Swiss School Of Business and Management Geneva

Introduction The increasing smoking population in Africa necessitates intensified tobacco cessation services. Assessing nicotine dependence with tools such as the heaviness of smoking index (HSI) aids in cessation efforts. This study evaluated the association between HSI and quit attempts and quit intentions among African cigarette smokers. Methods Global Adult Tobacco Survey (GATS) data from eight African countries collected between 2011 and 2018 were analysed. The time to first smoke (TTFS) and number of cigarettes per day (CPD) were used to estimate the HSI score and dependence level. The association between HSI dependence level and quit attempts and quit intentions was evaluated using likelihood ratios (LR). Results Among 2,399 daily cigarette users, 1,618 (67.5%) were exclusive cigarette users. The average and median CPDs were 8.27 ± 8.3 and 5 (IQR: 2–18), respectively, with Ethiopia having the highest mean CPD (11.6). In Ethiopia, 51.5% of exclusive and 42% of nonexclusive users had low dependence (HSI < 2), whereas 43.9% and 31.3% of other African countries had low dependence. The association between low dependence and quit attempts was significant only for exclusive cigarette smokers in Ethiopia, with LR of 1.45 (95% CI: 1.08–1.93). The association between low dependence and quit intentions was significant in Ethiopia for both exclusive (LR = 1.42) and nonexclusive (LR = 1.53) users. None of the LR estimates were > 10 or < 0.1. Conclusion The association between low dependence (HSI < 2) and quit behaviour was limited in African settings, highlighting the complex interplay between usage patterns, dependence assessment, and quit behaviour. A contextual tool for assessing nicotine dependence tailored to African settings is warranted for effective smoking cessation.

The prompts "What emotions does the thought of your own death arouse in you?" and "What will happen to you when your body dies?" have been used to induce anxiety in Terror Management Theory. The current study investigated how the responses to these prompts may reveal cross-national differences by using a text-mining approach. Undergraduates in the US (n = 298) and Japan (n = 212) participated in the study. Across both groups, anxiety was the most common emotion. Cross-national differences also emerged, such that students in the US were more likely to mention sadness, funeral, and religiosity for the first prompt, and acceptance, spiritual change, and religiosity for the second prompt. Students in Japan were more likely to mention regret for the first, and sadness, emptiness, and funeral for the second prompt. Results revealed differences and similarities in thoughts and emotions people associate with when thinking about own death.

As global crises become increasingly complex and intertwined, crisis readiness has emerged as a pivotal factor for an organisation's survival. Despite a growing, interdisciplinary corpus of research underscoring the necessity for systematic crisis management and strategic communication, a comprehensive understanding of corporate crisis readiness and methodologies for its achievement remain elusive and inadequately addressed. To bridge this knowledge gap, we conducted a systematic literature review, meticulously analysing 7287 articles to identify 40 eligible studies from 1987 to 2022. Through inductive content analysis and narrative synthesis, we have delineated the concept of crisis readiness and devised a comprehensive 10‐step process framework that organisations are advised to implement in preparation for potential crises. This investigation enriches the academic dialogue on crisis readiness by integrating insights from strategic management, crisis handling, organisational development, and learning theories. This offers a more nuanced understanding of how organisations can equip themselves for crisis situations. Additionally, our study sheds light on promising directions for future research in this vital field.

