Search

This research aims to comprehensively evaluate the performance of cold recycled mixtures treated with cement and plasticizer in laboratory and field settings. The study employed two recycled asphalt fractions, 0–40 mm and 0–20 mm, combined with varying proportions of portland cement (M500), water, and a plasticizer (ZHBI-S). Initial performance assessments were conducted using standard tests, followed by additional tests focusing on rutting resistance, beam fatigue, and elastic modulus of selected recycled asphalt pavement (RAP) compositions. Furthermore, field performance evaluation involved the collection of RAP mixture samples from the asphalt paver's bunker and core samples from the finished pavement. The investigation's outcomes suggest that the cold RAP mixture treated with cement and plasticizer can be applied as a binder or base course. [ABSTRACT FROM AUTHOR]

As part of this research, the shear process during asphalt paving with the paver screed was investigated in the laboratory. An extensive international literature search revealed that the influence of the shear process on the compaction success during asphalt paving has not been investigated to date. In addition, only limited data on the coefficient of friction between the asphalt mix and the screed plate exist from in-situ measurements. The investigations in the laboratory were carried out with a customized frame shear device of the Institute of Geotechnics at the Technical University of Darmstadt. The asphalt mix types AC 11 DS, SMA 11 DS and AC 16 BS (according to the German guideline TL Asphalt-StB) were used for this purpose. In addition, the influence of temperature changes and grading curve changes was investigated using the AC 11 DS asphalt mix. On the basis of the results, it was possible to prove for the first time that a significant increase in density takes place as a result of the shear process under constant normal stress. Where the influence on the compaction success depends on the asphalt composition. In addition, in-situ measurements could be validated based on determined sliding friction coefficients. The friction coefficients showed a material and temperature-dependent behavior. [ABSTRACT FROM AUTHOR]

In this paper, the experimental study of the asphalt paver is performed to assess the effect of the vibrational screed system's operating parameters on the asphalt paver's working performance. Then, the nonlinear dynamic models of the vibrational screed system and tamper system are built to assess in detail the effect of the working parameters of the vibrational screed system and optimise its working parameters to ameliorate the asphalt paver's working performance. Both the vertical acceleration and angular acceleration of the vibrational screed system computed via their Root Mean Square (RMS a ) value have been selected as the evaluation indexes. Results showed that the acceleration at the measured points has been unevenly distributed on the vibrational screed system's length, thus, the working performance of the asphalt paver is quite low. With the vibrational screed system's working parameters optimised, the average value of RMS a with optimisation is increased by 7.67% whereas the average deviation of RMS a between the measurement points with optimisation is greatly reduced by 34.2% in comparison without optimisation. Consequently, the working performance of the asphalt paver has obviously ameliorated. [ABSTRACT FROM AUTHOR]

Multivariate analysis models were developed to evaluate the results obtained from a compatibility study designed for ibuprofen with a large group of different types of excipients, as a possible approach for rapid screening of the incompatibility between the active pharmaceutical ingredient (API) and excipients. The solid-state characterization of the binary mixtures was performed by Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC). Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) using SIMCA® software were applied for evaluation of the experimentally obtained results. The optimal PCA model for the FTIR spectra explains 96.2 % of the variations in the dataset with good statistical indicators (R2X = 0.960, Q2 = 0.900), which was also the case for the PCA model for the DSC curves (R2X = 0.981, Q2 = 0.866). The applied PLS-DA models have shown similar behaviour to the PCA. Moreover, the main spectral variations in the FTIR spectra and the thermal events in the DSC data were attributed the highest variable importance for the projection (VIP) scores in the corresponding VIP plots, confirming the model capability for predicting ibuprofen interactions. Furthermore, the prediction power of the optimal models for FTIR and DSC experimental data was evaluated by the root mean square error of prediction (RMSEP) of 0.10 and 0.16, respectively. The obtained results demonstrated the potential of multivariate statistical analysis as a machine learning-based technique for screening and prediction of ibuprofen-excipients solid-state compatibility in the preformulation phase of the pharmaceutical development of dosage forms. [ABSTRACT FROM AUTHOR]

