Estimation of actual evapotranspiration and water requirements of strategic crops under different stresses.

According to the importance of water conservation in water scarcity regions, estimating the exact amount of required water for crops under different stress conditions in irrigation networks is vital. One of the challenges in water management is estimating these stresses with crop models. AquaCrop is a robust model that can simulate the actual evapotranspiration and the water needs under different biophysical and management conditions.

Optimizing irrigation and nitrogen levels to achieve sustainable rice productivity and profitability.

The global scarcity of irrigation water poses a significant challenge to the sustainable production of rice and its availability worldwide. With a growing population driving increased demand for rice, it is crucial to enhance rice production while minimizing water usage. Achieving this requires a comprehensive understanding of the complex interactions between water and nitrogen dynamics and the formulation of strategies to optimize the application of irrigation water and nitrogen fertilizers.

The impact of threshing unit structure and parameters on enhancing rice threshing performance.

Improving rice threshing unit performance and structure significantly enhances the thresher's overall efficiency by raising its productivity while lowering costs and power consumption. This study evaluated and optimized longitudinal axial flow threshing device performance using two threshers' structures, namely conical thresher and drum thresher, under different rotating speeds of 1100, 1300, and 1500 rpm and feeding rates of 0.8, 1.

Machine learning algorithm for estimating and optimizing the phytochemical content and physicochemical properties of okra slices in an infrared heating system.

This study investigates how different air temperatures and infrared intensities affect the physicochemical properties of dried okra at different airflow rates. The model was developed using machine learning, and Okra's physicochemical properties were optimized using a self-organizing map (SOM). The results showed that higher infrared intensity and air temperature improved rehydration and colour but reduced water activity and vitamin C levels.

Development of a sustainable portable Archimedes screw turbine for hydropower generation.

Portable hydropower turbines are turbines with a scale below 5 kW and which can be carried from one place to another easily by hand due to their light weight. This study was carried out to evaluate the potential of Archimedes Screw Turbine (AST) as an improved portable hydro-power turbine (PHPT) to address shortcomings in available portable turbines. The design of Archimedes screw hydro-power turbine is mainly concerned with screw geometry, which is determined by a variety of internal and external characteristics, including its length, external and internal diameter, Pitch of blades, and Number of the blades, which were 80 cm, 18 cm, 9.

Assessment of water quality and health hazards using water quality index and human health risk evaluation in district Talagang Pakistan.

This work was carried out for the determination of the water quality in the Talagang District of Pakistan, as water is essential for agriculture and drinking uses. This study aims to assess the water quality for irrigation, drinking, and health risks using the Water Quality Index (WQI) and Human Health Risk Assessment (HHRA) tools to identify regions with contaminated water, and to evaluate the associated risks. A total of 98 water samples were taken at various points from diverse sources such as hand pumps, streams, springs, dug wells, and tube wells for physio-chemical assessment.

Thin-layer modeling, drying parameters, and techno-enviro-economic analysis of a solar dried salted tilapia fish fillets.

This study focused on the development of an indirect forced solar dryer that incorporates a three-sided flat plate solar collector (TSFPSC) specifically designed for increasing thermal efficiency, and the system used for drying salted tilapia fish fillets (STFF). The investigation analyzed three fillet thicknesses-4 mm, 8 mm, and 12 mm, employing both open sun drying (OSD) and the developed solar dryer (DSD), with a constant airspeed of 0.5 m/s.

Hyperspectral technology and machine learning models to estimate the fruit quality parameters of mango and strawberry crops.

Using chemical laboratory procedures to estimate the fruit quality parameters (biochemical parameters) of mango "Succarri" and strawberry "Florida" as indicators of ripening degrees in a large area presents challenges such as low throughput, labor intensity, time consumption, and the need for multiple samples. So, using spectral reflectance-based proximal remote sensing to quickly and accurately measure biochemical parameters in different fruits is important to find the best time to harvest, make food ripen faster, and the processing of food easier. This has significant economic and ecological advantages.

Evaluating flood dynamics and effects in Nagpur city using remote sensing and Shannon's entropy analysis.

Flood is among the most disastrous natural disasters since they are responsible for massive damage to infrastructure, severe fatalities and injuries, innumerable economic losses, and social disruptions worldwide. These damages caused by floods have been worsening in recent years worldwide because of environmental degradation, climatic change, and high-speed urbanization. A rising precipitation rate increases the chances of floods in flood-vulnerable areas.

Estimation of yield and nitrogen use efficiencies in hybrid maize varieties through site specific nitrogen management based on leaf color chart (LCC).

Exorbitant praxis of nitrogen pioneered and opened up the usage of time-specific and need-based nitrogen management. The leaf color chart (LCC), being one of the handy tool, is put to use for the estimation of the indirect leaf N, and also heightens the competence of crop N administration. Integrating leaf color chart assessments into maize cultivation practices can provide a practical and cost-effective approach for tailoring nitrogen applications, leading to improved resource-use efficiency and sustainable maize production.

