Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effe...Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effects.This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-termisolator life.In this study,an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time.With the developed model,the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity,and the decrease in the damping property was tried to be determined.For this purpose,a data set was created by collecting the behavior of structures with single degrees of freedom(SDOF),different stiffness,damping ratio and natural period isolated from the foundation under far fault earthquakes.The data is divided into 5 different damping classes varying between 10%and 50%.Machine learning model was trained in damping classes with the data on the structure’s response to random seismic vibrations.As a result of the isolator behavior under randomly selected earthquakes,the recorded motion and structural acceleration of the structure against any seismic vibration were examined,and the decrease in the damping capacity was estimated on a class basis.The performance loss of the isolators,which are separated according to their damping properties,has been tried to be determined,and the reductions in the amounts to be taken into account have been determined by class.In the developed prediction model,using various supervised machine learning classification algorithms,the classification algorithm providing the highest precision for the model has been decided.When the results are examined,it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%,and it is an effective method in deciding whether there is a decrease in the damping capacity.展开更多
UAV marine monitoring plays an essential role in marine environmental protection because of its flexibility and convenience,low cost and convenient maintenance.In marine environmental monitoring,the similarity between...UAV marine monitoring plays an essential role in marine environmental protection because of its flexibility and convenience,low cost and convenient maintenance.In marine environmental monitoring,the similarity between objects such as oil spill and sea surface,Spartina alterniflora and algae is high,and the effect of the general segmentation algorithm is poor,which brings new challenges to the segmentation of UAV marine images.Panoramic segmentation can do object detection and semantic segmentation at the same time,which can well solve the polymorphism problem of objects in UAV ocean images.Currently,there are few studies on UAV marine image recognition with panoptic segmentation.In addition,there are no publicly available panoptic segmentation datasets for UAV images.In this work,we collect and annotate UAV images to form a panoptic segmentation UAV dataset named UAV-OUC-SEG and propose a panoptic segmentation method named PanopticUAV.First,to deal with the large intraclass variability in scale,deformable convolution and CBAM attention mechanism are employed in the backbone to obtain more accurate features.Second,due to the complexity and diversity of marine images,boundary masks by the Laplacian operator equation from the ground truth are merged into feature maps to improve boundary segmentation precision.Experiments demonstrate the advantages of PanopticUAV beyond the most other advanced approaches on the UAV-OUC-SEG dataset.展开更多
Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)scenarios.Furthermore,becaus...Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)scenarios.Furthermore,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is increased.In this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift velocity.The fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)algorithm.Unlike the previous work,the probability of bit error is expressed in a closed-form expression.It is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano receiver.The simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.展开更多
Formation and deposition of amyloid-beta(Aβ) are considered one of the main drivers of Alzheimer's disease(AD). For more than 30 years, Aβ has challenged researchers through its complex physicochemical propertie...Formation and deposition of amyloid-beta(Aβ) are considered one of the main drivers of Alzheimer's disease(AD). For more than 30 years, Aβ has challenged researchers through its complex physicochemical properties and multiple peptide processing steps that involve several proteases(Andreasson et al., 2007).展开更多
The high costs incurred due to attacks and the increasing number of different devices in the Internet of Things(IoT)highlight the necessity of the early detection of botnets(i.e.,a network of infected devices)to gain ...The high costs incurred due to attacks and the increasing number of different devices in the Internet of Things(IoT)highlight the necessity of the early detection of botnets(i.e.,a network of infected devices)to gain an advantage against attacks.However,early botnet detection is challenging because of continuous malware mutations,the adoption of sophisticated obfuscation techniques,and the massive volume of data.The literature addresses botnet detection by modeling the behavior of malware spread,the classification of malicious traffic,and the analysis of traffic anomalies.This article details ANTE,a system for ANTicipating botnEt signals based on machine learning algorithms.The system adapts itself to different scenarios and detects different types of botnets.It autonomously selects the most appropriate Machine Learning(ML)pipeline for each botnet and improves the classification before an attack effectively begins.The system evaluation follows trace-driven experiments and compares ANTE results to other relevant results from the literature over four representative datasets:ISOT HTTP Botnet,CTU-13,CICDDoS2019,and BoT-IoT.Results show an average detection accuracy of 99.06%and an average bot detection precision of 100%.展开更多