The smoothness achieved in pavement construction, or as-built roughness, has great importance in road engineering. Traditionally, as-built roughness has been studied in the literature from an empirical approach, based on the statistical correlation between the roughness IRI observed before and after the execution of paving works. In the case of asphalt overlays, this article highlight interactions in the construction process that limit the predictive capacity of the traditional empirical approach; demonstrating, through the frequency response of the paver screed and the IRI model, that fitting a before-after IRI type regression is insufficient to explain the variability observed in as-built roughness. From the preliminary analysis carried out an original approach to as-built IRI prediction is proposed, based on the modelling of the asphalt paver and its levelling response to surface roughness during the paving process. Formulation of linear systems that constitute the model are presented for the case of a wheeled paver without using of automatic levelling. Longitudinal profile data from the Long-Term Pavement Performance SPS-5 experiment was used to calibrate the model, obtaining a mean error of 0.22 m/km for the predicted IRI. The results obtained demonstrate the potential of the proposed approach as a modelling alternative. [ABSTRACT FROM AUTHOR]

India is confronted with the substantial issue of plastic debris due to the absence of an efficient waste management infrastructure. Recycled plastic has the potential to enhance various construction materials, such as roofing tiles, paving blocks, and insulation. The aforementioned materials possess notable attributes such as high strength, low weight, and exceptional resistance to extreme temperatures and humidity. The objective of this study is to ascertain feasible alternatives for manufacturing road paver blocks utilizing plastic waste (Polyethene terephthalate (PET)), and M-sand (stone dust). Three variations of a discarded plastic cube measuring 150 mm × 150 mm × 150 mm were prepared for the experiment. The experimental findings indicated that a ratio of 1:4 was determined to be the most effective in achieving the desired level of compressive strength. I-section road and brick paver blocks were produced as an alternative to the traditional concrete ones. Compressive strength tests were performed on I-sections and brick paver blocks, revealing that the 1:4 mix ratio exhibited the highest average compressive strength for both materials. The findings indicated that including plastic waste positively impacted the compressive strength of the I-sections and brick paver blocks. Additionally, the quality grading of these materials was evaluated using an ultrasonic pulse velocity test. The ultrasonic pulse velocity test results demonstrated a high-quality grading for the I-sections and brick paver blocks. Scanning electron microscopy (SEM) tests assessed the microstructural behavior and performance. The results of this study demonstrate that incorporating plastic waste in combination with M-sand can effectively improve the mechanical characteristics of composite materials, rendering them viable for use in construction-related purposes. [ABSTRACT FROM AUTHOR]

Copyright of Roads & Bridges / Drogi i Mosty is the property of Road & Bridge Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Maize is an important crop to ensure food safety. High-quality seeds can guarantee a good yield. The maize seed germination rate is the most important information for the maize industry, which can be obtained through the seed germination test. An essential stage in determining the germination rate is the planting of the seeds. The current seed planting process is fully manual, which is labor-intensive and costly, and it requires the development of an autonomous seeding machine. This research developed an automatic maize seeding machine, consisting of four operations: paving sand, seed layout, watering, and covering the seed. Among the four procedures, sand paving is a crucial step, the performance of which is affected by the gate opening size, conveyor speed, and sensor mounting location. Three performance evaluating factors are the weight of sand in the tray, the volume of sand left on the conveyor, and sand surface flatness. A full factorial experiment was designed with three variables and three levels to determine an appropriate factor combination. RGB-D information was used to calculate the volume of sand left on the conveyor and sand flatness. An analytic hierarchy process was employed to assign weights to the three evaluation indicators and score the various combinations of factors. The machine for paving sand achieved a satisfactory result with an opening size of 10.8 mm, a sensor distance of 9 cm, and a conveyor belt speed of 5.1 cm/s. With the most satisfactory factors determined, the machine shows superior performance to better meet practical applications. [ABSTRACT FROM AUTHOR]

The impact of vibrations on health in occupational environments has been extensively studied. Although the effects of vehicle vibrations on driving comfort has been investigated, the literature on the impact of vehicle vibrations on health is scarce. Accordingly, this study aimed to investigate the influence of e-scooter vibrations on driver health by considering both whole-body vibrations (WBVs) and hand–arm vibrations (HAVs). From the perspective of health, vibration zones were defined based on the UNE-2631 and UNE-5349 standards, as well as the European Vibration Directive. Real measurements obtained from an e-scooter acceleration database were used. The results of the study on WBVs show that, on average, 87.54% and 95.47% of non-desirable vibrations are caused by driving an e-scooter on pavers and asphalt, respectively. This shows that 'potentially non-healthy' and 'non-healthy' vibrations are 25.69% and 61.85%, respectively, when driving on pavers and 85.52% and 12.96%, respectively, when driving on asphalt. Therefore, the WBV levels reached by driving an e-scooter on any pavement could potentially harm health. However, the influence of HAV on the incidence of Raynaud's syndrome is low. The study results on WBV suggest that future e-scooter designs must be based on a more damped road–driver interface. [ABSTRACT FROM AUTHOR]

