Social Sciences Citation Index (Web of Science)

The caporalato system, a widespread form of labor exploitation in Italy and Europe, relies on caporali – intermediaries who manage and control migrant workers. Despite their central role, caporali remain understudied due to the difficulty of accessing and observing them directly. This study addresses this gap by analyzing narratives from three distinct datasets of recorded interactions with agricultural workers, using a dual framework of emerging social representations and ideologies. The analysis reveals that caporali are represented through overlapping and dynamic identities, including businessmen, criminal bosses, paternal figures, and religious leaders. These representations often merge, creating hybrid constructs that allow caporali to legitimize their power and maintain control. Similarly, the ideological values tied to productivity, loyalty, fear, and control interact, reinforcing the caporale’s dominance and making resistance difficult. This study highlights how the fluidity of representations and ideologies sustains the resilience of the caporalato system, offering new insights into its persistence and adaptability.

This work supports the circular economy and sustainable material by facilitating the creation of low-carbon materials with enhanced elimination of nutrients from wastewater, thereby assisting in preventing eutrophication. Porous geopolymers, owing to their distinctive pore structure and numerous superior properties, including noise reduction and thermal insulation, have a wide range of potential applications in the building sector, chemical industry, and water treatment. Developing low-carbon-footprint porous geopolymer materials is an important step toward creating multipurpose lightweight materials that can serve as structural materials and, at the same time, as adsorbents. In this study, it was revealed that the porous material created during the hydrothermal synthesis of (lime–Portland cement-based aerated composition), by replacement of sand with wood biomass bottom ash (WBA), can be used as porous aggregates (PA) for adsorbent development. PA was produced with an apparent porosity of 65%, a density of 610 kg/m3, and a compressive strength of 2.0 MPa. The effectiveness of employing an air-entraining additive (AEA) and creating PA in geopolymers was tested. A different-molarity activator was used, and wood biomass fly ash (WFA) and metakaolin (MK) waste were used as precursors for the synthesis of porous geopolymers. Using an air-entraining admixture in geopolymers allows for the production of lightweight geopolymers with densities up to 1400 kg/m3, compressive strengths up to 8.0 Mpa, and apparent porosities up to 38.4%. Such properties, together with their low cost, offer good prospects for geopolymers in the construction industry. By utilizing PA in the geopolymer composition, a lightweight geopolymer (GPA) with a density of 985 kg/m3 and a compressive strength of 3.9 Mpa, with 42.0% apparent porosity, was obtained. The materials effectively removed phosphorus from biologically treated wastewater: PA had an efficiency of up to 82.5%, the geopolymer with AEA had an efficiency of up to 88.4%, and GPA had an efficiency of up to 97%. The created GPA enhances the adsorbent’s sorption capacity, resulting in extremely high phosphorus uptake efficiency.

This article aims to define and name the contribution of the French sinologist and theorist François Jullien to the field of comparative studies, looking at the intersections of Chinese and Western civilisations. It reveals how this theorist, drawing on the legacy of Chinese thought and developing new concepts and individual methodology, challenges traditional comparative studies, opening up opportunities to look at the heritage of Western civilisation’s thinking from the outside, discovering what was not thought of in it. Such a rigorous deconstruction of the traditional Western norms of thinking opens the way to new possibilities for research on different civilisations.

