Dryland river regime shifts in Iran: Drivers and feedbacks

In the Anthropocene, human activities have created unprecedented changes and nonlinear relationships between humans and nature. These changes can be much faster and more intense in arid and semiarid areas that have been affected by intense human activities. Iran has climates from very humid to very dry, but arid and semiarid climates cover the country's largest area. Many of these arid areas have undergone severe changes in their surface and groundwater ecosystems in recent years, which have caused severe damage to humans and the environment in the area and surrounding areas. Therefore, in this study, using the theory of regime shifts, the time series of the Zayandeh-Rud River Basin in the center of Iran were analyzed. First, the data of the desired time series in the period of 1986–2018 was arranged seasonally. Then, using the sequential t-test method, regime shifts in these time series were identified, and then, causal loop diagrams of these shifts and their drivers and feedbacks were interpreted. The results showed that in the time series of quantity and quality of surface water and groundwater level in the studied stations and aquifers, regime shifts can be identified. Regime shifts were also identified in the time series of agricultural land area. These shifts have occurred with the increase in human activities since the early 1950s in the metropolis of Isfahan, the increase in agricultural and industrial exploitation, and consequently, the increase in population. When this reinforcing feedback loop becomes dominant, the Zayandeh-Rud River system has shifted from a regime of rich water resources to a regime of poor water resources. However, by recognizing and systematically analyzing these shifts, the Zayandeh-Rud River system can be directed toward a sustainable system through structural reform, negotiation, and redefining goals.     

Comparative study of one-pot and facile methods to synthesize codoped CQDs with low band gap and photovoltaic properties

Three different one-pot methods of electrochemical, solvothermal, and pyrolysis were applied for the synthesis of nitrogen-doped carbon quantum dots (N-CQDs), N-F codoped carbon quantum dots (CQDs), and N-S codoped CQDs from monoethanolamine and citric acid precursors. Ammonium fluoride and/or thiourea were used as the precursors of the second dopant corporation. The effective synthesis parameters were studied on the basis of the factorial experimental design methodology to maximize absorption edge and reduce band gap in UV-visible spectroscopy. Among the best results, the synthesized N-F/CQDs prepared from ammonium fluoride and citric acid in monoethanolamine revealed the highest absorption edge of 650 nm, the band gap of 1.91 eV, and the particle size of 24 ± 7 nm using the pyrolysis method. The X-ray photoelectron spectroscopy (XPS) analysis indicated simultaneous doping of F and N atoms in the CQDs structure, and the photoluminescence (PL) analysis revealed excitation-dependent properties, which are effective for optical sensor and solar cell applications.     

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images. Hyperspectral remote sensing contains acquisition of digital images from several narrow, contiguous spectral bands throughout the visible, Thermal Infrared (TIR), Near Infrared (NIR), and Mid-Infrared (MIR) regions of the electromagnetic spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the precise for agriculture as they can offer chemical and physical data on vegetation. With this motivation, this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification (HOADTL-CC) model on Hyperspectral Remote Sensing Images. The presented HOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images. To accomplish this, the presented HOADTL-CC model involves the design of HOA with capsule network (CapsNet) model for generating a set of useful feature vectors. Besides, Elman neural network (ENN) model is applied to allot proper class labels into the input HSI. Finally, glowworm swarm optimization (GSO) algorithm is exploited to fine tune the ENN parameters involved in this article. The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects. Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.     

Investigation of mixing performance in a semi-active T-micromixer actuated by magnetic nanoparticles: Characterization via Villermaux–Dushman reaction