Background:The management of suspected critical congenital heart defects(CCHD)relies on timely echocardiographic diagnosis.The availability of experienced echocardiographers is limited or even non-existent in many hos...Background:The management of suspected critical congenital heart defects(CCHD)relies on timely echocardiographic diagnosis.The availability of experienced echocardiographers is limited or even non-existent in many hospitals with obstetric units.This study evaluates remote-mentored echocardiography performed by physicians without experience in imaging of congenital heart defects(CHD).Methods:The setup included a pediatric cardiologist in a separate room,guiding a physician without experience in echocardiographic imaging of CHD in the examination of a symptomatic newborn.This remote-mentoring pair was blinded to the diagnosis of the newborn and presented with a simplified patient history.The echocardiographic images were streamed to the laptop of the mentor,along with a webcam feed showing the probe position.The task was to identify CCHD in need of immediate transfer to a pediatric cardiac surgical center.The result was compared to the previously completed echocardiographic report and the clinical decision of the patient-responsible pediatric cardiologist.Results:During 17 months,15 newborns were recruited.All six newborns with CCHD were correctly labeled by the remotementoring pair.One newborn with Tetralogy of Fallot was erroneously labeled as needing immediate transfer.Eight newborns without CCHD were correctly labeled.Conclusions:Remote-mentored echocardiography performed by examiners without experience in imaging CHD identified all newborns with CCHD in need of immediate transfer for specialist care.The setup shows promising results for improving the management of CCHD in hospitals without continuous pediatric cardiology service.展开更多
Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways:Prediction and forecast.Prediction is related to explaining past and current data bas...Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways:Prediction and forecast.Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role.Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences.The time series analysis approach has the advantage of being easier to use(in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average).Still,it is limited in forecasting time,unlike the classical models such as Susceptible-Exposed-Infectious-Removed.Its applicability in forecasting comes from its better accuracy for short-term prediction.In its basic form,it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures(governments,companies,etc.).Instead,it estimates from the data directly.Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread;be it school closures,emerging variants,etc.It can be used in mortality or hospital risk estimation from new cases,seroprevalence studies,assessing properties of emerging variants,and estimating excess mortality and its relationship with a pandemic.展开更多
Blood loss in peacetime is mainly due to the normal menstrual cycle in women or diseases with surgical intervention. In wartime, blood loss in military personnel is a characteristic sign of a closed or open injury of ...Blood loss in peacetime is mainly due to the normal menstrual cycle in women or diseases with surgical intervention. In wartime, blood loss in military personnel is a characteristic sign of a closed or open injury of the body during internal or external bleeding. Access to clinical care for wounded military personnel injured on the battlefield is limited and has long delays compared to patients in peacetime. Most of the deaths of wounded military personnel on the battlefield occur within the first hour after being wounded. The most common causes are delay in providing medical care, loss of time for diagnosis, delay in stabilization of pain shock and large blood loss. Some help in overcoming these problems is provided by the data in the individual capsule, which each soldier of the modern army possesses;however, data in an individual capsule is not sufficient to provide emergency medical care in field and hospital conditions. This paper considers a project for development of a smart real-time monitoring wearable system for blood loss and level of shock stress in wounded persons on the battlefield, which provides medical staff in field and hospital conditions with the necessary information to give timely medical care. Although the hospital will require additional information, the basic information about the victims will already be known before he enters the hospital. It is important to emphasize that the key term in this approach is monitoring. It is tracking, and not a one-time measurement of indicators, that is crucial in a valid definition of bleeding.展开更多
BACKGROUND The coronavirus disease 2019(COVID-19)pandemic affects all aspects of our lives,including children.With the advancement of the pandemic,children under five years old are at increased risk of hospitalization...BACKGROUND The coronavirus disease 2019(COVID-19)pandemic affects all aspects of our lives,including children.With the advancement of the pandemic,children under five years old are at increased risk of hospitalization relative to other age groups.This makes it paramount that we develop tools to address the two critical aspects of preserving children's health–new treatment protocols and new predictive models.For those purposes,we need to understand better the effects of COVID-19 on children,and we need to be able to predict the number of affected children as a proportion of the number of infected children.This is why our research focuses on clinical and epidemiological pictures of children with heart damage post-COVID,as a part of the general picture of post-COVID among this age group.AIM To demonstrate the role of children in the COVID-19 spread in Bulgaria and to test the hypothesis that there are no secondary transmissions in schools and from children to adults.METHODS Our modeling and data show with high probability that in Bulgaria,with our current measures,vaccination strategy and contact structure,the pandemic is driven by the children and their contacts in school.RESULTS This makes it paramount that we develop tools to address the two critical aspects of preserving children's health–new treatment protocols and new predictive models.For those purposes,we need to understand better the effects of COVID-19 on children,and we need to be able to predict the number of affected children as a proportion of the number of infected children.This is why our research focuses on clinical and epidemiological pictures of children with heart damage post-COVID,as a part of the general picture of post-Covid among this age group.CONCLUSION Our modeling rejects that hypothesis,and the epidemiological data supports that.We used epidemiological data to support the validity of our modeling.The first summer wave in 2020 from the listed here school proms endorse the idea of transmissions from students to teachers.展开更多
