Genetic Hybrid Research Articles

Lowering the PAPR of the optical OTFS-based 6G radio with a hybrid PTS-PSO genetic approach

Orthogonal Time Frequency Space (OTFS) is regarded as one of the best contenders for sixth-generation (6G) radio systems due to its ability to obtain excellent performance in high-speed scenarios. Peak-to-average power ratio (PAPR) significantly impacts and degrades the efficiency of the power amplifier used in OTFS-based 6G systems. The proposed article presents a genetic hybrid algorithm, Partial transmission sequence-particle swarm optimization (PTS-PSO), obtaining an optimal PAPR performance with low computation complexity. The PSO generates the best phase factor compared to the conventional phase generation method of PTS, making PTS-PSO more effective. The impacts such as PAPR, bit error rate (BER), power spectral density (PSD), and complexity of the proposed PTS-PSO are compared with the conventional selective mapping (SLM) and PTS methods for 64, 256, and 512 OTFS sub-carriers. The projected PTS-PSO attained a PAPR and PSD gain of 1–6 dB and − 540 while preserving the BER performance. The experimental results demonstrate that the projected PTS-PSO outperforms the conventional SLM and PTS algorithms with trivial intricacy.

Establishing the green algae Chlamydomonas incerta as a platform for recombinant protein production.

Chlamydomonas incerta, a genetically close relative of the model green alga Chlamydomonas reinhardtii, shows significant potential as a host for recombinant protein expression. Because of the close genetic relationship between C. incerta and C. reinhardtii, this species offers an additional reference point for advancing our understanding of photosynthetic organisms, and also provides a potential new candidate for biotechnological applications. This study investigates C. incerta's capacity to express three recombinant proteins: the fluorescent protein mCherry, the hemicellulose-degrading enzyme xylanase, and the plastic-degrading enzyme PHL7. We have also examined the capacity to target protein expression to various cellular compartments in this alga, including the cytosol, secretory pathway, cytoplasmic membrane, and cell wall. When compared directly with C. reinhardtii, C. incerta exhibited a distinct but notable capacity for recombinant protein production. Cellular transformation with a vector encoding mCherry revealed that C. incerta produced approximately 3.5 times higher fluorescence levels and a 3.7-fold increase in immunoblot intensity compared to C. reinhardtii. For xylanase expression and secretion, both C. incerta and C. reinhardtii showed similar secretion capacities and enzymatic activities, with comparable xylan degradation rates, highlighting the industrial applicability of xylanase expression in microalgae. Finally, C. incerta showed comparable PHL7 activity levels to C. reinhardtii, as demonstrated by the in vitro degradation of a polyester polyurethane suspension, Impranil® DLN. Finally, we also explored the potential of cellular fusion for the generation of genetic hybrids between C. incerta and C. reinhardtii as a means to enhance phenotypic diversity and augment genetic variation. We were able to generate genetic fusion that could exchange both the recombinant protein genes, as well as associated selectable marker genes into recombinant offspring. These findings emphasize C. incerta's potential as a robust platform for recombinant protein production, and as a powerful tool for gaining a better understanding of microalgal biology.

Model Optimization and Application of Straw Mulch Quantity Using Remote Sensing

Straw mulch quantity is an important indicator in the detection of straw returned to the field in conservation tillage, but there is a lack of large-scale automated measurement methods. In this study, we estimated global straw mulch quantity and completed the detection of straw returned to the field. We used an unmanned aerial vehicle (UAV) carrying a multispectral camera to acquire remote sensing images of straw in the field. First, the spectral index was selected using the Elastic-net (ENET) algorithm. Then, we used the Genetic Algorithm Hybrid Particle Swarm Optimization (GA-HPSO) algorithm, which embeds crossover and mutation operators from the Genetic Algorithm (GA) into the improved Particle Swarm Optimization (PSO) algorithm to solve the problem of machine learning model prediction performance being greatly affected by parameters. Finally, we used the Monte Carlo method to achieve a global estimation of straw mulch quantity and complete the rapid detection of field plots. The results indicate that the inversion model optimized using the GA-HPSO algorithm performed the best, with the coefficient of determination (R2) reaching 0.75 and the root mean square error (RMSE) only being 0.044. At the same time, the Monte Carlo estimation method achieved an average accuracy of 88.69% for the estimation of global straw mulch quantity, which was effective and applicable in the detection of global mulch quantity. This study provides a scientific reference for the detection of straw mulch quantity in conservation tillage and also provides a reliable model inversion estimation method for the estimation of straw mulch quantity in other crops.

