Development of high performance amine functionalized zeolitic imidazolate framework (ZIF-8)/cellulose triacetate mixed matrix membranes for CO2/CH4 separation

High cost and complex fabrication process of inorganic membranes and lower position of pristine polymeric membranes in the Robeson upper bound curve urged the researchers to develop mixed matrix membranes (MMMs). Cellulose acetate being most commercially used polymer, dominates the market of CO₂ separation mainly because of low cost and environmental friendly resource. In the present study, MMMs consists of amine functionalized zeolitic imidazolate framework (NH₂-ZIF-8) and cellulose triacetate were fabricated for the first time. NH₂-ZIF-8 was used as a filler because the pore size of ZIF-8 is between the kinetic diameter of separating gases (CO₂ and CH₄). Moreover,  NH₂ group attached on the surface of ZIF-8 has affinity with condensable gases like CO₂. Morphology, crystallinity, tensile strength and functional groups of fabricated membranes were investigated using different analytical techniques. Results revealed that the increase of feed pressure has increased CO₂ permeability and decreased permselectivity. However, improvements in gas separation performance were observed with the addition of nanofiller. Best position in Robeson's upper bound curve at 4 bar was obtained with 10 wt% loading with CO₂ permeability and CO₂/CH₄ permselectivity of 218 barrer and 13.84, respectively. The improvement in the gas separation performance with loading is attributed to the increased diffusion coefficients as well as solubility coefficients, which was increased to 33% and 3.8%, respectively.     

Designing Stimuli-Responsive Upconversion Nanoparticles that Exploit the Tumor Microenvironment

Tailoring personalized cancer nanomedicines demands detailed understanding of the tumor microenvironment. In recent years, smart upconversion nanoparticles with the ability to exploit the unique characteristics of the tumor microenvironment for precise targeting have been designed. To activate upconversion nanoparticles, various bio-physicochemical characteristics of the tumor microenvironment, namely, acidic pH, redox reactants, and hypoxia, are exploited. Stimuli-responsive upconversion nanoparticles also utilize the excessive presence of adenosine triphosphate (ATP), riboflavin, and Zn²⁺ in tumors. An overview of the design of stimulus-responsive upconversion nanoparticles that precisely target and respond to tumors via targeting the tumor microenvironment and intracellular signals is provided. Detailed understanding of the tumor microenvironment and the personalized design of upconversion nanoparticles will result in more effective clinical translation.     

Reliability-based fault analysis models with industrial applications: A systematic literature review

Effective and early fault detection and diagnosis techniques have tremendously enhanced over the years to ensure continuous operations of contemporary complex systems, control cost, and enhance safety in assets-intensive industries, including oil and gas, process, and power generation. The objective of this work is to understand the development of different fault detection and diagnosis methods, their applications, and benefits to the industry. This paper presents a contemporary state-of-the-art systematic literature survey focusing on a comprehensive review of the models for fault detection and their industrial applications. This study uses advanced tools from bibliometric analysis to systematically analyze over 500 peer-reviewed articles on focus areas published since 2010. We first present an exploratory analysis and identify the influential contributions to the field, authors, and countries, among other key indicators.  A network analysis is presented to unveil and visualize the clusters of the distinguishable areas using a co-citation network analysis. Later, a detailed content analysis of the top-100 most-cited papers is carried out to understand the progression of fault detection and artificial intelligence–based algorithms in different industrial applications. The findings of this paper allow us to comprehend the development of reliability-based fault analysis techniques over time, and the use of smart algorithms and their success. This work helps to make a unique contribution toward revealing the future avenues and setting up a prospective research road map for asset-intensive industry, researchers, and policymakers.     

