Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban development.Traditional mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely data.Recent advancements in Geographic Information Systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations.Nonetheless,challenges persist,particularly concerning accuracy and significant temporal delays.This study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing data.This methodology can facilitate the accurate and timely identification of urban redevelopment activities.The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment.The model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban redevelopment.This research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban development.The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

Hierarchical multihead self-attention for time-series-based fault diagnosis

Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.

Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.

Meteorological factors, ambient air pollution, and daily hospital admissions for depressive disorder in Harbin: A time-series study

BACKGROUND The literature has discussed the relationship between environmental factors and depressive disorders;however,the results are inconsistent in different studies and regions,as are the interaction effects between environmental factors.We hypo-thesized that meteorological factors and ambient air pollution individually affect and interact to affect depressive disorder morbidity.AIM To investigate the effects of meteorological factors and air pollution on depressive disorders,including their lagged effects and interactions.METHODS The samples were obtained from a class 3 hospital in Harbin,China.Daily hos-pital admission data for depressive disorders from January 1,2015 to December 31,2022 were obtained.Meteorological and air pollution data were also collected during the same period.Generalized additive models with quasi-Poisson regre-ssion were used for time-series modeling to measure the non-linear and delayed effects of environmental factors.We further incorporated each pair of environ-mental factors into a bivariate response surface model to examine the interaction effects on hospital admissions for depressive disorders.RESULTS Data for 2922 d were included in the study,with no missing values.The total number of depressive admissions was 83905.Medium to high correlations existed between environmental factors.Air temperature(AT)and wind speed(WS)significantly affected the number of admissions for depression.An extremely low temperature(-29.0℃)at lag 0 caused a 53%[relative risk(RR)=1.53,95%confidence interval(CI):1.23-1.89]increase in daily hospital admissions relative to the median temperature.Extremely low WSs(0.4 m/s)at lag 7 increased the number of admissions by 58%(RR=1.58,95%CI:1.07-2.31).In contrast,atmospheric pressure and relative humidity had smaller effects.Among the six air pollutants considered in the time-series model,nitrogen dioxide(NO_(2))was the only pollutant that showed significant effects over non-cumulative,cumulative,immediate,and lagged conditions.The cumulative effect of NO_(2) at lag 7 was 0.47%(RR=1.0047,95%CI:1.0024-1.0071).Interaction effects were found between AT and the five air pollutants,atmospheric temperature and the four air pollutants,WS and sulfur dioxide.CONCLUSION Meteorological factors and the air pollutant NO_(2) affect daily hospital admissions for depressive disorders,and interactions exist between meteorological factors and ambient air pollution.

Complication rates after direct anterior vs posterior approach for hip hemiarthroplasty in elderly individuals with femoral neck fractures

BACKGROUND Dislocation rates after hemiarthroplasty reportedly vary from 1%to 17%.This serious complication is associated with increased morbidity and mortality rates.Approaches to this surgery are still debated,with no consensus regarding the superiority of any single approach.AIM To compare early postoperative complications after implementing the direct anterior and posterior approaches(PL)for hip hemiarthroplasty after femoral neck fractures.METHODS This is a comparative,retrospective,single-center cohort study conducted at a university hospital.Between March 2008 and December 2018,273 patients(a total of 280 hips)underwent bipolar hemiarthroplasties(n=280)for displaced femoral neck fractures using either the PL(n=171)or the minimally invasive direct anterior approach(DAA)(n=109).The choice of approach was related to the surgeons’practices;the implant types were similar and unrelated to the approach.Dislocation rates and other complications were reviewed after a minimum followup of 6 mo.RESULTS Both treatment groups had similarly aged patients(mean age:82 years),sex ratios,patient body mass indexes,and patient comorbidities.Surgical data(surgery delay time,operative time,and blood loss volume)did not differ significantly between the groups.The 30 d mortality rate was higher in the PL group(9.9%)than in the DAA group(3.7%),but the difference was not statistically significant(P=0.052).Among the one-month survivors,a significantly higher rate of dislocation was observed in the PL group(14/154;9.1%)than in the DAA group(0/105;0%)(P=0.002).Of the 14 patients with dislocation,8 underwent revision surgery for recurrent instability(posterior group),and one of them had 2 additional procedures due to a deep infection.The rate of other complications(e.g.,perioperative and early postoperative periprosthetic fractures and infection-related complications)did not differ significantly between the groups.CONCLUSION These findings suggest that the DAA to bipolar hemiarthroplasty for patients with femoral neck fractures is associated with a lower dislocation rate(<1%)than the PL.