CsTs, a C-type lectin receptor-like kinase, regulates the development trichome development and cuticle metabolism in cucumber (Cucumis sativus).

Cucumber (Cucumis sativus) fruit spines are a classic material for researching the development of multicellular trichomes. Some key genes that influence trichome development have been confirmed to be associated with cuticle biosynthesis and secondary metabolism. However, the biological mechanisms underlying trichome development, cuticle biosynthesis, and secondary metabolism in cucumber remain poorly understood. CsTs, a C-type lectin receptor-like kinase gene, reportedly causes a tender trichome phenotype in cucumber when it mutates. In this study, the role of CsTs in cucumber fruit spines morphogenesis was confirmed using gene editing technology. Sectioning and cell wall component detection were used to analyse the main reason of tender fruit spines in the ts mutant. Subsequently, transcriptome data and a series of molecular biology experiments were used to further investigate the relationship between CsTs and cytoskeletal homeostasis in cucumber. CsTs overexpression partially compensated for the abnormal trichome phenotype of an Arabidopsis homolog mutant. Genetic hybridization and metabolic analysis indicated that CsTs and CsMict can affect trichome development and cuticle biosynthesis in the same pathway. Our findings provide important background information for further researching on the molecular mechanism underlying cucumber trichome development and contribute to understanding the biological function of C-type lectin receptor-like kinases.

Genetic fuzzy rules and hybrid QDCNN-F-DSAE for detecting attacker behavior with tuning of firewall

ABSTRACT The application of computer technology is increasing quickly as the field of information technology develops over time. Because of the growth of computer networks, there is a rise in network attacks. Moreover, the majority of transactions are conducted via the Internet, and then the web application security is becoming increasingly important. A network firewall shields the web servers from unauthorised and dangerous traffic. In this work, the hybrid Quantum Dilated Convolutional Neural Network fused Deep-Stacked Auto Encoder (QDCNN-F-DSAE) model with Genetic Fuzzy System (GFS) is developed for network protection. With the utilisation of genetic fuzzy system (GFS) and firewall tuning, the QDCNN-F-DSAE identified the attack, ransomware activity, and prompt decision-making. The data offered by the simulated environment is used to make decisions, and the decisions should be made quickly within a few seconds using the proposed QDCNN-F-DSAE with GFS. Furthermore, it identifies the attacks and alerts the users to store the data from the supplied source. The malicious function and protected private Local Area Network (LAN) are discovered and removed by the firewall after the formation of genetic fuzzy rules. Moreover, this model is estimated using the accuracy, sensitivity, and specificity measures that offered the finest values of 0.915, 0.908, and 0.920.

The Genetic Characterization of the Canarian Endemic Palm (Phoenix canariensis) by Simple Sequence Repeats and Chloroplast Markers: A Tool for the Molecular Traceability of Phoenix Hybridization

The endemic palm from the Canary Islands, Phoenix canariensis, is one of the most distinctive elements of the Canarian vegetation landscape, contributing to cultural, economic and environmental aspects. One of the main conservation problems facing this iconic palm is anthropogenic hybridization with other Phoenix species, particularly Phoenix dactylifera, which has been introduced extensively throughout its geographical range. Therefore, it is important to obtain a genetic tool that addresses different issues that may have an impact on the protection of P. canariensis, including ornamental applications and wild population conservation purposes. Our main goals were to detect a molecular tracer that could reliably distinguish between Phoenix canariensis and P. dactylifera in the Canary archipelago and to characterize the presence and extent of genetic hybridization events between the two species. We used 19 nuclear microsatellites and 1 chloroplast minisatellite set and analysed a large sample size (N = 433) of plants using both Bayesian methods and ordination techniques. Our data showed that a set of 13 nuclear markers revealed diagnostic alleles for P. canariensis, which were defined as the Canarian nuclear genotype (CNG). Moreover, P. canariensis exhibited an exclusive chlorotype of 266 bp that together with the GNC serve as an indicator of genetic purity in the Canarian palm. These markers are sufficient to detect any hybrid, even if it is not related to morphological differences. The occurrence of a considerable number of specimens with different degrees of hybridization is discussed in terms of the existence of different generations of hybrids and different types of crosses. Thus, the genetic tracers represent an invaluable tool to address any proposal for the genetic conservation of Phoenix canariensis.

