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Maize is a sensitive crop to drought and heat stresses, particularly at the reproductive stages of development. The present study investigated the individual and interactive effects of drought (50% field capacity) and heat (38 °C/30 °C) stresses on morpho-physiological growth, yield, nutrient uptake and oxidative metabolism in two maize hybrids i.e., ‘Xida 889’ and ‘Xida 319’. The stress treatments were applied at tasseling stage for 15 days. Drought, heat and drought + heat stress caused oxidative stress by the over-production of ROS (O 2− , H 2 O 2 , OH − ) and enhanced malondialdehyde contents, which led to reduced photosynthetic components, nutrients uptake and yield attributes. The concurrent occurrence of drought and heat was more severe for maize growth than the single stress. However, both stresses induced the metabolites accumulation and enzymatic and non-enzymatic antioxidants to prevent the oxidative damage. The performance of Xida 899 was more prominent than the Xida 319. The greater tolerance of Xida 889 to heat and drought stresses was attributed to strong antioxidant defense system, higher osmolyte accumulation, and maintenance of photosynthetic pigments and nutrient balance compared with Xida 319.

Drought is one of the major environmental stresses that negatively affect the maize ( Zea mays L.) growth and production throughout the world. Foliar applications of plant growth regulators, micronutrients or osmoprotectants for stimulating drought-tolerance in plants have been intensively reported. A controlled pot experiment was conducted to study the relative efficacy of salicylic acid (SA), zinc (Zn), and glycine betaine (GB) foliar applications on morphology, chlorophyll contents, relative water content (RWC), gas-exchange attributes, activities of antioxidant enzymes, accumulations of reactive oxygen species (ROS) and osmolytes, and yield attributes of maize plants exposed to two soil water conditions (85% field capacity: well-watered, 50% field capacity: drought stress) during critical growth stages. Drought stress significantly reduced the morphological parameters, yield and its components, RWC, chlorophyll contents, and gas-exchange parameters except for intercellular CO 2 concentration, compared with well water conditions. However, the foliar applications considerably enhanced all the above parameters under drought. Drought stress significantly (p < 0.05) increased the hydrogen peroxide and superoxide anion contents, and enhanced the lipid peroxidation rate measured in terms of malonaldehyde (MDA) content. However, ROS and MDA contents were substantially decreased by foliar applications under drought stress. Antioxidant enzymes activity, proline content, and the soluble sugar were increased by foliar treatments under both well-watered and drought-stressed conditions. Overall, the application of GB was the most effective among all compounds to enhance the drought tolerance in maize through reduced levels of ROS, increased activities of antioxidant enzymes and higher accumulation of osmolytes contents.

Plants face a combination of different abiotic stresses under field conditions which are lethal to plant growth and production. Simultaneous occurrence of chilling and drought stresses in plants due to the drastic and rapid global climate changes, can alter the morphological, physiological and molecular responses. Both these stresses adversely affect the plant growth and yields due to physical damages, physiological and biochemical disruptions, and molecular changes. In general, the co-occurrence of chilling and drought combination is even worse for crop production rather than an individual stress condition. Plants attain various common and different physiological and molecular protective approaches for tolerance under chilling and drought stresses. Nevertheless, plant responses to a combination of chilling and drought stresses are unique from those to individual stress. In the present review, we summarized the recent evidence on plant responses to chilling and drought stresses on shared as well as unique basis and tried to find a common thread potentially underlying these responses. We addressed the possible cross talk between plant responses to these stresses and discussed the potential management strategies for regulating the mechanisms of plant tolerance to drought and/or chilling stresses. To date, various novel approaches have been tested in minimizing the negative effects of combine stresses. Despite of the main improvements there is still a big room for improvement in combination of drought and chilling tolerance. Thus, future researches particularly using biotechnological and molecular approaches should be carried out to develop genetically engineered plants with enhanced tolerance against these stress factors.

