White Shark Research Articles

Structural safety and failure analysis of buried jointed high-density polyethylene corrugated pipelines subjected to blast vibration

The reliability of buried jointed high-density polyethylene (HDPE) corrugated pipelines under urban blast vibration loading needs to be emphasized. This study combines field blasting tests on directly buried single-section pipelines and numerical simulations to investigate the mechanical performance of jointed HDPE corrugated pipelines under complex blasting conditions. Intelligent prediction models based on genetic algorithm (GA), particle swarm optimization (PSO), and white shark optimization (WSO) combined with extreme learning machine (ELM) are proposed, namely GA-ELM, PSO-ELM, and WSO-ELM models, respectively. The numerical model is established using the orthogonal experimental principle to supplement the predicted sample data. By comparing the predictions of RPPV (resultant peak particle velocity) and RPPD (resultant peak particle displacement) for the pipelines under various working conditions using the four optimized ELM models, the results indicate that the WSO-ELM model is the best intelligent model for predicting the pipeline response characteristics. By combining the pipeline deflection criterion with the WSO-ELM prediction model, adjustments to the field blast parameters can be made to ensure the safety of the pipeline while improving construction efficiency.

Marine predators segregate interspecifically by space and time in a sheltered coastal bay.

Marine predators are vital to the healthy functioning of coastal ecosystems, but to understand their roles, it is necessary to elucidate their movement ecology, particularly in relation to one another. A decade's worth of acoustic telemetry data (2011-2020) from Algoa Bay, South Africa, was investigated to determine how two mesopredatory species (teleosts: dusky kob Argyrosomus japonicus, n = 11, and leervis Lichia amia, n = 16) and two top predatory species (sharks: ragged-tooth sharks Carcharias taurus, n = 45, and white sharks Carcharodon carcharias, n = 31) used and shared this bay ecosystem. Multi-annual seasonal fidelity to the bay was exhibited by all species, but differences in residency were observed among species. Similarly, species used space in the bay differently-the teleosts moved less and had movements restricted to the central and western inshore regions of the bay. Conversely, the sharks roamed more, but detections were concentrated in the western part of the bay for C. taurus and in the eastern part of the bay for C. carcharias. Social network analysis showed that species segregated in space and time on a fine scale. However, there was some interaction observed between C. taurus, L. amia, and A. japonicus, but to varying degrees. This is likely because of strong habitat preferences exhibited by each species and predator-prey relationships between these predatory guilds. Results highlight that the sheltered marine Algoa Bay is a resource-rich environment, supporting multiple predators with different hunting strategies albeit similar prey preferences. Finally, these species are likely afforded some protection by the current Greater Addo Elephant National Park Marine Protected Area in the bay but are vulnerable to fishing pressure when they leave this ecosystem.

Predicting whaler shark presence and interactions with humans in southern Queensland, Australia

The Queensland Shark Control Program (QSCP) started in 1962 to reduce the number of shark-human incidents by deploying nets and drumlines across the most popular beaches. The program targets large shark species (white, tiger and bull sharks) that are potentially hazardous to bathers. However, this strategy is lethal for other sharks and marine wildlife, including threatened and endangered species. Thus, finding non-lethal strategies is a priority. To better manage shark-human interactions, establishing a better understanding of the factors that drive shark movement is key. Here we used sea surface temperature (SST), rainfall and distance to rivers as environmental variables to predict the presence of whaler sharks in southern Queensland based on 26 years of catch data from the QSCP. We found that SST is positively corelated to sharks caught by drumlines, while rainfall was associated with the number of sharks captured in shark nets. In addition, more sharks were captured by nets and drumlines further away from rivers, and nets captured roughly 10 times more sharks than drumlines over the period of study. In contrast to tiger sharks, the catch data indicate the number of whalers has not declined over the past 26 years. Our findings suggest that environmental variables can be used to predict the movement of large sharks and by incorporating this knowledge into management plans and public education programs, may ultimately reduce shark-human incidents.

Identifying and documenting osseous trauma from shark attacks by post mortem CT examination and autopsy.