Analyzing intelligent tourism development and public services based on a fuzzy genetic hybrid system to promote environmental and cultural values.

Environmental, cultural, and public service-dependent factors encourage the development of a country's tourism. In recent years, automated tourism development using statistical and accumulated data has been exploited to recommend attractive tourist features. This article thus discloses an intelligent development assessment method (IDAM) using cumulative factors (CFs) for deriving development-focused improvement in tourism. This method accounts for public services and environmental and cultural factors that promote tourism for better assessment. The fuzzy process identifies the maximum possible impacting factors by independently evaluating the reviewed values. Based on the reviewed values, the manipulation of factor relationships is derived to identify even trivial factors impacting development. The fuzzy outputs are thus integrated with optimistically impacting development factors to provide attractive recommendations. Such recommendations are analyzed using fuzzy data for previous and current development factors for new decisions. The system's efficiency was evaluated using the recommendation ratio, ensuring a 48.58% success rate, a development rate of 0.105%, a 4-factor detection rate, and a review-based assessment rate of 55.5% for a sample size of 5,000 visitors.

Hard Ticks as Vectors: The Emerging Threat of Tick-Borne Diseases in India.

Hard ticks (Ixodidae) play a critical role in transmitting various tick-borne diseases (TBDs), posing significant global threats to human and animal health. Climatic factors influence the abundance, diversity, and vectorial capacity of tick vectors. It is imperative to have a comprehensive understanding of hard ticks, pathogens, eco-epidemiology, and the impact of climatic changes on the transmission dynamics of TBDs. The distribution and life cycle patterns of hard ticks are influenced by diverse ecological factors that, in turn, can be impacted by changes in climate, leading to the expansion of the tick vector's range and geographical distribution. Vector competence, a pivotal aspect of vectorial capacity, involves the tick's ability to acquire, maintain, and transmit pathogens. Hard ticks, by efficiently feeding on diverse hosts and manipulating their immunity through their saliva, emerge as competent vectors for various pathogens, such as viruses, parasites and bacteria. This ability significantly influences the success of pathogen transmission. Further exploration of genetic diversity, population structure, and hybrid tick vectors is crucial, as they play a substantial role in influencing vector competence and complicating the dynamics of TBDs. This comprehensive review deals with important TBDs in India and delves into a profound understanding of hard ticks as vectors, their biology, and the factors influencing their vector competence. Given that TBDs continue to pose a substantial threat to global health, the review emphasizes the urgency of investigating tick control strategies and advancing vaccine development. Special attention is given to the pivotal role of population genetics in comprehending the genetic diversity of tick populations and providing essential insights into their adaptability to environmental changes.

A New Hybrid Approach for Product Management in E-Commerce

Nowadays, due to the developments in technology and the effects of the pandemic, people have largely switched to e-commerce instead of traditional face-to-face commerce. In this sector, the product variety reaches tens of thousands, which has made it difficult to manage and to make quick decisions on inventory, promotion, pricing, and logistics. Therefore, it is thought that obtaining accurate and fast forecasting for the future will provide significant benefits to such companies in every respect. This study was built on the proposal of creating a cluster-based–genetic algorithm hybrid forecasting model including genetic algorithm (GA), cluster analysis, and some forecasting models as a new approach. In this study, unlike the literature, an attempt was made to create a more successful forecasting model for many products at the same time inside of single product forecasting. The proposed CBGA model success was compared separately to both the single prediction method successes and only genetic algorithm-based hybrid model successes by using real values from a popular B2C company. As a result, it has been observed that the forecasting success of the model proposed in this study is more successful than the forecasting made using single models or only the genetic algorithm.

Promiscuous potato: elucidating genetic identity and the complex genetic relationships of a cultivated potato germplasm collection.

