Search Results (2,104)

Grapevines (Vitis vinifera L.) are one of the most economically relevant crops worldwide, yet they are highly vulnerable to various diseases, causing substantial economic losses for winegrowers. This systematic review evaluates the application of remote sensing and proximal tools for vineyard disease detection, addressing current capabilities, gaps, and future directions in sensor-based field monitoring of grapevine diseases. The review covers 104 studies published between 2008 and October 2024, identified through searches in Scopus and Web of Science, conducted on 25 January 2024, and updated on 10 October 2024. The included studies focused exclusively on the sensor-based detection of grapevine diseases, while excluded studies were not related to grapevine diseases, did not use remote or proximal sensing, or were not conducted in field conditions. The most studied diseases include downy mildew, powdery mildew, Flavescence dorée, esca complex, rots, and viral diseases. The main sensors identified for disease detection are RGB, multispectral, hyperspectral sensors, and field spectroscopy. A trend identified in recent published research is the integration of artificial intelligence techniques, such as machine learning and deep learning, to improve disease detection accuracy. The results demonstrate progress in sensor-based disease monitoring, with most studies concentrating on specific diseases, sensor platforms, or methodological improvements. Future research should focus on standardizing methodologies, integrating multi-sensor data, and validating approaches across diverse vineyard contexts to improve commercial applicability and sustainability, addressing both economic and environmental challenges. Full article

The Solanaceae family, which includes vital crops such as tomatoes, peppers, eggplants, and potatoes, is increasingly impacted by drought due to climate change. Recent research has concentrated on unraveling the molecular mechanisms behind drought resistance in these crops, with a focus on abscisic acid (ABA) signaling pathways, transcription factors (TFs) like MYB (Myeloblastosis), WRKY (WRKY DNA-binding protein), and NAC (NAM, ATAF1/2, and CUC2- NAM: No Apical Meristem, ATAF1/2, and CUC2: Cup-shaped Cotyledon), and the omics approaches. Moreover, transcriptome sequencing (RNA-seq) has been instrumental in identifying differentially expressed genes (DEGs) crucial for drought adaptation. Proteomics studies further reveal changes in protein expression under drought conditions, elucidating stress response mechanisms. Additionally, microRNAs (miRNAs) have been identified as key regulators in drought response. Advances in proteomics and transcriptomics have highlighted key proteins and genes that respond to drought stress, offering new insights into drought tolerance. To address the challenge of drought, future research should emphasize the development of drought-resistant varieties through precision breeding techniques such as gene editing, marker-assisted selection (MAS), and the integration of artificial intelligence. Additionally, the adoption of environmentally sustainable cultivation practices, including precision irrigation and the use of anti-drought agents, is crucial for improving water-use efficiency and crop resilience. International collaboration and data sharing will be essential to accelerate progress and ensure global food security in increasingly arid conditions. These efforts will enable Solanaceae crops to adapt the challenges posed by climate change, ensuring their productivity and sustainability. Full article

Intelligent ships are a key focus for the future development of maritime transportation, relying on efficient decision-making and autonomous control within complex environments. To enhance the perception, prediction, and decision-making capabilities of these ships, the present study proposes a novel approach for constructing a time-series knowledge graph, utilizing real-time Automatic Identification System (AIS) data analyzed via a sliding window technique. By integrating advanced technologies such as knowledge extraction, representation learning, and semantic fusion, both static and dynamic navigational data are systematically unified within the knowledge graph. The study specifically targets the extraction and modeling of critical events, including variations in ship speed, course changes, vessel encounters, and port entries and exits. To evaluate the urgency of encounters, mathematical algorithms are applied to the Distance to Closest Point of Approach (DCPA) and Time to Closest Point of Approach (TCPA) metrics. Furthermore, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is employed to identify suitable docking berths. Additionally, multi-source meteorological data are integrated with ship dynamic data, providing a more comprehensive representation of the maritime environment. The resulting knowledge system effectively combines ship attributes, navigational status, event relationships, and environmental factors, thereby offering a robust framework for supporting intelligent ship operations. Full article

