THE IMPACT OF DIGITAL MARKETING ON THE DEVELOPMENT OF TOURISM IN REPUBLIC OF ALBANIA

The tremendous growth of tourism in Albania in recent decades, made important the understanding of the role that digital marketing and mobile technology is playing in this field. Tourism in Albania is one of the most important economic sectors of the country, and is growing year after year. It is emphasized that digitalization is a new form of communication between producers and consumers of tourism services, becoming a source of competitive advantages for tourism organizations. The main goal of the study is to give us a clear overview of the use of the Internet, information technologies and digital marketing in Albania. For the realization of this study, we used a methodology that combines primary data with secondary ones. The research was conducted through questionnaires that were sent to Albanian travel agencies via email. The questionnaire contains 17 questions, and was sent to 150 travel agencies, of which 102 agencies responded. Regarding the study, digital marketing plays an important role in improving the image of Albanian tourism throughout the world. It has created facilities in the way of doing marketing and reducing the costs of businesses. Through digital marketing, travel agencies have managed to promote our country online, personalize services and, above all, be closer to customers. The research found that the most effective digital marketing tools used by the agencies are Instagram and Facebook.

Cutting through the noise during crisis by enhancing the relevance of research to policymakers

Background:It is widely recognised that policymakers use research deemed relevant, yet little is understood about ways to enhance perceived relevance of research evidence. Observing policymakers' access of research online provides a pragmatic way to investigate predictors of relevance.Aims and objectives:This study investigates a range of relevance indicators including committee assignments, public statements, issue prevalence, or the policymaker's name or district.Methods:In a series of four rapid-cycle randomised control trials (RCTs), the present work systematically explores science communication strategies by studying indicators of perceived relevance. State legislators, state staffers, and federal staffers were emailed fact sheets on issues of COVID (Trial 1, N = 3403), exploitation (Trial 2, N = 6846), police violence (Trial 3, N = 3488), and domestic violence (Trial 4, N = 3888).Findings:Across these trials, personalising the subject line to the legislator's name or district and targeting recipients based on committee assignment consistently improved engagement. Mentions of subject matter in public statements was inconsistently associated, and state-level prevalence of the issue was largely not associated with email engagement behaviour.Discussion and conclusions:Together, these results indicate a benefit of targeting legislators based on committee assignments and of personalising the subject line with legislator information. This work further operationalises practical indicators of personal relevance and demonstrates a novel method of how to test science communication strategies among policymakers. Building enduring capacity for testing science communication will improve tactics to cut through the noise during times of political crisis.

Development of Infectious Disease Emergency Response Competencies for Nurses in China: A Delphi Study and an Analytic Hierarchy Process

Aim. To develop a set of infectious disease emergency response competencies specific to frontline nurses in China. Background. Nurses play an important role in the infectious disease emergency response. Competency-based training is the cornerstone of the professionalization of disaster rescue, including the infectious disease emergency response. Accordingly, reaching a consensus on a set of core competencies is essential. However, information regarding the competencies needed for nurses in the infectious disease emergency response is limited. Methods. A literature review and in-depth expert interviews were conducted to establish a draft of competencies, which consisted of 53 items, including 3 first-level index items, 12 second-level index items, and 38 third-level index items. Eighteen experts with the knowledge of infectious disease management and experience with infectious disease emergency rescue from different regions in China were recruited for Delphi consultation. A two-round Delphi survey was conducted via email. Consensus was defined as a mean importance value >4.5 and the coefficient of variation <0.25 among the experts. Finally, the analytic hierarchy process was used to determine the weight of each index on which consensus had been reached. Results. An index system of infectious disease emergency response competencies for nurses was constructed, including 3 first-level indices (knowledge, attitudes, and skills), 10 second-level indices, and 32 third-level indices. The response rates of the two rounds of the Delphi survey were both 100%, and the authority coefficient of the 18 experts was 0.903. The weighted value of each index was established with a consistency ratio <0.1, demonstrating that skill (0.5396) ranked first among the three first-level indices, followed by knowledge (0.2970) and attitudes (0.1634). Conclusion. The study developed a consensus on infectious disease emergency response competencies required for nurses in China, which provides guidance for the assessment and training of nurses on infectious disease emergency response. Implications for Nursing Management. According to the competency index system, nursing managers could develop effective training programs of infectious disease emergency response competency for nurses and select competent nurses for emergency response to infectious diseases.

