In this study we theoretically demonstrate ultrahigh-resolution two-dimensional atomic localization within a three-levelλ-type atomic medium via superposition of asymmetric and symmetric standing wave fields.Our anal...In this study we theoretically demonstrate ultrahigh-resolution two-dimensional atomic localization within a three-levelλ-type atomic medium via superposition of asymmetric and symmetric standing wave fields.Our analysis provides an understanding of the precise spatial localization of atomic positions at the atomic level,utilizing advanced theoretical approaches and principles of quantum mechanics.The dynamical behavior of a three-level atomic system is thoroughly analyzed using the density matrix formalism within the realm of quantum mechanics.A theoretical approach is constructed to describe the interaction between the system and external fields,specifically a control field and a probe field.The absorption spectrum of the probe field is thoroughly examined to clarify the spatial localization of the atom within the proposed configuration.A theoretical investigation found that symmetric and asymmetric superposition phenomena significantly influence the localized peaks within a two-dimensional spatial domain.Specifically,the emergence of one and two sharp localized peaks was observed within a one-wavelength domain.We observed notable influences of the intensity of the control field,probe field detuning and decay rates on atomic localization.Ultimately,we have achieved an unprecedented level of ultrahigh resolution and precision in localizing an atom within an area smaller thanλ/35×λ/35.These findings hold promise for potential applications in fields such as Bose-Einstein condensation,nanolithography,laser cooling,trapping of neutral atoms and the measurement of center-of-mass wave functions.展开更多
A model of focal cerebral ischemic infarction was established in dogs through middle cerebral artery occlusion of the right side.Thirty minutes after occlusion,models were injected with nerve growth factor adjacent to...A model of focal cerebral ischemic infarction was established in dogs through middle cerebral artery occlusion of the right side.Thirty minutes after occlusion,models were injected with nerve growth factor adjacent to the infarct locus.The therapeutic effect of nerve growth factor against cerebral infarction was assessed using the hemisphere anomalous volume ratio,a quantitative index of diffusion-weighted MRI.At 6 hours,24 hours,7 days and 3 months after modeling,the hemisphere anomalous volume ratio was significantly reduced after treatment with nerve growth factor. Hematoxylin-eosin staining,immunohistochemistry,electron microscopy and neurological function scores showed that infarct defects were slightly reduced and neurological function significantly improved after nerve growth factor treatment.This result was consistent with diffusion-weighted MRI measurements.Experimental findings indicate that nerve growth factor can protect against cerebral infarction,and that the hemisphere anomalous volume ratio of diffusion-weighted MRI can be used to evaluate the therapeutic effect.展开更多
Animal behavior researchers often face problems regarding standardization and reproducibility oftheir experiments. This has led to the partial substitution of live animals with artificial virtual stim-uli. In addition...Animal behavior researchers often face problems regarding standardization and reproducibility oftheir experiments. This has led to the partial substitution of live animals with artificial virtual stim-uli. In addition to standardization and reproducibility, virtual stimuli open new options for re-searchers since they are easily changeable in morphology and appearance, and their behavior canbe defined. In this article, a novel toolchain to conduct behavior experiments with fish is presentedby a case study in sailfin mollies Poecilia latipinna. As the toolchain holds many different and novelfeatures, it offers new possibilities for studies in behavioral animal research and promotes thestandardization of experiments. The presented method includes options to design, animate, andpresent virtual stimuli to live fish. The designing tool offers an easy and user-friendly way to definesize, coloration, and morphology of stimuli and moreover it is able to configure virtual stimuli ran-domly without any user influence. Furthermore, the toolchain brings a novel method to animatestimuli in a semiautomatic way with the help of a game controller. These created swimming pathscan be applied to different stimuli in real time. A presentation tool combines models and swim-ming paths regarding formerly defined playlists, and presents the stimuli onto 2 screens.Experiments with live sailfin mollies validated the usage of the created virtual 3D fish models inmate-choice experiments.展开更多
This paper focuses on facilitating state-of-the-art applications of big data analytics(BDA) architectures and infrastructures to telecommunications(telecom) industrial sector.Telecom companies are dealing with terabyt...This paper focuses on facilitating state-of-the-art applications of big data analytics(BDA) architectures and infrastructures to telecommunications(telecom) industrial sector.Telecom companies are dealing with terabytes to petabytes of data on a daily basis. Io T applications in telecom are further contributing to this data deluge. Recent advances in BDA have exposed new opportunities to get actionable insights from telecom big data. These benefits and the fast-changing BDA technology landscape make it important to investigate existing BDA applications to telecom sector. For this, we initially determine published research on BDA applications to telecom through a systematic literature review through which we filter 38 articles and categorize them in frameworks, use cases, literature reviews, white papers and experimental validations. We also discuss the benefits and challenges mentioned in these articles. We find that experiments are all proof of concepts(POC) on a severely limited BDA technology stack(as compared to the available technology stack), i.e.,we did not find any work focusing on full-fledged BDA implementation in an operational telecom environment. To facilitate these applications at research-level, we propose a state-of-the-art lambda architecture for BDA pipeline implementation(called Lambda Tel) based completely on open source BDA technologies and the standard Python language, along with relevant guidelines.We discovered only one research paper which presented a relatively-limited lambda architecture using the proprietary AWS cloud infrastructure. We believe Lambda Tel presents a clear roadmap for telecom industry practitioners to implement and enhance BDA applications in their enterprises.展开更多
Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt ver...Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelihood of collapse during moderate or severe earthquakes. Identifying and retrofitting buildings with such irregularities—generally termed as soft-story buildings—is, therefore, vital in earthquake preparedness and loss mitigation efforts. Soft-story building identification through conventional means is a labor-intensive and time-consuming process. In this study, an automated procedure was devised based on deep learning techniques for identifying soft-story buildings from street-view images at a regional scale. A database containing a large number of building images and a semi-automated image labeling approach that effectively annotates new database entries was developed for developing the deep learning model. Extensive computational experiments were carried out to examine the effectiveness of the proposed procedure, and to gain insights into automated soft-story building identification.展开更多
Classical localization methods use Cartesian or Polar coordinates, which require a priori range information to determine whether to estimate position or to only find bearings. The modified polar representation (MPR) u...Classical localization methods use Cartesian or Polar coordinates, which require a priori range information to determine whether to estimate position or to only find bearings. The modified polar representation (MPR) unifies near-field and farfield models, alleviating the thresholding effect. Current localization methods in MPR based on the angle of arrival (AOA) and time difference of arrival (TDOA) measurements resort to semidefinite relaxation (SDR) and Gauss-Newton iteration, which are computationally complex and face the possible diverge problem. This paper formulates a pseudo linear equation between the measurements and the unknown MPR position,which leads to a closed-form solution for the hybrid TDOA-AOA localization problem, namely hybrid constrained optimization(HCO). HCO attains Cramér-Rao bound (CRB)-level accuracy for mild Gaussian noise. Compared with the existing closed-form solutions for the hybrid TDOA-AOA case, HCO provides comparable performance to the hybrid generalized trust region subproblem (HGTRS) solution and is better than the hybrid successive unconstrained minimization (HSUM) solution in large noise region. Its computational complexity is lower than that of HGTRS. Simulations validate the performance of HCO achieves the CRB that the maximum likelihood estimator (MLE) attains if the noise is small, but the MLE deviates from CRB earlier.展开更多
Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semicon...Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method.展开更多
The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals.In this paper,we develop a model-based classification method to dete...The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals.In this paper,we develop a model-based classification method to detect epileptic seizures that relies on this algorithm to filter electroencephalogram(EEG) signals.The underlying idea was to design an EEG filter that enhances the waveform of epileptic signals.The filtered signal was fitted to a quadratic linear-parabolic model using the curve fitting technique.The model fitting was assessed using four statistical parameters,which were used as classification features with a random forest algorithm to discriminate seizure and non-seizure events.The proposed method was applied to 66 epochs from the Children Hospital Boston database.Results showed that the method achieved fast and accurate detection of epileptic seizures,with a92% sensitivity,96% specificity,and 94.1% accuracy.展开更多
Infections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span.Corona Virus Disease-2019(COVID-19),also known as novel coronavirus or SARS...Infections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span.Corona Virus Disease-2019(COVID-19),also known as novel coronavirus or SARSCoV-2(Severe Acute Respiratory Syndrome-Coronavirus-2),is a respiratory based touch contiguous disease.The catastrophic situation resulting from the COVID-19 pandemic posed a serious threat to societies globally.The whole world is making tremendous efforts to combat this life-threatening disease.For taking remedial action and planning preventive measures on time,there is an urgent need for efficient prediction models to confront the COVID-19 outbreak.A deep learning-based ARIMA-LSTM hybrid model is proposed in this article for predicting the COVID-19 outbreak by utilizing real-time information from the WHO’s daily bulletin report as well as provides information regarding clinical trials across the world.To evaluate the suitability and performance of our proposed model compared to other well-established prediction models,an experimental study has been performed.To estimate the prediction results,the three performance measures,i.e.,Root Mean Square Error(RMSE),Coefficient of determination(R2 Score),and Mean Absolute Percentage Error(MAPE)have been employed.The prediction results of fifty countries substantiated the fact that the proposed ARIMA-LSTM hybrid model performs very well as compared to other models.The proposed model archives the lowest RMSE,lowest MAPE,and highest R2 Score throughout the testing,under varied selection criteria(country-wise).This article aims to contribute a deep learning-based solution for the wellbeing of livings and to provide the current status of clinical trials across the globe.展开更多
Markov decision process(MDP)offers a general framework for modelling sequential decision making where outcomes are random.In particular,it serves as a mathematical framework for reinforcement learning.This paper intro...Markov decision process(MDP)offers a general framework for modelling sequential decision making where outcomes are random.In particular,it serves as a mathematical framework for reinforcement learning.This paper introduces an extension of MDP,namely quantum MDP(q MDP),that can serve as a mathematical model of decision making about quantum systems.We develop dynamic programming algorithms for policy evaluation and finding optimal policies for q MDPs in the case of finite-horizon.The results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.展开更多
