Search Results (3,576)

Soil heavy metal contamination poses a serious threat to soil ecosystems and human health. Geochemistry is often used in soil heavy metal contamination research to identify pollution sources, identify elemental cycling mechanisms, and assess the spatial distribution and risk of contamination. However, it is difficult to directly reflect the spatial continuity and deep distribution patterns of contamination. Three-dimensional electrical resistivity tomography (3D ERT) technology often indirectly predicts the distribution of soil contamination by leveraging the electrical structure of the subsurface medium. However, many factors influence this electrical structure, leading to biased predictions. This paper combines geochemistry with 3D ERT technology. A nonlinear statistical model is established based on the geochemical analysis results and resistivity of soil samples. A 3D ERT model is then constructed. This model is used to further investigate the spatial distribution patterns of soil heavy metal contamination and assess the extent of contamination. This study investigated soil sample collection and chemical analysis of heavy metal content at a heavy metal contaminated site in Hunan Province. Antimony contamination was particularly severe in the soil. The 3D ERT data collection and inversion imaging were performed in the soil sample collection area. A 3D ERT model was established to analyze and evaluate the distribution range and extent of antimony contamination in the area. Comparing the antimony content predicted by the model with the actual test data, the results show that the error range is 0.6–16.6%, and the average error is 5.8%. The model has high accuracy, achieving good overall prediction and evaluation results. Full article

In recent years, there has been an ever increasing interest in UxVs and the technology surrounding them. A more recent area of interest within the UxV ecosystem is the development of UxV swarms. In these systems, multiple UxVs synchronize, continuously exchange information, and operate as a cohesive unit. This evolution requires a higher level of autonomy, enhanced coordination, and more efficient communication channels. In this survey, we present relevant research on swarms of UxVs, always considering artificial intelligence (AI) as the key technological enabler for the swarm operations. We view the swarm from three distinct perspectives; these are intelligence-wise, communication-wise, and security-wise. Our main goal is to explore in which ways and to what extent AI has been integrated in these aspects. We aim to identify which of these aspects are the most researched and which need deeper investigation, the types of AI that are mainly used, and which types of vehicles are preferred. We then discuss the results of our work and present current limitations as well as areas of future research in the realm of UxVs, AI, swarm intelligence, communications, and security. Full article

Global supply chains face increasing disruptions from cyber threats, geopolitical instability, extreme weather events, and a range of economic, social, and environmental sustainability challenges. As these disruptions intensify, enhancing Supply Chain Resilience (SCR) has become a strategic priority. This study investigates how Distributed Ledger Technology (DLT) can contribute to SCR by mitigating vulnerabilities and strengthening key capabilities within global supply chains. A qualitative research approach is employed, utilizing expert evaluations to examine DLT’s impact on supply chain vulnerabilities and capabilities. Five workshops were conducted with 25 industry professionals from logistics, IT, procurement, and risk management. Experts examined how DLT could address disruptions stemming from supplier instability, poor traceability, and regulatory and environmental pressures, while highlighting its potential to drive ethical sourcing and environmentally responsible practices. The structured discussions were guided by theoretical frameworks and expert evaluations were synthesized into two analytical matrices illustrating DLT’s influence on SCR. The findings reveal that the contribution of DLT to SCR and sustainability is highly context-dependent, with its effectiveness hinging on how it is embedded within governance structures and aligned with the interplay of complementary technologies. Building on these insights, the study presents the DLT-LFL (Distributed Ledger Technology–Learning Feedback Loop) framework, which integrates sensing, decision-making, adaptation, and predictive learning from distributed operational data, allowing supply chains to better anticipate disruptions, adjust processes dynamically, and continuously strengthen resilience and sustainable practices. The study also develops a practical checklist to assess how effective DLT applications and their integration with predictive and AI-driven analytics reduce vulnerabilities, strengthen capabilities, mitigate risks, and support adaptive decision-making. Full article

Under the combined effects of climate change, overexploitation, and intense grazing, temperate steppe in northern China is experiencing increasing deterioration, which is typified by a shift from structural degradation to functional disruption. Accurately tracking steppe degradation using remote sensing technology has emerged as a crucial scientific concern. Prior research failed to integrate ecosystem structure and function and lacked reference baselines, relying only on individual indicators to quantify degradation. To resolve these gaps, this study established a novel degradation evaluation index system integrating ecosystem structure and function, incorporating vegetation community distribution and proportions of degradation-indicator species to define reference states and quantify degradation severity. Analyzed spatiotemporal evolution and drivers across the temperate typical steppe (2013–2022). Key findings reveal (1) non-degraded and slightly degraded areas dominated (75.57% mean coverage), showing an overall fluctuating improvement trend; (2) minimal transitions between degradation levels, with stable conditions prevailing (59.52% unchanged area), indicating progressive degradation reversal; and (3) natural factors predominated as degradation drivers. The integrated structural–functional framework enables more sensitive detection of early degradation signals, thereby informing more effective steppe restoration management. Full article