Data Analysis using Robot Process Automation Study on Web Scraping using UI Path Studio

Automation has been into existence since the mid Fifties but had simplest began to gain attention lately. The RPA software program makes use of existing generation's interface to automate the human detail in the technique. So, essentially, there's no want for human intervention. web scraping is a software of robot system Automation that is used in almost all of the industries. either or not it's a e-trade internet site, commodities buying and selling web sites, or any internet site and so forth. you can scrape the information from any of them based on your hobby. Now, the problem with guide scraping by hand is that it's miles at risk of mistakes and takes numerous times. also, the facts available on websites does now not change in any respect. up to date regularly, for this reason facts saved domestically might not usually be terrible. So, industries can actually automate this mission. The main objective of the project is to save time and send the updated information to the person using RPA technology. As this COVID-19 Global Pandemic going on, we thought of creating a project around COVID-19. So, in the project we will use Data Scrapping to extract Web Table (which contains COVID-19 data such as number of affected people, recovered people etc.) from web page. And, write the extracted data into Excel then we will send that excel over the email as attachment. In this project we researched how we can send data through email using RPA and extract the data from live covid_19 website. For software automation, there are many software's that are available in market. The main RPA vendors are UiPath, Automation Anywhere, and Blue Prism. So, to complete our RPA project, we have chosen UiPath which the best in the field of automation. You should be familiar with at least one of these tools before working on the following projects. This paper aims to provide RPA reviews as technical, as well as its implementation applications. © 2022 IEEE.

Transformer Models for Activity Mining in Knowledge-Intensive Processes

Mining useful information to analyze knowledge-intensive business processes requires data that describes activities of knowledge workers. Emails are widely used in organizations to provide support in the functioning of knowledge-intensive processes. The recent COVID-19 pandemic has increased reliance on technologies such as email to help facilitate communication within organizations to make up for the lack of face-to-face contact. In this work, we propose an activity mining technique, which receives an incoming email message, classifies the sender's intent and translates it into a set of business process activities. Specifically, we leverage deep learning language models to first classify the email body into a group of intents, which are then mapped to related activities. To our knowledge, we propose the first transfer-learning based solution for mining activity information from emails. The effectiveness of our solution was evaluated on real-world data coming from email exchanges between knowledge workers. Our results based on unsupervised experiments and a field study show that transformer models can be used to semantically label emails and that mapping activities to matched intents is highly accurate. © 2023, Springer Nature Switzerland AG.

HELPHED: Hybrid Ensemble Learning PHishing Email Detection

Phishing email attack is a dominant cyber-criminal strategy for decades. Despite its longevity, it has evolved during the COVID-19 pandemic, indicating that adversaries exploit critical situations to lure victims. Plenty of detectors have been proposed over the years, which mainly focus on the contents or the textual information of emails;however, to cope with the evolution of phishing emails more sophisticated approaches should be introduced that will exploit all the emails' traits to enhance the detection capability of Machine Learning/Deep Learning classifiers. To tackle the limitations of existing works, this paper proposes a phishing email detection methodology, named HELPHED that focuses on the detection of phishing emails by combining Ensemble Learning methods with hybrid features. The hybrid features provide an accurate representation of emails by fusing their content and textual traits. We propose two methods of HELPHED, the first one employs the Stacking Ensemble Learning method, while the second method utilizes the Soft Voting Ensemble Learning. Both methods deploy two different Machine Learning algorithms to handle the hybrid features separately, yet in parallel, minimizing the features' complexity and improving the model's performance. A thorough evaluation analysis is carried out considering innovative guidelines that aim to prevent partial and misleading results. Experimental tests verified that the combination of hybrid features with Ensemble Learning, overall, accomplishes better detection performance than when employing only content-based or text-based features. Numerical results on a rich imbalanced dataset (i.e., 32,051 benign and 3,460 phishing email samples) that considers the evolution of phishing emails show that Soft Voting Ensemble Learning outperforms other prominent Machine Learning/Deep Learning algorithms and existing works yielding F1-score equal to 0.9942. © 2022 Elsevier Ltd