OBJECTIVE:To explore the correlation between diagnostic information of tongue and gastroscopy results of patients with chronic gastritis.METHODS:Frequent pattern growth(FP-Growth),SPSS Modeler was used to analyze the ...OBJECTIVE:To explore the correlation between diagnostic information of tongue and gastroscopy results of patients with chronic gastritis.METHODS:Frequent pattern growth(FP-Growth),SPSS Modeler was used to analyze the correlation rules between the image information of tongue parameters and the characteristics of the stomach and duodenum seen under gastroscopy.RESULTS:Ranking in order of confidence:cyanotic tongue,slippery fur,yellow fur and spotted tongue were sequently associated with both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.L,one value of tongue coating color,which counted among(30,60),tooth-marked tongue and b,one value of tongue coating color,which counted in the range of(5,20)were sequently associated with gastric antrum mucosal erythema/macula.A,one value of tongue body color,which counted in the range of(0,20),was related to both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.a,one value of tongue coating color,which counted in the range of(15,35),was associated with gastric antrum mucosal erythema/macula.There are a total of 9 strong correlation rules.CONCLUSIONS:Cyanotic tongue,slippery fur,yellow fur,the CIE Lab value of tongue coating,a,the value of tongue body color,spotted tongue,and tooth-marked tongue are all related to the gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.The conditions of gastric mucosa could be predicted by the examination of the above related image information of tongue.展开更多
The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale.Analysis of this big da...The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale.Analysis of this big data allows for boundless potential outcomes for discovering knowledge.Big data analytics(BDA)in healthcare can,for instance,help determine causes of diseases,generate effective diagnoses,enhance Qo S guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments,generate accurate predictions of readmissions,enhance clinical care,and pinpoint opportunities for cost savings.However,BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners.In this paper,we present a comprehensive roadmap to derive insights from BDA in the healthcare(patient care)domain,based on the results of a systematic literature review.We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on No SQL databases.We also identify the limitations and challenges of these applications and justify the potential of No SQL databases to address these challenges and further enhance BDA healthcare research.We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm.We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare.Finally,we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work.The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators,practitioners and professionals to successfully implement BDA initiatives in their organizations.展开更多
We present a model for self-adjustment of social conventions to small perturbations, and investigate how perturbations can influence the convergence of social convention in different situations. The experimental resul...We present a model for self-adjustment of social conventions to small perturbations, and investigate how perturbations can influence the convergence of social convention in different situations. The experimental results show that the sensitivity of social conventions is determined by not only the perturbations themselves but also the agent adjustment functions for the perturbations; and social conventions are more sensitive to the outlier agent number than to the strategy fluctuation magnitudes and localities of perturbations.展开更多
There has been a significant interest of researchers to combine different schemes focused on optimizing energy performance while developing aMAC protocol for Wireless Sensor Networks(WSNs).In this paper,we propose to ...There has been a significant interest of researchers to combine different schemes focused on optimizing energy performance while developing aMAC protocol for Wireless Sensor Networks(WSNs).In this paper,we propose to integrate two cross-layer schemes:dynamic channel polling and packet concatenation using a recent asynchronous MAC protocol“Adaptive&Dynamic Polling MAC”(ADPMAC).ADP-MAC dynamically selects the polling interval distribution based on characterization of incoming traffic patterns using Coefficient of variation(CV).Packet Concatenation(PC)refers to combining the individually generated data packets into a single super packet and sending it at the polling instant.Also,the Block Acknowledgement(BA)scheme has been developed for ADP-MAC to work in conjunction with the packet concatenation.The proposed schemes have been implemented in Tiny-OS for Mica2 platform and Avrora emulator has been used for conducting experiments.Simulation results have revealed that the performance both in terms of energy&packet loss improves when ADP-MAC is used in conjunction with the additional features of PC&BA.Furthermore,the proposed scheme has been compared with a stateof-art packet concatenation primitive PiP(Packet-in-Packet).It has been observed that ADP-MAC supersedes the performance of PiP in terms of PDR(Packet Delivery Ratio)due to better management of synchronization between source and sink.展开更多
Geometric morphometrics （GM） is an important method of shape analysis and increasingly used in a wide range of scientific disciplines. Presently, a single character comparison system of geometric morphometric data i...Geometric morphometrics （GM） is an important method of shape analysis and increasingly used in a wide range of scientific disciplines. Presently, a single character comparison system of geometric morphometric data is used in almost all empirical studies, and this approach is sufficient for many scientific problems. However, the estimation of overall similarity among taxa or objects based on multiple characters is crucial in a variety of contexts （e.g. （semi-）automated identification, phenetic relationships, tracing of character evolution, phylogenetic reconstruction）. Here we propose a new web-based tool for merging several geometric morphometrics data files from multiple characters into a single data file. Using this approach information from multiple characters can be compared in combination and an overall similarity estimate can be obtained in a convenient and geometrically rigorous manner. To illustrate our method, we provide an example analysis of 25 dung beetle species with seven Procrustes superimposed landmark data files representing the morphological variation of body features： the epipharynx, right mandible, pronotum, elytra, hindwing, and the metendosternite in dorsal and lateral view. All seven files were merged into a single one containing information on 649 landmark locations. The possible applications of such merged data files in different fields of science are discussed.展开更多
