Search Results (22,860)

Despite considerable advancements in integrating the Internet of Things (IoT) and artificial intelligence (AI) within the industrial maintenance framework, the increasing reliance on these innovative technologies introduces significant vulnerabilities due to cybersecurity risks, potentially compromising the integrity of decision-making processes. Accordingly, this study aims to offer comprehensive insights into the cybersecurity challenges associated with predictive maintenance, proposing a novel methodology that leverages generative AI for data augmentation, enhancing threat detection capabilities. Experimental evaluations conducted using the NASA Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset affirm the viability of this approach leveraging the state-of-the-art TimeGAN model for temporal-aware data generation and building a recurrent classifier for attack discrimination in a balanced dataset. The classifier’s results demonstrate the satisfactory and robust performance achieved in terms of accuracy (between 80% and 90%) and how the strategic generation of data can effectively bolster the resilience of intelligent maintenance systems against cyber threats. Full article

Leakages in water distribution networks still pose major challenges to water utilities. Despite numerous technological advances, the adoption of digital leakage detection technology remains a slow process. Here, we present the project iOLE—intelligent Online LEakage detection, where we aim to increase the applicability of automated leak detection in practice through enhanced user experience and detection robustness. iOLE employs a human-centered design approach that involves the feedback of potential users during its development process to maximize subsequent user acceptance. To this end, we design a graphical user interface, combine model-based and data-driven leakage detection, and conduct a comprehensive robustness analysis. Full article

This review provides a detailed analysis of zero waste (ZW) initiatives, focusing on national policies, strategies, and case studies aimed at minimising municipal solid waste (MSW). It evaluates the environmental, social, and economic impacts of waste and explores the transition from conventional landfill reliance to sustainable waste management practices. Key ZW approaches, including circular economy frameworks and extended producer responsibility (EPR), are examined through case studies from countries such as China, Germany, and the United States. The review highlights advancements in waste-to-energy (WTE) technologies, the development of zero waste cities, and the critical role of policies in achieving significant MSW reduction. Additionally, it identifies key challenges such as infrastructure gaps and regulatory weaknesses and offers practical solutions to overcome these barriers. This study serves as a valuable resource for policymakers aiming to implement effective waste reduction strategies and enhance sustainable waste management systems globally. Full article

This study explores the application of artificial intelligence, specifically ChatGPT-4o, in constructing and managing a portfolio of cybersecurity stocks over the period from Q1 2018 to Q1 2024. Leveraging advanced machine learning models, fundamental analysis, sentiment analysis, and optimization techniques, the AI-driven portfolio significantly outperformed leading cybersecurity ETFs, as well as broader market indices such as the Nasdaq 100 (QQQ) and S&P 500 (SPY). The methodology employed included data collection, stock filtering, predictive modeling using Random Forests and Support Vector Machines (SVMs), sentiment analysis through natural language processing (NLP), and portfolio optimization using Mean-Variance Optimization (MVO), with quarterly rebalancing to ensure responsiveness to evolving market conditions. The AI-selected portfolio achieved a total return of 273%, with strong risk-adjusted performance as demonstrated by key metrics such as the Sharpe ratio, highlighting the effectiveness of an AI-based approach in navigating market complexities and generating superior returns. The results of this study indicate that AI-driven portfolio management can uncover investment opportunities that traditional methods may overlook, offering a competitive edge in the cybersecurity sector and promising enhanced predictive accuracy, efficiency, and overall investment success as AI technologies continue to evolve. Full article

The Brassica oleracea L. species embrace important horticultural crops, such as broccoli, cauliflower, and cabbage, which are highly valued for their beneficial nutritional effects. However, the complexity of flower emasculation in these species has forced breeders to adopt biotechnological approaches such as somatic hybridization to ease hybrid seed production. Protoplasts entail a versatile tool in plant biotechnology, supporting breeding strategies that involve genome editing and hybridization. This review discusses the use of somatic hybridization in B. oleracea L. as a biotechnological method for developing fusion products with desirable agronomic traits, particularly cytoplasmic male sterile (CMS) condition. These CMS lines are critical for implementing a cost-effective, efficient, and reliable system for producing F1 hybrids. We present recent studies on CMS systems in B. oleracea L. crops, providing an overview of established models that explain the mechanisms of CMS and fertility restoration. Additionally, we emphasize key insights gained from protoplast fusion applied to B. oleracea L. breeding. Key steps including pre-treatments of donor plants, the main tissues used as sources of parental protoplasts, methods for obtaining somatic hybrids and cybrids, and the importance of establishing a reliable plant regeneration method are discussed. Finally, the review explores the incorporation of genome editing technologies, such as CRISPR-Cas9, to introduce multiple agronomic traits in Brassica species. This combination of advanced biotechnological tools holds significant promise for enhancing B. oleracea breeding programs in the actual climate change context. Full article

