Explore the vast range of titles available.

The Internet of Things (IoT) devices generate a large amount of data over networks; therefore, the efficiency, complexity, interfaces, dynamics, robustness, and interaction need to be re-examined on a large scale. This phenomenon will lead to seamless network connectivity and the capability to provide support for the IoT. The traditional IoT is not enough to provide support. Therefore, we designed this study to provide a systematic analysis of next-generation advancements in the IoT. We propose a systematic catalog that covers the most recent advances in the traditional IoT. An overview of the IoT from the perspectives of big data, data science, and network science disciplines and also connecting technologies is given. We highlight the conceptual view of the IoT, key concepts, growth, and most recent trends. We discuss and highlight the importance and the integration of big data, data science, and network science along with key applications such as artificial intelligence, machine learning, blockchain, federated learning, etc. Finally, we discuss various challenges and issues of IoT such as architecture, integration, data provenance, and important applications such as cloud and edge computing, etc. This article will provide aid to the readers and other researchers in an understanding of the IoT’s next-generation developments and tell how they apply to the real world.

Summary Hybrid crop varieties have been repeatedly demonstrated to produce significantly higher yields than their parental lines; however, the low efficiency and high cost of hybrid seed production has limited the broad exploitation of heterosis for cotton production. One option for increasing the yield of hybrid seed is to improve pollination efficiency by insect pollinators. Here, we report the molecular cloning and characterization of a semidominant gene, Beauty Mark (BM), which controls purple spot formation at the base of flower petals in the cultivated tetraploid cotton species Gossypium barbadense. BM encodes an R2R3 MYB113 transcription factor, and we demonstrate that GbBM directly targets the promoter of four flavonoid biosynthesis genes to positively regulate petal spot development. Introgression of a GbBM allele into G. hirsutum by marker‐assisted selection restored petal spot formation, which significantly increased the frequency of honeybee visits in G. hirsutum. Moreover, field tests confirmed that cotton seed yield was significantly improved in a three‐line hybrid production system that incorporated the GbBM allele. Our study thus provides a basis for the potentially broad application of this gene in improving the long‐standing problem of low seed production in elite cotton hybrid lines.

The sensitivity to abscisic acid (ABA) by its receptors, pyrabactin resistance-like proteins (PYLs), is considered a most important factor in activating the ABA signal pathway in response to abiotic stress. However, it is still unknown which PYL is the crucial ABA receptor mediating response to drought stress in cotton ( L.). Here, we reported the identification and characterization of highly induced ABA receptor GhPYL9-11A in response to drought in cotton. It is observed that was highly induced by ABA treatment. GhPYL9-11A binds to protein phosphatase 2Cs (PP2Cs) in an ABA-independent manner. Moreover, the GhPYL-11A-PP2C interactions are partially disrupted by mutations, proline (P84) and histidine (H111), in the gate-latch region. Transgenic overexpressing plants were hypersensitive to ABA during seed germination and early seedling stage. Further, the increased in root growth and up regulation of drought stress-related genes in transgenic as compared to wild type confirmed the potential role of in abiotic stress tolerance. Consistently, the expression level of is on average higher in drought-tolerant cotton cultivars than in drought-sensitive cottons under drought treatment. In conclusion, the manipulation of expression could be a useful strategy for developing drought-tolerant cotton cultivars.

Narrow genetic base and complex allotetraploid genome of cotton (Gossypium hirsutum L.) is stimulating efforts to avail required polymorphism for marker based breeding. The availability of draft genome sequence of G. raimondii and G. arboreum and next generation sequencing (NGS) technologies facilitated the development of high-throughput marker technologies in cotton. The concepts of genetic diversity, QTL mapping, and marker assisted selection (MAS) are evolving into more efficient concepts of linkage disequilibrium, association mapping, and genomic selection, respectively. The objective of the current review is to analyze the pace of evolution in the molecular marker technologies in cotton during the last ten years into the following four areas: (i) comparative analysis of low- and high-throughput marker technologies available in cotton, (ii) genetic diversity in the available wild and improved gene pools of cotton, (iii) identification of the genomic regions within cotton genome underlying economic traits, and (iv) marker based selection methodologies. Moreover, the applications of marker technologies to enhance the breeding efficiency in cotton are also summarized. Aforementioned genomic technologies and the integration of several other omics resources are expected to enhance the cotton productivity and meet the global fiber quantity and quality demands.

