Multi-Objective Seagull Optimization Algorithm with Deep Learning-Enabled Vulnerability Detection for Secure Cloud Environments.

Cloud computing (CC) is an internet-enabled environment that provides computing services such as networking, databases, and servers to clients and organizations in a cost-effective manner. Despite the benefits rendered by CC, its security remains a prominent concern to overcome. An intrusion detection system (IDS) is generally used to detect both normal and anomalous behavior in networks. The design of IDS using a machine learning (ML) technique comprises a series of methods that can learn patterns from data and forecast the outcomes consequently. In this background, the current study designs a novel multi-objective seagull optimization algorithm with a deep learning-enabled vulnerability detection (MOSOA-DLVD) technique to secure the cloud platform. The MOSOA-DLVD technique uses the feature selection (FS) method and hyperparameter tuning strategy to identify the presence of vulnerabilities or attacks in the cloud infrastructure. Primarily, the FS method is implemented using the MOSOA technique. Furthermore, the MOSOA-DLVD technique uses a deep belief network (DBN) method for intrusion detection and its classification. In order to improve the detection outcomes of the DBN algorithm, the sooty tern optimization algorithm (STOA) is applied for the hyperparameter tuning process. The performance of the proposed MOSOA-DLVD system was validated with extensive simulations upon a benchmark IDS dataset. The improved intrusion detection results of the MOSOA-DLVD approach with a maximum accuracy of 99.34% establish the proficiency of the model compared with recent methods.

Bio-Inspired Spotted Hyena Optimizer with Deep Convolutional Neural Network-Based Automated Food Image Classification.

Food image classification, an interesting subdomain of Computer Vision (CV) technology, focuses on the automatic classification of food items represented through images. This technology has gained immense attention in recent years thanks to its widespread applications spanning dietary monitoring and nutrition studies to restaurant recommendation systems. By leveraging the developments in Deep-Learning (DL) techniques, especially the Convolutional Neural Network (CNN), food image classification has been developed as an effective process for interacting with and understanding the nuances of the culinary world. The deep CNN-based automated food image classification method is a technology that utilizes DL approaches, particularly CNNs, for the automatic categorization and classification of the images of distinct kinds of foods. The current research article develops a Bio-Inspired Spotted Hyena Optimizer with a Deep Convolutional Neural Network-based Automated Food Image Classification (SHODCNN-FIC) approach. The main objective of the SHODCNN-FIC method is to recognize and classify food images into distinct types. The presented SHODCNN-FIC technique exploits the DL model with a hyperparameter tuning approach for the classification of food images. To accomplish this objective, the SHODCNN-FIC method exploits the DCNN-based Xception model to derive the feature vectors. Furthermore, the SHODCNN-FIC technique uses the SHO algorithm for optimal hyperparameter selection of the Xception model. The SHODCNN-FIC technique uses the Extreme Learning Machine (ELM) model for the detection and classification of food images. A detailed set of experiments was conducted to demonstrate the better food image classification performance of the proposed SHODCNN-FIC technique. The wide range of simulation outcomes confirmed the superior performance of the SHODCNN-FIC method over other DL models.

PAPR reduction using SLM-PTS-CT hybrid PAPR method for optical NOMA waveform.

In this article, we focus on optimising the SLM-PTS-CT (selective mapping, partial transmission sequence, circular transformation) hybrid method for optical non-orthogonal multiple access (O-NOMA) waveforms. The goal is to enhance the spectrum performance and practicality of O-NOMA systems while mitigating the PAPR issue through a hybrid approach. The SLM-PTS-CT hybrid method is applicable to O-NOMA waveforms, providing effective PAPR reduction. By dividing the data sequence into sub-blocks, applying phase factors, and rotating the phase of the subcarriers in such a way that the peaks of the signal are distributed more uniformly, the proposed SLM-PTS-CT achieves an optimal PAPR reduction while maintaining the benefits of O-NOMA. The efficiency of the proposed method is analysed by estimating the performance of several parameters, such as bit error rate (BER), PAPR, and power spectral density (PSD), by increasing the number of sub-blocks (S) and phase factor (P). Further, the proposed SLM-PTS-CT is compared with the conventional SLM-PTS, SLM, and PTS. The simulation results demonstrate that the proposed approach efficiently improves spectral efficiency, preserves BER performance, and reduces PAPR as compared with conventional methods.

Role of Yeast and Yeast-Derived Products as Feed Additives in Broiler Nutrition.

