Zagreb School of Economics and Management

Fiber membranes have attracted extensive attention due to their tunable porosity, high surface-to-volume ratio, and adaptability for functionalization. This study presents a bibliometric analysis of fiber membrane applications in environmental (air purification, water treatment) and energy (hydrogen production, energy storage) fields from 2000 to 2024, leveraging data from Web of Science and Incopat. Results reveal China is dominant in publication papers (32–75% across subfields) and patent applications (35–90%), yet nations like Singapore and Germany exhibit a higher citation impact. Environmental applications, though historically active, show declining research in recent years, while energy-related domains─particularly energy storage (144 papers, 32.9 citations/paper) and hydrogen production (135 papers, 34.37 citations/paper)─surged post-2020, driven by clean energy demands. Keyword clustering identifies the evolving priorities: CO2 capture and mixed-matrix membranes dominate air purification, desalination and ultrafiltration define water treatment, and lithium-ion batteries lead energy storage. International collaboration networks highlight the central role of China, with strong ties to the USA and Southeast Asia. Despite the quantitative China leadership, qualitative gaps persist, emphasizing the need for cross-disciplinary innovation and industrial translation. This review underscores the transformative potential of fiber membranes in addressing sustainability challenges while mapping strategic pathways for future research, policy, and technology commercialization.

Reducing the environmental and economic impacts of food waste is essential for achieving the Sustainable Development Goals and advancing the circular economy. This study evaluates the sustainability potential of anaerobic digestion of food waste (ADFW) in Beijing, a pioneer in China’s waste separation and ADFW implementation. By integrating a complementary judgment matrix, life cycle assessment, and techno-economic assessment, we assess ADFW projects from social, environmental, and economic perspectives. Results highlight that technology cost indicators are key to ADFW sustainability, while environmental indicators strongly correlate with other metrics (correlation coefficient >0.8). Treating all sorted food waste in Beijing─20% of total waste─via anaerobic digestion could generate $96.5 million in annual revenues and offset 0.295 million tonnes of CO2 equivalent per year. Sensitivity analysis suggests that implementing a food waste disposal charge of $52.24 per tonne is critical for mitigating market risks, particularly those linked to crude oil price fluctuations. These findings demonstrate that ADFW offers significant environmental and economic benefits, underscoring the importance of targeted policies and technological innovations to promote sustainable waste management and resource recovery, aligning with the goals of the circular economy.

The cross-border intelligent marketing algorithm based on traditional linear models is relatively single in information feature extraction, making it difficult to effectively handle complex scenarios containing a large amount of implicit information in users and markets, resulting in poor personalized marketing effectiveness. To address this issue, this article proposes a cross-border intelligent marketing model that integrates rating information and user labels using a multi-layer perceptron grey wolf optimization and convolutional neural network (MLP-GWO-CNN). This model extracts implicit high-order information through nonlinear methods and can handle complex and sparse marketing data. Firstly, a dual path deep network structure was designed, in which one path was modeled using a multi-layer perceptron (MLP) to extract user interest features based on historical interaction ratings; Another path utilizes Convolutional Neural Networks (CNN) to extract semantic features from user label information and construct item feature representations. In response to the sensitivity of MLP algorithm to initial values and its tendency to fall into local optima, this paper uses GWO algorithm to optimize MLP. Next, the latent feature vectors generated by MLP and CNN are fused in the output layer to generate the final predictive marketing strategy last. Experiments were conducted using a real cross-border e-commerce dataset, and the results showed that compared with traditional recommendation algorithms, the MLP-GWO-CNN model proposed in this paper performs better in utilizing user tag information, effectively improving the accuracy and personalization of marketing recommendations. The accuracy of the model is over 89%, and the recall rate is over 90%.

Abstract China strives to achieve low-carbon agricultural development, and its agricultural trade deficit has become a reality. Therefore, this paper empirically analyzes the impact of agricultural products import trade on agricultural carbon productivity and its mechanism. The following conclusions are drawn: firstly, agricultural products import trade positively impacts agricultural carbon productivity. Secondly, there is a single threshold effect based on economic development level in the impact relationship between the above two. Thirdly, agricultural products import trade increases agricultural carbon productivity by reducing agricultural production factor inputs, adjusting the agricultural structure, and upgrading the agricultural production technology level.

