Multiple Environments Research Articles

Multi-UAV simultaneous target assignment and path planning based on deep reinforcement learning in dynamic multiple obstacles environments.

Target assignment and path planning are crucial for the cooperativity of multiple unmanned aerial vehicles (UAV) systems. However, it is a challenge considering the dynamics of environments and the partial observability of UAVs. In this article, the problem of multi-UAV target assignment and path planning is formulated as a partially observable Markov decision process (POMDP), and a novel deep reinforcement learning (DRL)-based algorithm is proposed to address it. Specifically, a target assignment network is introduced into the twin-delayed deep deterministic policy gradient (TD3) algorithm to solve the target assignment problem and path planning problem simultaneously. The target assignment network executes target assignment for each step of UAVs, while the TD3 guides UAVs to plan paths for this step based on the assignment result and provides training labels for the optimization of the target assignment network. Experimental results demonstrate that the proposed approach can ensure an optimal complete target allocation and achieve a collision-free path for each UAV in three-dimensional (3D) dynamic multiple-obstacle environments, and present a superior performance in target completion and a better adaptability to complex environments compared with existing methods.

Effect of temperature and moisture composite environments on the mechanical properties and mechanisms of woven carbon fiber composites

AbstractTemperature and moisture are important environmental conditions governing the usage of carbon fiber‐reinforced plastics (CFRP). In this study, tensile, compression, and shear tests of woven CFRP composites were conducted in four composite environments: cold‐temperature dry state, room‐temperature dry state, elevated‐temperature dry state, and elevated‐temperature wet state. Fourier transform infrared (FTIR) spectroscopy was used to analyze the molecular composition and structure under these environments. The fracture morphology was also analyzed using scanning electron microscopy (SEM) to understand the deterioration mechanism of the material properties at the macro and micro scales. Finally, a two‐parameter Weibull statistical model was used to evaluate the scattering of the composites. The results demonstrate that the tensile, compressive, and shear properties of the material decrease under harsh conditions, and the degradation effect on the ultimate strength is much greater than that of the modulus. Cold‐temperature dry and elevated‐temperature wet conditions are particularly severe. Composite environments significantly affect the macroscopic failure process and ultimate failure mode of materials. In general, the temperature and humidity of the matrix system acts at the core of the deterioration mechanism of composites, affecting the properties of the fiber/matrix and orthogonal woven fiber interfaces, and eventually resulting in a loss of their constitutive strength and bearing capacity. The theoretical ultimate strength were consistent with the experimental values.Highlights Comprehensive mechanical property testing in multiple composite environments. Degradation mechanisms were revealed from multiscale morphology analysis. Scattering of composites were evaluated using statistical analyses. Cold temperature dry and elevated temperature wet were the worst states. Deterioration effect of environment on matrix and interface is the core failure mechanism.

Germanene saturable absorber for mode-locked operation in an all-fiber laser with multiple dispersion environments.

In this paper, a high-quality germanene-polyvinyl alcohol (PVA) saturable absorber (SA) with a modulation depth of 3.05% and a saturation intensity of 17.95M W/c m 2 was prepared. Stable conventional mode-locking and harmonic mode-locking (HML) were achieved in germanene-based Er-doped fiber lasers (EDFL) using dispersion management techniques. In a cavity with a net dispersion value of -0.22p s 2, the conventional soliton had a center wavelength of 1558.2nm, a repetition frequency of 19.09MHz, and a maximum 3dB spectrum bandwidth of 3.5nm. The highest repetition frequencies achieved in cavities with net dispersion values of -2.81p s 2, -1.73p s 2, and -1.09p s 2 were 9.48MHz, 12.75MHz, and 12.10MHz for HML, respectively. Furthermore, the effects of dispersion, power, and the polarization state on HML were systematically investigated. Our research results fully demonstrate the capability of germanene as an optical modulator in generating conventional mode-locked and harmonic mode-locked solitons. This provides meaningful references for promoting its application in ultrafast fiber lasers.

The effect of complex cognitive context on the dynamic stability during gait initiation in older women.

