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Enhancing Business Value Creation Via Social Media Metrics Evaluation: A Machine Learning and Data Analytics Approach
No full textSocial Media Marketing (SMM) plays an important role in business growth and expansion. With its growing capability and impact, SMM offers a major challenge for decision makers seeking to quantify the value of emergent SMM channels in their marketing mix. The inherent risks and challenges of big data makes the return valuation more pertinent in SMM. Conventional methods used for measuring return on investment (ROI) of marketing activities do not seamlessly translate to SMM operations due to the active involvement of external participants and significant differences in the cost structure. There is no well-established approach to systematically relate organizational social media activities to various revenue streams hindering efforts to justify these investments. This study analyzes social network characteristics and typology to evaluate business performance. Social network typology is a relatively new and important research topic for business performance quantification and evaluation. Historically, performance measurements in SMM research have always been viewed in terms of numbers of followers, comments, likes, retweets and such. No organization is formed with the end goal of increasing its likes or followers. The main goal of every organization is to increase shareholder value. The contribution of this paper is multi-fold. It aims to start drawing research from these vanity and/or actionable metrics towards organizational performance metrics measurement. The research also introduces a multi-dimensional model that can be instrumental in evaluating the added value of SMM expenditures at the corporate level
Dry Needling for Spine Related Disorders: a Scoping Review
No full textIntroduction/Background: The depth and breadth of research on dry needling (DN) has not been evaluated specifically for symptomatic spine related disorders (SRD) from myofascial trigger points (TrP), disc, nerve and articular structures not due to serious pathologies. Current literature appears to support DN for treatment of TrP. Goals of this review include identifying research published on DN treatment for SRD, sites of treatment and outcomes studied. Methods: A scoping review was conducted following Levac et al.’s five part methodological framework to determine the current state of the literature regarding DN for patients with SRD. Results: Initial and secondary search strategies yielded 55 studies in the cervical (C) region (71.43%) and 22 in the thoracolumbar-pelvic (TLP) region (28.57%). Most were randomized controlled trials (60% in C, 45.45% in TLP) and clinical trials (18.18% in C, 22.78% in TLP). The most commonly treated condition was TrP for both the C and TLP regions. In the C region, DN was provided to 23 different muscles, with the trapezius as treatment site in 41.88% of studies. DN was applied to 31 different structures in the TLP region. In the C region, there was one treatment session in 23 studies (41.82%) and 2–6 treatments in 25 (45.45%%). For the TLP region, one DN treatment was provided in 8 of the 22 total studies (36.36%) and 2–6 in 9 (40.9%). The majority of experimental designs had DN as the sole intervention. For both C and TLP regions, visual analogue scale, pressure pain threshold and range of motion were the most common outcomes. Conclusion: For SRD, DN was primarily applied to myofascial structures for pain or TrP diagnoses. Many outcomes were improved regardless of diagnosis or treatment parameters. Most studies applied just one treatment which may not reflect common clinical practice. Further research is warranted to determine optimal treatment duration and frequency. Most studies looked at DN as the sole intervention. It is unclear whether DN alone or in addition to other treatment procedures would provide superior outcomes. Functional outcome tools best suited to tracking the outcomes of DN for SRD should be explored.https://doi.org/10.1186/s12998-020-00310-
Detection of Cardiovascular CRP Protein Biomarker Using a Novel Nanofibrous Substrate
No full textIt is known that different diseases have characteristic biomarkers that are secreted very early on, even before the symptoms have developed. Before any kind of therapeutic approach can be used, it is necessary that such biomarkers be detected at a minimum concentration in the bodily fluids. Here, we report the fabrication of an interdigitated sensing device integrated with polyvinyl alcohol (PVA) nanofibers and carbon nanotubes (CNT) for the detection of an inflammatory biomarker, C-reactive protein (CRP). The limit of detection (LOD) was achieved in a range of 100 ng mL−1 and 1 fg mL−1 in both phosphate buffered saline (PBS) and human serum (hs). Furthermore, a significant change in the electrochemical impedance from 45% to 70% (hs) and 38% to 60% (PBS) over the loading range of CRP was achieved. The finite element analysis indicates that a non-redox charge transduction at the solid/liquid interface on the electrode surface is responsible for the enhanced sensitivity. Furthermore, the fabricated biosensor consists of a large electro-active surface area, along with better charge transfer characteristics that enabled improved specific binding with CRP. This was determined both experimentally and from the simulated electrochemical impedance of the PVA nanofiber patterned gold electrode.https://doi.org/10.3390/bios1006007
