Assessing Adaptability of Cyclic and Non-Cyclic Approach to Conjunctive use of Groundwater and Surface water for Sustainable Management Plans under Climate Change

Groundwater overdraft in many regions throughout the world has been threatening the sustainability of this valuable resource. It has been argued that climate change may contribute to the severity of the issue; hence “impact assessment” is being replaced by “adaptation,” which explores more adapting scenarios and approaches. This study explores the adaptability of the proposed cyclic and non-cyclic conjunctive use of groundwater and surface water resources in increasing groundwater sustainability while increasing the sustainability of water allocation to the agricultural sector under possible climate change scenarios. To simulate climate change in the study area, precipitation and temperature variables are extracted from the results of three global atmospheric circulation models (Ensemble, CMCC-CMS, MRI-CGCM3) under RCP2.6 and RCP8.5 greenhouse gas emission scenarios in the period of 2021–2031. Spatial downscaling is performed using the M5 decision tree algorithm. The Wavelet-M5 hybrid model is used to predict runoff values as a rainfall-runoff model. Also, the Kharrufa method is applied to calculate evaporation in the future seasons. The system's adaptability to climate change is examined using the multi-objective cyclic and non-cyclic conjunctive use of surface and groundwater models. The study reveals that cyclic operation strategy improves the conjunctive use system adaptability compared to the optimal operation strategy that employs the non-cyclic approach. In this study's case study, the improvement in groundwater sustainability index exceeds 27 percent over the non-cyclic conjunctive use strategy.

     

Spectral analysis of GFDM modulated signal under nonlinear behavior of power amplifier

Wireless Networks - Generalized frequency division multiplexing (GFDM) is a flexible non-orthogonal waveform candidate for 5G which can offer some advantages such as low out-of-band emission and...     

A new fast settling low power CMOS gain stage architecture

This paper presents a new gain stage for high accuracy and fast settling applications. In the proposed structure a novel combination of closed loop and open loop amplifiers is employed to achieve high accuracy and enhanced settling behavior while adding only negligible power to the main circuit power constraint. To evaluate the functionality of the proposed idea, a zero cross based circuit and a switch capacitor amplifier are designed to implement the open loop and the closed loop stages, respectively. Though, other topologies for implementation of open loop and closed loop amplifiers are applicable in the presented gain stage. The proposed structure is implemented in 0.18 μm CMOS technology. HSPICE simulation results, using level 49 models, demonstrate that the new configuration improves the power efficiency and the settling behavior as well as the system accuracy. The proposed scheme shows very fast settling times of 0.8, 1.01, 1.41 ns for the gain accuracies of 6, 8 and 10 bits, respectively, while loaded with 1 pF capacitance and the output swing is 1.6 V. In comparison with a conventional switched capacitor closed loop amplifier, the proposed architecture improves the settling performance by a factor of 3 for 6 bit resolution, while it adds only 0.63 mW power to the total power consumption that is 8.68 mW.     

Correction to: Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling

Water Resources Management - A correction to this paper has been published: https://doi.org/10.1007/s11269-021-02861-z     

Detecting intrusion transactions in database systems:a novel approach

The security of computers and their networks is of crucial concern in the world today. One mechanism to safeguard information stored in database systems is an Intrusion Detection System (IDS). The purpose of intrusion detection in database systems is to detect malicious transactions that corrupt data. Recently researchers are working on using data mining techniques for detecting such malicious transactions in database systems. Their approach concentrates on mining data dependencies among data items. However, the transactions not compliant with these data dependencies are identified as malicious transactions. Algorithms that these approaches use for designing their data dependency miner have limitations. For instance, they need to experimentally determine appropriate settings for minimum support and related constraints, which does not necessarily lead to strong data dependencies. In this paper we propose a new data mining algorithm, called the Optimal Data Access Dependency Rule Mining (ODADRM), for designing a data dependency miner for our database IDS. ODADRM is an extension of k-optimal rule discovery algorithm, which has been improved to be suitable in database intrusion detection domain. ODADRM avoids many limitations of previous data dependency miner algorithms. As a result, our approach is able to track normal transactions and detect malicious ones more effectively than existing approaches.     