Stone Mastic Asphalt (SMA) pavements have advantages such as increasing durability and resistance to cracks. Gap-graded SMA pavements are more preferred because they reduce rutting and delay fatigue cracks. In SMA pavements, the filler and bitumen ratio is higher than conventional asphalt pavements. In addition to the positive aspects of SMA pavements, there are also some negative aspects that need to be taken care of. The bitumen draindown during the time it takes from the production in the plant to the application on the road. The draindown of the bitumen causes disruption of the homogeneity in the pavement. In addition, as a result of laying the SMA mixture on the road with a paver and compacting it with a roller, the bituminous binder rises from the pavement to the surface and desorption. In this study, the effect of using different aggregates and bitumen for fiber and non-fiber SMA mixtures with constant gradation on the percentage of draindown was investigated. As a result of the Schellenberger bitumen draindown tests, it was observed that the increase in the specific gravity and softening point of the bitumen decreased the drain down percentage. In mixtures prepared from the same refinery bitumen with different aggregates, it was found that a decrease in bitumen absorption of aggregates increases the draindown percentage, but an increase in the peeling strength of aggregates and the use of aggregates from the Sedimentary Rock class in the fine aggregate part of the mixture reduces the draindown percentage. [ABSTRACT FROM AUTHOR]

Seamless positioning and navigation requires an integration of outdoor and indoor positioning systems. Until recently, these systems mostly function in-silos. Though GNSS has become a standalone system for outdoors, no unified positioning modality could be found for indoor environments. Wi-Fi and Bluetooth signals are popular choices though. Increased adoption of different machine learning techniques for indoor–outdoor context detection and localization could be witnessed in the recent literature. The difficulty in precise data annotation, need for sensor fusion, the effect of different hardware configurations pose critical challenges that affect the success of indoor–outdoor (IO) positioning systems. Wireless sensor-based techniques are explicitly programmed, hence estimating locations dynamically becomes challenging. Machine learning and deep learning techniques can be used to overcome such situations and react appropriately by self-learning through experiences and actions without human intervention or reprogramming. Hence, the focus of the work is to present the readers a comprehensive survey of the applicability of machine learning and deep learning to achieve seamless navigation. The paper systematically discusses the application perspectives, research challenges, and the framework of ML (mostly) and DL (a few) based positioning approaches. The comparisons against various parameters like the technology used, the procedure applied, output metric and challenges are presented along with experimental results on benchmark datasets. The paper contributes to bridging the IO localization approaches with IO detection techniques so as to pave the way into the research domain for seamless positioning. Recent advances and hence, possible future research directions in the context of IO localization have also been articulated. • IO localization methods are presented from pragmatic view of seamless positioning. • ML techniques are categorized based on their applications in seamless positioning. • Performance results of ML techniques on benchmark datasets are reported. • The open issues along with future directions are discussed. [ABSTRACT FROM AUTHOR]

To develop standard optical biosensors, the simulation procedure takes a lot of time. For reducing that enormous amount of time and effort, machine learning might be a better solution. Effective indices, core power, total power, and effective area are the most crucial parameters for evaluating optical sensors. In this study, several machine learning (ML) approaches have been applied to predict those parameters while considering the core radius, cladding radius, pitch, analyte, and wavelength as the input vectors. We have utilized least squares (LS), LASSO, Elastic-Net (ENet), and Bayesian ridge regression (BRR) to make a comparative discussion using a balanced dataset obtained with the COMSOL Multiphysics simulation tool. Furthermore, a more extensive analysis of sensitivity, power fraction, and confinement loss is also demonstrated using the predicted and simulated data. The suggested models were also examined in terms of R 2 -score, mean average error (MAE), and mean squared error (MSE), with all of the models having an R 2 -score of more than 0.99, and it was also shown that optical biosensors had a design error rate of less than 3%. This research might pave the way for machine learning-based optimization approaches to be used to improve optical biosensors. [ABSTRACT FROM AUTHOR]