Partial Observer Decision Process Model for Crane-Robot Action
No full textThe most common use of robots is to effectively decrease the human’s effort with desirable output. In the human-robot interaction, it is essential for both parties to predict subsequent actions based on their present actions so as to well complete the cooperative work. A lot of effort has been devoted in order to attain cooperative work between human and robot precisely. In case of decision making , it is observed from the previous studies that short-term or midterm forecasting have long time horizon to adjust and react. To address this problem, we suggested a new vision-based interaction model. The suggested model reduces the error amplification problem by applying the prior inputs through their features, which are repossessed by a deep belief network (DBN) though Boltzmann machine (BM) mechanism. Additionally, we present a mechanism to decide the possible outcome (accept or reject). The said mechanism evaluates the model on several datasets. Hence, the systems would be able to capture the related information using the motion of the objects. And it updates this information for verification, tracking, acquisition, and extractions of images in order to adapt the situation. Furthermore, we have suggested an intelligent purifier filter (IPF) and learning algorithm based on vision theories in order to make the proposed approach stronger. Experiments show the higher performance of the proposed model compared to the state-of-the-art methods.https://doi.org/10.1155/2020/634934
Retinol Levels in Hashimoto's Thyroiditis
No full textHashimoto’s thyroiditis (HT) is one of the most common autoimmune diseases in the United States. Previous studies have proven that HT patients often have low vitamin D levels and benefit from vitamin D supplementation to help manage their autoimmune disease. Research is currently underway to investigate vitamin A’s benefits in the management of autoimmune diseases. Both of these vitamins have immune-modulating properties, and both affect thyroid function. This dissertation aims to establish whether HT patients had lower retinol (vitamin A) levels than participants that did not have HT. Data regarding retinol levels and thyroid function markers were gathered from a database of results from a small study conducted at Health Matters Clinic, in Northeast Arkansas. The study participants were sorted into two groups: HT and non-HT, and then 26 participants were randomly selected for each group. The HT group had participants that were positive for either or both thyroperoxidase and thyroglobulin antibodies, and that were not on thyroid medications such as levothyroxine. The non-HT group had participants that did not have thyroid autoantibodies, and did not have any other known autoimmune disease and had normal Thyroid Stimulating Hormone (TSH) levels. An independent sample t-test for differences in retinol levels was performed, as well as Pearson’s correlation for retinol and TSH and retinol and the anti-thyroid antibodies. The results revealed no statistical differences in retinol levels between the groups and no correlation between retinol and the level of thyroid antibodies. Retinol’s tight homeostatic control, which maintains steady serum levels regardless of liver reserve status, can explain the lack of statistical difference in retinol levels between the groups. A positive correlation was found between retinol and TSH levels (high TSH with high retinol), which may indicate some novel mechanism of retinol’s effect on the thyroid or retinol’s involvement in the etiology of HT, therefore, needs to be validated with more data. In conclusion, serum retinol levels do not appear to correlate with HT; in particular, serum retinol levels appear not to be decreased in patients with HT. At the same time, our data seem to indicate some involvement of retinol, or its signaling pathway, in thyroid disorders
Strategic Road-Mapping for Small-to-Medium Nonprofit Organizations
No full textLong-term strategic planning is not typical among small-to-medium nonprofit organizations (SMNOs), since the focus is on tactical mission priorities and urgencies. This prioritization is due to limited resources and capabilities, although a strategic plan is still considered critical for the social nonprofit enterprise’s sustained viability. Road-mapping provides an operationalizable strategic planning methodology for nonprofits, especially SMNOs. This research develops a strategic roadmap (SRM) model based on a long-term strategy for such nonprofits. The roadmap includes swim-lanes representing multiple stakeholder perspectives and is used to identify the policies and procedures required to achieve the long-term vision. The perspectives covered are social, technical, economic, environmental, and political (STEEP). The strategic roadmap model is validated using expert judgments and by the case study of a children’s mental health nonprofit organization agency located in Southeastern Connecticut, USA. It is shown to be effective for the case study because it is a visual model and could be operationalized with tasks for the practitioners