Air transport plays a pivotal role in enhancing economic development by supporting trade, tourism, and regional competitiveness. The growing environmental concerns and social expectations have necessitated the transition towards sustainable air transport systems. Sustainable air transport refers to aviation activities that balance environmental, economic, and social objectives, aiming to minimize carbon emissions, promote renewable energy usage, and enhance socio-economic welfare. In this study, a novel multi-criteria decision-making (MCDM)-based decision support system (DSS) is proposed to evaluate the sustainable air transport performance of the European countries. The main objective of this research is to develop a comprehensive and integrative framework for measuring and ranking the sustainable air transport performance of nations. A hybrid method, termed fractional fuzzy–ranking comparison-response to criteria weighting (RANCOM)–response to criteria weighting (RECA)–ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS), is introduced. DSS consists of five main stages: expert-based subjective weighting using fractional fuzzy RANCOM, objective weighting via RECA, aggregation of weights, and final performance ranking through the RATGOS method. The results indicate that Germany ranks highest, while Cyprus has the lowest sustainable air transport performance among the evaluated countries. The criterion "commercial aircraft fleet by age of aircraft" is determined to have the highest importance among the sustainable air transport performance indicators. The study provides a comprehensive, replicable framework for policymakers and stakeholders aiming to monitor and improve sustainable aviation systems. It contributes to the literature by addressing the gap in national-level sustainable air transport performance evaluation.

The complex dynamics of global development have turned crude oil from a critical strategic commodity into a powerful tool for political‒military influence. This study aims to forecast the price of OPEC crude oil by simultaneously considering the complex interaction between financial markets, the multifaceted political landscape of the Middle East, and the inherent complexity of oil markets. What they all have in common is their inherent uncertainty. To address this challenge, since this work seeks to identify a robust set of explanatory variables affecting oil prices, the adaptive neural-fuzzy inference system (ANFIS) was employed. A set of input variables with different points of view, which are the time series of monthly data from 2001 to 2019, are considered. In ANFIS, the Sugeno system and the C-Means clustering method are used. The results show the influence of different oil markets on each other, and among the unconventional variables, economic policy uncertainty is not influential, whereas terrorist attacks and geopolitical risk influence it. The numerical results following the implementation of the proposed model show a significant increase of 0.4417 compared with the conventional modeling approaches in this field. This discrepancy highlights the advantages of the proposed methodology.

In the context of global climate change and increasing resource constraints, the concepts of strong and weak sustainability have become vital frameworks for assessing the effectiveness of national sustainable development policies. However, significant gaps persist in understanding how to evaluate the progress toward implementation of these two sustainability approaches, especially in comparative studies covering diverse economic systems. This paper seeks to fill this gap by assessing the sustainability performance of OECD and BRIC countries through Data Envelopment Analysis (DEA) based on by-production technology. The study employs the by-production model, utilizing a logarithmic transformation to improve representation of the productive technology. The results show that OECD countries, driven by technological innovation and favorable policies, tend to exhibit higher sustainability efficiency. Conversely, BRIC countries face considerable challenges in balancing economic growth with environmental preservation. This research provides policymakers with a more comprehensive framework for optimizing sustainable development strategies, highlighting the critical importance of conserving natural resources for long-term sustainability.

Nowadays, in order to follow the trends and principles of sustainability, natural materials are often investigated in acoustics and noise prevention. Hemp fibre is a sustainable alternative to conventional sound-absorbing or insulating materials. The aim of the research is to investigate the acoustic properties of different types of hemp fibre. Five different types of hemp fibre were tested: bleached, cottonized, boiled cottonized, well-stripped decorticated, and short, not combed decorticated fibres. The hemp fibre samples were varied in thickness from 20, 40, and 60 mm and density from 50 to 250 kg/m3 in steps of 50 kg/m3. The sound transmission loss of the material was measured using an impedance tube. In order to predict the sound absorption properties of the samples, the airflow resistivity of the hemp fibre was determined. Based on the theoretical calculations proposed by Delany, Bazley, and Miki, a theoretical analysis of the sound absorption of hemp fibre was performed. In order to determine the dependence on different fibre types, all fibres were examined using SEM. It has been found that hemp fibre can be used as an insulating or sound-absorbing material in noise prevention, as a sustainable alternative to conventional materials.