Identification of Latent Topics in Patients Surviving COVID-19 in Mexico

With the outbreak of the SARS-CoV-2 o COVID-19 pandemic, multiple studies of risk factors and their influence on patient deaths have been developed. However, little attention is often paid to analyzing patients in risk groups despite the fact that they have been infected and inpatients can survive. In this article, with the dataset available from the Ministery of the health of Mexico, this paper proposes the use of the latent topic extraction algorithm Latent Dirichlet Allocation (LDA) for the study of COVID-19 survival factors in Mexico. The results let us conclude that in the year before strategies for prevention and control of COVID-19, the latent topics support that patients without comorbidities have a low risk of death, compared with the period of 2021, wherein in spite of having some risk factors patients can survive.

"Thunderbirds Are Go!” Rapid Response HTA Outputs For COVID-19

IntroductionThe COVID-19 pandemic has highlighted the need for rapid assessment of potential health technologies that can improve health outcomes in COVID-19 patients, as well as helping pressurized health service provision. Medical technologies play a key role in the COVID-19 pandemic, especially diagnostic tests and respiratory technologies. This study evaluates the rapid response work that the medical technology evaluation programme (MTEP) at the National Institute for Health and Care Excellence (NICE) has done in response to the COVID-19 pandemic.MethodsCompanies routinely submit medical technologies for evaluation by NICE through HealthTech Connect, which is an online portal for devices, diagnostics and digital technologies intended for use in the NHS or wider United Kingdom health and care system. During the COVID-19 pandemic, companies were able to use a designated email address if they perceived their technology may benefit the healthcare system regarding the COVID-19 pandemic. This new system bypassed the usual full registration and data submission. All technologies were reviewed that were submitted via HealthTech connect and email between March 2020 and June 2021.ResultsDuring this period, 20 technologies were submitted to MTEP. Most of these technologies were submitted via email. These technologies consisted of a mix of digital, diagnostic, and respiratory technologies. Seven technologies were selected for a rapid COVID-19 MedTech innovation briefing (MIB), with one specifically addressing issues around waiting lists because of knock-on effects of COVID-19 restricting normal clinical work. A further six technologies were not selected because of limited evidence, while one was not selected because it was not perceived as innovative. The other five technologies were progressed as normal MIBs as there was not enough evidence of potential benefits related to COVID-19 to expedite to a rapid COVID-19 MIB. In total, two technologies were selected for medical technology guidance (myCOPD and Anaconda) and are currently in development.ConclusionsMTEP has responded to the COVID-19 pandemic by prioritising and producing rapid COVID-19 MIBs on technologies to improve health and social care.

Community bushfire safety awareness: a community-led initiative progress report

Over the last 30 years, approaches to community-based disaster risk management (CBDRM) in Australia have moved from the margins towards the mainstream of policy and practice. CBDRM is now understood to be an important pillar for building resilience to increasing risks. Compared with previous top-down, command-and-control approaches, CBDRM orients disaster management around principles of community participation, ownership and capacity-building.1 This paper describes a locally led initiative in an outer-eastern Melbourne community that is helping residents recognise their bushfire risks and how to take action to mitigate them.

Virtual Care and Electronic Patient Communication During COVID-19: Cross-sectional Study of Inequities Across a Canadian Tertiary Cancer Center.