Dietary imbalance and overeating can lead to an increasingly widespread disease-obesity.Aesthetic considerations aside,obesity is defined as an excess of adipose tissue that can lead to serious health problems and can...Dietary imbalance and overeating can lead to an increasingly widespread disease-obesity.Aesthetic considerations aside,obesity is defined as an excess of adipose tissue that can lead to serious health problems and can predispose to a number of pathological changes and clinical diseases,including diabetes;hypertension;atherosclerosis;coronary artery disease and stroke;obstructive sleep apnea;depression;weight-related arthropathies and endometrial and breast cancer.A body weight 20%above ideal for age,gender and height is a severe health risk.Bariatric surgery is a set of surgical methods to treat morbid obesity when other treatments such as diet,increased physical activity,behavioral changes and drugs have failed.The two most common procedures currently used are sleeve gastrectomy and gastric bypass.This procedure has gained popularity recently and is generally considered safe and effective.Although current data show that perioperative mortality is low and better control of comorbidities and short-term complications is achieved,more randomized trials are needed to evaluate the long-term outcomes of bariatric procedures.This review aims to synthesize and summarize the growing evidence on the long-term effectiveness,outcomes and complications of bariatric surgery.展开更多
Computer Algebra Systems have been extensively used in higher education. The reasons are many e.g., visualize mathematical problems, correlate real-world problems on a conceptual level, are flexible, simple to use, ac...Computer Algebra Systems have been extensively used in higher education. The reasons are many e.g., visualize mathematical problems, correlate real-world problems on a conceptual level, are flexible, simple to use, accessible from anywhere, etc. However, there is still room for improvement. Computer algebra system (CAS) optimization is the set of best practices and techniques to keep the CAS running optimally. Best practices are related to how to carry out a mathematical task or configure your system. In this paper, we are going to examine these techniques. The documentation sheets of CASs are the source of data that we used to compare them and examine their characteristics. The research results reveal that there are many tips that we can follow to accelerate performance.展开更多
Online Computer Algebra Systems (CAS) have become increasingly popular among students and teachers. The reasons are many such as being more flexible, simple to use, accessible from anywhere, etc. However, as with any ...Online Computer Algebra Systems (CAS) have become increasingly popular among students and teachers. The reasons are many such as being more flexible, simple to use, accessible from anywhere, etc. However, as with any educational tool, they also have some disadvantages that we should know. The purpose of this study is to analyze advantages and disadvantages of online CASs and propose some techniques to optimize CAS performance in order to reduce weaknesses. The research results reveal that online CAS versions are on the rise but lag in some capabilities in comparison with desktop versions.展开更多
The compliance modeling and rigidity performance evaluation for the lower mobility parallel manipulators are still to be remained as two overwhelming challenges in the stage of conceptual design due to their geometric...The compliance modeling and rigidity performance evaluation for the lower mobility parallel manipulators are still to be remained as two overwhelming challenges in the stage of conceptual design due to their geometric complexities. By using the screw theory, this paper explores the compliance modeling and eigencompliance evaluation of a newly patented 1T2R spindle head whose topological architecture is a 3-RPS parallel mechanism. The kinematic definitions and inverse position analysis are briefly addressed in the first place to provide necessary information for compliance modeling. By considering the 3-RPS parallel kinematic machine(PKM) as a typical compliant parallel device, whose three limb assemblages have bending, extending and torsional deflections, an analytical compliance model for the spindle head is established with screw theory and the analytical stiffness matrix of the platform is formulated. Based on the eigenscrew decomposition, the eigencompliance and corresponding eigenscrews are analyzed and the platform's compliance properties are physically interpreted as the suspension of six screw springs. The distributions of stiffness constants of the six screw springs throughout the workspace are predicted in a quick manner with a piece-by-piece calculation algorithm. The numerical simulation reveals a strong dependency of platform's compliance on its configuration in that they are axially symmetric due to structural features. At the last stage, the effects of some design variables such as structural, configurational and dimensional parameters on system rigidity characteristics are investigated with the purpose of providing useful information for the structural design and performance improvement of the PKM. Compared with previous efforts in compliance analysis of PKMs, the present methodology is more intuitive and universal thus can be easily applied to evaluate the overall rigidity performance of other PKMs with high efficiency.展开更多
Encouraged by next-generation networks and autonomous vehicle systems,vehicular networks must employ advanced technologies to guarantee personal safety,reduce traffic accidents and ease traffic jams.By leveraging the ...Encouraged by next-generation networks and autonomous vehicle systems,vehicular networks must employ advanced technologies to guarantee personal safety,reduce traffic accidents and ease traffic jams.By leveraging the computing ability at the network edge,multi-access edge computing(MEC)is a promising technique to tackle such challenges.Compared to traditional full offloading,partial offloading offers more flexibility in the perspective of application as well as deployment of such systems.Hence,in this paper,we investigate the application of partial computing offloading in-vehicle networks.In particular,by analyzing the structure of many emerging applications,e.g.,AR and online games,we convert the application structure into a sequential multi-component model.Focusing on shortening the application execution delay,we extend the optimization problem from the single-vehicle computing offloading(SVCOP)scenario to the multi-vehicle computing offloading(MVCOP)by taking multiple constraints into account.A deep reinforcement learning(DRL)based algorithm is proposed as a solution to this problem.Various performance evaluation results have shown that the proposed algorithm achieves superior performance as compared to existing offloading mechanisms in deducing application execution delay.展开更多