Polarization-Insensitive, High-Efficiency Metasurface with Wide Reception Angle for Energy Harvesting Applications.

This research presents an innovative polarization-insensitive metasurface (MS) harvester designed for energy harvesting applications at 5 GHz, capable of operating efficiently over wide reception angles. The proposed MS features a novel wheel-shaped resonator array whose symmetrical structure ensures insensitivity to the polarization of incident electromagnetic (EM) waves, enabling efficient energy absorption and minimizing reflections. Unlike conventional designs, the metasurface achieves near-unity harvesting efficiency, exceeds 94% under normal incidence, and maintains superior performance across various incident angles for TE and TM polarizations.

Optimization of dried garlic physicochemical properties using a self-organizing map and the development of an artificial intelligence prediction model.

The experiments were conducted at different levels of infrared power, airflow, and temperature. The relationships between the input process factors and response factors' physicochemical properties of dried garlic were optimized by a self-organizing map (SOM), and the model was developed using machine learning. Artificial Neural Network (ANN) with 99% predicting accuracy and Self-Organizing Maps (SOM) with 97% clustering accuracy were used to determine the quality characteristics of garlic.

Development and laboratory evaluation of a novel IoT-based electric-driven metering system for high precision garlic planter.

In order to address many issues, such as the inconsistent and unreliable seeding process in traditional mechanical garlic seed metering systems (SMS), as well as the lack of ability to monitor the effectiveness of the seeding, a highly accurate electric-driven metering system (EDMS) was developed and created specifically for garlic seed planters. This study provided a description of the overall structure and functioning principle, as well as an analysis of the mechanism for smooth transit and delivery. A combination of an infrared (IR) sensor, Arduino Mega board, stepper motor, speed sensor, and a Wi-Fi module was employed to operate the EDMS, as well as monitor and count the quantity of garlic seeds during the planting process and determine the qualified rate (QR) and missing rate (MR).

Hybrid Pennisetum colonization by Bacillus megaterium BM18-2 labeled with green fluorescent protein (GFP) under Cd stress.

Researchers have reported that Bacillus megaterium BM18-2 reduces Cd toxicity in Hybrid Pennisetum, but understanding the interaction between plants and associated endophytes is crucial for understanding phytoremediation strategies under heavy metal stress. The current study aims to monitor the colonization patterns of GFP-labeled endophytic bacteria BM18-2 on Hybrid Pennisetum grass. Additionally, it will monitor Cd's effect on plant bacterial colonization.

Novel garden cress-fish gelatin based ointment: Improvement of skin wound healing in rats through modulation of anti-inflammatory and antioxidant states.

This study aimed to investigate the ability of aqueous extract of seeds (LSE) to improve the wound healing process in rat models. The gelatin, extracted from the skin of smooth-hound shark using citric acid, was used as a support material for ointment. Animals were divided into four groups of six rats each: an untreated control group, a control group treated with Moist Exposed Burn Ointment (MEBO), a treated group with gelatin gel, and a treated group with gelatin gel fortified with 20 mg/mL LSE.

Securing China's rice harvest: unveiling dominant factors in production using multi-source data and hybrid machine learning models.

Ensuring the security of China's rice harvest is imperative for sustainable food production. The existing study addresses a critical need by employing a comprehensive approach that integrates multi-source data, including climate, remote sensing, soil properties and agricultural statistics from 2000 to 2017. The research evaluates six artificial intelligence (AI) models including machine learning (ML), deep learning (DL) models and their hybridization to predict rice production across China, particularly focusing on the main rice cultivation areas.

Food applications of bioactive biomaterials based on gelatin and chitosan.

Food packaging must guarantee the products' quality during the different operations including packing and maintenance throughout transportation and storage until to consumption. Thus, it should satisfy, both, food freshness and quality preservation and consumers health safety. Natural bio-sourced polymers have been explored as safe edible materials for several packaging applications, being interestingly carrier of bioactive substances, once added to improve films' properties.

Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures.

Liquefaction is a devastating consequence of earthquakes that occurs in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameter is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines Extreme Learning Machine (ELM) with Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance.

Investigation into the Effects of Crystalline Admixtures and Coatings on the Properties of Self-Healing Concrete.

One fascinating concept for enhancing the durability and lifespan of concrete buildings involves the use of self-healing concrete. This study focuses on the effect of crystalline admixtures and coatings on various properties of self-healing concrete and provides a comparison with traditional concrete. Four different concrete mixtures were prepared to assess their effectiveness in bridging crack openings, their flexural and compressive strengths, and water absorption.

Enhancing groundwater vulnerability assessment for improved environmental management: addressing a critical environmental concern.

Groundwater serves as a primary water source for various purposes. Therefore, aquifer pollution poses a critical threat to human health and the environment. Identifying the aquifer's highly vulnerable areas to pollution is necessary to implement appropriate remedial measures, thus ensuring groundwater sustainability.