This study employed a deductive approach to examine external HRM factors influencing job satisfaction in the post-pandemic hybrid work environment. Explores the intermediary functions of age, gender, and work experience in this particular environment. The data-gathering procedure consisted of conducting semi-structured interviews with carefully chosen 50 managers representing various sectors, industries, organizations, and professions. The applied approach was adopted to allow a systematic and unbiased investigation of the mediating variables. The study used SPSS 25 and Smart PLS 4 to analyze the model, enhancing understanding of HRM challenges in a constantly evolving workplace. The findings offer valuable insights for HR experts and businesses, highlighting the value of comprehending what methods HRM components influence job satisfaction to optimize employee well-being and productivity. The study provides applied recommendations designed for enhancing employee contentment in the AI-evolving professional atmosphere, shedding light on the importance of supportive leadership strategies, particularly during AI-triggered downsizing. Additionally, we welcome a new era to push forward in integrating and managing AI tools and technologies to automate decision-making and data processing. Results propose that Exogenous influences of human resource management (HRM) influence manager job satisfaction considerably. Specifically, downsizing caused by AI was found to have negative consequences, whereas diversity and restructuring have favorable effects. Gender was recognized as a crucial factor that influences outcomes, then age and years of experience have the most visible effect.

The rise of fintech in the financial sector presents a transformative shift towards digitalisation and sustainability on a global scale, leveraging technologies like AI to minimise environmental footprint. Neobanks not only challenge traditional banking models but also offer innovative solutions that align with sustainable objectives. The purpose of this paper is to analyse the impact of neobanks on global sustainability from economic, environmental, and social points of view. A comprehensive literature review of existing literature and current sustainable practices of neobanks was conducted. Results reveal that neobanks significantly positively contribute towards environmental sustainability with reduced paper use and logistics requirements of banking services. By offering more accessible and affordable banking services they importantly contribute towards higher financial inclusion, and with innovative products towards more competitive and innovative financial markets. AI-based tools they employ are increasing financial literacy and social inclusion. This article also highlights concerns regarding electronic waste management, potential high energy consumption, required digital literacy and cybersecurity risks. In conclusion, despite the mentioned risks, neobanks importantly contribute to global sustainability in many ways and will even more in the future. These findings can help neobanks shape sustainable practices and guide policymaking, as well as spread awareness of the sustainable impact of banking services.

Bibliometric analysis is a commonly used tool to assess scientific collaborations within the researchers, community, institution, regions and countries. The analysis of publication records can provide a wealth of information about scientific collaboration, including the number of publications, the impact of the publications, and the areas of research where collaborations are most common. By providing detailed information on the patterns and trends in scientific collaboration, these tools can help to inform policy decisions and promote the development of effective strategies to support and enhance scientific collaborations between countries. This study aimed to analyze and visualize the scientific collaboration between Japan and Russia, using bibliometric analysis of collaborative publications from the Web of Science (WoS) database. The analysis utilized the bibliometrix package within the R statistical program. The analysis covered a period of two decades, from 2000 to 2021. The results showed a slight decrease in co-authored publications, with an annual growth rate of −1.26%. The keywords and thematic trends analysis confirmed that physics is the most co-authored field between the two countries. The study also analyzed the collaboration network and research funding sources. Overall, the study provides valuable insights into the current state of scientific collaboration between Japan and Russia. The study also highlights the importance of research funding sources in promoting and sustaining scientific cooperation between countries. The analysis suggests that more efforts in government funding are needed to increase collaboration between the two countries in various fields.

The effects of different storage temperatures (2, 4 and 8 ℃) and their corresponding optimal heat treatment conditions on the quality, physiological and biochemical indexes of Cucumber Fruits during storage were studied by using the quadratic regression orthogonal rotation combination design. The effects of different storage temperatures (2, 4 and 8 ℃) and their corresponding optimal heat treatment conditions on the chilling injury, hardness, weightlessness rate, polyphenol oxidase (PPO), catalase (CAT), peroxidase (POD), H₂O₂, super oxygen anion free radical (O^2-), ASA and GSH were determined. The results showed that heat treatment could inhibit chilling injury, while heat treatment combined with 4 ℃ low temperature storage could effectively inhibit the decline of fruit hardness and weight loss rate, delay the increase of peroxidase (POD) and polyphenol oxidase (PPO) activities, inhibit the increase of H₂O₂ and superoxide anion free radical O^2- and significantly inhibit the browning of cucumber, delay the decline of ascorbic acid and maintain the content of GSH, it was beneficial to adjust the balance of active oxygen system. The results showed that under the storage condition of 4 ℃, the hot water treatment condition of cucumber was 39.4 ℃ and 24.3 min, which could delay the senescence of cucumber fruit and better maintain the quality of cucumber fruit.

Low temperature is one of the most significant environmental factors that threaten the survival of subtropical and tropical plant species. By conducting a study, which was arranged in a completely randomized design with three replicates, the relative freezing tolerance (FT) of four Iranian pomegranate cultivars, including ‘Alak Torsh’, ‘Tabestaneh Torsh’, ‘Poost Sefid’, and ‘Poost Syah’, as well as its correlation with some biochemical indices, were investigated. From each cultivar, pieces of one-year-old shoot samples were treated with controlled freezing temperatures (−11, −14, and −17 ℃) to determine lethal temperatures (LT₅₀) based on survival percentage, electrolyte leakage, phenolic leakage, and tetrazolium staining test (TST) methods. Results showed that FT was higher in the second year with a lower minimum temperature and a higher concentration of cryoprotectants. The stronger correlation of electrolyte leakage with survival percentage (r = 0.93***) compared to the other three indices explained that this index could be the most reliable injury index in determining the pomegranate FT to investigate freezing effects. Of all four cultivars, ‘Poost Syah’ was the hardest by presenting a higher FT than ~ −14 ℃ in mid-winter. Accordingly, this pomegranate cultivar seems to be promising to grow in regions with a higher risk of freezing and to be involved in breeding programs to develop novel commercial cultivars.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.