Machine learning (ML) models have been increasingly employed to predict osteoporosis. However, the incorporation of hair minerals into ML models remains unexplored. This study aimed to develop ML models for predicting low bone mass (LBM) using health checkup data and hair mineral analysis. A total of 1206 postmenopausal women and 820 men aged 50 years or older at a health promotion center were included in this study. LBM was defined as a T-score below − 1 at the lumbar, femur neck, or total hip area. The proportion of individuals with LBM was 59.4% (n = 1205). The features used in the models comprised 50 health checkup items and 22 hair minerals. The ML algorithms employed were Extreme Gradient Boosting (XGB), Random Forest (RF), Gradient Boosting (GB), and Adaptive Boosting (AdaBoost). The subjects were divided into training and test datasets with an 80:20 ratio. The area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and an F1 score were evaluated to measure the performances of the models. Through 50 repetitions, the mean (standard deviation) AUROC for LBM was 0.744 (± 0.021) for XGB, the highest among the models, followed by 0.737 (± 0.023) for AdaBoost, and 0.733 (± 0.023) for GB, and 0.732 (± 0.021) for RF. The XGB model had an accuracy of 68.7%, sensitivity of 80.7%, specificity of 51.1%, PPV of 70.9%, NPV of 64.3%, and an F1 score of 0.754. However, these performance metrics did not demonstrate notable differences among the models. The XGB model identified sulfur, sodium, mercury, copper, magnesium, arsenic, and phosphate as crucial hair mineral features. The study findings emphasize the significance of employing ML algorithms for predicting LBM. Integrating health checkup data and hair mineral analysis into these models may provide valuable insights into identifying individuals at risk of LBM.

A person's feelings and thoughts about a communication robot have a significant impact on how the two connect. Robots that are both effective and sympathetic must first have a firm grasp of these emotional dynamics. The complex relationship between people's feelings, thoughts, and actions in relation to robots is investigated in this empirical study. Preprocessing input, selecting features, and sequencing are the three main steps in training a model. The Iliou preprocessing method improves the quality of the original data by creating a refined dataset from it. Feature selection makes use of principal component analysis (PCA), which finds important features by analyzing class-feature covariance values. In order to train KDCNN models to operate at their best, feature selection must be done effectively. With an impressive accuracy rate of 96.55%, the suggested method outperforms two state-of-the-art algorithms, KD and CNN, according to the comparative research. More intuitive and responsive robotic systems can be created as a result of this research, which adds to our knowledge of the dynamics of human-robot interaction.

A serious transformation of methods for implementing innovative activities, which is happening today in Russia, is difficult without improvement of the processes for managing project innovation activities of universities. This issue is not widely studied in the scientific community despite its relevance and significance. The purpose of this work is to study the theoretical foundations of project innovation activities of universities in the Russian Federation, as well as to analyze the demand for project innovation activities of domestic universities. The work presents a map of concepts in the innovation sphere, which depicts the relationship between such concepts as: innovation, project, innovation activity, project activity, innovation infrastructure, design-innovation activity, innovative developments and innovative project. The paper examines key problems and trends in project innovation activities of universities in the Russian Federation, as well as the factors without which successful implementation of this activity can’t be possible. Owing to the current study, it is obvious that the development of a new approach to innovation activities project management in universities is required, taking into account current trends in the country’s socio-economic development and the geopolitical situation.

Chronic granulomatous disease (CGD) is a rare inborn error of immunity characterized by recurrent fungal and bacterial infections due to defective nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activity. This case report describes an 11-month-old female who was initially diagnosed with tubercular lymphadenitis and presented with fever and bilateral neck swelling. Despite receiving anti-tubercular treatment (ATT) and intravenous antibiotics, the patient experienced recurrent infections and abscesses, prompting further investigation. Laboratory tests revealed normal immunoglobulin levels but abnormal nitroblue tetrazolium (NBT) and dihydrorhodamine (DHR) tests, indicating CGD. Genetic analysis (clinical exome by next-generation sequencing) confirmed a novel NCF2 gene mutation associated with autosomal recessive CGD. This patient was treated with prophylactic antibiotics and antifungals and subsequently underwent successful hematopoietic stem cell transplantation (HSCT). This highlights the diagnostic challenges associated with CGD, particularly in tuberculosis-endemic regions such as India, emphasizing the importance of considering primary immunodeficiency disorders in patients with recurrent infections. Early diagnosis and appropriate treatment, including HSCT, can significantly improve patient outcomes. The patient remained infection-free on prophylactic antimicrobials for 1.5 years post-discharge, demonstrating the potential for a favorable prognosis with timely intervention and comprehensive management.

