CGST: Provably Secure Lightweight Certificateless Group Signcryption Technique Based on Fractional Chaotic Maps

In recent years, there has been a lot of research interest in analyzing chaotic constructions and their associated cryptographic structures. Compared with the essential combination of encryption and signature, the signcryption scheme has a more realistic solution for achieving message confidentiality and authentication simultaneously. However, the security of a signcryption scheme is questionable when deployed in modern safety-critical systems, especially as billions of sensitive user information is transmitted over open communication channels. In order to address this problem, a lightweight, provably secure certificateless technique that uses Fractional Chaotic Maps (FCM) for group-oriented signcryption (CGST) is proposed. The main feature of the CGST-FCM technique is that any group signcrypter may encrypt data/information with the group manager (GM) and have it sent to the verifier seamlessly. This implies the legitimacy of the signcrypted information/data is verifiable using the public conditions of the group, but they cannot link it to the conforming signcrypter. In this scenario, valid signcrypted information/data cannot be produced by the GM or any signcrypter in that category alone. However, the GM is allowed to reveal the identity of the signcrypter when there is a legal conflict to restrict repudiation of the signature. Generally, the CGST-FCM technique is protected from the indistinguishably chosen ciphertext attack (IND-CCA). Additionally, the computationally difficult Diffie-Hellman (DH) problems have been used to build unlinkability, untraceability, unforgeability, and robustness of the projected CGST-FCM scheme. Finally, the security investigation of the presented CGST-FCM technique shows appreciable consistency and high efficiency when applied in real-time security applications

Comparison of cubic, quadratic, and quintic splines for soil erosion modeling

Abstract Approximate curve is constructed using quadratic, quintic, and cubic splines and examination between these splines. The point of this construction is to predict sediment yield index (SYI) corresponding to curve number. This strategy is outlined with a contextual analysis of Manot watershed of Narmada Basin, India. The relation among calculated SYI and observed SYI esteems is associated with a coefficient of determination (R 2) of 0.36 and 0.48 for the corresponding quadratic and quintic splines, while the cubic spline showed R 2 of 0.87 (Meshram et al. Arab J Geosci 10:155–168, 2017b; Appl Water Sci 7:1773–1779, 2017c). Numerical results seemed to indicate that the cubic spline method is more accurate than the quadratic/quintic spline method

Statistical evaluation of rainfall time series in concurrence with agriculture and water resources of Ken River basin, Central India (1901–2010)

Trend analysis of long-term rainfall records can be used to facilitate better agriculture water management decision and climate risk studies. The main objective of this study was to identify the existing trends in the long-term rainfall time series over the period 1901–2010 utilizing 12 hydrological stations located at the Ken River basin (KRB) in Madhya Pradesh, India. To investigate the different trends, the rainfall time series data were divided into annual and seasonal (i.e., pre-monsoon, monsoon, post-monsoon, and winter season) sub-sets, and a statistical analysis of data using the non-parametric Mann–Kendall (MK) test and the Sen’s slope approach was applied to identify the nature of the existing trends in rainfall series for the Ken River basin. The obtained results were further interpolated with the aid of the Quantum Geographic Information System (GIS) approach employing the inverse distance weighted approach. The results showed that the monsoon and the winter season exhibited a negative trend in rainfall changes over the period of study, and this was true for all stations, although the changes during the preand the post-monsoon seasons were less significant. The outcomes of this research study also suggest significant decreases in the seasonal and annual trends of rainfall amounts in the study period. These findings showing a clear signature of climate change impacts on KRB region potentially have implications in terms of climate risk management strategies to be developed during major growing and harvesting seasons and also to aid in the appropriate water resource management strategies that must be implemented in decision-making process

ORIENTAL JOURNAL OF CHEMISTRY Characteristics and Seasonal Variation of Carbonaceous and Water Soluble Organic Components in the Aerosols over East India

ABSTRACT The present investigation intends to measurement of PM 2.5 and PM 10 samples from agricultural (AG) and an Adityapur industrial (AI) site of East India to better characterize the carbonecous and water-soluble organic carbon (WSOC). The current study aimed (a) to determine variation ratio of OC/ PM, EC/PM, WSOC/EC, OC/EC in the study area (b) assess and quantity the Correlation between OC and EC, WSOC and OC, WSOC and PM, WSOC and EC of AG and AI site (c) Analyse the abundance pattern, at AG site indicating dominant contribution from biomass burning sources (woodfuel and agriculture waste) and in AI site sharp contrast influenced by emissions from coal-fired industries. Th