ZnO–SnO2 nanocubes for fluorescence sensing and dye degradation applications

ZnO–SnO₂ nanocubes were used as promising material for efficient sensing of p-nitrophenol and faster photocatalytic degradations of dyes like methyl orange (MO), methylene Blue (MB) and acid orange 74 (AO74). ZnO–SnO₂ nanocubes were prepared by the facile solution process at 50 °C using Zn(NO₃)₂·6H₂O and SnCl₂·2H₂O as a precursor in the presence of ethylenediammine. The synthesized material was examined for its morphological, structural, crystalline, optical, vibrational, and compositional studies by using field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), Fourier Transform Infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS) and photoluminescence spectroscopy. FESEM studies revealed the formation of well-defined ZnO–SnO₂ nanocubes where the structural examinations revealed the formation of a crystalline tetragonal rutile phase for SnO₂ with some crystal sites doped with Zn. The as-synthesized nanocubes were explored for their photocatalytic activities towards three different dye viz. MO, MB, and AO74. Practically, complete degradation of AO74 was seen within 4 minutes of photo-irradiation in the presence of 0.05 g ZnO–SnO₂ nanocubes. However, 97.17% and 41.63% degradations were observed for MB and MO within 15 and 60 minutes, respectively. All the dye degradation processes followed the pseudo-first-order kinetic model. Moreover, the as-synthesized nanocubes were utilized to fabricate highly sensitive and selective fluorescent chemical sensor for the detection of p-nitrophenol (PNP). ZnO–SnO₂ nanocubes showed a very low detection limit of 4.09 μM for the detection of PNP as calculated according to the 3σ IUPAC criteria. Further, the as-synthesized ZnO–SnO₂ nanotubes were found to be highly selective for p-nitrophenol as compared to the other two isomers.     

Ferroelectric,dielectric, magnetic,structural and photocatalytic properties of Co and Fe doped LaCrO3 perovskite synthesized via micro-emulsion route

In the present investigation, La_1-xCo_xCr_1-yFe_yO₃ (x,y = 0.0, 0.12, 0.36, 0.60) perovskite was fabricated via a facile micro-emulsion route. The synthesized perovskites were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) techniques to examine the effect of Co and Fe ions on the physico-chemical properties. The ferroelectric, dielectric, and magnetic properties of La_1-xCo_xCr_1-yFe_yO₃ were changed significantly as a function of dopants contents (Co and Fe ions). Outcomes revealed that the dielectric, ferroelectric and magnetic properties of LaCrO₃ perovskite can be tuned significantly via Co and Fe doping and La_0.40Co_0.60Cr_0.40Fe_0.60O₃ have potential for photocatalytic dye removal under (visible) light expoure. The photocatalytic activity (PCA) of the pristine LaCrO₃ and La_0.40Co_0.60Cr_0.40Fe_0.60O₃ photocatalyst was evaluated under (visible) light irradiation for crystal violet (CV) dye. Experimental results revealed that La_0.40Co_0.60Cr_0.40Fe_0.60O₃ photocatalyst degrdae almost 77.21% CV dye with the rate constant value of 0.01475 min^?1. In the presence of isopropyl alcohol (IPA) scavenger, the PCA of the La_0.40Co_0.60Cr_0.40Fe_0.60O₃ photocatalyst and rate constant value of the photocatalytic reaction decreased to 32.5% and 0.00491 min^?1, suggesting the superoxide as main active specie. Results revealed that Co and Fe doping doped material is efficient for photocatalytic presentations under solar light expoure.     

A Smart Wellness Service Platform and Its Practical Implementation

Advances in the field of medical sciences and medical technology, and present-day challenges, such as an aging population, rising medical expenses, and lifestyle-related diseases, have collectively catalyzed a research ecosystem termed “smart wellness.” This article describes the establishment of a smart wellness service platform designed to empower individuals to create a sense of balance in their lives. Step-by-step details include service model, design, and architectural considerations. As a proof of concept, implementation details of a Health Improvement and Management Systems (HIMS) Hub, a Smart Wellness Service Platform deployed in six cities in South Korea, are presented. An on-site survey conducted in Busan Metropolitan City reveals the percentage of satisfied users to be 91.3%. Furthermore, data gathered from 27,236 physical evaluations of users from a Busan city center over the period of April 2013 to May 2018 reveal that males and females in their 50s and 60s account for the highest number of participants, while males in their 70s have a higher rate of participation than females in the same age group.     

