Search Results (65)

Brassica campestris L. var. toria, a major oilseed crop cultivated in India, is primarily an entomophilic species. Hymenopteran flower-visiting species provide important ecological services like pollination or pest control in Brassica crops. In this context, a study was conducted during 2015–2017 in three localities in Assam, a state in northeast India that falls under two global biodiversity hotspots—Indo–Burma and Himalayan—to bring data on the diversity of hymenopteran flower visitors of toria crops by using multiple sampling techniques and to compare the efficiency of these techniques. Altogether, nine sampling treatments were used. To assess the sampling effectiveness of the different treatments, the data from the two cropping periods of toria in each locality were analysed cumulatively and comparatively. Variable transect outperformed the other sampling methods with the highest number of hymenopteran flower visitor species recorded in toria crops at 54, representing 84.4% of the total number of species, and was followed by standard transect (34 species, 53.1%), elevated yellow trap (22 species, 34.4%), and observation plot (21 species, 32.8%). However, the importance of multiple sampling methods in this diversity study was noticed; one method alone could not sample all the species recorded. The cluster of traps and netting with transect walks was proven to be complementary and considered useful for future research studies in the upstream basin of the Burhidihing River of Assam, India. Full article

Brent oil holds a significant position in the global energy market, as oil prices in many regions are indexed to it. Therefore, forecasting the future price of Brent oil is of great importance. In recent years, artificial intelligence techniques have been widely applied in modeling and prediction tasks. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS), a well-established AI approach, was employed for the time-series forecasting of Brent oil prices. To ensure effective learning and improve prediction accuracy, ANFIS was trained using nine different metaheuristic algorithms: Artificial Bee Colony (ABC), Selfish Herd Optimizer (SHO), Biogeography-Based Optimization (BBO), Multi-Verse Optimizer (MVO), Teaching–Learning-Based Optimization (TLBO), Cuckoo Search (CS), Moth Flame Optimization (MFO), Marine Predator Algorithm (MPA), and Flower Pollination Algorithm (FPA). Symmetric training procedures were applied across all algorithms to ensure fair and consistent evaluation. The analyses were conducted on the lowest and highest daily, weekly, and monthly Brent oil prices. Mean squared error (MSE) was used as the primary performance metric. The results showed that all algorithms achieved effective prediction performance. Among them, BBO and TLBO demonstrated superior accuracy and stability, particularly in handling the complexities of Brent oil forecasting. This study contributes to the literature by combining ANFIS and metaheuristics within a symmetric framework of experimentation and evaluation. Full article

An attempt was made to quantify soil properties using hyperspectral remote-sensing techniques and machine-learning algorithms. In total, 100 soil samples representing various locations and soil-nutrient statuses were collected, and the samples were analyzed for soil pH, EC, soil organic carbon, available nitrogen (AN), available phosphorus (AP), and available potassium (AK) by following standard methods. Soil had a wide range of properties, i.e., pH varied from 5.62 to 8.49, EC varied from 0.08 to 1.78 dS/m, soil organic carbon varied from 0.23 to 0.94%, available nitrogen varied from 154 to 344 kg/ha, available phosphorus varied from 9.5 to 25.5 kg/ha, and available potassium varied from 131 to 747 kg/ha. The same set of soil samples were subjected to spectral reflectance measurement using SVC GER 1500 Spectroradiometer (spectral range: 350 to 1050 nm). The measured spectral signatures of various soils were organized for developing a spectral library and for deriving various spectral indices to correlate with soil properties to quantify the nutrients. The soil samples were partitioned into 60:40 ratios for training and validation, respectively. In order to select optimum bands (wavelength) from the soil spectra, we have employed metaheuristic algorithms i.e., Particle Swarm Optimization (PSO), Moth–Flame optimization (MFO), Flower Pollination Optimization (FPO), and Battle Royale Optimization (BRO) algorithm. Further partial least square regression (PLSR) was used to find the latent variable and to evaluate various algorithms for their performance in predicting soil properties. The results indicated that nutrients could be quantified from spectral reflectance measurement with fair to good accuracy through the Battle Royale Optimization technique with a $R^2$ value of 0.45, 0.32, 0.48, 0.21, 0.71, and 0.35 for pH, EC, soil organic carbon, available-N, available-P, and available-K, respectively. Full article