Bacillus thuringiensis (Bt) is a gram negative soil bacterium. This bacterium secretes various proteins during different growth phases with an insecticidal potential against many economically important crop pests. One of the important families of Bt proteins is vegetative insecticidal proteins (Vip), which are secreted into the growth medium during vegetative growth. There are three subfamilies of Vip proteins. Vip1 and Vip2 heterodimer toxins have an insecticidal activity against many Coleopteran and Hemipteran pests. Vip3, the most extensively studied family of Vip toxins, is effective against Lepidopteron. Vip proteins do not share homology in sequence and binding sites with Cry proteins, but share similarities at some points in their mechanism of action. Vip3 proteins are expressed as pyramids alongside Cry proteins in crops like maize and cotton, so as to control resistant pests and delay the evolution of resistance. Biotechnological- and in silico-based analyses are promising for the generation of mutant Vip proteins with an enhanced insecticidal activity and broader spectrum of target insects.

Protoplasts, which lack cell walls, are ideal research materials for genetic engineering. They are commonly employed in fusion (they can be used for more distant somatic cell fusion to obtain somatic hybrids), genetic transformation, plant regeneration, and other applications. Cotton is grown throughout the world and is the most economically important crop globally. It is therefore critical to study successful extraction and transformation efficiency of cotton protoplasts. In the present study, a cotton callus protoplast extraction method was tested to optimize the ratio of enzymes (cellulase, pectinase, macerozyme R-10, and hemicellulase) used in the procedure. The optimized ratio significantly increased the quantity and activity of protoplasts extracted. We showed that when enzyme concentrations of 1.5% cellulase and 1.5% pectinase, and either 1.5% or 0.5% macerozyme and 0.5% hemicellulase were used, one can obtain increasingly stable protoplasts. We successfully obtained fluorescent protoplasts by transiently expressing fluorescent proteins in the isolated protoplasts. The protoplasts were determined to be suitable for use in further experimental studies. We also studied the influence of plasmid concentration and transformation time on protoplast transformation efficiency. When the plasmid concentration reaches 16 µg and the transformation time is controlled within 12–16 h, the best transformation efficiency can be obtained. In summary, this study presents efficient extraction and transformation techniques for cotton protoplasts.

In the contemporary digital era, the Internet of Things (IoT) and its applications have proliferated extensively, particularly within smart city environments, resulting in increased network traffic and raising the significance of efficient service discovery (SD) mechanisms. The social Internet of Things (SIoT) is an emerging paradigm that enables IoT devices to autonomously establish social relationships based on rules defined by their owners, thereby enhancing services through social relations. Things can interact with others; thus, the huge volume of traffic is increased. Each node or device could select an appropriate peer for the discovery of services, which is thus helpful for human beings. Although numerous service discovery and query processing models have been proposed in the recent literature. However, the existing state-of-the-art approaches often lack a comprehensive analysis of the parameters. Most traditional state-of-the-art models primarily focus on relationships or device similarity. Also neglecting the vital factors, for instance, query processing, efficiency, spatial-temporal dynamics, and service provisioning, etc. Thus, to solve this issue, this research proposes an exhaustive analysis of the main parameters needed to implement service discovery mechanisms for Social IoT and studies their relative importance based on a dataset of real objects. Based on the advanced parameters’ selection, an efficient service discovery algorithm is proposed. The proposed model emphasizes efficiency by optimizing the service discovery through reduced social graph traversal (i.e., fewer hops), consideration of the service types, and integration of caching mechanisms. We have conducted a comprehensive analysis of key parameters essential for implementing an effective service discovery mechanism in SIoT, prioritizing based on their importance. Experimental validation demonstrates the superiority of the proposed over state-of-the-art models, confirming its efficacy, scalability.