Changes in cognitive control are considered potential factors affecting voluntary motor movements during gait initiation (GI). Simulating environments with higher cognitive resource demands have an effect on the stability of GI task performance, which is of significant importance for assessing fall risk in the older adults and devising fall risk management measures in multiple environments. This study aims to reveal the influence of complex cognitive competitive environment with increased cognitive demands on the dynamic stability during GI in the older women. Twenty-three older females and twenty-three younger females performed walking tests under three conditions: voluntary initiation (SI), visual light reaction time task (LRT), and cognitive interference + visual light reaction time task (C + LRT). Eight cameras (Qualisys, Sweden, model: Oqus 600) and three force plates (Kistler, Switzerland, model: 9287C) are used to obtain kinematic and kinetic data. To recorde the trajectory of center of pressure (CoP) and the position of the foot placement, and compute the anterior-posterior (A-P) and medio-lateral (M-L) dynamic stability at the onset and end moments of the single-leg support by means of center of mass (CoM) and gait spatiotemporal parameters. Older women responded to the effect of complex environments involving cognitive competition on body stability by prolonging the lateral displacement time of the CoP during the anticipatory postural adjustments (APAs) phase, reducing step length and velocity, and increasing step width and foot inclination angle. Complex initiation environments lead to competition for cognitive resources in the brain, resulting in decreased stability of GI motor control in older adults. The higher the complexity of the cognitive resource demands environment, the lower the stability of GI in older adults, and the greater the effect on their M-L stability at the onset of stepping.

Identification of reliable QTLs and designed QTL breeding for grain shape and milling quality in the reciprocal introgression lines in rice

Milling quality (MQ) and grain shape (GS) of rice (Oryza sativa L.) are correlated traits, both determine farmers’ final profit. More than one population under multiple environments may provide valuable information for breeding selection on these MQ-GS correlations. However, suitable analytical methods for reciprocal introgression lines with linkage map for this kind of correlation remains unclear. In this study, our major tasks were (1) to provide a set of reciprocal introgression lines (composed of two BC2RIL populations) suitable for mapping by linkage mapping using markers/bins with physical positions; (2) to test the mapping effects of different methods by using MQ-GS correlation dissection as sample case; (3) to perform genetic and breeding simulation on pyramiding favorite alleles of QTLs for representative MQ-GS traits. Finally, with four analysis methods and data collected under five environments, we identified about 28.4 loci on average for MQ-GS traits. Notably, 52.3% of these loci were commonly detected by different methods and eight loci were novel. There were also nine regions harboring loci for different MQ-GS traits which may be underlying the MQ-GS correlations. Background independent (BI) loci were also found for each MQ and GS trait. All these information may provide useful resources for rice molecular breeding.

Transposon sequencing reveals the essential gene set and genes enabling gut symbiosis in the insect symbiont Caballeronia insecticola.

Caballeronia insecticola is a bacterium belonging to the Burkholderia genus sensu lato, which is able to colonize multiple environments like soils and the gut of the bean bug Riptortus pedestris. We constructed a saturated Himar1 mariner transposon library and revealed by transposon-sequencing that 498 protein-coding genes constitute the essential genome of Caballeronia insecticola for growth in free-living conditions. By comparing essential gene sets of Caballeronia insecticola and seven related Burkholderia s.l. strains, only 120 common genes were identified, indicating that a large part of the essential genome is strain-specific. In order to reproduce specific nutritional conditions that are present in the gut of Riptortus pedestris, we grew the mutant library in minimal media supplemented with candidate gut nutrients and identified several condition-dependent fitness-defect genes by transposon-sequencing. To validate the robustness of the approach, insertion mutants in six fitness genes were constructed and their growth deficiency in media supplemented with the corresponding nutrient was confirmed. The mutants were further tested for their efficiency in Riptortus pedestris gut colonization, confirming that gluconeogenic carbon sources, taurine and inositol, are nutrients consumed by the symbiont in the gut. Thus, our study provides insights about specific contributions provided by the insect host to the bacterial symbiont.