Pollen rain studies are rare in arid and semi-arid regions worldwide. Interpretations related to the dynamics of plant communities and possible paleoclimatic changes in these areas face significant limitations due to this lack of data. The global biome of Seasonally Dry Tropical Forests and Shrublands (SDTFS) is represented in Northeast Brazil by the caatinga, which is composed of xerophytic vegetation. This study aimed to generate information about the pollen rain in this area and to understand its relationship with species flowering, pollination syndromes, life forms, and climatic aspects. A caatinga area in Canudos, Bahia, Brazil (09°54′ S 39°07′ W), was selected for this purpose. Artificial pollen collectors were installed and exchanged monthly over two years for palynological analyses of the collected material, using standard palynological techniques. A total of 124 pollen types were identified, with approximately 8823 pollen grains deposited per cm² over the two years. Several vegetation components were represented in the pollen rain, reflecting local plant diversity, life forms, and physiognomies. A positive relationship was observed between increased temperature and pollen production from trees and shrubs, and new pollen types were associated with indicator species of caatinga vegetation. Full article

Background/Objectives: Essential oils (EOs) have been exploited by humans for centuries, but many sources remain poorly investigated, mainly due to the low yields associated with conventional extraction. Recently, new techniques have been developed, like solvent-free microwave extraction (SFME), able to enhance efficiency and sustainability. The use of Papaver rhoeas L. in traditional medicine has led researchers to investigate non-volatile fractions, but there are little data available on EOs. Methods: In the present work, we prepared EOs from the petals and leaves of P. rhoeas by SFME. GC/MS analysis of EOs revealed the presence of 106 compounds belonging to 13 different classes. Isomers of the different alkenes were identified thanks to an alkylthiolation reaction. Results: The results highlighted a predominance of saturated and unsaturated hydrocarbons, alcohols, and esters that might suggest a specific relationship with pollinators. Each population has been compared using PCA, heatmap, and barplot tools, highlighting differences in terms of composition by both comparing leaves and flowers and hill and lowland samples. Furthermore, cantharidin, a metabolite usually produced by insects, was detected in the flowers, possible present for attractiveness purposes. Conclusions: These results could contribute to ensuring a better understanding of the pollination process and of the biological activities of EOs from P. rhoeas. Full article

The natural pollinator of the major species of commercially-grown vanilla, Vanilla planifolia, is unknown, and the crop requires hand pollination to achieve significant levels of fruit set; however, the traditional technique (using a toothpick) is costly, as it requires skilled personnel. To overcome this problem, two native Australian bees, Tetragonula carbonaria and Austroplebeia australis, and the blowfly, Lucilia cuprina, were trialled as pollinators. Three alternatives to the toothpick method were also trialled. The appearance of vanilla flowers under ultraviolet radiation was examined to determine the presence of cryptic pollination guides, and the chemical composition of nectar from extrafloral nectaries and aroma volatiles from the flowers were characterised. None of the three insects effected pollination due to their small size and behaviour; other insect pollinators need to be identified. The alternative mechanical methods of pollination trialled resulted in fruit set; however, the percentages of fruit set were lower than the traditional toothpick method, and the fruit were of inferior quality. The nectar produced predominantly consisted of sucrose and melezitose. Melezitose is a strong attractant of various ant species, which may explain the concentration of ants around the nectaries and the apparent lack of nectar production in part of this study. The aroma volatiles included monoterpenoids, terpenes, sesquiterpenoids, aromatics, nitrogen-containing compounds and fatty acids, the most abundant being a-pinene and eucalyptol. Illumination of the flowers with UV-A radiation revealed fluorescence from the stamens, the column and the callus, which is located on the labellum. These observations may aid the identification and use of potential pollinators. Full article

