Captures and Diet of Three Sharks Species in the Veracruz Reef System

During July to November of 2008, the artisanal fisheries captured juvenile sharks belonging to the Carcharhinus and Sphyrnidae family in the Veracruz Reef System (south western Gulf of Mexico). The three most abundant organisms were of the species Sphyrna lewini, Carcharhinus brevipinna and Rhizoprionodon terraenovae. Local fisherman recognized five captured areas of sharks as a direct way or bycatch. Some of these areas are located near to eddies formations and river discharges (high productivity areas). These top predators fed on benthic and demersal prey of coastal and reef habits had been the Teleost group the most important item in its diet. However it is possible to observe differences in its feeding tendency.

Evolution of white and megatooth sharks, and evidence for early predation on seals, sirenians, and whales

The early white shark Carcharodon Smith, 1838 with the fossil Carcharodon auriculatus (Blainville, 1818) and the extinct megatooth shark Otodus Agassiz, 1843 with species Otodus sokolovi (Jaeckel, 1895) were both present in the European proto North Sea Basin about 47.8 - 41.3 m.y. ago (Lutetian, early Middle Eocene), as well as in the Tethys realm around the Afican-Eurasian shallow marine habitats. Both top predators developed to be polyphyletic, with possible two different lamnid shark ancestors within the Early Paleocene to Early Eocene timespan with Carcharodon (white shark line-age) and Otodus (megatooth shark lineage). Their sawblade teeth developed during the early Paleogene as the result of adaptation to feeding on various marine new rising mammals, coinciding with three main waves of evolutionary emergence of seals, sirenians, and whales in parallel with the evolution of these large predatory sharks. Megatooth sharks specialized in hunting whales and sirenians only on the coastal shelves of warm oceans and disappeared globally in the Pleistocene due to climate change and ocean cooling. The cold-water adapted early white sharks have survived until the present day with body temperate change adaptation in warm to temperate oceans and are proposed to have specialized on coastal seal hunting already50 m.y. ago.

Sharks senses and shark repellents

Despite over 70 years of research on shark repellents,few practical and reliable solutions to prevent shark attacks on humans or reduce shark bycatch and depredation in commercial fisheries have been developed.In large part,this deficiency stems from a lack of fundamental knowledge of the sensory cues that drive predatory behavior in sharks.However,the widespread use of shark repellents is also hampered by the physical constraints and technical or logistical difficulties of deploying substances or devices in an open-water marine environment to prevent an unpredictable interaction with a complex animal.Here,we summarize the key attributes of the various sensory systems of sharks and highlight residual knowledge gaps that are relevant to the development of effective shark repellents.We also review the most recent advances in shark repellent technology within the broader historical context of research on shark repellents and shark sensory systems.We conclude with suggestions for future research that may enhance the efficacy of shark repellent devices,in particular,the continued need for basic research on shark sensory biology and the use of a multi-sensory approach when developing or deploying shark repellent technology.

某海上石油设施发电机信号采集模块升级改造

某海上石油设施配有3台生产发电机,每台发电机配置UMT模块,由国外厂家生产,分别监测发电机电压、电流、频率等参数,并将相关参数计算得出的有功功率、无功功率等信号传输至电力管理系统(Power Management System,PMS)。由于其中1台发电机的UMT模块故障后,无法修复且该型号已停产,提出采用高性能多功能仪表Shark200进行升级改造,以同样的模拟量输出至PMS实现发电机控制。

New Sharks,Rays Discovered in Indonesia Fish Markets

海洋是神秘的,它是生命孕育的摇篮。各种各样的生物游弋其间,逍遥自在。海洋为它们的生存和繁衍提供了广阔无垠的舞台。对于大海,自古以来,人们就有一种本能的渴望。好奇心驱使人类不断探索大海的深处,搜寻缤纷多彩的海洋生物。而对于作为海霸王的鲨鱼,人们更是怀着一种敬畏的心情。它的神秘与强大为人们提供了巨大的幻想空间。近年来,随着研究的不断深入和科技的日新月异,鲨鱼的面纱正被揭开。许多鲨鱼新品种的被发现就是很好的例证。