Identification of road surfaces can help drivers controlling the speed of their vehicles and avoid accidents. Smartphones sensors are widely used to detect road obstacles and alert drivers in advance. However, use of data stream generated by smartphone sensors for road surface detection needs to be studied further. This paper presents a novel way of combination of Mel Frequency Cepstrum Coefficients (MFCC), typically used in audio signal processing, Delta–Delta Energy Feature and dynamic time warping technique for unsupervised learning—clustering to divide data set of accelerometer acceleration values into 4 different types of roads, commonly found in India viz, Unpaved, Bituminous Roads, Concrete Roads, Paver blocks road. Promising results based upon silhouette values in the range 0.6–0.7, Lower Davies–Bouldin index (DBI) (≈ 0.15–0.51) and Higher Dunn's Index (≈ 0.24–0.54) indicates probability of correct cluster assignment of extracted data points, with reduction of any size of data set to fixed size of 12 Delta–Delta Energy features. [ABSTRACT FROM AUTHOR]

The spatial and temporal coverage of spaceborne optical imaging systems are well suited for automated marine litter monitoring. However, developing machine learning-based detection and identification algorithms requires large amounts of data. Indeed, when it comes to marine debris, ground validated data is scarce. In this study, we propose a general methodology that leverages synthetic data in order to avoid overfitting and generalizes well. The idea is to utilize realistic models of spaceborne optical image acquisition and marine litter to generate large amounts of data to train the machine learning algorithms. These can then be used to detect marine pollution automatically on real satellite images. The main contribution of our study is showing that algorithms trained on simulated data can be successfully transferred to real-life situations. We present the general components of our framework, our modeling of satellites and marine debris and a proof of concept implementation for macro-plastic detection with Sentinel-2 images. In this case study, we generated a large dataset (more than 16,000 pixels of marine debris) composed of seawater, plastic, and wood and trained a Random Forest classifier on it. This classifier, when tested on real satellite images, successfully discriminates marine litter from seawater, thus proving the effectiveness of our approach and paving the way for machine learning-based marine litter detection with even more representative simulation models. [ABSTRACT FROM AUTHOR]

Nowadays, finite element (FE) codes are increasingly employed for simulating large deformation problems. Thus, to reliably represent the strain hardening behavior, a proper calibration of constitutive laws is essential. Focusing on tensile tests, the main issue with ductile metals is necking occurrence, because of the consequent triaxiality and non-uniformity of the strain and stress states. Over the past decades many strain hardening identification approaches have been proposed. Among them, FE-based inverse methods are widely used, but computationally expensive and time consuming. Hence, the authors propose an efficient method which exploits a database for relating the plastic flow rule and the specimen necking profile. The explicit solver of the nonlinear FE code LS-DYNA was used to build the database, whose size could be limited thanks to physical considerations. The developed methodology was applied to experimental quasi-static tensile tests performed on different metals. The predicted hardening laws showed good agreement with those identified with FE-based inverse methods, thus verifying the applicability of the proposed strategy. This study paves the way for machine learning tools having as main input the necking shape: indeed, the present work suggests their feasibility and provides insights into how to establish datasets for a proper and efficient training. • A specimen deformed profile allows to find the post-necking hardening law. • The post-necking behavior is estimated starting from FE results saved in a database. • Good accordance with other methodologies proves the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Means of transport should be able to fulfil their main function safely and comfortably for travellers and drivers. The effects of vibrations on ride comfort are in the frequency range of 0.5 to 80 Hz and can be analysed using the UNE-2631 standard. This type of analysis has been conducted for several means of transport (bicycles, motorcycles, cars, trucks, etc.), but the literature on e-scooter comfort is very scarce. Existing research describes methodologies, simulation models, and a few measurements related to e-scooter comfort. This paper presents, for the first time, a comfort analysis using an Arduino-based data acquisition system at a sampling frequency of 200 Hz (higher than that in previous studies). Acceleration and speed measurements were obtained by sensorising an e-scooter with inflated wheels without any additional damping systems, which is one of the commonly used e-scooter types. In this study, the comfort for two different speeds (20 and 28 km/h), two types of pavements (pavers and asphalt), and two drivers with different weights was investigated. The results indicate the lowest comfort values for higher velocities and paver pavement. Furthermore, the comfort values were extremely low for all scenarios. In addition, the results demonstrate the necessity of using a sampling rate of at least 80 Hz for this e-scooter model. [ABSTRACT FROM AUTHOR]