Whether digital village construction can effectively promote agriculture’s green development is essential for modernizing agriculture and rural areas. Using panel data from 30 provinces in China between 2011 and 2022, this study empirically examines the relationship between digital village construction and agroecological efficiency and explores its mechanism of action and threshold effect, contributing to the exploration of agricultural digitization and sustainable development. This study shows that (1) AEE is positively associated with digital village construction; (2) the positive association size varies in regions and construction levels; (3) agricultural land transfer and technological innovation play a mediating role in the positive effect; and (4) there is a single threshold value for the positive effect of digital village construction, and after crossing the threshold value, its marginal effect shows a positive and increasing nonlinear characteristic. This study enhances our comprehension of digital village development to advance agroecological efficiency and offers theoretical insights and policy recommendations for optimizing the rural digital infrastructure and fostering sustainable agricultural growth. Full article

Entrepreneurship has gained significant relevance in contemporary societies due to its role in generating economic and social value, including job creation, new businesses, and technological and social innovations. Scientific interest in entrepreneurship, which dates back to the 17th century, has increased since the 1990s. This field of study has evolved to encompass not only strict business creation but also impactful social initiatives. This article explores the intersection of academic and social entrepreneurship, examining factors to understand impactful initiatives through the seminal ideas presented by Joseph Schumpeter. The text offers insights and recommendations for advancing the transdisciplinary study of academic social entrepreneurship starting from an Economic Sociology perspective. Full article

Using provincial panel data from China spanning 2011 to 2022, this paper analyzes the impact, mechanisms, and regional differences in digital trade’s effects on regional green innovation. It also explores the threshold effect between digital trade and green innovation, with environmental regulation serving as the threshold variable. The results indicate the following: first, after accounting for government intervention, foreign direct investment, human capital, industrialization, information technology infrastructure, and economic development, digital trade significantly promotes regional green innovation. This conclusion remains valid after a series of robustness tests. Second, digital trade promotes regional green innovation through three mechanisms: accelerating industrial structure upgrading, promoting industrial agglomeration, and enhancing technology transfer. Third, environmental regulation leads to a non-linear relationship between digital trade and green innovation. Higher levels of environmental regulation make digital trade’s contribution to green innovation more significant. Finally, the effects of digital trade on green innovation vary by region in China. This impact is more pronounced in eastern provinces, regions with advanced digital economies, areas with well-developed transport infrastructure, and provinces with a higher degree of trade openness. These findings hold substantial implications for advancing green innovation and promoting sustainable social development in China. Full article

The digital economy (DE) is characterized by invention, low energy consumption, cross-sector integration, and open sharing. It can effectively enhance social production methods, influence consumer behavior, and provide new pathways to enhance total factor energy efficiency (TFEE). This paper studies 280 Chinese cities, employing the entropy method and data envelopment analysis (DEA) model to evaluate and analyze urban DE and TFEE. It also constructs a system generalized method of moments model (SGMM model) and a threshold regression model (TR model) to examine the impact of the DE on TFEE in China. The main study findings include the following: (1) The regression results of the SGMM model indicate that the effect of DE on TFEE in Chinese cities shows a U-shaped trend. (2) The regression results of the TR model further confirm a U-shaped association connecting DE and TFEE, with the threshold estimated at 0.304. (3) The economic factors and industrial structure have a major impact on inhibiting the improvement of TFEE, whereas technological advancements and environmental regulations significantly facilitate its improvement. Full article

