Search Results (6,411)

This study analyzes the application of the agile SCRUM methodology in the Peruvian cocoa production chain, aiming to improve organizational efficiency, bean quality, and environmental sustainability. Four experimental SPRINTS were implemented in a cocoa plantation located in San Martín, Peru, addressing practices such as the use of bio-organic fertilizers, monitoring of the fermentation process, and cadmium reduction in cocoa beans. The results showed significant physiological improvements: treatment R3 increased plant height by +10.5 cm (p < 0.005), and stem diameter reached a mean value of 11.36 mm in treated living plants compared to 6.46 mm in the untreated control group. The fermentation process remained under statistical control, with an overall mean temperature of 34.57 °C and no deviations beyond the established control limits (UCL: 50.71 °C, LCL: 18.43 °C). Regarding cadmium reduction, treatment T6 (MycoUp 3 kg/ha + MBB 2 kg/ha) reduced cadmium concentration to 0.039 mg/kg, below the maximum limit established by the European Union (0.05 mg/kg), in contrast to the control group, which reached 0.134 mg/kg. The implementation of SCRUM facilitated iterative planning, clear role allocation, and data-driven decision-making, demonstrating its adaptability to the agribusiness sector. This research proposes a replicable model that integrates technological innovation, agroecology, and collaborative management to address the structural and commercial challenges of Peruvian cocoa production. Full article

With the rapid development of technology and increasing material consumption, the efficient management of waste streams has become a critical challenge within the circular economy, particularly in resource-intensive sectors such as electronic waste recycling. This study examines how artificial intelligence can improve the assessment and forecasting of circular economy investment efficiency, with particular attention paid to resource-intensive sectors such as electronic waste recycling. The study reviews data from European Union countries for the period 2010–2024, including economic, technological, and environmental indicators. A machine learning model system based on ensemble predictive methods was developed to assess the effectiveness of circular economy investments. The results show that artificial intelligence-based models have higher forecasting accuracy than traditional econometric methods, and the most important factors determining investment efficiency are the level of automation, recycling efficiency, and the stringency of environmental policies. The study provides a new, data-driven methodological approach to assessing circular economy investments and discusses their implications for sustainable real estate development and resource management. Full article

In hospitality SMEs, digital transformation is increasingly linked to sustainability goals. However, evidence on how corporate social responsibility (CSR) relates to the adoption of artificial intelligence (AI) in owner-managed firms remains limited. This study examines CSR practices, managerial attitudes toward AI, and AI adoption in micro and small restaurant SMEs in a small European Union (EU) economy. Using survey data from 157 Slovenian restaurant SMEs and structural equation modelling, CSR is conceptualised as an enacted, practice-based orientation. At the same time, managerial attitudes toward AI are modelled as the key mechanism preceding adoption. Results reveal an asymmetric relationship between CSR and AI. Employee-related CSR practices, which are mainly institutionalised, do not significantly influence managerial AI attitudes. In contrast, environmental CSR practices are negatively associated with AI attitudes, indicating more cautious evaluations among environmentally responsible managers. Managerial attitudes toward AI are positively and significantly associated with AI adoption, confirming their central role in adoption decisions. Financial performance, measured by objective revenue data, does not emerge as a direct outcome of AI adoption but rather operates as a contextual condition shaping how CSR practices relate to managerial attitudes and how those attitudes translate into adoption decisions. Overall, the findings indicate that CSR does not uniformly translate into managerial attitudes toward AI and subsequent AI adoption in restaurant SMEs. Full article