The production of apples plays a crucial role in global agriculture. In 2023, the world production of these fruits amounted to nearly 150 million tonnes, cultivated on 6.6 million ha. Today’s horticulture faces the difficult challenge of maintaining high productivity while simultaneously reducing negative environmental impact. Traditional methods based on chemical pesticides encounter increasing problems, such as biodiversity loss, toxic residues in food, development of pest resistance, and disrupted balance of ecosystems. Integrated Pest Management (IPM) responds to these challenges by combining biological and agrotechnical methods with selective use of chemicals. Biopesticides are a crucial component of IPM, and they include antagonist microorganisms, substances of natural origin, and other biological methods of control, which represent effective alternatives to conventional measures. Their development is driven by consumer requirements concerning food safety, as well as by the need to protect the environment. The aim of this article is to highlight current problems in apple production, describe microorganisms and natural substances used as biopesticides used for the protection of apple orchards, as well as present the characteristics of modern technologies used for biocontrol in apple orchards. Full article

Entrepreneurship plays a significant role in the economic development of emerging economies, particularly by addressing persistent issues such as youth unemployment and growth challenges. Developing nations perceive their startup ecosystems as critical engines of economic progress. Policymakers in these countries strive to reduce uncertainties and mitigate risks that could impede the growth of this essential sector. However, they face a significant obstacle: the lack of accurate and reliable data necessary to comprehend the challenges and requirements of the startup ecosystem. To effectively navigate these challenges, policymakers must utilize advanced analytical tools and technologies, including big data analytics, artificial intelligence, and machine learning. These technologies are crucial for the comprehensive collection and analysis of data from diverse sources. This research aims to identify current trends and challenges within the startup ecosystem in developing countries through the meticulous collection and analysis of news data on the topic. To achieve this objective, we developed a detailed plan to collect news data on Iran’s startup ecosystem spanning from 2017 to 2022. By employing advanced natural language processing techniques, we intended to conduct a thorough analysis of the collected data. Our goal is to extract significant insights that will inform and shape effective policymaking. Full article

This paper presents a pioneering and novel analysis of the synergistic relationship between China’s leadership in electric vehicle (EV) adoption and the rapid advancement of autonomous driving (AD) technologies within the nation’s mobility ecosystem. Challenging the conventional view of electrification as a parallel trend, this study introduces a new perspective by demonstrating how EV infrastructure serves as a fundamental enabler of autonomy, providing the necessary high-voltage architectures for critical AD functions like real-time sensor fusion and over-the-air updates. In doing so, it addresses the central research question: How does large-scale electrification influence the architecture, deployment, and safety development of autonomous driving vehicles, particularly in the context of China’s intelligent mobility ecosystem? Through technical analysis and industry examples, the paper offers original contributions by illustrating how EV-driven platforms overcome the inherent limitations of internal combustion engine systems, enhancing autonomous execution and system reliability. Furthermore, this research provides novel insights into China’s unique public–private innovation ecosystem, highlighting the role of vertically integrated startups and cross-sector coordination in driving AD development. By analyzing these previously overlooked systemic interactions, the paper posits that China’s EV dominance strategically amplifies its autonomous vehicle ambitions, positioning the nation to lead the next generation of intelligent transportation systems. Full article

This systematic review examines the role of rural tourism in promoting sustainable development, focusing on its interaction with forest ecosystems and the essential ecosystem services they provide. A comprehensive literature search across Scopus, PubMed, and Google Scholar identified 142 peer-reviewed articles, analyzed through qualitative synthesis and bibliometric mapping. The review highlights four thematic clusters in rural tourism research: impacts on rural areas, destination management, resident perspectives and cultural sustainability, and emerging themes like place attachment. It emphasizes the reliance of rural tourism on ecosystem services, including provisioning, regulating, cultural, and supporting, especially those linked to forest ecosystems. Examples from Monteverde, Costa Rica, and Tuscany, Italy, illustrate the role of rural tourism in supporting biodiversity conservation, habitat restoration, and sustainable agriculture. However, uncontrolled tourism in forested regions can lead to deforestation and ecosystem degradation, as seen in the Lake District, Masai Mara, and Rajasthan. The review stresses the need for sustainable practices to mitigate the negative impacts of tourism, advocating for an integrated sustainability framework that balances economic, environmental, and governance aspects. Best practices include eco-friendly infrastructure, community participation, and environmental education. The potential of emerging technologies, such as eco-certification systems and smart tourism, is explored to reduce the environmental footprint of tourism. The review calls for stronger policy integration, equitable benefit-sharing, capacity building, and longitudinal research to ensure resilient rural tourism that harmonizes ecosystem conservation with socio-economic development. In conclusion, the integration of sustainable practices and community involvement is crucial for aligning rural tourism with forest ecosystem conservation. Full article

