Medical image segmentation has consistently been a significant topic of research and a prominent goal,particularly in computer vision.Brain tumor research plays a major role in medical imaging applications by providin...Medical image segmentation has consistently been a significant topic of research and a prominent goal,particularly in computer vision.Brain tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy preparation.To prevent or minimize manual segmentation error,automated tumor segmentation,and detection became the most demanding process for radiologists and physicians as the tumor often has complex structures.Many methods for detection and segmentation presently exist,but all lack high accuracy.This paper’s key contribution focuses on evaluating machine learning techniques that are supposed to reduce the effect of frequently found issues in brain tumor research.Furthermore,attention concentrated on the challenges related to level set segmentation.The study proposed in this paper uses the Population-based Artificial Bee Colony Clustering(P-ABCC)methodology to reliably collect initial contour points,which helps minimize the number of iterations and segmentation errors of the level-set process.The proposed model measures cluster centroids(ABC populations)and uses a level-set approach to resolve contour differences as brain tumors vary as they have irregular form,structure,and volume.The suggested model comprises of three major steps:first,pre-processing to separate the brain from the head and improves contrast stretching.Secondly,P-ABCC is used to obtain tumor edges that are utilized as an initial MRI sequence contour.The level-set segmentation is then used to detect tumor regions from all volume slices with fewer iterations.Results suggest improved model efficiency compared to state-of-the-art methods for both datasets BRATS 2019 and BRATS 2017.At BRATS 2019,dice progress was achieved for Entire Tumor(WT),Tumor Center(TC),and Improved Tumor(ET)by 0.03%,0.03%,and 0.01%respectively.At BRATS 2017,an increase in precision for WT was reached by 5.27%.展开更多
Monitoring techniques are a key technology for examining the conditions in various scenarios, e.g., structural conditions, weather conditions, and disasters. In order to understand such scenarios, the appropriate extr...Monitoring techniques are a key technology for examining the conditions in various scenarios, e.g., structural conditions, weather conditions, and disasters. In order to understand such scenarios, the appropriate extraction of their features from observation data is important. This paper proposes a monitoring method that allows sound environments to be expressed as a sound pattern. To this end, the concept of synesthesia is exploited. That is, the keys, tones, and pitches of the monitored sound are expressed using the three elements of color, that is, the hue, saturation, and brightness, respectively. In this paper, it is assumed that the hue, saturation, and brightness can be detected from the chromagram, sonogram, and sound spectrogram, respectively, based on a previous synesthesia experiment. Then, the sound pattern can be drawn using color, yielding a “painted sound map.” The usefulness of the proposed monitoring technique is verified using environmental sound data observed at a galleria.展开更多
To establish a proper evaluation method for spatial cognitive deficits and a form of developmental disorder, we have used an immersive virtual reality (VR) device to develop a game that involves actually walking throu...To establish a proper evaluation method for spatial cognitive deficits and a form of developmental disorder, we have used an immersive virtual reality (VR) device to develop a game that involves actually walking through a VR space to search for a target object. In this paper, we presented the results of control experiment with 22 healthy elementary school students as participants. The complexity of the VR space was controlled according to the number of pillars present and whether an overall view was possible (controlled by the height of the pillars). For each set of conditions, 24 trials were performed, and the route and time taken to search for the target were recorded. The starting point was changed in each subsequent trial. Results showed that the search time decreased as the number of trials increase, suggesting a process whereby a cognitive map was formed. We also compared the present results to results from our previous experiment with university students using the same experimental conditions, and we discussed the influence of developmental stage on spatial cognition.展开更多
In this paper, we have presented the numerical investigation of the geometric phase and field entropy squeezing for a two-level system interacting with coherent field under decoherence effect during the time evolution...In this paper, we have presented the numerical investigation of the geometric phase and field entropy squeezing for a two-level system interacting with coherent field under decoherence effect during the time evolution. The effects of the initial state setting and atomic dissipation damping parameter on the evolution of the geometric phase and entropy squeezing have been examined. We have reported some new results related to the periodicity and regularity of geometric phase and entropy squeezing.展开更多