Neuroinformatics is a fascinating research field that applies computational models and analytical tools to high dimensional experimental neuroscience data for a better understanding of how the brain functions or dysfu...Neuroinformatics is a fascinating research field that applies computational models and analytical tools to high dimensional experimental neuroscience data for a better understanding of how the brain functions or dysfunctions in brain diseases. Neuroinformaticians work in the intersection of neuroscience and informatics supporting the integration of various sub-disciplines(behavioural neuroscience, genetics, cognitive psychology, etc.) working on brain research. Neuroinformaticians are the pathway of information exchange between informaticians and clinicians for a better understanding of the outcome of computational models and the clinical interpretation of the analysis. Machine learning is one of the most significant computational developments in the last decade giving tools to neuroinformaticians and finally to radiologists and clinicians for an automatic and early diagnosis-prognosis of a brain disease. Random forest(RF) algorithm has been successfully applied to high-dimensional neuroimaging data for feature reduction and also has been applied to classify the clinical label of a subject using single or multi-modal neuroimaging datasets. Our aim was to review the studies where RF was applied to correctly predict the Alzheimer's disease(AD), the conversion from mild cognitive impairment(MCI) and its robustness to overfitting, outliers and handling of non-linear data. Finally, we described our RF-based model that gave us the 1 ^(st) position in an international challenge for automated prediction of MCI from MRI data.展开更多
In the past decade,online Peer-to-Peer(P2P)lending platforms have transformed the lending industry,which has been historically dominated by commercial banks.Information technology breakthroughs such as big data-based ...In the past decade,online Peer-to-Peer(P2P)lending platforms have transformed the lending industry,which has been historically dominated by commercial banks.Information technology breakthroughs such as big data-based financial technologies(Fintech)have been identified as important disruptive driving forces for this paradigm shift.In this paper,we take an information economics perspective to investigate how big data affects the transformation of the lending industry.By identifying how signaling and search costs are reduced by big data analytics for credit risk management of P2P lending,we discuss how information asymmetry is reduced in the big data era.Rooted in the lending business,we propose a theory on the economics of big data and outline a number of research opportunities and challenging issues.展开更多
The financial industry has been strongly influenced by digitalization in the past few years reflected by the emergence of“FinTech,”which represents the marriage of“finance”and“information technology.”FinTech pro...The financial industry has been strongly influenced by digitalization in the past few years reflected by the emergence of“FinTech,”which represents the marriage of“finance”and“information technology.”FinTech provides opportunities for the creation of new services and business models and poses challenges to traditional financial service providers.Therefore,FinTech has become a subject of debate among practitioners,investors,and researchers and is highly visible in the popular media.In this study,we unveil the drivers motivating the FinTech phenomenon perceived by the English and German popular press including the subjects discussed in the context of FinTech.This study is the first one to reflect the media perspective on the FinTech phenomenon in the research.In doing so,we extend the growing knowledge on FinTech and contribute to a common understanding in the financial and digital innovation literature.These study contributes to research in the areas of information systems,finance and interdisciplinary social sciences.Moreover,it brings value to practitioners(entrepreneurs,investors,regulators,etc.),who explore the field of FinTech.展开更多
In this work, we analyzed only the patients of the NSTEMI (non ST-segment elevation myocardial infarction) who arrived at the hospital within 12 h after symptoms started. Using NSTEMI follow-up data within, the charac...In this work, we analyzed only the patients of the NSTEMI (non ST-segment elevation myocardial infarction) who arrived at the hospital within 12 h after symptoms started. Using NSTEMI follow-up data within, the characteristics of the clinical data, the risk factor, and the blood tested in the hospital visit were analyzed for MACE (major adverse cardiac events) patients. MACE includes cardiac death, MI (myocardial infarction), Re-PCI, and CABG (coronary artery bypass graft). As a result, from the NSTEMI patients which can be followed up for over 12 m, NT-ProBNP (p=0.014) and age (p=0.045) are found to be the independent risk factors related to MACE. Accordingly, they can be useful for the diagnosis and prognosis for NSTEMI patients as a biomarker.展开更多
In this paper, the inverse problem of reconstructing reflectivity function of a medium is examined within a blind deconvolution framework. The ultrasound pulse is estimated using higher-order statistics, and Wiener fi...In this paper, the inverse problem of reconstructing reflectivity function of a medium is examined within a blind deconvolution framework. The ultrasound pulse is estimated using higher-order statistics, and Wiener filter is used to obtain the ultrasonic reflectivity function through wavelet-based models. A new approach to the parameter estimation of the inverse filtering step is proposed in the nondestructive evaluation field, which is based on the theory of Fourier-Wavelet regularized deconvolution (ForWaRD). This new approach can be viewed as a solution to the open problem of adaptation of the ForWaRD framework to perform the convolution kernel estimation and deconvolution interdependently. The results indicate stable solutions of the esti- mated pulse and an improvement in the radio-frequency (RF) signal taking into account its signal-to-noise ratio (SNR) and axial resolution. Simulations and experiments showed that the proposed approach can provide robust and optimal estimates of the reflectivity function.展开更多
基金the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2020R1A2C1A01011131)the Energy Cloud R&D Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(2019M3F2A1073164).
