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Sustainability usually refers to the ability to maintain a certain standard of human lifestyle without causing environmental damage or any other destruction to nature and biodiversity. It is beneficial for human health and well-being in addition to socio-economic benefits and contributes to environmental integrity. However, as Robert Swan (1956) once stated: “the greatest threat to our planet is the belief that someone else will save it”—this is true even today; namely, there are still serious threats to the planet and there is “a short time period” for action. We all have to be obliged to save the planet in which we live throughout our daily and local activities. In this study, the authors provide an overview of the meaning of sustainable tourism and its relationship to all global Sustainable Development Goals established by UN (2015) as one way to achieve a brighter sustainable future and society for everyone, leaving no one behind. More specifically, the case of Kosovo and its potential for the development of sustainable tourism is analyzed. In this case, the authors aim to present the potential to serve the local tradition, culture, diversity, and inclusiveness through sustainable actions in the tourism and hospitality sector.

This work intended to explore some motivations that influence people’s eating habits towards sustainability. This was an observational, cross-sectional study, carried out by questionnaire survey on a non-probabilistic sample of 10,067 participants from 13 countries (Argentina, Brazil, Croatia, Greece, Hungary, Latvia, Lithuania, Poland, Portugal, Serbia, Slovenia, Romania and United States). Results indicated that people prefer fresh local foods from the season, being important because it allows transportation and storage to be reduced, which in many cases implies refrigeration systems and consequent energy expenditure. Although people avoid food waste at home, the awareness for the waste at restaurants still needs to be improved. Consumers seem to prefer foods that have been produced and packed in sustainable ways but still give importance to the package—understandable for food products. The results also indicated significant differences in the food choice motivations between groups for all sociodemographic variables tested (age, sex, marital status, education, professional area, living environment and country), but the association was high only for variable country. Additionally, a tree classification analysis allowed to identify the relative importance of the influential variables on the sustainable food choices, with country being the most important, followed by age and sex. Additionally, discriminant function analysis allowed establishing a model for the relation between country and six variables accounting for preservation of biodiversity, respect for life, save natural resources, save energy, reduce industrial pollution and minimal packaging. Although with some limitations, this study brings valuable insight into some aspects linked with sustainable food choices on a number of countries and how people shape their food choices according to some sustainability issues.

The research aims to prioritize the solutions to overcome the challenges of Lean Six Sigma 4.0 (LSS 4.0). It is an integrated approach with lean, six sigma, and Industry 4.0 attributes. This integrated approach helps to achieve organizational excellence and sustainable development goals. Fuzzy stepwise weight assessment ratio analysis (fuzzy-SWARA) was used to estimate the weights of LSS 4.0 challenges. Furthermore, fuzzy-weighted aggregated sum product assessment (fuzzy-WASPAS) was used to prioritize the LSS 4.0 solutions. In this study, 23 challenges and 23 solutions of LSS 4.0 implementation were identified with the help of an extensive literature review and discussion with the area experts having vast experience. Management participation in LSS 4.0 implementation and planning for long-term vision were found to be the topmost solutions to overcome LSS 4.0 challenges. To the best of our knowledge, to date, the prioritization of solutions to overcome the challenges of LSS 4.0 have not yet been investigated in the developing economic context.

Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%.

This study’s objective is to examine the influence of entrepreneurial self-efficacy and internal locus of control on the entrepreneurial intention of small Saudi entrepreneurs during adverse times, with entrepreneurial resilience as a moderator. The study, which targeted a sample of 207 small entrepreneurs working in various sectors in Saudi Arabia, gathered data through an online questionnaire sent to respondents and analysed the results using PLS-SEM. The study revealed intriguing findings, such as the existence of a positive significant relationship between entrepreneurial self-efficacy, internal locus of control and entrepreneurial intention amongst small Saudi entrepreneurs. It also demonstrated that in times of adversity, such as during the COVID-19 pandemic and other environmental challenges, entrepreneurial resilience can act as a moderator between entrepreneurial intention and entrepreneurial self-efficacy. Entrepreneurial resilience, in particular, has the potential to strengthen the relationship between entrepreneurial self-efficacy and entrepreneurial intention. Accordingly, the government, along with other sectors and stakeholders in Saudi Arabia, should continue to support the psychological characteristics of small Saudi entrepreneurs, notably their internal locus of control, entrepreneurial self-efficacy, and entrepreneurial resilience to ensure greater sustainability and the continuity of their small businesses.

On a global scale, wheat (Triticum aestivum L.) is a widely cultivated crop among all cereals. Increasing pollution, population expansion, socio-economic development, ecological and industrial policies have induced changes in overall climatic attributes. The impact of these factors on agriculture dynamics has led to various biotic and abiotic stresses, i.e., significant decline in rainfall, directly affect sustainable agriculture. Increasing abiotic stresses have a direct negative effect on worldwide crop production. More promising and improved stress-tolerant strategies that can help to feed the increasing global population are required. A laboratory experiment was performed on two of the latest wheat (Triticum aestivum L.) genotypes (Akbar 2019 and Anaj 2017) from Punjab Pakistan, to determine the influence of seed priming with thiamine (vitamin B1) along with soil inoculation of Endophytic bacterial strains to mitigate the effects of drought stress at different degrees. Results revealed that thiamine helped in the remote germination; seeds of Anaj 2017 germinated within 16 hours while Akbar 2019 germinated after one day. Overall growth parameters of Anaj 2017 were negatively affected even under higher levels of drought stress, while Akbar 2019 proved to be a susceptible cultivar. A significant increase in RFW (54%), SFW (85%), RDW (69%), SDW (67%) and TChl (136%) validated the effectiveness of D-T3 compared to C-T0 in drought stress. Significant decrease in MDA, EL and H2O2 signified the imperative function of D-T3 over C-T0 under drought stress. In conclusion and recommendation, we declare that farmers can get better wheat growth under drought stress by application of D-T3 over C-T0.

In 2021, 56% of the global population lived in cities, and by 2050 the ratio of urban-to-rural population is expected to reach 67% [...]

The development of e-systems has given consumers and businesses access to a plethora of information, which has complicated the process of decision making. Document classification is one of the main decisions that any business adopts in their decision making to categorize documents into groups according to their structure. In this paper, we combined multi-agent systems (MASs), which is one of the IDSS systems, with Bayesian-based classification to filter out the specialization, collaboration, and privacy of distributed business sources to produce an efficient distributed classification system. Bayesian classification made use of MAS to eliminate distributed sources’ specialization and privacy. Therefore, incorporating the probabilities of various sources is a practical and swift solution to such a problem, where this method works the same when all the data are merged into a single source. Each intelligent agent can collaborate and ask for help from other intelligent agents in classifying cases that are difficult to classify locally. The results demonstrate that our proposed technique is more accurate than those of the non-communicated classification, where the results proved the ability of the utilized productive distributed classification system.