BACKGROUND: Virtual care (VC) visits (telephone or video) and email-based patient communication have been rapidly adopted to facilitate cancer care during the COVID-19 pandemic. Inequities in access and patient experience may arise as these digital health tools become prevalent. OBJECTIVE: We aimed to characterize inequities in access and patient-reported experience following adoption of digital health tools at a tertiary cancer center during the COVID-19 pandemic. METHODS: We designed a cross-sectional study of outpatients with visits from September to December 2020. Patient characteristics and responses to an email-based patient-experience survey were collated. Inequities in access were assessed across three pairs of comparison groups: (1) patients with VC and in-person visits, (2) patients with and without documented email addresses, and (3) responders and nonresponders to the survey. Inequities in patient-reported experience were assessed among survey responders. Demographics were mapped to area-level averages from national census data. Socioeconomic status was mapped to area-level dimensions of the Canadian Index of Multiple Deprivation. Covariate balance between comparison groups was assessed using standardized mean differences (SMDs), with SMD≥0.2 indicating differences between groups. Associations between patient experience satisfaction scores and covariates were assessed using multivariable analyses, with P<.05 indicating statistical significance. RESULTS: Among the 42,194 patients who had outpatient visits, 62.65% (n=26,435) had at least one VC visit and 31.15% (n=13,144) were emailable. Access to VC and email was similar across demographic and socioeconomic indices (SMD<0.2). Among emailable patients, 21.84% (2870/13,144) responded to the survey. Survey responsiveness was similar across indices, aside from a small difference by age (SMD=0.24). Among responders, 24.4% received VC and were similar to in-person responders across indices (SMD<0.2). VC and in-person responders had similar satisfaction levels with all care domains surveyed (all P>.05). Regardless of visit type, patients had variable satisfaction with care domains across demographic and socioeconomic indices. Patients with higher ethnocultural composition scores were less satisfied with the cultural appropriateness of their care (odds ratio [OR] 0.70, 95% CI 0.57-0.86). Patients with higher situational vulnerability scores were less satisfied with discussion of physical symptoms (OR 0.67, 95% CI 0.48-0.93). Patients with higher residential instability scores were less satisfied with discussion of both physical (OR 0.81, 95% CI 0.68-0.97) and emotional (OR 0.86, 95% CI 0.77-0.96) symptoms, and also with the duration of their visit (OR 0.85, 95% CI 0.74-0.98; P=.02). Male patients were more satisfied with how their health care provider had listened to them (OR 1.64, 95% CI 1.11-2.44; P=.01). CONCLUSIONS: Adoption of VC and email can equitably maintain access and patient-reported experience in cancer care across demographics and socioeconomic indices. Existing health inequities among structurally marginalized patients must continue to be addressed to improve their care experience.

CRITICAL STORYTELLING DURING THE COVID-19 PANDEMIC: BEREA COLLEGE STUDENTS SHARE THEIR EXPERIENCES

Pohl reviews Critical Storytelling During the COVID-19 Pandemic: Berea College Students Share Their Experiences edited by Nicholas D. Hartlep, Christopher V. Stuchell, Nathaniel Elisha Whitt and Brandon O. Hensley.

Phish Responder: A Hybrid Machine Learning Approach to Detect Phishing and Spam Emails

Using technology to prevent cyber-attacks has allowed organisations to somewhat automate cyber security. Despite solutions to aid organisations, many are susceptible to phishing and spam emails which can make an unwanted impact if not mitigated. Traits that make organisations susceptible to phishing and spam emails include a lack of awareness around the identification of malicious emails, explicit trust, and the lack of basic security controls. For any organisation, phishing and spam emails can be received and the consequences of an attack could result in disruption. This research investigated the threat of phishing and spam and developed a detection solution to address this challenge. Deep learning and natural language processing are two techniques that have been employed in related research, which has illustrated improvements in the detection of phishing. Therefore, this research contributes by developing Phish Responder, a solution that uses a hybrid machine learning approach combining natural language processing to detect phishing and spam emails. To ensure its efficiency, Phish Responder was subjected to an experiment in which it has achieved an average accuracy of 99% with the LSTM model for text-based datasets. Furthermore, Phish Responder has presented an average accuracy of 94% with the MLP model for numerical-based datasets. Phish Responder was evaluated by comparing it with other solutions and through an independent t-test which demonstrated that the numerical-based technique is statistically significantly better than existing approaches.

Deconstructing the Challenges of COVID-19 on First-Year Rural University Students in South Africa

Assumptions and facts exist about the various challenges rural learners face when transitioning into university education in South Africa due to the pedagogical differences between secondary and university education. However, the advent of the COVID-19 pandemic compounded the transitioning challenges of students because most of the universities, especially the selected university, utilise online learning, which is alien to first-year students who are transitioning from rural high schools to the university. This study explores the challenges and solutions associated with first-year students transitioning to a new level of education during the COVID-19 pandemic. An asset-based approach was used to theorise the study within the Transformative Paradigm (TP), while Participatory Research (PR) was used to design the study. These are relevant because both TP and PR are targeted towards transforming people's predicaments. The participants consisted of ten first-year students selected using a convenient sampling technique. Data was collected using electronic interviews such as email, WhatsApp messages, and phone calls. The data were analysed using thematic analysis. The study revealed that first-year rural university students' inability to use online learning tools effectively and unstable internet connections in the rural community are major challenges. The study, therefore, concludes that the provision of internet access and students' readiness for adaptability are the possible solutions.