This study develops a systemic framework to conceptualize digital organizational resilience in micro, small, and medium-sized enterprises (MSMEs) as an emergent property of entangled socio-technical systems. Building on theories of distributed cognition, sociomateriality, and resilience engineering, this paper argues that resilience does not reside in isolated elements—such as leadership, technologies, or procedures—but in their dynamic interplay. Four interdependent dimensions—human, technological, organizational, and institutional—are identified as constitutive of resilience capacities. The research design is conceptual and exploratory in nature. Two theory-driven conceptual statements are formulated: first, that natural language mediation in human–machine interaction enhances coordination and adaptability; and second, that distributed cognition and prototyping practices strengthen collective problem-solving and adaptive capacity. These conceptual statements are not statistically tested but serve as conceptual anchors for the model and as guiding directions for future empirical studies. Empirical illustrations from Ecuadorian MSMEs ground the framework in practice. The evidence highlights three insights: (1) structural fragility, as micro and small firms dominate the economy but face high mortality and financial vulnerability; (2) uneven digitalization, with limited adoption of BPM, ERP, and AI due to skill and resource constraints; and (3) disproportionate gains from modest interventions, such as optimization models or collaborative prototyping. This study contributes to organizational theory by positioning MSMEs as socio-technical ecosystems, providing a conceptual foundation for future empirical validation. Full article

Lower respiratory tract infections (LRTIs) represent a significant global health concern, and the accurate identification of pathogens is crucial for patient care. Culture-based methods are the gold standard, but their detection abilities are limited. Next-generation sequencing (NGS) offers a promising method for comprehensive microbial detection, providing valuable information for clinical practice. In this study, 144 bronchoalveolar lavage fluid samples were collected, culture-based diagnostics were performed, and bacterial microbiome profiles were generated by short-read sequencing of the V4 region of the 16S rRNA gene using Illumina technologies and long-read sequencing with Oxford Nanopore Technologies (ONT) to determine the full-length 16S rRNA gene. The most common genera detected by NGS included Streptococcus, Staphylococcus, Veillonella, Prevotella, Rothia, Enterococcus, and Haemophilus. Short-read sequencing detected cultured bacteria at the genus level in ~85% of cases, while long-read sequencing demonstrated agreement with cultured species in ~62% of cases. In three cases, long-read sequencing identified the uncommon potential lung pathogen Tropheryma whipplei not detected with traditional culturing techniques. The NGS results showed a partial overlap with culture as the current diagnostic gold standard in LRTI. Additionally, NGS detected a broader spectrum of bacteria, revealed fastidious potential pathogens, and offered deeper insights into the complex microbial ecosystem of the lungs. Full article

This study analyzes the impact of research and development (R&D) investment on economic growth in Panama, an emerging economy with structural challenges in its innovation system. Using a multivariate econometric approach that included elastic net regularization and fixed-effect panel data estimation, the analysis incorporated key explanatory variables such as public education expenditure, inflation, infrastructure investment, population growth, and exports. The results indicated that both R&D and education spending have a positive and statistically significant effect on GDP growth, while inflation has a negative impact and exports show no significant effect. To ensure robustness, the study applied the augmented Dickey–Fuller test for stationarity, nonparametric bootstrapping (1000 replications), and multiple diagnostic tests, including RMSE, adjusted R², Durbin–Watson statistic, and White’s test. Scenario-based projections suggest that gradual and sustained increases in R&D investment, supported by stronger institutional coordination and absorptive capacity, could enhance Panama’s long-term productivity and innovation outcomes. The findings underscore that improving R&D funding alone is not sufficient; effective governance and coherent science, technology, and innovation (STI) policies are essential. This research contributes empirical evidence to a relatively underexplored area in the development literature and offers strategic insights for policymakers seeking to build more integrated and sustainable STI ecosystems in emerging economies. Full article

