Search Results (8,313)

An in-depth understanding of the spatiotemporal heterogeneity of ecosystem service (ES) trade-offs and synergies, along with their driving factors, is crucial for formulating key ecological restoration strategies and effectively allocating ecological environmental resources in the Hulunbuir region. This study employed an integrated analytical approach combining the InVEST model, ArcGIS geospatial processing, R software environment, and Optimal Parameter Geographical Detector (OPGD). The spatiotemporal patterns and driving factors of the interaction of four major ES functions in Hulunbuir area from 2000 to 2020 were studied. The research findings are as follows: (1) carbon storage (CS) and soil conservation (SC) services in the Hulunbuir region mainly show a distribution pattern of high values in the central and northeast areas, with low values in the west and southeast. Water yield (WY) exhibits a distribution pattern characterized by high values in the central–western transition zone and southeast and low values in the west. For forage supply (FS), the overall pattern is higher in the west and lower in the east. (2) The trade-off relationships between CS and WY, CS and SC, and SC and WY are primarily concentrated in the western part of Hulunbuir, while the synergistic relationships are mainly observed in the central and eastern regions. In contrast, the trade-off relationships between CS and FS, as well as FS and WY, are predominantly located in the central and eastern parts of Hulunbuir, with the intensity of these trade-offs steadily increasing. The trade-off relationship between SC and FS is almost widespread throughout HulunBuir. (3) Fractional vegetation cover, mean annual precipitation, and land use type were the primary drivers affecting ESs. Among these factors, fractional vegetation cover demonstrates the highest explanatory power, with a q-value between 0.6 and 0.9. The slope and population density exhibit relatively weak explanatory power, with q-values ranging from 0.001 to 0.2. (4) The interactions between factors have a greater impact on the inter-relationships of ESs in the Hulunbuir region than individual factors alone. The research findings have facilitated the optimization and sustainable development of regional ES, providing a foundation for ecological conservation and restoration in Hulunbuir. Full article

The spatial variation of forest ecosystem services at regional scales remains poorly understood, and few studies have explicitly analyzed how ecosystem services are distributed across different forest management types. This study assessed the spatial overlap between forest management types and ecosystem service hotspots in the Southeastern United States (SEUS) and the Pacific Northwest (PNW) forests. We used the InVEST suite of tools and GIS to quantify carbon storage and water yield. Carbon storage was estimated, stratified by forest group and age class, and literature-based biomass pool values were applied. Average annual water yield and its temporal changes (2001–2020) were modeled using the annual water yield model, incorporating precipitation, potential evapotranspiration, vegetation type, and soil characteristics. Ecosystem service outputs were classified to identify hotspot zones (top 20%) and to evaluate the synergies and tradeoffs between these services. Hotspots were then overlaid with forest management maps to examine their distribution across management types. We found that only 2% of the SEUS and 11% of the PNW region were simultaneous hotspots for both services. In the SEUS, ecological and preservation forest management types showed higher efficiency in hotspot allocation, while in PNW, production forestry contributed relatively more to hotspot areas. These findings offer valuable insights for decision-makers and forest managers seeking to preserve the multiple benefits that forests provide at regional scales. Full article

In this paper a new state model is introduced, an adaptative state model in a binary temporal representation (ASMBRT) as well as its application in constructing an algorithmic trading system. The presented model uses the binary temporal representation, which allows for a precise analysis of exchange rates without losing any informative value of the data. The basis of the model is the trajectory analysis for the ensuing changes in price quotations and dependencies between the duration of each change. The main advantage of the model is to eliminate the threshold analysis, used in existing state models. This solution allows for a more accurate identification of investor behavior patterns, which translates into a reduction of investment risk. In order to verify obtained results in practice, the paper presents a concept of creating an algorithmic trading system and an analysis of its financial effectiveness for the exchange rate most popular among investors, namely EUR/USD. Full article

In this study, we consider monthly load forecasting, which is an essential decision for energy infrastructure planning and investment. This study focuses on the Texas power grid, where electricity consumption has surged due to rising industrial activity and the increased construction of data centers driven by growing demand for AI. Based on an extensive exploratory data analysis, we identify key characteristics of monthly electricity demand in Texas, including an accelerating upward trend, strong seasonality, and temperature sensitivity. In response, we propose a regression-based forecasting model that incorporates a carefully designed set of input features, including a nonlinear trend, lagged demand variables, a seasonality-adjusted month variable, average temperature of a representative area, and calendar-based proxies for industrial activity. We adopt a rolling forecasting approach, generating 12-month-ahead forecasts for both 2023 and 2024 using monthly data from 2013 onward. Comparative experiments against benchmarks including Holt–Winters, SARIMA, Prophet, RNN, LSTM, Transformer, Random Forest, LightGBM, and XGBoost show that the proposed model achieves superior performance with a mean absolute percentage error of approximately 2%. The results indicate that a well-designed regression approach can effectively outperform even the latest machine learning methods in monthly load forecasting. Full article