Predicting Emerging Trends on Social Media by Modeling it as Temporal Bipartite Networks
No full textThe behavior of peoples' request for a post on online social media is a stochastic process that makes post's ranking highly skewed in nature. We mean peoples interest for a post can grow/decay exponentially or linearly. Considering this nature of the evolutionary peoples' interest, this paper presents a Growth-based Popularity Predictor (GPP) model for predicting and ranking the web-contents. Three different kinds of web-based real datasets namely Movielens, Facebook-wall-post and Digg are used to evaluate the performance of the proposed model. This performance is measured based on four information-retrieval metrics Area Under receiving operating Characteristic (AUC), Novelty, Precision, and Kendal's Tau. The obtained results show that the prediction performance can be further improved if the score is mapped onto a cumulative predicted item's ranking.https://doi.org/10.1109/ACCESS.2020.297613
Graphene Quantum Dot Oxidation Governs Noncovalent Biopolymer Adsorption
No full textGraphene quantum dots (GQDs) are an allotrope of carbon with a planar surface amenable to functionalization and nanoscale dimensions that confer photoluminescence. Collectively, these properties render GQDs an advantageous platform for nanobiotechnology applications, including optical biosensing and delivery. Towards this end, noncovalent functionalization offers a route to reversibly modify and preserve the pristine GQD substrate, however, a clear paradigm has yet to be realized. Herein, we demonstrate the feasibility of noncovalent polymer adsorption to GQD surfaces, with a specific focus on single-stranded DNA (ssDNA). We study how GQD oxidation level affects the propensity for polymer adsorption by synthesizing and characterizing four types of GQD substrates ranging ~60-fold in oxidation level, then investigating noncovalent polymer association to these substrates. Adsorption of ssDNA quenches intrinsic GQD fluorescence by 31.5% for low-oxidation GQDs and enables aqueous dispersion of otherwise insoluble no-oxidation GQDs. ssDNA-GQD complexation is confirmed by atomic force microscopy, by inducing ssDNA desorption, and with molecular dynamics simulations. ssDNA is determined to adsorb strongly to no-oxidation GQDs, weakly to low-oxidation GQDs, and not at all for heavily oxidized GQDs. Finally, we reveal the generality of the adsorption platform and assess how the GQD system is tunable by modifying polymer sequence and type.https://www.nature.com/articles/s41598-020-63769-
Android Malware Family Classification and Analysis: Current Status and Future Directions
No full textAndroid receives major attention from security practitioners and researchers due to the influx number of malicious applications. For the past twelve years, Android malicious applications have been grouped into families. In the research community, detecting new malware families is a challenge. As we investigate, most of the literature reviews focus on surveying malware detection. Characterizing the malware families can improve the detection process and understand the malware patterns. For this reason, we conduct a comprehensive survey on the state-of-the-art Android malware familial detection, identification, and categorization techniques. We categorize the literature based on three dimensions: type of analysis, features, and methodologies and techniques. Furthermore, we report the datasets that are commonly used. Finally, we highlight the limitations that we identify in the literature, challenges, and future research directions regarding the Android malware family.https://doi.org/10.3390/electronics906094
DRNN-based shift decision for automatic transmission
No full textIn research on intelligent shift for automatic transmission, the neural network selected has no feedback and lacks an associative memory function. Thus, its adaptability needs to be improved. To achieve this, an automatic shift strategy based on a deep recurrent neural network (DRNN) is proposed. First, a neural network framework was designed in combination with an eight-speed gearbox that matches a particular type of vehicle. Then, the working principle of the DRNN was applied to the shifting process of an automatic gearbox, and the implementation model of the shift logic was established in MATLAB/Stateflow. A data sample obtained from the model was used to train the DRNN. Training and evaluation of the DRNN were accomplished in Python. Finally, a simulation comparison of the DRNN with a back-propagation (BP) neural network proved that after the epochs have been increased, the DRNN has higher precision and adaptation than a BP neural network. This research provides a theoretical basis and technical support for intelligent control of automatic transmission.https://doi.org/10.1177/168781402097529