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Soil is the backbone of the agricultural economy of any country. Soil salinity refers to the higher concentration of soluble salts in the soil. Soil salinity is a ruinous abiotic stress that has emerged as a threatening issue for food security. High salt concentration causes an ionic imbalance that hampers water uptake, affecting photosynthesis and other metabolic processes, ultimately resulting in inferior seed germination and stunted plant growth. A wide range of strategies have been adopted to mitigate the harmful effects of salinity such as efficient irrigation techniques, soil reclamation, habitat restoration, flushing, leaching or using salt-tolerant crops, but all the methods have one or more limitations. An alternative and effective strategy is the exploitation of salt-tolerant plant growth-promoting bacteria (ST-PGPB) to mitigate salt stress and improve crop productivity. ST-PGPB can survive in salinity-tainted environments and perform their inherent plant growth-promoting and biocontrol functions effectively. Additionally, ST-PGPB can rescue plants via stress-responsive mechanisms including production of growth regulators, maintenance of osmotic balance, aminocyclopropane-1-carboxylate (ACC) deaminase activity, exopolysaccharides (EPS) activity, improvement in photosynthesis activity, synthesis of compatible solutes, antioxidant activity and regulation of salt overly sensitive (SOS) signaling pathway. Several well-known ST-PGPB, specifically Azospirillum, Bacillus, Burkholderia, Enterobacter, Pseudomonas and Pantoea, are used as bioinoculants to improve the growth of different crops. The application of ST-PGPB allows plants to cope with salt stress by boosting their defense mechanisms. This review highlights the impact of salinity stress on plant growth and the potential of ST-PGPB as a biofertilizer to improve crop productivity under salt stress. [ABSTRACT FROM AUTHOR]

Feature selection and classification are widely used in machine learning in the context of big data. In many data sets, both attributes and decision classes can be preference ordered. Therefore, to process the data and information based on preference-ordered attributes, dominance-based rough set approach (DRSA) has been proposed. DRSA considers dominance relation between objects and can process the information with preference-ordered attribute domains. The it should be noted that the majority of the algorithms based on DRSA use dependency as an underlying criterion measure for different tasks. However, calculating dependency using the conventional DRSA approach requires the calculation of lower and upper approximations which is a computationally expensive task. A new approach has been proposed in this paper which calculates the dominance-based rough set dependency measure without calculating the lower and upper approximations. The proposed methodology is called the "Incremental Dominance-based Dependency Calculation Method" (IDDC). To justify the proposed approach, both IDDC and conventional approaches are compared using various data sets from the UCI data set repository. Results have shown that the proposed approach outperforms the conventional approach by depicting on average 46 % and 98 % decrease in execution time and required runtime memory, respectively. [ABSTRACT FROM AUTHOR]

Pectinases are applied in many commercial processes, particularly for fruit juice clarification. Cost effective production of pectinase necessitates utilization of agro-industrial wastes as fermentation raw materials. Biomass from halophytic plants provides a prospect for its sustainable utilization in industrial processes. Here, we report utilization of two halophytic biomasses, Suaeda fruticosa and Cressa cretica to grow a pectinolytic strain, Geotrichum candidum AA15 and its mutant strain AHC1. Amount of pectinase produced was compared by cultivating the strains in the medium containing apple peels and orange peels. The data showed that G. candidum AHC1 can produce pectinase titers of 15.43 and 28.15 IU mL−1 within 25 h when cultivated in the medium containing biomass from C. cretica and S. fruticosa, respectively. The results of FTIR and Scanning Electron microscopy affirmed the bacterial growth mediated changes in the structure of the substrate that was associated with the pectinase production. The crude pectinase preparation was applied to hydrolyze an agro-waste, sugarcane bagasse, that resulted in the release of 86 mg g−1 of reducing sugars. Thus, this study proposed the usage of halophytic plant biomass for cost effective pectinase production and its application for saccharification of agro-waste. [ABSTRACT FROM AUTHOR]