The biopharmaceutical industry is undergoing a fundamental transformation from traditional batch manufacturing to continuous manufacturing (CM) for recombinant drugs and biosimilars, driven by regulatory support through the International Council for Harmonization (ICH) Q13 guidance and compelling economic advantages. This comprehensive review examines the technical, economic, and regulatory aspects of implementing continuous manufacturing specifically for recombinant protein production and biosimilar development, synthesizing validated data from peer-reviewed research, regulatory sources, and global implementation case studies. The analysis demonstrates that continuous manufacturing offers substantial benefits, including a reduced equipment footprint of up to 70%, a 3- to 5-fold increase in volumetric productivity, enhanced product quality consistency, and facility cost reductions of 30–50% compared to traditional batch processes. Leading biomanufacturers across North America, Europe, and the Asia–Pacific region are successfully integrating perfusion upstream processes with connected downstream bioprocesses, enabling the fully end-to-end continuous manufacture of biopharmaceuticals with demonstrated commercial viability. The regulatory framework has been comprehensively established through ICH Q13 guidance and region-specific implementations across the FDA, EMA, PMDA, and emerging market authorities. This review provides a critical analysis of advanced technologies, including single-use perfusion bioreactors, continuous chromatography systems, real-time process analytical technology, and Industry 4.0 integration strategies. The economic modeling presents favorable return-on-investment profiles, accompanied by a detailed analysis of global market dynamics, regional implementation patterns, and supply chain integration opportunities. Full article

This article examines training itineraries for early childhood education professionals in Portugal, focusing on promoting social and human development through music and the arts for infants. The training models discussed are categorized as short-term and long-term, encompassing both theory and practice through a transdisciplinary approach. Based on initiatives promoted by the Companhia de Música Teatral (CMT) and the Education and Human Development Group of the Centre for the Study of Sociology and Musical Aesthetics (CESEM) at NOVA University Lisbon, the article highlights projects such as: (i) Opus Tutti and GermInArte, developed between 2011 and 2018; (ii) the Postgraduate Course Music in Childhood: Intervention and Research, offered at the University since 2020/21, which integrates art, health, and education, promoting collaborative work between professionals; and (iii) Mil Pássaros (Thousand Birds), developed since 2020, which exemplifies the integration of environmental education and artistic practices. The theoretical basis of these training programs combines neuroscientific and educational evidence, emphasizing the importance of the first years of life for integral development. Studies, such as those by Heckman, reinforce the impact of early investment in children’s development. Edwin Gordon’s Music Learning Theory and Malloch and Trevarthen’s concept of ‘communicative musicality’ structure the design of these courses, recognizing music as a catalyst for cognitive, emotional, and social skills. The transformative role of music and the arts in educational and social contexts is emphasized, in line with the Sustainable Development Goals of the 2030 Agenda, by proposing approaches that articulate creation, intervention, and research to promote human development from childhood onwards. Full article

This study examines whether regional infrastructure can predict economic resilience in European regions, focusing on resistance, recovery, and reorientation during the COVID-19 crisis. While infrastructure is widely recognized as a key factor influencing regional resilience, its explicit role has been underexplored in the European context. Using a comprehensive literature review and spatial econometric models applied to NUTS-2 level data from 2017 to 2024, we investigate the direct and spatial spillover effects of various infrastructure types—transportation, healthcare, tourism, education, and digital access—on regional resilience outcomes. We apply OLS and four spatial models (SEM, SLX, SDEM, SDM) under 29 spatial weighting matrices to account for spatial autocorrelation. Results show that motorway density, early school leaving, and healthcare infrastructure in neighbouring regions significantly affect resistance. For recovery, railway density and GDP per capita emerge as key predictors, with notable spatial spillovers. Reorientation is shaped by population structure, railway density, and tourism infrastructure, with both positive and negative spatial dynamics observed. The findings underscore the importance of infrastructure not only in isolation but also within regional systems, revealing complex interdependencies. We conclude that policymakers must consider spatial externalities and coordinate infrastructure investments to enhance regional economic resilience across interconnected Europe. Full article

