Short Term Traffic Flow Prediction Using Hybrid Deep Learning

Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential features.Secondly, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error value.An extensive experimental analysis was performed on the benchmark dataset.The evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.

Predicting and Curing Depression Using Long Short Term Memory and Global Vector

In today’s world, there are many people suffering from mentalhealth problems such as depression and anxiety. If these conditions are notidentified and treated early, they can get worse quickly and have far-reachingnegative effects. Unfortunately, many people suffering from these conditions,especially depression and hypertension, are unaware of their existence until theconditions become chronic. Thus, this paper proposes a novel approach usingBi-directional Long Short-Term Memory (Bi-LSTM) algorithm and GlobalVector (GloVe) algorithm for the prediction and treatment of these conditions.Smartwatches and fitness bands can be equipped with these algorithms whichcan share data with a variety of IoT devices and smart systems to betterunderstand and analyze the user’s condition. We compared the accuracy andloss of the training dataset and the validation dataset of the two modelsnamely, Bi-LSTM without a global vector layer and with a global vector layer.It was observed that the model of Bi-LSTM without a global vector layer hadan accuracy of 83%,while Bi-LSTMwith a global vector layer had an accuracyof 86% with a precision of 86.4%, and an F1 score of 0.861. In addition toproviding basic therapies for the treatment of identified cases, our model alsohelps prevent the deterioration of associated conditions, making our methoda real-world solution.

Prediction of Attention and Short-Term Memory Loss by EEG Workload Estimation

Mental workload plays a vital role in cognitive impairment. The impairment refers to a person’s difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that seriously affect everyday life. In this paper, the simultaneous capacity (SIMKAP) experiment-based EEG workload analysis was presented using 45 subjects for multitasking mental workload estimation with subject wise attention loss calculation as well as short term memory loss measurement. Using an open access preprocessed EEG dataset, Discrete wavelet transforms (DWT) was utilized for feature extraction and Minimum redundancy and maximum relevancy (MRMR) technique was used to select most relevance features. Wavelet decomposition technique was also used for decomposing EEG signals into five sub bands. Fourteen statistical features were calculated from each sub band signal to form a 5 × 14 window size. The Neural Network (Narrow) classification algorithm was used to classify dataset for low and high workload conditions and comparison was made using some other machine learning models. The results show the classifier’s accuracy of 86.7%, precision of 84.4%, F1 score of 86.33%, and recall of 88.37% that crosses the state-of-the art methodologies in the literature. This prediction is expected to greatly facilitate the improved way in memory and attention loss impairments assessment.

Short-TermWind Power Prediction Based on Combinatorial Neural Networks

Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation.Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections.For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model,the short-term prediction of wind power based on a combined neural network is proposed.First,the Bi-directional Long Short Term Memory(BiLSTM)network prediction model is constructed,and the bi-directional nature of the BiLSTM network is used to deeply mine the wind power data information and find the correlation information within the data.Secondly,to avoid the limitation of a single prediction model when the wind power changes abruptly,the Wavelet Transform-Improved Adaptive Genetic Algorithm-Back Propagation(WT-IAGA-BP)neural network based on the combination of the WT-IAGA-BP neural network and BiLSTM network is constructed for the short-term prediction of wind power.Finally,comparing with LSTM,BiLSTM,WT-LSTM,WT-BiLSTM,WT-IAGA-BP,and WT-IAGA-BP&LSTM prediction models,it is verified that the wind power short-term prediction model based on the combination of WT-IAGA-BP neural network and BiLSTM network has higher prediction accuracy.

Short-Term Prediction of Photovoltaic Power Generation Based on LMD Permutation Entropy and Singular Spectrum Analysis

The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.

Ankle Fractures, Short Term Operative Outcome: A Retrospective Case Series

Introduction and Background: Ankle injury is one of the most frequent presenting injuries to the emergency room and ankle fractures are common fracture in the lower limbs injuries that may require operative treatment with variable outcomes. Materials and Methods: Sixty-three patients were included in my retrospective study, and all with a displaced fracture of the ankle caused by high energy trauma were treated by open reduction and rigid internal fixation. Results: After follow-up at six weeks and twelve weeks, the results were satisfactory in fifty-five percent out of all the sixty-three patients. Conclusions and Recommendations: Ankle fractures occur mainly in young males of the age group between 26 and 35 years, mostly caused by fall down and motor vehicle accident.

