Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism

A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.

Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

Simple bone cysts of the proximal humerus presented with limb length discrepancy:A case report

BACKGROUND Simple bone cysts(SBC)are benign tumor-like bone lesions typically identified in children.While SBC may lead to growth disturbances or growth arrest,such cases are uncommon.The mechanisms behind these observations remain unclear.Additionally,research on the etiology of SBC remains inconclusive,and there has been no consensus on the appropriate timing and methodology for treatment.CASE SUMMARY Here,we present our experience in the successful surgical management of a 10-year-old girl with SBC,who presented with a pathological fracture complicated by malunion of the displaced fracture,varus deformity,and limb length discrepancy.We hypothesized two possible etiologies for the patient’s growth arrest and subsequent humerus varus deformity:(1)Direct disruption of the physis by fluid from the cyst itself;and(2)damage to the epiphysis due to repetitive pathological fractures associated with SBC.In addressing this case,surgical intervention was undertaken to correct the proximal humerus varus deformity.This approach offered the advantages of simultaneously correcting angular abnormalities,achieving mild limb lengthening,providing definitive SBC treatment,and reducing the overall treatment duration.CONCLUSION As per current literature,acute correction of acute angular deformity in proximal humeral SBC is not well comprehended.However,in this specific case,acute correction was considered an optimal solution.

正月趣品牌设计,中国传统文化习俗的现代表达

“正月趣”是一个以中国新年为主题的文化创意品牌。此设计是为了唤起人们对中国文化习俗的记忆,保护和传承这一独特的文化传统。设计中融合了与传统习俗相关的,具有吉祥寓意的动物角色。产品的主色调是红色,这在中国通常代表着幸福、快乐和繁荣。设计师在小册子中或在信封、桌布、灯笼等物品上使用这些独特的元素,巧妙地将历史与现代生活相结合。

Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.

Traffic Flow Prediction with Heterogenous Data Using a Hybrid CNN-LSTM Model

Predicting traffic flow is a crucial component of an intelligent transportation system.Precisely monitoring and predicting traffic flow remains a challenging endeavor.However,existingmethods for predicting traffic flow do not incorporate various external factors or consider the spatiotemporal correlation between spatially adjacent nodes,resulting in the loss of essential information and lower forecast performance.On the other hand,the availability of spatiotemporal data is limited.This research offers alternative spatiotemporal data with three specific features as input,vehicle type(5 types),holidays(3 types),and weather(10 conditions).In this study,the proposed model combines the advantages of the capability of convolutional(CNN)layers to extract valuable information and learn the internal representation of time-series data that can be interpreted as an image,as well as the efficiency of long short-term memory(LSTM)layers for identifying short-term and long-term dependencies.Our approach may utilize the heterogeneous spatiotemporal correlation features of the traffic flowdataset to deliver better performance traffic flow prediction than existing deep learning models.The research findings show that adding spatiotemporal feature data increases the forecast’s performance;weather by 25.85%,vehicle type by 23.70%,and holiday by 14.02%.

Comment on: Bibliometric analysis of glaucoma-related literature based on SCIE database: a 10-year literature analysis from 2009 to 2018

Dear Editor,Sun et al[1]recently published a bibliometric paper in the International Journal of Ophthalmology entitled“Bibliometric analysis of glaucoma-related literature based on SCIE database:a 10-year literature analysis from 2009 to 2018”.The authors mentioned in section MATERIALS AND METHODS that“SCIE database established by Institute for Scientific Information(ISI)was used for the purpose of this study.”and“The search strategy was as follows:theme=(glaucoma OR ocular hypertension),and the‘theme’field contains:title,abstract,author keywords,and keywords plus.Document type=(article),and other types of articles,such as case reports,reviews,letters to the editors,and so on,were excluded.The time period of publications was focused on the latest 10y from 2009 to 2018.And the search date was 2019-3-29.”

Introduction to the Special Issue on Hybrid Intelligent Methods for Forecasting in Resources and Energy Field

Precise resources and energy forecasting are important to facilitate the decision-making process in order to achieve higher efficiency and reliability in energy system planning,maintenance,operation,security,and so on.In the past decades,many resources and energy forecasting models have been continuously proposed to increase the forecasting accuracy,especially intelligence models(e.g.,artificial neural networks,support vector regression,evolutionary computation models,etc.).Meanwhile,due to the great development of optimization methods(e.g.,quadratic programming method,differential empirical mode method,evolutionary algorithms,etc.),many novel hybrid methods combined with the above-mentioned intelligent-optimization-based methods have also been proposed to achieve satisfactory forecasting accuracy levels.It is worthwhile to explore the tendency and development of intelligent-optimization-based hybrid methodologies and to enrich their practical performances,particularly for resources and energy forecasting.

