Lower Mean Research Articles

Predictive modeling of shallow tunnel behavior: Leveraging machine learning for maximum convergence displacement estimation

Accurate prediction of maximum convergence in unsupported, shallow tunnel construction is crucial for optimizing the lining and ensuring tunnel safety. Machine learning (ML) algorithms, especially through boosting techniques, enable effective solution of complex engineering problems and demonstrate their capabilities in problem solving and optimization. In this study, the FLAC 3D package was used to create a robust and validated database of 954 datasets. Five tree-based ML algorithms, including extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), gradient boosting machine (GBM), histogram-based gradient boosting (HGB) and categorical boosting (CatBoost), were used to predict the maximum convergence displacement for unsupported shallow tunnels. For the test dataset, XGBoost outperformed the other models with an excellent coefficient of determination of 0.9633, a minimum mean absolute error of 0.0021 and a low root mean squared error of 0.00725. HGB followed closely behind, and GBM and CatBoost showed strong performances, while Adaboost was less effective. The superior performance of XGBoost highlights its effectiveness in predicting maximum convergence in shallow tunnels. An in-depth sensitivity analysis within the XGBoost model showed the significant influence of soil elastic modulus on the maximum convergence displacement in unsupported tunnels. The remarkable results achieved by the XGBoost algorithm on our complex tunnel convergence predictions illustrate the profound ability of ML to tackle complicated geotechnical challenges. This interdisciplinary collaboration demonstrates the potential of advanced algorithms to improve safety and efficiency in construction, underlining the crucial role of technology in tackling complex problems and establishing a new paradigm for innovation in the field.

Deep learning-based prediction of float model performance in floatplanes: A case study on lift-to-drag coefficient ratio

Developing an engineering design is resource-intensive and time-consuming, particularly for the floats of a floatplane design, due to its complexity and limited testing facilities. Intelligent-based computational design (IBCD) techniques, which integrate computational design techniques and machine learning (ML) algorithms, offer a solution to reduce required testing by providing predictions. This paper proposes a deep learning (DL)-based IBCD method for modeling floats' lift-to-drag coefficient ratio (C<sub>L</sub>/C<sub>D</sub>), where DL is one of the most powerful ML. The proposed method consists of two phases: hyper-parameter optimization and DL model training and evaluation. A genetic algorithm (GA) is employed in the first phase to explore complex hyper-parameter combinations efficiently. Evaluation of the predicted C<sub>L</sub>/C<sub>D</sub> of the floats using the DL model resulted in a satisfactory R-squared of 0.9329 and the lowest mean squared error (MSE) of 0,001536. These results demonstrate the ability of DL model to predict the float's performance accurately and can facilitate further design optimization. Thus, the proposed method can offer a time-efficient and cost-effective solution for predicting float performance, aiding in optimizing floatplane designs and enhancing their functionalities.

Procurement Planning of Coffee Bean at Small Scale Agro-Industry in Kalibaru Banyuwangi Regency

Abstract Procurement planning with forecasting techniques is constructive for the agro-industry in dealing with price fluctuations and uncertainty in the availability of quality raw materials and controlling procurement costs. However, not all agro-industries can implement procurement planning accurately and precisely because it is unsuitable for the data characteristics and limited data. This study aims to forecast the amount of raw material needed for KUB Srikandi in the future using the ARIMA Model. The results of the study show that there are two models in the ARIMA family: ARIMA (1,1,0) and ARIMA (0,1,1), both of which have significant parameters with p-values below 0.05. But the ARIMA (0,1,1) model is the best model, according to the results of the model with the lowest Mean Squared Error (MSE) value among all the models that were looked at. The purchase planning quantitative analysis results are 191 kg/month at the lower limit and 213 kg/month at the upper limit.