Snowmelt Flood Mapping and Land Surface Short-Term Dynamics Assessment in a “Before-During-After” Scenario Based on Radar and Optical Satellite Imagery: Case Study Around the Lewisville Lake (Dallas/Fort Worth Metropolitan, Texas, USA)

The main goal of this study has been to map flood and assess land surface short-term dynamics in relation with snowy weather. The two recent snowfall events, which happened in, February 14th and 15th, of year 2021, and February 3rd and 4th, of year 2022, were chosen. A pre-analysis correlation was assumed between, the snow events, recurrency of floods, and changes in the land surface characteristics (i.e., wetness, energy, temperature), in a “Before-During-After” scenario. Active and passive microwave satellites data such as, Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral instrument (MSI) and Landsat-9 Operation Land Imager-2/Thermal Infrared Sensors-2 (OLI-2/TIRS-2), as well as cloud databased global models for water and urban layers were used. The first step of processing was thresholding of SAR image, at 0.25 cutoff, based on bimodal histogram distribution, followed by the change analysis. The following processing consisted in the images transformation, by computing the tasseled cap transformation wetness (TCTw) and the surface albedo on MSI image. In addition, the land surface temperature (LST) was modeled from OLI-2/TIRS-2 image. Then, a 5th order polynomial regression was computed, between TCTw as dependent variable and, albedo and LST as independent variables. As a first result, an area of 5.6 km2 has been mapped as recurrently flooded from the two years assessment. The other output highlighted a constant increase of wetness (TCTw), considered most influential on land surface dynamics, comparatively to energy exchange (albedo) and temperature (LST). The “After” event dependency between the three indicators was highest, with a correlation coefficient, R2 = 0.682, confirming the persistence of wetness after-snowmelt. Validation over topographic layers confirmed that, recurrently flooded areas are mostly distributed on, lowest valley depth points, farthest distances from channel network (i.e., from perennial waters), and lowest relative slope position areas. Whereas, 88.9% of the validation sampling were confirmed in the laboratory, and 86.7% of urban validation points were assessed as recurrently flooded when combining pre-/post-field-work campaign.

Identification of the Breach of Short-Term Rental Regulations in Irish Rent Pressure Zones

The housing crisis in Ireland has rapidly grown in recent years. To make a more significant profit, many landlords are no longer renting out their houses under long-term tenancies but under short-term tenancies. Regulating rentals in Rent Pressure Zones with the highest and rising rents is becoming a tricky issue. In this paper, we develop a breach identifier to check short-term rentals located in Rent Pressure Zones with potential breaches only using publicly available data from Airbnb (an online marketplace focused on short-term home-stays) and Irish government websites. First, we use a Residual Neural Network to filter out outdoor landscape photos that negatively impact identifying whether an owner has multiple rentals in a Rent Pressure Zone. Second, a Siamese Neural Network is used to compare the similarity of indoor photos to determine if multiple rental posts correspond to the same residence. Next, we use the Haversine algorithm to locate short-term rentals within a circle centered on the coordinate of a permit. Short-term rentals with a permit will not be restricted. Finally, we improve the occupancy estimation model combined with sentiment analysis, which may provide higher accuracy.

Short-Term Memory Capacity across Time and Language Estimated from Ancient and Modern Literary Texts. Study-Case: New Testament Translations

We study the short-term memory capacity of ancient readers of the original New Testament written in Greek, of its translations to Latin and to modern languages. To model it, we consider the number of words between any two contiguous interpunctions Ip, because this parameter can model how the human mind memorizes “chunks” of information. Since IP can be calculated for any alphabetical text, we can perform experiments—otherwise impossible— with ancient readers by studying the literary works they used to read. The “experiments” compare the IP of texts of a language/translation to those of another language/translation by measuring the minimum average probability of finding joint readers (those who can read both texts because of similar short-term memory capacity) and by defining an “overlap index”. We also define the population of universal readers, people who can read any New Testament text in any language. Future work is vast, with many research tracks, because alphabetical literatures are very large and allow many experiments, such as comparing authors, translations or even texts written by artificial intelligence tools.