Spatial scan statistics is one of the most important models in order to detect high activity or hotspots in real world applications such as epidemiology,public health,astronomy and criminology applications on geograph...Spatial scan statistics is one of the most important models in order to detect high activity or hotspots in real world applications such as epidemiology,public health,astronomy and criminology applications on geographic data.Traditional scan statistic uses regular shapes like circles to detect areas of high activity;the same model was extended to eclipses to improve the model.More recent works identify irregular shaped hotspots for data with geographical boundaries,where information about population within the geographical boundaries is available.With the introduction of better mapping technology,mapping individual cases to latitude and longitude became easier compared to aggregated data for which the previous models were developed.We propose an approach of spatial hotspot detection for point data set with no geographical boundary information.Our algorithm detects hotspots as a polygon made up of a set of triangles that are computed by a Polygon Propagation algorithm.The time complexity of the algorithm is non-linear to the number of observations,which does not scale well for larger datasets.To improve the model,we also introduce a MapReduce version of our algorithm to identify hotspots for larger datasets.展开更多
COVID-19 is spreading within the sort of an enormous epidemic for the globe.This epidemic infects a lot of individuals in Egypt.The World Health Organization states that COVID-19 could be spread from one person to ano...COVID-19 is spreading within the sort of an enormous epidemic for the globe.This epidemic infects a lot of individuals in Egypt.The World Health Organization states that COVID-19 could be spread from one person to another at a very fast speed through contact and respiratory spray.On these days,Egypt and all countries worldwide should rise to an effective step to investigate this disease and eliminate the effects of this epidemic.In this paper displayed,the real database of COVID-19 for Egypt has been analysed from February 15,2020,to June 15,2020,and predicted with the number of patients that will be infected with COVID-19,and estimated the epidemic final size.Several regression analysis models have been applied for data analysis of COVID-19 of Egypt.In this study,we’ve been applied seven regression analysis-based models that are exponential polynomial,quadratic,thirddegree,fourth-degree,fifth-degree,sixth-degree,and logit growth respectively for the COVID-19 dataset.Thus,the exponential,fourth-degree,fifth-degree,and sixth-degree polynomial regression models are excellent models specially fourth-degree model that will help the government preparing their procedures for one month.In addition,we have applied the well-known logit growth regression model and we obtained the following epidemiological insights:Firstly,the epidemic peak could possibly reach at 22-June 2020 and final time of epidemic at 8-September 2020.Secondly,the final total size for cases 1.6676Et05 cases.The action from government of interevent over a relatively long interval is necessary to minimize the final epidemic size.展开更多
It is unequivocal that human influence has warmed the planet,which is seriously affecting the planetary health including human health.Adapting climate change should not only be a slogan,but requires a united,holistic ...It is unequivocal that human influence has warmed the planet,which is seriously affecting the planetary health including human health.Adapting climate change should not only be a slogan,but requires a united,holistic action and a paradigm shift from crisis response to an ambitious and integrated approach immediately.Recognizing the urgent needs to tackle the risk connection between climate change and One Health,the four key messages and recommendations that with the intent to guide further research and to promote international cooperation to achieve a more climate-resilient world are provided.展开更多
Plastic offers a new niche for microorganisms,the plastisphere.The everincreasing emission of plastic waste makes it critical to understand the microbial ecology of the plastisphere and associated effects.Here,we pres...Plastic offers a new niche for microorganisms,the plastisphere.The everincreasing emission of plastic waste makes it critical to understand the microbial ecology of the plastisphere and associated effects.Here,we present a global fingerprint of the plastisphere,analyzing samples collected from freshwater,seawater,and terrestrial ecosystems.The plastisphere assembles a distinct microbial community that has a clearly higher heterogeneity and a more deterministically dominated assembly compared to natural habitats.New coexistence patterns—loose and fragile networks with mostly specialist linkages among microorganisms that are rarely found in natural habitats—are seen in the plastisphere.Plastisphere microbiomes generally have a great potential to metabolize organic compounds,which could accelerate carbon turnover.Microorganisms involved in the nitrogen cycle are also altered in the plastisphere,especially in freshwater plastispheres,where a high abundance of denitrifiers may increase the release of nitrite(aquatic toxicant)and nitrous oxide(greenhouse gas).Enrichment of animal,plant,and human pathogens means that the plastisphere could become an increasingly mobile reservoir of harmful microorganisms.Our findings highlight that if the trajectory of plastic emissions is not reversed,the expanding plastisphere could pose critical planetary health challenges.展开更多