Contribution to the biosystematics of the wild Vaccinium species distributed in Turkey

The genus Vaccinium L. (Ericaceae) consists of shrubs or dwarf shrubs species widely distributed in Southeast Asia, America, and Europe. In the present paper, members of the genus (V. arctostaphylos, V. myrtillus, V. uliginosum, V. vitis-idaea) naturally grown in Northeast Anatolia were compared in terms of leaf venation, seed micromorphology, and palynological properties based on 16 populations in detail. Present findings revealed that number of areoles (the small area bounded by secondary veinlet) is 8–9 for V. arctostaphylos, 5–7 for V. myrtillus, 3–4 for V. uliginosum and V. vitis-idaea. Scanning electron microscope observation shows that the leaf adaxial surface is fiber-fluted in V. arctostaphylos, ruminate in V. myrtillus, V. uliginosum, and V. vitis-idaea due to epicuticular waxes. In addition, the seed shape of V. arctostaphylos in outline varies from triangular, tetragonal to oval, whereas they are lunate to reniform or falcate in the rest of the examined taxa. Epidermal cells of the seed are quadrilateral with straight anticlinal walls in V. arctostaphylos, however, they are most elongate rectangle with weakly undulate anticlinal walls in the rest species. The mean tetrad diameter (D) of pollen belonging to all examined species is 29.89 (21.47–43.22) μm while the lowest mean D was found in V. uliginosum (24.59 μm) and the highest mean D was found in V. arctostaphylos (36.74 μm). Principal component analysis based on 47 traits (flower: 9, leaf: 18, seed and fruit: 9 and pollen: 11) showed that the leaf length and width, teeth numbers, tetrad diameter, equatorial axis, and polar axis were the most valuable characters to distinguish the examined taxa in the present paper.

Effect of Face Masks on Voice Quality Associated with Young and Older Chinese Adult Speakers

ObjectivesFace masks have become important after the pandemic, but the change in voice when wearing a face mask is still unclear. The study investigated the effect of face masks on the acoustic and perceptual characteristics of voice quality associated with young and older Chinese adults. MethodsVoice samples of the sustained vowel /a/ and continuous speech produced by 44 older and 61 young adults with and without an ASTM level-3 surgical face mask were recorded and analyzed. Perceptual and acoustic parameters including mean fundamental frequency (F0) and intensity, perturbation measures (jitter and shimmer), harmonic-to-noise ratio (HNR), smoothed cepstral peak prominence (CPPs), and long-term average spectrum (LTAS) measures were obtained and compared. ResultsWhen comparing masked to unmasked voices, for both male and female speakers, F0 and intensity showed no significant changes, except for F0 of continuous speech, which increased significantly. Meanwhile, perturbation measures including jitter and shimmer were reduced, HNR and CPPs were increased, and LTAS measures encompass low frequency mean spectral energy (MSE), high frequency MSE, and spectral tilt (ST) were different. For perceptual measures, the overall grade of dysphonia, and roughness were reduced, except for the breathiness among older male speakers, while the other vocal qualities were not changed. Between young and older speakers, significant differences in shimmer, CPPs, and perceived breathiness among male speakers, and low frequency MSE among female speakers were found. ConclusionWearing a surgical mask appeared to change the perceived voice quality. This is supported by the change in perturbation and LTAS measures, and HNR and CPPs values. In addition, some differences between young and older male adults were observed. Communication effectiveness may be affected when wearing surgical masks due to changes in voice quality. Additionally, clinicians need to exercise caution in evaluating the voice quality of clients when wearing face masks.

Long-term outcomes in sacral agenesis.

Sacral agenesis (SA) includes a range of clinical presentations of varying severity, with implications for function and quality of life (QoL). Diagnosis is often made perinatally, and prognostic discussions become an important aspect of parental counselling. This study engaged SA sufferers and their caregivers to obtain objective, long-term patient reported outcome data. Patients with radiologically confirmed SA from a single tertiary spinal unit underwent retrospective medical record review. Patients were then contacted by telephone to complete QoL questionnaires including EQ-ED-5L for adults and EQ-ED-Y for < 16-year-olds. Additional information including Renshaw grade, employment, living situation and bladder function was also collected. Twenty-six patients with SA were identified. Mean age is 23.35years (range 0.92-63.53), 13M:17F. Renshaw grade ranged from 1 to 4. Sixty-eight percent had associated kyphoscoliotic deformities. The majority (70%) had either impaired or absent bladder control, and 80% need walking aids to mobilise. Twenty patients completed the questionnaire (10 adults and 10 < 16-year-olds). Mean EQ-ED-5L index for adults was +0.474 (range -0.1 to +0.089, 1 = best), with a lower mean value of +0.287 (range -0.54 to +1) for the < 16-year cohort. Those undergoing spinal fusion procedures had significantly lower scores (-0.08 v +0.44, p = 0.022). This study provides an objective record of the QoL of individuals with SA, illustrating a wide variety of outcomes, with differences between younger and older individuals which may reflect the results of a long-term adaptive process. The implications for individuals should be carefully tailored to the specific deformity and the likely underlying neurological deficits.

Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning models

For discovering uncharted chemical space of ionic liquids (ILs) for CO2 dissolution, a reliable generative framework combining re-balanced variational autoencoder (VAE), artificial neural network (ANN), and particle swarm optimization (PSO) is developed based on a comprehensive experimental solubility database from literature. The re-balanced VAE transforms the chemical space of ILs into continuous latent space, which is demonstrated by t-distributed stochastic neighbor embedding (t-SNE) visualization and sampled ions of the latent space. ANN is connected with the re-balanced VAE to predict the CO2 solubility and the resultant VAE-ANN model achieves a low mean absolute error (MAE) of 0.022 on the test set. Lastly, the PSO algorithm is employed to search the latent space for optimal IL structures with the highest predicted solubility. A total of 5120 ILs are generated and optimized through 10 parallel runs of PSO. Their CO2 solubilities are predicted and compared to those of the 3735 ILs combined with the already-known cations and anions in the CO2 solubility database under 298.15 K and 100 kPa. The results demonstrate a notably larger distribution of higher CO2 solubility in optimized ILs after PSO, which effectively points out the significance and directions for exploring the wide IL chemical space.

Lithospheric Evolution of the South‐Central United States Constrained by Joint Inversion of Receiver Functions and Surface Wave Dispersion

AbstractIn the present study, we use broadband seismic data recorded by 190 stations of the EarthScope program's Transportable Array to construct a 3‐D shear wave velocity model for the upper 180 km using a non‐linear Bayesian Monte‐Carlo joint inversion of receiver functions (RFs) and Rayleigh wave dispersion curves. Ambient noise and teleseismic data are used for obtaining Rayleigh wave phase velocity dispersion curves. A resonance removal filtering technique is applied to the RFs contaminated by reverberations from the thick sedimentary layers that cover most of the region. Our observations of the higher crustal shear velocities (∼3.40 km/s) beneath the Sabine Block (SB), along with the estimated relatively thicker crust (∼34.0 km) and lower crustal Vp/Vs estimates (∼1.80) in comparison with the rest of the Gulf Coastal Plain (GCP) (∼3.10 km/s for crustal shear velocities, ∼29.0 km for crustal thickness, and ∼1.90 for crustal Vp/Vs estimates), indicating that this crustal block has different crustal properties from the surrounding coastal plain regions. The southern Ouachita Mountains have a thin crust (∼30.0 km) and low mean crustal Vp/Vs value (∼1.73), suggesting that lower crustal delamination has occurred in this region. Low velocities in the upper mantle beneath most of the GCP are interpreted as a combined result of thin lithosphere, higher‐than‐normal temperatures, and possibly compositional variations.

Performance of the District Health Boards within the district health system in Chiang Mai Province, Thailand

The objectives of this cross-sectional study were to examine the performance of the District Health Boards (DHBs), and to investigate factors affecting DHB performance within the district health system in Chiang Mai Province, Thailand. The samples of the study were 399 participants from the DHBs. The data were collected by using a questionnaire. The collected data were analyzed with descriptive statistics (percentage, mean, and standard deviation) and inferential statistics (stepwise multiple regression analysis). The study’s results showed that the overall DHB performance within the district health system was at a moderate level (mean = 3.38, S.D. = 0.79). Among the components, the highest mean was found for setting objectives and plans at a high level (mean = 3.52, S.D. = 0.69), while the lowest mean was found for the evaluation of sustainable work development at the moderate level (mean = 3.17, S.D. = 0.70). Regarding factors affecting the DHB performances, eight co-predictive variables were found: 1) internal communication, 2) DHB potential development, 3) work assignment in teams, 4) community participation, 5) teamwork, 6) management experiences, 7) status of divorce/widow/separation, and 8) participation period in the DHB role. These eight factors could significantly co-predict the performances of the DHBs within the district health system at 65.40%. The results of this study can later be used as data for determining policies and plans for the implementation of the DHBs in Chiang Mai Province, Thailand.