文摘Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effects.This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-termisolator life.In this study,an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time.With the developed model,the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity,and the decrease in the damping property was tried to be determined.For this purpose,a data set was created by collecting the behavior of structures with single degrees of freedom(SDOF),different stiffness,damping ratio and natural period isolated from the foundation under far fault earthquakes.The data is divided into 5 different damping classes varying between 10%and 50%.Machine learning model was trained in damping classes with the data on the structure’s response to random seismic vibrations.As a result of the isolator behavior under randomly selected earthquakes,the recorded motion and structural acceleration of the structure against any seismic vibration were examined,and the decrease in the damping capacity was estimated on a class basis.The performance loss of the isolators,which are separated according to their damping properties,has been tried to be determined,and the reductions in the amounts to be taken into account have been determined by class.In the developed prediction model,using various supervised machine learning classification algorithms,the classification algorithm providing the highest precision for the model has been decided.When the results are examined,it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%,and it is an effective method in deciding whether there is a decrease in the damping capacity.
基金This work was partially supported by the National Key Research and Development Program of China under Grant No.2018AAA0100400the Natural Science Foundation of Shandong Province under Grants Nos.ZR2020MF131 and ZR2021ZD19the Science and Technology Program of Qingdao under Grant No.21-1-4-ny-19-nsh.
文摘UAV marine monitoring plays an essential role in marine environmental protection because of its flexibility and convenience,low cost and convenient maintenance.In marine environmental monitoring,the similarity between objects such as oil spill and sea surface,Spartina alterniflora and algae is high,and the effect of the general segmentation algorithm is poor,which brings new challenges to the segmentation of UAV marine images.Panoramic segmentation can do object detection and semantic segmentation at the same time,which can well solve the polymorphism problem of objects in UAV ocean images.Currently,there are few studies on UAV marine image recognition with panoptic segmentation.In addition,there are no publicly available panoptic segmentation datasets for UAV images.In this work,we collect and annotate UAV images to form a panoptic segmentation UAV dataset named UAV-OUC-SEG and propose a panoptic segmentation method named PanopticUAV.First,to deal with the large intraclass variability in scale,deformable convolution and CBAM attention mechanism are employed in the backbone to obtain more accurate features.Second,due to the complexity and diversity of marine images,boundary masks by the Laplacian operator equation from the ground truth are merged into feature maps to improve boundary segmentation precision.Experiments demonstrate the advantages of PanopticUAV beyond the most other advanced approaches on the UAV-OUC-SEG dataset.
基金the Researchers Supporting Project Number(RSP2023R 102)King Saud University,Riyadh,Saudi Arabia.
文摘Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)scenarios.Furthermore,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is increased.In this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift velocity.The fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)algorithm.Unlike the previous work,the probability of bit error is expressed in a closed-form expression.It is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano receiver.The simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.
基金Ministerium für Wissenschaft und Gesundheit (MWG),Rheinland Pfalz,Neurodeg X Forschungskolleg (to BB)。
文摘Formation and deposition of amyloid-beta(Aβ) are considered one of the main drivers of Alzheimer's disease(AD). For more than 30 years, Aβ has challenged researchers through its complex physicochemical properties and multiple peptide processing steps that involve several proteases(Andreasson et al., 2007).
基金This work was supported by National Council for Scientific and Technological Development(CNPq/Brazil)grants#309129/2017-6 and#432204/2018-0,by Sao Paulo Research Foundation(FAPESP)+2 种基金grant#2018/23098-0,by the Coordination for the Improvement of Higher Education Personnel CAPES/Brazilgrants#88887.501287/2020-00 and#88887.509309/2020–00by the National Teaching and Research Network(RNP)by the GT-Periscope project.
文摘The high costs incurred due to attacks and the increasing number of different devices in the Internet of Things(IoT)highlight the necessity of the early detection of botnets(i.e.,a network of infected devices)to gain an advantage against attacks.However,early botnet detection is challenging because of continuous malware mutations,the adoption of sophisticated obfuscation techniques,and the massive volume of data.The literature addresses botnet detection by modeling the behavior of malware spread,the classification of malicious traffic,and the analysis of traffic anomalies.This article details ANTE,a system for ANTicipating botnEt signals based on machine learning algorithms.The system adapts itself to different scenarios and detects different types of botnets.It autonomously selects the most appropriate Machine Learning(ML)pipeline for each botnet and improves the classification before an attack effectively begins.The system evaluation follows trace-driven experiments and compares ANTE results to other relevant results from the literature over four representative datasets:ISOT HTTP Botnet,CTU-13,CICDDoS2019,and BoT-IoT.Results show an average detection accuracy of 99.06%and an average bot detection precision of 100%.
