Does Bariatric Surgery Affect Pregnancy in Women in the Childbearing Period? A Retrospective Single Center Study.

Background: Severe obesity is a significant public health challenge, affecting women's reproductive health in the childbearing period. Bariatric surgery is an effective weight loss method for management of severe obesity and increase fertility. It may result in nutritional deficiency which adversely affects the maternal and fetal outcome.

Comparative analysis of drying kinetics, diffusivity, and thermodynamic properties of hybrid solar and oven dryers for Egyptian sweet marjoram.

Egyptian sweet marjoram leaves (ESML) are aromatic herbs long valued for their ability to enhance flavor and extend shelf life by inhibiting the autoxidation of food lipids. Despite their widespread use, limited research exists on how various drying techniques, air temperatures, and thin-layer thicknesses affect the drying behavior, mathematical modeling, effective moisture diffusivity (EMD), activation energy, and thermodynamic properties of ESML. This study addresses this gap by examining the drying characteristics of ESML using a hybrid solar drying system (HSDS) at three air temperatures (50, 60, and 70 °C) and three layer thicknesses (1, 2, and 3 cm), comparing with a conventional oven drying (OD).

Yield and water productivity of rice as influenced by crop establishment and irrigation methods under temperate environment.

This study compares direct-seeded rice (DSR) with transplanted rice under three different irrigation methods: conventional flooding, saturation, and alternate wetting and drying. The findings indicate that DSR outperforms transplanted rice in several key aspects. Specifically, DSR exhibited a greater number of tillers (635-650) and panicles (510-529) m, along with lower spikelet sterility (9.

Enhanced residual-attention deep neural network for disease classification in maize leaf images.

Disease classification in maize plant is necessary for immediate treatment to enhance agricultural production and assure global food sustainability. Recent advancements in deep learning, specifically convolutional neural networks, have shown outstanding potential for image classification. This study presents Maize Net, a convolutional neural network model that precisely identifies diseases in maize leaves.

Long-term conservation agriculture to enhance soil properties and quality in rice-wheat cropping system.

Conservation agriculture (CA) presents a promising substitute to the tillage-intensive rice-wheat cropping system (RWS) prevalent in the Indo-Gangetic plains (IGPs). In the northwestern IGPs, on-farm studies examining the impact of CA durations on soil properties and quality are limited. This study assessed the effects of CA practised for 2 (CA2), 4 (CA4), 8 (CA8), and 12 (CA12) years and conventional tillage (CT) on soil quality in the Nilokheri block of Haryana, India.

Enzymeless electrochemical detection of hydrogen peroxide using NiO octahedron decorated 3D graphene hydrogel.

Nowadays, electrochemical biosensors have gained extensive consensus to detect a wide variety of analytes such as hydrogen peroxide (HO), glucose, dopamine, uric acid, and so on. However, the detection of HO is more and more important because HO plays a vital role in our daily life. Hence, a new nonenzymatic HO biosensor was developed by decorating NiO octahedrons on the 3-dimensional graphene hydrogel (3DGH).

Artificial intelligence and machine learning models for predicting and evaluating the influence of shelf-life environments and packaging materials on garlic ( L) physicochemical and phytochemical compositions.

The nutritional content and quality of garlic, a crop widely consumed, must be preserved after harvesting by overcoming several challenges. The necessity of this study arises from the growing demand for effective postharvest management solutions that can extend shelf life, maintain the nutritional integrity of garlic and enhance consumer satisfaction. This study explores the application of machine learning (ML) and artificial intelligence (AI) to predict the effects of storage environments and packaging materials on garlic's physicochemical properties, enzymatic activities, phytochemical content, and antioxidant characteristics.

Comparative analysis of drying kinetics, thermodynamic properties, and mathematical modeling of pomegranate peel (Punica granatum L.) in a hybrid solar dryer and an oven dryer.

Drying pomegranate peels, a by-product of juice extraction, offers an effective means to preserve bioactive compounds while reducing waste. This study investigates the influence of drying temperature and layer thickness on the drying kinetics, thermodynamic properties, and modeling of pomegranate peels using a hybrid solar dryer (HSD) and a conventional oven dryer (OD). Fresh peels with an average initial MC of 325.