一种基于long short-term memory的唇语识别方法

唇动视觉信息是说话内容的重要载体。受嘴唇外观、背景信息和说话习惯等影响,即使说话者说相同的内容,唇动视觉信息也会相差很大。为解决唇语视觉信息多样性的问题,提出一种基于long short-term memory(LSTM)的新的唇语识别方法。以往大多数的方法从嘴唇外表信息入手。本方法用嘴唇关键点坐标描述嘴唇形变信息作为唇语视频的特征,它具有类内一致性和类间区分性的特点。然后利用LSTM对特征进行时序编码,它能学习具有区分性和泛化性的空间-时序特征。在公开的唇语数据集GRID、MIRACL-VC和Oulu VS上对本方法做了针对分割的单词或短语的说话者独立的唇语识别评估。在GRID和MIRACL-VC上,本方法的准确率比传统方法至少高30%;在Oulu VS上,本方法的准确率接近于最优结果。以上实验结果表明,本文提出的基于LSTM的唇语识别方法有效地解决了唇语视觉信息多样性的问题。

The spatial characteristic of the short－term and imminent anomalies of waterradonbe┐foreearthquakeinthemainlandofChina

The changes of radon content in underground water(water radon)recorded at about 200 stations in 32 earthquakes occurred in the mainland of China are studied in this paper. The result shows that the spatial distribution of short term and imminent anomalies of water radon before earthquake seems to be mainly related to the active master fault nearby the hypocenter of an earthquake and the earthquake generating mechanism. Finally, some understandings on the mechanism of the aomalies and the imminent earthquake prediction are set forth.

Tool Health Condition Recognition Method for High Speed Milling of Titanium Alloy Based on Principal Component Analysis (PCA) and Long Short Term Memory (LSTM)

The healthy condition of the milling tool has a very high impact on the machining quality of the titanium components.Therefore,it is important to recognize the healthy condition of the tool and replace the damaged cutter at the right time.In order to recognize the health condition of the milling cutter,a method based on the long short term memory(LSTM)was proposed to recognize tool health state in this paper.The various signals collected in the tool wear experiments were analyzed by time-domain statistics,and then the extracted data were generated by principal component analysis(PCA)method.The preprocessed data extracted by PCA is transmitted to the LSTM model for recognition.Compared with back propagation neural network(BPNN)and support vector machine(SVM),the proposed method can effectively utilize the time-domain regulation in the data to achieve higher recognition speed and accuracy.

A Hybrid Short Term Load Forecasting Model of an Indian Grid

This paper describes an application of combined model of extrapolation and correlation techniques for short term load forecasting of an Indian substation. Here effort has been given to improvise the accuracy of elec-trical load forecasting considering the factors, past data of the load, respective weather condition and finan-cial growth of the people. These factors are derived by curve fitting technique. Then simulation has been conducted using MATLAB tools. Here it has been suggested that consideration of 20 years data for a devel-oping country should be ignored as the development of a country is highly unpredictable. However, the im-portance of the past data should not be ignored. Here, just previous five years data are used to determine the above factors.

The effect of fire disturbance on short-term soil respiration in typical forest of Greater Xing'an Range, China

We investigated the effect of fire disturbance on short-term soil respiration in birch （Betula platyphylla Suk.） and larch （Larix gmelinii Rupr.） forests in Greater Xing’an range, northeastern China for further understanding of its effect on the carbon cycle in ecosystems. Our study show that post-fire soil respiration rates in B. platyphylla and L. gmelinii forests were reduced by 14%and 10%, respectively. In contrast, the soil heterotrophic respiration rates in the two types of forest were similar in post-fire and control plots. After fire, the contribution of root respiration to total soil respiration was dramatically reduced. Variation in soil respiration rates was explained by soil moisture （W） and soil tem-perature （T） at a depth of 5 cm. Exponential regression fitted T and W models explained Rs rates in B. platyphylla control and post-fire plots （83.1% and 86.2%） and L. gmelinii control and post-fire plots （83.7%and 88.7%）. In addition, the short-term temperature coefficients in B.