New approach for sediment yield forecasting with a two-phase feed forward neuron network-particle swarm optimization model integrated with the gravitational search algorithm

Predicting sediment yield is an important task for decision-makers in environmental monitoring and water management since the benefits of applying non-linear, artificial intelligence (AI) models for optimal prediction can be far reaching in real-life decision support systems. AI-based models are considered to be favorable predictive tools since the nonlinear nature of suspended sediment data series warrants the utilization of nonlinear predictive methods for feature extraction, and for accurate simulation of suspended sediment load. In this study, Artificial Neural Network (ANN) approaches are employed to estimate the monthly sediment load where the two-phase Feed-forward Neuron Network Particle Swarm Optimization Gravitational Search Algorithm (FNN-PSOGSA) is developed, and then evaluated in respect to 3 distinct algorithms: the Adaptive Neuro-Fuzzy Inference System (ANFIS), Feed-forward Neuron Network (FNN) and the single-phase Feed-forward Neuron Network Particle Swarm Optimization (FNN-PSO). The study is carried out using the monthly rainfall, runoff and sediment data spanning a 10 year period (2000–2009) where about 75% of data are used in model training phase, 25% in testing phase. Three statistical performance criteria namely: the mean absolute error (MAE), Nash-Sutcliffe coefficient (NSE) and the Willmott’s Index (WI) and diagnostic plots visualizing the tested results are used to evaluate the performance of four AI-based models. The results reveal that the objective model (the two-phase FNN-PSOGSA model) and the single-phase FNN-PSO model yielded more precise results compared to the other forecast models. This result accorded to an NSE value of 0.612 (for the FNN-PSOGSA model) vs. an NS value of 0.500, 0.331 and 0.244 for the FNN-PSO, FNN and ANFIS models, and WI = 0.832 vs. 0.771, 0.692 and 0.726, respectively The study also demonstrated that the FNN model generated slightly better results than the ANFIS model for the estimation of sediment load data but overall, the two-phase FNN-PSOGSA model outperformed all comparison models. In light of the superior performance, this research advocates that the fully-optimized two-phase FNN-PSOGSA model can be explored as a decision-support tool for monthly sediment load forecasting using the rainfall and runoff values as the predictor datasets

Impact of roof rain water harvesting of runoff capture and household consumption

In recent years, the occurrence of floods is one of the most important challenges facing in Hamadan city. In the absence/inefficiency of urban drainage systems, rainwater harvesting (RWH) systems as low-impact development (LID) methods can be considered as a measure to reduce the floods. In this study, three scenarios concerning the RWH from the roof surfaces are studied to evaluate the type of the harvested water on reducing flooding. In the first scenario, which indicates the current situation in the studied area, it is indicated that there is no harvest of the roof surfaces in the studied area. The second scenario is about the use of water harvested from the roof surfaces for household purposes. The third scenario also refers to the use of harvested water for irrigation of gardens. The simulation results of these three scenarios using the Soil Conservation Service (SCS) method in the Hydrologic Modeling System (HEC-HMS) model reveal that if the second scenario is implemented, the runoff volume decreases from 28 to 12% for the return period from 2 to 100 years. However, in the third scenario, this reduction in runoff volume will be 48 and 27% for return periods of 2 to 100 years, respectively. Therefore, the results of this study indicate that the use of harvested water can also affect the reduction on runoff volume

Comparative implementation between neuro-emotional genetic algorithm and novel ensemble computing techniques for modelling dissolved oxygen concentration

Accurate prediction of dissolved oxygen (DO) concentration is important for managing healthy aquatic ecosystems. This study investigates the comparative potential of the emotional artificial neural network-genetic algorithm (EANN-GA), and three ensemble techniques, i.e. emotional artificial neural network (EANN), feedforward neural network (FFNN), and neural network ensemble (NNE), to predict DO concentration in the Kinta River basin of Malaysia. The performance of EANN-GA, EANN, FFNN, and NNE models in predicting DO was evaluated using statistical metrics and visual interpretation. Appraisal of the results revealed a promising performance of the NNE-M3 model (Nash-Sutcliffe efficiency (NSE) = 0.8743/0.8630, correlation coefficient (CC) = 0.9351/0.9113, mean square error (MSE) = 0.5757/0.6833 mg/L, root mean square error (RMSE) = 0.7588/0.8266 mg/L, and mean absolute percentage error (MAPE) = 20.6581/14.1675) during the calibration/validation period compared to EANN-GA, EANN, and FFNN models in DO prediction in the study basin