The use of informal language on social media and the sheer volume of information make it difficult for a computer system to analyse it automatically. The aim of this work is to design a new group decision-making method that applies two new consensus methods based on sentiment analysis. This method is designed for application in the analysis of texts on social media. To test the method, we will use posts from the so called social network X. The proposed model differs from previous work in this field by defining a new degree of subjectivity and a new degree of reliability associated with user opinions. This work also presents two new consensus measures, one focused on measuring the number of words classified as positive and negative and the other on analysing the percentage of occurrence of those words. Our method allows us to automatically extract preferences from the transcription of the texts used in the debate, avoiding the need for users to explicitly indicate their preferences. The application to a real case of public investment demonstrates the effectiveness of the approach in collaborative contexts that used natural language. Full article

Virginia’s coastal location and abundant marine resources make its seafood industry a vital contributor to the state’s economy, supporting both local communities and tourism. This study applied input–output models and updates the economic contributions of the Virginia seafood industry using 2023 data, building on models developed for 2019 that capture both direct effects and broader economic ripple effects. In 2023, the industry generated USD 1.27 billion in total economic output and supported over 6500 jobs—including watermen, aquaculture farmers, processors, and distributors—resulting in USD 238.3 million in labor income. Contributions to state GDP totaled USD 976.7 million, and tax revenues exceeded USD 390.4 million. The study also evaluates the economic role of Virginia seafood products sold in retail stores and restaurants, based on secondary data sources. In 2023, these sectors generated USD 458 million in economic output, supported more than 3600 jobs, produced USD 136.7 million in labor income, and USD 280.8 million in value-added. Combined tax contributions surpassed USD 74 million. Importantly, the analysis results for the Virginia seafood products from retail and restaurant should not be summed to the seafood industry totals to avoid double-counting, as seafood products move as output from one sector as an input to another. These results provide evidence-based insights to guide decision-making, inform stakeholders, and support continued investment in Virginia’s seafood supply chain and related economic activities. Full article

The water-yield function in the Yellow River Basin (YRB) of China for maintaining the basin’s ecological water balance plays a crucial role. Understanding its spatiotemporal variation and the underlying drivers in the basin is crucial for the management, utilization, and development of water resources. Thus, we used the InVEST model to explore its spatiotemporal dynamics across multiple scales (“basin–county–pixel”). Then, we integrated socio-economic and natural factors to elucidate the driving forces and spatial heterogeneity of water-yield dynamics. Our findings indicated that water-yield trends increased in 71.76% of the YRB, and significant water-yield increases were detected in 13.9% of the basin over the past 40 years. A phase-wise comparison revealed a shift in water yield from a decreasing trend in the first two decades to a significant increasing trend in the last two decades. Hotspot analysis revealed that hotspots of increasing water-yield trends have shifted from the downstream section of the basin toward the southwest, while hotspots of decreasing water-yield trends first concentrated in the basin’s southern part and then disappeared. Both natural and socioeconomic factors have exerted positive and negative impacts on water-yield dynamics. Among them, the dynamics of water yield have been predominantly driven by natural variables. Full article

This paper explores the intervening role in SEP performance of corporate environmental, cultural, and ethnic activities (ECEAs) and diversity, equity, inclusion, and social initiatives (DEISIs) on blockchain adoption (BCA) strategy, particularly useful in the Western Balkans (WB), which demands transparency due to extended fraud and ethnic complexities. In this domain, a question has been raised: In BCA strategies, is there any correlation between SEP performance and ECEAs and DEISIs in a mediating role? A serial mediation model was tested on a dataset of 630 WB and EU companies, and the research conceptual model was validated by CFA (Confirmation Factor Analysis), and the SEM (Structural Equation Model) fit was assessed. We found a statistically sound (significant, positive) correlation between BCA and ESG success performance, especially in the innovation and integrity ESG performance success indicators, when DEISIs mediate. The findings confirmed the influence of technology, and environmental, cultural, ethnic, and social factors on BCA strategy. The findings revealed some important issues of BCA that are of worth to WB companies’ managers to address BCA for better performance. This study adds to the literature on corporate blockchain transformation, especially for organizations seeking investment opportunities in new international markets to diversify their assets and skill pool. Furthermore, it contributes to a deeper understanding of how DEI initiatives impact the correlation between business transformation and socioeconomic performance, which is referred to as the “social impact”. Full article