基金This study was funded through a grant from the European Union's Project Horizon 2020 and 5G HEART,under Grant Agreement Number 857034[15]the Norwegian Association for Children with Congenital Heart Disease.
文摘Background:The management of suspected critical congenital heart defects(CCHD)relies on timely echocardiographic diagnosis.The availability of experienced echocardiographers is limited or even non-existent in many hospitals with obstetric units.This study evaluates remote-mentored echocardiography performed by physicians without experience in imaging of congenital heart defects(CHD).Methods:The setup included a pediatric cardiologist in a separate room,guiding a physician without experience in echocardiographic imaging of CHD in the examination of a symptomatic newborn.This remote-mentoring pair was blinded to the diagnosis of the newborn and presented with a simplified patient history.The echocardiographic images were streamed to the laptop of the mentor,along with a webcam feed showing the probe position.The task was to identify CCHD in need of immediate transfer to a pediatric cardiac surgical center.The result was compared to the previously completed echocardiographic report and the clinical decision of the patient-responsible pediatric cardiologist.Results:During 17 months,15 newborns were recruited.All six newborns with CCHD were correctly labeled by the remotementoring pair.One newborn with Tetralogy of Fallot was erroneously labeled as needing immediate transfer.Eight newborns without CCHD were correctly labeled.Conclusions:Remote-mentored echocardiography performed by examiners without experience in imaging CHD identified all newborns with CCHD in need of immediate transfer for specialist care.The setup shows promising results for improving the management of CCHD in hospitals without continuous pediatric cardiology service.
基金Supported by European Union-NextGenerationEU,Through the National Recovery and Resilience Plan of the Republic of Bulgaria,No.BG-RRP-2.004-0008-C01.
文摘Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways:Prediction and forecast.Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role.Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences.The time series analysis approach has the advantage of being easier to use(in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average).Still,it is limited in forecasting time,unlike the classical models such as Susceptible-Exposed-Infectious-Removed.Its applicability in forecasting comes from its better accuracy for short-term prediction.In its basic form,it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures(governments,companies,etc.).Instead,it estimates from the data directly.Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread;be it school closures,emerging variants,etc.It can be used in mortality or hospital risk estimation from new cases,seroprevalence studies,assessing properties of emerging variants,and estimating excess mortality and its relationship with a pandemic.
文摘Blood loss in peacetime is mainly due to the normal menstrual cycle in women or diseases with surgical intervention. In wartime, blood loss in military personnel is a characteristic sign of a closed or open injury of the body during internal or external bleeding. Access to clinical care for wounded military personnel injured on the battlefield is limited and has long delays compared to patients in peacetime. Most of the deaths of wounded military personnel on the battlefield occur within the first hour after being wounded. The most common causes are delay in providing medical care, loss of time for diagnosis, delay in stabilization of pain shock and large blood loss. Some help in overcoming these problems is provided by the data in the individual capsule, which each soldier of the modern army possesses;however, data in an individual capsule is not sufficient to provide emergency medical care in field and hospital conditions. This paper considers a project for development of a smart real-time monitoring wearable system for blood loss and level of shock stress in wounded persons on the battlefield, which provides medical staff in field and hospital conditions with the necessary information to give timely medical care. Although the hospital will require additional information, the basic information about the victims will already be known before he enters the hospital. It is important to emphasize that the key term in this approach is monitoring. It is tracking, and not a one-time measurement of indicators, that is crucial in a valid definition of bleeding.
文摘BACKGROUND The coronavirus disease 2019(COVID-19)pandemic affects all aspects of our lives,including children.With the advancement of the pandemic,children under five years old are at increased risk of hospitalization relative to other age groups.This makes it paramount that we develop tools to address the two critical aspects of preserving children's health–new treatment protocols and new predictive models.For those purposes,we need to understand better the effects of COVID-19 on children,and we need to be able to predict the number of affected children as a proportion of the number of infected children.This is why our research focuses on clinical and epidemiological pictures of children with heart damage post-COVID,as a part of the general picture of post-COVID among this age group.AIM To demonstrate the role of children in the COVID-19 spread in Bulgaria and to test the hypothesis that there are no secondary transmissions in schools and from children to adults.METHODS Our modeling and data show with high probability that in Bulgaria,with our current measures,vaccination strategy and contact structure,the pandemic is driven by the children and their contacts in school.RESULTS This makes it paramount that we develop tools to address the two critical aspects of preserving children's health–new treatment protocols and new predictive models.For those purposes,we need to understand better the effects of COVID-19 on children,and we need to be able to predict the number of affected children as a proportion of the number of infected children.This is why our research focuses on clinical and epidemiological pictures of children with heart damage post-COVID,as a part of the general picture of post-Covid among this age group.CONCLUSION Our modeling rejects that hypothesis,and the epidemiological data supports that.We used epidemiological data to support the validity of our modeling.The first summer wave in 2020 from the listed here school proms endorse the idea of transmissions from students to teachers.
