Do Millennials share similar perceptions of brand experience? A clusterization based on brand experience and other brand-related constructs: the case of Netflix

AbstractThis study aims to explore, in the case of the Over-The-Top (OTT) sector, Millennials' perceptions of brand experience in relation to the well-established brand Netflix. In particular, the work addresses a clusterization of Millennials on the basis of their experience with the brand. The study first explores the theoretical background, highlighting current perspectives on Over-The-Top industry and on brand experience as a strategic process for creating holistic customer value, achieving differentiation and sustainable competitive advantage. Second, it offers a quantitative study (using a survey) and highlights the principal results related to the brand. Moreover, this work will attempt to use cluster analysis methodology exploiting brand experience validated scale and other related brand and behavioural constructs to cluster consumers. Both academics and marketing managers should focus on approaches able to deliver strong and memorable brand experiences. A positive and durable brand experience is related to other important consequences for consumer action and behaviour, such as the willingness to place brand trust, consumer loyalty towards the brand, an enduring consumer-brand relationship, repurchase intentions, and lastly, the long-term life of the bran

Shallow Depth Factoring Based on Quantum Feasibility Labeling and Variational Quantum Search

Large integer factorization is a prominent research challenge, particularly in the context of quantum computing. This holds significant importance, especially in information security that relies on public key cryptosystems. The classical computation of prime factors for an integer has exponential time complexity. Quantum computing offers the potential for significantly faster computational processes compared to classical processors. In this paper, we propose a new quantum algorithm, Shallow Depth Factoring (SDF), to factor a biprime integer. SDF consists of three steps. First, it converts a factoring problem to an optimization problem without an objective function. Then, it uses a Quantum Feasibility Labeling (QFL) method to label every possible solution according to whether it is feasible or infeasible for the optimization problem. Finally, it employs the Variational Quantum Search (VQS) to find all feasible solutions. The SDF utilizes shallow-depth quantum circuits for efficient factorization, with the circuit depth scaling linearly as the integer to be factorized increases. Through minimizing the number of gates in the circuit, the algorithm enhances feasibility and reduces vulnerability to errors.Comment: 10 pages, 3 figure

A Framework for Burnt Area Mapping and Evacuation Problem Using Aerial Imagery Analysis

The study aims to develop a holistic framework for maximum area coverage of a disaster region during a bushfire event. The monitoring and detection of bushfires are essential to assess the extent of damage, its direction of spread, and action to be taken for its containment. Bushfires limit human’s access to gather data to understand the ground situation. Therefore, the application of Unmanned Aerial Vehicles (UAVs) could be a suitable and technically advanced approach to grasp the dynamics of fires and take measures to mitigate them. The study proposes an optimization model for a maximal area coverage of the fire-affected region. The advanced Artificial Bee Colony (ABC) algorithm will be applied to the swarm of drones to capture images and gather data vital for enhancing disaster response. The captured images will facilitate the development of burnt area maps, locating access points to the region, estimating damages, and preventing the further spread of fire. The proposed algorithm showed optimum responses for exploration, exploitation, and estimation of the maximum height of the drones for the coverage of wildfires and it outperformed the benchmarking algorithm. The results showed that area coverage of the affected region was directly proportional to drone height. At a maximum drone height of 121 m, the area coverage was improved by 30%. These results further led to a proposed framework for bushfire relief and rescue missions. The framework is grounded on the ABC algorithm and requires the coordination of the State Emergency Services (SES) for quick and efficient disaster response

UAVs in disaster management: application of integrated aerial Imagery and convolutional neural network for flood detection