To face the increasing requirement for grains as the global population continues to grow, improving both crop yield and quality has become essential. Plant health directly impacts crop quality and yield, making the development of plant health-monitoring technologies essential. Variable sensing technologies for outdoor/indoor farming based on different working principles have emerged as important tools for monitoring plants and their microclimates. These technologies can detect factors such as plant water content, volatile organic compounds (VOCs), and hormones released by plants, as well as environmental conditions like humidity, temperature, wind speed, and light intensity. To achieve comprehensive plant health monitoring for multidimensional assessment, multimodal sensors have been developed. Non-invasive monitoring approaches are also gaining attention, leveraging biocompatible and flexible sensors for plant monitoring without interference with its natural growth. Furthermore, wireless data transmission is crucial for real-time monitoring and efficient farm management. Reliable power supplies for these systems are vital to ensure continuous operation. By combining wearable sensors with intelligent data analysis and remote monitoring, modern agriculture can achieve refined management, resource optimization, and sustainable production, offering innovative solutions to global food security and environmental challenges. Full article

Livestock behavior serves as a crucial indicator of physiological health. Leveraging deep learning techniques to automatically recognize dairy goat behaviors, particularly abnormal ones, enables early detection of potential health and environmental issues. To address the challenges of recognizing small-target behaviors in complex environments, a multi-scale and lightweight behavior recognition model for dairy goats called GSCW-YOLO was proposed. The model integrates Gaussian Context Transformation (GCT) and the Content-Aware Reassembly of Features (CARAFE) upsampling operator, enhancing the YOLOv8n framework’s attention to behavioral features, reducing interferences from complex backgrounds, and improving the ability to distinguish subtle behavior differences. Additionally, GSCW-YOLO incorporates a small-target detection layer and optimizes the Wise-IoU loss function, increasing its effectiveness in detecting distant small-target behaviors and transient abnormal behaviors in surveillance videos. Data for this study were collected via video surveillance under varying lighting conditions and evaluated on a self-constructed dataset comprising 9213 images. Experimental results demonstrated that the GSCW-YOLO model achieved a precision of 93.5%, a recall of 94.1%, and a mean Average Precision (mAP) of 97.5%, representing improvements of 3, 3.1, and 2 percentage points, respectively, compared to the YOLOv8n model. Furthermore, GSCW-YOLO is highly efficient, with a model size of just 5.9 MB and a frame per second (FPS) of 175. It outperforms popular models such as CenterNet, EfficientDet, and other YOLO-series networks, providing significant technical support for the intelligent management and welfare-focused breeding of dairy goats, thus advancing the modernization of the dairy goat industry. Full article

Against the background of the dual challenges of global energy demand growth and environmental protection, this paper focuses on the study of microgrid optimization and scheduling technology and constructs a smart microgrid system integrating energy production, storage, conversion, and distribution. By integrating high-precision load forecasting, dynamic power allocation algorithms, and intelligent control technologies, a microgrid scheduling model is proposed. This model simultaneously considers environmental protection and economic efficiency, aiming to achieve the optimal allocation of energy resources and maintain a dynamic balance between supply and demand. The goose optimization algorithm (GO) is innovatively introduced and improved, enhancing the algorithm’s ability to use global search and local fine search in complex optimization problems by simulating the social aggregation of the goose flock, the adaptive monitoring mechanism, and the improved algorithm, which effectively avoids the problem of the local optimal solution. Meanwhile, the combination of super-Latin stereo sampling and the K-means clustering algorithm improves the data processing efficiency and model accuracy. The results demonstrate that the proposed model and algorithm effectively reduce the operating costs of microgrids and mitigate environmental pollution. Using the improved goose algorithm (IGO), the combined operating and environmental costs are reduced by 16.15%, confirming the model’s effectiveness and superiority. Full article

A fresh paradigm for classifying current studies on flood management systems is proposed in this review. The literature has examined methods for managing different flood management activities from a variety of fields, such as machine learning, image processing, data analysis, and remote sensing. Prediction, detection, mapping, evacuation, and relief efforts are all part of flood management. This can be improved by adopting state-of-the-art tools and technology. Preventing floods and ensuring a prompt response after floods is crucial to ensuring the lowest number of fatalities as well as minimizing environmental and financial damages. The following noteworthy research questions are addressed by the framework: (1) What are the main methods used in flood control? (2) Which stages of flood management are the majority of research currently in existence focused on? (3) Which systems are being suggested to address issues with flood control? (4) In the literature, what are the research gaps regarding the use of technology for flood management? To classify the many technologies that have been studied, a framework for classification has been provided for flood management. It was found that there were few hybrid models for flood control that combined machine learning and image processing. Furthermore, it was discovered that there was little use of machine learning-based techniques in the aftermath of a disaster. To provide efficient and comprehensive disaster management, future efforts must concentrate on integrating image processing methods, machine learning technologies, and the understanding of disaster management across all phases. The study has proposed the use of Generative Artificial Intelligence. Full article