基金Supported by the European Union-NextGenerationEU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,No. BG-RRP-2.004-0008-C01。
文摘Dietary imbalance and overeating can lead to an increasingly widespread disease-obesity.Aesthetic considerations aside,obesity is defined as an excess of adipose tissue that can lead to serious health problems and can predispose to a number of pathological changes and clinical diseases,including diabetes;hypertension;atherosclerosis;coronary artery disease and stroke;obstructive sleep apnea;depression;weight-related arthropathies and endometrial and breast cancer.A body weight 20%above ideal for age,gender and height is a severe health risk.Bariatric surgery is a set of surgical methods to treat morbid obesity when other treatments such as diet,increased physical activity,behavioral changes and drugs have failed.The two most common procedures currently used are sleeve gastrectomy and gastric bypass.This procedure has gained popularity recently and is generally considered safe and effective.Although current data show that perioperative mortality is low and better control of comorbidities and short-term complications is achieved,more randomized trials are needed to evaluate the long-term outcomes of bariatric procedures.This review aims to synthesize and summarize the growing evidence on the long-term effectiveness,outcomes and complications of bariatric surgery.
文摘Computer Algebra Systems have been extensively used in higher education. The reasons are many e.g., visualize mathematical problems, correlate real-world problems on a conceptual level, are flexible, simple to use, accessible from anywhere, etc. However, there is still room for improvement. Computer algebra system (CAS) optimization is the set of best practices and techniques to keep the CAS running optimally. Best practices are related to how to carry out a mathematical task or configure your system. In this paper, we are going to examine these techniques. The documentation sheets of CASs are the source of data that we used to compare them and examine their characteristics. The research results reveal that there are many tips that we can follow to accelerate performance.
文摘Online Computer Algebra Systems (CAS) have become increasingly popular among students and teachers. The reasons are many such as being more flexible, simple to use, accessible from anywhere, etc. However, as with any educational tool, they also have some disadvantages that we should know. The purpose of this study is to analyze advantages and disadvantages of online CASs and propose some techniques to optimize CAS performance in order to reduce weaknesses. The research results reveal that online CAS versions are on the rise but lag in some capabilities in comparison with desktop versions.
基金Supported by National Natural Science Foundation of China(Grant No.51375013)Anhui Provincial Natural Science Foundation of China(Grant No.1208085ME64)Open Research Fund of Key Laboratory of High Performance Complex Manufacturing,Central South University(Grant No.Kfkt2013-12)
文摘The compliance modeling and rigidity performance evaluation for the lower mobility parallel manipulators are still to be remained as two overwhelming challenges in the stage of conceptual design due to their geometric complexities. By using the screw theory, this paper explores the compliance modeling and eigencompliance evaluation of a newly patented 1T2R spindle head whose topological architecture is a 3-RPS parallel mechanism. The kinematic definitions and inverse position analysis are briefly addressed in the first place to provide necessary information for compliance modeling. By considering the 3-RPS parallel kinematic machine(PKM) as a typical compliant parallel device, whose three limb assemblages have bending, extending and torsional deflections, an analytical compliance model for the spindle head is established with screw theory and the analytical stiffness matrix of the platform is formulated. Based on the eigenscrew decomposition, the eigencompliance and corresponding eigenscrews are analyzed and the platform's compliance properties are physically interpreted as the suspension of six screw springs. The distributions of stiffness constants of the six screw springs throughout the workspace are predicted in a quick manner with a piece-by-piece calculation algorithm. The numerical simulation reveals a strong dependency of platform's compliance on its configuration in that they are axially symmetric due to structural features. At the last stage, the effects of some design variables such as structural, configurational and dimensional parameters on system rigidity characteristics are investigated with the purpose of providing useful information for the structural design and performance improvement of the PKM. Compared with previous efforts in compliance analysis of PKMs, the present methodology is more intuitive and universal thus can be easily applied to evaluate the overall rigidity performance of other PKMs with high efficiency.
基金the National Natural Science Foundation of China(NSFC)(Grant No.61671072).
文摘Encouraged by next-generation networks and autonomous vehicle systems,vehicular networks must employ advanced technologies to guarantee personal safety,reduce traffic accidents and ease traffic jams.By leveraging the computing ability at the network edge,multi-access edge computing(MEC)is a promising technique to tackle such challenges.Compared to traditional full offloading,partial offloading offers more flexibility in the perspective of application as well as deployment of such systems.Hence,in this paper,we investigate the application of partial computing offloading in-vehicle networks.In particular,by analyzing the structure of many emerging applications,e.g.,AR and online games,we convert the application structure into a sequential multi-component model.Focusing on shortening the application execution delay,we extend the optimization problem from the single-vehicle computing offloading(SVCOP)scenario to the multi-vehicle computing offloading(MVCOP)by taking multiple constraints into account.A deep reinforcement learning(DRL)based algorithm is proposed as a solution to this problem.Various performance evaluation results have shown that the proposed algorithm achieves superior performance as compared to existing offloading mechanisms in deducing application execution delay.