A traits-based approach to assess aquaculture’s contributions to food, climate change, and biodiversity goals

Aquaculture has the potential to support a sustainable and equitable food system in line with the United Nations Sustainable Development Goals (SDG) on food security, climate change, and biodiversity (FCB). Biological diversity amongst aquaculture organisms can drive diverse contributions to such goals. Existing studies have assessed the performance of a limited number of taxa in the general context of improving aquaculture production, but few explicitly consider the biological attributes of farmed aquatic taxa at the FCB nexus. Through a systematic literature review, we identify key traits associated with FCB and evaluate the potential of aquaculture to contribute to FCB goals using a fuzzy logic model. The majority of identified traits are associated with food security, and two-thirds of traits linked with food security are also associated with climate change or biodiversity, revealing potential co-benefits of optimizing a single trait. Correlations between FCB indices further suggest that challenges and opportunities in aquaculture are intertwined across FCB goals, but low mean FCB scores suggest that the focus of aquaculture research and development on food production is insufficient to address food security, much less climate or biodiversity issues. As expected, production-maximizing traits (absolute fecundity, the von Bertalanffy growth function coefficient K, macronutrient density, maximum size, and trophic level as a proxy for feed efficiency) highly influence a species’ FCB potential, but so do species preferences for environmental conditions (tolerance to phosphates, nitrates, and pH levels, as well as latitudinal and geographic ranges). Many highly farmed species that are typically associated with food security, especially finfish, score poorly for food, climate, and biodiversity potential. Algae and mollusc species tend to perform well across FCB indices, revealing the importance of non-fish species in achieving FCB goals and potential synergies in integrated multi-trophic aquaculture systems. Overall, this study provides decision-makers with a biologically informed assessment of desirable aquaculture traits and species while illuminating possible strategies to increase support for FCB goals. Our findings can be used as a foundation for studying the socio-economic opportunities and barriers for aquaculture transitions to develop equitable pathways toward FCB-positive aquaculture across nuanced regional contexts.

Superpixel Segmentation-Enabled Transmission Electron Microscopy Images for Rapid and Accurate Detection of Coronavirus

Worldwide, SARS-CoV-2 has been responsible for millions of fatalities and extensive disability. Hence, to stop the spread of novel viruses like SARS-CoV-2, Omicron, and other worrying types, rapid and accurate diagnostic techniques are needed to identify symptomatic and asymptomatic carriers as soon as feasible. Early recognition and diagnosis are essential to effective epidemic management. However, different viral strains' shapes and spatial characteristics are similar, complicating image classification, especially in medical virology. This study uses a super-pixels segmentation technique based on transmission electron microscopy (TEM) images to differentiate SARS-CoV-2 from SARS-CoV. This paper aims to develop a method that enables virologists to detect and diagnose viral infections more accurately. In results, SARS-CoV-2 had a median area of 25,145.54 pixels and SARS-CoV of 38,591.35 pixels. The model can help to better understand how viruses develop, spread, diagnose and contain outbreaks. Furthermore, an exceptionally low root mean square error (RMSE) of 0.0275 between the segmentation of the viral area between humans and machines is obtained. Indeed, this low error rate indicates the accuracy of this automated measurement technique. Finally, the developed superpixel segmentation technique provides quick and reliable identification of coronaviruses, promising to significantly contribute to medical virology and help manage epidemics by simplifying prompt viral diagnosis.