One of the common defects of flexible road pavement is the loss of bonding between two layers of asphalt concrete: the base course and the binder course. The occurrence of this phenomenon has a major impact on the observed state of deflection and deformation of the pavement. This effect affects the results of non-destructive tests which are used to calculate material parameters and then are used in the diagnostics of the pavement condition or design of structural strengthening. This paper discusses the influence of the various level of bonding on the result of backcalculation and the obtained elastic moduli. For the obtained values of moduli, calculations of key deformations and pavement durability were performed. Improper assumptions about the interaction between the layers affects the observed results. Additionally paper discusses the effect of pavement displacement discontinuity on the observed deflection basin and compares the results with those for a model with continuity. Numerical calculations were carried out using Simulia Abaqus software, the computational model was verified using analytical solution. [ABSTRACT FROM AUTHOR]

Ceramic matrix composites (CMCs) are highly promising for the hot components of the high thrust-to-weight ratio aeroengines because of their excellent high-temperature resistance and lightweight. Submillimeter cooling holes are necessary cooling structures for the extremely high working temperature of a CMCs hot component. However, CMCs are hard, brittle, and poorly conductive. Current machining is trapped in severe tool wear, poor hole quality, and low efficiency in machining such small size holes. This paper proposes high-frequency ultrasonic vibration-assisted drilling (UAD) to machine submillimeter holes of CMCs. It reveals that increasing vibration frequency during machining can enlarge the cutting edge's effective rake angle and reduce drilling forces. Correspondingly, the tool life and hole quality are improved. The machinability of high-frequency UAD is rationalized by both theoretical analysis and experiments. Results show that compared with low-frequency UAD and Conventional drilling (CD), the drilling force in high-frequency UAD is reduced by 47 % and 81 %, the tool life is more than three times, and the hole quality is improved by 12 % and 35 %, respectively. Therefore, the study paved the way for the machining of submillimeter cooling holes in CMCs, which is important to the industrial application of CMCs. [Display omitted] • The change of effective rake angle in drilling is revealed • Effects of ultrasonic vibration frequency on effective rake angle are clarified. • A drilling force model of submillimeter holes is built. • Performance of UAD in drilling small holes in CMC is demonstrated. • Effects of ultrasonic vibration frequency on drilling performance are revealed. [ABSTRACT FROM AUTHOR]

The application of stabilization technologies to a radiologically contaminated surface has the potential for reducing the spread of contamination and, as a result, decreasing worker exposure to radiation. Three stabilization technologies, calcium chloride (CaCl2), flame retardant Phos-Chek® MVP-Fx, and Soil2OTM were investigated to evaluate their ability to reduce the resuspension and tracking of radiological contamination during response activities such as vehicle and foot traffic. Concrete pavers, asphalt pavers, and sandy soil walking paths were used as test surfaces, along with simulated fallout material (SFM) tagged with radiostrontium (Sr-85) applied as the contaminant. Radiological activities were measured using gamma spectrometry before and after simulated vehicle operation and foot traffic experiments, conducted with each stabilization technology and without application as a nonstabilized control. These measurements were acquired separately for each combination of surface and vehicle/foot traffic experiment. The resulting data describes the extent of SFM removed from each surface onto the tires or boots, the extent of SFM transferred to adjacent surfaces, and the residual SFM remaining on the tires or boots after each experiment. The type of surface and response worker actions influenced the stabilization results. For instance, when walked over, less than 2% of particles were removed from nonstabilized concrete, 4% from asphalt, and 40% of the particles were removed from the sand surface. By contrast, for vehicle experiments, ~40% of particles were again removed from the sand, but 7% and 15% from concrete and asphalt, respectively. In most cases, the stabilization technologies did provide improved stabilization. The improvement was related to the type of surface, worker actions, and stabilizer; a statistical analysis of these variables is presented. Overall, the results suggest an ability to utilize these technologies during the planning and implementation of response activities involving foot and vehicle traffic. In addition, resuspension of aerosolizable range SFM was monitored during walking path foot traffic experiments, and all stabilizing agents decreased the measured radioactivity, with the Soil2OTM decrease being 3 fold, whereas the CaCl2 and Phos-Chek MVP-Fx surfaces generated no detectable radioactivity. Overall, these results suggest that the stabilization technologies decrease the availability of particles respirable by response workers under these conditions. [ABSTRACT FROM AUTHOR]