NS-2 based simulation framework for cognitive radio sensor networks

In this paper, we propose a simulation model for cognitive radio sensor networks (CRSNs) which is an attempt to combine the useful properties of wireless sensor networks and cognitive radio networks. The existing simulation models for cognitive radios cannot be extended for this purpose as they do not consider the strict energy constraint in wireless sensor networks. Our proposed model considers the limited energy available for wireless sensor nodes that constrain the spectrum sensing process—an unavoidable operation in cognitive radios. Our model has been thoroughly tested by performing experiments in different scenarios of CRSNs. The results generated by the model have been found accurate which can be considered for realization of CRSNs.     

Assisting software engineering students in analyzing their performance in software development

Collecting product and process measures in software development projects, particularly in education and training environments, is important as a basis for assessing current performance and opportunities for improvement. However, analyzing the collected data manually is challenging because of the expertise required, the lack of benchmarks for comparison, the amount of data to analyze, and the time required to do the analysis. ProcessPAIR is a novel tool for automated performance analysis and improvement recommendation; based on a performance model calibrated from the performance data of many developers, it automatically identifies and ranks potential performance problems and root causes of individual developers. In education and training environments, it increases students’ autonomy and reduces instructors’ effort in grading and feedback. In this article, we present the results of a controlled experiment involving 61 software engineering master students, half of whom used ProcessPAIR in a Personal Software Process (PSP) performance analysis assignment, and the other half used a traditional PSP support tool (Process Dashboard) for performing the same assignment. The results show significant benefits in terms of students’ satisfaction (average score of 4.78 in a 1–5 scale for ProcessPAIR users, against 3.81 for Process Dashboard users), quality of the analysis outcomes (average grades achieved of 88.1 in a 0–100 scale for ProcessPAIR users, against 82.5 for Process Dashboard users), and time required to do the analysis (average of 252 min for ProcessPAIR users, against 262 min for Process Dashboard users, but with much room for improvement).

     

Industrial wireless sensor and actuator networks in industry 4.0: Exploring requirements,protocols, and challenges—A MAC survey

The vision to connect everyday physical objects to the Internet promises to create the Internet of Things (IoT), which is expected to integrate the diverse technologies such as sensors, actuators, radio frequency identification, communication technologies, and Internet protocols. Thus, IoT promises to transfer traditional industry to advance digital industry known as the Industry 4.0. At the core of the Industry 4.0 are the wireless sensor networks (WSNs) and wireless sensor and actuator networks (WSANs) that led to the development of industrial wireless sensor networks (IWSNs) and industrial wireless sensor and actuator networks (IWSANs). These networks play a central role of connecting machines, parts, products, and humans and create a diverse set of new applications to support intelligent and autonomous decision making. The IWSAN is a promising technology for numerous industrial applications because of their several potential benefits such as simple deployment, low cost, less complexity, and mobility support. However, despite such benefits, they impose several unique challenges at different layers of the protocol stack when deploying them for various monitoring and control applications in the Industry 4.0. In this article, we explore IWSAN, its applications, requirements, challenges, and solutions in the context of industrial control applications. Our main focus is on the medium access control (MAC) layer that can be exploited to satisfy such requirements. Our discussion presents extensive background study of the MAC schemes and it reviews the MAC protocols of the existing wireless standards and technologies. A number of application‐specific MAC protocols developed to support industrial applications, which are not part of these standards, are also elaborated. We rationalize to what extent the existing standards and protocols help in solving such requirements as laid down by the Industry 4.0. In the end, we emphasize on existing challenges and present important future directions.