Graph Construction Method for GNN-Based Multivariate Time-Series Forecasting

Multivariate time-series forecasting(MTSF)plays an important role in diverse real-world applications.To achieve better accuracy in MTSF,time-series patterns in each variable and interrelationship patterns between variables should be considered together.Recently,graph neural networks(GNNs)has gained much attention as they can learn both patterns using a graph.For accurate forecasting through GNN,a well-defined graph is required.However,existing GNNs have limitations in reflecting the spectral similarity and time delay between nodes,and consider all nodes with the same weight when constructing graph.In this paper,we propose a novel graph construction method that solves aforementioned limitations.We first calculate the Fourier transform-based spectral similarity and then update this similarity to reflect the time delay.Then,we weight each node according to the number of edge connections to get the final graph and utilize it to train the GNN model.Through experiments on various datasets,we demonstrated that the proposed method enhanced the performance of GNN-based MTSF models,and the proposed forecasting model achieve of up to 18.1%predictive performance improvement over the state-of-the-art model.

Sentinel-1 In SAR observations and time-series analysis of co-and postseismic deformation mechanisms of the 2021 Mw 5.8 Bandar Ganaveh Earthquake,Southern Iran

In the past two decades,because of the significant increase in the availability of differential interferometry from synthetic aperture radar and GPS data,spaceborne geodesy has been widely employed to determine the co-seismic displacement field of earthquakes.On April 18,2021,a moderate earthquake(Mw 5.8)occurred east of Bandar Ganaveh,southern Iran,followed by intensive seismic activity and aftershocks of various magnitudes.We use two-pass D-InSAR and Small Baseline Inversion techniques via the LiCSBAS suite to study the coseismic displacement and monitor the four-month post-seismic deformation of the Bandar Ganaveh earthquake,as well as constrain the fault geometry of the co-seismic faulting mechanism during the seismic sequence.Analyses show that the co-and postseismic deformation are distributed in relatively shallow depths along with an NW-SE striking and NE dipping complex reverse/thrust fault branches of the Zagros Mountain Front Fault,complying with the main trend of the Zagros structures.The average cumulative displacements were obtained from-137.5 to+113.3 mm/yr in the SW and NE blocks of the Mountain Front Fault,respectively.The received maximum uplift amount is approximately consistent with the overall orogen-normal shortening component of the Arabian-Eurasian convergence in the Zagros region.No surface ruptures were associated with the seismic source;therefore,we propose a shallow blind thrust/reverse fault(depth~10 km)connected to the deeper basal decollement fault within a complex tectonic zone,emphasizing the thin-skinned tectonics.

Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features.Electric kickboards are gradually growing in popularity in tourist and education-centric localities.In the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is essential.Due to its freefloating nature,the shared electric kickboard is a common and practical means of transportation.Relocation plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is crucial.Predicting demand accurately with small data is troublesome.Extensive data is necessary for training machine learning algorithms for effective prediction.Data generation is a method for expanding the amount of data that will be further accessible for training.In this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data.We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results.We modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster convergence.Finally,we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction.We used various evaluation criteria and visual representations to compare our proposed model’s performance.Synthetic data generated by our suggested GAN model is also evaluated.The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.The presented model’s performance is compared to existing ensemble and baseline models.The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error(MAPE)of 4.476 and increase prediction accuracy.

Which approach of total hip arthroplasty is the best efficacy and least complication?