The growing interest in safeguarding agroecosystem biodiversity has led to interest in studying ecological interactions among the various organisms present within the agroecosystem. Indeed, mutualisms between weeds and pollinators are of crucial importance as they influence the respective survival dynamics. In this review, the mutualistic role of flower visitors and the possible (often predominant) abiotic alternatives to insect pollination (self- and wind-pollination) are investigated. Mutualistic relations are discussed in terms of reward (pollen and/or nectar) and attractiveness (color, shape, scent, nectar quality and quantity), analyzing whether and to what extent typical weeds are linked to pollinators by rigid (specialization) or flexible (generalization) mutualistic relations. The entomofauna involved is composed mainly of solitary and social bees, bumblebees, Diptera, and Lepidoptera. While some of these pollinators are polylectic, others are oligolectic, depending on the shape of their mouthparts, which can be suited to explore the flower corollas as function of their depths. Consequently, the persistence dynamics of weed species show more successful survival in plants that are basically (occasional insect pollination) or totally (self and/or wind pollination) unspecialized in mutualistic relations. However, even weed species with typical abiotic pollination are at times visited during periods such as late summer, in which plants with more abundant rewards are insufficiently present or completely absent. Many typically insect-pollinated weeds can represent a valid indicator of the ecological sustainability of crop management techniques, as their survival dynamics are closely dependent on the biodiversity of the surrounding entomofauna. In particular, the presence of plant communities of species pollinated above all by butterflies (e.g., several Caryophyllaceae) gives evidence to the ecological compatibility of the previous agronomic management, in the sense that butterflies require certain weed species for oviposition and subsequent larva rearing and, therefore, provide further evidence of plant biodiversity in the environment. Full article

Most banana improvement programs are restricted to using a sub-set of edible landraces for sexual hybridization as the majority are female sterile. This results from an array of factors that work in tandem and lead to sterility. Use of pollen germination media (PGM) during pollination significantly increases seed set, but it is a very small fraction compared to the potential seed set. This research therefore explored early pollination (a day before anthesis), evening pollination, saline treatment, plant growth regulators (PGRs) treatment, and ovule culture as potential techniques for overcoming sterility in bananas. Early and evening pollinations did not increase seed set because of immature flowers and a mismatch of male and female flower opening, respectively (t-prob. = 0.735 and 0.884). Immersion of bunches in a saline solution before pollination and ovule culture also did not overcome pollination barriers. Auxin antagonists (TIBA and salicylic acid) increased seed set, though their respective increases were not statistically significant (t-prob. = 0.123 and 0.164, respectively). The use of auxin antagonists shows great potential for overcoming pollination barriers in bananas. However, application rates and time of application have to be optimized and used holistically with other promising techniques, such as use of PGM. Full article

Due to increased complexity and interactions between various subsystems, higher-order MIMO systems present difficulties in terms of stability and control performance. This study effort provides a novel, all-encompassing method for creating a decentralized fractional-order control technique for higher-order systems. Given the greater number of variables that needed to be optimized for fractional order control in higher-order, multi-input, multi-output systems, the modified flower pollination optimization algorithm (MFPOA) optimization technique was chosen due to its rapid convergence speed and minimal computational effort. The goal of the design is to improve control performance. Maximum overshoot (Mp), rising time (tr), and settling time (ts) are the performance factors taken into consideration. The MFPOA approach is used to improve the settings of the proposed decentralized fractional-order proportional-integral-derivative (FOPID) controller. By exploring the parameter space and converging on the best controller settings, the MFPOA examines the parameter space and satisfies the imposed constraints by maintaining system stability. To evaluate the suggested approach, simulation studies on two systems are carried out. The results show that by decreasing the loop interactions between subsystems with improved stability, the decentralized control with the MFPOA-based FOPID controller provides better control performance. Full article