This study evaluates three precipitation products including the Tropical Rainfall Measuring Mission (TRMM), Multi-satellite Precipitation Analysis (TMPA), Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE, hereafter abbreviated as APH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network Climate Data Record (PERSIANN-CDR, hereafter abbreviated as PCDR) using surface precipitation gauge (SPG) data of 43 stations over five climatic zones of Pakistan for the period 1998 − 2015 on multiple temporal scales. Multiple statistical performance metrics and categorical statistics were utilized for evaluating the precipitation products such as mean error (ME), mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (CC), standard deviation (SD), probability of detection (POD), critical success index (CSI), and false alarm ratio (FAR), whereas probability distribution function (PDF) technique was employed to evaluate the precipitation intensities, and Mann–Kendall (MK) test was used for assessing the trends. Blend of over- and underestimation between SPG-TMPA, SPG-APH, and SPG-PCDR were perceived in different climatic zones on all temporal scales. MAE and RMSE of daily were higher than monthly and annual temporal scales. TMPA demonstrated slightly better results in comparison to APH and PCDR in all five climatic zones by analyzing precipitation intensity. The magnitude of the trend was less in the first half (1998 − 2006) as compared to the second half (2007 − 2015). All precipitation products performed better in climatic zones situated in plain areas in comparison to high mountainous regions. It is concluded that TMPA can be a better substitute of SPG for agriculture modeling, weather analysis, and water resource management studies. [ABSTRACT FROM AUTHOR]

Since sustainable development concerns emerged, researchers have studied which environmental regulations motivate companies to improve their environmental performance. Environmental goals are best achieved by either changing people's behavior or modifying technology. Thus, this study contributes to the existing literature on corporate governance and social and environmental accountability by examining the impact of CEO's tournament incentives (TI) on Chinese firms' environmental reporting and performance. Furthermore, the study consequently establishes how much tournament incentives and environmental practices connection are explained with green innovation. Using the panel data regression model, the study finds that CEOs' tournament incentives are favorably connected with Chinese enterprises' environmental performance and negatively with environmental reporting over 10 years (2010–2019). Furthermore, our findings suggest that green innovation positively mediates the TI environmental performance nexus. The study provided excellent implications for Chinese society, which can lead to achieving a "greener GDP" and a "harmonious society" for which China is dreaming and striving. The study findings have significant implications for stakeholders, including CEOs' policymakers, corporate regulators, environmentalists, and policymakers. [ABSTRACT FROM AUTHOR]

The effluent load from textile industries has forced traders and industrialists to use sustainable tools that not only save energy, money, and labor but also make the process cleaner. The purpose of this study is to improve the dyeing of proteinous fabrics using Acid Violet 49 dye under microwave radiation. Aqueous and acidic dye solutions were prepared and treated with MW radiations for up to 10 min. MW treated and untreated dye solutions were used to color treated and untreated fabrics to observe color yield. It has been found that dyeing of irradiated silk at 65 °C for 35 min, using 55 mL of irradiated dye solution containing 1 g/100 mL salt, has given excellent results. Whereas good color characteristics are obtained if irradiated wool fabric is dyed at 85 °C for 55 min, using 55 mL irradiated dye solution using 1 g/100 mL salt. Physicochemical analysis reveals that MW rays have physically modified the fabric without altering its chemistry. ISO standard methods employed for colorfastness show that under optimal conditions, the color developed is fast; statistical analysis shows that the dyeing process has given significant results. It is concluded that MW rays have excellent potential to improve acidic dyeing of proteinous fabric under mild conditions which show that the utilization of MW rays is a cost-, time-, and energy-effective process. [ABSTRACT FROM AUTHOR]

Carbon emissions bring significant risks and opportunities, and organisations have responded by adopting different strategies and environmental control systems, such as carbon accounting systems (CASs). However, it remains unclear whether a CAS can help reduce emissions, and what role is played by a CAS in the relationship between carbon strategy and carbon performance. Therefore, this paper analyses the strategy-accounting-performance nexus by drawing on 1672 firm-year observations of firms participating in the CDP in 2014 and 2015. The results suggest that the quality of a CAS is influenced by strategic choices; with a proactive carbon strategy being associated with a higher quality CAS. Further, proactive strategies and CASs are found to be associated with carbon savings and emissions reduction. The results indicate a moderating role of CASs on the strategy-performance relationship, with carbon strategy enabling higher carbon savings and lower emissions intensity in the presence of a high-quality CAS. Our findings suggest that formulation of carbon strategies and establishment of carbon measures can drive effective carbon mitigation. [ABSTRACT FROM AUTHOR]