Antibiotic resistance is looming problem in broiler production globally and there has been an increasing interest to look for sustainable alternatives to antibiotics. Yeast and its derived products are recognized as potential feed additives because of their beneficial impacts on poultry. Particularly, yeast exhibited positive effects on the humoral immunity by increasing serum immunoglobulin (Ig) A levels. Moreover, yeast and its products showed immune adjuvant-like properties that helped the broilers chicken to develop faster and stronger innate immune response under pathogenic challenges. Use of yeast and its products as prebiotic/probiotic improves the gut architecture mainly by improving the gut development and gut microbiome, reduction in colonization of pathogens through competitive exclusion, binding of toxins and enhancing digestion and absorption of nutrients. These unique properties of yeast and yeast products enhance animal welfare and productivity; warrant them to be used as a promising feed additive. This article, therefore, provides insights into the functional role of yeast and its products in the broiler diets and highlights its importance as a commercially viable alternative of synthetic antibiotic growth promoters in the broiler feed industry.

An updated review on behavior of domestic quail with reference to the negative effect of heat stress.

Japanese quail originated from the wildlife environment and was first domesticated in Japan in 1595. Japanese quail has widely distributed in various parts of the world. This bird is characterized by its rapid growth rate, high rate of egg production, much lower space requirements, small size, good reproductive potential, short life cycle, resistance to diseases, early sexual maturity (from 39 to 50 days), better laying ability and shorter time of hatching compared with the different species of poultry. All these characteristics rendered it an excellent laboratory animal and a good economical animal protein source (for both egg and meat). Thermal stress was found to be the major limiting variable in poultry production, directly influencing bird welfare conditions. Previous research showed that heat stress in the production environment, induced by high ambient temperatures, may have a direct detrimental effect on welfare, meat quality, carcass characteristics, productivity, egg mass and egg quality. Furthermore, heat stress directly decreases quails' reproductive performance. As tiny, ground-dwelling birds, quail may appear unable to handle extreme temperatures, yet they have methods of fighting the heat. This review will help in developing and strengthening the core of the quail-based poultry sector. In addition, it provides aggregate information on the characteristics of the quail bird as a production unit in poultry farms as well as being an animal model for laboratory experiments. Also, this review provided deep insight into the domestication process and the impact of heat stress on production characteristics, which altered the domestic or Japanese quail substantially.

Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification.

Biomedical engineering is the application of the principles and problem-solving methods of engineering to biology along with medicine. Computation intelligence is the study of design of intelligent agents which are systems acting perceptively. The computation intelligence paradigm offers more advantages to the enhancement and maintenance of the field of biomedical engineering. Liver cancer is the major reason of mortality worldwide. Earlier-stage diagnosis and treatment might increase the survival rate of liver cancer patients. Manual recognition of the cancer tissue is a time-consuming and difficult task. Hence, a computer-aided diagnosis (CAD) is employed in decision making procedures for accurate diagnosis and effective treatment. In contrast to classical image-dependent "semantic" feature evaluation from human expertise, deep learning techniques could learn feature representation automatically from sample images using convolutional neural network (CNN). This study introduces a Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification (HRO-DLBLCC) model. The proposed HRO-DLBLCC model majorly focuses on the identification of liver cancer in the medical images. To do so, the proposed HRO-DLBLCC model employs preprocessing in two stages, namely, Gabor filtering (GF) based noise removal and watershed transform based segmentation. In addition, the proposed HRO-DLBLCC model involves NAdam optimizer with DenseNet-201 based feature extractor to generate an optimal set of feature vectors. Finally, the HRO algorithm with recurrent neural network-long short-term memory (RNN-LSTM) model is applied for liver cancer classification, in which the hyperparameters of the RNN-LSTM model are tuned by the use of HRO algorithm. The HRO-DLBLCC model is experimentally validated and compared with existing models. The experimental results assured the promising performance of the HRO-DLBLCC model over recent approaches.

Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images.

Decision-making medical systems (DMS) refer to the design of decision techniques in the healthcare sector. They involve a procedure of employing ideas and decisions related to certain processes such as data acquisition, processing, judgment, and conclusion. Pancreatic cancer is a lethal type of cancer, and its prediction is ineffective with current techniques. Automated detection and classification of pancreatic tumors can be provided by the computer-aided diagnosis (CAD) model using radiological images such as computed tomography (CT) and magnetic resonance imaging (MRI). The recently developed machine learning (ML) and deep learning (DL) models can be utilized for the automated and timely detection of pancreatic cancer. In light of this, this article introduces an intelligent deep-learning-enabled decision-making medical system for pancreatic tumor classification (IDLDMS-PTC) using CT images. The major intention of the IDLDMS-PTC technique is to examine the CT images for the existence of pancreatic tumors. The IDLDMS-PTC model derives an emperor penguin optimizer (EPO) with multilevel thresholding (EPO-MLT) technique for pancreatic tumor segmentation. Additionally, the MobileNet model is applied as a feature extractor with optimal auto encoder (AE) for pancreatic tumor classification. In order to optimally adjust the weight and bias values of the AE technique, the multileader optimization (MLO) technique is utilized. The design of the EPO algorithm for optimal threshold selection and the MLO algorithm for parameter tuning shows the novelty. A wide range of simulations was executed on benchmark datasets, and the outcomes reported the promising performance of the IDLDMS-PTC model on the existing methods.