Modeling specific capacitance of carbon nanotube-based supercapacitor electrodes by machine learning algorithms

Carbon nanotubes (CNTs) have emerged as promising materials for supercapacitors (SCs) due to their unique properties and exceptional electrical conductivity. These cylindrical structures composed of carbon atoms offer several advantages for SC electrode applications. The electrochemical performance of CNT-based electrodes is strongly influenced by factors such as surface area, pore structure, and ID/IG ratio. However, the lack of a credible physical model capable of accurately predicting the performance of SCs based on these physicochemical properties of CNTs poses a challenge. In this study, we propose the utilization of a data-driven approach employing various models including a gradient boosting regression (GBR), Bayesian regression (BR), ridge regression (RR), and stochastic gradient descent (SGD) model to predict the performance of SCs with CNT electrodes based on the microstructural properties of the electrode material and electrochemical operational parameters. The developed GBR model demonstrates its feasibility by achieving a low root mean square error (RMSE) value of approximately 36.31 for the prediction of specific capacitance for test split. Additionally, a sensitivity analysis was conducted to investigate the influence of independent input parameters on a single output parameter, specifically the specific capacitance. This analysis provides insights into the relative importance and impact of various input parameters on the specific capacitance of CNT-based SCs.

Flood susceptibility mapping leveraging open-source remote-sensing data and machine learning approaches in Nam Ngum River Basin (NNRB), Lao PDR

Frequent floods caused by monsoons and rainstorms have significantly affected the resilience of human and natural ecosystems in the Nam Ngum River Basin, Lao PDR. A cost-efficient framework integrating advanced remote sensing and machine learning techniques is proposed to address this issue by enhancing flood susceptibility understanding and informed decision-making. This study utilizes remote sensing geo-datasets and machine learning algorithms (Random Forest, Support Vector Machine, Artificial Neural Networks, and Long Short-Term Memory) to generate comprehensive flood susceptibility maps. The results highlight Random Forest’s superior performance, achieving the highest train and test Area Under the Curve of Receiver Operating Characteristic (AUROC) (1.00 and 0.993), accuracy (0.957), F1-score (0.962), and kappa value (0.914), with the lowest mean squared error (0.207) and Root Mean Squared Error (0.043). Vulnerability is particularly pronounced in low-elevation and low-slope southern downstream areas (Central part of Lao PDR). The results reveal that 36%–53% of the basin’s total area is highly susceptible to flooding, emphasizing the dire need for coordinated floodplain management strategies. This research uses freely accessible remote sensing data, addresses data scarcity in flood studies, and provides valuable insights for disaster risk management and sustainable planning in Lao PDR.

A practical equation for predicting saturated hydraulic conductivity of fine-grained soils

The saturated hydraulic conductivity (ks) of soils plays a crucial role in various agricultural and environmental engineering applications, particularly in relation to pollution prevention and water migration. Firstly, the traditional Kozeny-Carman (KC) equation, which are commonly used to estimate ks, has limitations in predicting ks for different types of fine-grained soils based on the analyses of permeability test data in the literature. Secondly, to tackle this issue, a modified KC equation was proposed by introducing an expression for the shape parameter and establishing a relationship between the tortuosity equation and the plastic limit. Additionally, the validation of the proposed equation was conducted using ks values of 25 fine-grained soils obtained from other sources. Finally, a comparison was made with the four commonly used ks models to evaluate its performance. The results indicate that the proposed equation demonstrates a relatively high coefficient of determination and a low root mean square error. This suggests that the proposed equation has the potential to accurately assess ks for fine-grained soils. The predictions for ks using the modified equation showed good agreement with the experimental data mentioned in the literature.