A total of 3,860 accessions from the global in trust clonal potato germplasm collection w3ere genotyped with the Illumina Infinium SolCAP V2 12K potato SNP array to evaluate genetic diversity and population structure within the potato germplasm collection. Diploid, triploid, tetraploid, and pentaploid accessions were included representing the cultivated potato taxa. Heterozygosity ranged from 9.7% to 66.6% increasing with ploidy level with an average heterozygosity of 33.5%. Identity, relatedness, and ancestry were evaluated using hierarchal clustering and model-based Bayesian admixture analyses. Errors in genetic identity were revealed in a side-by-side comparison of in vitro clonal material with the original mother plants revealing mistakes putatively occurring during decades of processing and handling. A phylogeny was constructed to evaluate inter- and intraspecific relationships which together with a STRUCTURE analysis supported both commonly used treatments of potato taxonomy. Accessions generally clustered based on taxonomic and ploidy classifications with some exceptions but did not consistently cluster by geographic origin. STRUCTURE analysis identified putative hybrids and suggested six genetic clusters in the cultivated potato collection with extensive gene flow occurring among the potato populations, implying most populations readily shared alleles and that introgression is common in potato. Solanum tuberosum subsp. andigena (ADG) and S. curtilobum (CUR) displayed significant admixture. ADG likely has extensive admixture due to its broad geographic distribution. Solanum phureja (PHU), Solanum chaucha (CHA)/Solanum stenotomum subsp. stenotomum (STN), and Solanum tuberosum subsp. tuberosum (TBR) populations had less admixture from an accession/population perspective relative to the species evaluated. A core and mini core subset from the genebank material was also constructed. SNP genotyping was also carried out on 745 accessions from the Seed Savers potato collection which confirmed no genetic duplication between the two potato collections, suggesting that the collections hold very different genetic resources of potato. The Infinium SNP Potato Array is a powerful tool that can provide diversity assessments, fingerprint genebank accessions for quality management programs, use in research and breeding, and provide insights into the complex genetic structure and hybrid origin of the diversity present in potato genetic resource collections.

Use of simulation to optimize a sweet corn breeding program: implementing genomic selection and doubled haploid technology.

Genomic selection and doubled haploids hold significant potential to enhance genetic gains and shorten breeding cycles across various crops. Here, we utilized stochastic simulations to investigate the best strategies for optimize a sweet corn breeding program. We assessed the effects of incorporating varying proportions of old and new parents into the crossing block (3:1, 1:1, 1:3, and 0:1 ratio, representing different degrees of parental substitution), as well as the implementation of genomic selection in two distinct pipelines: one calibrated using the phenotypes of testcross parents (GSTC scenario) and another using F1 individuals (GSF1). Additionally, we examined scenarios with doubled haploids, both with (DH) and without (DHGS) genomic selection. Across 20 years of simulated breeding, we evaluated scenarios considering traits with varying heritabilities, the presence or absence of genotype-by-environment effects, and two program sizes (50 vs 200 crosses per generation). We also assessed parameters such as parental genetic mean, average genetic variance, hybrid mean, and implementation costs for each scenario. Results indicated that within a conventional selection program, a 1:3 parental substitution ratio (replacing 75% of parents each generation with new lines) yielded the highest performance. Furthermore, the GSTC model outperformed the GSF1 model in enhancing genetic gain. The DHGS model emerged as the most effective, reducing cycle time from 5 to 4 years and enhancing hybrid gains despite increased costs. In conclusion, our findings strongly advocate for the integration of genomic selection and doubled haploids into sweet corn breeding programs, offering accelerated genetic gains and efficiency improvements.

A Hybrid Genetic Algorithm for Ground Station Scheduling Problems

In recent years, the substantial growth in satellite data transmission tasks and volume, coupled with the limited availability of ground station hardware resources, has exacerbated conflicts among missions and rendered traditional scheduling algorithms inadequate. To address this challenge, this paper introduces an improved tabu genetic hybrid algorithm (ITGA) integrated with heuristic rules for the first time. Firstly, a constraint satisfaction model for satellite data transmission tasks is established, considering multiple factors such as task execution windows, satellite–ground visibility, and ground station capabilities. Leveraging heuristic rules, an initial population of high-fitness chromosomes is selected for iterative refinement. Secondly, the proposed hybrid algorithm iteratively evolves this population towards optimal solutions. Finally, the scheduling plan with the highest fitness value is selected as the best strategy. Comparative simulation experimental results demonstrate that, across four distinct scenarios, our algorithm achieves improvements in the average task success rate ranging from 1.5% to 19.8% compared to alternative methods. Moreover, it reduces the average algorithm execution time by 0.5 s to 28.46 s and enhances algorithm stability by 0.8% to 27.7%. This research contributes a novel approach to the efficient scheduling of satellite data transmission tasks.