Blockchain technology has rapidly evolved beyond cryptocurrencies, underpinning diverse applications such as supply chains, healthcare, and finances, yet its security vulnerabilities remain a critical barrier to safe adoption. However, attackers increasingly exploit weaknesses in consensus protocols, smart contracts, and network layers with threats such as Denial-of-Chain (DoC) and Black Bird attacks, posing serious challenges to blockchain ecosystems. We conducted anomaly detection using two independent datasets (A and B) generated from simulation attack scenarios including hash rate, Sybil, Eclipse, Finney, and Denial-of-Chain (DoC) attacks. Key blockchain metrics such as hash rate, transaction authorization status, and recorded attack consequences were collected for analysis. We compared both class-balanced and imbalanced datasets, applying Synthetic Minority Oversampling Technique (SMOTE) to improve representation of minority-class samples and enhance performance metrics. Supervised models such as Random Forest, Gradient Boosting, and Logistic Regression consistently outperformed unsupervised models, achieving high F1-scores (0.90), while balancing the training data had only a modest effect. The results are based on simulated environment and should be considered as preliminary until the experiment is performed in a real blockchain environment. Based on identified gaps, we recommend the exploration and development of multifaceted defense approaches that combine prevention, detection, and response to strengthen blockchain resilience. Full article

Blockchain technologies have profoundly transformed information systems by providing decentralized infrastructures that enhance transparency, security, and traceability. Ethereum, in particular, supports smart contracts and facilitates the development of decentralized finance (DeFi), non-fungible tokens (NFTs), and Web3 applications. However, its openness also enables illicit activities, including fraud and money laundering, through anonymous wallets. Identifying wallets involved in large transfers or abnormal transactional patterns is therefore critical to ecosystem security. This study proposes an AI-based framework employing XGBoost, LightGBM, and CatBoost to detect suspicious Ethereum wallets, achieving test accuracies between 95.83% and 96.46%. The system provides near real-time predictions for individual or recent wallet addresses using a pre-trained XGBoost model. To improve interpretability, SHAP (SHapley Additive exPlanations) visualizations are integrated, highlighting the contribution of each feature. The results demonstrate the effectiveness of AI-driven methods in monitoring and securing Ethereum transactions against fraudulent activities. Full article

The NewEcoSmart project addresses the need to foster inclusive green and digital transitions in rural habitat sectors by systematically assessing local socio-technical readiness and tailoring capacity-building interventions. We adapted the validated SCIROCCO Exchange Maturity Self-Assessment Tool—selecting eight dimensions relevant to environmental, technological and social innovation—and conducted a two-phase evaluation across three pilot sites in Italy, Portugal and Spain. Phase 1 mapped stakeholder evidence against predefined criteria; Phase 2 engaged local actors (45+ adults, SMEs and micro-firms) in a self-assessment to determine digital, green and entrepreneurial skill gaps. For each domain of the SCIROCCO Tool, local actors can assign a minimum of 0 to a maximum of 5. The final score of the SCIROCCO tool can be a minimum of 0 to a maximum of 40. Quantitative maturity scores revealed heterogeneous profiles (Pacentro and Majella Madre = 5; Yecla = 10; Adelo Area = 23), underscoring diverse ecosystem strengths and limitations. A qualitative analysis, framed by Smart Healthy Age-Friendly Environments (SHAFE) domains, identified emergent training needs that are clustered at three levels: MACRO (community-wide awareness and engagement), MESO (decision-maker capacity for strategic planning and governance) and MICRO (industry-specific practical skills). The adapted SCIROCCO tool effectively proposes the assessment of socio-technical maturity in rural contexts and guides the design of a modular, multi-layered training framework. These findings support the need for scalable deployment of interventions that are targeted to the maturity of the local ecosystems to accelerate innovations through equitable green and digital transformations in complex socio-cultural settings. Full article

The dairy sector produces considerable amounts of nutrient-rich effluents, which are frequently undervalued as simple by-products or waste. In particular, Second Cheese Whey (SCW), also known as scotta, exhausted whey, or deproteinized whey, represents the liquid fraction from ricotta cheese production. Despite its abundance and high organic and saline content, SCW is often improperly discharged into terrestrial and aquatic ecosystems, causing both environmental impact and resource waste. The available purification methods are expensive for dairy companies, and, at best, SCW is reused as feed or fertilizer. In recent years, increasing awareness of sustainability and circular economy principles has increased interest in the valorization of SCW. Biological treatment of SCW using microalgae represents an attractive strategy, as it simultaneously reduces the organic load and converts waste into algal biomass. This biomass can be further valorized as a source of proteins, pigments, and bioactive compounds with industrial relevance, supporting applications in food, nutraceuticals, biofuels, and cosmetics. This review, starting from analyzing the characteristics, production volumes, and environmental issues associated with SCW, focused on the potential of microalgae application for their valorization. In addition, the broader regulatory and sustainability aspects related to biomass utilization and treated SCW are considered, highlighting both the promises and limitations of microalgae-based strategies by integrating technological prospects with policy considerations. Full article