Recommendation and Distillation of IoT Multi-Environments

ABSTRACT The Internet of Things enhances the quality of life by automating tasks and streamlining human-device interactions. However, manual device management remains time-consuming, especially in multiple or new environments that demand new settings and interactions. Learning systems aid in automating task management, but their learning times hinder personalization and struggle when the system has to interact with multiple IoT environments, impacting user experience. This paper aims to optimize knowledge sharing for IoT environments, proposing a framework that utilizes recommender systems to find optimal and reusable configurations among IoT environments and users. To that end, this work leverages teacher-student relationships in Knowledge Distillation, facilitating knowledge sharing and enhancing knowledge reuse in learning models. In addition, real-time processing eliminates training time. This approach achieves a remarkable 93.15% accuracy.

Identification of Putative Quantitative Trait Loci for Improved Seed Oil Quality in Peanuts.

Improving seed oil quality in peanut (Arachis hypogaea) has long been an aim of breeding programs worldwide. The genetic resources to achieve this goal are limited. We used an advanced recombinant inbred line (RIL) population derived from JH5 × KX01-6 to explore quantitative trait loci (QTL) affecting peanut oil quality and their additive effects, epistatic effects, and QTL × environment interactions. Gas chromatography (GC) analysis suggested seven fatty acids components were obviously detected in both parents and analyzed in a follow-up QTL analysis. The major components, palmitic acid (C16:0), oleic acid (C18:1), and linoleic acid (C18:2), exhibited considerable phenotypic variation and fit the two major gene and minor gene mixed-inheritance model. Seventeen QTL explained 2.57-38.72% of the phenotypic variation in these major components, with LOD values of 4.12-37.56 in six environments, and thirty-five QTL explained 0.94-32.21% of the phenotypic variation, with LOD values of 5.99-150.38 in multiple environments. Sixteen of these QTL were detected in both individual and multiple environments. Among these, qFA_08_1 was a novel QTL with stable, valuable and major effect. Two other major-effect QTL, qFA_09_2 and qFA_19_3, share the same physical position as FAD2A and FAD2B, respectively. Eleven stable epistatic QTL involving nine loci explained 1.30-34.97% of the phenotypic variation, with epistatic effects ranging from 0.09 to 6.13. These QTL could be valuable for breeding varieties with improved oil quality.

Diversity of Gibberellin 2-oxidase genes in the barley genome offers opportunities for genetic improvement

IntroductionGibberellin (GA) is a vital phytohormone in regulating plant growth and development. During the “Green Revolution”, modification of GA-related genes created semi-dwarfing phenotype in cereal crops but adversely affected grain weight. Gibberellin 2-oxidases (GA2oxs) in barley act as key catabolic enzymes in deactivating GA, but their functions are still less known. ObjectivesThis study investigates the physiological function of two HvGA2ox genes in barley and identifies novel semi-dwarf alleles with minimum impacts on other agronomic traits. MethodsVirus-induced gene silencing and CRISPR/Cas9 technology were used to manipulate gene expression of HvGA2ox9 and HvGA2ox8a in barley and RNA-seq was conducted to compare the transcriptome between wild type and mutants. Also, field trials in multiple environments were performed to detect the functional haplotypes. ResultsThere were ten GA2oxs that distinctly expressed in shoot, tiller, inflorescence, grain, embryo and root. Knockdown of HvGA2ox9 did not affect plant height, while ga2ox8a mutants generated by CRISPR/Cas9 increased plant height and significantly altered seed width and weight due to the increased bioactive GA4 level. RNA-seq analysis revealed that genes involved in starch and sucrose metabolism were significantly decreased in the inflorescence of ga2ox8a mutants. Furthermore, haplotype analysis revealed one naturally occurring HvGA2ox8a haplotype was associated with decreased plant height, early flowering and wider and heavier seed. ConclusionOur results demonstrate the potential of manipulating GA2ox genes to fine tune GA signalling and biofunctions in desired plant tissues and open a promising avenue for minimising the trade-off effects of Green Revolution semi-dwarfing genes on grain size and weight. The knowledge will promote the development of next generation barley cultivars with better adaptation to a changing climate.