Cybersecurity and Data Science

The unprecedented increase in data availability in many science and technology fields (e.g., genomic data, data from industrial environments, sensory data of smart cities, and social network data) require new methods and solutions for data processing, information extraction, and decision support. ‘Multi-Language Spam/Phishing Classification by Email Body Text: Toward Automated Security Incident Investigation’ by Rastenis et al. The authors proposed a semi-automatic information security model, which can deal with situational awareness data, strategies prevailing information security activities, and protocols monitoring specific types of the network next to the real-time information environment. [...]the paper entitled ‘Simulation of Authentication in Information-Processing Electronic Devices Based on Poisson Pulse Sequence Generators’ by Maksymovych [17] was devoted to modelling authenticators of information-processing electronic devices by creating a bit template simulator based on a Poisson pulse sequence generator.

Combating Fake News: Stakeholder Interventions and Potential Solutions

The fake news "infodemic", facilitated by social media and mobile message sharing platforms, has progressed from causing a nuisance to seriously impacting law and order through deliberate and large-scale manipulation of public sentiments. There are social, religious, political, and economic dimensions to the fake news phenomenon, providing enough motivation for interested parties to push biased opinions, claims, conspiracies and fraud to many naive information consumers. The ease with which fake news can be created and propagated makes it extremely challenging to detect and mitigate. To combat the fake news, the researchers have utilized mechanisms which are largely based on Artificial Intelligence (AI) algorithms and social network analysis. However, no viable solution has yet been deployed at a scale. This paper present a comprehensive survey on combating fake news and evaluates the challenges involved in its detection with the help of existing detection mechanisms and techniques to control its spread. The challenges associated with combating fake news have been addressed based on the various aspects such as psychological, economic, and technical. Furthermore, we consider the fake news combat spectrum to analyze the stakeholder interventions due to the spread of fake news. Finally, various technology-based solutions have been presented for combating fake news and the associated future challenges and opportunities.

Application to Send Certificates of Participation in The Workshops of Al-Maarif University College

During recent times and after the spread of the new Coronavirus (Covid-19), all researchers and teachers have been interested in participating in conferences, seminars, workshops, and electronic courses to increase scientific expertise in their fields of specialization. These workshops and seminars required the issuance of certificates of participation for them to support their joining, and the establishment of a special system to facilitate the issuance of certificates and their delivery to the participants as soon as possible. During the launch of an electronic questionnaire about the quality of the certificate sending system, participating in the electronic workshops held at the Faculty of Al-Maarif University College (AUC), (1142) of both sexes (males and females) answered the questionnaire. Data were collected from the date of 6/6/2020 until 4/7/2020. By asking participants about how they would like to receive certificates via e-mail or other means such as (Google drive or Media Fire), it was found that 99% of the participants want to receive certificates via e-mail and 1 % do not wish to receive certificates via e-mail. © 2022 IEEE.

Aspects of ICT connectivity among older adults living in rural subsidized housing: reassessing the digital divide

Purpose>This article examines aspects of information communication technology (ICT) connectivity among the understudied population of low-income older adults living in rural and peri-urban subsidized housing. We aim to investigate if variations exist in access and connectivity when economic and housing conditions are constant and use data from northern New England.Design/methodology/approach>The multidisciplinary, mixed-methods approach involved administering structured surveys using iPads with senior residents (n = 91) from five housing sites, qualitative observations by field researchers and an ecological assessment of ICT resources at housing, community and state levels.Findings>All subsidized housing sites were broadband accessible and nearby libraries. Fewer sites had Wi-Fi freely available to residents, and individual residents disparately accessed the Internet. Age and education demonstrably influenced ICT use of social media and email. Technology in the form of iPads used for surveys posed functional challenges for some older adults, but these technology-mediated interactions were also perceived as important sites of sociability.Originality/value>Older adults disparately access and use ICT relative to socioeconomic status even as housing conditions remain constant, and access and use influences frequency of social connections with friends and family. The findings reveal factors that contribute to the existing digital divide facing older adults and broader lack of digital equity.

