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This study implements hybrid machine learning models that utilize six commonly employed meta-heuristic algorithms to predict the compressive strength (CS) of manufactured sand concrete (MSC). Six hybrid artificial neural network (ANN) models were created utilizing multiple meta-heuristic algorithms of different groups. A sum of 275 records were used to determine concrete CS of MSC. The hybrid framework, combining ANN and firefly algorithm, i.e., ANN-FF, shows exceptional accuracy in predicting the CS. During the model development stage, the ANN-FF model achieved R2= 0.9536 and RMSE = 0.0498. During testing phase, the values of these indices are R2= 0.9276 and RMSE = 0.0656. The results of the sensitivity analysis demonstrate that the constructed ANN-FF framework effectively estimates the magnitude of the correlation between influential parameters and the CS. The evaluation of outcomes was examined using a variety of tools including Taylor diagram, error matrix, and OBJ criterion. In terms of objective criterion, ANN-FF achieved the best predictive precision. Based on the findings, the constructed ANN-FF can serve as a viable alternative for supporting engineers in civil engineering endeavours. The MATLAB developed ANN-FF model (constructed using eleven distinct influencing parameters) is also attached that can readily be implemented to predict the CS of MSC.

Liquefaction is one of the natural hazards that occurs due to earthquakes and has a significant impact on the loss of human lives and various civil infrastructures. In this study, metaheuristic ANN with optimization techniques (i.e., ANN-GWO, ANN-GTO, ANN-GAO, ANN-HHO, ANN-SSA, and ANN-SMA), machine learning techniques are used to predict the probability of liquefaction (PL) from the SPT-based dataset. A dataset of 834 case histories, including seven geotechnical and seismic parameters, was used for training and testing different metaheuristic algorithms. The performance of the proposed machine learning algorithm used at every stage of analysis includes statistical parameters evaluation, score analysis, actual vs.predicted curve, error matrix, Taylor diagram, OBJ criteria, DDR criteria, and AIC criteria. The ANN-GTO model has been found to be the best model for the prediction of the probability of liquefaction potential of soil. However, all proposed models can successfully predict the liquefaction potential of soil with reasonably good accuracy. The proposed models can be used as a key tool in the prediction of the liquefaction susceptibility of any soil deposit.

•The study addresses a literature gap on the utilization, reuse and disposal of thermoplastic masks.•Thermoplastic masks are often reused in curative treatments, government-run and busy centres.•The preferred way of disposal of used thermoplastic masks is through biomedical waste management.•Radiotherapy technologists are conscious for environmental effects of thermoplastic mask disposal.

Thin layer drying of the Citrus macropterasamples pretreated with 2% NaCl, 10% sucrose, and 10% glucose solution was accomplished in a humidity chamber to evaluate the effects of drying temperatures (45, 50, and 55 °C) on drying kinetics and check the relative biochemical properties, and the microbial load. The drying data were fitted using the moisture ratio (MR) to predict the drying kinetics. The fitness was measured for the Newton, Henderson and Pabis, and Page model. The changes in water loss were fitted to Peleg model and Weibull distribution model to predict the water loss distribution during drying. Page’s mathematical model was selected as the best-fitted model for explaining drying kinetics based on the highest R2and the lowest Root Mean Square Error (RMSE) values. The Peleg’s model was found to fit well for equilibrium moisture content at different temperatures. Drying of the Satkara samples at 45 °C showed better results in the retention of color, total flavonoid content, total phenolic content (TPC), vitamin C, citric acid, vitamin B1, and B6at different pre-treatment conditions. Antioxidant activity was significantly higher (p < 0.05) in the dried Satkara slices dried at 55 °C. Among the B-vitamins, vitamin B3was comparatively stable during the drying process. Sucrose pre-treated samples showed better results in the retention of color, TPC, citric acid, ascorbic acid, and antioxidant activity. The microbial load (bacterial load: 3.4 × 103to 1.5 × 103CFU/g and fungal load < 104CFU/g) was at an acceptable level in all the dried samples. According to the findings, drying of the Satkara at 55 °C pretreated with 10% sucrose was comparatively better while considering the minimum time required for drying and considerable loss of nutritional factors.

Abstract: The ambient noise analysis was performed at ten installed broadband seismic stations of the Eastern Dharwar Craton in Telangana state, India. We calculated the power spectral densities and their corresponding probability density functions, using different types of noise to assess the stations’ performance. One year of continuous data from January 2021 to December 2021 was used for the analysis. The results suggest that the observed vertical noise levels are within the defined global noise limits for all stations. In the short-period band, the stations are dominated by cultural noise due to human activities, industrial machinery, automobile traffic, etc., which also causes a strong variation between daytime and night-time. At the long-period band, the vertical component lies above the new low-noise model (NLNM) and the horizontal components marginally overshoot the new high-noise model (NHNM) due to the tilt effect. An assessment of the noise variation was also performed during the partial lockdown due to COVID-19 in 2021 imposed by the Telangana state government in India, which revealed no significant variations in short-period noise levels. It may show the effect of remote installation away from cultural noise. Overall, our estimation suggests stability and validates the performance of all the installed stations of the Eastern Dharwar Craton in Telangana state. Research Highlights:

In this study, we performed ambient noise analysis for ten broadband seismic stations of the Eastern Dharwar Craton, in Telangana state.