In this paper,we propose a two-tiered segment-based Device-toDevice(S-D2D)caching approach to decrease the startup and playback delay experienced by Video-on-Demand(VoD)users in a cellular network.In the S-D2D caching...In this paper,we propose a two-tiered segment-based Device-toDevice(S-D2D)caching approach to decrease the startup and playback delay experienced by Video-on-Demand(VoD)users in a cellular network.In the S-D2D caching approach cache space of each mobile device is divided into two cache-blocks.The rst cache-block reserve for caching and delivering the beginning portion of the most popular video les and the second cacheblock caches the latter portion of the requested video les‘fully or partially’depending on the users’video watching behaviour and popularity of videos.In this approach before caching,video is divided and grouped in a sequence of xed-sized fragments called segments.To control the admission to both cacheblocks and improve the system throughput,we further propose and evaluate three cache admission control algorithms.We also propose a video segment access protocol to elaborate on how to cache and share the video segments in a segmentation based D2D caching architecture.We formulate an optimisation problem and nd the optimal cache probability and beginning-segment size that maximise the cache-throughput probability of beginning-segments.To solve the non-convex cache-throughout maximisation problem,we derive an iterative algorithm,where the optimal solution is derived in each step.We used extensive simulations to evaluate the performance of our proposed S-D2D caching system.展开更多
Sentiment Analysis, an un-abating research area in text mining, requires a computational method for extracting useful information from text. In recent days, social media has become a really rich source to get informat...Sentiment Analysis, an un-abating research area in text mining, requires a computational method for extracting useful information from text. In recent days, social media has become a really rich source to get information about the behavioral state of people(opinion) through reviews and comments. Numerous techniques have been aimed to analyze the sentiment of the text, however, they were unable to come up to the complexity of the sentiments. The complexity requires novel approach for deep analysis of sentiments for more accurate prediction. This research presents a three-step Sentiment Analysis and Prediction(SAP) solution of Text Trend through K-Nearest Neighbor(KNN). At first, sentences are transformed into tokens and stop words are removed. Secondly, polarity of the sentence, paragraph and text is calculated through contributing weighted words, intensity clauses and sentiment shifters. The resulting features extracted in this step played significant role to improve the results. Finally, the trend of the input text has been predicted using KNN classifier based on extracted features. The training and testing of the model has been performed on publically available datasets of twitter and movie reviews. Experiments results illustrated the satisfactory improvement as compared to existing solutions. In addition, GUI(Hello World) based text analysis framework has been designed to perform the text analytics.展开更多
Throughout the use of the small battery-operated sensor nodes encou-rage us to develop an energy-efficient routing protocol for wireless sensor networks(WSNs).The development of an energy-efficient routing protocol is...Throughout the use of the small battery-operated sensor nodes encou-rage us to develop an energy-efficient routing protocol for wireless sensor networks(WSNs).The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of WSN.Many routing protocols are available,but the issue is still alive.Clustering is one of the most important techniques in the existing routing protocols.In the clustering-based model,the important thing is the selection of the cluster heads.In this paper,we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each cluster.Initially,the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small distance.The proposed scheme performs hierarchal routing and direct routing with some energy thresholds.The simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its performance.Moreover,the simulations will be performed in two scenarios,gate-way-based and without gateway to achieve more energy-efficient results.展开更多
文摘In this study we theoretically demonstrate ultrahigh-resolution two-dimensional atomic localization within a three-levelλ-type atomic medium via superposition of asymmetric and symmetric standing wave fields.Our analysis provides an understanding of the precise spatial localization of atomic positions at the atomic level,utilizing advanced theoretical approaches and principles of quantum mechanics.The dynamical behavior of a three-level atomic system is thoroughly analyzed using the density matrix formalism within the realm of quantum mechanics.A theoretical approach is constructed to describe the interaction between the system and external fields,specifically a control field and a probe field.The absorption spectrum of the probe field is thoroughly examined to clarify the spatial localization of the atom within the proposed configuration.A theoretical investigation found that symmetric and asymmetric superposition phenomena significantly influence the localized peaks within a two-dimensional spatial domain.Specifically,the emergence of one and two sharp localized peaks was observed within a one-wavelength domain.We observed notable influences of the intensity of the control field,probe field detuning and decay rates on atomic localization.Ultimately,we have achieved an unprecedented level of ultrahigh resolution and precision in localizing an atom within an area smaller thanλ/35×λ/35.These findings hold promise for potential applications in fields such as Bose-Einstein condensation,nanolithography,laser cooling,trapping of neutral atoms and the measurement of center-of-mass wave functions.