BACKGROUND Total hip arthroplasty is as an effective intervention to relieve pain and improve hip function.Approaches of the hip have been exhaustively explored about pros and cons.The efficacy and the complications of hip approaches remains inconclusive.This study conducted an umbrella review to systematically appraise previous meta-analysis(MAs)including conventional posterior approach(PA),and minimally invasive surgeries as the lateral approach(LA),direct anterior approach(DAA),2-incisions method,mini-lateral approach and the newest technique direct superior approach(DSA)or supercapsular percutaneouslyassisted total hip(SuperPath).AIM To compare the efficacy and complications of hip approaches that have been published in all MAs and randomized controlled trials(RCTs).METHODS MAs were identified from MEDLINE and Scopus from inception until 2023.RCTs were then updated from the latest MA to September 2023.This study included studies which compared hip approaches and reported at least one outcome such as Harris Hip Score(HHS),dislocation,intra-operative fracture,wound compliData were independently selected,extracted and assessed by two reviewers.Network MA and cluster rank and surface under the cumulative ranking curve(SUCRA)were estimated for treatment efficacy and safety.RESULTS Finally,twenty-eight MAs(40 RCTs),and 13 RCTs were retrieved.In total 47 RCTs were included for reanalysis.The results of corrected covered area showed high degree(13.80%).Among 47 RCTs,most of the studies were low risk of bias in part of random process and outcome reporting,while other domains were medium to high risk of bias.DAA significantly provided higher HHS at three months than PA[pooled unstandardized mean difference(USMD):3.49,95%confidence interval(CI):0.98,6.00 with SUCRA:85.9],followed by DSA/SuperPath(USMD:1.57,95%CI:-1.55,4.69 with SUCRA:57.6).All approaches had indifferent dislocation and intraoperative fracture rates.SUCRA comparing early functional outcome and composite complications(dislocation,intra-operative fracture,wound complication,and nerve injury)found DAA was the best approach followed by DSA/SuperPath.CONCLUSION DSA/SuperPath had better earlier functional outcome than PA,but still could not overcome the result of DAA.This technique might be the other preferred option with acceptable complications.

Minimalistic approach to left atrial appendage occlusion guided by cardiac computed tomography angiography

OBJECTIVE To assess the feasibility and safety of the minimalistic approach to left atrial appendage occlusion(LAAO) guided by cardiac computed tomography angiography(CCTA).METHODS Ninety consecutive patients who underwent LAAO, with or without CCTA-guided, were matched(1:2). Each step of the LAAO procedure in the computed tomography(CT) guidance group(CT group) was directed by preprocedural CT planning. In the control group, LAAO was performed using the standard method. All patients were followed up for 12 months, and device surveillance was conducted using CCTA.RESULTS A total of 90 patients were included in the analysis, with 30 patients in the CT group and 60 matched patients in the control group. All patients were successfully implanted with Watchman devices. The mean ages for the CT group and the control group were 70.0 ± 9.4 years and 68.4 ± 11.9 years(P = 0.52), respectively. The procedure duration(45.6 ± 10.7 min vs. 58.8 ± 13.0 min,P < 0.001) and hospital stay(7.5 ± 2.4 day vs. 9.6 ± 2.8 day, P = 0.001) in the CT group was significantly shorter compared to the control group. However, the total radiation dose was higher in the CT group compared to the control group(904.9 ± 348.0 m Gy vs.711.9 ± 211.2 m Gy, P = 0.002). There were no significant differences in periprocedural pericardial effusion(3.3% vs. 6.3%, P = 0.8) between the two groups. The rate of postprocedural adverse events(13.3% vs. 18.3%, P = 0.55) were comparable between both groups at 12 months follow-up.CONCLUSIONS CCTA is capable of detailed LAAO procedure planning. Minimalistic LAAO with preprocedural CCTA planning was feasible and safe, with shortened procedure time and acceptable increased radiation and contras consumption. For patients with contraindications to general anesthesia and/or transesophageal echocardiography, this promising method may be an alternative to conventional LAAO.