In computer network security, the escalating use of computer networks and the corresponding increase in cyberattacks have propelled Intrusion Detection Systems (IDSs) to the forefront of research in computer science. IDSs are a crucial security technology that diligently monitor network traffic and host activities to identify unauthorized or malicious behavior. This study develops highly accurate models for detecting a diverse range of cyberattacks using the fewest possible features, achieved via a meticulous selection of features. We chose 5, 9, and 10 features, respectively, using the Artificial Bee Colony (ABC), Flower Pollination Algorithm (FPA), and Ant Colony Optimization (ACO) feature-selection techniques. We successfully constructed different models with a remarkable detection accuracy of over 98.8% (approximately 99.0%) with Ant Colony Optimization (ACO), an accuracy of 98.7% with the Flower Pollination Algorithm (FPA), and an accuracy of 98.6% with the Artificial Bee Colony (ABC). Another achievement of this study is the minimum model building time achieved in intrusion detection, which was equal to 1 s using the Flower Pollination Algorithm (FPA), 2 s using the Artificial Bee Colony (ABC), and 3 s using Ant Colony Optimization (ACO). Our research leverages the comprehensive and up-to-date CSE-CIC-IDS2018 dataset and uses the preprocessing Discretize technique to discretize data. Furthermore, our research provides valuable recommendations to network administrators, aiding them in selecting appropriate machine learning algorithms tailored to specific requirements. Full article

Binaural beats are a low-frequency form of acoustic stimulation that may be heard between 200 and 900 Hz and can help reduce anxiety as well as alter other psychological situations and states by affecting mood and cognitive function. However, prior research has only looked at the impact of binaural beats on state and trait anxiety using the STA-I scale; the level of anxiety has not yet been evaluated, and for the removal of artifacts the improper selection of wavelet parameters reduced the original signal energy. Hence, in this research, the level of anxiety when hearing binaural beats has been analyzed using a novel optimized wavelet transform in which optimized wavelet parameters are extracted from the EEG signal using the flower pollination algorithm, whereby artifacts are removed effectively from the EEG signal. Thus, EEG signals have five types of brainwaves in the existing models, which have not been analyzed optimally for brainwaves other than delta waves nor has the level of anxiety yet been analyzed using binaural beats. To overcome this, deep convolutional neural network (CNN)-based signal processing has been proposed. In this, deep features are extracted from optimized EEG signal parameters, which are precisely selected and adjusted to their most efficient values using the flower pollination algorithm, ensuring minimal signal energy reduction and artifact removal to maintain the integrity of the original EEG signal during analysis. These features provide the accurate classification of various levels of anxiety, which provides more accurate results for the effects of binaural beats on anxiety from brainwaves. Finally, the proposed model is implemented in the Python platform, and the obtained results demonstrate its efficacy. The proposed optimized wavelet transform using deep CNN-based signal processing outperforms existing techniques such as KNN, SVM, LDA, and Narrow-ANN, with a high accuracy of 0.99%, precision of 0.99%, recall of 0.99%, F1-score of 0.99%, specificity of 0.999%, and error rate of 0.01%. Thus, the optimized wavelet transform with a deep CNN can perform an effective decomposition of EEG data and extract deep features related to anxiety to analyze the effect of binaural beats on anxiety levels. Full article

The study of the semiochemicals produced by the flowers of Impatiens spp. is an important topic that may explain the reason for the rapid expansion of some species in this genus. Impatiens L. belongs to the Balsaminaceae family, which includes several species considered to be invasive plants in Europe. This study aimed to characterize the phytochemistry of four naturally occurring plant species in Poland, including three invasive alien taxa (Impatiens parviflora, I. glandulifera, and I. capensis) and one native species (I. noli-tangere). Gas chromatographic techniques were used to assess phytochemical profiles of chemical attractant cues in their pollination biology. We detected differences in the scent profiles of the investigated species. All the examined Impatiens species produce various alcohols, i.e., heptacosanol, octacosanol, aldehydes (e.g., octadecanal, eicosanal, etc.), and fatty acids, as well as long-chain hydrocarbons such as dodecane, tricosane, petacosane, hexacosane, and farnesene. Impatiens parviflora, I. glandulifera, and I. capensis produce geraniol and linalool, which attract members of the Apidae family, including bumblebees and honeybees. Impatiens parviflora also produces linalool-derived monoterpenes (linalool oxide and 8-hydroxylinalool), which are a strong attractant for Diptera; this may clarify why the species is mainly visited and pollinated by syrphid flies. A list of insect visitors to the Impatiens species under study can be found in the article. Full article