Abstract Background: Ayurveda, yoga, naturopathy, Unani, Siddha, and homeopathy (AYUSH) form an alternative system of medicine in India. Understanding the utilization of AYUSH practitioners’ services is crucial to substantiating the current government initiatives to mainstream AYUSH in the Indian health system. The utilization of AYUSH practitioners’ services among different sub-populations, including older adults, for various health conditions is underexplored. The present study explores the utilization of AYUSH practitioners’ service among older Indian adults and its correlates. Methods: During 2017-2018, the Longitudinal Aging Study in India (LASI) conducted a nationally representative study among adults aged 45 years or more and their spouses. The study leveraged this data from publicly available LASI. Descriptive analysis and cross-tabulation were performed using a subset of older adults (age ≥ 60 years, n = 31,464). The utilization of AYUSH practitioners’ services was taken as the outcome variable. A logistic regression model was employed to understand the independent effect of various explorative variables on the use of AYUSH practitioners' services. Results: One in 14 older adults utilized the services of AYUSH practitioners. The socio-demographic factors that were looked at, including religion, residence, and caste were significant independent factors for AYUSH consultation. Among chronic conditions, hypertension (use-5.6%, AOR: 1.24, CI: 1.09-1.40), diabetes (use 4.2%, AOR: 1.31, CI: 1.09-1.57), and arthritis (use-9.1%, AOR: 0.59, CI: 0.52-0.67) were independent determinants of AYUSH practitioners' service utilization. In the fully adjusted model, the effect of explanatory variables is almost similar to that in the minimally adjusted model. Only the effect of the female gender was accentuated in magnitude, whereas the effect of diabetes was partially attenuated. Conclusion: The preference for AYUSH practitioners’ service among older adults is determined by the complex interplay between socio-demographic factors and disease conditions. Though utilization of AYUSH practitioners’ service was high among certain underprivileged sections, it is assuring that education and income do not affect older populations’ preference for AYUSH practitioners' service.

Understanding entrepreneurial dynamics during crises increasingly gains scholars’ and practitioners’ attention, highlighting the adaptability and innovation inherent in entrepreneurial ventures. This study investigates the intertwined role of organizational resilience (OR) and business model innovation (BMI) on the performance of startups amidst the COVID-19 pandemic using data from 258 startups in Tanzania. It employs a stepwise multiple regression analysis to assess the moderating effect of BMI on the relationship between OR and the start-ups’ performance. The findings suggest that service sector startups exhibited higher performance than those in manufacturing and merchandising. The results show that startups that actively participate in BMI exhibited greater resilience and outperformed their counterparts during the pandemic. The study underscores the imperative for startups to consistently innovate their business models to maintain their competitiveness and success during disruptive moments.

Credit rating is crucial in the fast-changing 21st-century banking industry to determine creditworthiness. Traditional credit score systems may not be able to handle today's complex money habits because they are focused on statistics and prior data. This research advises adding management, human resources, and organizational factors to machine learning credit evaluations in addition to financial data. Structure of the research describes different machine learning types. Logistic regression, decision trees, random forests, gradient boosting, and neural networks. The algorithms are trained using this dataset's financial metrics, management practices, HR indicators, and organizational procedures. Feature engineering strategies pull data from various sources to get a full picture of someone's reputation. The research argues that machine learning models should be transparent, especially in the highly regulated banking business. Using LIME and SHAP values helps make credit scoring determinations more dependable and understandable. Credit scoring will be more precise, and financial institutions will understand credit risk aspects better. Banks can improve loan selections, portfolio performance, and risk by adding management, human resources, and organizational data to financial data. This research helps financial organizations analyze credit risk in the age of machine learning and big data, resulting in more accurate credit score models.