Environmental quality has been pondered as an essential aspect of sustainable development across the global economies. Several factors such as economic development, income inequality, transportation, and environmental expenditures drastically influence environmental quality. More specifically, the transport sector is a major contributor to carbon emissions which deteriorate the environmental quality. Therefore, this study investigates whether economic development, transportation, environmental expenditures, and income inequality affect transport-carbon emissions for the OECD countries. Furthermore, panel time-series data period from 2000 to 2020 and cross-sectional autoregressive distributed lag method are used for transport-oriented environmental examination. Results demonstrate that transportation upsurges transport-carbon emission level by 46.45% on average. Moreover, the joint effect of economic development and environmental taxes significantly reduces transport-carbon emissions by 14.70%. Findings further suggest that an inverted U-shaped relationship exists between economic development and transport emission. Besides, income inequality, environmental expenditures, and green transportation are negatively associated with the coefficient of transport-carbon emissions. More interestingly, income inequality is negatively correlated with transport-carbon emissions across the sample countries. Furthermore, the joint effect of income inequality and economic development increases the emission level released by the transport sector. Thus, this research recommends some policies: countries should control traffic movements and increase environmental expenditures, and produce green transport vehicles to tackle environmental issues. [ABSTRACT FROM AUTHOR]

Accurate forecasting of key hydrological processes, such as rainfall, generally requires the use of auxiliary predictive hydrological variables. Data requirements can be reduced by using artificial intelligence models that are able to successfully capture the information contained in the historic observations of the target variable of interest. In this study, a novel Wavelet-coupled Multi-order Time Lagged Neural Network (WMTLNN) model is developed to accurately forecast rainfall by using previous rainfall records only. The study is conducted using daily rainfall data recorded in the period 2015–2017 at three meteorological stations (Astore, Chillas, and Gilgit) located in Upper Indus Basin (UIB), Pakistan. WMTLNN models are developed by introducing time lags up to ten days, Symlets 4 (sym4) wavelets and Daubechies wavelets with three vanishing moments (db3), and Maximal Overlap Discrete Wavelet Transformation (MODWT) to account for boundary effects in the forecasting mode. The performance of WMTLNN models is compared with that of Time Lagged Neural Network (TLNN) models, Wavelet-coupled Time Lagged Neural Network (WTLNN), and deep learning Long Short-Term Memory (LSTM) models. Comparative analysis indicates that WMTLNN models overcome the other models, with more than 80% forecasting accuracy for most of the cases, and a typical range of 0.85–0.95 accuracy in terms of NSE. The highest NSE value is 0.97 at Astore with LSTM model, 0.96 at Chillas with WMTLNN model, and 0.95 at Gilgit with WMTLNN model. Overall, wavelet transformation of time series data enhances efficiency and accuracy of rainfall forecast. [ABSTRACT FROM AUTHOR]

In this study, a simple but efficient bilevel multiobjective model (BLMOM) has been formulated for the optimal allocation of available water (AW) among competing water users. Upper-level decision makers (DMs), being the leader in the hierarchy (i.e., river authorities), allocate AW to lower-level DMs (i.e., canal authorities) based on equity and stability, whereas lower-level DMs allocate AW among competing users based on two single- and one multiobjective functions. The first objective function (OF1) maximizes the satisfaction rate (SR) of various water users, whereas the second objective function (OF2) maximizes the net economic benefits (NEB). The multiobjective function (OF12) maximizes the combined effect of the first two single objectives. The multiobjective function has been solved by using the simultaneous compromise constraint (SICCON) technique which creates a compromise between single-objective functions. The model was applied at Taunsa Barrage, Pakistan, for the optimal allocation of AW. Various scenarios were analyzed by varying priorities assigned to different objective functions and water users to evaluate the model applicability under various conditions. When OF1 was considered, maximum SR of 61% was achieved. In case of priority given to OF2, maximum NEB of 77 million USD was attained. However, in the case of OF12, both SR and NEB were maximized, whereas wheat, cotton, sugarcane, rice, onion and sunflower water users attained NEB of 14.36, 5.03, 22.96, 27.85, 1.34 and 0.13 million USD, respectively, with overall NEB of 72 million USD against satisfaction rate of 52%. [ABSTRACT FROM AUTHOR]

