Search Results (4)

The conflict between exploitation of coal resources and environmental protection is highly pronounced in the Wanli mining area, located in the arid and semi-arid region of Inner Mongolia, China. The impact of mining operations has led to varying degrees of surface subsidence, which further threatens the ecological environment as coal extraction continues. The Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique offers significant advantages over traditional subsidence monitoring methods, particularly in complex terrain with vertical and horizontal valleys. This approach enables large-scale, low-cost, and all-weather monitoring. Based on 64 Sentinel-1A SAR images from 2018 to 2023, this study aims to promptly identify the location, deformation degree, and evolution characteristics of mining-induced subsidence within the study area using SBAS-InSAR techniques. The results indicate that the area affected by mining-induced subsidence covers 109.73 km², with a maximum cumulative subsidence of 283.41 mm and a maximum subsidence velocity of 46.45 mm/y. Additionally, during the field verification, 29 ground fractures, predominantly located along the precipitous borders of subsidence areas, were identified, validating the credibility of the monitoring results. This study demonstrates that SBAS-InSAR technology remains highly effective in the erosional terrain of the Loess Plateau. The monitoring data can help in-production mining to accurately identify the characteristics and patterns of surface subsidence induced by coal mining operations. It provides reliable policymaking data support and makes significant contributions to optimize cost-efficiency and guide targeted monitoring efforts in subsequent management work of the Wanli mining area as well as other mining areas. Full article

Cyberattacks always remain the major threats and challenging issues in the modern digital world. With the increase in the number of internet of things (IoT) devices, security challenges in these devices, such as lack of encryption, malware, ransomware, and IoT botnets, leave the devices vulnerable to attackers that can access and manipulate the important data, threaten the system, and demand ransom. The lessons from the earlier experiences of cyberattacks demand the development of the best-practices benchmark of cybersecurity, especially in modern Smart Environments. In this study, we propose an approach with a framework to discover malware attacks by using artificial intelligence (AI) methods to cover diverse and distributed scenarios. The new method facilitates proactively tracking network traffic data to detect malware and attacks in the IoT ecosystem. Moreover, the novel approach makes Smart Environments more secure and aware of possible future threats. The performance and concurrency testing of the deep neural network (DNN) model deployed in IoT devices are computed to validate the possibility of in-production implementation. By deploying the DNN model on two selected IoT gateways, we observed very promising results, with less than 30 kb/s increase in network bandwidth on average, and just a 2% increase in CPU consumption. Similarly, we noticed minimal physical memory and power consumption, with 0.42 GB and 0.2 GB memory usage for NVIDIA Jetson and Raspberry Pi devices, respectively, and an average 13.5% increase in power consumption per device with the deployed model. The ML models were able to demonstrate nearly 93% of detection accuracy and 92% f1-score on both utilized datasets. The result of the models shows that our framework detects malware and attacks in Smart Environments accurately and efficiently. Full article

Market-based initiatives like agriculture value chain (AVC) are becoming progressively pervasive to support smallholder rural farmers and assist them in entering larger market interventions and providing a pathway of enhancing their socioeconomic well-being. Moreover, it may also foster staggering effects towards the post-era poverty alleviation in rural areas and possessed a significant theoretical and practical influence for modern agricultural development. The prime objective of the study is to explore the effects of smallholder farmers’ participation in the agricultural value chain for availing rural development and poverty alleviation. Specifically, we have crafted the assessment employing pre-production (improved fertilizers usage), in-production (modern preservation technology), and post-production (supply chain) participation and interventions of smallholder farmers. The empirical data has been collected from a micro survey dataset of 623 kiwifruit farmers from July to September in Shaanxi, China. We have employed propensity score matching (PSM), probit, and OLS models to explore the multidimensional poverty reduction impact and heterogeneity of farmers’ participation in the agricultural value chain. The results show that the total number of poor farmers who have experienced one-dimensional and two-dimensional poverty is relatively high (66.3%). We also find that farmers’ participation in agricultural value chain activities has a significant poverty reduction effect. The multidimensional poverty level of farmers using improved fertilizer, organizational acquisition, and using storage technology (compared with non-participating farmers) decreased by 30.1%, 46.5%, and 25.0%, respectively. The multidimensional poverty reduction degree of male farmers using improved fertilizer and participating in the organizational acquisition is greater than that of women. The multidimensional poverty reduction degree of female farmers using storage and fresh-keeping technology has a greater impact than the males using storage and improved storage technology. Government should widely promote the value chain in the form of pre-harvest, production, and post-harvest technology. The public–private partnership should also be strengthened for availing innovative technologies and infrastructure development. Full article

Anomaly detection is becoming increasingly important to enhance reliability and resiliency in the Industry 4.0 framework. In this work, we investigate different methods for anomaly detection on in-production manufacturing machines taking into account their variability, both in operation and in wear conditions. We demonstrate how the nature of the available data, featuring any anomaly or not, is of importance for the algorithmic choice, discussing both statistical machine learning methods and control charts. We finally develop methods for automatic anomaly detection, which obtain a recall close to one on our data. Our developed methods are designed not to rely on a continuous recalibration and hand-tuning by the machine user, thereby allowing their deployment in an in-production environment robustly and efficiently. Full article