We observed a significant diurnal variation as well as seasonal variation at all stations.

We compared the noise variation during the period of partial lockdown in 2021 imposed by the Telangana state government in India and found no significant variations in short-period noise levels before and after partial lockdown.

ABSTRACTSilica fume is a recognized pozzolan which can contribute unique properties to Portland cement products. The use of silica fume in cement and concrete technology has unexpectedly increased over the most recent couple of years with the advent of high-performance concrete. All the previous researches in estimating the cementing efficiency factor of silica fume have been built on the strength prediction of concrete at different w/c ratio for different percentage replacement of silica fume. In the present work, the cementing efficiency factor for silica fume at different percentage replacement level has been assumed, and at the constant water to binder ratio, the compressive strength has been obtained. Further, the results of 28-day compressive strengths of silica fume concrete have been examined using a response surface model and a relationship has been projected. This model, which relates compressive strength with water to binder ratio, cementing efficiency factor and percentage silica fume, might find a useful application in the strength prediction of silica fume concretes.

Compressive strength is an important and pertinent property for the characterization of concrete. It is known that compressive strength of hardened concrete depends on the efficiency factor (K) of silica fume and its percentage replacement (%SF). Consequently, the ultrasonic pulse velocity (UPV) is also an important factor for predicting compressive strength. The article examines the capability of relevance vector machine (RVM) and emotional neural network (ENN) for determination of compressive strength using the efficiency factor, percentage silica fume replacement and UPV as an input parameter. A comparative study has been carried out for the developed model using RVM and ENN. The analysis confirms that both RVM and ENN are a robust model for the prediction of compressive strength of hardened concrete.

The complexity of concrete's composition makes it difficult to predict its compressive strength, which is a highly valuable and desired characteristic. Traditional methods for prediction are expensive and time-consuming, resulting in limited data availability. However, modern soft-computing models have emerged as a reliable solution for accurately forecasting concrete's compressive strength. The research proposes a novel Deep Neural Network (DNN), Multivariate Adaptive Regression Splines (MARS) and Extreme Learning Machine (ELM) based machine learning (ML) models for forecasting the compressive strength of concrete added with various proportions of fly ash and silica fume. For this purpose, a dataset of 144 trials, having 8 input parameters is taken from the literature. The performance of the models is confirmed using various statistical parameters. Rank Analysis reveals that DNN is the best-performing model (Rank =52, RTR2=0.983 and RTs2=0.954), closely followed by MARS (Rank =38, RTR2=0.974 and RTs2=0.956); while ELM lags behind the other two counterparts. The results are further confirmed using an error matrix, external validation and AIC criteria. The visual interpretation is provided using the Taylor diagram. MARS has the edge over the other two models in terms of providing a user-friendly solution.

In finite element approximation of the Oseen problem, one needs to handle two major difficulties, namely, the lack of stability due to convection dominance and the incompatibility between the approximating finite element spaces for the velocity and the pressure. These difficulties are addressed in this article by using an edge patch-wise local projection (EPLP) stabilization technique. The article analyses the EPLP stabilized nonconforming finite element methods for the Oseen problem. For approximating the velocity, the lowest-order Crouzeix–Raviart (CR) nonconforming finite element space is considered; whereas for approximating the pressure, two discrete spaces are considered, namely, the piecewise constant polynomial space and the lowest-order CR finite element space. The proposed discrete weak formulation is a combination of the standard Galerkin method, EPLP stabilization and weakly imposed boundary condition by using Nitsche’s technique. The resulting bilinear form satisfies an inf-sup condition with respect to EPLP norm, which leads to the well-posedness of the discrete problem. A priori error analysis assures the optimal order of convergence in both the cases, that is, order one in the case of piecewise constant approximation and 32\frac{3}{2}in the case of CR-finite element approximation for pressure. The numerical experiments illustrate the theoretical findings.