Impact of soil type and harvest season on the ratooning ability of sugarcane varieties

Summary Sugarcane varieties differ in their ratooning ability (RA), and it is hypothesized that soil types and harvest seasons impact varieties’ RA. However, the effects of these factors on varieties’ RA remain unclear. This study aimed to assess the RA of different commercial sugarcane varieties (NCo376, N19, N23, N25, and N36), and establish the effects of soils and seasons on ratoon yields of these varieties in Eswatini. Fifteen years data on tons cane per ha per annum (TCHA) and tons sucrose per ha per annum (TSHA) achieved by plant cane and seven ratoon crops were collected from four commercial growers and analysed using linear regression models. The varieties significantly differed in RA. Variety N25, which had the highest plant cane yields (121.3 TCHA and 16.7 TSHA), had the sharpest yield decline over ratoon crops (–2.74 TCHA and −0.33 TSHA), suggesting that this variety is more suitable for short crop cycles. Variety N36 had second highest plant cane yields (111.7 TCHA and 16.4 TSHA) and a lower ratoon yield decline (–1.38 TCHA and −0.16 TSHA) than N25, suggesting that it is suitable for longer ratoon crop cycles. While soil type and harvest season significantly affected the relative yields of varieties, they did not significantly impact their RA, indicating that differences in varieties’ RA were driven by genotype and were relatively stable across environments. This suggests that tests to assess the adaptability of varieties should be conducted in multiple environments, while testing the RA of varieties may be conducted in fewer environments.

Genome-wide association analysis identifies a consistent QTL for powdery mildew resistance on chromosome 3A in Nordic and Baltic spring wheat

Key messageQPm.NOBAL-3A is an important QTL providing robust adult plant powdery mildew resistance in Nordic and Baltic spring wheat, aiding sustainable crop protection and breeding.Powdery mildew, caused by the biotrophic fungal pathogen Blumeria graminis f. sp. tritici, poses a significant threat to bread wheat (Triticum aestivum L.), one of the world’s most crucial cereal crops. Enhancing cultivar resistance against this devastating disease requires a comprehensive understanding of the genetic basis of powdery mildew resistance. In this study, we performed a genome-wide association study (GWAS) using extensive field trial data from multiple environments across Estonia, Latvia, Lithuania, and Norway. The study involved a diverse panel of recent wheat cultivars and breeding lines sourced from the Baltic region and Norway. We identified a major quantitative trait locus (QTL) on chromosome 3A, designated as QPm.NOBAL-3A, which consistently conferred high resistance to powdery mildew across various environments and countries. Furthermore, the consistency of the QTL haplotype effect was validated using an independent Norwegian spring wheat panel. Subsequent greenhouse seedling inoculations with 15 representative powdery mildew isolates on a subset of the GWAS panel indicated that this QTL provides adult plant resistance and is likely of race non-specific nature. Moreover, we developed and validated KASP markers for QPm.NOBAL-3A tailored for use in breeding. These findings provide a critical foundation for marker-assisted selection in breeding programs aimed at pyramiding resistance QTL/genes to achieve durable and broad-spectrum resistance against powdery mildew.

Performance and Adaptability of Common Bean-Released Cultivars at Three Agro-Ecological Zones in Tanzania