In recent years, the rapid growth of e-commerce and on-demand delivery services has placed a significant strain on urban logistics systems. Technological advances such as unmanned aerial vehicle (UAV)-based logistics systems have thus emerged as promising solutions in urban environments and are increasingly being piloted worldwide. However, the implementation of UAV logistics risks exacerbating social inequities, particularly in marginalized communities that may disproportionately bear the noise and safety risks. To mitigate these risks, it is crucial to integrate social equity considerations into urban UAV logistics. This study explores social equity factors through a systematic literature review and social media analysis of Xiaohongshu (the Little Red Book), a popular Chinese social media platform known for its extensive user base and active discussions on social issues. This literature review involves a full-text examination, while latent Dirichlet allocation (LDA) topic modeling is used to analyze social media comment datasets. Each method identifies social equity factors and separately assesses their relative importance, resulting in the final identification of 24 key factors that provide a holistic view of public sentiment and academic discourse. The findings reveal a divide between academic concerns around systemic risks and a public focus on immediate needs. By synthesizing these insights, this study provides a social equity landscape for urban UAV logistics and actionable references for policymakers and stakeholders. Full article

Recognizing and classifying localized lesions on apple fruit surfaces during automated sorting is critical for improving product quality and increasing the sustainability of fruit production. This study is aimed at developing sustainable methods for fruit sorting by applying hyperspectral analysis and machine learning to improve product quality and reduce losses. The employed hyperspectral technologies and machine learning algorithms enable the rapid and accurate detection of defects on the surface of fruits, enhancing product quality and reducing the number of rejects, thereby contributing to the sustainability of agriculture. This study seeks to advance commercial fruit quality control by comparing hyperspectral image classification algorithms to detect apple lesions caused by pathogens, including sunburn, scab, and rot, on three apple varieties: Honeycrisp, Gala, and Jonagold. The lesions were confirmed independently using expert judgment, real-time PCR, and 3D fluorimetry, providing a high accuracy of ground truth data and allowing conclusions to be drawn on ways to improve the sustainability and safety of the agrocenosis in which the fruits are grown. Hyperspectral imaging combined with mathematical analysis revealed that Venturia inaequalis is the main pathogen responsible for scab, while Botrytis cinerea and Penicillium expansum are the main causes of rot. This comparative study is important because it provides a detailed analysis of the performance of both supervised and unsupervised classification methods for hyperspectral imagery, which is essential for the development of reliable automated grading systems. Support Vector Machines (SVM) proved to be the most accurate, with the highest average adjusted Rand Index (ARI) scores for sunscald (0.789), scab (0.818), and rot (0.854), making it the preferred approach for classifying apple lesions during grading. K-Means performed well for scab (0.786) and rot (0.84) classes, but showed limitations with lower metrics for other lesion types. A design and technological scheme of an optical system for identifying micro- and macro-damage to fruit tissues is proposed, and the dependence of the percentage of apple damage on the rotation frequency of the sorting line rollers is obtained. The optimal values for the rotation frequency of the rollers, at which the damage to apples is less than 5%, are up to 6 Hz. The results of this study confirm the high potential of hyperspectral data for the non-invasive recognition and classification of apple diseases in automated sorting systems with an accuracy comparable to that of human experts. These results provide valuable insights into the optimization of machine learning algorithms for agricultural applications, contributing to the development of more efficient and accurate fruit quality control systems, improved production sustainability, and the long-term storage of fruits. Full article

Atmospheric visibility is a crucial meteorological element impacting urban air pollution monitoring, public transportation, and military security. Traditional visibility detection methods, primarily manual and instrumental, have been costly and imprecise. With advancements in data science and computing, deep learning-based visibility detection technologies have rapidly emerged as a research hotspot in atmospheric science. This paper systematically reviews the applications of various deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformer networks—in visibility estimation, prediction, and enhancement. Each model’s characteristics and application methods are discussed, highlighting the efficiency of CNNs in spatial feature extraction, RNNs in temporal tracking, GANs in image restoration, and Transformers in capturing long-range dependencies. Furthermore, the paper addresses critical challenges in the field, including dataset quality, algorithm optimization, and practical application barriers, proposing future research directions, such as the development of large-scale, accurately labeled datasets, innovative learning strategies, and enhanced model interpretability. These findings highlight the potential of deep learning in enhancing atmospheric visibility detection techniques, providing valuable insights into the literature and contributing to advances in the field of meteorological observation and public safety. Full article