In developing countries, rural households instinctively burn huge amounts of biomass fuel, which has become a concern at present. In this study, an attempt has been made to assess the consumption pattern of biomass fuel in relation to socio-economic factors in rural households in the three upazilas (subdistricts) in Khulna district of Bangladesh. The consumption pattern of biomass fuel was assessed by multistage random sampling techniques with a semi-structured questionnaire. The study revealed that households incorporate a variety of fuel sources, of which 97% use various types of biomass fuel, e.g., firewood, leaves and twigs, coconut shell and coir, crop residues, cow dung, etc. The average monthly household biomass fuel consumption was 193 kg, with a monthly expenditure of 5.86 USD. The largest share of biomass fuel was derived from firewood, which amounted to 105 kg per month. The middle income group and Kacha(clay) type households consume more biomass than other types of households. The major sources of biomass fuel were found in their home gardens and agricultural lands. These biomass fuels are mainly used for domestic cooking, and most of the households in the study area used general stoves. Among commonly available species, ten firewood species were identified as the most preferred by the majority of households in the study area. Insights from our research may be utilized to promote accessibility to clean energy for households in Bangladesh and other countries alike.

In this study machine learning models are used that forecast the compressive strength of self-compacting concrete (SCC) depending on percentage replacement of cement by supplementary cementitious material. In order to predict the mechanical property of SCC, a hybrid artificial neural network (ANN) along with metaheuristic optimization techniques and two traditional models were employed which comprised of 300 datasets. Several input factors are employed in the modelling process, including cement, the water-binder ratio, coarse aggregate, fine aggregate, fly ash (FA) and the superplasticizer. ANN hybridised with RUNge Kutta optimizer (RUN) was chosen as one of the top predictive models based on coefficient of determination ((R2= 0.933 (in training) and R2= 0.9203 (in testing))) findings and model validation. According to the rank analysis, the ANN-RUN surpassed other models with a score of 128. Permutation feature importance, statistical analysis, and comparisons amongst regression models are used to determine the models' efficacy. The best input parameters were selected based on person correlation and density plot. Also, the use of Taylor diagrams and accuracy matrices was also examined for the visual comprehension of the data which has shown similar results as discussed above. The results show that the suggested machine learning models attained consistent accuracy and can be used as a robust technique to predict the compressive strength of SCC.

The Indian subcontinent comprises geological terranes of varied age and structural character. In this study, we provide new constraints to existing crustal models by inverting the P‐to‐sreceiver functions (RFs) at 317 broadband seismic stations. Inversion results fill crucial gaps in existing velocity models (CRUST1.0 and SEAPS) by capturing regions which are less represented. The final model produced is much more heterogeneous and is able to capture the structural variations between closely spaced seismic stations. In comparison to the global models, major differences are seen for seismic stations located over various rift zones (e.g., Godavari, Narmada, and Cambay) and those close to the coastal regions where transition from oceanic to continental crust is expected to create drastic changes in the crustal configuration. Seismic images are produced along various profiles using 49,682 individual RFs recorded at 442 seismic stations. Lateral variations captured using migrated images across the Himalayan collisional front revealed the hitherto elusive southern extent of the Moho and intracrustal features south of the Main Central Thrust (MCT). Poisson's ratio and crustal thickness estimates obtained using H‐kstacking technique and inversion of RFs are grossly similar lending credence to the robustness of inversions. An updated crustal thickness map produced using 1,525 individual data points from controlled source seismics and RFs reveals a (a) thickened crust (>55 km) at the boundary of Dharwar Craton and Southern Granulite Terrain, (b) clear difference in crustal thickness estimates between Eastern Dharwar Craton and Western Dharwar Craton, (c) thinner crust beneath Cambay Basin between southwest Deccan Volcanic Province and Delhi‐Aravalli Fold Belt, (d) thinner crust (<35 km) beneath Bengal Basin, (e) thicker crust (>40 km) beneath paleorift zones like Narmada Son Lineament and Godavari Graben, and (f) very thick crust beneath central Tibet (>65 km) with maximum lateral variations along the Himalayan collision front. An undated crustal model for Indian subcontinent is produced using new constraints through inversion of receiver functionsCrustal architecture of the Indian subcontinent is better imaged with fills in less explored regions of central India and HimalayaAn updated crustal thickness map is produced for the Indian subcontinent using controlled source seismics and receiver functions

The supplementary cementitious materials (SCMs) are used as an alternative material to reduce environmental concerns. Using industrial waste as an SCM can also help to reduce the amount of garbage deposited in lagoons and landfills and can have strength, durability, economic and environmental benefits. Ultra-high performance concrete (UHPC) is a new type of concrete with improved mechanical and durability properties. There is just a little amount of information on the impact of different SCMs on UHPC mechanical and durability performance. This paper presents the mechanical and durability properties of UHPC containing Silica Fume (SF) and Ground Granulated Blast Furnace Slag (GGBS). Mechanical properties of UHPC such as compressive, splitting tensile and flexural strengths were studied. Moreover, its resistance to chloride ions penetration was evaluated along with Sorptivity. According to the test findings, the strength properties of GGBS-based UHPC are significant up to 40% cement replacement level under normal water curing. Furthermore, the inclusion of 12% SF and 40% GGBS demonstrated superior mechanical and durability characteristics as compared to their binary counterpart. In terms of the synergistic effects of GGBS and SF, the efficiency factor (k value) was computed as an indicator to predict the compressive strength of UHPC. Oven curing at elevated temperature for 48 h and thereafter normal curing improved the results of early age compressive strength significantly.