INCREASING CONFIDENCE OF FOUNDATION DOCTORS IN MAKING AND DISCUSSING ESCALATION DECISIONS

Introduction The Covid-19 pandemic has resulted in renewed emphasis on escalation decisions and discussions, often conducted by junior doctors without any training. Our local Foundation Year (FY) teaching does not address these topics. The distress caused by poor communication regarding escalation is well recognised. Our goal was to improve FY doctors’ confidence in this area and consequently improve quality of patient care. Method In PDSA cycle one, email questionnaires established a lack of confidence amongst FY1 doctors. This cycle resulted in the design of a one-hour workshop by middle-grade trainees interested in geriatrics or acute medicine. This workshop included an interactive teaching session, followed by demonstrative and participant role play. Feedback from the first workshop closed cycle two. In cycle three, a subsequent workshop was amended according to feedback. Workshop participants completed anonymous feedback, rating the impact on their confidence. Results Thirteen FY1 doctors responded positively to the initial questionnaire, with nine able to attend a workshop. Pre- and post-workshop questionnaires asked respondents to rate their confidence from 1 (low) to 5 (high). Comparison demonstrated an increase in confidence making decisions from an average of 1.8 to 3.7 and discussing decisions with patients from 2 to 3.1. Qualitative feedback emphasised benefit from participant role play and the need for longer workshops. Conclusion Our project highlighted the need and desire for FY training in making and discussing escalation decisions. A one-hour workshop increased confidence in this group, though we acknowledge this is a surrogate marker of improved care. Workshop uptake was limited by a small local FY1 cohort, leave and clinical commitments. Though convenient, a one-hour session did not provide adequate time to realise full benefit. We hope to address these issues by integrating longer sessions into the protected teaching for all FY doctors in our health board.

Performance Assessment of some Phishing predictive models based on Minimal Feature corpus

The credentials harvested normally include bank account numbers, passwords or PINs, credit card numbers, security questions, security codes etc. In most instances, vulnerability to phishing threat is due to the ease with which unsuspecting online users navigate web pages using links or URL within a body of an online message (Han et al. 2012). [...]there is an increased motivation for phishers as the number of mobile-connected devices accessing social media sites continues to grow. The limitation is often connected with superfluous training/testing time which may result in high memory overheads, delay in detection time, expensive maintenance/update etc. [...]responsiveness is used to measure prediction accuracy with commensurate processing time while the response time is used to ensure that the detection time for any window of vulnerability is reasonable and insignificant (Silva et al. 2020). In this work, we proposed an approach to examining the different state of art predictive model using reduced phishing feature corpus to resolve the uncertainties that result from performance issues (responsiveness) and other inconsistencies (response time, computational overhead etc.) in the feature set corpus.

Replacing Human Input in Spam Email Detection Using Deep Learning

The Covid-19 pandemic has been a driving force for a substantial increase in online activity and transactions across the globe. As a consequence, cyber-attacks, particularly those leveraging email as the preferred attack vector, have also increased exponentially since Q1 2020. Despite this, email remains a popular communication tool. Previously, in an effort to reduce the amount of spam entering a users inbox, many email providers started to incorporate spam filters into their products. However, many commercial spam filters rely on a human to train the filter, leaving a margin of risk if sufficient training has not occurred. In addition, knowing this, hackers employ more targeted and nuanced obfuscation methods to bypass in-built spam filters. In response to this continued problem, there is a growing body of research on the use of machine learning techniques for spam filtering. In many cases, detection results have shown great promise, but often still rely on human input to classify training datasets. In this study, we explore specifically the use of deep learning as a method of reducing human input required for spam detection. First, we evaluate the efficacy of popular spam detection methods/tools/techniques (freeware). Next, we narrow down machine learning techniques to select the appropriate method for our dataset. This was then compared with the accuracy of freeware spam detection tools to present our results. Our results showed that our deep learning model, based on simple word embedding and global max pooling (SWEM-max) had higher accuracy (98.41%) than both Thunderbird (95%) and Mailwasher (92%) which are based on Bayesian spam filtering. Finally, we postulate whether this improvement is enough to accept the removal of human input in spam email detection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.