This study focused on examining the genetic performance and stability of common bean genotypes across multiple environments in Tanzania using an alpha-lattice experimental design. The aim was to minimize environmental variability and maximize genetic expression. Three experimental sites were selected to represent the ecologies of the main bean growing areas of Tanzania, which are Tropical Savannah represented by TARI-Seliani, Tropical highlands represented by TARI-Uyole and semi-arid regions represented by Babati region. The sites were planted with are diverse of common bean genotypes, all of which were released for use in Tanzania. Agronomic practices, such as hand-hoe weeding and fertilizer application, were consistently applied. Key data collected included days to 50 percent flowering, growth habit, plant height, pod and seed count, yield per plot, and 100 seed weight. Advanced statistical analyses, including ANOVA, AMMI, and stability tests, were conducted using R software to evaluate yield and yield components. This paper findings discuss about the yield performance, stability, and the discriminating verses representative power across locations. In terms of yield, Babati was the leading site with mean yield of 1413.07 kilogram per hectare (kg/h)with Uyole 96 being the lead genotype (2845.567kg/h). Genotypes that were found to be stable and high-yielding in multiple locations include, Rojo, SUA Kalima, SAKILA, Fibea, and Nyeupe Uyole with the mean yields of 1045.83kg/h, 1023.73kg/h, 1003.33kg/h, 670.4kg/h and 544.77kg/h respectively. In discriminativeness and representativeness, Babati was the most discriminating site among the three locations while Seliani was the most representative among the three. These findings revealed significant variationsand allowed the assessment of genotype performance and environmental interactions.

Improving Enterprise Resilience TTrough Research on Employee Mental Health and Stress Management in Multiple Environment

Improving Enterprise Resilience TTrough Research on Employee Mental Health and Stress Management in Multiple Environment

Incorporating Privacy by Design Principles in the Modification of AI Systems in Preventing Breaches across Multiple Environments, Including Public Cloud, Private Cloud, and On-prem

Incorporating Privacy by Design Principles in the Modification of AI Systems in Preventing Breaches across Multiple Environments, Including Public Cloud, Private Cloud, and On-prem

Phenomic Selection for Hybrid Rapeseed Breeding.

Phenomic selection is a recent approach suggested as a low-cost, high-throughput alternative to genomic selection. Instead of using genetic markers, it employs spectral data to predict complex traits using equivalent statistical models. Phenomic selection has been shown to outperform genomic selection when using spectral data that was obtained within the same generation as the traits that were predicted. However, for hybrid breeding, the key question is whether spectral data from parental genotypes can be used to effectively predict traits in the hybrid generation. Here, we aimed to evaluate the potential of phenomic selection for hybrid rapeseed breeding. We performed predictions for various traits in a structured population of 410 test hybrids, grown in multiple environments, using near-infrared spectroscopy data obtained from harvested seeds of both the hybrids and their parental lines with different linear and nonlinear models. We found that phenomic selection within the hybrid generation outperformed genomic selection for seed yield and plant height, even when spectral data was collected at single locations, while being less affected by population structure. Furthermore, we demonstrate that phenomic prediction across generations is feasible, and selecting hybrids based on spectral data obtained from parental genotypes is competitive with genomic selection. We conclude that phenomic selection is a promising approach for rapeseed breeding that can be easily implemented without any additional costs or efforts as near-infrared spectroscopy is routinely assessed in rapeseed breeding.

Demonstration the effectiveness of NDVI with GGE biplot model in determination of high yielding durum wheat (Triticum durum Desf.) genotypes

The use of spectral indices such as normalised difference vegetation index (NDVI) is becoming an important element in the evaluation of physiological traits in cereal crops. Determining the correlation between spectral readings taken at different phenological stages of wheat plants and grain yield (GY) is crucial. This study aimed to demonstrate the effectiveness of NDVI in combination with the GGE biplot model in identifying high yielding durum wheat genotypes. Field experiments were conducted in multiple environments and growth stages to evaluate the relationship between NDVI and grain yield (GY). Twenty-five durum wheat genotypes were tested for two years under rainfed and supplementary irrigation conditions using a split plot experimental design. The results showed significant positive correlations between NDVI and GY, especially at the generative stages under rain-fed conditions. In contrast, NDVI at the grain-filling stage showed a weaker relationship with GY under supplemental irrigation. Genotypes exhibiting high NDVI values up to the stem elongation stage tended to have lower grain yields, emphasizing the importance of considering growth stage dynamics. GGE biplot analysis provided visual information on genotype-trait relationships, helping to identify genotypes with consistent performance in different environments. This study highlights that NDVI can be used to predict yield potential and guide selection in durum wheat breeding programs and the GGE biplot model serves as a valuable tool for genotype evaluation and selection.