The objective of this study is to explore the time-varying shock transmission mechanism between aggregated financial stress indices (FSIs) of developed economies (the U.S., the U.K., the European Union (EU) and Japan) and the emerging economy of China. We employ a novel Time-Varying Parameter Vector Auto-Regression (TVP-VAR)-based “connectedness approach” to capture dynamic shock spillovers without the limitations of arbitrarily chosen rolling windows, loss of observations, or excessive sensitivity to outliers, as it is grounded in a multivariate Kalman filter structure. The aggregated measures of the FSIs of China, the U.S., the U.K., the EU and Japan are incorporated from the Asian Development Bank’s data repository by using time-series observations from January 2010 to September 2023. The findings indicate that the FSI of China is influenced by financial stress shocks originating from Japan (18.35%) and the U.S. (16.86%) the most, whereas the U.K. (EU) contributes to only 8.42% (6.54%) of FSI shocks in China. This research article significantly captures China’s heightened vulnerability to external financial stress shocks from developed economic systems and underscores the critical importance of reinforcing financial resilience, strengthening macro-prudential regulations and early-warning systems, and expanding financial buffers during episodes of trade uncertainty like restrictions on China’s rare earth exports and solar panels, U.S. restrictions on industrial metal imports, Brexit, supply chain disruptions amid COVID-19, and geopolitical uncertainties like the Russia–Ukraine war. Overall, this study provides actionable guidance for mitigating the impact of global financial stresses, improving risk management, and safeguarding economic stability in an increasingly interconnected and volatile international environment. Full article

Antibiotic residues in water buffalo milk are a food-safety concern, yet depletion data are scarce. The purpose of this study was to characterize the depletion profiles of amoxicillin (AMOX) and its two major metabolites, amoxicilloic acid (AMA) and amoxicillin diketopiperazine-2′,5′-dione (2,5-DKP), in Anatolian water buffalo milk after a single intramuscular administration and to estimate a milk withdrawal time relative to the EU MRL. We tested the hypothesis that AMOX concentrations would decrease below the EU MRL over successive milkings and that AMA and 2,5-DKP would exhibit depletion kinetics distinct from the parent compound. Five lactating Anatolian water buffaloes received a single intramuscular injection of amoxicillin (15 milligrams per kilogram). Milk was collected at each milking (twice daily) for seven days and analyzed by liquid chromatography–tandem mass spectrometry with quantification limits below the European Union maximum residue limit for amoxicillin in milk (4 micrograms per kilogram). Amoxicillin peaked at the second milking (mean 13.65 micrograms per kilogram), mean concentrations fell below the maximum residue limit from the sixth milking, and they became non-quantifiable from the tenth milking onward. Two major metabolites, amoxicillinic acid and amoxicillin diketopiperazine-2′,5′-dione, peaked earlier (2,5-DKP Tmax 12 h) or at higher concentrations (AMA Cmax 32.64 µg/kg vs. AMOX 13.65 µg/kg) and remained detectable up to the thirteenth milking, with longer apparent terminal half-lives (32.0 and 52.8 h) than amoxicillin (23.5 h); the mixed-effects model confirmed different depletion rates among analytes (milking × analyte interaction p = 4.63 × 10⁻⁵). A log-linear withdrawal model applying the EMA 95/95 tolerance limit indicated that the first time point at which the upper tolerance limit fell below the EU MRL was 84.7 h after dosing; rounded up to the next 12 h milking interval, this corresponds to a reported withdrawal period of 96 h (≈8 milkings). These results provide species-specific residue kinetics for amoxicillin in Anatolian buffalo milk and support considering metabolites in monitoring and withdrawal-time decisions. Full article

