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A 25-year-old intensive monoculture of cucumber (Cucumis sativus ‘Monolit F1’) has been chosen for monitoring the effect of continuous fertilization, drip irrigation and various soil disinfection on free-living nematodes in soil. Densities, relative abundance of nematode genera, trophic groups and c-p classes, in addition, numerous nematological indices were determined besides certain soil properties. Concerning the nematological indices, all the values proved a stressed, depleted and unstable environment with degraded food web condition. The exaggerated application of fertilizers through irrigation caused high salinity values (0.62 %) which is much higher than the indicated toxicity threshold (0.4 %) for cultivated plants. The number of individuals of the bacterivore Acrobeloides and Cuticularia genera, which have a very high stress tolerance, was outstanding in the area. Besides juveniles, the appearance of Meloidogyne incognita males was also recorded which could lead to a change in the genetic composition of the population to adapt to environmental stressors. All these changes may result from salt stress indicated by intensive drip irrigation and the use of artificial fertilizers.

The Industrial Internet of Things (IIoT) has become a transformative force in various healthcare applications, providing integrated services for daily life. The app healthcare based on the IIoT framework is broadly used to remotely monitor clients health using advanced biomedical sensors with wireless technologies, managing activities such as monitoring blood pressure, heart rate, and vital signs. Despite its widespread use, IIoT in healthcare faces challenges such as security concerns, inefficient work scheduling, and associated costs. To address these issues, this paper proposes and evaluates the Blockchain-Based Deep Reinforcement Learning System for Optimizing Healthcare (BDRL) framework. BDRL aims to enhance security protocols and maximize makespan efficiency in scheduling medical applications. It facilitates the sharing of legitimate and secure data among linked network nodes beyond the initial stages of data validation and assignment. This study presents the design, implementation, and statistical evaluation of BDRL using a new dataset and varying platform resources. The evaluation shows that BDRL is versatile and successfully addresses the security, privacy, and makespan needs of healthcare applications on distributed networks, while also delivering excellent performance. However, the framework utilizes high resources as the size of inserted data increases. [ABSTRACT FROM AUTHOR]