Development of technologies and innovations encouraged companies to look for and implement innovative solutions in their practice seeking not only to increase the efficiency of activity but also towards sustainability. In this context, the aim of the research is to reveal innovative solutions for the improvement of the warehousing processes towards sustainability in the case of manufacturing companies. The methodological setup consists of two steps. First, a comprehensive literature analysis was conducted seeking to reveal and present a theoretical model based on the conceptual framework on this topic. Then, a semi-structured interview was conducted with 8 managers holding managerial positions in four Lithuanian manufacturing companies. The manufacturing companies were chosen for the research due to their durable experience in the market, which use advanced warehouse management methods in their operations. Main findings showed, that innovative solutions such as Big Data Datasets, smart networks, Drones, Robots, Internet of Things and etc., are important for the efficient warehousing processes. Furthermore, it is also necessary to emphasize the benefits of implementing of innovative solutions in warehousing processes not only in economic terms, but also for solving of social and environmental issues towards sustainability. The novelty of this study lies in its dual objective of filling a theoretical gap and of drawing the attention of companies and policy makers to the importance of innovative solutions implementation in the warehousing process towards sustainability.

Introduction: In contemporary healthcare education, the integration of technology has emerged as an essential factor in enhancing the efficiency and efficacy of training methodologies. Particularly within the domain of cardiopulmonary resuscitation (CPR) training, the adoption of technology-driven approaches holds considerable potential for enriching the skills and proficiencies of healthcare practitioners. Through the utilization of innovative technologies, such as simulation software and leveraging smartphones as primary tools, CPR training programs can be customized to provide immersive, interactive, and authentic learning experiences. This study aims to validate a comprehensive CPR training module tailored explicitly for healthcare professionals, to integrate it into smartphones as a medium for delivering CPR training. Methods: Two validity tests, namely content validity and face validity were conducted to evaluate the validity of the Smart-CPR training module. A self-constructed measurement scale was utilized to assess four parameters: consistency, representativeness, clarity, and relevancy. Content validity employed the content validity ratio, with scores ranging between 1 and −1, indicating the level of consensus among experts regarding the significance of each item. Face validity was assessed using two indices: the item face validity index and the scale face validity index. Ratings of 3 or 4 were given a score of 1, while ratings of 1 or 2 received a score of 0. Result: The content validity shows that CVI values for ‘consistency’ and ‘representativeness’ were 0.99 for the module and questionnaire, and 0.96 and 0.97, respectively. ‘Clarity’ scored 0.99 for the module and 0.96 for the questionnaire, while ‘relevance’ achieved 0.99 for both. All 44 items exceeded the 0.83 threshold for face validity. The Lawshe’s content validity ratio (CVR) and content validity index (CVI) value were used to evaluate the content validity of both the CRSTP module and questionnaire, with CVR values result ranging from 0.80 to 0.99 across dimensions. These findings demonstrate robust content validity. Additionally, high CVI scores, mostly exceeding 0.95, suggest favorable outcomes and indicate no need for revisions. In face validity method, all 44 items surpassed the minimum threshold of 0.83, signifying a favourable outcome. Thus, all items were deemed acceptable. Conclusion: The Smart-CPR training module and questionnaires were meticulously developed to meet both face and content validity standards. All 44 items demonstrated appropriate levels of validity, ensuring they effectively enhance and maintain CPR competency among healthcare providers and potentially benefit the broader community. The positive results of the Smart-CPR training module confirm the high validity of the CPR competency assessment. Content validity, evaluated by experts, received a perfect score, demonstrating agreement on the relevance of each module component. Similarly, face validity, assessed by healthcare professionals, also received a flawless score, indicating consensus on the module’s clarity and relevance. These findings validate the module’s effectiveness in teaching CPR techniques to a diverse audience and ensuring compliance with established standards. With such strong validity, digitizing the module becomes more straightforward, facilitating easier sharing and use across digital platforms. Ultimately, the module’s high validity facilitates its integration into digital platforms, thereby enhancing CPR education and improving outcomes during real emergencies.

