Horticultural crops are rich in constituents such as proteins, carbohydrates, vitamins, and minerals important for human health. Under biotic and abiotic stress conditions, rhizospheric bacteria are powerful sources of phytohormones such as indole acetic acid (IAA), gibberellic acid (GA), abscisic acid (ABA) and Plant growth regulators including cytokines, ammonia, nitrogen, siderophores, phosphate, and extra cellular enzymes. These phytohormones help horticultural crops grow both directly and indirectly. In recent agricultural practices, the massive use of chemical fertilizers causes a major loss of agricultural land that can be resolved by using the potent plant growth-promoting rhizospheric bacteria that protect the agricultural and horticultural crops from the adverse effect of phytopathogens and increase crop quality and yield. This review highlights the role of multifunctional rhizospheric bacteria in the growth promotion of horticultural crops in greenhouse conditions and agricultural fields. The relevance of plant growth hormones in horticultural crops highlighted in the current study is crucial for sustainable agriculture.

Introduction: the presence of anti-CCP is an important prognostic tool for rheumatoid arthritis (RA), but its relationship with the activity of the disease and functional capacity is still being investigated. Objectives: to study the relationship between anti-CCP and the indices of disease activity, functional capacity and structural damage, by means of conventional radiography (CR) and magnetic resonance imaging (MRI), in stabilized RA. Methods: cross-sectional study of RA patients with one to 10 years of disease. The participants were subjected to clinical evaluation with anti-CCP screening. Disease activity was assessed by means of the Clinical Disease Activity Index (CDAI) and functional capacity by means of the Health Assessment Questionnaire (HAQ). CR was analyzed by the Sharp van der Heijde index (SmvH) and MRI by the Rheumatoid Arthritis Magnetic Resonance Image Scoring System (RAMRIS). Results: 56 patients were evaluated, with median (IIq) of 55 (47.5–60.0) years, 50 (89.3%) were female among whom 37 (66.1%) were positive for anti-CCP. The median (IIq) of CDAI, HAQ, SmvH and RAMRIS were 14.75 (5.42–24.97), 1.06 (0.28–1.75), 2 (0–8) and 15 (7–35), respectively. There was no association between anti-CCP and CDAI, HAQ, SmvH and RAMRIS. Conclusion: our results did not establish the association of anti-CCP with the severity of the disease. So far, we cannot corroborate the anti-CCP as a prognostic tool in RA established.

Unmanned Aerial Vehicles (UAVs) have gained spotlighted attention in the recent past and has experienced exponential advancements. This research focuses on UAV-based data acquisition and processing to generate highly accurate outputs pertaining to orthomosaic imagery, elevation, surface and terrain models. The study addresses the challenges inherent in the generation and analysis of orthomosaic images, particularly the critical need for correction and enhancement to ensure precise application in fields like detailed mapping and continuous monitoring. To achieve superior image quality and precision, the study applies advanced image processing techniques encompassing Fuzzy Logic and edge-detection techniques. The study emphasizes on the necessity of an approach for countering the loss of information while mapping the UAV deliverables. By offering insights into both the challenges and solutions related to orthomosaic image processing, this research lays the groundwork for future applications that promise to further increase the efficiency and effectiveness of UAV-based methods in geomatics, as well as in broader fields such as engineering and environmental management.

Electricity consumption in Europe has risen significantly in recent years, with households being the largest consumers of final electricity. Managing and reducing residential power consumption is critical for achieving efficient and sustainable energy management, conserving financial resources, and mitigating environmental effects. Many studies have used statistical models such as linear, multinomial, ridge, polynomial, and LASSO regression to examine and understand the determinants of residential energy consumption. However, these models are limited to capturing only direct effects among the determinants of household energy consumption. This study addresses these limitations by applying a path analysis model that captures the direct and indirect effects. Numerical and theoretical comparisons that demonstrate its advantages and efficiency are also given. The results show that Sub-metering components associated with specific uses, like cooking or water heating, have significant indirect impacts on global intensity through active power and that the voltage affects negatively the global power (active and reactive) due to the physical and behavioral mechanisms. Our findings provide an in-depth understanding of household electricity power consumption. This will improve forecasting and enable real-time energy management tools, extending to the design of precise energy efficiency policies to achieve SDG 7’s objectives.