Developing Business Model Competences in the Enterprise

Despite the COVID-19 pandemic, 2021 saw a growing interest in starting own business: as per the Census Bureau's Business Formation Statistics, the number of applications to form new businesses filed in the U.S. was the highest compared to any other year on record, reaching the total of 5.4 million (Economic Innovation Group, 2022), while in the EU, after an initial downward trend recorded in the first and second quarters of 2020, the number of new business registrations grew again in the third quarter of that year, and this upward trend continued throughout 2021 (Eurostat, 2022). Of course, as a result of Russia's invasion on Ukraine and related economic crisis, a downward tendency could be observed, but business registration levels in the EU in the first quarter of 2022 were still higher than during the pre-COVID 19 pandemic period (2015-2019) (Eurostat, 2022) and online searches indicating and intent to open a business spiked by 76% from 2018 to 2022 (Search Engine Journal, 2022). This shows that despite many external impediments, people are still tempted to start their own business, and many influencers, motivational speakers and coaches, as well as various popular TV shows broadcast worldwide (like the Apprentice, Dragons' Den, Shark Tank or Planet of the Apps) encourage them to do so. Becoming an entrepreneur has become a goal many people, especially 20-, 30- and 40-year-olds, strive to achieve. However, many of those people fail to realise that the very entry in the business register does not automatically make them entrepreneurs or their business successful. Neither does a good (or even excellent and innovative) business idea that attracts customers, as it was in Kodak's, Blockbuster's, or Ask Jeeves' case. What is required, is the ability to stay attractive to existing and prospective customers, i.e., the ability to win and retain customers, and to adapt to the changing demands, trends and economic conditions. All this can be achieved thanks to a meticulously designed and regularly reviewed and updated business model. The aim of this paper is to present and analyse the learning process of acquiring and building competences in the area of business models with the use of different innovative tools. The results presented and discussed in this article come from surveys as well as face-to-face and on-line meetings conducted in the ProBM 2 ERASMUS+ project (Understanding and Developing Business Models in the Era of Globalisation), in which the total of 261 respondents from seven (7) European countries, i.e. Poland, Italy, Greece, Romania, Portugal, Malta, and Switzerland, took part between 2019 and 2022. From the meetings and surveys it follows that much more awareness of business models needs to be encouraged and developed, particularly as regards improving competences helping future business owners and their employees assess profitability and efficiency of their operations and ensure that the business will be a going concern. © 2022 Authors. All rights reserved.

Real-Time Tracking of Health Parameters Using IOT and Data Logger: Application to COVID-19

There is an alarming upward trend in COVID-19 cases, and the existing healthcare system is unable to cater the day-to-day requirements from testing followed by appropriate patient care. The use of monitoring devices has found the importance of detecting the presence of the virus early. In order to expand the exciting monitoring system, every technocrat needed to come forward and donate his money to this. Authors as a responsible persons deemed it proper to work on the development design of multipurpose all in one reliable device to help the front like warriors in identifying persons with COVID-19 symptoms like temperature, heartbeat, humidity and positioning real time. After putting in an extra effort, author has succeeded in translating my idea into action. A device that is so well-designed and will increase the medical brotherhood effort to continue monitoring patient health parameters using a variety of sensors connected to the Arduino board. The generated data is then transmitted via Wi-Fi module to IOT platform, i.e., ThingSpeak and which can be monitored on devices like desktop, laptop or smartphone. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Systematic Threat Analysis of Modern Unified Healthcare Communication Systems

Recently, smart medical devices have become preva-lent in remote monitoring of patients and the delivery of medication. The ongoing Covid-19 pandemic situation has boosted the upward trend of the popularity of smart medical devices in the healthcare system. Simultaneously, different device manufacturers and technologies compete for a share in a smart medical device's market, which forces the integration of diverse smart medical de-vices into a common healthcare ecosystem. Hence, modern unified healthcare communication systems (UHCSs) combine ISO/IEEE 11073 and Health Level Seven (HL7) communication standards to support smart medical devices' interoperability and their communication with healthcare providers. Despite their advantages in supporting various smart medical devices and communication technologies, these standards do not provide any security and suffer from vulnerabilities. Existing studies provide stand-alone security solutions to components of UHCSs and do not cover UHCSs holistically. In this paper, we perform a systematic threat analysis of UHCSs that relies on attack-defense tree (ADTree) formalisms. Considering the attack landscape and defense ecosys-tem, we build an ADTree for UHCSs and convert the ADTree to stochastic timed automata (STA) to perform quantitative analysis. Our analysis using UPPAAL SMC shows that the Man-in-the-Middle and unauthorized remote access attacks are the most probable attacks that a malicious entity could pursue, causing mistreatment to patients. We also extract valuable information about the top threats, the likelihood of performing different individual and simultaneous attacks, and the expected cost for attackers. © 2022 IEEE.

Smart Tourism: How Can We Count on Digital in the Recovery of Post-covid Tourism in Portugal

The number of tourists who visited Portugal in 2019 reached historic highs, a trend that was eventually interrupted by the emergence of the pandemic caused by the SARS-CoV-2 virus. Several experts point out that tourism was one of the first sectors to suffer the impacts of the pandemic and one of the hardest to be hit. Tourism recovery is desirable and what needs to be clarified with this study is how the digital transition so present, for example, in the Recovery and Resilience Plan (PRR) can help to rescue this upward trend of visitors/tourists. The present research work intends to emphasize how digital can be decisive in the resumption of tourism in Portugal, taking into account the measures announced for this sector. The results are discussed in the light of the literature and future work is identified with a view to enhancing tourism, smart tourism and smart destinations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Prediction New Cases of COVID-19 in Indonesia Using Vector Autoregression (VAR) and Long-Short Term Memory (LSTM) Methods

The addition of Covid-19 cases is still uncontrolled, especially in Indonesia. Often the addition of Covid-19 cases in Indonesia always experiences a significant upward trend after a slightly loose government policy. This is because the government does not think there will be a spike in cases after cases go down. This is where the importance of predicting new cases of Covid-19 in Indonesia to be a reference for the government in taking policy. With deep learning, the prediction results will be more accurate. The implementation of vector autoregression (VAR) and long-short term memory (LSTM) methods can reach an accretion rate of up to 98%. With this method, the prediction results can be used for the government in anticipating if there is a surge in new cases per day because it has been predicted from the beginning. In fact, this method can predict new cases for up to a year. © 2021 IEEE.