Conditional Random Field Tracking Model Based on a Visual Long Short Term Memory Network

In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is established by using a visual long short term memory network in the three-dimensional(3D)space and the motion estimations jointly performed on object trajectory segments.Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation.To address the uncertainty of the length and interval of trajectory segments,a multimode long short term memory network is proposed for the object motion estimation.The tracking performance is evaluated using the PETS2009 dataset.The experimental results show that the proposed method achieves better performance than the tracking methods based on the independent motion estimation.

Investigation on short-term burst pressure of plastic pipes reinforced by cross helically wound steel wires

Plastic pipes reinforced by cross helically wound steel wires (PSP), which have exhibited excellent mechanical performance, consist of inner polyethylene (PE) layer, winding layer and outer PE layer. The winding layer is composed of two monolayers where steel wires are cross helically wound. An analytical procedure is developed to predict the short-term burst pressure of PSP as the monolayer is assumed to be elastic and orthotropic. The 3D anisotropic elasticity and Maximum Stress Failure Criterion are employed in the formulation of the elasticity problem. Good agreement between the theoretical results and the experimental data shows that the proposed approach can well predict the short-term burst pressure of PSP.

Predictive value of serum levels of transforming growth factor beta 1 for the short-term effects of radiotherapy and chemotherapy in patients with esophageal cancer

Objective To investigate variation in levels of transforming growth factor beta 1(TGF-β1)before and after radiotherapy in patients with esophageal cancer in order to evaluate the predictive value of TGF-β1 for the effects of radiotherapy Methods A total of 140 patients with esophageal squamous carcinoma undergoing radical radiation therapy in the Department of Oncology from March 2015 to December 2017 were enrolled.The patients were divided into the effective(115 cases)and ineffective(25 cases)groups according to World Health Organization(WHO)criteria for the evaluation of solid tumors(2009 RECIST standard).TGF-β1 levels were measured in all patients by using enzyme-linked immunosorbent assay(ELISA).Multiple-factor analysis of the predictive value of the treatment efficacy was performed by Cox regression analysis.Results After radiotherapy,36,79,and 25 cases experienced complete response(CR),partial response(PR),and no response(NR),respectively,with a total effective rate of 82.14%.The TGF-β1 level was significantly lower in the effective group than that in the ineffective group(P<0.05)and covariance analysis revealed significantly reduced TGF-β1 level in esophageal cancer patients following radiotherapy.The multi-factor Cox regression model revealed that the predictive value of TGF-β1 for the effect of radiotherapy was largest,with a hazard ratio[HR]of 1.955(P=0.002),followed by exposure dose,with(HR=1.367;P=0.035).Conclusion Serum TGF-β1 level can serve as a predictor for the short-term effects of radiotherapy in patients with esophageal cancer.

Colonoscopy evaluation after short-term anti-tuberculosis treatment in nonspecific ulcers on the ileocecal area

AIM: To evaluate the eff icacy of colonoscopy follow-up after short-term anti-tuberculosis treatment in patients with nonspecific ulcers on ileocecal areas being suspicious of tuberculous colitis. METHODS: We prospectively analyzed the colonoscopic fi ndings before and after short term anti- tuberculosis treatment in 18 patients with nonspecifi c ulcers on the ileocecal area and compared them with 7 patients of confi rmed tuberculous colitis by acid-fast bacilli or caseating granuloma on colonic biopsy. RESULTS: Mean duration for short-term follow- up was 107.3 d with combined chemotherapy containing isoniazid, rifampicin, ethambutol and pyrazinamide. Seven patients with tuberculous colitis showed complete healing of active ulcers after short- term medication. After short-term anti-tuberculosis treatment, follow-up colonoscopy findings devided 18 patients with nonspecific ulcers into two groups by ulcer state. One is the "suspicious tuberculous colitis group" showing healing of ulcers and erosions and another is the "suspicious inflammatory bowel disease group" showing active ulcers with or without aggravation of the lesion. Finally, all 9 of the "suspicious tuberculous colitis group" were diagnosed as tuberculous colitis showing no recurrence of ulcers after termination of 9 mo of anti-tuberculosis medication. Patients of the "suspicious inflammatorybowel disease group" were f inally diagnosed as Crohn's disease or nonspecifi c colonic ulcers during long-term follow up. CONCLUSION: Follow-up colonoscopy shows a healing stage ulcer or scarring change without an active ulcer with just 2 mo to 3 mo of medication in patients with tuberculous colitis. Colonoscopy follow-up after short term anti-tuberculosis trial in patients with nonspecif ic ulcers on the ileocecal area is valuable in making early differential diagnosis of tuberculous colitis.

