Delphinapterus Research Articles

Unsupervised beluga whistle end-point detection under burst pulse interferences

Passive acoustic monitoring is an increasingly important tool for whale research. Accurately detecting the end-point of whale whistle is an essential task in the study of marine mammal calls. However, the detection accuracy of the beluga whistle is reduced due to the non-stationary burst pulse interference in the marine environment. In this paper, an unsupervised two-stage beluga whistle end-point detection method is proposed to solve the above problem. Based on the high Q-factor wavelet decomposition, a rough whistle detection method is proposed to remove the silent time and the low intensity pulse. On this basis, local density adaptive spectral clustering is designed to further distinguish whistle and strong pulse interference based on the sparsity difference in time-frequency domain. The performance of the detector is tested with real signals of beluga whales, and its F 1-score is calculated. The result shows that the detector is obviously better than the traditional whistle detectors under the background of burst pulse interferences, and has higher robustness. In the future, the presented method is supposed to be applied for detecting whistles of some other whale species.

Investigating the Historical Background of Mocha Dick's Legend

Abstract: It is widely acknowledged that Herman Melville's Moby-Dick has been inspired by a variety of sources. One of these is the legend of Mocha Dick, about which Jeremiah N. Reynolds published the earliest known account in 1839. In his narration, Reynolds describes an evening he spent on board an unspecified whaling ship off Mocha Island, Chile, during which the whaler's first mate claimed to have killed Mocha Dick on a previous whaling voyage. This essay aims to substantiate the setting of Reynolds's account, in an attempt to identify the alleged murderer of Mocha Dick and eventually to investigate his previous voyages to seek any evidence of a large and / or white whale killed near Mocha Island. Overall, Reynolds's text was found to be consistent with the sources consulted: primarily, the logbook of the schooner Penguin . Reynolds's narration was identified as having taken place on April 3 or 4, 1830, on board the whaling ship Cincinnatus , which was built in New York in 1818. The identity of the whaling ship's first mate, however, could not be established due to a lack of supporting documentation, which in turn prevents further investigation into the origins of Mocha Dick's legend.

Using epigenetic clocks to investigate changes in the age structure of critically endangered Māui dolphins.

The age of an individual is an essential demographic parameter but is difficult to estimate without long-term monitoring or invasive sampling. Epigenetic approaches are increasingly used to age organisms, including nonmodel organisms such as cetaceans. Māui dolphins (Cephalorhynchus hectori maui) are a critically endangered subspecies endemic to Aotearoa New Zealand, and the age structure of this population is important for informing conservation. Here we present an epigenetic clock for aging Māui and Hector's dolphins (C. h. hectori) developed from methylation data using DNA from tooth aged individuals (n = 48). Based on this training data set, the optimal model required only eight methylation sites, provided an age correlation of .95, and had a median absolute age error of 1.54 years. A leave-one-out cross-validation analysis with the same parameters resulted in an age correlation of .87 and median absolute age error of 2.09 years. To improve age estimation, we included previously published beluga whale (Delphinapterus leucas) data to develop a joint beluga/dolphin clock, resulting in a clock with comparable performance and improved estimation of older individuals. Application of the models to DNA from skin biopsy samples of living Māui dolphins revealed a shift from a median age of 8-9 years to a younger population aged 7-8 years 10 years later. These models could be applied to other dolphin species and demonstrate the ability to construct a clock even when the number of known age samples is limited, removing this impediment to estimating demographic parameters vital to the conservation of critically endangered species.

Multimodal representative answer extraction in community question answering

To solve the information overload problem of multimodal answers in community question answering (CQA), this paper proposes a multimodal representative answer extraction method. First, the method of similarity calculation between multimodal answers is constructed, and multimodal clustering is used to cluster answers. Then, a binary multi-objective optimization model with three objective functions including multimodal answer coverage, multimodal answer redundancy, and multimodal answer consistency is constructed to extract a representative subset of answers. The improved Beluga whale optimization algorithm (MTRL-BWO), based on tent mapping, reinforcement learning, and multiple swarm strategy, is designed to increase the diversity of the population while avoiding local optima to improve the search capability and solution accuracy of the algorithm. Experimental results show the feasibility and superior performance of the proposed method.

Assessing of the use of proteins A, G, and chimeric protein AG to detect marine mammal immunoglobulins.