NDVI estimation using Sentinel-1 data over wheat fields in a semiarid Mediterranean region

ABSTRACT Annual crop monitoring is a key parameter for managing agricultural strategies. Several studies have relied on remote sensing products such as the normalized difference vegetation index (NDVI) as a vegetation dynamic metric. However, the dependence of optical data on weather conditions limits its availability. In this study, we reconstruct the NDVI time series of wheat fields using the moving averages of the Sentinel-1 normalized VH/VV cross-polarization ratio (IN) and the interferometric coherence in VV polarization over wheat selected fields in a semiarid site in Tunisia during two seasons, from 2018 to 2020. The crop cycle is divided into two periods: before and after the heading phase, which occurs in approximately the middle of March. Due to the volume-scattering impact, the second phase is divided into the ripening and maturation phase (NDVI ≥0.4) and senescence phase (NDVI <0.4). To estimate the NDVI values, different methods are used: curve-fitting equations and machine learning regressors such as the random forest (RF) and the support vector regressor (SVR). Low root mean square error (RMSE) values characterize NDVI estimation during the first period. In the second period, the RMSE values reach 0.06 when the NDVI is lower than 0.4. When the NDVI values exceed 0.4 in the second period, lower accuracy marks the NDVI estimation using the curve-fitting equations as a function of IN or coherence. Relative low accuracy characterizes the regression algorithms’ estimations when NDVI ≥ 0.4 compared to their performance during the aforementioned periods. The proposed approach was tested on different wheat fields. The NDVI estimations are characterized by RMSE values varying between 0.12 and 0.19. The use of RF and SVR outperformed the curve-fitting methods with an RMSE equal to 0.12. The present findings revealed the high accuracy of the proposed approach to estimate the missing values of wheat fields NDVI values during the vegetation development period until heading and the senescence phase. The presence of the mutual effect of the vegetation water content and its volume complicated the NDVI estimation using the C-band data.

Enhancing tribological performance of hybrid fiber-reinforced composites through machine learning and response surface methodology

This study delves into the significant effects of sodium hydroxide (NaOH) treatment on the tribological properties of hybrid fiber-reinforced composites, specifically focusing on the combination of paddy straw (PS) and pineapple leaf (PALF) in a polyester matrix. By leveraging Artificial Neural Networks (ANNs) to predict the Specific Wear Rate (SWR) and Coefficient of Friction (COF), the research employs a grid search approach for hyperparameter optimization. This optimization process results in an optimal ANN architecture with impressive accuracy, showcasing low mean absolute error and high R-squared values of 0.991 and 0.986 for SWR and COF predictions, respectively. Utilizing the Design of Experiments (DOE), the study systematically analyzes the intricate interplay of disc speed, wear duration, and NaOH treatment percentage, with a specific focus on SWR and COF as pivotal tribological metrics. The Analysis of Variance (ANOVA) results underscore the substantial impact of duration and treatment percentage on wear characteristics. Additionally, quadratic regression models reveal nuanced correlations, highlighting the sensitivity of SWR to NaOH percentage and the influence of disc speed, duration, and treatment percentage on COF. This outcome emphasizes the efficacy of these parameters in achieving superior tribological performance in hybrid composites. Beyond contributing to a profound understanding of wear characteristics, this work introduces an innovative dimension through optimized ANN modeling, ensuring a more accurate and fine-tuned predictive model.

Construction of a Green and Low-Carbon Travel Order Prediction Model Based on Shared Bicycle Big Data

In the era of big data, traditional analysis methods are insufficient to meet the growing demand for green and low-carbon travel orders in shared bicycle systems. To address this issue, a new order demand forecasting model, named the “convolutional neural network (CNN)”—“long short-term memory (LSTM)” model (CNN-LSTM), is proposed by integrating CNN and LSTM techniques. The research further validates the spatiotemporal prediction performance of this model. The experimental results demonstrate that LSTM exhibits favorable prediction performance in terms of time feature analysis, as evidenced by the overlapping of the true value (TV) and predicted value (PV) curves. Notably, LSTM achieves an impressively low mean squared error (MSE) value of 0.0063, which is significantly lower compared to CNN (0.0082) and XGBoost (0.0074). Upon incorporating date and weather characteristics, the predictive performance improves significantly, achieving an outstanding MSE value of 0.0043. However, when it comes to spatial feature analysis, the LSTM algorithm alone proves inadequate, obtaining a MSE value of 0.0084. Thus, by employing the CNN-LSTM combination model, a lower MSE value of 0.0066 is achieved, outperforming the comparison algorithms. Overall, the CNN-LSTM model exhibits strong predictive capabilities regarding the temporal and spatial requirements of shared bicycles. This model plays a key role in accurately forecasting order demands, facilitating urban transportation planning and management, as well as guiding the planning and location of non-motorized vehicle stops.

