,basalts,Copernicus ejecta,and regolith.The returned samples are valuable to explore the speculated urKREEP,to reveal the relationship between heat-producing elements and volcanism,to refine the lunar cratering chronology function,and to investigate volatiles in the regolith.
文摘Lymphomas represent one of the most common malignant diseases in young men and an important issue is how treatments will affect their reproductive health.It has been hypothesized that chemotherapies,similarly to environmental chemicals,may alter the spermatogenic epigenome.Here,we report the genomic and epigenomic profiling of the sperm DNA from a 31-year-old Hodgkin lymphoma patient who faced recurrent spontaneous miscarriages in his couple 11-26 months after receiving chemotherapy with adriamycin,bleomycin,vinblastine,and dacarbazine(ABVD).In order to capture the potential deleterious impact of the ABVD treatment on mutational and methylation changes,we compared sperm DNA before and 26 months after chemotherapy with whole-genome sequencing(WGS)and reduced representation bisulfite sequencing(RRBS).The WGS analysis identified 403 variants following ABVD treatment,including 28 linked to genes crucial for embryogenesis.However,none were found in coding regions,indicating no impact of chemotherapy on protein function.The RRBS analysis identified 99high-quality differentially methylated regions(hqDMRs)for which methylation status changed upon chemotherapy.Those hqDRMs were associated with 87 differentially methylated genes,among which 14 are known to be important or expressed during embryo development.While no variants were detected in coding regions,promoter regions of several genes potentially important for embryo development contained variants or displayed an altered methylated status.These might in turn modify the corresponding gene expression and thus affect their function during key stages of embryogenesis,leading to potential developmental disorders or miscarriages.