In the face of growing challenges in modern agriculture, such as climate change, sustainable resource management, and food security, drones are emerging as essential tools for transforming precision agriculture. This systematic review, based on an in-depth analysis of recent scientific literature (2020–2024), provides a comprehensive synthesis of current drone applications in the agricultural sector, primarily focusing on studies from this period while including a few notable exceptions of particular interest. Our study examines in detail the technological advancements in drone systems, including innovative aerial platforms, cutting-edge multispectral and hyperspectral sensors, and advanced navigation and communication systems. We analyze diagnostic applications, such as crop monitoring and multispectral mapping, as well as interventional applications like precision spraying and drone-assisted seeding. The integration of artificial intelligence and IoTs in analyzing drone-collected data is highlighted, demonstrating significant improvements in early disease detection, yield estimation, and irrigation management. Specific case studies illustrate the effectiveness of drones in various crops, from viticulture to cereal cultivation. Despite these advancements, we identify several obstacles to widespread drone adoption, including regulatory, technological, and socio-economic challenges. This study particularly emphasizes the need to harmonize regulations on beyond visual line of sight (BVLOS) flights and improve economic accessibility for small-scale farmers. This review also identifies key opportunities for future research, including the use of drone swarms, improved energy autonomy, and the development of more sophisticated decision-support systems integrating drone data. In conclusion, we underscore the transformative potential of drones as a key technology for more sustainable, productive, and resilient agriculture in the face of global challenges in the 21st century, while highlighting the need for an integrated approach combining technological innovation, adapted policies, and farmer training. Full article

Millets are recognized as future foods due to their abundant nutrition and resilience, increasing their value on the global stage. Millets possess a broad spectrum of nutrients, antinutrients, and antioxidants, making it imperative to understand the effects of various processing methods on these components. Antinutritional factors interfere with the digestibility of macro-nutrients and the bioavailability and bio accessibility of minerals. This necessitates methods to reduce or eliminate antinutrients while improving nutritive and antioxidant value in food. This review aims to elucidate the rationale behind processing choices by evaluating the scientific literature and examining the mechanisms of processing methods, categorized as physiochemical, bio, thermal, novel non-thermal, and their combination techniques. Physiochemical and bioprocessing methods alter antinutrients and antioxidant profiles through mass transfer, enzyme activation, product synthesis, microbial activity, and selective removal of grain layers. Thermal methods break functional bonds, modify the chemical or physical structures, enhance kinetics, or degrade heat-labile components. Non-thermal techniques preserve heat-sensitive antioxidants while reducing antinutrients through structural modifications, oxidation by ROS, and break down the covalent and non-covalent bonds, resulting in degradation of compounds. To maximize the trade-off between retention of beneficial components and reducing detrimental ones, exploring the synergy of combination techniques is crucial. Beyond mitigating antinutrients, these processing methods also stimulate the release of bioactive compounds, including phenolics, flavonoids, and peptides, which exhibit potent health-promoting properties. This review underscores the transformative potential of processing technologies in enhancing millets as functional ingredients in modern diets, promoting health and advancing sustainable food practices. Full article

This study applies UAV-based photogrammetry to map and examine the stratigraphy and archaeological artifact distribution in two localities within the Post research area in northwest Texas. A DJI Inspire 1 UAV equipped with a Zenmuse X5 camera captured nadir and oblique images. These were processed using Agisoft Metashape to generate 3D models. These models enabled the precise mapping of stratigraphic boundaries, revealing the distinctions between Triassic-age bedrock, Pleistocene-age alluvial deposits, and Holocene-age aeolian sediments. Field surveys from 2022 to 2024 documented over 5000 artifacts with sub-centimeter accuracy, including diagnostic projectile points and ceramics. This research highlights the advantages of UAV-derived 3D models in rapidly and accurately documenting stratigraphy and archaeological data. It demonstrates the value of UAV technology for visualizing landscape-scale processes and artifact contexts, offering a new approach to understanding the interactions between geomorphology and archaeology. The findings contribute to advancing UAV applications in both geomorphological and archaeological research. Full article