Extensive research has been conducted on the effects of pollen activation temperature, cultivation temperature, and ambient temperature on germination rates and fruit sets. Yet, the influence of the temperature (pre-germination temperature) at which the pollen suspension is prepared within the storage container (tank) remains unexplored. In this study, we initially established the optimal time for pollen activation. Subsequently, pollen suspensions at varying temperatures were prepared, and their germination rates were analyzed using microscopy. Pollen suspensions of different temperatures were then sprayed onto Fojianxi pear flowers, and parameters such as flower fruit set rate, inflorescence fruit set rate, fruit longitudinal dimension, fruit transverse dimension, and fruit shape index were evaluated. The findings revealed that the optimal pollen activation duration was 6 h. A pollen suspension temperature of 30 °C resulted in the highest germination rate (72.06 ± 6.87%). However, a suspension temperature of 25 °C achieved the highest flower fruit set rate (57.29 ± 5.58%) and inflorescence fruit set rate (87.50 ± 4.84%). The fruit longitudinal dimension (68.12 ± 3.94 mm), transverse dimension (73.77 ± 4.04 mm), and fruit shape index (108.42 ± 6.02%) were most favorable at a suspension temperature of 15 °C. Our study concludes that the pollen in lower pollen suspension temperatures (15~25 °C) has higher germination energy and have higher stigmatic capacity. Thus, we advocate for a pollen suspension temperature range of 15~25 °C when employing liquid spray pollination techniques with Xuehuali (Pyrus bretschneiderilia) pollen. Full article

Native flowering forbs plantings have been found to support diverse arthropod communities by providing year-round forage and refuge from prey and chemical pollutants. Typically, pollinator habitats are planted from seeds; however, poor establishment and weed pressure often result in limited success of the planting. In this study, we evaluated two bed preparation techniques, a glyphosate treatment with no tillage and tillage with soil fumigant (dazomet) to determine their impact on herbaceous perennial transplants establishment, weed control, and arthropod presence. Our results suggest that forbs grown in beds that are tilled and treated with dazomet exhibit greater growth, produce more blooms, and experience reduced weed pressure compared to plants grown in untilled beds treated with glyphosate. We also found that the bed preparation method had no effect on arthropod activity, despite higher bloom counts in plants grown in beds prepared with tillage and dazomet applications. This research indicates that of the fifteen native flowering forbs examined in this study, a majority attracted abundant and diverse insect populations, despite variations in plant growth and bloom counts due to bed preparation treatments. Full article

Amber is a fossilized tree resin that ranges in age from the Carboniferous to the Cenozoic. It occurs globally from the Arctic to Antarctica. As the resin petrifies and turns into amber, it can enclose and preserve other materials. Amber with inclusions can help reconstruct past biodiversity and ecosystems. Some amber contains fossils representing the oldest and most detailed records of critical evolutionary traits or markers. Inclusions can even capture behavioral indicators previously only observed in extant organisms. Evidence of insect pollination of flowering plants and dragonfly mating behavior appears in amber, as does the morphological specialization of insects, indicating sociality and social parasitism. Dating amber deposits can help calibrate evolutionary events and inform reconstructions of past ecosystems. While the direct dating of amber remains impossible, age constraints on most amber deposits are based on correlations or relative dating, methods that come with significant uncertainties. This study discusses two cases using ⁴⁰Ar/³⁹Ar and U–Pb geochronologic methods to constrain the ages of amber deposits in China and the paleo-ecosystems they record. This paper also summarizes how radio-isotopic dating and other techniques combined with the analysis of inclusions in amber can help elucidate biogeography and the dynamic relationship between life and the physical environment. Full article