Abstract This article introduces an artificial intelligence (AI)‐based system for forecasting food insecurity in data‐limited settings, employing unsupervised neural networks for topic modeling on news data. Unlike traditional methods, our system operates without relying on expert assumptions about food insecurity factors. Through a case study in Somalia, we show that the method can yield competitive performance, even in the absence of traditional food security indicators such as food prices. This system is valuable in supporting expert assessments of food insecurity, unlocking a wealth of untapped information from news outlets, and offering a path toward more frequent and automated food insecurity monitoring for timely crisis intervention.

As a critical part of entrepreneurial process, entrepreneurial exit has great influence on the fate of new ventures. The reasons why entrepreneurial founder team (EFT) members choose to exit the new ventures they help to found have long intrigued researchers and practitioners. Based on psychological contract violation literature, our study investigates the psychological antecedents of EFT members’ entrepreneurial exit. We offer a cross-level contingent decision-making model to explain how and when psychological contract violations affect entrepreneurial exit. Using a metric conjoint experiment, we study 3968 exit decision-making judgements made by 124 participants. Our findings indicate that EFT members’ psychological contract violations motivate entrepreneurial exit intentions, and beneficial financial consequence of exit positively moderate the positive impact of psychological contract violations on entrepreneurial exit. Moreover, when perceiving psychological contract violations, EFT members with lower risk propensity are more likely to make entrepreneurial exit decisions.

The empirical study aimed to explore the relationships among social media exposure, job stress, anxiety, and identity disturbance in a nonclinical setting in the COVID-19 pandemic context. An online questionnaire was administered to 282 participants in the United States of America (USA) during the COVID-19 pandemic. The study utilized a two-step Structural Equation Modeling (SEM) approach consisting of both measurement model and structural model testing. Relationships between the model variables of social media exposure, identity disturbance, anxiety, and job stress were analyzed using standardized beta coefficients, standard errors, t-values, and p-values. The results indicate that both social media exposure and job stress are associated with increased anxiety levels, which, in turn, influence identity disturbance. Moreover, there is a moderating effect of job stress on the relationship between social media exposure and anxiety, as well as the mediating effect of anxiety on the relationship between social media exposure and identity disturbance. The findings are valuable for organizations and can be used to develop programs aimed at mitigating the adverse effects of social media exposure on mental health. Prioritizing employee mental health through awareness and support initiatives is paramount, especially for those facing high stress and extensive social media use.

To understand dynamics in climate change, informing policy decisions and prompting timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of year-on-year CO2 emission changes across ten countries, considering a broad range of factors including socioeconomic, CO2-related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO2 emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of variations in the target. To capture emissions trend variation, we categorized countries by the change in CO2 emission volatility (high, moderate, low with upward or downward trends), assessed using standard deviation. We employed machine learning techniques, including feature importance analysis, Partial Dependence Plots (PDPs), sensitivity analysis, and Pearson and Canonical correlation analyses, to identify influential factors driving these short-term changes. The Decision Tree Classifier was the most accurate model, with an accuracy of 96%. It revealed population size, CO2 emissions from coal, the three-year average change in CO2 emissions, GDP, CO2 emissions from oil, education level (incomplete primary), and contribution to temperature rise as the most significant predictors, in order of importance. Furthermore, this study estimates the likelihood of a country transitioning to a higher emission category. Our findings provide valuable insights into the temporal dynamics of factors influencing CO2 emissions changes, contributing to global efforts to address climate change

European Muslims (some of which may be jihadists or are motivated by jihadists) and misunderstanding (tolerating) this misuse of jihad by European non-Muslims, both participating in pro-Palestinian protests in Europe which creates a paradoxical situation for both. Non-Muslims are against militant jihadism symbolism and are at the same time tolerating such symbolism. In order to analyze this paradox three steps are taken. In the first part, the description and levels of the paradox are supplied. In the second part, three elements are given: the description of the circumstances in Europe after the Palestinian attack on Israel on October 7, 2023, the elements of possible misuse of jihad (by jihadists), and the description of jihad and jihadism in Europe. In the third part, a conclusion is drawn from the previous premises. Since jihadism is a fact in Europe as well as terrorist attacks by jihadists, there is a possibility and even some probability that jihadists, misusing jihad, are related to Muslims among pro-Palestinian protesters in Europe, which creates a paradoxical situation for non-Muslim members of pro-Palestinian protesters in Europe since they are in the same time against jihad, and jihadist terrorism (Hamas) and pro-Palestine (civilians).