In recent years, there has been an increase in infectious diseases in marine mammals, including brucellosis, infections of morbillivirus, herpesvirus, and poxvirus. Several serological diagnostic methods, including enzyme-linked immunosorbent assays, immunofluorescence assays (ELISA), and western blotting, have been used to detect antibodies against pathogens in marine mammals. However, options for commercial secondary antibodies used to detect antibodies in marine mammals are limited; therefore, the use of proteins A, G, or chimeric protein AG may provide a suitable alternative. This study aimed to assess the use of proteins A, G, and chimeric protein AG to detect marine mammal immunoglobulins. Currently, there are no comparative studies on the use of proteins A, G, and chimeric protein AG for the detection of immunoglobulins in marine mammals. In this study, we used ten pinnipeds' species (Baikal seal, California sea lion, harbor seal, northern fur seal, ringed seal, South American fur seal, South American sea lion, spotted seal, Steller sea lion, and walrus) and five cetacean species (beluga whale, bottlenose dolphin, harbor porpoise, killer whale, and Pacific white-sided dolphin) and compare binding ability to proteins A, G, or chimeric protein AG by ELISA. The results revealed that the immunoglobulins from pinniped and cetacean species reacted more strongly to protein A than protein G. In addition, the immunoglobulins of pinnipeds and cetaceans showed a strong binding ability to chimeric protein AG. These results suggest that proteins A, G, and chimeric protein AG would be used to help further develop serological assays.

Preventive maintenance optimization for key components of subway train bogie with consideration of failure risk

With the acceleration of urbanization, the number of urban rail transit trains has increased dramatically, and the economy and safety of subway trains have become important standards. As an essential subsystem of the subway train, the bogie has a long maintenance process and serious failure consequences. So, it is essential to formulate a reasonable maintenance strategy to ensure its operation. To solve the above problems, a preventive maintenance (PM) strategy for key components of train bogie is established with consideration of failure risk in this paper. Firstly, the failure risk factors of the bogie components are scored and weighted, and the failure risk cost model and PM cost model are established. Next, a Weibull distribution parameter estimation method for bogie components based on the improved beluga whale optimization algorithm (IBWO) is proposed, providing a theoretical basis for PM decision-making optimization. Then, the Lévy flight is introduced into particle swarm optimization to enhance the optimization performance of the model and obtain the optimal solution. Finally, the bogie key components of Nanning Metro line 1 are selected as a case study. The results show that the IBWO algorithm has strong applicability and feasibility, and can accurately calculate the Weibull parameter values of the bogie key components. Compared with the PM plan without considering the failure risk, the proposed method is more economical and safer, which can provide necessary theoretical support for the maintenance decision optimization of urban rail transit train components.

Improving multi-month hydrological drought forecasting in a tropical region using hybridized extreme learning machine model with Beluga Whale Optimization algorithm

Improving multi-month hydrological drought forecasting in a tropical region using hybridized extreme learning machine model with Beluga Whale Optimization algorithm

Integrative soft computing approaches for optimizing thermal energy performance in residential buildings.

As is known, early prediction of thermal load in buildings can give valuable insight to engineers and energy experts in order to optimize the building design. Although different machine learning models have been promisingly employed for this problem, newer sophisticated techniques still require proper attention. This study aims at introducing novel hybrid algorithms for estimating building thermal load. The predictive models are artificial neural networks exposed to five optimizer algorithms, namely Archimedes optimization algorithm (AOA), Beluga whale optimization (BWO), forensic-based investigation (FBI), snake optimizer (SO), and transient search algorithm (TSO), for attaining optimal trainings. These five integrations aim at predicting the annual thermal energy demand. The accuracy of the models is broadly assessed using mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) indicators and a ranking system is accordingly developed. As the MAPE and R2 reported, all obtained relative errors were below 5% and correlations were above 92% which confirm the general acceptability of the results and all used models. While the models exhibited different performances in training and testing stages, referring to the overall results, the BWO emerged as the most accurate algorithm, followed by the AOA and SO simultaneously in the second position, the FBI as the third, and TSO as the fourth accurate model. Mean absolute error (MAPE) and Considering the wide variety of artificial intelligence techniques that are used nowadays, the findings of this research may shed light on the selection of proper techniques for reliable energy performance analysis in complex buildings.

Multi-objective distributed generation hierarchical optimal planning in distribution network: Improved beluga whale optimization algorithm

This study constructs a distributed generation (DG) multi-objective hierarchical optimal planning model. A solution method is proposed based on an improved beluga whale optimization algorithm (IBWO). Reasonable planning of the location and capacity of DG access to the distribution network plays an important role in minimizing actual power losses, reducing grid operating costs, and improving voltage distribution. The output power of DG has uncertainties and the demand response on the load side can also affect the DG planning; however, previous studies have ignored these two critical factors. This study aims to determine the optimal location and capacity of DG access to distribution network considering the demand response and the uncertainty of DG output power to improve the economic benefit, power quality and energy utilization efficiency. This study proposes an IBWO algorithm with better performance, aiming at the multi-objective, multi-constrained and nonlinear DG planning problem and a DG multi-objective hierarchical optimal planning model is established. The results show that proposed method can reduce the annual comprehensive cost, total voltage deviation and power loss of system by 11.66%, 40.55% and 38.61%.

