Ion implantation effects of negative oxygen on copper nanowires

Copper nanowires of diameter 80 nm were synthesized in polycarbonate membrane using template technique. Samples were then implanted with 160 keV O^?1 ion beam with varying particle fluence of 1?×?10¹², 5?×?10¹² and 1?×?10¹³ ions/cm². The SRIM (Stopping and range of ions in matter) software was used to study the processes involved. Compositional analysis confirms implantation of oxygen ions and the stoichiometry of Cu:O was found to be 6:1 by weight % when implanted at 1?×?10¹³ ions/cm². Scanning electron microscopy reveals no changes in morphology of nanowires on implantation. X-ray diffraction analysis showed no shifting in the ‘2θ’ position of diffraction peaks however some new diffraction peaks of oxygen were seen. Implantation with oxygen ion led to the increased crystallite size and reduced strain. The conductivity of the nanowires was found to increase linearly with the ion fluence presenting constructive effect of negative ion implantation on copper nanowires.     

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users’ emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu language. Therefore, in this paper, we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’ emotions from the text. To accomplish this task, we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis. After that, we have preprocessed the data and selected dialogues with common emotions. Once the dataset is prepared, we have used different deep learning and machine learning techniques for the classification of emotion. We have tuned the algorithms according to the Urdu language datasets. The experimental evaluation has shown encouraging results with 67% accuracy for the Urdu dialogue datasets, more than 10, 000 dialogues are classified into five emotions i.e., joy, fear, anger, sadness, and neutral. We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.     

Stability Analysis of Epidemic Modeling Based on Spatial Correlation for Wireless Sensor Networks

Wireless Personal Communications - In wireless sensor networks (WSNs), the main task of each sensor node is to sense the physical activity and then report it to a remote central monitoring sink...     

The Impact of Social Media on Consumers’ Acculturation and Purchase Intentions

Social media has emerged as a significant and effective means of assisting and endorsing activities and communications among peers, consumers and organizations that outdo the restrictions of time and space. While the previous studies acknowledge the role of agents of culture change, it largely remains silent on the role of social media in influencing acculturation outcomes and consumption choices. This study uses self-administered questionnaire to collect data from 514 Turkish-Dutch respondents and examines how their use of social media affects their acculturation and consumption choices. This research makes a significant contribution to consumer acculturation research by showing that social media is a vital means of culture change and a driver of acculturation strategies and consumption choices. This study is the first to investigate the role of social media as an agent of culture change in terms of how it impacts acculturation and consumption. The paper discusses implications for theory development and for practice.     

The impact of big data analytics on firms’ high value business performance

Big Data Analytics (BDA) is an emerging phenomenon with the reported potential to transform how firms manage and enhance high value businesses performance. The purpose of our study is to investigate the impact of BDA on operations management in the manufacturing sector, which is an acknowledged infrequently researched context. Using an interpretive qualitative approach, this empirical study leverages a comparative case study of three manufacturing companies with varying levels of BDA usage (experimental, moderate and heavy). The information technology (IT) business value literature and a resource based view informed the development of our research propositions and the conceptual framework that illuminated the relationships between BDA capability and organizational readiness and design. Our findings indicate that BDA capability (in terms of data sourcing, access, integration, and delivery, analytical capabilities, and people’s expertise) along with organizational readiness and design factors (such as BDA strategy, top management support, financial resources, and employee engagement) facilitated better utilization of BDA in manufacturing decision making, and thus enhanced high value business performance. Our results also highlight important managerial implications related to the impact of BDA on empowerment of employees, and how BDA can be integrated into organizations to augment rather than replace management capabilities. Our research will be of benefit to academics and practitioners in further aiding our understanding of BDA utilization in transforming operations and production management. It adds to the body of limited empirically based knowledge by highlighting the real business value resulting from applying BDA in manufacturing firms and thus encouraging beneficial economic societal changes.     