基金supported by the Hebei Provincial Medical Science Research Key Youth Project,No.20100078
文摘A model of focal cerebral ischemic infarction was established in dogs through middle cerebral artery occlusion of the right side.Thirty minutes after occlusion,models were injected with nerve growth factor adjacent to the infarct locus.The therapeutic effect of nerve growth factor against cerebral infarction was assessed using the hemisphere anomalous volume ratio,a quantitative index of diffusion-weighted MRI.At 6 hours,24 hours,7 days and 3 months after modeling,the hemisphere anomalous volume ratio was significantly reduced after treatment with nerve growth factor. Hematoxylin-eosin staining,immunohistochemistry,electron microscopy and neurological function scores showed that infarct defects were slightly reduced and neurological function significantly improved after nerve growth factor treatment.This result was consistent with diffusion-weighted MRI measurements.Experimental findings indicate that nerve growth factor can protect against cerebral infarction,and that the hemisphere anomalous volume ratio of diffusion-weighted MRI can be used to evaluate the therapeutic effect.
文摘Animal behavior researchers often face problems regarding standardization and reproducibility oftheir experiments. This has led to the partial substitution of live animals with artificial virtual stim-uli. In addition to standardization and reproducibility, virtual stimuli open new options for re-searchers since they are easily changeable in morphology and appearance, and their behavior canbe defined. In this article, a novel toolchain to conduct behavior experiments with fish is presentedby a case study in sailfin mollies Poecilia latipinna. As the toolchain holds many different and novelfeatures, it offers new possibilities for studies in behavioral animal research and promotes thestandardization of experiments. The presented method includes options to design, animate, andpresent virtual stimuli to live fish. The designing tool offers an easy and user-friendly way to definesize, coloration, and morphology of stimuli and moreover it is able to configure virtual stimuli ran-domly without any user influence. Furthermore, the toolchain brings a novel method to animatestimuli in a semiautomatic way with the help of a game controller. These created swimming pathscan be applied to different stimuli in real time. A presentation tool combines models and swim-ming paths regarding formerly defined playlists, and presents the stimuli onto 2 screens.Experiments with live sailfin mollies validated the usage of the created virtual 3D fish models inmate-choice experiments.
基金supported in part by the Big Data Analytics Laboratory(BDALAB)at the Institute of Business Administration under the research grant approved by the Higher Education Commission of Pakistan(www.hec.gov.pk)the Darbi company(www.darbi.io)
文摘This paper focuses on facilitating state-of-the-art applications of big data analytics(BDA) architectures and infrastructures to telecommunications(telecom) industrial sector.Telecom companies are dealing with terabytes to petabytes of data on a daily basis. Io T applications in telecom are further contributing to this data deluge. Recent advances in BDA have exposed new opportunities to get actionable insights from telecom big data. These benefits and the fast-changing BDA technology landscape make it important to investigate existing BDA applications to telecom sector. For this, we initially determine published research on BDA applications to telecom through a systematic literature review through which we filter 38 articles and categorize them in frameworks, use cases, literature reviews, white papers and experimental validations. We also discuss the benefits and challenges mentioned in these articles. We find that experiments are all proof of concepts(POC) on a severely limited BDA technology stack(as compared to the available technology stack), i.e.,we did not find any work focusing on full-fledged BDA implementation in an operational telecom environment. To facilitate these applications at research-level, we propose a state-of-the-art lambda architecture for BDA pipeline implementation(called Lambda Tel) based completely on open source BDA technologies and the standard Python language, along with relevant guidelines.We discovered only one research paper which presented a relatively-limited lambda architecture using the proprietary AWS cloud infrastructure. We believe Lambda Tel presents a clear roadmap for telecom industry practitioners to implement and enhance BDA applications in their enterprises.
