Epoxide functionalized γ-Al2O3/Fe3O4/SiO2 nanocomposite and comparative adsorption behavior of a model reactive azo dye

In this investigation, magnetic γ-Al₂O₃ nanocomposite polymer particles with epoxide functionality were prepared following a multistep process. The prepared nanocomposite polymer particle was named as γ-Al₂O₃/Fe₃O₄/SiO₂/poly(glycidyl methacrylate (PGMA). The surface property was evaluated by carrying out the adsorption study of Remazol Navy RGB (RN), a model reactive azo dye, on both γ-Al₂O₃/Fe₃O₄/SiO₂ and γ-Al₂O₃/Fe₃O₄/SiO₂/PGMA nanocomposite particles, that is, before and after epoxide functionalization. A contact time, temperature, adsorbent dose, and dye concentration dependent change in adsorption behavior was observed on both nanocomposite particles. The adsorption amount reached equilibrium (q_e) value within 5 minutes at the respective point of zero charge (PZC). The adsorption density of RN per unit specific surface area on epoxide functional γ-Al₂O₃/Fe₃O₄/SiO₂/PGMA nanocomposite polymer particles (1.30 mg/m²) was higher relative to that on γ-Al₂O₃/Fe₃O₄/SiO₂ nanocomposite particles (0.87 mg/m²). The optimum adsorbent dose for obtaining the maximum adsorption density was 0.01 g. Comparatively, Langmuir isotherm model was better to describe the adsorption process and the adsorption process was favorable at low temperature (283 K). Batch kinetic adsorption experiment suggested that a pseudo-second-order rate kinetic model is more appropriate. Nanocomposite polymer particles were used as adsorbent up to third cycle with almost 99% adsorption efficiency.     

Boosted hydrogen evolution activity from Sr doped ZnO/CNTs nanocomposite as visible light driven photocatalyst

CNTs were decorated onto Sr doped ZnO nanoparticles to construct an efficient photocatalyst via a facile sol-gel method. The as-fabricated Sr doped ZnO/CNTs with recyclability exhibits Sr and CNTs content dependent hydrogen evolution activit under visible light illumination. The Sr doped ZnO/CNTs photocatalyst shows the highest hydrogen evolution rate of 2732.2 μmolh^?1g^?1, which is 33.7 and 2.83 times higher than pure ZnO and Sr doped ZnO photocatalysts, respectively. The improved hydrogen evolution activity of Sr doped ZnO/CNTs is primarily assigned to high surface area, Sr doping and construction of heterojunction, which can extend the light absorption, decrease the optical band gap and improve the charge separation. Moreover, the underlying photocatalytic mechanism is proposed on the basis of Mott-Schottky study and explains the interfacial charge transfer process from ZnO to CNTs and Sr. This work open new strategies to synthesize CNTs based nanocomposite for hydrogen evolution.     

Exploring Regional Profile of Drought History- a New Procedure to Characterize and Evaluate Multi-Scaler Drought Indices Under Spatial Poisson Log-Normal Model

Water Resources Management - Drought is recurrently occurring in many parts of the globe. In contrast to other natural hazards, drought has complex climatic characteristics. Several environmental...     

Vertex-Edge Degree Based Indices of Honey Comb Derived Network

Chemical graph theory is a branch of mathematics which combines graph theory and chemistry. Chemical reaction network theory is a territory of applied mathematics that endeavors to display the conduct of genuine compound frameworks. It pulled the research community due to its applications in theoretical and organic chemistry since 1960. Additionally, it also increases the interest the mathematicians due to the interesting mathematical structures and problems are involved. The structure of an interconnection network can be represented by a graph. In the network, vertices represent the processor nodes and edges represent the links between the processor nodes. Graph invariants play a vital feature in graph theory and distinguish the structural properties of graphs and networks. In this paper, we determined the newly introduced topological indices namely, first -degree Zagreb index, first -degree Zagreb index, second -degree Zagreb index, -degree Randic index, -degree atom-bond connectivity index, -degree geometric-arithmetic index, -degree harmonic index and -degree sum-connectivity index for honey comb derived network. In the analysis of the quantitative structure property relationships (QSPRs) and the quantitative structureactivity relationships (QSARs), graph invariants are important tools to approximate and predicate the properties of the biological and chemical compounds. Also, we give the numerical and graphical representation of our outcomes.     