Design of solar air conditioning system integrated with photovoltaic panels and thermoelectric coolers: Experimental analysis and machine learning modeling by random vector functional link coupled with white whale optimization

Design of solar air conditioning system integrated with photovoltaic panels and thermoelectric coolers: Experimental analysis and machine learning modeling by random vector functional link coupled with white whale optimization

Do beluga whales truly migrate? Testing a key trait of the classical migration syndrome

BackgroundMigration enables organisms to access resources in separate regions that have predictable but asynchronous spatiotemporal variability in habitat quality. The classical migration syndrome is defined by key traits including directionally persistent long-distance movements during which maintenance activities are suppressed. But recently, seasonal round-trip movements have frequently been considered to constitute migration irrespective of the traits required to meet this movement type, conflating common outcomes with common traits required for a mechanistic understanding of long-distance movements. We aimed to test whether a cetacean ceases foraging during so-called migratory movements, conforming to a trait that defines classical migration.MethodsWe used location and dive data collected by satellite tags deployed on beluga whales (Delphinapterus leucas) from the Eastern Beaufort Sea population, which undertake long-distance directed movements between summer and winter areas. To identify phases of directionally persistent travel, behavioural states (area-restricted search, ARS; or Transit) were decoded using a hidden-Markov model, based on step length and turning angle. Established dive profiles were then used as a proxy for foraging, to test the hypothesis that belugas cease foraging during these long-distance transiting movements, i.e., they suppress maintenance activities.ResultsBelugas principally made directed horizontal movements when moving between summer and winter residency areas, remaining in a Transit state for an average of 75.4% (range = 58.5–87.2%) of the time. All individuals, however, exhibited persistent foraging during Transit movements (75.8% of hours decoded as the Transit state had ≥ 1 foraging dive). These data indicate that belugas actively search for and/or respond to resources during these long-distance movements that are typically called a migration.ConclusionsThe long-distance movements of belugas do not conform to the traits defining the classical migration syndrome, but instead have characteristics of both migratory and nomadic behaviour, which may prove adaptive in the face of unpredictable environmental change. Such patterns are likely present in other cetaceans that have been labeled as migratory. Examination of not only horizontal movement state, but also the vertical behaviour of aquatic animals during directed movements is essential for identifying whether a species exhibits traits of the classical migration syndrome or another long-distance movement strategy, enabling improved ecological inference.

Piece-Wise Linear Chaotic Mapping-based Beluga Whale Optimization Algorithm-based Indoor Activity Monitoring for Elderly and Visually Impaired Persons

Currently, the methods of mobile communications and Internet of Things (IoT) are designed to collect human and environmental data for various intelligent applications and services. Remote monitoring of disabled and elderly people living in smart homes is challenging. Localization and positioning in indoor surroundings need unique solutions. Moreover, positioning remains a crucial feature of any navigation system that assists visually impaired persons (VIPs) in mobility. Other indispensable features of a common indoor navigation system are obstacle avoidance, pathfinding, and abilities for user communication. In recent times, the arrival of smartphones, artificial intelligence, IoT, wearables, etc. makes it possible to devise indoor monitoring systems for smart homecare. Therefore, this study presents an Improved Beluga Whale Optimization Algorithm with fuzzy-based Indoor Activity Monitoring (IBWOA-FIMS) for elderly and VIPs. The presented IBWOA-FIMS technique mainly focused on the identification and classification of indoor activities of elderly and disabled people. To accomplish this, the IBWOA-FIMS technique employs an adaptive neuro fuzzy inference system (ANFIS) model for the indoor monitoring process. In order to improve the monitoring results of the IBWOA-FIMS technique, the IBWOA is used to adjust the parameters related to the ANFIS model. For illustrating the enhanced indoor monitoring results of the IBWOA-FIMS technique, a series of simulations were performed. The simulation values portrayed the betterment of the IBWOA-FIMS technique in terms of different metrics.