Advanced Bioresponsive Multitasking Hydrogels in the New Era of Biomedicine

Advanced forms of hydrogels have many inherently desirable properties and can be designed with different structures and functions. In particular, bioresponsive multifunctional hydrogels can carry out sophisticated biological functions. These include in situ single-cell approaches, capturing, analysis, and release of living cells, biomimetics of cell, tissue, and tumor-specific niches. They can allow in vivo cell manipulation and act as novel drug delivery systems, allowing diagnostic, therapeutic, vaccination, and immunotherapy methods. In the present review of multitasking hydrogels, new approaches and devices classified into point-of-care testing (POCT), microarrays, single-cell/rare cell approaches, artificial membranes, biomimetic modeling systems, nanodoctors, and microneedle patches are summarized. The potentials and application of each format are critically discussed, and some limitations are highlighted. Finally, how hydrogels can enable an “all-in-one platform” to play a key role in cancer therapy, regenerative medicine, and the treatment of inflammatory, degenerative, genetic, and metabolic diseases is being looked forward to.     

Assessing Consumers’ Co‐production and Future Participation On Value Co‐creation and Business Benefit: an F-P-C-B Model Perspective

Information Systems Frontiers - Co-production and active participation of the consumers are considered to have enhanced the value co-creation activities that would ensure business benefits of a...     

Design and simulation of pyroelectric detectors with nanometer size spider-web for low thermal conductance

We report the design and simulation of uncooled pyroelectric detectors which utilizes a nanometer sized mesh or truss to support the suspended detector. Pyroelectric detector is a class of thermal detector in which the change in temperature causes the change in the spontaneous polarization in the sensing material. Ca modified lead titanate (PCT) was selected as the thermometer in the detector because of its high pyroelectric figure of merit. The design and simulation of pyroelectric detectors have been conducted by simulating the structure with Intellisuite™. Finite element method (FEM) was used to simulate the structural and thermal properties of the device. The simulated detectors had a spider web-like structure with each of the strut (ring) of spider web had a width of 100 nm. In the design, the pyroelectric detectors utilized Ni_0.8Cr_0.2 absorber, PCT sensing layer, Ti electrodes, Al₂O₃ structural layer to obtain low thermal conductance between the detector and Si substrate. Three different types of pyroelectric detectors were designed and analyzed. The first design had linear electrode and simple spider web support. The value of the thermal conductance of this detector was found to be 3.98 × 10⁻⁸ W/K. The second design had a longer thermal path than the first one and the thermal conductivity of this device was found to be 2.41 × 10⁻⁸ W/K. High detectivity was obtained by reducing the thermal conductance between the sensing layer and the substrate or the heat sink in the third design. The design was optimized for the best result by modifying the shape, dimension and thickness of various layers namely absorber, electrodes, sensing layer, and struts. The thermal conductance between the sensor and the substrate using the third design was found to be as low as 4.57 × 10⁻⁹ W/K which is significantly lower than previously reported values. The thicknesses of the web structure, web support, electrodes, sensing layer, and absorber of the final structure were 2, 1, 0.5, 2, and 0.2 µm respectively for this value of thermal conductance. The absorber diameter was 50 µm and the diameter of the spider web was 200 µm. A total of 80 struts with 100 nm width were used in the design.

     

Au/PANA/PVAc and Au/P(ANA-co-CNTA)/PVAc electrospun nanofibers as tyrosinase immobilization supports

Au/poly anthranilic acid/poly vinyl acetate and Au/poly(anthranilic acid-co-3-carboxy-N-(2-thenylidene)aniline)/poly vinyl acetate nanofibers through electrospinning and their modification with covalent tyrosinase (Ty) immobilization was performed. It was realized by surface activation using N-(3-dimetylaminopropyl)-N-ethylcarbodimide hydrochloride/N-hydroxysuccinimide chemistry. Electrochemical impedance spectroscopy (EIS), FTIR–ATR, Raman spectroscopy, and bicinchoninic acid assay analyses demonstrated that Ty was stably and covalently bonded onto the nanofibers. Increase in surface roughness [atomic force microscopy (AFM)] and the presence of Cu atoms in the nanofiber composition after enzyme immobilization confirmed the Ty immobilization. The charge transfer resistances of the nanofibers decreased due to changes in the nanofiber surfaces after attachment of enzyme.