Enhancement of drought tolerance in diverse Vicia faba cultivars by inoculation with plant growth-promoting rhizobacteria under newly reclaimed soil conditions.

Water deficit has devastating impacts on legume production, particularly with the current abrupt climate changes in arid environments. The application of plant growth-promoting rhizobacteria (PGPR) is an effective approach for producing natural nitrogen and attenuating the detrimental effects of drought stress. This study investigated the influence of inoculation with the PGPR Rhizobium leguminosarum biovar viciae (USDA 2435) and Pseudomonas putida (RA MTCC5279) solely or in combination on the physio-biochemical and agronomic traits of five diverse Vicia faba cultivars under well-watered (100% crop evapotranspiration [ETc]), moderate drought (75% ETc), and severe drought (50% ETc) conditions in newly reclaimed poor-fertility sandy soil. Drought stress substantially reduced the expression of photosynthetic pigments and water relation parameters. In contrast, antioxidant enzyme activities and osmoprotectants were considerably increased in plants under drought stress compared with those in well-watered plants. These adverse effects of drought stress reduced crop water productivity (CWP) and seed yield-related traits. However, the application of PGPR, particularly a consortium of both strains, improved these parameters and increased seed yield and CWP. The evaluated cultivars displayed varied tolerance to drought stress: Giza-843 and Giza-716 had the highest tolerance under well-watered and moderate drought conditions, whereas Giza-843 and Sakha-4 were more tolerant under severe drought conditions. Thus, co-inoculation of drought-tolerant cultivars with R. leguminosarum and P. putida enhanced their tolerance and increased their yield and CWP under water-deficit stress conditions. This study showed for the first time that the combined use of R. leguminosarum and P. putida is a promising and ecofriendly strategy for increasing drought tolerance in legume crops.

Genetic Stability, Phenolic, Flavonoid, Ferulic Acid Contents, and Antioxidant Activity of Micropropagated Lycium schweinfurthii Plants.

Lycium schweinfurthii is a Mediterranean wild shrub rich in plant secondary metabolites. In vitro propagation of this plant may support the production of valuable dietary supplements for humanity, introduction of it to the world market, and opportunities for further studies. The presented study aimed to introduce an efficient and reproducible protocol for in vitro micropropagation of L. schweinfurthii and assess the genetic stability of micropropagated plants (MiPs) as well as to estimate phenolic, flavonoid, ferulic acid contents, and the antioxidant activity in leaves of micropropagated plants. Two DNA-based techniques, random amplified polymorphic DNA (RAPD) and inter-simple sequence repeats (ISSR), and one biochemical technique, sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), were used to assess the genetic stability in MiPs. Spectrophotometric analysis was performed to estimate total phenolic and flavonoid contents and antioxidant activity of MiPs leaves, while ferulic acid content was estimated using high-performance thin-layer chromatography (HPTLC). Sufficient shoot proliferation was achieved at MS (Murashige and Skoog) medium supplemented with 0.4 mg L-1 kinetin and rooted successfully on half-strength MS medium fortified with 0.4 mg L-1 Indole-3-butyric acid (IBA). The Jaccard's similarity coefficients detected in MiPs reached 52%, 55%, and 82% in the RAPD, ISSR, and SDS-PAGE analyses, respectively. In the dried leaves of MiPs, the phenolic, flavonoid, and ferulic acid contents of 11.53 mg gallic acid equivalent, 12.99 mg catechin equivalent, and 45.52 mg were estimated per gram, respectively. However, an IC50 of 0.43, and 1.99 mg mL-1 of MiP dried leaves' methanolic extract was required to scavenge half of the DPPH, and ABTS free radicals, respectively. The study presented a successful protocol for in vitro propagation of a valued promising plant source of phenolic compounds.

Thermal stress and high stocking densities in poultry farms: Potential effects and mitigation strategies.

Environmental changes pose significant threats to agricultural activities particularly animal production. These changes have induced major concerns which will negatively affect the poultry health and productivity under the current climate changes. Moreover, they also alter the immunological status of the exposed birds and make them susceptible to different diseases. The adverse effects of environmental stress also include poor performance of birds (reduced feed intake, growth, feed efficiency, immunity, and egg production) and inferior product quality. The adverse effect of heat stress on different quail breeds like Japanese quail, bobwhite quail, scaled quail, and Gambel's quail ranged from decreased growth rates (11.0-14.5%), body weight (7.7-13.2%), feed intake (6.1-21.6%), feed efficiency (4.3-8.6%), and egg production (6.6-23.3%). Also, birds reared under heat stress (34 °C) had significantly decreased Haugh units by 10.8% and egg weight by 14.3% in comparison with the control group (reared at 22 °C). On the other hand, increasing stoking density from 30 to 45 kg/m2 also negatively affected the feed intake and body weight. Recent studies have focused on evaluating the potential adverse effects of different environmental stresses on poultry performance, behavior, welfare, and reproduction. It is imperative to understand better the interaction of different environmental factors and their subsequent effects on avian physiology, to spotlights on the effective management and nutritional strategies to alleviate the adverse effects of different stresses in poultry. This review aims to present a comprehensive overview of physiological manifestations of major environmental stresses including thermal stress (heat and cold stress) and high stocking densities on poultry health and production. Moreover, we have also critically evaluated the scope and efficacy of some potential strategies to mitigate the influences of these environmental stressors in different poultry species.