To enhance the compacting performance and the asphalt density during the tamper-asphalt mixture interaction (TAMI), a dynamics model of the TAMI is established to analyze the compacting energy of TAMI under the different excitation frequencies of the compacting mechanism. The MATLAB/Simulink software is then applied to solve the vibration equations of TAMI based on the lumped parameters of the compacting mechanism and hot mix asphalt. The experimental research is also performed to verify simulation results and force distribution characteristics of the compacting mechanism on the asphalt pavement. The research results indicate that the vibration excitation of the compacting mechanism should be used in a range from 18 to 22 Hz and the number of vibrations of the compacting mechanism at the same position should be used by N = 4 or 5 to improve the compacting performance and the asphalt density. Additionally, the experimental results also show that the compacting force distribution of compacting mechanisms is asymmetric, thus, the design parameters of the vibration screed system should be optimized to enhance the compacting performance of the asphalt paver. [ABSTRACT FROM AUTHOR]

Clostridium difficile (C. diff) infection is one of the most contagious diseases associated with high morbidity and mortality rates in hospitalised patients. Accurate diagnosis can slow its spread by determining the most effective treatment. Herein, we report a novel testing platform as a proof-of-concept for the selective, sensitive, rapid and cost-effective diagnosis of C. diff infection (CDI) based on a duplex measurement. This was achieved by detecting two specific biomarkers, surface layer protein A (SlpA) and toxin B (ToxB), using a surface enhanced Raman scattering-based lateral flow assay (SERS-based LFA). The simultaneous duplex detection of SlpA with ToxB has not been described for the clinical diagnosis of CDI previously. The SlpA biomarker "AKDGSTKEDQLVDALA" was first reported by our group in 2018 as a species-specific identification tool. The second biomarker, ToxB, is the essential virulence biomarker of C. diff pathogenic strains and is required to confirm true infection pathogenicity. The proposed SERS-based LFA platform enabled rapid duplex detection of SlpA and ToxB on separate test lines using a duplex LF test strip within 20 minutes. The use of a handheld Raman spectrometer to scan test lines allowed for the highly sensitive quantitative detection of both biomarkers with a lowest observable concentration of 0.01 pg μL−1. The use of a handheld device in this SERS-based LFA instead of benchtop machine paves the way for rapid, selective, sensitive and cheap clinical evaluation of CDI at the point of care (POC) with minimal sample backlog. [ABSTRACT FROM AUTHOR]

Rationale: The low speed and low flexibility of most liquid chromatography/tandem mass spectrometry (LC/MS/MS) approaches in early drug discovery delay sample analysis from routine in vivo studies within the same day. A high‐throughput platform for the rapid quantification of drug compounds in various in vivo assays was developed and established in routine bioanalysis. Methods: Automated selection of an efficient and adequate LC method was realized by autonomous sample qualification for ultrafast batch gradients (9 s/sample) or for fast linear gradients (45 s/sample) if samples required chromatography. The hardware and software components of our Rapid and Integrated Analysis System (RIAS) were streamlined for increased analytical throughput via state‐of‐the‐art automation while maintaining high analytical quality. Results: Online decision‐making was based on a quick assay suitability test (AST), based on a small and dedicated sample set evaluated by two different strategies. 84% of the acquired data points were within ±30% accuracy and 93% of the deviations between the lower limit of quantitation (LLOQ) values were ≤2‐fold compared with standard LC/MS/MS systems. Speed, flexibility and overall automation significantly improved. Conclusions: The developed platform provided an analysis time of only 10 min (batch‐mode) and 47 min (gradient‐mode) per standard pharmacokinetic (PK) study (62 injections). Automation, data evaluation and results handling were optimized to pave the way for machine learning based on decision‐making regarding the evaluation strategy of the AST. [ABSTRACT FROM AUTHOR]