Virtual source approach for maximizing resolution in high-penetration gamma-ray imaging

High-energy gamma-ray radiography has exceptional penetration ability and has become an indispensable nondestructive testing(NDT)tool in various fields.For high-energy photons,point projection radiography is almost the only feasible imaging method,and its spatial resolution is primarily constrained by the size of the gamma-ray source.In conventional industrial applications,gamma-ray sources are commonly based on electron beams driven by accelerators,utilizing the process of bremsstrahlung radiation.The size of the gamma-ray source is dependent on the dimensional characteristics of the electron beam.Extensive research has been conducted on various advanced accelerator technologies that have the potential to greatly improve spatial resolution in NDT.In our investigation of laser-driven gamma-ray sources,a spatial resolution of about 90μm is achieved when the areal density of the penetrated object is 120 g/cm^(2).A virtual source approach is proposed to optimize the size of the gamma-ray source used for imaging,with the aim of maximizing spatial resolution.In this virtual source approach,the gamma ray can be considered as being emitted from a virtual source within the convertor,where the equivalent gamma-ray source size in imaging is much smaller than the actual emission area.On the basis of Monte Carlo simulations,we derive a set of evaluation formulas for virtual source scale and gamma-ray emission angle.Under optimal conditions,the virtual source size can be as small as 15μm,which can significantly improve the spatial resolution of high-penetration imaging to less than 50μm.

Debate on direct-anterior vs posterior approach for hip hemiarthroplasty:The authors’insights

We read and discussed the study entitled“Complication rates after direct anterior vs posterior approach for Hip Hemiarthroplasty in elderly individuals with femoral neck fractures”with great interest.The authors have done justice to the topic of comparison of anterior and posterior surgical approaches for bipolar hemiarthroplasty which has been an everlasting debate in the existing literature.However,there are certain aspects of this study that need clarification from the authors.

Direct anterior compared to posterior approach for hip hemiarthroplasty following femoral neck fractures

The differences in complication rates between the direct anterior and posterior approaches for hemiarthroplasty in elderly patients with femoral neck fractures are not yet fully understood.Dislocation,a severe complication associated with increased mortality and often requiring additional surgery,may occur less frequently with the direct anterior approach compared to the posterior approach.Careful consideration of patient demographics is essential when planning the surgical approach.Future research in this area should focus on robust randomized controlled trials involving elderly patients recovering from femoral neck fractures.

A FLEXIBLE OBJECTIVE-CONSTRAINT APPROACH AND A NEW ALGORITHM FOR CONSTRUCTING THE PARETO FRONT OF MULTIOBJECTIVE OPTIMIZATION PROBLEMS

In this article, a novel scalarization technique, called the improved objective-constraint approach, is introduced to find efficient solutions of a given multiobjective programming problem. The presented scalarized problem extends the objective-constraint problem. It is demonstrated that how adding variables to the scalarized problem, can lead to find conditions for (weakly, properly) Pareto optimal solutions. Applying the obtained necessary and sufficient conditions, two algorithms for generating the Pareto front approximation of bi-objective and three-objective programming problems are designed. These algorithms are easy to implement and can achieve an even approximation of (weakly, properly) Pareto optimal solutions. These algorithms can be generalized for optimization problems with more than three criterion functions, too. The effectiveness and capability of the algorithms are demonstrated in test problems.

A Deep Learning Based Broadcast Approach for Image Semantic Communication over Fading Channels

We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel,with a finite number of channel states.A deep learning-aided broadcast approach scheme is proposed to benefit the adaptive semantic transmission in terms of different channel states.We combine the classic broadcast approach with the image transformer to implement this adaptive joint source and channel coding(JSCC)scheme.Specifically,we utilize the neural network(NN)to jointly optimize the hierarchical image compression and superposition code mapping within this scheme.The learned transformers and codebooks allow recovering of the image with an adaptive quality and low error rate at the receiver side,in each channel state.The simulation results exhibit our proposed scheme can dynamically adapt the coding to the current channel state and outperform some existing intelligent schemes with the fixed coding block.

Lychrel Numbers in Base 10: A Probabilistic Approach

For decades, Lychrel numbers have been studied on many bases. Their existence has been proven in base 2, 11 or 17. This paper presents a probabilistic proof of the existence of Lychrel number in base 10 and provides some properties which enable a mathematical extraction of new Lychrel numbers from existing ones. This probabilistic approach has the advantage of being extendable to other bases. The results show that palindromes can also be Lychrel numbers.