Floods have been a major cause of destruction, instigating fatalities and massive damageto the infrastructure and overall economy of the affected country. Flood-related devastation resultsin the loss of homes, buildings, and critical infrastructure, leaving no means of communicationor travel for the people stuck in such disasters. Thus, it is essential to develop systems that candetect floods in a region to provide timely aid and relief to stranded people, save their livelihoods,homes, and buildings, and protect key city infrastructure. Flood prediction and warning systemshave been implemented in developed countries, but the manufacturing cost of such systems istoo high for developing countries. Remote sensing, satellite imagery, global positioning system,and geographical information systems are currently used for flood detection to assess the flood-related damages. These techniques use neural networks, machine learning, or deep learning methods.However, unmanned aerial vehicles (UAVs) coupled with convolution neural networks have not beenexplored in these contexts to instigate a swift disaster management response to minimize damage toinfrastructure. Accordingly, this paper uses UAV-based aerial imagery as a flood detection methodbased on Convolutional Neural Network (CNN) to extract flood-related features from the imagesof the disaster zone. This method is effective in assessing the damage to local infrastructures in thedisaster zones. The study area is based on a flood-prone region of the Indus River in Pakistan, whereboth pre-and post-disaster images are collected through UAVs. For the training phase,2150 imagepatches are created by resizing and cropping the source images. These patches in the training datasettrain the CNN model to detect and extract the regions where a flood-related change has occurred.The model is tested against both pre-and post-disaster images to validate it, which has positive flooddetection results with an accuracy of 91%. Disaster management organizations can use this modelto assess the damages to critical city infrastructure and other assets worldwide to instigate properdisaster responses and minimize the damages. This can help with the smart governance of the citieswhere all emergent disasters are addressed promptl

UAV Assisted Spatiotemporal Analysis and Management of Bushfires: A Case Study of the 2020 Victorian Bushfires

Australia is a regular recipient of devastating bushfires that severely impacts its economy, landscape, forests, and wild animals. These bushfires must be managed to save a fortune, wildlife, and vegetation and reduce fatalities and harmful environmental impacts. The current study proposes a holistic model that uses a mixed-method approach of Geographical Information System (GIS), remote sensing, and Unmanned Aerial Vehicles (UAV)-based bushfire assessment and mitigation. The fire products of Visible Infrared Imager Radiometer Suite (VIIRS) and Moderate-resolution Imaging Spectroradiometer (MODIS) are used for monitoring the burnt areas within the Victorian Region due to the 2020 bushfires. The results show that the aggregate of 1500 m produces the best output for estimating the burnt areas. The identified hotspots are in the eastern belt of the state that progressed north towards New South Wales. The R2 values between 0.91–0.99 indicate the fitness of methods used in the current study. A healthy z-value index between 0.03 to 2.9 shows the statistical significance of the hotspots. Additional analysis of the 2019–20 Victorian bushfires shows a widespread radius of the fires associated with the climate change and Indian Ocean Dipole (IOD) phenomenon. The UAV paths are optimized using five algorithms: greedy, intra route, inter route, tabu, and particle swarm optimization (PSO), where PSO search surpassed all the tested methods in terms of faster run time and lesser costs to manage the bushfires disasters. The average improvement demonstrated by the PSO algorithm over the greedy method is approximately 2% and 1.2% as compared with the intra route. Further, the cost reduction is 1.5% compared with the inter-route scheme and 1.2% compared with the intra route algorithm. The local disaster management authorities can instantly adopt the proposed system to assess the bushfires disasters and instigate an immediate response plan

Drone-as-a-Service (DaaS) for COVID-19 self-testing kits delivery in smart healthcare setups: A technological perspective

Drones have gained increasing attention in the healthcare industry for mobility and accessibility to remote areas. This perspective-based study proposes a drone-based sample collection system whereby COVID-19 self-testing kits are delivered to and collected from potential patients. This is achieved using the drone as a service (DaaS). A mobile application is also proposed to depict drone navigation and destination location to help ease the process. Through this app, the patient could contact the hospital and give details about their medical condition and the type of emergency. A hypothetical case study for Geelong, Australia, was carried out, and the drone path was optimized using the Artificial Bee Colony (ABC) algorithm. The proposed method aims to reduce person-to-person contact, aid the patient at their home, and deliver any medicine, including first aid kits, to support the patients until further assistance is provided. Artificial intelligence and machine learning-based algorithms coupled with drones will provide state-of-the-art healthcare systems technology

Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare

The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of ‘Big Data’ because, whenever a drug interaction is investigated, it is typically reported and published in healthcare and clinical pharmacology journals. However, it is crucial to automate the extraction of the interactions taking place between drugs because the medical literature is being published in immense volumes, and it is impossible for healthcare professionals to read and collect all of the investigated DDI reports from these Big Data. To avoid this time-consuming procedure, the Information Extraction (IE) and Relationship Extraction (RE) techniques that have been studied in depth in Natural Language Processing (NLP) could be very promising. Since 2011, a lot of research has been reported in this particular area, and there are many approaches that have been implemented that can also be applied to biomedical texts to extract DDI-related information. A benchmark corpus is also publicly available for the advancement of DDI extraction tasks. The current state-of-the-art implementations for extracting DDIs from biomedical texts has employed Support Vector Machines (SVM) or other machine learning methods that work on manually defined features and that might be the cause of the low precision and recall that have been achieved in this domain so far. Modern deep learning techniques have also been applied for the automatic extraction of DDIs from the scientific literature and have proven to be very promising for the advancement of DDI extraction tasks. As such, it is pertinent to investigate deep learning techniques for the extraction and classification of DDIs in order for them to be used in the smart healthcare domain. We proposed a deep neural network-based method (SEV-DDI: Severity-Drug–Drug Interaction) with some further-integrated units/layers to achieve higher precision and accuracy. After successfully outperforming other methods in the DDI classification task, we moved a step further and utilized the methods in a sentiment analysis task to investigate the severity of an interaction. The ability to determine the severity of a DDI will be very helpful for clinical decision support systems in making more accurate and informed decisions, ensuring the safety of the patients

Synergistic effects of activated carbon and nano-zerovalent copper on the performance of hydroxyapatite-alginate beads for the removal of As\u3csup\u3e3+\u3c/sup\u3e from aqueous solution

© 2019 Elsevier Ltd In this study, activated carbon (AC) and nano-zerovalent copper (nZVCu) functionalized hydroxyapatite (HA) and alginate beads were synthesized and used for the removal of As3+ from aqueous solution. The characterization by X-ray diffraction, scanning electron microscopy, X-ray energy dispersive spectroscopy, X-ray photoelectron spectroscopy, transmission electron microscopy, high resolution transmission electron microscopy, BET surface area analysis, thermogravimetric analysis, and Fourier transform infrared spectroscopy revealed successful formation of the AC/nZVCu/HA-alginate, nZVCu/HA-alginate, AC/HA-alginate, and HA-alginate beads. The scanning electron microscopy and surface analysis revealed the prepared beads to be highly mesoporous which led to the maximum adsorption of As3+, i.e., 13.97, 29.33, 30.96, and 39.06 mg/g by HA-alginate, AC/HA-alginate, nZVCu/HA-alginate, and AC/nZVCu/HA-alginate beads, respectively. The thermogravimteric analysis showed the nZVCu/HA-alginate beads to be highly stable while the AC composite beads as the least stable to heat treatment. The HA-alginate beads achieved 39% removal of As3+, however, removal efficiency was promoted to 95% by coupling AC and nZVCu with HA-alginate beads at a reaction time of 120 min. The removal of As3+ by the prepared AC & nZVCu coupled HA-alginate beads was promoted with increasing [As3+]0 and [AC/nZVCu/HA-alginate]0. The pH of aqueous solution significantly influenced the removal of As3+ by AC/nZVCu/HA-alginate beads and maximum removal was achieved at pH 5.8. Freundlich adsorption isotherm and pseudo-second-order kinetic models were found to best fit the removal of As3+ by the synthesized beads. The high performance of AC/nZVCu/HA-alginate beads in the removal of As3+ even after seven cyclic treatment as well as least leaching of Cu ions into aqueous solution suggest enhanced reusability and stability of HA-alginate beads by coupling with AC and nZVCu. The results suggest that the synthesized beads have good potential for the removal of As3+ from aqueous solutions