Respondents were placed into a bad or good mood and then provided with reading choices. They chose magazine articles that featured either bad or good news. In agreement with theoretical expectations, women in a bad mood were drawn to good news, sampling significantly more of it than women in a good mood. Men did not show this preference, however. Gender-specific selection of news stories was further evident in that women in a bad mood sampled less bad news than did men in a bad mood, whereas women in a good mood sampled more bad news than men in a good mood did.

•Red mud pulverization resulted in increase of its binder’s compressive strength.•Contribution of iron phases in the enhanced strength was investigated.•Mössbauer Spectroscopy test revealed increase in Fe3+ions in geopolymeric network.•The result was validated by FTIR test.

We present Real-time Automated Photometric IDentification (RAPID), a novel time series classification tool capable of automatically identifying transients from within a day of the initial alert, to the full lifetime of a light curve. Using a deep recurrent neural network with gated recurrent units (GRUs), we present the first method specifically designed to provide early classifications of astronomical timeseries data, typing 12 different transient classes. Our classifier can process light curves with any phase coverage, and it does not rely on deriving computationally expensive features from the data, making RAPIDwell suited for processing the millions of alerts that ongoing and upcoming wide-field surveys such as the Zwicky Transient Facility (ZTF), and the Large Synoptic Survey Telescope (LSST) will produce. The classification accuracy improves over the lifetime of the transient as more photometric data becomes available, and across the 12 transient classes, we obtain an average area under the receiver operating characteristic curve of 0.95 and 0.98 at early and late epochs, respectively. We demonstrate RAPID's ability to effectively provide early classifications of observed transients from the ZTF data stream. We have made RAPIDavailable as an open-source software package8for machine-learning-based alert brokers to use for the autonomous and quick classification of several thousand light curves within a few seconds.

The Zwicky Transient Facility (ZTF), a public-private enterprise, is a new time-domain survey employing a dedicated camera on the Palomar 48-inch Schmidt telescope with a 47 deg2field of view and an 8 second readout time. It is well positioned in the development of time-domain astronomy, offering operations at 10% of the scale and style of the Large Synoptic Survey Telescope (LSST) with a single 1-m class survey telescope. The public surveys will cover the observable northern sky every three nights in gand rfilters and the visible Galactic plane every night in gand r. Alerts generated by these surveys are sent in real time to brokers. A consortium of universities that provided funding ("partnership") are undertaking several boutique surveys. The combination of these surveys producing one million alerts per night allows for exploration of transient and variable astrophysical phenomena brighter than r? 20.5 on timescales of minutes to years. We describe the primary science objectives driving ZTF, including the physics of supernovae and relativistic explosions, multi-messenger astrophysics, supernova cosmology, active galactic nuclei, and tidal disruption events, stellar variability, and solar system objects.

We identify minimal observing cadence requirements that enable photometric astronomical surveys to detect and recognize fast and explosive transients and fast transient features. Observations in two different filters within a short time window (e.g., g-and-i, or r-and-z, within <0.5 hr) and a repeat of one of those filters with a longer time window (e.g., >1.5 hr) are desirable for this purpose. Such an observing strategy delivers both the color and light curve evolution of transients on the same night. This allows the identification and initial characterization of fast transient-or fast features of longer timescale transients-such as rapidly declining supernovae, kilonovae, and the signatures of SN ejecta interacting with binary companion stars or circumstellar material. Some of these extragalactic transients are intrinsically rare and generally all hard to find, thus upcoming surveys like the Large Synoptic Survey Telescope (LSST) could dramatically improve our understanding of their origin and properties. We colloquially refer to such a strategy implementation for the LSST as the Presto-Color strategy (rapid-color). This cadence's minimal requirements allow for overall optimization of a survey for other science goals.

The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids. We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective.

The Zwicky Transient Facility (ZTF) is a new optical time-domain survey that uses the Palomar 48 inch Schmidt telescope. A custom-built wide-field camera provides a 47 deg2field of view and 8 s readout time, yielding more than an order of magnitude improvement in survey speed relative to its predecessor survey, the Palomar Transient Factory. We describe the design and implementation of the camera and observing system. The ZTF data system at the Infrared Processing and Analysis Center provides near-real-time reduction to identify moving and varying objects. We outline the analysis pipelines, data products, and associated archive. Finally, we present on-sky performance analysis and first scientific results from commissioning and the early survey. ZTF's public alert stream will serve as a useful precursor for that of the Large Synoptic Survey Telescope.