Probabilistic Analysis of Slope Using Finite Element Approach and Limit Equilibrium Approach around Amalpata Landslide of West Central, Nepal

The stability study of the ongoing and recurring Amalpata landslide in Baglung in Nepal’s Gandaki Province is presented in this research. The impacted slope is around 200 meters high, with two terraces that have different slope inclinations. The lower bench, located above the basement, consistently fails and sets others up for failure. The fluctuating water level of the slope, which travels down the slope masses, exacerbates the slide problem. The majority of these rocks are Amalpata landslide area experiences several structural disruptions. The area’s stability must be evaluated in order to prevent and control more harm from occurring to the nearby agricultural land and people living along the slope. The slopes’ failures increase the damages of house existing in nearby area and the erosion of the slope. Two modeling techniques the finite element approach and the limit equilibrium method were used to simulate the slope. The findings show that, in every case, the terrace above the basement is where the majority of the stress is concentrated, with a safety factor of near unity. Using probabilistic slope stability analysis, the failure probability was predicted to be between 98.90% and 100%.

“Instruction-Category” Approach of Test Suite Construction for Oven Embedded System

Being different from testing for popular GUI software, the “instruction-category” approach is proposed for testing embedded system. This approach is constructed by three steps including refining items, drawing instruction-brief and instruction-category, and constructing test suite. Consequently, this approach is adopted to test oven embedded system, and detail process is deeply discussed. As a result, the factual result indicates that the “instruction-category” approach can be effectively applied in embedded system testing as a black-box method for conformity testing.

Watson-Jones Anatomical Approach for Open Reduction and Internal Fixation of Proximal Femoral Fractures without Image Intensifier in a Low-Resource Setting

Introduction: Standard procedures for surgical fixation of proximal femoral fractures (PFF) require an image intensifier which in developing countries remains a luxury. We hypothesized that, with a well-codified technique, the Watson Jones approach (WJA) without image intensifier nor traction table, can allow open reduction and internal fixation (ORIF) of PFF using Dynamic hip screw (DHS), with satisfactory outcome. Patients and methods: Forty one consecutive patients (mean age 59.5 ± 21.6 years, 61% males) who were followed in a Teaching Hospital for PFF treated by ORIF using the WJA and DHS from January 2016 to December 2020 were reassessed. The outcome measures were the quality of the reduction, the positioning of the implants, the tip-apex distance (TAD), the rate and delay of consolidation, the functional results using Postel Merle d’Aubigné (PMA) score, the rate of surgical site infection (SSI) and the overall mortality. Logistic regression was used to determine factors associated with mechanical failure. Results: The mean follow-up period was 33.8 ± 15.0 months. Fracture reduction was good in 31 (75.6%) cases and acceptable in 8(19.5%) cases. Implant position was fair to good in 37 (90.2%) patients. The mean TAD was 26.1 ± 3.9 mm. Three patients developed SSI. Consolidation was achieved in 38 (92.6%) patients. The functional results were good to excellent in 80.5% of patients. The overall mortality rate was 7.3%. There were an association between mechanical failure and osteoporosis (p = 0.04), fracture reduction (p = 0.003), and TAD (p = 0.025). In multivariate logistic regression, no independent factors were predictive of mechanical failure. Conclusion: This study shows that ORIF using DHS for PFF via the Watson-Jones approach without an image intensifier can give satisfactory anatomical and functional outcomes in low-resource settings. It provides and validates a reliable and reproducible technique that deserves to be diffused to surgeons in austere areas over the world.

Gastrointestinal tuberculosis:Diagnostic approaches for this uncommon pathology

A case report entitled“Primary gastroduodenal tuberculosis presenting as gastric outlet obstruction”recently published in the World Journal of Clinical Cases presented a rare cause of gastric outlet obstruction and highlighted the atypical manner in which gastrointestinal tuberculosis(TB)can present.The literature with regards to this rare pathology is limited to case reports and case series with the largest being published using data from between 2003 and 2013.However,since then the diagnostic tools available have significantly changed with more modern and increasingly accurate tests now available.This editorial reviews the current state of the art with regards to diagnosis in gastrointestinal TB.