Voluntary Disclosure from Multiple Sources and Information Environment

Voluntary Disclosure from Multiple Sources and Information Environment

How Online Consumers Use Multiple Advice Sources: An Empirical Exploration Using Verbal Protocol Analysis

With more online stores providing recommendations and reviews simultaneously from multiple advice sources—such as recommendation agents, other consumers, and experts—consumers are facing the challenge of deciding how to use wide-ranging and possibly conflicting sets of information to improve their product selection. Although previous studies have investigated the classical decision-making strategies used in preferential choice problems, most of these are not directly applicable to multiple advice source environments. In addition, since most of these studies have mainly focused on a variance model rather than a process model, they cannot fully explain how decision makers reach their product selection decisions. To shed light on the processes that online consumers use in making product selections when using multiple advice sources, this study: (1) explores if, how, and when consumers use consistency as a part of their decision-making strategies through developing process models; (2) identifies the consistency strategies utilized when using multiple advice sources; and (3) proposes a new consistency-based decision-making model. Through concurrent verbal protocol analysis, we identified four consistency strategies and found that the use of consistency strategies increases decision quality more than traditional nonconsistency strategies. Our findings are triangulated through the theoretical lens of cognitive dissonance theory, information search process model, and confirmation bias for rigorous validation. We also describe the theoretical and practical implications of our findings.

Influence of temperature on corrosion behavior of N80 Steel in multiple thermal fluid environment

During offshore oil production, O2 could be introduced into the multiple thermal fluid, and the temperature of the wellbore can reach up to 200°C, which may result severe corrosion of tubing. However, the corrosion mechanism of downhole pipes in such aggressive environment remains unclear in the high-temperature and high-pressure CO2-O2-H2O mixed environment. Herein, we investigated the corrosion behaviour of N80 steel with different temperatures in CO2-O2-H2O environment. The influence of temperature on composition and structure of the corrosion product film of N80 steel was characterized by scanning electron microscope, energy-dispersive spectroscopy and X-ray diffraction. The results show that the corrosion rates of N80 steel performed a downward trend with the increase of the temperature, but it increased slightly at 180°C. The corrosion rate of N80 steel was up to 1.6 mm/y at 60°C, owing to the damage to corrosion product film by O2 and reduced its protection at lower temperature. As the temperature increased, the formation of Fe3O4 enhanced the protection of the inner corrosion product film, thereby greatly reducing the corrosion rate. However, the product of (FeCa)CO3 in the corrosion film completely lost its protection at 180°C, and the corrosion rate increased slightly to 0.84 mm/y. With the temperature increased to 240°C the corrosion rate of N80 steel reduced to 0.24 mm/y on account of the formation of dense and complete Fe3O4.

Perception-Guided Quality Metric of 3D Point Clouds Using Hybrid Strategy.

Full-reference point cloud quality assessment (FR-PCQA) aims to infer the quality of distorted point clouds with available references. Most of the existing FR-PCQA metrics ignore the fact that the human visual system (HVS) dynamically tackles visual information according to different distortion levels (i.e., distortion detection for high-quality samples and appearance perception for low-quality samples) and measure point cloud quality using unified features. To bridge the gap, in this paper, we propose a perception-guided hybrid metric (PHM) that adaptively leverages two visual strategies with respect to distortion degree to predict point cloud quality: to measure visible difference in high-quality samples, PHM takes into account the masking effect and employs texture complexity as an effective compensatory factor for absolute difference; on the other hand, PHM leverages spectral graph theory to evaluate appearance degradation in low-quality samples. Variations in geometric signals on graphs and changes in the spectral graph wavelet coefficients are utilized to characterize geometry and texture appearance degradation, respectively. Finally, the results obtained from the two components are combined in a non-linear method to produce an overall quality score of the tested point cloud. The results of the experiment on five independent databases show that PHM achieves state-of-the-art (SOTA) performance and offers significant performance improvement in multiple distortion environments. The code is publicly available at https://github.com/zhangyujie-1998/PHM.