文摘Medical image segmentation has consistently been a significant topic of research and a prominent goal,particularly in computer vision.Brain tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy preparation.To prevent or minimize manual segmentation error,automated tumor segmentation,and detection became the most demanding process for radiologists and physicians as the tumor often has complex structures.Many methods for detection and segmentation presently exist,but all lack high accuracy.This paper’s key contribution focuses on evaluating machine learning techniques that are supposed to reduce the effect of frequently found issues in brain tumor research.Furthermore,attention concentrated on the challenges related to level set segmentation.The study proposed in this paper uses the Population-based Artificial Bee Colony Clustering(P-ABCC)methodology to reliably collect initial contour points,which helps minimize the number of iterations and segmentation errors of the level-set process.The proposed model measures cluster centroids(ABC populations)and uses a level-set approach to resolve contour differences as brain tumors vary as they have irregular form,structure,and volume.The suggested model comprises of three major steps:first,pre-processing to separate the brain from the head and improves contrast stretching.Secondly,P-ABCC is used to obtain tumor edges that are utilized as an initial MRI sequence contour.The level-set segmentation is then used to detect tumor regions from all volume slices with fewer iterations.Results suggest improved model efficiency compared to state-of-the-art methods for both datasets BRATS 2019 and BRATS 2017.At BRATS 2019,dice progress was achieved for Entire Tumor(WT),Tumor Center(TC),and Improved Tumor(ET)by 0.03%,0.03%,and 0.01%respectively.At BRATS 2017,an increase in precision for WT was reached by 5.27%.
文摘Monitoring techniques are a key technology for examining the conditions in various scenarios, e.g., structural conditions, weather conditions, and disasters. In order to understand such scenarios, the appropriate extraction of their features from observation data is important. This paper proposes a monitoring method that allows sound environments to be expressed as a sound pattern. To this end, the concept of synesthesia is exploited. That is, the keys, tones, and pitches of the monitored sound are expressed using the three elements of color, that is, the hue, saturation, and brightness, respectively. In this paper, it is assumed that the hue, saturation, and brightness can be detected from the chromagram, sonogram, and sound spectrogram, respectively, based on a previous synesthesia experiment. Then, the sound pattern can be drawn using color, yielding a “painted sound map.” The usefulness of the proposed monitoring technique is verified using environmental sound data observed at a galleria.
文摘To establish a proper evaluation method for spatial cognitive deficits and a form of developmental disorder, we have used an immersive virtual reality (VR) device to develop a game that involves actually walking through a VR space to search for a target object. In this paper, we presented the results of control experiment with 22 healthy elementary school students as participants. The complexity of the VR space was controlled according to the number of pillars present and whether an overall view was possible (controlled by the height of the pillars). For each set of conditions, 24 trials were performed, and the route and time taken to search for the target were recorded. The starting point was changed in each subsequent trial. Results showed that the search time decreased as the number of trials increase, suggesting a process whereby a cognitive map was formed. We also compared the present results to results from our previous experiment with university students using the same experimental conditions, and we discussed the influence of developmental stage on spatial cognition.
文摘In this paper, we have presented the numerical investigation of the geometric phase and field entropy squeezing for a two-level system interacting with coherent field under decoherence effect during the time evolution. The effects of the initial state setting and atomic dissipation damping parameter on the evolution of the geometric phase and entropy squeezing have been examined. We have reported some new results related to the periodicity and regularity of geometric phase and entropy squeezing.
基金partially supported by the National Science Foundation under[grant number IIP-1160958],[grant number CNS-1650551],and[grant number CNS-1429526].
文摘Spatial scan statistics is one of the most important models in order to detect high activity or hotspots in real world applications such as epidemiology,public health,astronomy and criminology applications on geographic data.Traditional scan statistic uses regular shapes like circles to detect areas of high activity;the same model was extended to eclipses to improve the model.More recent works identify irregular shaped hotspots for data with geographical boundaries,where information about population within the geographical boundaries is available.With the introduction of better mapping technology,mapping individual cases to latitude and longitude became easier compared to aggregated data for which the previous models were developed.We propose an approach of spatial hotspot detection for point data set with no geographical boundary information.Our algorithm detects hotspots as a polygon made up of a set of triangles that are computed by a Polygon Propagation algorithm.The time complexity of the algorithm is non-linear to the number of observations,which does not scale well for larger datasets.To improve the model,we also introduce a MapReduce version of our algorithm to identify hotspots for larger datasets.