Development of millifluidic device and electronic extrusion system for fabrication of sodium alginate/carbon nanotube microfibers.

The article presents a milli-fluidic device and electronic extrusion system for fabricating alginate/carbon nanotube microfibers using calcium chloride as a crosslink agent. The device was designed, simulated, and prototyped, and successfully applied the mixture of alginate and carbon nanotube to generate microfibers in different concentrations and stepper motor speeds. The flow extrusion pump was used to pump fluid flow to the milli-fluidic device and crosslink calcium chloride to form sodium alginate-CNT microfibers.

Aqua-MC as a simple open access code for uncountable runs of AquaCrop.

Understanding uncertainty in crop modeling is essential for improving prediction accuracy and decision-making in agricultural management. Monte Carlo simulations are widely used for uncertainty and sensitivity analysis, but their application to closed-source models like AquaCrop presents significant challenges due to the lack of direct access to source code. This study introduces Aqua-MC, an automated framework designed to facilitate Monte Carlo simulations in AquaCrop by integrating probabilistic parameter selection, iterative execution, and uncertainty quantification within a structured workflow.

SPAD dynamics in maize crop with precision nitrogen management under rain-fed and irrigated conditions.

Real-time monitoring of canopy chlorophyll content is crucial for understanding crop growth and guiding precision agricultural management. The SPAD chlorophyll meter is a valuable tool for assessing nitrogen status in maize (Zea mays L.), a key cereal crop used for food, feed, and biofuels.

Integrated approach of extreme learning machines and locally weighted linear regression for improved discharge coefficient prediction.

Accurate determination of the discharge coefficient (Cd) is essential for calculating discharge over side weirs. The current study aims to enhance the prediction accuracy of Cd for rectangular sharp-crested side weirs by addressing the limitation of the output layer of the Extreme learning machine (ELM). The output layer of ELM depends mainly on the linear system which limits its generalization capabilities.

Evaluation of different spectral indices for wheat lodging assessment using machine learning algorithms.

Wheat lodging is a recurrent phenomenon that significantly affects grain yield and impedes the harvesting efficiency. Therefore, the precise and rapid assessment of wheat lodging is crucial in minimizing its impact on grain yield and quality. Recently few studies related to machine learning based wheat lodging have been reported; however, the literature still lacks comprehensive assessments of machine learning algorithms for wheat lodging over Indian agricultural fields.

Mathematical modeling, drying kinetics, and economic analysis of a hybrid photovoltaic thermal solar dryer for henna leaves.

Solar dryers offer a sustainable and efficient method for drying many agricultural products, preserving their quality, color, and medicinal properties while minimizing energy consumption and environmental impact. Current research on henna processing reveals a significant gap in drying engineering studies, creating a critical barrier to process optimization and quality enhancement. While numerous studies have investigated henna's physical and chemical characteristics, the engineering aspects of drying-including heat and mass transfer mechanisms, equipment design, and process parameter optimization-remain substantially understudied.

Rapid GC-MS method for screening seized drugs in forensic investigations: optimization and validation.

Indroduction: The escalating incidence of drug-related crimes requires rapid and reliable forensic methods for drug screening.

Methods: This study develops and optimizes a rapid Gas Chromatography-Mass Spectrometry (GC-MS) method that significantly reduces the total analysis time from 30 to 10 minutes, facilitating faster judicial processes and law enforcement responses. Enhanced by optimizing temperature programming and operational parameters, the method efficiently shortens the run time while ensuring the accuracy essential for forensic applications.

Air temperature estimation and modeling using data driven techniques based on best subset regression model in Egypt.

Air temperature plays a critical role in estimating agricultural water requirements, hydrological processes, and the climate change impacts. This study aims to identify the most accurate forecasting model for daily minimum (T) and maximum (T) temperatures in a semi-arid environment. Five machine learning models-linear regression (LR), additive regression (AR), support vector machine (SVM), random subspace (RSS), and M5 pruned (M5P)-were compared for T and T forecasting in Gharbia Governorate, Egypt, using data from 1979 to 2014.

Comparative assessment of standalone and hybrid deep neural networks for modeling daily pan evaporation in a semi-arid environment.