A 15-year-old male was attacked by a large white shark while surfing. CT examination revealed an above-knee amputation of the right lower extremity with stripping of soft tissues from the groin distally. 3-dimensional volume rendering did not show any fragments of shark teeth but did reveal linear gouges, areas of shaving of cortical bone and an inverted 'V'-shaped defect at the distal margin of the femoral shaft. At autopsy these injuries were confirmed in addition to areas with fine parallel cross-striations matching the marginal serrations of the teeth of a white shark. Thus, while post mortem CT with 3-dimensional reconstruction at high resolution can show the nature and number of the bony injuries following shark attack, it is complimented by pathological examination which may find fine parallel grooves from teeth serrations. Post mortem 3-dimensional volume rendering may also help to find or exclude fragments of teeth, and silicone casting may provide a permanent record of bone lesions.

Efficient malicious node detection by multi-objective energy trust aware hybrid optimization based maximizing lifetime of wireless sensor networks

Wireless sensor network (WSN) collect and detect data in real time, but their battery life limits their lifetime. The CH selection process increases network overhead and reduces lifetime, but it considers node processing and energy limitations. To solve that problem this research methodology proposed Multi Objective Energy trust - Aware Optimal Clustering and Secure Routing (MOETAOCSR) protocol. At first, the trust factors such as direct and indirect factors are calculated. Thus, the calculated values are given as input to the SDLSTM to detect the malicious node and normal node. Here, the network deployment process is initially carried out and then the cluster is formed by HWF-FCM. From the clustered sensor nodes, the cluster head is selected using Golden Jackal Siberian Tiger Optimization (GJSTO) approach. Then, the selection of CH the paths are learned by using the Beta Distribution and Scaled Activation Function based Deep Elman Neural Network (BDSAF-DENN) and from the detected paths the optimal paths are selected using the White Shark Optimization (WSO). From the derived path sensed data securely transferred to the BS for further monitoring process using FPCCRSA. The proposed technique is implemented in a MATLAB platform, where its efficiency is assessed using key performance metrics including network lifetime, packet delivery ratio, and delay. Compared to existing models such as EAOCSR, RSA, and Homographic methods, the proposed technique achieves superior results. Specifically, it demonstrates a 0.95 improvement in throughput, 0.8 enhancement in encryption time, and a network lifetime of 7.4.

The effectiveness of the semi‐automated Identifin software to accurately fast‐track dorsal fin photographic identifications of sharks

Abstract For some marine species, photo‐identification (photo‐ID) data can be used to generate population estimates critical for conservation efforts. For the white shark (Carcharadon carcharias), different photo‐ID methods have been proposed: (1) the traditional manual matching method based on non‐organized databases, (2) the notches code method based on organized databases and (3) the semi‐automatic software, Identifin. However, the effectiveness of these different methods and the applicability to other shark species have not been adequately explored. Eleven inexperienced and two experienced investigators used the three different photo‐ID methods to record the accuracy and time taken to identify individual white sharks from photo‐ID images. Both the traditional and notches code methods allowed for the accurate matching of individuals to the databases provided. For all investigators, the notches code method used significantly less time (14 min ± 0.14 min) than the traditional matching method (19 min ± 1.34 min). The semi‐automated method, Identifin, further shortened the identification time to 2 min (± 0.10 min) and provided a significantly higher matching accuracy (96% correct matches) compared with traditional manual matching (78% correct matches) and the notches code method (70% correct matches). The use of Identifin was tested to identify bronze whaler sharks (Carcharhinus brachyurus), a species with finer notches patterns when compared with white sharks. Identifin showed less efficiency in matching individual photographs to the database, but when very‐high‐quality (VHQ) photographs were available, matching improved substantially. Identifin software shows great potential in assisting researchers to accurately identify individual sharks in large databases, and this can contribute significantly to the conservation of shark species. The software and automated process need to be adapted to be functional on a variety of different shark species, but it proves to be an effective tool in managing large photo‐ID databases.