Air quality improvement represents a critical challenge for the European Union, with particulate matter (PM) being the most harmful pollutant in urban areas. Urban Green Infrastructures (UGIs) provide essential ecosystem services that mitigate air pollution, notably through PM₁₀ removal via deposition on leaf surfaces, reducing health risks associated with poor air quality. This study quantifies the PM₁₀ removal supplied by urban forests in the Bydgoszcz–Toruń area (Poland) using a spatially explicit modeling framework. Remotely sensed Leaf Area Index, vegetation cover, and PM₁₀ concentration data were integrated within a GIS environment, with all analyses conducted on a seasonal basis to capture temporal variability in vegetation phenology and pollutant levels. Resulting maps of mean seasonal PM₁₀ removal efficiency (kg/ha) reveal distinct functional group patterns: deciduous broadleaves reach peak efficiency in summer, whereas conifers provide a more consistent year-round contribution, resulting in the highest annual removal. Monetary valuation was performed using externality costs from the European Environmental Agency. Overall, urban forests remove 3360.40 Mg of PM₁₀ annually, corresponding to an estimated value of 255.69 M€. Integrating biophysical and economic perspectives supports urban planning and highlights UGIs as nature-based solutions to enhance air quality, protect public health and promote ecosystem biodiversity and resilience. Full article

Since their adoption by the United Nations General Assembly in 2015, the 17 Sustainable Development Goals (SDGs), while non-binding, have served as a strategic compass, encouraging nations to adopt consistent sustainability measures within their own institutional contexts. To analyze how European Union (EU) countries are progressing towards the SDGs, combining an analysis of the relationships between different SDGs with an assessment of the relationships between countries, a Generalized STATIS method, called GSTATIS, is presented. Following a three-way approach designed for the analysis of complex data, the proposed method uses two different sets of weights for classes of countries and groups of SDGs to explicitly account for the role they play in the three-way variability. Unlike the standard STATIS method, which accounts for relationships between countries but overlooks the potential interactions between SDGs, GSTATIS recognizes that different groups of SDGs and classes of countries with group-correlated SDGs may contribute unequally to the three-way variability. Therefore, the “compromise” matrix is defined by using different optimal sets of weights for both classes of countries and groups of SDGs. As a result, GSTATIS offers a novel perspective on sustainability analysis, leveraging a three-way approach that simultaneously addresses within- and between-country variability by identifying groups of SDGs with highly correlated trends across countries. Full article

Despite the well-documented acceleration of climate-related economic losses in Europe, existing research has largely treated these damages as isolated stochastic events rather than as structurally embedded fiscal risks. This gap leaves EU fiscal governance frameworks inadequately prepared for the persistent, spatially concentrated, and temporally dependent nature of such losses. This study addresses this gap by investigating the systemic transformation of climate-related economic risks within the European Union, arguing that climate losses have evolved from unpredictable stochastic shocks into a persistent, structural burden on the European economy. Adopting a comprehensive multi-methodological approach, the research quantifies this transition by integrating spatial concentration metrics (HHI), advanced time-series modelling (OLS, ARIMA, ETS), and anomaly detection techniques to analyse loss patterns across the EU-27 from 1980 to 2023. The empirical results demonstrate three critical systemic dimensions: (1) a statistically significant upward shift in the baseline of economic damages; (2) a high geographical concentration of losses, with Germany, Italy, and France consistently bearing the largest share of climate-driven fiscal pressure; and (3) the emergence of volatility clustering, indicating that climate risks are becoming increasingly non-linear and embedded in the macroeconomic environment. The study contributes to the literature by reframing climate-related economic losses as a systemic fiscal phenomenon requiring structural governance reform, rather than ad hoc disaster response. The findings suggest that existing reactive policy frameworks are insufficient to address the scale of these structural risks. Consequently, the paper advocates for a paradigm shift in EU climate policy—moving toward anticipatory fiscal instruments, harmonised resilience financing, and monitoring systems designed to mitigate systemic volatility and cross-country economic asymmetry rather than merely responding to isolated disaster events. Full article