基金supported by the US National Science Foundation under Grant No. 1612843. NHERI Design Safe (Rathje et al., 2017)Texas Advanced Computing Center (TACC)。
文摘Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelihood of collapse during moderate or severe earthquakes. Identifying and retrofitting buildings with such irregularities—generally termed as soft-story buildings—is, therefore, vital in earthquake preparedness and loss mitigation efforts. Soft-story building identification through conventional means is a labor-intensive and time-consuming process. In this study, an automated procedure was devised based on deep learning techniques for identifying soft-story buildings from street-view images at a regional scale. A database containing a large number of building images and a semi-automated image labeling approach that effectively annotates new database entries was developed for developing the deep learning model. Extensive computational experiments were carried out to examine the effectiveness of the proposed procedure, and to gain insights into automated soft-story building identification.
基金supported by the National Natural Science Foundation of China (62101359)Sichuan University and Yibin Municipal People’s Government University and City Strategic Cooperation Special Fund Project (2020CDYB-29)+1 种基金the Science and Technology Plan Transfer Payment Project of Sichuan Province (2021ZYSF007)the Key Research and Development Program of Science and Technology Department of Sichuan Province (2020YFS0575,2021KJT0012-2 021YFS-0067)。
文摘Classical localization methods use Cartesian or Polar coordinates, which require a priori range information to determine whether to estimate position or to only find bearings. The modified polar representation (MPR) unifies near-field and farfield models, alleviating the thresholding effect. Current localization methods in MPR based on the angle of arrival (AOA) and time difference of arrival (TDOA) measurements resort to semidefinite relaxation (SDR) and Gauss-Newton iteration, which are computationally complex and face the possible diverge problem. This paper formulates a pseudo linear equation between the measurements and the unknown MPR position,which leads to a closed-form solution for the hybrid TDOA-AOA localization problem, namely hybrid constrained optimization(HCO). HCO attains Cramér-Rao bound (CRB)-level accuracy for mild Gaussian noise. Compared with the existing closed-form solutions for the hybrid TDOA-AOA case, HCO provides comparable performance to the hybrid generalized trust region subproblem (HGTRS) solution and is better than the hybrid successive unconstrained minimization (HSUM) solution in large noise region. Its computational complexity is lower than that of HGTRS. Simulations validate the performance of HCO achieves the CRB that the maximum likelihood estimator (MLE) attains if the noise is small, but the MLE deviates from CRB earlier.
基金Supported by the National Key Basic Research and Development Program of China (2009CB320602)the National Natural Science Foundation of China (60834004, 61025018)+2 种基金the Open Project Program of the State Key Lab of Industrial ControlTechnology (ICT1108)the Open Project Program of the State Key Lab of CAD & CG (A1120)the Foundation of Key Laboratory of System Control and Information Processing (SCIP2011005),Ministry of Education,China
文摘Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method.
文摘The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals.In this paper,we develop a model-based classification method to detect epileptic seizures that relies on this algorithm to filter electroencephalogram(EEG) signals.The underlying idea was to design an EEG filter that enhances the waveform of epileptic signals.The filtered signal was fitted to a quadratic linear-parabolic model using the curve fitting technique.The model fitting was assessed using four statistical parameters,which were used as classification features with a random forest algorithm to discriminate seizure and non-seizure events.The proposed method was applied to 66 epochs from the Children Hospital Boston database.Results showed that the method achieved fast and accurate detection of epileptic seizures,with a92% sensitivity,96% specificity,and 94.1% accuracy.
文摘Infections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span.Corona Virus Disease-2019(COVID-19),also known as novel coronavirus or SARSCoV-2(Severe Acute Respiratory Syndrome-Coronavirus-2),is a respiratory based touch contiguous disease.The catastrophic situation resulting from the COVID-19 pandemic posed a serious threat to societies globally.The whole world is making tremendous efforts to combat this life-threatening disease.For taking remedial action and planning preventive measures on time,there is an urgent need for efficient prediction models to confront the COVID-19 outbreak.A deep learning-based ARIMA-LSTM hybrid model is proposed in this article for predicting the COVID-19 outbreak by utilizing real-time information from the WHO’s daily bulletin report as well as provides information regarding clinical trials across the world.To evaluate the suitability and performance of our proposed model compared to other well-established prediction models,an experimental study has been performed.To estimate the prediction results,the three performance measures,i.e.,Root Mean Square Error(RMSE),Coefficient of determination(R2 Score),and Mean Absolute Percentage Error(MAPE)have been employed.The prediction results of fifty countries substantiated the fact that the proposed ARIMA-LSTM hybrid model performs very well as compared to other models.The proposed model archives the lowest RMSE,lowest MAPE,and highest R2 Score throughout the testing,under varied selection criteria(country-wise).This article aims to contribute a deep learning-based solution for the wellbeing of livings and to provide the current status of clinical trials across the globe.