Fault diagnosis of power transformers based on t-SNE and ECOC-TEWSO-SVM

Support Vector Machines (SVMs) have achieved significant success in the field of power transformer fault diagnosis. However, challenges such as determining SVM hyperparameters and their suitability for binary classification still exist. This paper proposes a novel method for power transformer fault diagnosis, called ECOC-WSO-SVM, which utilizes a White Shark Optimizer (WSO) and error correcting output codes to optimize SVMs. First, t-distributed Stochastic Neighbor Embedding (t-SNE) is employed to reduce the dimensionality of Dissolved Gas Analysis (DGA) features constructed using the correlation ratio method, from 26 dimensions. In addition, to effectively solve the hyperparameters of SVMs, a multi-strategy fusion method is proposed to improve the WSO, incorporating tent chaos initialization, elite opposite learning, and selection strategies, forming TEWSO, and its superior optimization performance is validated using IEEE CEC2021 test functions. Furthermore, to address the limitations of SVMs as a binary classifier, an error correcting output code fusion SVM is introduced, thus constructing a multi-class SVM model. Finally, the diagnostic performance of the ECOC-TEWSO-SVM model is validated using real-world data. Results demonstrate that the proposed model exhibits the best diagnostic performance compared to traditional models and those in the literature, thereby proving the significance and effectiveness of the proposed model.

Enhancing Medium Term Wind Power Forecasting Accuracy With Dual Stage Attention Based TCN-GRU Model and White Shark Optimization

This research introduces a creative strategy for addressing the challenges of wind power predicting, crucial for effective renewable energy integration into the power grid. We propose a dual-stage attention-based Temporal Convolutional Network and Gated Recurrent Units (ATCN-AGRU) method for predicting wind energy over the medium term. In the first stage, a local attention mechanism captures fine-grained details and local dependencies, while the second stage employs a global attention mechanism to emphasize broader context and long-range dependencies within the data sequence. Our experimentation employs wind turbine data at 1-hr resolution, examining various time horizons from 24 hr to 1 week to assess multi-step forecasting precision and computational efficiency. Through rigorous statistical assessments, we demonstrate the model’s validity, with the mean absolute percentage errors (MAPE) consistently below 9% for week-ahead forecasting. Model parameters were fine-tuned using white shark optimization (WSO), enhancing convergence and overall performance. The proposed hybrid model significantly outperforms standard forecasting methods, achieving a maximum MAPE of 8.07% for week-ahead forecasting and a minimal 4.44% for day-ahead forecasting. To test the model’s stability, we extend the experiment to month-ahead forecasting, with the mean absolute error (MAE) ranging from 2.42 to 6.2% for weekly predictions.

The influence of micro-scale thermal habitat on the movements of juvenile white sharks in their Southern California aggregation sites

While juvenile white sharks (JWS) can display regional endothermy, the need to maintain internal temperatures within an energetically favorable range likely drives thermoregulatory movements to maximize growth and foraging efficiency. Many JWS from the northeastern Pacific population aggregate seasonally in nearshore nursery habitats throughout the Southern California Bight and historic data show that inter-seasonal movement patterns may be heavily dependent on ambient water temperature. However, the degree to which micro-scale (e.g., ~10 m2) water temperature heterogeneity influences JWS three-dimensional movement within nearshore aggregation sites is unknown. High-resolution temperature and passive acoustic-telemetry geo-positional data were used to quantify how temperature and vertical thermal stratification influenced JWS movement using several modeling approaches. JWS selected for water temperatures between 16 – 22°C and depths shallower than 2 m. Sharks occupied deeper waters during dawn and dusk periods, and their distance from the seafloor and spatial orientation of their depth distribution was significantly related to the thermal structure of the water column across the monitored area. Tagged sharks remained above a 16°C thermal threshold and altered their horizontal and vertical distributions accordingly. While high-resolution movement and environmental data provide improved predictability of micro-scale habitat use and distribution, other variables such as prey distribution, behavior, and competition would further improve habitat use models for this highly mobile species.

Development of single-pin, un-barbed, pole-tagging of free-swimming dolphins and sharks with satellite-linked transmitters