Evaporation represents a fundamental hydrological cycle process that demands dependable methods to quantify its fluctuation to ascertain sustainable agriculture, irrigation systems, and overall water resource management. Meteorological variables such as relative humidity, temperature, wind speed, and sunshine hours affect evaporation non-linearly, resulting in challenges while developing prediction models. To combat this, the study aimed to develop robust models for estimating evaporation in semi-arid environments by applying machine learning techniques.

Investigating the impact of meteorological parameters on daily soil temperature changes using machine learning models.

Soil temperature (ST) is one of the critical parameters in agricultural meteorology and significantly influences physical, chemical, and biological activities in the soil environment. One of the major challenges in agricultural studies is the limited number of synoptic stations for measuring ST. Novel data mining methods offer effective solutions for obtaining reliable estimations while reducing costs and improving accuracy.

Predicting mass transfer activation energy and physicochemical properties of dried onion using numerical modeling and artificial intelligence.

The quality of the onion slices was statistically evaluated based on the variables of drying conditions, considering the following characteristics: drying time, color, shrinkage, water activity, and rehydration ratio, critical parameters in evaluating food processing and preservation methods. The onion slices were subjected to drying under the ensuing settings: infrared powers of 1500, 3500, and 5500 W/m; airflow rates of 0.3, 0.

Optimized design and performance evaluation of a highly precise variable rate mis-planting and replanting potato electronic-metering mechanism.

Introduction: Precise seed placement during potato planting critically determines crop distribution and density, yet mis-planting remains a persistent agricultural challenge. Current manual detection and correction methods introduce inefficiencies, increase labor costs, and risk human error.

Methodology: To address these limitations, this study developed and evaluated a high-precision variable-rate electronic metering mechanism (EMM) capable of automated mis-planting detection and replanting under controlled laboratory conditions.

Automated vacuum drying kinetics, thermodynamics, and economic analysis of sage leaves.

Vacuum drying of sage leaves is important for preserving their essential oils, flavor, and medicinal properties by reducing oxidation and thermal degradation, but previous research has not investigated its impact on drying speed, thermodynamic properties, mathematical modeling, or economic viability. This study employed an automatic vacuum dryer at temperatures of 40 °C, 50 °C, and 60 °C under different pressure conditions (atmospheric, -5 kPa, and - 10 kPa) with a 1 cm layer thickness. Results showed that increasing temperature and decreasing pressure significantly improved drying efficiency, reducing the process time to just 90 min while achieving a drying rate of 22.

Forecasting monthly runoff in a glacierized catchment: A comparison of extreme gradient boosting (XGBoost) and deep learning models.

Accurate monthly runoff forecasting is vital for water management, flood control, hydropower, and irrigation. In glacierized catchments affected by climate change, runoff is influenced by complex hydrological processes, making precise forecasting even more challenging. To address this, the study focuses on the Lotschental catchment in Switzerland, conducting a comprehensive comparison between deep learning and ensemble-based models.

Genetic diversity and agro-morphological characterization of cassava varieties provides insight for breeding and crop improvement.

The lack of knowledge about genetic variation in cassava is a problem for Fiji's efforts to improve its genetics. Using agro-morphological features, this study aimed to assess the genetic diversity and interrelationships among 33 cassava cultivars. A field investigation was conducted at the Dobuilevu Research Station using a randomized complete block design.

Optimizing cultivation practices to enhance growth and yield of Indian mustard.

Since mustard is a significant oilseed crop in India, improving cultivation practises is essential for enhancing the productivity. The experiment was laid out in randomized block design with three replications at the farm of Department of Agronomy, Lovely Professional University, Phagwara during the rabi season. The work was carried out for two years to analyse the pooled data (2022-2023; 2023-2024) of Indian mustard.

Enhancing rapid nitrogen freezer performance for Litchi fruit: a Taguchi-based approach to reduce cracking, time, and nitrogen consumption.

This study employed the Taguchi technique to investigate the influence of various liquid nitrogen quick-freezing parameters on litchi fruit cracking, nitrogen consumption, and freezing time. The experiments included testing different freezing temperatures (- 40, - 50, - 60, and - 70 °C), two types of nitrogen spraying nozzles (Hollow-cone and Full-cone), and two fan speeds (800 and 1200 rpm). Before freezing, the litchis were soaked in a solution and precooled as a pre-treatment to mitigate peel cracking and preserve quality.