Improvement of type 2 diabetes mellitus after gastric cancer surgery:Short-term outcome analysis after gastrectomy

AIM:To evaluate the effect of gastrectomy on diabetes control in patients with type 2 diabetes mellitus and early gastric cancer.METHODS:Data from 64 patients with early gastric cancer and type 2 diabetes mellitus were prospectively collected.All patients underwent curative gastrectomy(36 subtotal gastrectomy with gastroduodenostomy,16subtotal gastrectomy with gastrojejunostomy,12 total gastrectomy)and their physical and laboratory data were evaluated before and 3,6 and 12 mo after surgery.RESULTS:Fasting blood glucose(FBS),HbA1c,insulin,C-peptide,and homeostasis model assessment-estimated insulin resistance were significantly improved 3mo after surgery,regardless of operation type,and the significant improvement in all measured values,except HbA1c,was sustained up to 12 mo postoperatively.Approximately 3.1%of patients stopped diabetes medication and had HbA1c<6.0%and FBS<126 mg/dL.54.7%of patients decreased their medication,and had reduced FBS or HbA1c.In multivariate analysis,good diabetic control was not associated with operation type,but was associated with diabetes duration.CONCLUSION:Diabetes improved in more than 50%of patients during the first year after gastric cancer surgery.The degree of diabetes control was related to diabetes duration.

Rapid afterslip and short-term viscoelastic relaxation following the 2008 M_W7.9 Wenchuan earthquake

Significant postseismic deformation of the 2008 M W 7.9 Wenchuan earthquake has been observed from GPS data of the first 14 days after the earthquake. The possible mechanisms for the rapid postseismic deformation are assumed to be afterslip on the earthquake rupture plane and viscoelastic relaxation of coseismiclly stress change in the lower crust or upper mantle. We firstly use the constrained least squares method to find an afterslip model which can fit the GPS data best. The afterslip model can explain near-field data very well but shows considerable discrepancies in fitting far-field data. To estimate the effect due to the viscoelastic relaxation in the lower crust, we then ignore the contribution from the afterslip and attempt to invert the viscosity structure beneath the Longmenshan fault where the Wenchuan earthquake occurred from the postseismic deformation data. For this purpose, we use a viscoelastic model with a 2D geometry based on the geological and seismological observations and the coseismic slip distribution derived from the coseismic GPS and InSAR data. By means of a grid search we find that the optimum viscosity is 9×10 18 Pa·s for the middle-lower crust in the Chengdu Basin, 4×10 17 Pa·s for the middle-lower crust in the Chuanxi Plateau and 7×10 17 Pa·s for the low velocity zone in the Chuanxi plateau. The viscoelastic model explains the postseismic deformation observed in the far-field satisfactorily, but it is considerably worse than the afterslip model in fitting the near-fault data. It suggests therefore a hybrid model including both afterslip and relaxation effects. Since the viscoelastic model produces mainly the far-field surface deformation and has fewer degree of freedoms （three viscosity parameters） than the afterslip model with a huge number of source parameters, we fix the viscositiy structure as obtained before but redetermine the afterslip distribution using the residual data from the viscoelastic modeling. The redetermined afterslip distribution becomes physically more reasonable; it is more localized and exhibits a pattern spatially complementary with the coseismic rupture distribution. We conclude that the aseismic fault slip is responsible for the near-fault postseismic deformation, whereas the viscoelastic stress relaxation might be the major cause for the far-field postseismic deformation.