Genomic and epidemiological evidence for the emergence of a L. infantum/L. donovani hybrid with unusual epidemiology in northern Italy.

Leishmania (L.) infantum is one of the main causative agents of animal and human leishmaniasis across many endemic areas in South America, Europe, North Africa, and Asia. Despite its clinical significance, little is known about the genetic diversity of L. infantum circulating in a given endemic area. Here, we investigate this important open question by applying a comparative genomics approach to seven L. infantum isolates from different hosts and Italian regions, including the northern part of the country (Emilia-Romagna, RER), Sicily, and Sardinia, as an initial attempt to explore the breadth of parasite genetic heterogeneity in Italy. Additionally, microsatellite analysis was carried out to compare the isolates from RER with other 70 L. infantum strains from the same region as well as 65 strains belonging to the L. donovani complex from other countries. We revealed important karyotypic instability and identified strain-specific changes in gene dosage, which affected important virulence factors such as amastins and surface antigen-like proteins. Single nucleotide polymorphism-based clustering analysis of these genomes together with over 80 publicly available L. infantum and L. donovani genomes placed the Italian isolates into three geographically distinct clusters within the Mediterranean basin and uncovered three isolates clustering with putative L. infantum/L. donovani hybrids isolated in Cyprus. As judged by microsatellite profiling, these hybrid isolates are representative of a sub-population of parasites circulating in northern Italy that preferentially infect humans but not dogs. Our results place Italy at the crossroads of L. infantum infection in the Mediterranean and call attention to the public health risk represented by the introduction of non-European Leishmania species.IMPORTANCEThis study closes important knowledge gaps with respect to Leishmania (L.) infantum genetic heterogeneity in a given endemic country, as exemplified here for Italy, and reveals genetic hybridization as a main cause for re-emerging human leishmaniasis in northern Italy. The observed high diversity of Leishmania parasites on the Italian peninsula suggests different geographical origins, with genomic adaptation to various ecologies affecting both pathogenicity and transmission potential. This is documented by the discovery of a putative L. infantum/L. donovani hybrid strain, which has been shown to preferentially infect humans but not dogs. Our results provide important information to health authorities, which need to consider the public health risk represented by the introduction of new Leishmania species into EU countries due to population displacement or travel from countries where exotic/allochthonous parasite species are endemic.

Hyperspectral remote sensing of forage stoichiometric ratios in the senescent stage of alpine grasslands

Senescence is an important phenological stage in the life cycle of grassland plants, and estimating grass senescence in alpine grasslands is particularly crucial for animal husbandry and grazing the utilization of grassland resources for grazing. However, the application of proximal sensing technologies to capture the variations in forage growth parameters in the senescent stage, especially regarding some key stoichiometric ratios, still faces challenges associated with indiscernible spectral properties and indeterminate absorption and reflectance features. The present study aims to demonstrate the potential and effectiveness of applying hyperspectral data to assess the forage carbon-nitrogen (C:N) ratio and calcium-phosphorus (Ca:P) ratio during the senescent stage (September to November). A progressive variable dimensionality reduction strategy based on a genetic algorithm (GA) and hybrid optimization method (ant optimization colony (ACO) and extreme learning machine (ELM)) coupled with multiple linear and nonlinear statistical methods is applied to develop C:N ratio and Ca:P ratio estimation models via combination with data from 205 sample sites collected from six field campaigns (2016–2019) on the eastern Tibetan Plateau. The results show that the important spectral bands favorable for forage C:N ratio and Ca:P ratio retrieval are generally in the red and shortwave infrared (SWIR) regions, and the proposed model presents satisfactory performance in the estimation of the forage C:N ratio (V-R2 = 0.81, V-RMSE = 6.44) and Ca:P ratio (V-R2 = 0.64, V-RMSE = 2.54) during senescence. Moreover, the model can effectively overcome the spatial differences among sampling areas and perform optimally in capturing the variations in the forage C:N ratio and Ca:P ratio in the early and middle stages of senescence. Overall, our study demonstrates that it is feasible and promising to estimate these stoichiometric ratios using hyperspectral feature bands during grass senescence at the canopy level, with potential applications that may further enhance the monitoring of forage quality and quantity.