Federated Feature Concatenate Method for Heterogeneous Computing in Federated Learning

Federated learning is an emerging machine learning techniquethat enables clients to collaboratively train a deep learning model withoutuploading raw data to the aggregation server. Each client may be equippedwith different computing resources for model training. The client equippedwith a lower computing capability requires more time for model training,resulting in a prolonged training time in federated learning. Moreover, it mayfail to train the entire model because of the out-of-memory issue. This studyaims to tackle these problems and propose the federated feature concatenate(FedFC) method for federated learning considering heterogeneous clients.FedFC leverages the model splitting and feature concatenate for offloadinga portion of the training loads from clients to the aggregation server. Eachclient in FedFC can collaboratively train a model with different cutting layers.Therefore, the specific features learned in the deeper layer of the serversidemodel are more identical for the data class classification. Accordingly,FedFC can reduce the computation loading for the resource-constrainedclient and accelerate the convergence time. The performance effectiveness isverified by considering different dataset scenarios, such as data and classimbalance for the participant clients in the experiments. The performanceimpacts of different cutting layers are evaluated during the model training.The experimental results show that the co-adapted features have a criticalimpact on the adequate classification of the deep learning model. Overall,FedFC not only shortens the convergence time, but also improves the bestaccuracy by up to 5.9% and 14.5% when compared to conventional federatedlearning and splitfed, respectively. In conclusion, the proposed approach isfeasible and effective for heterogeneous clients in federated learning.

Bi-Objective Optimization: A Pareto Method with Analytical Solutions

Multiple objectives to be optimized simultaneously are prevalent in real-life problems. This paper develops a new Pareto Method for bi-objective optimization which yields analytical solutions. The Pareto optimal front is obtained in closed-form, enabling the derivation of various solutions in a convenient and efficient way. The advantage of analytical solution is the possibility of deriving accurate, exact and well-understood solutions, which is especially useful for policy analysis. An extension of the method to include multiple objectives is provided with the objectives being classified into two types. Such an extension expands the applicability of the developed techniques.

Long-term rare giant sialolithiasis for 30 years:A case report and review of literature

BACKGROUND Sialolithiasis is one of the most common salivary gland disorders,most commonly affecting the submandibular gland.Submandibular sialolithiasis can be treated using non-invasive conservative measures and invasive treatments.Treatment selection was based on the ductal system anatomy and the size and location of the stones.This study aimed to review the updates on sialolithiasis treatment and compare the different management strategies of the variables.CASE SUMMARY This report presents a case of a long-term,rare,and giant sialolithiasis within the submandibular gland parenchyma for 30 years in an older adult.Our patient presented with painless right submandibular swelling.Computed tomography revealed a calcified mass measuring 35 mm×20 mm within the right submandibular gland.In this case,the infection and fibrosis of the affected gland and size of the stone did not provide us with other alternatives except for the excision of the involved gland.Thus,right submandibular sialoadenectomy was performed via the transcervical approach.After the surgery,the patient recovered without any complaints,side effects,or complications.CONCLUSION Tailored management is important for preserving gland function,maintaining low risk,and reducing patient discomfort.

Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor younger.There is a need to develop new technologies andmethods to deal with this disease.Many researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a challenge.Several different types of angiograms are adopted for CAD diagnosis.This paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art results.We have collected the 2D X-ray images from the hospital,and the proposed model has been applied to them.Image augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s size.Also,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high accuracy.As the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.

H1-antihistamine use and head and neck cancer risk in type 2 diabetes mellitus

This study aimed to examine the association between the use of H1-antihistamines(AHs)and head and neck cancer(HNC)risk in patients with type 2 diabetes mellitus(T2DM).Data from the National Health Insurance Research Database of Taiwan were analyzed for the period from 2008 to 2018.A propensity-score-matched cohort of 54,384 patients each in the AH user and nonuser groups was created and analyzed using Kaplan-Meier method and Cox proportional hazards regression.The results showed that the risk of HNC was significantly lower in AH users(adjusted hazard ratio:0.55,95%CI:0.48 to 0.64)and the incidence rate was also lower(5.16 vs.8.10 per 100,000 person-years).The lower HNC incidence rate in AH users(95%CI:0.63;0.55 to 0.73)suggests that AH use may reduce the risk of HNC in T2DM patients.