BackgroundTo tag large marine vertebrates, without the need to catch them, avoiding using barbs for tag retention, and precisely controlling tag location, the remote Tag Attachment Device on a pole (TADpole) was developed. This allows single-pin tags (Finmount, Wildlife Computers) to be attached to the dorsal fins of free-swimming large marine vertebrates.ResultsTADpole comprises a pole-mounted holster that carries a tag. It uses compressed air, and a micro-controller, to rapidly insert a stainless-steel pin through a corrodible metal retaining ring in the first tag attachment wing, the animal’s dorsal fin, and then a press fit Delrin retaining ring in the tag wing on the other side of the fin. Tagging only occurs when the trailing edge of the dorsal fin touches a trigger bar in the holster, ensuring optimal pin placement. It was developed using fins from cadavers, then trialed on briefly restrained coastal dolphins that could be followed in successive days and weeks, and then on free-swimming animals in the field. The latter showed very short touch/response intervals and highlighted the need for several iterative revisions of the pneumatic system. This resulted in reducing the total time from triggering to tag application to ~ 20 ms. Subsequent efforts expanded the TADpole’s applicability to sharks. One free-swimming Atlantic spotted dolphin, two white sharks, and one whale shark were then tagged using the TADpole.ConclusionsBeing able to tag free-swimming dolphins and sharks remotely and precisely with satellite-linked telemetry devices may contribute to solving conservation challenges. Sharks were easier to tag than dolphins. Dolphin touch-to-response times were 28 ms or less. Delphinid skin has unique polymodal axon bundles that project into the epidermis, perhaps a factor in their uniquely fast response, which is 10 × faster than humans. Their primary reaction to tagging is to abduct the flippers and roll the fin out of the TADpole holster. This device has the potential to deliver high-quality tag data from large vertebrates with dorsal fins without the stress and logistics associated with catch-and-release, and without the trauma of tags that use barbs for retention. It also collects a dorsal fin biopsy core.

Diverse habitats shape the movement ecology of a top marine predator, the white shark Carcharodon carcharias

AbstractAn animal's movement is influenced by a plethora of internal and external factors, leading to individual‐ and habitat‐specific movement characteristics. This plasticity is thought to allow individuals to exploit diverse environments efficiently. We tested whether the movement characteristics of white sharks Carcharodon carcharias differ across ontogeny and among habitats along the coast of Central California. In doing so, we elucidate how changes in internal state (physiological changes coinciding with body size) and external environments (differing seascapes and/or diel phases) shape the movement of this globally distributed predator. Twenty‐one white sharks, from small juveniles to large adults, were equipped with motion‐sensitive biologging tags at four contrasting seascapes: two islands, a headland, and an inshore cove. From multisensor biologging data, 20 metrics characterizing movement (i.e., depth use, vertical velocities, activity, turning rates, and bursting events) were derived and subjected to multivariate analyses. Movement characteristics were most different across seascapes, followed by ontogeny and diel phase. Juvenile sharks, which were only encountered at the cove, displayed the most distinct movement characteristics. Sharks at this seascape remained close to the shore traveling over smaller areas, shallower depth ranges, and with lower levels of tail beat frequencies, when corrected for size, than sub‐adult and adult sharks tagged elsewhere. Distinct tortuous daytime versus linear nighttime horizontal movements were recorded from sharks at island seascapes but not from those at the headland or inshore cove. At the offshore islands, the linear nighttime swimming patterns coincided with repeated dives to and from deeper water. The availability of prey and access to deeper water are likely drivers of the differences in movement characteristics described, with varying demographics of pinniped prey found at the subadult and adult aggregation areas and juvenile sharks being piscivorous and their habitat neither adjacent to pinniped haul out areas nor deeper water. This study demonstrates plasticity in the movements of a top predator, which adapts its routine to suit the habitat it forages within.

A profiled side‐channel attack detection using deep learning model with capsule auto‐encoder network

AbstractSide‐channel analysis (SCA) is a type of cryptanalytic attack that uses unintended ‘side‐channel’ leakage through the real‐world execution of the cryptographic algorithm to crack a secret key of an embedded system. These side‐channel errors can be discovered through tracking the energy usage of the device performing the technique, electromagnetic radiations while the encryption process, execution time, cache hits/misses, and others. Nowadays, deep learning‐based detection techniques are considered as emerging techniques that have been proposed for attack detection. Deep learning architectures have the ability to learn autonomously and concentrate on difficult features, in contrast to machine learning models. In light of these factors, the work's motive is thought to be the proposal of a deep learning‐based attack detection method. Many methods are used to decrease these assaults, however, the majority of them are inefficient and time‐demanding. In order to address these challenges, this study employs a novel deep learning‐based methodology. Pre‐processing, feature extraction, and SCA classification are the three stages of the approach proposed in this work. First, pre‐processing is used to remove unnecessary information and improve the quality of the input using data cleaning and min‐max normalization. The previously processed data are then fed as input into the proposed hybrid deep learning architecture. A Deep Residual Capsule Auto‐Encoder (DR_CAE) model is introduced in the proposed study. The deep residual neural network‐50 (DRNN‐50) is utilized to extract relevant features in this case, while the side channel analysis is done by using capsule auto‐encoder (CAE). The parameters of the proposed model are adjusted using the modified white shark optimization (MWSO) technique to improve its performance. In the results section, the proposed model is compared to various existing models in terms of accuracy, precision, recall, F‐measures, time, and so on. The proposed framework has an accuracy of 98.802%, F‐measures of 98.801%, kappa coefficient of 97.6%, the precision value of 98.81%, and recall value of 98.80%.