Changes in the corn yield structure depending on genetic characteristics, background mineral nutrition and sowing time in the Khabarovsk Krai

This study presents the results of a three-factor experiment on corn yield structure depending on the genetic characteristics and hybrids, mineral nutrition background and sowing dates. The experiment was carried out in Khabarovsk Krai in fourfold repetition on corn crops of local selection varieties Birsu, ALitet 2 and Guran 2 and hybrids of foreign selection Moldavsky 215 SV, P 7515, P 8521, P 7460; on three mineral nutrition backgrounds: № 1 (control) – N90P90K90, № 2 – N110P110K90, № 3 – N130P130K90; in 2 sowing dates with 5 days apart. The studies were carried out in 2021–2022 on the experimental field of the Far Eastern Agricultural Research Institute, the soil of the experimental section was meadow-brown, heavy loamy. The highest grain yield up to 125.3 c/ha was noted on the mineral nutrition background № 2 (N110P110K90) and № 3 (N130P130K90). Yield structure indicators depended more on varietal features of plants (46.0–73.4%) rather than on mineral nutrition background (6.2–17.8%) and sowing dates had sufficient influence (0.18–2.59%). However, the weight of 1000 grains was more influenced by additional application of nitrogen and phosphorus (40.67%) rather than varietal features (32.71%). In this study varieties of local breeding Alitet 2 and Guran 2 are recommended for cultivation in Khabarovsk Krai as they give a good grain yield, responsive to the additional application of nitrogen and phosphorus, correspond to the region conditions in terms of precocity, suitable both for harvesting silage and green fodder and for harvesting grain forage.

Unveiling Genetic Reinforcement Learning (GRLA) and Hybrid Attention-Enhanced Gated Recurrent Unit with Random Forest (HAGRU-RF) for Energy-Efficient Containerized Data Centers Empowered by Solar Energy and AI

The adoption of renewable energy sources has seen a significant rise in recent years across various industrial sectors, with solar energy standing out due to its eco-friendly characteristics. This shift from conventional fossil fuels to solar power is particularly noteworthy in energy-intensive environments such as cloud data centers. These centers, which operate continuously to support active servers via virtual instances, present a critical opportunity for the integration of sustainable energy solutions. In this study, we introduce two innovative approaches that substantially advance data center energy management. Firstly, we introduce the Genetic Reinforcement Learning Algorithm (GRLA) for energy-efficient container placement, representing a pioneering approach in data center management. Secondly, we propose the Hybrid Attention-enhanced GRU with Random Forest (HAGRU-RF) model for accurate solar energy prediction. This model combines GRU neural networks with Random Forest algorithms to forecast solar energy production reliably. Our primary focus is to evaluate the feasibility of solar energy in meeting the energy demands of cloud data centers that utilize containerization for virtualization, thereby promoting green cloud computing. Leveraging a robust German photovoltaic energy dataset, our study demonstrates the effectiveness and adaptability of these techniques across diverse environmental contexts. Furthermore, comparative analysis against traditional methods highlights the superior performance of our models, affirming the potential of solar-powered data centers as a sustainable and environmentally responsible solution.

Meta-heuristic algorithms for integrating manufacturing and supply chain functions

This study proposes an integrated framework that incorporates essential manufacturing and supply chain functions. These functions encompass process planning, scheduling, due-date assignment, and delivery optimization. The objective of this integrated approach is to achieve multiple benefits, including balanced workload distribution, enhanced company performance, generation of more realistic planning schedules, and ultimately, the achievement of shorter due dates. As a result, the overall efficiency of operations is substantially improved, with approximately a 50 % increase over isolated function management. Additionally, the isolated integration of the delivery function within systems comprising three integrated functions has been found to improve efficiency by 18%. The study employs various heuristic techniques, including genetic algorithms, simulated annealing, random search, hybrid search, and evolutionary strategy, to assess the optimal solution method and rules for these functions. The Taguchi technique is employed to ascertain the optimal values for critical parameters, such as population size, mutation rate, crossover points, and random search rate. Among the solution methods investigated, genetic algorithms consistently yielded superior results Additionally, the weighted slack rule consistently exhibited notable effectiveness compared to other due-date assignment rules. Similarly, the savings algorithm outperformed other delivery optimization rules. However, it is important to note that among the scheduling rules evaluated, none has emerged as dominant.