A semi-active T-type micromixer is designed to intensify micromixing by actuating magnetic nanoparticles (MNPs). Five permanent magnets in a zig-zag arrangement are located next to the mixing channel of the micromixer to apply the magnetic field to the fluid flow. Micromixing performance is considered in terms of the segregation index (X_S) by the Villermaux/Dushman reaction test. The effects of magnetic flux intensity (B = 380–500 mT), the concentration of MNPs (φ = 0.002–0.01 [w/v]), and flow rate ratios on X_S and pressure drop are investigated. By increasing MNPs concentration from φ = 0.002–0.008 (w/v), X_S decreased and the rise in φ up to 0.008 (w/v) has not been significant on X_S. Maximum mixing efficiency (i.e., minimum X_S = 0.0088) is achieved for B = 500 mT and φ = 0.01 (w/v). By applying the magnetic field, the mixing performance increased due to the motion of MNPs, but its negative effect is an increase in the pressure drop along the micromixer reactor. Generally, with the formation of MNPs barriers inside the mixing channel, the main fluid flows through these layers and creates the sinusoidal flow paths compared to no magnetic field conditions, and thus, a superior mixing efficiency could be attained.     

Exploring the Design and Spectroscopic Characteristics of PVA/Si3N4/SiBr4 New Structures for Electronics and Optics Devices

Silicon - This work aims to design of silicon nitride (Si3N4) and silicon bromide (SiBr4) doped polyvinyl alcohol (PVA) as promising semiconductors materials which can be used in various...     

Challenges and Strategies for Optimizing Corrosion and Biodegradation Stability of Biomedical Micro- and Nanoswimmers: A Review

The last two decades have witnessed the emergence of micro- and nanoswimmers (MNSs). Researchers have invested significant efforts in engineering motile micro- and nanodevices to address current limitations in minimally invasive medicine. MNSs can move through complex fluid media by using chemical fuels or external energy sources such as magnetic fields, ultrasound, or light. Despite significant advancements in their locomotion and functionalities, the gradual deterioration of MNSs in human physiological media is often overlooked. Corrosion and biodegradation caused by chemical reactions with surrounding medium and the activity of biological agents can significantly affect their chemical stability and functional properties during their lifetime performance. It is therefore essential to understand the degradation mechanisms and factors that influence them to design ideal biomedical MNSs that are affordable, highly efficient, and sufficiently resistant to degradation (at least during their service time). This review summarizes recent studies that delve into the physicochemical characteristics and complex environmental factors affecting the corrosion and biodegradation of MNSs, with a focus on metal-based devices. Additionally, different strategies are discussed to enhance and/or optimize their stability. Conversely, controlled degradation of non-toxic MNSs can be highly advantageous for numerous biomedical applications, allowing for less invasive, safer, and more efficient treatments.     

Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

Biomedical data classification has become a hot research topic in recent years, thanks to the latest technological advancements made in healthcare. Biomedical data is usually examined by physicians for decision making process in patient treatment. Since manual diagnosis is a tedious and time consuming task, numerous automated models, using Artificial Intelligence (AI) techniques, have been presented so far. With this motivation, the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI (BDC-CMBOAI) technique. The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical data. Besides, the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection (CMBO-FS) technique to derive a useful subset of features. In addition, Ridge Regression (RR) model is also utilized as a classifier to identify the existence of disease. The novelty of the current work is its designing of CMBO-FS model for data classification. Moreover, CMBO-FS technique is used to get rid of unwanted features and boosts the classification accuracy. The results of the experimental analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures.     

Numerical Solution of the Flow of Power‐Law Gel Propellants in Converging Injectors

The governing equations of the steady, incompressible, isothermal, laminar flow of a power‐law, shear‐thinning gel propellant in a converging injector were formulated. The equations were transformed to a ψ–ω system, then discretized and solved. A numerical algorithm was developed for the solution of the flowfield. A parametric investigation was conducted to evaluate the effect of the injector geometry and the flow rate on the velocity and viscosity fields, for various gel propellants. In comparison to a straight injector, a convergent one can produce additional decrease in the mean apparent viscosity of the fluid. The mean apparent viscosity decreases significantly with increasing the convergence angle of the injector. In general, increasing the gel flow rate results in a decrease of the mean apparent viscosity of the fluid. Increasing the gellant content of gel propellants results in an increase of the fluid viscosity that decreases when the gel flows through the injector. Gels of different gellant content that flow through the same geometry injector exhibit similar relative reduction in viscosity.