Patterns of trace element deposition in beluga whale teeth reflect early life history

Determination of trace element concentrations in continuously growing biological structures such as otoliths, whiskers, and teeth can provide important insight into physiological and ontogenetic processes. We examined concentrations of 11 trace elements (Li, Mg, Mn, Cu, Zn, Se, Rb, Sr, Cs, Ba, Pb) in the annual dentine growth layer groups (GLGs) of teeth of 66 Eastern Canadian Arctic belugas (Delphinapterus leucas). Several of these trace elements displayed clear and consistent patterns in early life, though few longer term trends or signals were present in trace element data for either females or males. Large changes in Sr and Ba concentrations in fetal dentine reflected in utero shifts in element deposition in the teeth of developing belugas. Marked changes in these elements during the first years after birth were likely associated with the onset of nursing and subsequent weaning. Mg, Mn, and Zn also displayed clear and consistent patterns in early life that correlated strongly with dentine stable nitrogen isotope (δ15N) data, suggesting these elements merit further study as potential tools for studying nursing and weaning. Depositional patterns of Zn and Pb, which have been linked to sexual maturation in female Pacific walruses (Odobenus rosmarus divergens), were inconsistent in beluga teeth. Some individuals (including males) displayed patterns strongly resembling those observed in female walruses, whereas many animals did not, perhaps because they had not yet reached sexual maturity. The lack of clear patterns in trace element deposition after the first few years of life may have resulted from pooling samples from multiple populations/regions collected across more than two decades, but may also indicate that elemental concentrations are primarily driven by other, extrinsic processes later in life, and might be useful as biomonitors of environmental element concentrations or tools for delineating population structure.

Modeling landslide susceptibility based on convolutional neural network coupling with metaheuristic optimization algorithms

ABSTRACT Landslides are one of the most common geological hazards worldwide, especially in Sichuan Province (Southwest China). The current study's main purposes are to explore the potential applications of convolutional neural networks (CNN) hybrid ensemble metaheuristic optimization algorithms, namely beluga whale optimization (BWO) and coati optimization algorithm (COA), for landslide susceptibility mapping in Sichuan Province (China). For this aim, fourteen landslide conditioning factors were compiled in a spatial database. The effectiveness of the conditioning factors in the development of the landslide predictive model was quantified using the linear support vector machine model. The receiver operating characteristic (ROC) curve (AUC), the root mean square error, and six statistical indices were used to test and compare the three resultant models. For the training dataset, the AUC values of the CNN-COA, CNN-BWO and CNN models were 0.946, 0.937 and 0.855, respectively. In terms of the validation dataset, the CNN-COA model exhibited a higher AUC value of 0.919, while the AUC values of the CNN-BWO and CNN models were 0.906 and 0.805, respectively. The results indicate that the CNN-COA model, followed by the CNN-BWO model, and the CNN model, offers the best overall performance for landslide susceptibility analysis.

Dynamic game optimization control for shared energy storage in multiple application scenarios considering energy storage economy

In response to poor economic efficiency caused by the single service mode of energy storage stations, a double-level dynamic game optimization method for shared energy storage systems in multiple application scenarios considering economic efficiency is proposed in this paper. By analyzing the needs of multiple stakeholders involved in grid auxiliary services, fully tap into the profitability potential of energy storage stations. The capacity of the shared energy storage system is optimized by the non-dominant sorting beluga whale optimization algorithm (NSBWOA) in the upper level, and the operation strategy under multiple scenarios is optimized by the adaptive greedy search algorithm (AGSA) in the lower level. With the goal of maximizing the gross annual total income and high-value peak regulation ratio, and minimizing the cost- income ratio, the optimal capacity configuration and operation strategy of the shared energy storage system are obtained through collaborative optimization between upper and lower level models. The effectiveness of the proposed method is verified through the simulation testing of actual operating data of a certain power grid in China. Simulation results show that the gross annual income and high-value peak regulation ratio across multiple scenarios (Scenario III) are the highest, and the cost-income ratio is at an acceptable low level, which can provide a theoretical basis for the large-scale application of energy storage systems in new power systems.

Breast Milk Oligosaccharides Contain Immunomodulatory Glucuronic Acid and LacdiNAc

Breast milk is abundant with functionalized milk oligosaccharides (MOs) to nourish and protect the neonate. Yet we lack a comprehensive understanding of the repertoire and evolution of MOs across Mammalia. We report ∼400 MO-species associations (>100 novel structures) from milk glycomics of nine mostly understudied species: alpaca, beluga whale, black rhinoceros, bottlenose dolphin, impala, L'Hoest's monkey, pygmy hippopotamus, domestic sheep, and striped dolphin. This revealed the hitherto unknown existence of the LacdiNAc motif (GalNAcβ1-4GlcNAc) in MOs of all species except alpaca, sheep, and striped dolphin, indicating the widespread occurrence of this potentially antimicrobial motif in MOs. We also characterize glucuronic acid-containing MOs in the milk of impala, dolphins, sheep, and rhinoceros, previously only reported in cows. We demonstrate that these GlcA-MOs exhibit potent immunomodulatory effects. Our study extends the number of known MOs by >15%. Combined with >1900 curated MO-species associations, we characterize MO motif distributions, presenting an exhaustive overview of MO biodiversity.