This study aims to develop an efficient recovery solution for waste transformer insulating oil, addressing the challenge of incomplete separation of residual oil in existing recovery technologies. A multi-module integrated system is constructed, comprising a waste oil extraction module, a residual oil vaporization module, an exhaust gas treatment module, and an online monitoring module. By combining steps such as oil extraction, residual oil absorption, hot air circulation heating, and negative-pressure low-frequency induction heating, the complete recovery of waste oil is achieved. The recovery process incorporates oil–gas saturation monitoring and an oil–gas precipitation assessment algorithm based on neural networks to enable intelligent control, ensuring thorough recovery of residual oil from transformers. The proposed system and methods demonstrate excellent recovery efficiency and environmental protection effects during the pre-treatment of waste transformer oil. Experiments conducted on 50 discarded transformers showed an average recovery efficiency exceeding 99%, with 49 transformers exhibiting no damage to core components after the recovery process. From a theoretical perspective, this research introduces monitoring and control methods for transformer insulating oil recovery, providing significant support for the green processing and reutilization of discarded transformer insulating oil. From an application value perspective, the recovery process helps reduce environmental pollution and facilitates the disassembly of transformers. This enables better analysis of transformer operating characteristics, thereby enhancing the reliability and safety of power systems. Full article

Marine vessel design plays a key role in optimizing global trade, environmental sustainability, and technological advancements in naval architecture. However, a comprehensive review of research trends, key advancements, and future directions in sustainable marine vessel design has been lacking. This study addresses this gap by conducting a bibliometric analysis of 1701 publications from the Web of Science Core Collection database from 2000 to 2024. Using CiteSpace and VOSviewer, this research explores global research patterns, key institutions, and the evolution of thematic areas in sustainable marine vessel design over the last 24 years. The results reveal significant contributions from countries such as China, the USA, and South Korea, emphasizing sustainable technologies, safety, structural integrity, and intelligent systems in vessel design. Key research hotspots include “optimization”, “modeling”, “simulation”, and “computational fluid dynamics (CFD)”, reflecting the growing use of advanced technologies to improve vessel efficiency, environmental sustainability, and safety. This study also highlights the importance of interdisciplinary collaboration involving structural engineering, fluid mechanics, materials science, and environmental science. By mapping the historical landscape, current dynamics, and future directions of sustainable marine vessel design, this study aims to provide a foundation for advancing scientific discourse and practical applications in this field. Full article

The rapid development of Industrial Internet of Things (IIoT) technology has highlighted the critical role of wireless sensor networks in enabling intelligent production and equipment monitoring. Effective sensor deployment is essential for ensuring communication quality and transmission speed in IIoT environments. This paper presents a novel sensor deployment strategy that integrates four key metrics: deployment cost, energy consumption, network connectivity, and sensing probability. To address the challenges of multi-dimensional optimization, the proposed method normalizes these metrics and assigns appropriate weights based on their relative importance. A major innovation of this approach is the inclusion of larger-scale environmental obstacles, which enhances its adaptability to diverse industrial settings and specific deployment scenarios. Through a comprehensive set of simulation experiments across different scenarios, the proposed particle swarm/genetic hybrid algorithm demonstrates superior performance compared to existing methods, even surpassing 10% in performance. Specifically, it excels in optimizing the newly introduced network performance metric and significantly improves search convergence time, making it a highly efficient and effective solution for sensor network optimization in IIoT applications. Full article

Wastewater treatment is a critical process for ensuring water quality and public health, particularly in the context of increasing environmental challenges such as pollution and water scarcity. Artificial intelligence (AI) has emerged as a transformative technology capable of optimizing various wastewater treatment processes, such as contaminant removal, energy consumption, and cost-efficiency. This study presents a comprehensive bibliometric analysis of AI applications in wastewater treatment, utilizing data from Scopus and Web of Science covering 4335 publications from 1985 to 2024. Utilizing machine learning techniques such as neural networks, fuzzy logic, and genetic algorithms, the analysis reveals key trends in the role of the AI in optimizing wastewater treatment processes. The results show that AI has increasingly been applied to solve complex problems like membrane fouling, nutrient removal, and biofouling control. Regional contributions highlight a strong focus on advanced oxidation processes, microbial sludge treatment, and energy optimization. The Latent Dirichlet Allocation (LDA) model further identifies emerging topics such as real-time process monitoring and AI-driven effluent prediction as pivotal areas for future research. The findings provide valuable insights into the current state and future potential of AI technologies in wastewater management, offering a roadmap for researchers exploring the integration of AI to address sustainability challenges in the field. Full article