Boosting white shark optimizer for global optimization and cloud scheduling problem

Boosting white shark optimizer for global optimization and cloud scheduling problem

Efficient economic operation based on load dispatch of power systems using a leader white shark optimization algorithm

This article proposes the use of a leader white shark optimizer (LWSO) with the aim of improving the exploitation of the conventional white shark optimizer (WSO) and solving the economic operation-based load dispatch (ELD) problem. The ELD problem is a crucial aspect of power system operation, involving the allocation of power generation resources to meet the demand while minimizing operational costs. The proposed approach aims to enhance the performance and efficiency of the WSO by introducing a leadership mechanism within the optimization process, which aids in more effectively navigating the complex ELD solution space. The LWSO achieves increased exploitation by utilizing a leader-based mutation selection throughout each generation of white sharks. The efficacy of the proposed algorithm is tested on 13 engineer benchmarks non-convex optimization problems from CEC 2020 and compared with recent metaheuristic algorithms such as dung beetle optimizer (DBO), conventional WSO, fox optimizer (FOX), and moth-flame optimization (MFO) algorithms. The LWSO is also used to address the ELD problem in different case studies (6 units, 10 units, 11 units, and 40 units), with 20 separate runs using the proposed LWSO and other competitive algorithms being statistically assessed to demonstrate its effectiveness. The results show that the LWSO outperforms other metaheuristic algorithms, achieving the best solution for the benchmarks and the minimum fuel cost for the ELD problem. Additionally, statistical tests are conducted to validate the competitiveness of the LWSO algorithm.

Automated multi-class skin cancer classification using white shark optimizer with ensemble learning classifier on dermoscopy images

Automated multi-class skin cancer classification using white shark optimizer with ensemble learning classifier on dermoscopy images

Power Quality (PQ) Analyses of DG Utilizing Unified Power Quality Conditioner (UPQC) by White Shark Optimizer and Recalling-Enhanced Recurrent Neural Network

This paper proposes a novel hybrid technique for enhancing power quality (PQ) in distributed generation (DG) systems by deploying a unified power quality conditioner (UPQC). Here, the proposed hybrid method is the joint execution of white shark optimizer (WSO) and recalling-enhanced recurrent neural network (RERNN), called the WSO-RERNN technique. The primary objective of this novel approach is to effectively mitigate voltage sag and reduce voltage harmonics under varying load conditions. It is important to investigate the voltage sag, swell and harmonic distortion of the system to obtain an enhanced PQ of the energy supply. Therefore, this paper shows the brief impact of PQ in DG utilizing the proposed unified PQ conditioner controller. The WSO-RERNN control technique enhances the performance of the UPQC controller by providing the optimal control signal. By then, the efficiency of the proposed approach is done in MATLAB, and the performance is compared with those of existing optimization techniques, including Ant Lion Optimizer (ALO), Grey wolf optimization (GWO) and Salp swarm algorithm (SSA) methods.