Exploiting hybrid vigor to enhance survival and growth for whiteleg shrimp (Litopenaeus vannamei): A complete diallel cross experiment

Whiteleg shrimp (Litopenaeus vannamei) is an economically important cultured species that is extensively cultured in China. However, there is the potential for further optimization of its survival and growth traits that are key determinants of productivity in aquaculture. This study investigated the efficacy of hybridization, a genetic improvement technology leveraging hybrid vigor, as a strategy for enhancing these traits in L. vannamei. The study was conducted using a 4 × 4 complete diallel cross experiment involving four selected strains (5, 8, 9, and 10), with the aim of identifying potential crosses exhibiting hybrid vigor. The most suitable crosses were those between strains 8 and 9, where the mid-parent heterosis (H89) was 16.52% for survival and 18.93% for growth. Our findings revealed that most reciprocal hybrids displayed higher rates of survival than their inbred parents. Additionally, an age-dependent increase in the heterosis effect on weight was observed over mid-parents and single-parents in most hybrid offspring. These findings suggest that hybridization could be a promising approach to improving the survival and growth of whiteleg shrimp. Our crossing experiment has yielded novel genetic resources for shrimp breeding, ultimately enhancing the economic importance of whiteleg shrimp.

Empirical Enhancement of Intrusion Detection Systems: A Comprehensive Approach with Genetic Algorithm-based Hyperparameter Tuning and Hybrid Feature Selection

Machine learning-based IDSs have demonstrated promising outcomes in identifying and mitigating security threats within IoT networks. However, the efficacy of such systems is contingent on various hyperparameters, necessitating optimization to elevate their performance. This paper introduces a comprehensive empirical and quantitative exploration aimed at enhancing intrusion detection systems (IDSs). The study capitalizes on a genetic algorithm-based hyperparameter tuning mechanism and a pioneering hybrid feature selection approach to systematically investigate incremental performance improvements in IDS. Specifically, our work proposes a machine learning-based IDS approach tailored for detecting attacks in IoT environments. To achieve this, we introduce a hybrid feature selection method designed to identify the most salient features for the task. Additionally, we employed the genetic algorithm (GA) to fine-tune hyperparameters of multiple machine learning models, ensuring their accuracy in detecting attacks. We commence by evaluating the default hyperparameters of these models on the CICIDS2017 dataset, followed by rigorous testing of the same algorithms post-optimization through GA. Through a series of experiments, we scrutinize the impact of combining feature selection methods with hyperparameter tuning approaches. The outcomes unequivocally demonstrate the potential of hyperparameter optimization in enhancing the accuracy and efficiency of machine learning-based IDS systems for IoT networks. The empirical nature of our research method provides a meticulous analysis of the efficacy of the proposed techniques through systematic experimentation and quantitative evaluation. Consolidated in a unified manner, the results underscore the step-by-step enhancement of IDS performance, especially in terms of detection time, substantiating the efficacy of our approach in real-world scenarios.

Arabidopsis ERD15 regulated by BBX24 plays a positive role in UV-B signaling

Ultraviolet-B (UV-B, 280-315 nm) is a minor component of solar radiation, but it has a major regulatory impact on plant growth and development. Solar UV-B regulates numerous aspects of plant metabolism, morphology and physiology through altering the expression of hundreds of genes. EARLY RESPONSIVE TO DEHYDRATION 15 (ERD15) is a drought-induced rapid response gene, formerly known as a negative regulator of the abscisic acid (ABA) signaling pathway. It is unclear whether ERD15 is involved in UV-B-induced photomorphogenesis. Previously, we reported that the BBX24 transcriptional factor negatively regulated UV-B signaling. In the present study, we identified that ERD15 is involved in UV-B photomorphogenesis as a positive regulator at phenotypic, physiological and molecular levels. Our results indicated that ERD15 expression is suppressed by UV-B, inhibited the elongation of Arabidopsis hypocotyls in a UV-B-dependent manner, promoted the expression of related UV-B signaling genes and increased the total antioxidant capacity of Arabidopsis under UV-B. Genetic hybridization results show that ERD15 acts downstream of BBX24, and BBX24 protein mediated the expression of ERD15 by binding to its promoter. Thus, ERD15 is a novel positive regulator of the UV-B signaling pathway, which is downstream of BBX24 and regulated by BBX24 protein to participate in UV-B photomorphogenesis.