Distributed generators (DGs) are increasingly employed in radial distribution systems owing to their ability to reduce electrical energy losses, better voltage levels, and increased dependability of the power supply. This research paper deals with the utilization of a Particle Swarm Optimization algorithm by handling its random constraints to determine the most appropriate size and location of photovoltaic-based DG (PVDG) to keep the asymmetries of the phases minimal in the grid. It is thus expected that this algorithm will provide an efficient and consistent solution to improve the overall performance of the power system. The placement and sizing of the DG are done in a way that minimizes power losses, enhances the voltage profile, i.e., bringing symmetry in the voltage profile of the system, and provides maximum cost savings. The model has been tested on an IEEE 33-bus radial distribution system using MATLAB software, in both conditions, i.e., with and without PVDG. The simulation results were successful, indicating the viability of the proposed model. The proposed PSO-based PVDG model further reduced active power losses as compared to the models based on the teaching–learning artificial bee colony algorithm (TLABC), pathfinder algorithm (PFA), and ant lion optimization algorithm (ALOA). With the proposed model, active power losses have reduced to 17.50%, 17.48%, and 8.82% compared to the losses found in the case of TLABC, PFA, and ALOA, respectively. Similarly, the proposed solution lessens the reactive power losses compared to the losses found through existing TLABC, PFA, and ALOA techniques by an extent of 23.06%, 23%, and 23.08%, respectively. Moreover, this work shows cost saving of 15.21% and 6.70% more than TLABC and ALOA, respectively. Additionally, it improves the voltage profile by 3.48% of the power distribution system.

Allotetraploid cotton (Gossypium hirsutum and Gossypium barbadense) are cultivated worldwide for its white fiber. For centuries, conventional breeding approaches increase cotton yield at the cost of extensive erosion of natural genetic variability. Sea Island cotton (G. barbadense) is known for its superior fiber quality, but show poor adaptability as compared to Upland cotton. Here, in this study, we use ethylmethanesulfonate (EMS) as a mutagenic agent to induce genome-wide point mutations to improve the current germplasm resources of Sea Island cotton and develop diverse breeding lines with improved adaptability and excellent economic traits. We determined the optimal EMS experimental procedure suitable for construction of cotton mutant library. At M6 generation, mutant library comprised of lines with distinguished phenotypes of the plant architecture, leaf, flower, boll, and fiber. Genome-wide analysis of SNP distribution and density in yellow leaf mutant reflected the better quality of mutant library. Reduced photosynthetic efficiency and transmission electron microscopy of yellow leaf mutants revealed the effect of induced mutations at physiological and cellular level. Our mutant collection will serve as the valuable resource for basic research on cotton functional genomics, as well as cotton breeding.

Sentiment analysis plays an important role in understanding employee feedback and improving workplace culture. By leveraging NLP techniques to analyze this feedback accurately, organizations can pinpoint specific areas that need improvement, address employee concerns, and foster a positive work environment. These NLP-driven deep learning models offer valuable tools for E-Commerce HR and sales departments, enabling monitoring employee and users’ sentiment trends over time and assisting in implementing targeted interventions. Focusing on the e-commerce industry, this work utilizes NLP-driven deep learning methodologies to analyze employee and user feedback, aiming to identify sentiments. The proposed NLP-driven, deep learning-based framework is designed to classify user feedback into positive, negative, or neutral sentiments. The key steps in this framework include data collection, NLP-enhanced feature extraction using BERT-BiGRU, and final classification using a Graph Neural Network-based finite-state automata. The effectiveness of this NLP-centric approach was tested on diverse datasets of customer feedback from the e-commerce industry. The results demonstrate the framework’s efficacy, achieving an impressive 93.35% accuracy rate, surpassing existing benchmark methods. The research significantly benefits e-commerce by refining product portfolios and enhancing workplace culture.