Promoting energy efficiency in the Italian production sector is significantly hampered by the lack of knowledge, the scarcity and the limited distribution of tools for supporting energy audits in small and medium-sized enterprises (SMEs) in a wide range of Italian economic sectors (industry, tertiary sector, transport). The Advanced Tool for ENErgy Audit for SMEs, ATENEA4SME, is intended to help SMEs promote energy-efficiency projects, supports energy audits and self-evaluation of energy consumption. The tool uses an original mathematical model that takes into account the results of questionnaires and a multi-criteria analysis to generate recommendations for energy efficiency investments. This article will give a thorough explanation of the tool, emphasizing and outlining the sections as well as the procedures to get the ultimate summary of the energy usage of the enterprises under investigation and the potential for energy saving. From a technological and financial perspective, the tool helps to remove obstacles to the development of energy-efficiency measures. In this article, the IT and methodological structure of the tool will therefore be extensively described, and its operation for the context of SMEs will be illustrated, with application cases. Ample space will be allocated to the dissemination campaign and the replicability of the tool for all economic sectors of the industrial and tertiary sectors. Full article

Rough sets and fuzzy sets are two complementary approaches for modeling uncertainty and imprecision. Their integration enables a more comprehensive representation of complex, uncertain systems. However, existing rough fuzzy sets models lack the expressive power to fully capture the interactions among structural uncertainty, cognitive hesitation, and multi-level granular information. To address these limitations, we achieve the following: (1) We propose intuitionistic fuzzy covering rough membership and non-membership degrees based on maximal description and construct a new single-granulation model that more effectively captures both the structural relationships among elements and the semantics of fuzzy information. (2) We further extend the model to a multi-granulation framework by defining optimistic and pessimistic approximation operators and analyzing their properties. Additionally, we propose a neutral multi-granulation covering rough intuitionistic fuzzy sets based on aggregated membership and non-membership degrees. Compared with single-granulation models, the multi-granulation models integrate multiple levels of information, allowing for more fine-grained and robust representations of uncertainty. Finally, a case study on real estate investment was conducted to validate the effectiveness of the proposed models. The results show that our models can more precisely represent uncertainty and granularity in complex data, providing a flexible tool for knowledge representation in decision-making scenarios. Full article

To evaluate the efficiency of innovation development in China’s high-tech manufacturing industry, this paper constructs a two-stage dynamic network cross-efficiency model. This model divides innovation activities into two stages: technology research and development and achievement transformation and introduces a 2-year lag period in the technology research and development stage and a 1-year lag period in the achievement transformation stage. It proposes the overall efficiency and efficiency models for each stage. The model was applied to 30 provinces in China, and the results showed that most provinces have achieved relatively ideal results in the overall efficiency and achievement transformation stage of high-tech manufacturing, while the efficiency in the technology research and development stage is generally lower than that in the achievement transformation stage. It is recommended that enterprises increase their R&D investments, break through technological barriers, and optimize the innovation chain. Full article

The right to development, an inherent human right for all, emphasizes that all individuals and communities have the right to participate in, contribute to, and benefit from development that ensures the full realization of human rights. In Kenya, where a significant portion of the population faces poverty and vulnerability to climate change, access to rights-based needs such as clean water, healthcare, and education still remains a critical challenge. This study explored the implementation of a Human Rights-Based approach to community development through a Public–Private Development Partnership model (PPDP), with a focus on alleviating poverty and improving access to rights-based services at the community level in Narok and Nakuru counties. The research aimed to identify critical success factors for scaling the PPDP model and explore its effects on socio-economic empowerment. The study employed a mixed-methods approach for data collection, using questionnaires to obtain quantitative data, focus group discussions, and key informant interviews with community members, local leaders, and stakeholders to gather qualitative data. We cleaned and analyzed all our data in R (version 4.4.3) and used the chi-square to establish the significance of differences between areas where the PPDP model was implemented and control areas where it was not. Results reveal that communities with the PPDP model experienced statistically significant improvements in employment, income levels, and access to rights-based services compared to control areas. The outcomes underscore the potential of the PPDP model to address inclusive and sustainable development. This study therefore proposes a scalable pathway beginning with access to rights-based needs, followed by improved service delivery, and culminating in economic empowerment. These findings offer valuable insights for governments, development practitioners, investment agencies, and researchers seeking community-driven developments in similar socio-economic contexts across Africa. For the first time, it can be adopted in the design and implementation of development projects in rural and local communities across Africa bringing into focus the need to integrate rights-based needs at the core of the project. Full article