The Present study deals with synthesis of PVC/ABS/ZnO nanocomposites with Zinc oxide nanoparticles of particle size less than 50 nm by sonication and solution casting techniques. After characterization, such nanocomposite materials were subjected to thermal study, antibiofilm, antibacterial and antioxidant screening. Nanocomposites films showed higher thermal stability than pure polymer matrix loaded with different ZnO-Nps concentration with homogeneous distribution. Antibacterial studies were carried out against selected gram-positive bacteria: Staphylococcus aureus and gram-negative bacteria: Pseudomonas aeruginosa. Selective antibiofilm activity was studied against Staphylococcus aureus and Pseudomonas aeruginosa, which showed a higher to lower activity as a model pathogenic strains (∼93 and ∼89 at 160 µg/ml concentration, respectively), while free radical scavenging capacity was assessed by DPPH, ABTH·+ and FRAP methods. PVC/ABS/ZnO nanocomposite showed larger zones of inhibition and higher antibiofilm and antioxidant activity than PVC/ABS polymer matrix. PVC/ABS/ZnO nanocomposite showed enhanced thermal stability and biological properties that qualify them for different biomedical and industrial applications. [ABSTRACT FROM AUTHOR]

In this study, numerical simulations were performed to investigate the flow and turbulence characteristics of rectangular spur dikes with varying permeability using the Reynolds stress turbulence model developed by three-dimensional (3-D) numerical code FLUENT (ANSYS). In this research, both permeable and impermeable spur dikes were investigated with varying permeability (25%, 37%, 49%, 62%, and 74%) to show its effect on the flow structure. At zh=3.5 cm (where z is the maximum flow depth, and zh is mid of flow depth), 3-D flow velocities and depth-averaged velocities were measured in the horizontal plane. Different velocity profiles and contours, turbulent intensities (T.I), and turbulent kinetic energy (TKE) were analyzed at various selected positions. The findings indicate that an increase in the permeability of the spur dikes up to 74% had a reciprocal effect on the mean stream-wise velocity. Moreover, the recirculation regions created within the impermeable spur dike field decreased with increased permeability of spur dikes. The permeable spur dike head showed a significant decrease in T.I, and TKE compared to the impermeable spur dike. Therefore, to protect the spur dike head from severe turbulent flow during floods and reduce the spur dike field's recirculation region, a permeable spur dike is preferred. [ABSTRACT FROM AUTHOR]

Drought is generally associated with variation in multi geomorphic-climatic variables rather than a single drought driver. Hence, it is better to consider all possible drought drivers for comprehensive spatiotemporal analysis of historical and future drought in the region with an erratic, scant, and unstable climate like the south-west agricultural region of Pakistan. This study attempted to quantify the multivariable future drought projection based on project CMIP5 climatic data under the Representative Concentration Pathway (RCP 8.5) scenario. The drought projection under the climate change scenario was performed in the historical segment (HS: 1996–2018) and two future segments (2019–2034 and 2035–2050) using trend analysis and composite drought index (CDI). The overall results indicated that temperature and evapotranspiration tend to increase with an average relative value of ≥ 5% and in the case of precipitation, the ≥ 40% (relative average) decline was observed from HS to the future, which indicated an increase in numbers of drought in near future. Moreover, drought analysis also showed that the probability of occurrence and intensity of drought would increase in the near future, especially during 2019–2034. It was observed that the sub-regions, i.e., Sindh and Balochistan, would experience more intense long-term droughts as compared to Punjab. Hence, considering the future variation of drought, substantial attention is needed for contingency planning, mitigation, and adaptation strategies to minimize the impact of probable future drought on society in drought-prone agricultural areas of Pakistan. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ETo) is considered as an essential component in hydrological and agro meteorological processes. Its accurate estimation becomes an imperative in the planning and management of irrigation practices. ETo estimation also plays a vital role in improving the irrigation efficiency, water reuse and irrigation scheduling. The conventional physical model of Penmen Montieth (PM56) developed by Food and agriculture organization (FAO) has been recommended worldwide for ETo estimation. This model was firstly used in this study to determine ETo by using required meteorological data and obtained results used as referenced values. Afterward, five data machine learning algorithms/data driven models, support vector machine (SVM), multilayer perceptron (MLP), group method of data handling (GMDH), general regression neural network (GRNN) and cascade correlation neural network (CCNN), were applied to estimate ETo values. The climatic data of maximum and minimum temperatures, wind speed, average relative humidity and sunshine hours of six stations from Pakistan was used to train and test data driven model. Data driven models were also applied on other climatic stations without training data which lie in China, New Zealand and USA to further validate and investigate their performance. Comparison results indicated that model efficiency (ME) and correlation coefficient (r) of SVM were obtained (ranges: ME = 95–99%; r = 0.96–1) maximum for all the selected stations. Alternatively, model errors (RMSE = 0.016, MSE = 0.0001 & MAE = 0.08) for SVM were found minimum in comparison to GMDH, MLP, CCNN and GRNN. In addition, all data driven models show enough divergence from hyper arid to high humid climate except SVM which shows almost identical results for all the climatic zones in comparison to standard FAO-PM56 method. Finally, it can be concluded that SVM could be considered as a reliable alternative method for ETo estimation among data driven models. [ABSTRACT FROM AUTHOR]