Achieving high density in complex powder metallurgy components like spur gears is often hindered by friction-induced density gradients and ejection defects. This study investigates a novel elastic die system designed to mitigate these issues through controlled radial deformation. Spur gears were compacted using Ancorsteel 2000 powder under pressures of 400–700 MPa, utilizing a tapered elastic sleeve to apply radial compression. Green and sintered densities were measured, while porosity distribution was quantified via image analysis. Additionally, a 3D finite element simulation using FORGE software was conducted to model the thermo-mechanical behavior and stress distribution during the process. Experimental trials demonstrated that the elastic relaxation of the sleeve enabled free ejection of the compacts without requiring an extraction force. Image analysis confirmed a homogenous porosity distribution across the gear teeth, and higher die pre-stressing strokes were found to correlate with increased sintered density. Finite element modeling accurately predicted critical stress concentrations of 700 MPa at the die–sleeve interface and validated the strain distribution. The results confirm that elastic die technology effectively eliminates ejection friction and improves density uniformity in complex gears, offering a viable solution for reducing tool wear and manufacturing defects in high-precision powder metallurgy. Full article

The acceleration of climate change poses a growing threat to human health, particularly by exacerbating non-communicable diseases (NCDs) such as cardiovascular and respiratory conditions. Rising global temperatures amplify air pollution and environmental toxins, disproportionately affecting vulnerable populations. This narrative review explores the complex pathways linking climate-related environmental stressors to adverse health outcomes, focusing on the intermediary roles of epigenetic modifications and alterations in the microbiota. Epigenetic processes, including DNA methylation and histone modifications, may mediate how environmental exposures influence gene expression and disease susceptibility. Concurrently, changes in microbiota composition induced by pollutants and temperature fluctuations can promote inflammatory responses and immune dysfunction. Elucidating these molecular mechanisms is essential for developing targeted interventions and adaptive strategies to mitigate the health impacts of climate change. This review underscores the importance of identifying epigenetic and microbiota-based biomarkers for early risk stratification and for informing public health prevention and adaptation policies. A transdisciplinary approach, grounded in the One Health framework, is critical to addressing the growing burden of climate-sensitive diseases and reducing health inequalities. Full article

Platinum group elements (PGE) are six rare highly siderophile metals which have similar chemical characteristics and occur together in mineral deposits: platinum (Pt), palladium (Pd), rhodium (Rh), ruthenium (Ru), iridium (Ir) and osmium (Os). In nature, they tend to exist in a metallic state or bond with sulfur and arsenic and occur as trace accessory minerals predominantly in mafic and ultramafic rocks. High industrial demand together with their scarcity in crustal rocks has been reflected in their inclusion in 2025 US Government’s List of Critical Minerals, European Union’s List of Critical Raw Materials and Mongolian List of 11 Critical Minerals. Although Mongolia is not currently a producer, it hosts four types of potentially economic PGE deposits: (1) Podiform chromitites associated with ophiolites; (2) Ni-Cu-PGE sulfide mineralization of rift-related mafic–ultramafic intrusions; (3) Alaskan–Uralian type arc related zoned mafic–ultramafic intrusions; and (4) Placers. Particularly promising are Permian Ni-Cu-PGE sulfide bearing mafic–ultramafic intrusions of the Khangai large igneous province which bear resemblance to mineralized Permian intrusions in Russia (e.g., Norilsk-Talnakh) and N.W. China (e.g., Kalatongke; Tarim basin). In addition, sub-economic ophiolite-hosted PGE mineralization can be extracted as a by-product during chromite mining. There is also the potential for PGE recovery as a by-product in existing gold placer operations in areas hosting ophiolitic massifs and Alaskan–Uralian type intrusions. Mongolia is a promising frontier for PGE exploration and mining. Full article