Comparative Study between Exogenously Applied Plant Growth Hormones versus Metabolites of Microbial Endophytes as Plant Growth-Promoting for Phaseolus vulgaris L.

Microbial endophytes organize symbiotic relationships with the host plant, and their excretions contain diverse plant beneficial matter such as phytohormones and bioactive compounds. In the present investigation, six bacterial and four fungal strains were isolated from the common bean (Phaseolus vulgaris L.) root plant, identified using molecular techniques, and their growth-promoting properties were reviewed. All microbial isolates showed varying activities to produce indole-3-acetic acid (IAA) and different hydrolytic enzymes such as amylase, cellulase, protease, pectinase, and xylanase. Six bacterial endophytic isolates displayed phosphate-solubilizing capacity and ammonia production. We conducted a field experiment to evaluate the promotion activity of the metabolites of the most potent endophytic bacterial (Bacillus thuringiensis PB2 and Brevibacillus agri PB5) and fungal (Alternaria sorghi PF2 and, Penicillium commune PF3) strains in comparison to two exogenously applied hormone, IAA, and benzyl adenine (BA), on the growth and biochemical characteristics of the P. vulgaris L. Interestingly, our investigations showed that bacterial and fungal endophytic metabolites surpassed the exogenously applied hormones in increasing the plant biomass, photosynthetic pigments, carbohydrate and protein contents, antioxidant enzyme activity, endogenous hormones and yield traits. Our findings illustrate that the endophyte Brevibacillus agri (PB5) provides high potential as a stimulator for the growth and productivity of common bean plants.

Impact of Foliar Application of Chitosan Dissolved in Different Organic Acids on Isozymes, Protein Patterns and Physio-Biochemical Characteristics of Tomato Grown under Salinity Stress.

In this study, the anti-stress capabilities of the foliar application of chitosan, dissolved in four different organic acids (acetic acid, ascorbic acid, citric acid and malic acid) have been investigated on tomato (Solanum lycopersicum L.) plants under salinity stress (100 mM NaCl). Morphological traits, photosynthetic pigments, osmolytes, secondary metabolites, oxidative stress, minerals, antioxidant enzymes activity, isozymes and protein patterns were tested for potential tolerance of tomato plants growing under salinity stress. Salinity stress was caused a reduction in growth parameters, photosynthetic pigments, soluble sugars, soluble proteins and potassium (K+) content. However, the contents of proline, ascorbic acid, total phenol, malondialdehyde (MDA), hydrogen peroxide (H2O2), sodium (Na+) and antioxidant enzyme activity were increased in tomato plants grown under saline conditions. Chitosan treatments in any of the non-stressed plants showed improvements in morphological traits, photosynthetic pigments, osmolytes, total phenol and antioxidant enzymes activity. Besides, the harmful impacts of salinity on tomato plants have also been reduced by lowering MDA, H2O2 and Na+ levels. Chitosan treatments in either non-stressed or stressed plants showed different responses in number and density of peroxidase (POD), polyphenol oxidase (PPO) and superoxide dismutase (SOD) isozymes. NaCl stress led to the diminishing of protein bands with different molecular weights, while they were produced again in response to chitosan foliar application. These responses were varied according to the type of solvent acid. It could be suggested that foliar application of chitosan, especially that dissolved in ascorbic or citric acid, could be commercially used for the stimulation of tomato plants grown under salinity stress.

Post-transcriptional regulation of gene expression in plants during abiotic stress.

Land plants are anchored in one place for most of their life cycle and therefore must constantly adapt their growth and metabolism to abiotic stresses such as light intensity, temperature and the availability of water and essential minerals. Thus, plants' subsistence depends on their ability to regulate rapidly gene expression in order to adapt their physiology to their environment. Recent studies indicate that post-transcriptional regulations of gene expression play an important role in how plants respond to abiotic stresses. We will review the different mechanisms of post-transcriptional regulation of nuclear genes expression including messenger RNA (mRNA) processing, stability, localization and protein translation, and discuss their relative importance for plant adaptation to abiotic stress.