Cross-layer optimization for wireless mesh networks with smart antennas

A cross-layer optimization framework for wireless mesh networks is presented where at each node, various smart antenna techniques such as beam-forming, spatial division multiple access and spatial division multiplexing are employed. These techniques provide interference suppression, capability for simultaneous communication with several nodes and transmission with higher data rates, respectively, through multiple antennas. By integrating different combinations of the multi-antenna techniques in physical layer with various constraints from MAC and network layers, three Mixed Integer Linear Programming (MILP) models are presented to minimize the system activation time. Since these optimization problems are complex combinatorial, the optimal solution is approached by a Column Generation decomposition method. The numerical results for different network scenarios with various node densities, number of antennas, transmission ranges and number of sessions are provided. It is shown that the resulted directive, multiple access and multiplexing gains combined with scheduling, effectively increase both the spectrum spatial reuse and the capacity of the links and therefore, enhance the achievable system throughput. Our cross-layer approach is also extended to consider heterogeneous networks and we present a multi-criteria optimization framework to model the design problem where the objective is to jointly minimize the cost of deployment and the system activation time. Our results reveal the benefits of joint design in terms of reducing the cost of deployment while achieving higher system performance.     

Decision‐tree‐based methods for differential diagnosis of β‐thalassemia trait from iron deficiency anemia

Several discrimination indices have been proposed to distinct between β‐thalassemia trait (βTT) and iron deficiency anemia (IDA). This study is the first application of tree‐based methods for differential diagnosis of βTT from IDA. One hundred forty‐four patients with hypochromic microcytic anemia aged 18–40 years old from Ayat Hospital of Tehran were recruited. Classification and Regression tree, CHi‐squared Automatic Interaction Detector (CHAID), Exhaustive CHi‐squared Automatic Interaction Detector (E‐CHAID), Quick, Unbiased, Efficient Statistical Tree (QUEST), Classification Rule with Unbiased Interaction Selection and Estimation (CRUISE), and Generalized, Unbiased, Interaction Detection and Estimation (GUIDE) have been used to discriminate the diagnosis. Mean corpuscular volume (MCV) was found as the main predictor in discrimination. All the mentioned tree‐based methods showed acceptable sensitivity, specificity, accuracy, Youden's index, false positive and negative rate, positive and negative predictive values and AUC in differential diagnosis of βTT from IDA. However, Classification Rule with Unbiased Interaction Selection and Estimation revealed more precise classification with an area under the curve value of 0.99. Decision‐tree‐based methods can be used to develop sensitive and accurate diagnostic methods for differentiating βTT from IDA.     

Design of product polar quantizers for A/D conversion of measurement signals with Gaussian distribution

In this paper, a design of product polar quantizers for A/D (analog-to-digital) conversion of measurement signals and an analysis of their performances in the wide range of variances are considered, in a general manner, i.e. for any compression function. Special attention is devoted to the design of robust product polar quantizers, applying the developed theory on μ-law logarithmic compression function, which is very important since a lot of measurement signals are nonstationary. The proposed quantizers achieve much better performances than scalar quantizers (they have higher the maximal SQNR (signal-to-quantization noise ratio) for about 2.5 dB and they are more robust), without increasing of complexity. Theory is proved by simulation. These quantizers can be applied for many different types of measurement signals and can be used in many measurement systems (telemetry, telemedicine, wireless sensor networks, etc.).     

Vacancy‐Controlled Contact Friction in Graphene

Defects‐controlled friction in graphene is of technological importance in many applications, but the underlying mechanism remains a subject of debate. Here it is shown that, during the controlled oxidation in oxygen plasma and subsequent reduction induced by high‐energy photons, the contact friction in chemical vapor deposition grown graphene is dominantly determined by the vacancies formed instead of the bonding with add‐atoms. This effect is attributed to the vacancy‐enhanced out‐of‐plane deformation flexibility in graphene, which tends to produce large puckering of graphene sheet near the contact edge and thus increases the effective contact area. Modified graphene with large contact friction has a large density of defects, but remains a good electrical conductor, in which the carrier transport is strongly affected by quantum localization effects even at room temperature. It is also found that the oxidation process in graphene is substrate‐sensitive. Comparing to monolayer graphene on SiO₂ substrate, the oxidation process progresses much faster when the substrate is SrTiO₃, while bilayer graphene exhibits great oxidation resistance on both substrates. The collection of observations provides important information for tailoring the mechanical, electrical, and chemical properties of graphene through selected defects and substrates.