3D food printing is a digitally controlled process in which food materials are deposited by the number of layers placed one above the other. The extrusion-based printers are commercially used for 3D food printing into the various innovative designs for personalized food products. The present review is compiled based on investigations in extrusion-based 3D food printing emphasis on the characteristics of materials (cereal, starch, protein gel, dairy based, and non-native materials), printer control parameters, printing stability, and post-processing practices. Besides, the key aspects of technological advancements of 3D food printers and the major industrial challenges of 3D food printer are discussed. Recent research evidence that the extrusion-based 3D food printing greatly impacts on material characteristics, i.e., rheological, textural, morphological, and the printer control parameters. Formulations and printing conditions play a significant role to retain the structure of the 3D food product. Moreover, different food additives like xanthan gum, methylcellulose, and κ-carrageenan show a positive influence on printing stability. Post-processing, i.e., drying, baking, steaming, frying, etc., are the key challenges to retain the original shape of the 3D printed product. In addition, the potential of the 3D printer for customization of food product, the prospect for the research development and future scope that will bring the high benefit of the imminent researcher and the food industry.

In this paper we investigate a non-minimal, space-time derivative dependent, coupling between the k -essence field and a relativistic fluid using a variational approach. The derivative coupling term couples the space-time derivative of the k -essence field with the fluid 4-velocity via an inner product. The inner product has a coefficient whose form specifies the various models of interaction. By introducing a coupling term at the Lagrangian level and using the variational technique we obtain the k -essence field equation and the Friedmann equations in the background of a spatially flat Friedmann–Lemaitre–Robertson–Walker (FLRW) metric. Explicitly using the dynamical analysis approach we analyze the dynamics of this coupled scenario in the context of two kinds of interaction models. The models are distinguished by the form of the coefficient multiplying the derivative coupling term. In the simplest approach we work with an inverse square law potential of the k -essence field. Both of the models are not only capable of producing a stable accelerating solution, they can also explain different phases of the evolutionary universe.

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required.

Bio-inspired flow-sensing and actuation mechanisms offer a promising path for enhancing the stability of flapping-wing flying robots (FWFRs) operating in dynamic and noisy environments. This study introduces a bio-inspired sensing and actuation feather unit (SAFU) that mimics the covert feathers of falcons and serves simultaneously as a distributed flow sensor and an adaptive actuation element. Each electromechanical feather (EF) passively detects airflow disturbances through deflection and actively modulates its flaps through an embedded actuator, enabling real-time aerodynamic adaptation. A reduced-order bond-graph model capturing the coupled aero-electromechanical dynamics of the FWFR wing and SAFU is developed to provide a physics-based training environment for a proximal policy optimization (PPO) based reinforcement learning controller. Through closed-loop interaction with this environment, the PPO policy autonomously learns control actions that regulate feather displacement, reduce airflow-induced loads, and improve dynamic stability without predefined control laws. Simulation results show that the PPO-driven SAFU achieves fast, well-damped responses with rise times below 0.5 s, settling times under 1.4 s, near-zero steady-state error across varying gust conditions and up to 50% alleviation of airflow-induced disturbance effects. Overall, this work highlights the potential of bio-inspired sensing-actuation architectures, combined with reinforcement learning, to serve as a promising solution for future flapping-wing drone designs, enabling enhanced resilience, autonomous flow adaptation, and intelligent aerodynamic control during operations in gusts.

Rice belongs to tropical and subtropical environments and is extremely sensitive to chilling stress particularly during emergence and early stages of seedling development. Seed priming can be a good approach to enhance rice germination and stand establishment under chilling stress. The present study examined the role of different seed priming techniques viz., hydropriming, osmopriming, redox priming, chemical priming, and hormonal priming, in enhancing the chilling tolerance in rice. The most effective reagents and their pre-optimized concentrations based on preliminary experiments were used in this study. Two different rice cultivars were sown under chilling stress (18°C) and normal temperatures (28°C) in separate growth chambers. A non-primed control treatment was also maintained for comparison. Chilling stress caused erratic and delayed germination, poor seedling growth, reduced starch metabolism, and lower respiration rate, while higher lipid peroxidation and hydrogen peroxide accumulation in rice seedlings of both cultivars. Nevertheless, all the seed priming treatments effectively alleviated the negative effects of chilling stress. In addition, seed priming treatments triggered the activities of superoxide dismutase, peroxidase, and catalase, and enhanced the accumulations of glutathione and free proline in rice seedlings, which suggests that these measures help prevent the rice seedlings from chilling induced oxidative stress. Chemical priming with selenium and hormonal priming with salicylic acid remained more effective treatments for both rice cultivars under chilling stress than all other priming treatments. The better performance and greater tolerance of primed rice seedlings was associated with enhanced starch metabolism, high respiration rate, lower lipid peroxidation, and strong antioxidative defense system under chilling stress.

Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset.

Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.