The study’s purpose is to investigate the relationship effect of innovation on online organizational learning applications and employees’ engagement in the Jordanian public sector. Quantities and descriptive analytical approach were used, and the population was covered (10) Jordanian public departments in Amman capital. Convenience method was used, which covered all departments’ managers and assistances in the Jordanian public services department’s headquarters, with (284) employee. Electronic survey set used. The SPSS-V20 and AMOS-V24 were used for hypotheses statistical analysis testing. The study found a significant impact of online organizational learning applications in its dimensions (Zoom, Teams, Goto Meeting, and Google Meet) on employee’s engagement, and a significant relationship of innovation between online organizational learning applications and employee’s engagement in Jordanian public services departments. The study contributions show that employees are willing to engage with their occupied work to achieve work goals, and to control over of how they undertake the employees career development, empowerment, communication skills, and work completion competences. The study implications for organizations management to conduct more future studies concerning online organizational learning applications by other dimensions as well as social media and other digital workshop and training in different organizations environments.

This study focuses on the use of the Soil and Water Assessment Tool (SWAT) model for water budgeting and resource planning in Oued Cherraa basin. The combination of hydrological models such as SWAT with reliable meteorological data makes it possible to simulate water availability and manage water resources. In this study, the SWAT model was employed to estimate hydrological parameters in the Oued Cherra basin, utilizing meteorological data (2012–2020) sourced from the Moulouya Hydraulic Basin Agency (ABHM). The hydrology of the basin is therefore represented by point data from the Tazarhine hydrological station for the 2009–2020 period. In order to optimize the accuracy of a specific model, namely SWAT-CUP, a calibration and validation process was carried out on the aforementioned model using observed flow data. The SUFI-2 algorithm was utilized in this process, with the aim of enhancing its precision. The performance of the model was then evaluated using statistical parameters, with particular attention being given to Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R²). The NSE values for the study were 0.58 for calibration and 0.60 for validation, while the corresponding R² values were 0.66 and 0.63. The study examined 16 hydrological parameters for Oued Cherra, determining that evapotranspiration accounted for 89% of the annual rainfall, while surface runoff constituted only 6%. It also showed that groundwater recharge was pretty much negligible. This emphasized how important it is to manage water resources effectively. The calibrated SWAT model replicated flow patterns pretty well, which gave us some valuable insights into the water balance and availability. The study’s primary conclusions were that surface water is limited and that shallow aquifers are a really important source of water storage, especially for irrigation during droughts.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.

Based on the application of phase diagram calculation technique (CALPHAD), the Fe-Nd-B magnetic materials were investigated, and alloy design and microstructure evolution concerning. According to the thermodynamic database of Fe-Nd-B ternary system, the equilibrium solidification process of Fe78Nd15B7 alloy is simulated, and we explained well the reason of this experimental phenomenon by the metastable extension of the equilibrium phase diagram.

The application of nanotechnology in the food industry enables prioritization of consumers’ needs. Nanotechnology has the ability to provide new forms of control on food structure; therefore, this technology has higher industrial value. This paper briefly introduces the main concepts of nanotechnology and its correlation with size reduction performance. This paper also introduces the main nanobjects and their potential applications in food, and summarizes various studies and their applications in food industry.

Kinnow production is hampered due to the lack of micronutrient applications such as zinc (Zn), iron (Fe), and manganese (Mn), which play a significant role in the metabolic activities of the plant, affecting yield and quality. The farmers of the region use mineral micronutrient fertilizers, but it leads to phytotoxicity due to unoptimized fertilizer application dose. In the present investigation, an attempt has been made to optimize the Zn, Mn, and Fe minerals dose as tank mix foliar application for improvement of fruit yield, quality, and uptake of nutrients. The twelve combinations of different doses of zinc sulphate, manganese sulphate, and ferrous sulphate fertilizers replicated three times were tested at kinnow orchards established at Krishi Vigyan Kendra, Bathinda, Punjab, India. The data revealed that the fruit drop was significantly low in the treatment F₁₂ (43.4%) (tank mix spray of 0.3% ZnSO₄ + 0.2% MnSO₄ + 0.1% FeSO₄ ) compared to control treatment. The fruit yield per tree was significantly higher in the treatment F12 compared to untreated control. The juice percentage was also recorded higher in treatment F12 as compared to control, and the juice percentage improved by 2.6%. The leaf nutrient analysis also revealed translocation of higher amount of nutrient from leaf to fruit under optimized supply of micronutrient. Thus, the application of tank mix spray of 0.3% ZnSO₄ + 0.2% MnSO₄ + 0.1% FeSO₄ may be used for better fruit yield and quality.