Air pollution remains a major public-health concern, and exposure to particulate matter (PM), particularly PM10 (with a diameter ≤ 10 µm), is associated with adverse respiratory and cardiovascular outcomes. Most research relies on a singular model for PM10 surface estimation. This study is an assessment of a national-scale, daily PM10 estimation framework for Lithuania (2019–2024), using a hybrid machine-learning method that combines Random Forest (RF) and extreme gradient boosting (XGBoost) algorithms. Hourly PM10 observations were aggregated from 18 monitoring stations to obtain daily means and temporal means. The predictors integrated meteorological factors, such as temperature, wind, humidity, and precipitation, to determine satellite-based atmospheric composition from Sentinel-5P Tropospheric Monitoring Instruments (TROPOMI). Atmospheric components include nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), formaldehyde (HCHO), and the absorbing aerosol index (AI). Moderate-Resolution Imaging Spectroradiometers (MODIS) were used to record land-surface temperature and static spatial descriptors, such as elevation, land cover, Normalized Difference Vegetation Index (NDVI), population, and road proximity. The dataset was partitioned temporally into training (70%), validation (20%), and testing (10%). The hybrid model achieved an improved accuracy, compared with single-model baselines, reaching a coefficient of determination (R2) of 0.739 in validation and R2 = 0.75 in the tested dataset. Mean absolute error (MAE) was 3.15 µg/m3, and root mean square error (RMSE) was 3.98 µg/m3. The results indicate a slight tendency to overestimate PM10 concentrations at lower concentration levels. Feature-importance analysis revealed that short-term temporal persistence is the key to daily PM10 prediction, while meteorological variables provide secondary contributions. Temporal evaluation, using consecutive two-year windows, revealed a consistent improvement in predictive performance from 2019–2020 to 2023–2024, while station-level analysis showed moderate-to-strong agreement between the predicted and observed PM10 concentrations across monitoring stations, with R2 ranging from 0.455 to 0.760. This provides decision-support capabilities for air-quality management, the evaluation of mitigation measures, and integration of air-pollution considerations into sustainable urban planning strategies assessing public-health protection.

The optimal composition of an asphalt mixture, composed from the various mineral materials and bituminous binders, is produced in an asphalt mixing plant (AMP). Various AMP properties determine the suitability of this complex, long-term-use technological equipment in meeting contemporary requirements. Worn AMPs are replaced with new ones when they fail to satisfy these requirements. The AMP purchase process consists of two separate parts: (1) the decision-making process—whether there is a need to purchase a new AMP; and (2) AMP suitability—the evaluation of various technical and other properties, related to the AMP itself. This research paper considers the second part of the AMP purchase process and presents a new twenty-criteria system that identifies the most important AMP quality parameters indicating AMP suitability. The weights of the quality criteria for a new AMP were established by applying the Average Rank Transformation into Weight-Linear (ARTIW-L), Non-Linear (ARTIW-N) and Analytic Hierarchy Process (AHP) methods. Furthermore, the paper presents and verifies the Inverse Hierarchy for Assessment of Main Criteria Importance (IHAMCI) method. The findings of the research show that customers consider technological parameters to be the most important, followed by technical performance. Economical indicators rank third, while ecological (environmental) indicators receive the least attention. Future research will assess the sustainability requirements applied to operating AMPs in recent years.

This study examines how migrants’ narrative form changes over time when the host society is treated as an external object of evaluation. Two interview waves with five West African migrants in Southern Italy (2018; 2023–2024) are analyzed across three domains: Economic–Political, Socio–Cultural, and Environmental–Geographical. Paired comparisons are reported only where within-speaker narratives were comparable across waves. Using the crafting-styles framework, the analysis shows a shift from more stable, closure-oriented tellings in 2018 to more reversal-driven, deconstructive tellings in 2023–2024. This form drift tracks changes in evaluation, agency attribution, and the narratability of the host society as a setting for future planning. Methodologically, the study shows how narrative selection and crafting-style patterns support parsimonious longitudinal comparison and strengthen within-case temporal inference beyond content-focused readings, while also clarifying how coherence and closure become harder to sustain under prolonged institutional contact and precarity.