Black Tea: Chemical and Pharmacological Appraisal

Medicinal plants are gaining popularity as folk medicine due to future demand to get rid of synthetic health promoting medicines. Nowadays, black tea is gaining interest as the most frequently consumed therapeutic drink after the water. The importance of black tea is due to existence of flavonoids such as (Thearubigins (TRs) and theaflavins (TFs) and catechins) that are the main therapeutic agents and are more bio-direct and stable compounds compared to those exist in other herbal plants alongside some other promising compounds which enhance is credentials as therapeutic drug. Numerous scientific explorations have elucidated the biological worth of these bioactive moieties against plethora of ailments with special reference to metabolic disorder. The mandate of current chapter is to discuss the black tea chemistry for elucidating its pharmacological worth

Achieving benchmarks for national quality indicators reduces recurrence and progression in non-muscle-invasive bladder cancer

Background Noncompliance with evidence-based interventions and guidelines contributes to significant and variable recurrence and progression in patients with non–muscle-invasive bladder cancer (NMIBC). The implementation of a quality performance indicator (QPI) programme in Scotland’s National Health Service (NHS) aimed to improve cancer outcomes and reduce nationwide variance. Objective To evaluate the effect of hospitals achieving benchmarks for two specific QPIs on time to recurrence and progression in NMIBC. Design, setting, and participants QPIs for bladder cancer (BC) were enforced nationally in April 2014. NHS health boards collected prospective data on all new BC patients. Prospectively recorded surveillance data were pooled from 12 collaborating centres. Intervention QPIs of interest were (1) hospitals achieving detrusor muscle (DM) sampling target at initial transurethral resection of bladder tumour (TURBT) and (2) use of single instillation of mitomycin C after TURBT (SI-MMC). Outcome measurements and statistical analysis The primary and secondary endpoints were time to recurrence and progression, respectively. Kaplan-Meier and Cox multivariable regression analyses were performed. Key findings and limitations Between April 1, 2014 and March 31, 2017, we diagnosed 3899 patients with new BC, of which 2688 were NMIBC . With a median follow up of 60.3 mo, hospitals achieving the DM sampling target had a 5.4% lower recurrence rate at 5 yr than hospitals not achieving this target (442/1136 [38.9%] vs 677/1528 [44.3%], 95% confidence interval [CI] = 1.6–9.2, p = 0.005). SI-MMC was associated with a 20.4% lower recurrence rate (634/1791 [35.4%] vs 469/840 [55.8%], 95% CI = 16.4–24.5, p < 0.001). On Cox multivariable regression, meeting the DM target and SI-MMC were associated with significant improvement in recurrence (hazard ratio [HR] 0.81, 95% CI = 0.73–0.91, p = 0.0002 and HR 0.66, 95% CI = 0.59–0.74, p < 0.004, respectively) as well as progression-free survival (HR 0.62, 95% CI = 0.45–0.84, p = 0.002 and HR 0.65, 95% CI = 0.49–0.87, p = 0.004, respectively). We did not have a national multicentre pre-QPI control. Conclusions Within a national QPI programme, meeting targets for sampling DM and SI-MMC in the real world were independently associated with delays to recurrence and progression in NMIBC patients. Patient summary Following the first 3 yr of implementing a novel quality performance indicator programme in Scotland, we evaluated compliance and outcomes in non–muscle-invasive bladder cancer. In 2688 patients followed up for 5 yr, we found that achieving targets for sampling detrusor muscle and the single instillation of mitomycin C during and after transurethral resection of bladder tumour, respectively, were associated with delays in cancer recurrence and progression