Summary. Skip resistance of asphalt is an important parameter that can influence the safety of drivers on roads. Although there is a linear relationship between slipping on road surfaces and accidents, the impacts of pollutants for decreasing friction of roads is clear to researchers. Moisture and temperature influence friction and safety. In this research in SMA samples, three different gradations with the maximum nominal sizes of 19, 12.5 and 9.5, based on international standards were used. For polluting the surface, five materials that are found on roads were used, including fine-grained soil, sand, oil, soot and rubber powder. To measure the skip resistance, the British pendulum tester was used and for analysing macro-texture, the sand patch method was used. The results of this research showed that by increasing the maximum nominal size of aggregates, the depth of macro-texture in surfaces are grown and this is due to the decrease of fine aggregates in larger gradations. Because of the higher flexibility of pure bitumen, the applied compression pressure on rigid aggregates can cause indentations in the substrate and result in declining the roughness height of aggregates in the mixed surface. This leads to declining the hysteresis part of friction by increasing temperature. Keywords: road safety, skid resistance, pollutants, British pendulum tester, decision tree and result in declining the roughness height of aggregates in the mixed surface. This leads to declining the hysteresis part of friction by increasing temperature. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to better understand management accounting automation by exploring the programmability of management accounting work. Design/methodology/approach: We build upon the literature on digitalization in management accounting and draw upon the pragmatic constructivist methodology to understand how digitalization takes place at the individual actors' level in accounting practice. The paper uses a data set from an interventionist case study of a machinery manufacturer. Findings: We examine an actual process of automating management accounting tasks. During this development process, surprisingly, calculation tasks remained more fit for humans than machines though, initially, they were thought to be programmable. Research limitations/implications: According to our findings, practitioners may interpret experts' nonprogrammable work tasks as programmable and seek to automate them. Only identifying the factual possibilities for automating accounting-related work can lead to automation-improved efficiency. Our findings can be increasingly relevant for advanced analytics initiatives and applications within management accounting (e.g. robotic process automation, big data, machine learning and artificial intelligence). Practical implications: Practitioners need to carefully analyze the entity they wish to automate and understand the factual possibilities of using and maintaining the planned automatic system throughout its life cycle. Originality/value: The paper shows that when processes are assessed from a distance, the nonprogrammable management accounting tasks and expertise can become misinterpreted as programmable, and the goal of automating them has little chance of success. It also shows possibilities for human accountants to remain relevant in comparison to machines and paves the way for further studies on advanced decision technologies in management accounting. [ABSTRACT FROM AUTHOR]

To enhance the compression performance and improve the paving quality of the VSS of the pavers, the experimental research of the VSS is performed to assess the VSS’s vibration stability under various excitations of the tampers and vibrator screed. A VSS’s dynamic model is also established to simulate and evaluate the VSS’s working performance. The root-mean-square (RMS) acceleration responses of the vertical and pitching motions at centre of gravity of the screed floor are chosen as the objective functions. In order to increase the VSS’s working performance, the dynamic parameters of the angular deviations of tampers are then controlled based on Fuzzy control. The research results indicate that the RMS value of the vertical screed motion is remarkably increased, concurrently the RMS value of the pitching screed angle is significantly reduced by controlling the angular deviations of tampers under different excitation frequencies of the VSS. Therefore, the VSS’s working performance is significantly improved in comparison without the control of the angular deviations. [ABSTRACT FROM AUTHOR]

Residual Useful Life (RUL) prediction is a key step of Condition-Based Maintenance (CBM). Deep learning-based techniques have shown wonderful prospects on RUL prediction, although their performances depends on heavy structures and parameter tuning strategies of these deep-learning models. In this paper, we propose a novel Deep Belief Network (DBN) model constructed by improved conditional Restrict Boltzmann Machines (RBMs) and apply it in RUL prediction for hydraulic pumps. DBN is a deep probabilistic digraph neural network that consists of multiple layers of RBMs. Since RBM is an undirected graph model and there is no communication among the nodes of the same layer, the deep feature extraction capability of the original DBN model can hardly ensure the accuracy of modeling continuous data. To address this issue, the DBN model is improved by replacing RBM with the Improved Conditional RBM (ICRBM) that adds timing linkage factors and constraint variables among the nodes of the same layers on the basis of RBM. The proposed model is applied to RUL prediction of hydraulic pumps, and the results show that the prediction model proposed in this paper has higher prediction accuracy compared with traditional DBNs, BP networks, support vector machines and modified DBNs such as DEBN and GC-DBN. [ABSTRACT FROM AUTHOR]