In the context of growing environmental concerns and the increasing impact of the manufacturing sector on sustainability, this paper introduces the concept of “Product 4.0” (P4.0) as a novel approach to harnessing the potential of Artificial Intelligence (AI) within Industry 4.0 (I4.0) technologies. P4.0 focuses on optimizing the performance of the product throughout its lifecycle and improving recovery strategies at End of Use (EoU) and End of Life (EoL) stages. Through a comprehensive review of the literature, this study identifies critical gaps in the current application of AI within I4.0 for sustainable manufacturing, particularly in regard to smart product systems and their interactions with external environments. To address these gaps, the paper proposes a holistic approach for the P4.0 that leverages AI-driven data analysis and decision making to facilitate efficient product recovery and resource utilization. Additionally, a Causal Loop Diagram (CLD) model is developed to illustrate the relationships between sustainability dimensions—environmental, economic, and social—and product demand influenced by P4.0, while also discussing the challenges and limitations associated with its implementation. By bridging theoretical insights with practical recovery solutions, this research contributes to the sustainable manufacturing discourse and offers actionable directions for future investigations into AI-enhanced P4.0 applications within the manufacturing industry. Full article

Integrating circular economy (CE) principles into mining practices offers a promising path toward reducing environmental harm while promoting sustainable resource management. This shift boosts the industry’s efficiency and profitability and aligns it with global sustainability goals. This paper delves into strategies for closing material loops, such as waste valorization, resource recovery from mine tailings, and water reuse in mining processes. Additionally, this study highlights innovative technologies and their potential to transform traditional linear practices into sustainable, circular systems. This paper emphasizes the importance of strong collaboration among industry stakeholders and policymakers, including mining companies, researchers, and local communities, for the implementation of CE principles. This paper also discusses the role of emerging digital tools, automation, and artificial intelligence in advancing circular practices and improving operational efficiency. By exploring the economic, environmental, and social benefits of the CE, this paper demonstrates how these practices can contribute to sustainable mining. It addresses key challenges, including technological, economic, and regulatory hurdles, and offers recommendations for overcoming them to pave the way for a more sustainable and resilient mining industry. Full article

In the domain of supply chain resilience (SCR), research has disproportionately emphasized the environmental dimensions of environmental, social, and governance (ESG) factors, leading to an oversight regarding the roles played by social and governance factors. To address this gap, a questionnaire survey was conducted among 313 employees from five Chinese supply chain enterprises. Through factor analysis, this study identified four latent variables associated with environmental factors, three with social factors, and four with governance factors. A structural equation model was then developed to present a comprehensive analysis of the impacts of the three ESG dimensions and digital intelligence on SCR, while also examining the interplay among these ESG factors. The findings reveal that environmental factors positively influence SCR, whereas social and governance factors exert a negative impact. This study further observes that digital intelligence enhances ESG factors but does not directly influence SCR. These results underscore the intricate dynamics between ESG indicators, digital intelligence, and SCR, highlighting the imperative for supply chain entities to make balanced decisions. This research offers novel insights into the effects of ESG factors from the employees’ viewpoint, providing implications and recommendations for supply chain management. Full article

This review examines the integration of advanced ultrasonic techniques and artificial intelligence (AI) for monitoring and analyzing concrete structures, focusing on detecting and classifying internal defects. Concrete structures are subject to damage over time due to environmental factors and dynamic loads, compromising their integrity. Non-destructive techniques, such as ultrasonics, allow for identifying discontinuities and microcracks without altering structural functionality. This review addresses key scientific challenges, such as the complexity of managing the large volumes of data generated by high-resolution inspections and the importance of non-linear models, such as the Hammerstein model, for interpreting ultrasonic signals. Integrating AI with advanced analytical models enhances early defect diagnosis and enables the creation of detailed maps of internal discontinuities. Results reported in the literature show significant improvements in diagnostic sensitivity (up to 30% compared to traditional linear techniques), accuracy in defect localization (improvements of 25%), and reductions in predictive maintenance costs by 20–40%, thanks to advanced systems based on convolutional neural networks and fuzzy logic. These innovative approaches contribute to the sustainability and safety of infrastructure, with significant implications for monitoring and maintaining the built environment. The scientific significance of this review lies in offering a systematic overview of emerging technologies and their application to concrete structures, providing tools to address challenges related to infrastructure degradation and contributing to advancements in composite sciences. Full article