Osteoporosis Prediction for Trabecular Bone using Machine Learning: A Review

Trabecular bone holds the utmost importance due to its significance regarding early bone loss. Diseases like osteoporosis greatly affect the structure of the Trabecular bone which results in different outcomes like high risk of fracture. The objective of this paper is to inspect the characteristics of the Trabecular Bone by using the Magnetic Resonance Imaging (MRI) technique. These characteristics prove to be quite helpful in studying different studies related to Trabecular bone such as osteoporosis. The things that were considered before the selection of the articles for the systematic review were language, research field, and electronic sources. Only those articles written in the English language were selected as it is the most prominent language used in scientific, engineering, computer science, and biomedical researches. This literature review was conducted on the articles published between 2006 and 2020. A total of 62 research papers out of 1050 papers were extracted which were according to our topic of review after screening abstract and article content for the title and abstract screening. The findings from those researches were compiled at the end of the result section. This systematic literature review presents a comprehensive report on scientific researches and studies that have been done in the medical area concerning trabecular bone.     

Role of Fuzzy Approach towards Fault Detection for Distributed Components

Component-based software development is rapidly introducing numerous new paradigms and possibilities to deliver highly customized software in a distributed environment. Among other communication, teamwork, and coordination problems in global software development, the detection of faults is seen as the key challenge. Thus, there is a need to ensure the reliability of component-based applications requirements. Distributed device detection faults applied to tracked components from various sources and failed to keep track of all the large number of components from different locations. In this study, we propose an approach for fault detection from component-based systems requirements using the fuzzy logic approach and historical information during acceptance testing. This approach identified error-prone components selection for test case extraction and for prioritization of test cases to validate components in acceptance testing. For the evaluation, we used empirical study, and results depicted that the proposed approach significantly outperforms in component selection and acceptance testing. The comparison to the conventional procedures, i.e., requirement criteria, and communication coverage criteria without irrelevancy and redundancy successfully outperform other procedures. Consequently, the F-measures of the proposed approach define the accurate selection of components, and faults identification increases in components using the proposed approach were higher (i.e., more than 80 percent) than requirement criteria, and code coverage criteria procedures (i.e., less than 80 percent), respectively. Similarly, the rate of fault detection in the proposed approach increases, i.e., 92.80 compared to existing methods i.e., less than 80 percent. The proposed approach will provide a comprehensive guideline and roadmap for practitioners and researchers.     

Climate change and the UN-2030 agenda: Do mitigation technologies represent a driving factor? New evidence from OECD economies

Clean Technologies and Environmental Policy - The United Nations 2030 agenda states various sustainable development goals (SDGs) to counter climate change affecting individuals' health and...     

Optimal path planning in cluttered environment using RRT*-AB

Rapidly exploring Random Tree Star (RRT*) has gained popularity due to its support for complex and high-dimensional problems. Its numerous applications in path planning have made it an active area of research. Although it ensures probabilistic completeness and asymptotic optimality, its slow convergence rate and large dense sampling space are proven problems. In this paper, an off-line planning algorithm based on RRT* named RRT*-adjustable bounds (RRT*-AB) is proposed to resolve these issues. The proposed approach rapidly targets the goal region with improved computational efficiency. Desired objectives are achieved through three novel strategies, i.e., connectivity region, goal-biased bounded sampling, and path optimization. Goal-biased bounded sampling is performed within boundary of connectivity region to find the initial path. Connectivity region is flexible enough to grow for complex environment. Once path is found, it is optimized gradually using node rejection and concentrated bounded sampling. Final path is further improved using global pruning to erode extra nodes. Robustness and efficiency of proposed algorithm is tested through experiments in different structured and unstructured environments cluttered with obstacles including narrow and complex maze cases. The proposed approach converges to shorter path with reduced time and memory requirements than conventional RRT* methods.