To reduce shaking of a vibration screed system (VSS) and improve the paving performance of an asphalt paver (AP), the root-mean-square (RMS) acceleration responses at points on the front and rear screed floors are analyzed via an experimental method. A 3D nonlinear dynamic model of the VSS is also built to evaluate the influence of the dynamic parameters of the VSS on the compression efficiency, paving quality, and working stability of the AP based on the objective functions of the vertical, pitching, and rolling RMS values at the centre of gravity of the screed. The angular deviations, α and γ of the tamper are then controlled to improve the paving performance. The research results show that the excitation frequency, ft, and both angular deviations, α and γ of the tamper strongly affect the paving performance. The compression efficiency is quickly enhanced, while both paving quality and working stability are significantly reduced with increasing the excitation frequency ft and reducing the angular deviations.α and γ and vice versa. Additionally, the screed shaking and paving performance of the AP are remarkably improved by control of the angular deviations, α and γ under different working conditions. [ABSTRACT FROM AUTHOR]

Dementia is an ominous neurological disease. Scientists proposed a link between its occurrence and the presence of Toxoplasma gondii (T. gondii). The long-term sequels of anti-Toxoplasma premunition, chiefly dominated by TNF-α, on the neurons and their receptors as the insulin-like growth factor-1 receptor (IGF-1R), which is tangled in cognition and synaptic plasticity, are still not clear. IGF-1R mediates its action via IGF-1, and its depletion is incorporated in the pathogenesis of dementia. The activated TNF-α signaling pathway induces NF-κβ that may induce or inhibit neurogenesis. This study speculates the potential impact of anti-Toxoplasma immune response on the expression of IGF-1R in chronic cerebral toxoplasmosis. The distributive pattern of T. gondii cysts was studied in association with TNF-α serum levels, the in situ expression of NF-κβ, and IGF-1R in mice using the low virulent ME-49 T. gondii strain. There was an elevation of the TNF-α serum level (p value ≤ 0.004) and significant upsurge in NF-κβ whereas IGF-1R was of low abundance (p value < 0.05) compared to the controls. TNF-α had a strong positive correlation with the intracerebral expression of NF-κβ (r value ≈ 0.943, p value ≈ 0.005) and a strong negative correlation to IGF-1R (r value -0.584 and -0.725 for area% and O.D., respectively). This activated TNF-α/NF-κβ keeps T. gondii under control at the expense of IGF-1R expression, depriving neurons of the effect of IGF-1, the receptor's ligand. We therefore deduce that T. gondii immunopathological reaction may be a road paver for developing dementia. [ABSTRACT FROM AUTHOR]

The significance of accurate short-term load forecasting (STLF) for modern power systems' efficient and secure operation is paramount. This task is intricate due to cyclicity, non-stationarity, seasonality, and nonlinear power consumption time series data characteristics. The rise of data accessibility in the power industry has paved the way for machine learning (ML) models, which show the potential to enhance STLF accuracy. This paper presents a novel hybrid ML model combining Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGBoost), k-Nearest Neighbors (kNN), and Support Vector Regression (SVR), examining both standalone and integrated, coupled with signal decomposition techniques like STL, EMD, EEMD, CEEMDAN, and EWT. Through Automated Machine Learning (AutoML), these models are integrated and their hyperparameters optimized, predicting each load signal component using data from two sources: The National Operator of Electric System (ONS) and the Independent System Operators New England (ISO-NE), boosting prediction capacity. For the 2019 ONS dataset, combining EWT and XGBoost yielded the best results for very short-term load forecasting (VSTLF) with an RMSE of 1,931.8 MW, MAE of 1,564.9 MW, and MAPE of 2.54%. These findings highlight the necessity for diverse approaches to each VSTLF problem, emphasizing the adaptability and strength of ML models combined with signal decomposition techniques. • Novel hybrid structure of machine learning and signal decomposition approaches for VSTLF. • Combination of GBR, XGBoost, KNN, and SVR with STL, EMD, EEMD, CEEMDAN, and EWT. • Accurate predictions demonstrated for ONS and ISO-NE databases. • Valuable tool for power companies to enhance forecasting accuracy in VSTLF. • Addresses challenges of nonlinearity and non-stationarity in short-term load forecasting. [ABSTRACT FROM AUTHOR]