基金supported by Medical Research Council(MRC)grant MR/K004360/1 to SIDMARIE CURIE COFUND EU-UK Research Fellowship to SID
文摘Neuroinformatics is a fascinating research field that applies computational models and analytical tools to high dimensional experimental neuroscience data for a better understanding of how the brain functions or dysfunctions in brain diseases. Neuroinformaticians work in the intersection of neuroscience and informatics supporting the integration of various sub-disciplines(behavioural neuroscience, genetics, cognitive psychology, etc.) working on brain research. Neuroinformaticians are the pathway of information exchange between informaticians and clinicians for a better understanding of the outcome of computational models and the clinical interpretation of the analysis. Machine learning is one of the most significant computational developments in the last decade giving tools to neuroinformaticians and finally to radiologists and clinicians for an automatic and early diagnosis-prognosis of a brain disease. Random forest(RF) algorithm has been successfully applied to high-dimensional neuroimaging data for feature reduction and also has been applied to classify the clinical label of a subject using single or multi-modal neuroimaging datasets. Our aim was to review the studies where RF was applied to correctly predict the Alzheimer's disease(AD), the conversion from mild cognitive impairment(MCI) and its robustness to overfitting, outliers and handling of non-linear data. Finally, we described our RF-based model that gave us the 1 ^(st) position in an international challenge for automated prediction of MCI from MRI data.
文摘In the past decade,online Peer-to-Peer(P2P)lending platforms have transformed the lending industry,which has been historically dominated by commercial banks.Information technology breakthroughs such as big data-based financial technologies(Fintech)have been identified as important disruptive driving forces for this paradigm shift.In this paper,we take an information economics perspective to investigate how big data affects the transformation of the lending industry.By identifying how signaling and search costs are reduced by big data analytics for credit risk management of P2P lending,we discuss how information asymmetry is reduced in the big data era.Rooted in the lending business,we propose a theory on the economics of big data and outline a number of research opportunities and challenging issues.
文摘The financial industry has been strongly influenced by digitalization in the past few years reflected by the emergence of“FinTech,”which represents the marriage of“finance”and“information technology.”FinTech provides opportunities for the creation of new services and business models and poses challenges to traditional financial service providers.Therefore,FinTech has become a subject of debate among practitioners,investors,and researchers and is highly visible in the popular media.In this study,we unveil the drivers motivating the FinTech phenomenon perceived by the English and German popular press including the subjects discussed in the context of FinTech.This study is the first one to reflect the media perspective on the FinTech phenomenon in the research.In doing so,we extend the growing knowledge on FinTech and contribute to a common understanding in the financial and digital innovation literature.These study contributes to research in the areas of information systems,finance and interdisciplinary social sciences.Moreover,it brings value to practitioners(entrepreneurs,investors,regulators,etc.),who explore the field of FinTech.
基金Project(2012-0000478) supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MEST)
文摘In this work, we analyzed only the patients of the NSTEMI (non ST-segment elevation myocardial infarction) who arrived at the hospital within 12 h after symptoms started. Using NSTEMI follow-up data within, the characteristics of the clinical data, the risk factor, and the blood tested in the hospital visit were analyzed for MACE (major adverse cardiac events) patients. MACE includes cardiac death, MI (myocardial infarction), Re-PCI, and CABG (coronary artery bypass graft). As a result, from the NSTEMI patients which can be followed up for over 12 m, NT-ProBNP (p=0.014) and age (p=0.045) are found to be the independent risk factors related to MACE. Accordingly, they can be useful for the diagnosis and prognosis for NSTEMI patients as a biomarker.
基金Project (No. PRC 03-41/2003) supported by the Ministry of Con-struction of Cuba
文摘In this paper, the inverse problem of reconstructing reflectivity function of a medium is examined within a blind deconvolution framework. The ultrasound pulse is estimated using higher-order statistics, and Wiener filter is used to obtain the ultrasonic reflectivity function through wavelet-based models. A new approach to the parameter estimation of the inverse filtering step is proposed in the nondestructive evaluation field, which is based on the theory of Fourier-Wavelet regularized deconvolution (ForWaRD). This new approach can be viewed as a solution to the open problem of adaptation of the ForWaRD framework to perform the convolution kernel estimation and deconvolution interdependently. The results indicate stable solutions of the esti- mated pulse and an improvement in the radio-frequency (RF) signal taking into account its signal-to-noise ratio (SNR) and axial resolution. Simulations and experiments showed that the proposed approach can provide robust and optimal estimates of the reflectivity function.