基金partly supported by National Key R&D Program of China(No.2018YFA0306701)the Australian Research Council(Nos.DP160101652 and DP180100691)+1 种基金National Natural Science Foundation of China(No.61832015)the Key Research Program of Frontier Sciences,Chinese Academy of Sciences。
文摘Markov decision process(MDP)offers a general framework for modelling sequential decision making where outcomes are random.In particular,it serves as a mathematical framework for reinforcement learning.This paper introduces an extension of MDP,namely quantum MDP(q MDP),that can serve as a mathematical model of decision making about quantum systems.We develop dynamic programming algorithms for policy evaluation and finding optimal policies for q MDPs in the case of finite-horizon.The results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.
基金Key Special Project of the National Key Research and Development Program of Ministry of Science and Technology(No.2017YFB1002300):Topic One:Multimodal Heterogeneous Efficient Acquisition of Traditional Chinese Medicine Big Data and Resource Library Construction(No.2017YFB1002301)and Topic Three:Multi-Scale Cognition Methods and Treatment Analysis Model of Traditional Chinese Medicine Based on Deep Learning(No.2017YFB1002303)from Big Data-Driven Traditional Chinese Medicine Intelligent Auxiliary Diagnostic Service SystemGraduation Design of“Cultivation Program”for Cross-cultivation of High-level Talents in Beijing Colleges and Universities in 2010(Scientific Research):the Research on the Clinical Diagnosis and Prediction System of Gastric Precancerous Lesions Based on Artificial Intelligence+2 种基金National Natural Science Foundation of China(No.30701071)the Sixth Batch of Academic Experience Inheritance of Traditional Chinese Medicine Experts(2017)“3+3”Project of Beijing Traditional Chinese Medicine Inheritance(No.2012-SZ-C-41)。
文摘OBJECTIVE:To explore the correlation between diagnostic information of tongue and gastroscopy results of patients with chronic gastritis.METHODS:Frequent pattern growth(FP-Growth),SPSS Modeler was used to analyze the correlation rules between the image information of tongue parameters and the characteristics of the stomach and duodenum seen under gastroscopy.RESULTS:Ranking in order of confidence:cyanotic tongue,slippery fur,yellow fur and spotted tongue were sequently associated with both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.L,one value of tongue coating color,which counted among(30,60),tooth-marked tongue and b,one value of tongue coating color,which counted in the range of(5,20)were sequently associated with gastric antrum mucosal erythema/macula.A,one value of tongue body color,which counted in the range of(0,20),was related to both gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.a,one value of tongue coating color,which counted in the range of(15,35),was associated with gastric antrum mucosal erythema/macula.There are a total of 9 strong correlation rules.CONCLUSIONS:Cyanotic tongue,slippery fur,yellow fur,the CIE Lab value of tongue coating,a,the value of tongue body color,spotted tongue,and tooth-marked tongue are all related to the gastric antrum mucosal hyperemia or edema and gastric antrum mucosal erythema/macula.The conditions of gastric mucosa could be predicted by the examination of the above related image information of tongue.
基金supported by two research grants provided by the Karachi Institute of Economics and Technology(KIET)the Big Data Analytics Laboratory at the Insitute of Business Administration(IBAKarachi)。
文摘The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale.Analysis of this big data allows for boundless potential outcomes for discovering knowledge.Big data analytics(BDA)in healthcare can,for instance,help determine causes of diseases,generate effective diagnoses,enhance Qo S guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments,generate accurate predictions of readmissions,enhance clinical care,and pinpoint opportunities for cost savings.However,BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners.In this paper,we present a comprehensive roadmap to derive insights from BDA in the healthcare(patient care)domain,based on the results of a systematic literature review.We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on No SQL databases.We also identify the limitations and challenges of these applications and justify the potential of No SQL databases to address these challenges and further enhance BDA healthcare research.We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm.We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare.Finally,we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work.The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators,practitioners and professionals to successfully implement BDA initiatives in their organizations.
基金Supported by the National Natural Science Foundation of China under Grant No 60803060, and the Excellent Young Teachers Program of Southeast University.
文摘We present a model for self-adjustment of social conventions to small perturbations, and investigate how perturbations can influence the convergence of social convention in different situations. The experimental results show that the sensitivity of social conventions is determined by not only the perturbations themselves but also the agent adjustment functions for the perturbations; and social conventions are more sensitive to the outlier agent number than to the strategy fluctuation magnitudes and localities of perturbations.