文摘COVID-19 is spreading within the sort of an enormous epidemic for the globe.This epidemic infects a lot of individuals in Egypt.The World Health Organization states that COVID-19 could be spread from one person to another at a very fast speed through contact and respiratory spray.On these days,Egypt and all countries worldwide should rise to an effective step to investigate this disease and eliminate the effects of this epidemic.In this paper displayed,the real database of COVID-19 for Egypt has been analysed from February 15,2020,to June 15,2020,and predicted with the number of patients that will be infected with COVID-19,and estimated the epidemic final size.Several regression analysis models have been applied for data analysis of COVID-19 of Egypt.In this study,we’ve been applied seven regression analysis-based models that are exponential polynomial,quadratic,thirddegree,fourth-degree,fifth-degree,sixth-degree,and logit growth respectively for the COVID-19 dataset.Thus,the exponential,fourth-degree,fifth-degree,and sixth-degree polynomial regression models are excellent models specially fourth-degree model that will help the government preparing their procedures for one month.In addition,we have applied the well-known logit growth regression model and we obtained the following epidemiological insights:Firstly,the epidemic peak could possibly reach at 22-June 2020 and final time of epidemic at 8-September 2020.Secondly,the final total size for cases 1.6676Et05 cases.The action from government of interevent over a relatively long interval is necessary to minimize the final epidemic size.
基金Shanghai International Science and Technology Partnership Project(No. 21230780200)。
文摘It is unequivocal that human influence has warmed the planet,which is seriously affecting the planetary health including human health.Adapting climate change should not only be a slogan,but requires a united,holistic action and a paradigm shift from crisis response to an ambitious and integrated approach immediately.Recognizing the urgent needs to tackle the risk connection between climate change and One Health,the four key messages and recommendations that with the intent to guide further research and to promote international cooperation to achieve a more climate-resilient world are provided.
基金This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB40020102)the National Natural Science Foundation of China(22193063,32071523,and 42007229)+3 种基金the State Key Laboratory of Marine Pollution Collaborative Research Fund(SKLMP/CRF/0004 and SKLMP/SCRF/0030)the Hong Kong Branch of the Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)Open Collaborative Research Fund(SMSEGL20SC02)the Hong Kong LNG Terminal Marine Conservation Enhancement Fund(MCEF20030)the Start-up Funds of The Hong Kong Polytechnic University(P0036173 and P0038311).C.L.acknowledges support from the Distinguished Postdoctoral Fellowship of The Hong Kong Polytechnic University(1-YWCE).We are grateful to all of the principal investigators for uploading sequencing data as an open access resource.We also thank Mr.Lifei Wang of Shandong University for his contribution in the sampling process,Miss Yanfei Wang of Shanghai University of Electric Power for her input in programming,Prof.Beat Frey of Snow and Landscape Research(WSL)for kindly providing information on the samples,and Prof.Huijun Xie of Shandong University,Prof.Yong-Xin Liu of the Chinese Academy of Agricultural Sciences,and Dr.Robyn J.Wright of Dalhousie University for their constructive comments on the manuscript.
文摘Plastic offers a new niche for microorganisms,the plastisphere.The everincreasing emission of plastic waste makes it critical to understand the microbial ecology of the plastisphere and associated effects.Here,we present a global fingerprint of the plastisphere,analyzing samples collected from freshwater,seawater,and terrestrial ecosystems.The plastisphere assembles a distinct microbial community that has a clearly higher heterogeneity and a more deterministically dominated assembly compared to natural habitats.New coexistence patterns—loose and fragile networks with mostly specialist linkages among microorganisms that are rarely found in natural habitats—are seen in the plastisphere.Plastisphere microbiomes generally have a great potential to metabolize organic compounds,which could accelerate carbon turnover.Microorganisms involved in the nitrogen cycle are also altered in the plastisphere,especially in freshwater plastispheres,where a high abundance of denitrifiers may increase the release of nitrite(aquatic toxicant)and nitrous oxide(greenhouse gas).Enrichment of animal,plant,and human pathogens means that the plastisphere could become an increasingly mobile reservoir of harmful microorganisms.Our findings highlight that if the trajectory of plastic emissions is not reversed,the expanding plastisphere could pose critical planetary health challenges.