Catastrophes, like earthquakes, bring sudden and severe damage, causing fatalities, injuries, and property loss. This often triggers a rapid increase in insurance claims. These claims can encompass various types, such as life insurance claims for deaths, health insurance claims for injuries, and general insurance claims for property damage. For insurers offering multiple types of coverage, this surge in claims can pose a risk of financial losses or bankruptcy. One option for insurers is to transfer some of these risks to reinsurance companies. Reinsurance companies will assess the potential losses due to a catastrophe event, then issue catastrophe reinsurance contracts to insurance companies. This study aims to construct a valuation model for catastrophe reinsurance contracts that can cover claim losses arising from two types of insurance products. Valuation in this study is done using the Fundamental Theorem of Asset Pricing, which is the expected present value of the number of claims that occur during the reinsurance coverage period. The number of catastrophe events during the reinsurance coverage period is assumed to follow a Poisson process. Each impact of a catastrophe event, such as the number of fatalities and injuries that cause claims, is represented as random variables, and modeled using Peaks Over Threshold (POT). This study uses Clayton, Gumbel, and Frank copulas to describe various dependence characteristics between random variables. The parameters of the POT model and copula are estimated using Inference Functions for Margins method. After estimating the model parameters, Monte Carlo simulations are performed to obtain numerical solutions for the expected value of catastrophe reinsurance based on the Fundamental Theorem of Asset Pricing. The expected reinsurance value based on Monte Carlo simulations using Indonesian earthquake data from 1979–2021 is Rp 10,296,819,838.

Surveys are one of the most important tasks to be executed to get valued information. One of the main problems is how the data about many different persons can be processed to give good information about their environment. Modelling environments through Artificial Neural Networks (ANNs) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in dealing with sets of data with some noise, but they are fundamentally surjective mathematical functions, and they aren’t able to give different results for the same input. So, if an ANN is trained using data where samples with the same input configuration has different outputs, which can be the case of survey data, it can be a major problem for the success of modelling the environment. The environment used to demonstrate the study is a strategic environment that is used to predict the impact of the applied strategies to an organization financial result, but the conclusions are not limited to this type of environment. Therefore, is necessary to adjust, eliminate invalid and inconsistent data. This permits one to maximize the probability of success and precision in modeling the desired environment. This study demonstrates, describes and evaluates each step of a process to prepare data for use, to improve the performance and precision of the ANNs used to obtain the model. This is, to improve the model quality. As a result of the studied process, it is possible to see a significant improvement both in the possibility of building a model as in its accuracy.

The proposed scientific article aims to analyze the application of Lean Six Sigma in the food industry. To this end, a detailed methodology has been designed that ranges from the selection of the works to the synthesis and presentation of the results obtained. The methodology is based on rigorous inclusion criteria to ensure the relevance and quality of the selected sources, including books, academic articles, theses, and other relevant documents. Through extensive searches of academic databases and other reliable sources, key works were identified that specifically address the implementation of Lean Six Sigma in the context of food production. Once the relevant papers were collected, a critical analysis was conducted to identify common themes, trends, and key findings. The works were classified according to their main focus, such as process improvement, waste reduction, supply chain optimization and food safety assurance. This categorization allowed the information to be organized in a coherent way and to facilitate the synthesis of the results. The results obtained were presented in a table that included details about each selected work, such as title, author, year of publication, abstract and links to the original source. This structured and rigorous approach provides a clear and comprehensive view of the topic, contributing to the advancement of knowledge in this area and offering practical guidance for practitioners and researchers interested in the application of Lean Six Sigma in the food industry. The literature on Lean Six Sigma in the food industry highlights its importance in improving efficiency, quality, and safety. Key recommendations include gradual implementation, appropriate training, focus on quality, and continuous improvement.

This research aims to investigate the factors shaping the investment choices of individuals in Saudi Arabia concerning cryptocurrencies, particularly focusing on the influence of the Fear of Missing Out (FOMO) psychological phenomenon. This study employs a mixed-methods approach to comprehend the factors influencing Saudi investors' decisions in the cryptocurrency realm. Quantitative surveys are conducted to gauge perceptions of risk, return, regulatory factors, and social influence. Additionally, qualitative interviews delve into the nuanced interplay of these elements and the impact of FOMO on decision-making. Integrating the Theory of Planned Behavior and Behavioral Finance theories, this research offers a holistic understanding of cryptocurrency investment determinants. The combined quantitative and qualitative methods provide a comprehensive view, enabling an in-depth analysis of the subject matter. The study reveals that Saudi Arabian investors' decisions regarding cryptocurrencies are significantly influenced by multiple factors, including perceived risk, potential return, regulatory environment, and social dynamics. FOMO emerges as a crucial psychological factor, interacting with these influences and driving decision-making. This research underscores the intricate interplay between these factors and FOMO, shedding light on the dynamics of cryptocurrency investment choices in the Saudi Arabian market. The findings hold implications for policymakers, financial institutions, and investors seeking deeper insights into this evolving landscape. Drawing from the Theory of Planned Behavior and Behavioral Finance, it examines perceived risk, return, regulatory factors, and social influence in influencing cryptocurrency investment choices among Saudi investors, focusing on the influence of Fear of Missing Out (FOMO).  The research outcome provides insights for policymakers, financial institutions, and investors seeking to understand cryptocurrency investment dynamics in Saudi Arabia.