Abstract- The banking industry needs to set up strong detection systems to fight the continuing risk of fraud in order to keep people's trust in financial systems and keep their cash safe. Problems often arise with traditional rule-based detection systems when they are put up against complicated fraud plans. It is possible to find fake activities more easily now that machine learning and big data analytics are becoming more popular. In this research, a complete approach is introduced that makes it easier to spot fraud in banking systems. The system has algorithms for machine learning, important management parts, and big data analytics. using "big data" technologies to collect and examine a lot of data from a lot of different sources, such as external data streams, internal transaction records, and profiles of customers. Fraud detection systems get better at telling the difference by picking out key features from preprocessed data. Researching on a system that will constantly watch all incoming transfers and send alerts right away if any suspicious activity is seen. Because of this, it is necessary to set limits, create automatic systems for sending out warnings, and come up with ways to spot anomalies. The financial industry must make sure that the methods they use to find and stop fraud are legal and meet their compliance responsibilities.

Background Tobacco use remains a significant global public health concern, causing millions of preventable and premature deaths annually and imposing substantial economic burdens. India, the second-largest producer and consumer of tobacco products worldwide, bears a significant burden of tobacco-related morbidity and mortality. Medical and dental students represent the future healthcare workforce and role models; hence, their tobacco consumption and attitude would play a vital role in tobacco control. This study aims to estimate the prevalence and assess the knowledge, attitudes, and behaviors regarding tobacco use among medical and dental students in Bhubaneswar, Odisha. Methods A descriptive cross-sectional study was conducted using the Global Health Professional Students Survey (GHPSS) questionnaire. The study included third-year Bachelor of Medicine and Bachelor of Surgery (MBBS) and Bachelor of Dental Surgery (BDS) students from two private medical and two dental colleges in Bhubaneswar, Odisha. Data were collected from February to April 2019 through anonymous self-administered questionnaires, and descriptive and bivariate analyses were performed. Results A total of 400 students were surveyed, with 16% reporting being current smokers (24.3% males, 8.7% females). Furthermore, 36.8% had tried cigarettes and other tobacco products. Nonsmokers demonstrated stronger support for comprehensive tobacco control policies, such as banning advertising and smoking in public places, compared to current smokers. Most students acknowledged the importance of recording tobacco use history and providing educational materials; however, only around 40% had received formal training on smoking cessation. Conclusion The findings highlight the need for targeted intervention among medical and dental students for tobacco cessation. It is vital to foster a positive attitude toward tobacco control among future healthcare professionals. Health professional institutions should take proactive steps to prevent tobacco use among students and develop initiatives to motivate successful cessation training. Investing in tobacco control education for healthcare professionals is crucial to empower them in tobacco cessation efforts and promote healthier societies.

Introduction: Internet access, smartphones, and televisions have significantly boosted over-the-top (OTT) movies and web series viewing in India, especially among youths. Despite restrictions, OTT platforms continue to promote tobacco products. India has recently enforced the revised OTT Rules 2023 effective September 1, 2023, to counter tobacco promotion in OTT shows. This study explores compliance with the OTT Rules 2023 in popular movies and web series on select OTT platforms in India. Methods: About 29 movies and 31 web series from seven popular OTT platforms as of September 26, 2023, were analyzed in this study. The incidence of tobacco imagery and compliance with the OTT Rules 2023 were assessed using a standardized format with the help of seven trained coders. Descriptive statistics were used to describe instances of tobacco imagery and violations of the provisions of Indian law. Results: The average incidence of tobacco imagery per included show was 3.95. None of the movies and web series fully complied with the provisions of health spots and audio-visual warnings. Only 35.7% of the shows (movies: 57.1%, web series: 14.3%) fully complied with the anti-tobacco static message provisions. The foreign-origin movies had zero compliance with static messages, though they had fewer tobacco images. Half of the shows for children up to 12 years old had tobacco imagery but fully complied with the static warning message provisions. Conclusion: The portrayal of tobacco imagery in OTT shows is prevalent, and their poor compliance with the OTT Rules 2023 is a concern. Therefore, monitoring and stricter enforcement of the OTT Rules should be given priority to protect viewers from tobacco promotion in OTT shows.