文摘There has been a significant interest of researchers to combine different schemes focused on optimizing energy performance while developing aMAC protocol for Wireless Sensor Networks(WSNs).In this paper,we propose to integrate two cross-layer schemes:dynamic channel polling and packet concatenation using a recent asynchronous MAC protocol“Adaptive&Dynamic Polling MAC”(ADPMAC).ADP-MAC dynamically selects the polling interval distribution based on characterization of incoming traffic patterns using Coefficient of variation(CV).Packet Concatenation(PC)refers to combining the individually generated data packets into a single super packet and sending it at the polling instant.Also,the Block Acknowledgement(BA)scheme has been developed for ADP-MAC to work in conjunction with the packet concatenation.The proposed schemes have been implemented in Tiny-OS for Mica2 platform and Avrora emulator has been used for conducting experiments.Simulation results have revealed that the performance both in terms of energy&packet loss improves when ADP-MAC is used in conjunction with the additional features of PC&BA.Furthermore,the proposed scheme has been compared with a stateof-art packet concatenation primitive PiP(Packet-in-Packet).It has been observed that ADP-MAC supersedes the performance of PiP in terms of PDR(Packet Delivery Ratio)due to better management of synchronization between source and sink.
基金supported by the National Natural Science Foundation of China(31672345,51305057,61379087)the Research Equipment Development Project of Chinese Academy of Sciences(YZ201509)a Humboldt Fellowship(M.B.) from Alexander von Humboldt Foundation
文摘Geometric morphometrics （GM） is an important method of shape analysis and increasingly used in a wide range of scientific disciplines. Presently, a single character comparison system of geometric morphometric data is used in almost all empirical studies, and this approach is sufficient for many scientific problems. However, the estimation of overall similarity among taxa or objects based on multiple characters is crucial in a variety of contexts （e.g. （semi-）automated identification, phenetic relationships, tracing of character evolution, phylogenetic reconstruction）. Here we propose a new web-based tool for merging several geometric morphometrics data files from multiple characters into a single data file. Using this approach information from multiple characters can be compared in combination and an overall similarity estimate can be obtained in a convenient and geometrically rigorous manner. To illustrate our method, we provide an example analysis of 25 dung beetle species with seven Procrustes superimposed landmark data files representing the morphological variation of body features： the epipharynx, right mandible, pronotum, elytra, hindwing, and the metendosternite in dorsal and lateral view. All seven files were merged into a single one containing information on 649 landmark locations. The possible applications of such merged data files in different fields of science are discussed.
基金The author F.W.would like to express their gratitude to the Baihang university,Beijing,China for their nancial and technical support under Code No.BU/IFC/INT/01/008.
文摘In this paper,we propose a two-tiered segment-based Device-toDevice(S-D2D)caching approach to decrease the startup and playback delay experienced by Video-on-Demand(VoD)users in a cellular network.In the S-D2D caching approach cache space of each mobile device is divided into two cache-blocks.The rst cache-block reserve for caching and delivering the beginning portion of the most popular video les and the second cacheblock caches the latter portion of the requested video les‘fully or partially’depending on the users’video watching behaviour and popularity of videos.In this approach before caching,video is divided and grouped in a sequence of xed-sized fragments called segments.To control the admission to both cacheblocks and improve the system throughput,we further propose and evaluate three cache admission control algorithms.We also propose a video segment access protocol to elaborate on how to cache and share the video segments in a segmentation based D2D caching architecture.We formulate an optimisation problem and nd the optimal cache probability and beginning-segment size that maximise the cache-throughput probability of beginning-segments.To solve the non-convex cache-throughout maximisation problem,we derive an iterative algorithm,where the optimal solution is derived in each step.We used extensive simulations to evaluate the performance of our proposed S-D2D caching system.
文摘Sentiment Analysis, an un-abating research area in text mining, requires a computational method for extracting useful information from text. In recent days, social media has become a really rich source to get information about the behavioral state of people(opinion) through reviews and comments. Numerous techniques have been aimed to analyze the sentiment of the text, however, they were unable to come up to the complexity of the sentiments. The complexity requires novel approach for deep analysis of sentiments for more accurate prediction. This research presents a three-step Sentiment Analysis and Prediction(SAP) solution of Text Trend through K-Nearest Neighbor(KNN). At first, sentences are transformed into tokens and stop words are removed. Secondly, polarity of the sentence, paragraph and text is calculated through contributing weighted words, intensity clauses and sentiment shifters. The resulting features extracted in this step played significant role to improve the results. Finally, the trend of the input text has been predicted using KNN classifier based on extracted features. The training and testing of the model has been performed on publically available datasets of twitter and movie reviews. Experiments results illustrated the satisfactory improvement as compared to existing solutions. In addition, GUI(Hello World) based text analysis framework has been designed to perform the text analytics.
文摘Throughout the use of the small battery-operated sensor nodes encou-rage us to develop an energy-efficient routing protocol for wireless sensor networks(WSNs).The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of WSN.Many routing protocols are available,but the issue is still alive.Clustering is one of the most important techniques in the existing routing protocols.In the clustering-based model,the important thing is the selection of the cluster heads.In this paper,we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each cluster.Initially,the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small distance.The proposed scheme performs hierarchal routing and direct routing with some energy thresholds.The simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its performance.Moreover,the simulations will be performed in two scenarios,gate-way-based and without gateway to achieve more energy-efficient results.