This paper assesses the fairness of the Malaysian judicial system in handling cases pertaining to bribery and corruption. The assessment is based on an archival database of 1869 actual court cases that occurred between 2006 and 2013. The findings suggest that the Malaysian judicial system does not operate fairly. The punishment verdicts, in the form of fines and imprisonment, appear to be significantly influenced by offenders' social, political, and personal characteristics. Specifically, the results reveal that white-collar workers, government employees, and indigenous Malaysians (Bumiputera) receive more lenient treatment from the judicial system than do other offenders. Males receive harsher sentences than females, and offenders in those states controlled by the ruling party receive softer treatment from the judicial system than those in opposition-controlled states. An unfair justice system erodes public confidence and enables prospective or existing offenders to exploit the system's weaknesses for their personal gain. The contributions of this paper are as follows. First, unlike prior research which mostly draws upon public opinion surveys, this paper analyses real court cases. Second, it goes beyond the simple white-collar versus blue-collar distinction and considers offenders' social, political and personal characteristics. [ABSTRACT FROM AUTHOR]

The research work was carried out to synthesize a series of novel chitosan based water dispersible polyurethanes (CS-WDPUs). The three step synthesis involves the formation of end capped PU-prepolymer was formed through the reaction between polyethylene glycol (PEG) (Mn=600 g/mole, dimethylolpropionic acid (DMPA) and isophorone diisocyanate (IPDI) followed by the preparation of neutralized NCO terminated PU-prepolymer, which lead to the chain extension by using the chitosan. The dispersion of the obtained product was carried out by adding proper proportion of water. The synthesized CS-WDPUs were applied onto the different qualities of plain weave poly-cotton printed and dyed textile swatches by employing pad-dry-cure procedures. The textile assets of the treated and untreated textile swatches were assessed, as color fastness, pilling resistance, tear and tensile strength. The results showed that the chitosan incorporation into PU backbone has significant effect on the assets of treated textiles. These synthesized CS-WDPUs are eco-friendly bio-based finishes with potential applications for polyester/cotton textiles. [ABSTRACT FROM AUTHOR]

c-Myc is a highly pleiotropic transcription factor known to control cell cycle progression, apoptosis, and cellular transformation. Normally, ectopic expression of c-Myc is associated with promoting cell proliferation or triggering cell death via activating p53. However, it is not clear how the levels of c-Myc lead to different cellular responses. Here, we generated a series of stable RPE cell clones expressing c-Myc at different levels, and found that consistent low level of c-Myc induced cellular senescence by activating AP4 in post-confluent RPE cells, while the cells underwent cell death at high level of c-Myc. In addition, high level of c-Myc could override the effect of AP4 on cellular senescence. Further knockdown of AP4 abrogated senescence-like phenotype in cells expressing low level of c-Myc, and accelerated cell death in cells with medium level of c-Myc, indicating that AP4 was required for cellular senescence induced by low level of c-Myc. [ABSTRACT FROM AUTHOR]

This study estimates the sediment transport capacity of D.G. Khan Canal and analyses the impact of silt excluder on the sediment management of the canal. The average sediment load entering into the D.G. Khan Canal before the silt excluder’s intervention was about 258 ppm which is 58% more than the sediment transport capacity of the canal (163 ppm). However, the post-intervention average sediment load observed during 2006–2009 was 183 ppm which indicates that the intervention of silt excluder has reduced the sediment entry in the canal. [ABSTRACT FROM AUTHOR]