The world is undergoing rapid transformations driven by climate change, socio-economic pressures, and geopolitical tensions. Monitoring these dynamics is essential to understand and anticipate territorial change. Although initiatives such as the European Union’s Open Data program promote spatiotemporal datasets (e.g., population, land use), analyzing and interpreting these data over time remains complex and requires technical expertise, limiting their accessibility. This research proposes Semantic Web-based methods to detect and annotate trends in spatiotemporal series, thereby assisting in the systematic analysis of temporal patterns. We introduce the SETT ontology (SEmantic Trajectory of Territory) and its OFF-SETT framework (Ontological Framework For SETT), enabling the formal description of territorial trends and their publication as semantic trajectories in the Linked Open Data cloud. The study delivers (i) a generic methodology for detecting and describing trajectories in spatiotemporal datasets; (ii) a framework for automatically generating knowledge graphs capturing these trajectories; (iii) a knowledge graph describing trajectories of demographic and satellite-derived variables (e.g., temperature, water, vegetation) for study areas in France and Switzerland; and (iv) a web-based geovisualization platform. The approach shows that Semantic Web technologies bridge complex spatiotemporal analysis and public accessibility. By publishing territorial trajectories as knowledge graphs, it fosters transparency, interoperability, and reuse of data, supporting informed decision-making and citizen engagement. Full article

Growing water scarcity across the European Union (EU) increases the need for improved water-use efficiency in water-intensive sectors such as recreational facilities. This study evaluates the feasibility of integrating alternative water sources—including rainwater, graywater, and filter backwash water—into swimming pool operations through a comparative analysis of EU legislation and selected national regulatory frameworks. The study is based on a structured desk review of scientific literature, legal documents, and technical standards published between 2010 and 2025, complemented by a qualitative SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis. Previous studies indicate that public swimming pool facilities may consume approximately 20–50 m³ of water per day, highlighting the potential benefits of alternative water supply strategies. However, regulatory fragmentation and the absence of harmonized EU-level quality standards for recreational water reuse remain the main barriers to wider implementation. While Regulation (EU) 2020/741 establishes minimum requirements for reclaimed water reuse in agricultural irrigation, no dedicated framework exists for swimming pool facilities. Among the analyzed options, rainwater harvesting and graywater reuse appear to be the most feasible solutions. Clearer regulatory guidance and risk-management procedures could support the safe adoption of alternative water sources and contribute to improving water-use efficiency in the recreational sector. Full article

Corrosion resistance of steel wires can be achieved through several approaches, one of the most established being hot-dip galvanizing. The effectiveness of anticorrosive protection of a galvanized wire is considered to depend not only on the galvanizing process itself, namely bath composition, temperature, and immersion duration—but also on the post-galvanizing wiping method, which ultimately determines the final thickness and uniformity of the zinc coating. This study describes and quantifies the resulting parameters of the Zn layer, systematically comparing two technical variants. Four parameters were analyzed to characterize the coating: the effective thickness of the constituent layers, their morphology (examined by SEM), their compositional profile (EDX mapping), and their microhardness. To comprehensively assess the influence of the wiping method on the anticorrosion performance of the galvanized wire, the final corrosion tests, fifth in the sequence, will be conducted in a salt fog environment using an Erichsen chamber, in accordance with standardized procedures. Full article

The Galileo Signal Authentication Service (SAS) is the next new feature to be offered by Galileo, the European GNSS. Its signal-in-space initial capability is expected already in the next months of 2025, starting with the L3 (Launch 3) Galileo elliptical-orbit satellites. It is the first-ever navigation signal authentication feature offered globally and openly. Galileo SAS uses the existing Galileo E6-C signal to be encrypted, in combination with OSNMA (Open Service Navigation Message Authentication), through the so-called semi-assisted authentication concept. In this concept, portions of the E6-C are re-encrypted with OSNMA future keys and published in a server. The concept allows signal authentication openly and for free, and without private key management by users. In exchange, the time between authentications is 30 s, inherited from OSNMA, and it introduces a latency between the E6-C signal reception and its authentication down to a few seconds. This work presents the status of Galileo SAS. It outlines its latest technical definition, already shared in previous publications. It will also present the MMARIO (Message and Measurement Authentication Receiver for Initial Operations) project, developing the first SAS server, receiver and testing platform. The paper also outlines the Galileo SAS plans for the near future, up to the Initial Service Declaration. Full article