Abstract – This research addresses the challenge of providing accurate decision support for Gastrointestinal Neuroendocrine Tumors (GI NETs) by introducing a novel Inception-ResNet-v2 enhanced CNN-based framework. There are currently no all-inclusive systems that successfully combine genetic, imaging, and clinical data. The Inception-ResNet-v2 model, which is well-known for its feature extraction capabilities, is used in our proposed system to bridge this gap by integrating various modalities into a single architecture. Due to GI NETs' intrinsic complexity, a nuanced approach is required, and our solution outperforms the current techniques. In terms of accuracy, precision, recall, and F1 score, our model routinely surpasses previous studies in comparison evaluations. The model outperforms previous research with impressive results: 92% accuracy, 94% precision, 91% recall, and 92% F1 score. The confusion matrix shows that the model can produce more accurate classifications, with less false positives and negatives. Not only does this study present a state-of-the-art decision support framework, but it also proven its worth by comparing it to existing standards in great detail. This model has the potential to be a game-changer in the field of precision medicine, according to the outcomes that have been shown. It will help us better understand and treat gastrointestinal NETs, which is a complex condition.

This study explores orthodontists’ perspectives on risks associated with orthodontic treatment, as described by Greek and Slovak orthodontists. Informed by the foundational importance of effective communication of risk perspectives in health sciences, particularly in facilitating valid consent and shared decision-making, this research addresses gaps identified in the literature concerning the consistent communication of potential treatment risks based on demographic and cultural characteristics. This study identifies 15 potential critical risks during orthodontic treatment. These risks include root resorption; temporary undesired changes to the occlusion; sleep difficulties; not achieving an ideal result; development of black triangles between teeth; taking additional X-rays; speech difficulties; using a protective splint during sports; duration of treatment; number of visits; transmission of infectious diseases; and swallowing orthodontic appliances. A questionnaire, distributed electronically to orthodontists in Greece (N1 = 570) and Slovakia (N2 = 210) from September 2022 to December 2022, aimed to assess risk communication practices, taking into consideration socio-demographic factors, such as country, gender, age, and academic-degree-related variations. A total of 168 valid questionnaires (91 from Slovakia and 77 from Greece) were obtained, indicating significant disparities in the risks emphasized and preferred forms of consent. The Greek orthodontists focused more on the risks involved, such as relapse, root resorption, temporal occlusal changes, and failure of desired movement, while the Slovak practitioners tended to be more interested in sleeping difficulties, temporal occlusal changes, and not achieving an ideal result. They also obtained written or digital consent from patients or their parents/guardians more frequently than the Greek team. Male orthodontists discussed specific risks more frequently, including relapse and extractions, whereas females preferred written or digital consent. PhD-trained orthodontists prioritized certain risks, indicating the need for tailored approaches. This study underscores the dynamic nature of risk assessment in orthodontic practice, emphasizing its ethical and strategic dimensions. The findings advocate for tailored risk communication strategies that recognize individual, contextual, and cultural factors, and the need for an orthodontic informed consent protocol for a tailored communication approach for patients to elevate the standard of care in European orthodontics. The reliance on digital tools reflects contemporary trends in enhancing patient understanding, thereby supporting ongoing innovation in orthodontic practices.

Understanding the current situation of the European logistic sector is vital to predict how the industry may evolve in the coming years. Therefore, in this chapter, the current situation in the European logistics sector is analyzed. Specifically, experts from the industry give their opinion on the impact of megatrends on the European logistics sector and assess the current investment situation. The results are presented in 3 subchapters: Overview – European Logistics, Megatrends, and Current Investments.

Logistics properties in Europe are increasingly becoming the focus of real estate investors. Continued strong interest from investors is expected as the Covid-19 pandemic as well as economic and political instabilities underscore the important role of logistics and the robustness of this asset class. Based on the previous analyses, this chapter intends to provide investors with a guide for investments in European logistics.

Now that the current situation of European logistics has been analyzed, this chapter focuses on the future of European logistics. Experts predict how the industry may evolve in the coming years. They specifically shed light on climate-, environmental- and social aspects, as well as the opportunities and challenges investors should look out for in the future. Considering these trends, opportunities, and challenges the experts then give a market outlook for future investments in European logistics.