Stream salinity management is of prime importance for ensuring environmental sustainability of rivers, streams, lakes and other water bodies. Saline groundwater inflows are the main cause of deteriorating quality of stream flows; especially during periods of low flows. The Murray-Darling Basin is a good example for reducing groundwater recharge from irrigated agricultural areas with successful implementation of land and water management plans (LWMPs). In most cases, these plans help minimize the influence of saline groundwater flows on the stream salinity, as a preventive measure. As a remedial measure, this paper introduces a simple but an innovative approach for regulating saline groundwater inflows to natural streams in irrigated agricultural areas. The approach uses the flap gate with an automatic lowering and raising mechanism. During low stream flow, this gate is kept in a raised position. Depending upon the upstream inflows, the ponding situation will develop hydraulic gradient away from the stream; groundwater inflows to the creek will become minimal. During high stream flows, the gate is kept in a lower position to let the diluted water flows through the stream uninterrupted. To install, operate and manage such gates on multiple sites along the stream(s), the individual control panels can be joined into a central control station via telemetric link up. This approach, which holds a key for successfully regulating groundwater induced salinity to natural streams, should be considered in conjunction with other LWMPs to improve stream salinity in an irrigated agricultural area. [ABSTRACT FROM AUTHOR]

For completing Koch's postulates, I C. seifrizii i plants were inoculated by spraying spore suspension (1 × 10 SP 6 sp spores/ml), while control plants were treated with sterile distilled water only and covered with polythene bags during first 24 h to keep them moist for disease development (Kittimorakul et al. [2]). Keywords: Ornamental Palm; Leaf spots disease; Molecular identification; Curvularia lunata EN Ornamental Palm Leaf spots disease Molecular identification Curvularia lunata 713 713 1 05/06/21 20210501 NES 210501 I Chamaedorea seifrizii i Burret (Seifriz's chamaedorea) is shade loving indoor ornamental plant. [Extracted from the article]

Keywords: Ornamental palm; Fungal leaf spots; Pathogenicity confirmation; Molecular characterization EN Ornamental palm Fungal leaf spots Pathogenicity confirmation Molecular characterization 585 585 1 05/29/20 20200501 NES 200501 I Caryota mitis i L. (Clustering fishtail palm) is an important outdoor ornamental palm grown in landscapes of gardens and parks. Ornamental palm, Fungal leaf spots, Pathogenicity confirmation, Molecular characterization. [Extracted from the article]

An unexpected outbreak of deadly Covid-19 in later part of 2019 not only endangered the economies of the world but also posed threats to the cultural, social and psychological barriers of mankind. As soon as the virus emerged, scientists and researchers from all over the world started investigating the dynamics of this disease. Despite extensive investments in research, no cure has been officially found to date. This uncertain situation rises severe threats to the survival of mankind. An ultimate need of the time is to investigate the course of disease transfer and suggest a future projection of the disease transfer to be enabled to effectively tackle the always evolving situations ahead. In the present study daily new cases of COVID-19 was predicted using different forecasting techniques; Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing/Error Trend Seasonality (ETS), Artificial Neural Network Models (ANN), Gene Expression Programming (GEP), and Long Short-Term Memory (LSTM) in four countries; Pakistan, USA, India and Brazil. The dataset of new daily confirmed cases of COVID-19 from the date on which first case was registered in the respective country to 30 November 2020 is analyzed through these five forecasting models to forecast the new daily cases up to 31st January 2020. The forecasting efficiency of each model was evaluated using well known statistical parameters R 2 , RMSE, and NSE. A comparative analysis of all above-mentioned models was performed. Finally, the study concluded that Long Short-Term Memory (LSTM) neural network-based forecasting model projected the future cases of COVID-19 pandemic best in all the selected four stations. The accuracy of the model ranges from coefficient of determination value of 0.85 in Brazil to 0.96 in Pakistan. NSE value for the model in India is 0. 99, 0.98 in USA and Pakistan and 0.97 in Brazil. This high-accuracy forecast of COVID-19 cases enables the projection of possible peaks in near future in the aforementioned countries and, therefore, prove to be helpful in formulating strategies to get prepared for the potential hard times ahead., Competing Interests: Conflict of interestNo conflict of interest., (© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2021.)