A hybrid algorithm based on beluga whale optimization-forgetting factor recursive least square and improved particle filter for the state of charge estimation of lithium-ion batteries

A hybrid algorithm based on beluga whale optimization-forgetting factor recursive least square and improved particle filter for the state of charge estimation of lithium-ion batteries

OCCURRENCE AND COASTAL MORTALITY OF CETACEANS ON THE ARCTIC COAST OF CHUKOTKA PENINSULA IN THE WATERS OF CAPE VANKAREM IN 2017–2021

Six species of cetaceans have been found to occur in the Chukchi Sea adjacent to the Pacific walrus rookery at Cape Vankarem: the Grey whale, the Bowhead whale, the Humpback whale, the Killer whale, the Beluga whale, and the Fin whale. The Humpback whale is noted to have become abundant in the Chukchi Sea in the summer in recent years. The coastal mortality of cetaceans in a 100-km section in the western part of the distribution range of both Gray and Bowhead whales has been established to average 1.66 and 0.3 whales per year, respectively. Mostly young animals of 1–2 years of age appear to die, the main cause of death of which being predation by killer whales. The most common whales in that area in the summer-autumn period are humpback whales, which at the same time form significant concentrations in the coastal waters during the accumulation of the main food items. Both bowhead and gray whales are much less frequent, also being rather irregular to be found, while despite the relative scarcity of encounters these species account for the lion’s share of all dead cetaceans.

Identification of two novel papillomaviruses in belugas.

Papillomaviruses (PVs) can cause hyperplasia in the skin and mucous membranes of humans, mammals, and non-mammalian animals, and are a significant risk factor for cervical and genital cancers. Using next-generation sequencing (NGS), we identified two novel strains of papillomavirus, PV-HMU-1 and PV-HMU-2, in swabs taken from belugas (Delphinapterus leucas) at Polar Ocean Parks in Qingdao and Dalian. We amplified the complete genomes of both strains and screened ten belugas and one false killer whale (Pseudorca crassidens) for the late gene (L1) to determine the infection rate. In Qingdao, 50% of the two sampled belugas were infected with PV-HMU-1, while the false killer whale was negative. In Dalian, 71% of the eight sampled belugas were infected with PV-HMU-2. In their L1 genes, PV-HMU-1 and PV-HMU-2 showed 64.99 and 68.12% amino acid identity, respectively, with other members of Papillomaviridae. Phylogenetic analysis of combinatorial amino acid sequences revealed that PV-HMU-1 and PV-HMU-2 clustered with other known dolphin PVs but formed distinct branches. PVs carried by belugas were proposed as novel species under Firstpapillomavirinae. The discovery of these two novel PVs enhances our understanding of the genetic diversity of papillomaviruses and their impact on the beluga population.

Quantitative analysis of aflatoxin B1 of peanut by optimized support vector machine models based on near-infrared spectral features

This study designs a chemometric framework for quantitatively evaluating aflatoxin B1 (AFB1) in peanuts based on near-infrared (NIR) spectroscopy technique. The NIR spectra of peanut samples exhibiting diverse fungal contamination levels were acquired using a portable NIR spectrometer. Subsequently, appropriate pre-processing techniques were employed for data refinement. To streamline the analysis, the iterative variable subset optimization (IVSO) technique was employed to conduct an initial screening of the pre-processed NIR spectra, eliminating numerous irrelevant variables. Building upon this screening process, the beluga whale optimization (BWO) algorithm was utilized to optimize the selected feature variables further. Subsequently, support vector machine (SVM) models were developed using the refined near-infrared spectral features to test AFB1 in peanuts quantitatively. The results indicate that the SVM model significantly improves detection performance and generalization proficiency, particularly after secondary optimization using BWO-IVSO. Among the different models considered, the SVM model established after BWO-IVSO optimization exhibited the most extraordinary level of generalization, with a root mean square error of prediction of 24.6322 μg∙kg−1, a correlation coefficient of 0.9761, and a relative percent deviation of 4.6999. Overall, this investigation highlights the effectiveness of the proposed NIR spectroscopy model based on BWO-IVSO-SVM for quantitatively analyzing AFB1 in peanuts. The study contributes valuable technical and methodological insights that can serve as a reference for rapidly determining mycotoxins in cereal crops.