A multi-stage forecasting system for daily ocean tidal energy based on secondary decomposition, optimized gate recurrent unit and error correction

Tidal energy, as a new energy, has very high research potential and practical application value. For the characteristics of tidal energy such as nonstationarity and nonlinearity, a multi-stage forecasting system for daily ocean tidal energy is proposed. It is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), refined composite multi-scale dispersion entropy (RCMDE), empirical mode decomposition based on time-varying filter modified by white shark optimizer (WSOTVFEMD), improved gate recurrent unit using parasitic salp swarm algorithm based on differential evolution (PDESSAGRU) and error correction using CNN (CNN-EC), named as ICEEMDAN-RCMDE-WSOTVFEMD-PDESSAGRU–CNN–EC. Firstly, decompose tidal energy into a series of intrinsic mode functions (IMFs) by ICEEMDAN, and divide IMFs into high-complexity and low-complexity components by RCMDE. Next, secondly decompose the reconstructed high-complexity components into high-complexity parts by WSOTVFEMD. Afterwards, separately predict each component of the high-complexity parts and the low-complexity components by PDESSA, and reconstruct the predicted results to obtain original predicting results. In the end, decompose the error into error IMFs (EIMFs) by ICEEMDAN, predict EIMFs with convolutional neural network (CNN) respectively to acquire error predicting results, and reconstruct original predicting results and error predicting results to acquire the final results. Taking the tidal energy of the American cities including San Francisco, Sitka, and Wauna as case studies, the results show that the proposed system has high prediction accuracy after experiments with 13 comparative models in each city.

Frequency regulation of interconnected hybrid power system with Assimilation of electrical vehicles

Recent widespread connections of renewable energy resource (RESs) in place of fossil fuel supplies and the adoption of electrical vehicles in place of gasoline-powered vehicles have given birth to a number of new concerns. The control architecture of linked power networks now faces an increasingly pressing challenge: tie-line power fluctuations and reducing frequency deviations. Because of their nature and dependence on external circumstances, RESs are analogous to continually fluctuating power generators. Using a fractional order-based frequency regulator, this work presents a new method for improving the frequency regulation in a two-area interconnected power system. In order to deal with the frequency regulation difficulties of the hybrid system integrated with RES, the suggested controller utilizes the modified form of fractional order proportional integral derivative (FOPID) controller known as FOI-PDN controller. The new proposed controllers are designed using the white shark optimizer (WSO), a current powerful bioinspired meta heuristic algorithm which has been motivated by the learning abilities of white sharks when actively hunting in the environment. The suggested FOI-PDN controller's performance was compared to that of various control methodologies such as FOPID, and PID. Furthermore, the WSO findings are compared to those of other techniques such as the salp swarm algorithm, sine cosine algorithm and fitness dependent optimizer. The recommended controller and design approach have been tested and validated at different loading conditions and different circumstances, as well as their robustness against system parameter suspicions. The simulation outcomes demonstrate that the WSO-based tuned FOI-PDN controller successfully reduces peak overshoot by 73.33%, 91.03%, and 77.21% for region-2, region-1, and link power variation respectively, and delivers minimum undershoot of 89.12%, 83.11%, and 78.10% for both regions and tie-line. The obtained findings demonstrate the new proposed controller's stable function and frequency controlling performance with optimal controller parameters and without the requirement for a sophisticated design process.

Lone orca seen hunting a great white shark

Lone orca seen hunting a great white shark

Length at life stages of the white shark Carcharodon carcharias in the western North Atlantic

Length at life stages of the white shark Carcharodon carcharias is not well known for most of the 9 populations of this species, including in the western North Atlantic (WNA). We analyzed length and maturity data by sex for 87 white sharks with sizes ranging 138-501 cm total length (TL), captured, studied, and released by OCEARCH during 2012-2022, off the US and Canadian Atlantic coasts. A binary logistic regression was used to estimate the length-at-maturity (L50) for the WNA white shark with a Bayesian statistical framework using a Markov chain Monte Carlo method for numerical integration. Different trials using noninformative and informative priors were tested. The posterior probability distribution for L50, steepness of the model (φ), and 95% credible intervals (CI) of the logistic model for females were L50 = 411.3 cm TL (CI: 390.8-432.6 cm TL) and φ = 10.5 (CI: 5.7-17.8) and for males were L50 = 334.9 cm TL (CI: 321.2-348.2 cm TL) and φ = 7.5 (CI: 4.2-12.4). These L50 values are somewhat smaller than previously reported sizes-at-maturity for both sexes of this species. An ordinal logistic regression allowed us to determine the probability of being in the various life stages (young-of-the-year, juvenile, and adult) at a particular size. Estimating the length at any life-history stage of white sharks along with age estimates is useful for determining the reproductive value of the population and ultimately for estimating the relative contribution (elasticity) of vital rates to population growth.