Patient satisfaction followed by remote monitoring

Abstract Introduction Remote monitoring (RM) of cardiac implantable electronic devices has increased significantly in the last years. The safety and efficacy of RM have been demonstrated, however, there are few data on patient satisfaction. The objective of this study was to evaluate the satisfaction of patients with an implanted pacemaker (PM) followed through remote monitoring. Methods Patients implanted with a conventional or leadless PM who had been using RM system for at least 3 months were included. Patients had to be able to understand and answer questionnaires, having no significant cognitive impairment. An specific, and previous validate, patient satisfaction RM questionnaire with 30-item, self-completed, measured on a 5-point Likert scale (0=No, nothing, 4=Yes, a lot) was administered. The questionnaire is structured in 5 dimensions: Information on cardiac condition, device convenience, transmission process, satisfaction with medical monitoring, and benefits. The results are presented as mean. The difference between mean was analyzed using the T-Student test. Results A total of 98 patients were included (77.8±8.2 years old, 67% male), 82 % had conventional PM and 18 % leadless PM. According RM connectivity 70% had daily automatic transmission, 12% automatic transmission and 18% manual transmission. On average, participants answered 83% of the items. The overall satisfaction of the patient was high (3.66). The mean score for each dimension is summarized in Table 1. The Device convenience had the highest mean (3.75), and the Transmission process had the lowest mean (3.56). There was no difference between patients with a conventional pacemaker versus leadless pacemakers (Fig 1, p=0,30). Results Patients with PM followed through RM have a high level of satisfaction when evaluated using an specific questionnaire. The level of satisfaction is independent of the type of PM or the connectivity of the system.Table 1Mean dimension score by device type

Evaluation of health risk experienced by urban taxi drivers to whole-body vibration and multiple shocks

This cross-sectional study was done aiming at determining the exposure of taxi drivers to vibration on 35 safe taxis (19 cars with engine volume of 1.325 L, 9 cars with engine volume of 1.645 L, and 7 cars with engine volume of 1.761 L). Measurements were based on ‘ISO 2631-1’ on seats’ pans, by Castel GA2005 vibrometer. The results through two different evaluative methods indicated moderate exposure levels, corresponding to suggestive Health Guidance Caution Zone (HGCZ) in ‘ISO 2631-1’. Evaluation of exposure to the WBV among taxi drivers, in the taxis with low and high mean acceleration values, using the VDV method is preferred over the R.M.S method. In lower accelerations, in all conditions, the mean of the reckoned values in the VDV method was less than the R.M.S method, and in higher accelerations, where there are more differences, and the CF values were higher (PAIK 1761 cars), again the assessed mean duration in the VDV method is less than R.M.S method. In higher accelerations and with fewer crest factors, this matter didn’t hold, and the allowed time in the R.M.S method became lower.

Estimation of riverbank erosion by combining channel morphological models with AI techniques

Simulating riverbank erosion involves modeling the complex interactions between water flow, sediment transport, and bank stability. The present study employs a novel approach, combining the Self Organizing Map (SOM) algorithm and the Long Short-Term Memory (LSTM) network, to capture the behavior of riverbank erosion. A two-stage training procedure is promoted to enhance predictive accuracy and emphasize the need for preprocessing input data related to hydrological conditions, geomorphological features, and soil properties. Pivotal variables that can characterize changes in channel geometry are designed as the output targets, such as the vertical displacement of the riverbed, the horizontal displacement of the riverbank, and the channel width. The goal of this study is to create an alternative to addressing challenges associated with predicting riverbank erosion by utilizing new training methods of artificial intelligence (AI) models. The proposed method performs well in assessing three output variables, showing low mean relative errors, less than 0.081, and high correlation coefficients above 0.981, along with R-squared values over 0.963. These results highlight the effectiveness of this method in accurately describing cross-sectional changes. The proposed method is designed with practical applications in mind, satisfying the need for methods that are not only accurate but also operationally efficient.