Highly cited publication performance in the ophthalmology category in the Web of Science database:a bibliometric analysis

AIM:To determine and evaluate the features of highly cited articles(HCAs)in the ophthalmology category in the Science Citation Index Expanded(SCI-EXPANDED)from 1991 to 2020.METHODS:The Web of Science Core Collection documents with at least 100 citations from their publication year until December 31,2020,were evaluated as highly cited.The examined features were the distribution of yearly output and its average number of per publication,HCAs,authors,institutions,journals,and nations.The publication performance of nations and organizations was assessed using six publication indicators.The Y-index was employed to compare the research outputs of various authors.RESULTS:Publications that had cited the most references were highly published in high-impact factor journals.The United States of America came out on top across all six publication indicators,and it was home to eight of the top 10 most productive institutions.The articles written by Breivik et al(2006)and Farrar et al(2001)were highly cited and had a significant impact in 2020.The authors had a higher number of highly cited articles published as corresponding authors than as first authors.CONCLUSION:The findings of the present study highlight the current scope of global research in ophthalmology.The findings can help policy-makers and advisory groups of research centers to develop future policies.In addition,the findings can guide researchers in this field.

Age and Gender Classification Using Backpropagation and Bagging Algorithms

Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security systems.The main aim of the research is to develop a system able to predicate and classify gender,age,and accent.So,a newsystem calledClassifyingVoice Gender,Age,and Accent(CVGAA)is proposed.Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories.It has high precision compared to other algorithms used in this problem,as the adaptive backpropagation algorithm had an accuracy of 98%and the Bagging algorithm had an accuracy of 98.10%in the gender identification data.Bagging has the best accuracy among all algorithms,with 55.39%accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.

Optimal Strategies Trajectory with Multi-Local-Worlds Graph

This paper constructs a non-cooperative/cooperative stochasticdifferential game model to prove that the optimal strategies trajectory ofagents in a system with a topological configuration of a Multi-Local-Worldgraph would converge into a certain attractor if the system’s configuration isfixed. Due to the economics and management property, almost all systems aredivided into several independent Local-Worlds, and the interaction betweenagents in the system is more complex. The interaction between agents inthe same Local-World is defined as a stochastic differential cooperativegame;conversely, the interaction between agents in different Local-Worldsis defined as a stochastic differential non-cooperative game. We construct anon-cooperative/cooperative stochastic differential game model to describethe interaction between agents. The solutions of the cooperative and noncooperativegames are obtained by invoking corresponding theories, and thena nonlinear operator is constructed to couple these two solutions together.At last, the optimal strategies trajectory of agents in the system is proven toconverge into a certain attractor, which means that strategies trajectory arecertainty as time tends to infinity or a large positive integer. It is concluded thatthe optimal strategy trajectory with a nonlinear operator of cooperative/noncooperativestochastic differential game between agents can make agentsin a certain Local-World coordinate and make the Local-World paymentmaximize, and can make the all Local-Worlds equilibrated;furthermore, theoptimal strategy of the coupled game can converge into a particular attractorthat decides the optimal property.

The gut microbiome:implications for neurogenesis and neurological diseases

There is an increasing recognition of the strong links between the gut microbiome and the brain,and there is persuasive evidence that the gut microbiome plays a role in a variety of physiological processes in the central nervous system.This review summarizes findings that gut microbial composition alterations are linked to hippocampal neurogenesis,as well as the possible mechanisms of action;the existing literature suggests that microbiota influence neurogenic processes,which can result in neurological disorders.We consider this evidence from the perspectives of neuroinflammation,microbial-derived metabolites,neurotrophins,and neurotransmitters.Based on the existing research,we propose that the administration of probiotics can normalize the gut microbiome.This could therefore also represent a promising treatment strategy to counteract neurological impairment.

SCIE中化学工程高引用文章的文献计量学分析(英文)

This study aimed to identify and to analyze characteristics of top-cited articles published in the Web of Science chemical engineering subject category from 1899 to 2011.Articles that have been cited more than 100 times were assessed regarding publication outputs,and distribution of outputs in journals.Five bibliometric indicators were used to evaluate source countries,institution and authors.A new indicator,Y-index,was created to assess quantity and quality of contribution to articles.Results showed that 3828 articles,published between 1931 and 2010,had been cited at least 100 times.Among them 54% published before 1991,and 49% top-cited articles originated from US.The top eight productive institutions were all located in US.The top journals were Journal of Catalysis,AIChE Journal,Chemical Engineering Science and Journal of Membrane Science.Y-index was successfully applied to evaluate publication character of authors,institutions,and countries/regions.

Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality.The privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective search.Attribute-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud storage.However,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious computations.In a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE schemes.In the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain network.Thus,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical CCPS.With the assistance of blockchain technology,the proposed scheme offers two main benefits.First,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of failure.Second,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain network.Specifically,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus nodes.This eliminates the need of the trusted authority and reduces the burden of data users,respectively.Further,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret key associated with users.It has been verified both theoretically and practically in the performance analysis section.