Atlantic Sharks Seen Coming Closer to Shore

海中的鲨鱼也有其苦衷,它们的食物(鲱鱼)被过量捕捞,因此,鲨鱼就得逼近海岸线去寻觅另可选择的食物,而在海中享受游泳的人,便很自然地成了鲨鱼攻击目标。文章的末尾提出了5条“下海”时有效避免鲨鱼攻击的“须知”,很值得一读。

Sharks Enjoy Whale of a Feast Near Beach

一条24米长,80吨重的“少年”鲸鱼死了,它的尸体正漂浮在澳大利亚的海岸的一个冲浪胜地。死鲸散发出难闻的气味,但是,因体积和重量的关系,人们又无法移走它。请君放心,鲨鱼正在feasting on it。

Same or different?Abstract relational concept use in juvenile bamboo sharks and Malawi cichlids

Sorting objects and events into categories and concepts is an important cognitive prerequisite that spares an individual the learning of every object or situation encountered in its daily life.Accordingly,specific items are classified in general groups that allow fast responses to novel situations.The present study assessed whether bamboo sharks Chiloscyllium griseum and Malawi cichlids Pseudotropheus zebra can distinguish sets of stimuli(each stimulus consisting of two abstract,geometric objects)that meet two conceptual preconditions,i.e.,(1)"sameness"versus"difference"and(2)a certain spatial arrangement of both objects.In two alternative forced choice experiments,individuals were first trained to choose two different,vertically arranged objects from two different but horizontally arranged ones.Pair discriminations were followed by extensive transfer test experiments.Transfer tests using stimuli consisting of(a)black and gray circles and(b)squares with novel geometric patterns provided conflicting information with respect to the learnt rule"choose two different,vertically arranged objects",thereby investigating(1)the individuals'ability to transfer previously gained knowledge to novel stimuli and(2)the abstract relational concept(s)or rule(s)applied to categorize these novel objects.Present results suggest that the level of processing and usage of both abstract concepts differed considerably between bamboo sharks and Malawi cichlids.Bamboo sharks seemed to combine both concepts-although not with equal but hierarchical prominence-pointing to advanced cognitive capabilities.Conversely,Malawi cichlids had difficulties in discriminating between symbols and failed to apply the acquired training knowledge on new sets of geometric and,in particular,gray-level transfer stimuli.

Improved Shark Smell Optimization Algorithm for Human Action Recognition

Human Action Recognition(HAR)in uncontrolled environments targets to recognition of different actions froma video.An effective HAR model can be employed for an application like human-computer interaction,health care,person tracking,and video surveillance.Machine Learning(ML)approaches,specifically,Convolutional Neural Network(CNN)models had beenwidely used and achieved impressive results through feature fusion.The accuracy and effectiveness of these models continue to be the biggest challenge in this field.In this article,a novel feature optimization algorithm,called improved Shark Smell Optimization(iSSO)is proposed to reduce the redundancy of extracted features.This proposed technique is inspired by the behavior ofwhite sharks,and howthey find the best prey in thewhole search space.The proposed iSSOalgorithmdivides the FeatureVector(FV)into subparts,where a search is conducted to find optimal local features fromeach subpart of FV.Once local optimal features are selected,a global search is conducted to further optimize these features.The proposed iSSO algorithm is employed on nine(9)selected CNN models.These CNN models are selected based on their top-1 and top-5 accuracy in ImageNet competition.To evaluate the model,two publicly available datasets UCF-Sports and Hollywood2 are selected.

Securing Cloud Computing from Flash Crowd Attack Using Ensemble Intrusion Detection System

Flash Crowd attacks are a form of Distributed Denial of Service(DDoS)attack that is becoming increasingly difficult to detect due to its ability to imitate normal user behavior in Cloud Computing(CC).Botnets are often used by attackers to perform a wide range of DDoS attacks.With advancements in technology,bots are now able to simulate DDoS attacks as flash crowd events,making them difficult to detect.When it comes to application layer DDoS attacks,the Flash Crowd attack that occurs during a Flash Event is viewed as the most intricate issue.This is mainly because it can imitate typical user behavior,leading to a substantial influx of requests that can overwhelm the server by consuming either its network bandwidth or resources.Therefore,identifying these types of attacks on web servers has become crucial,particularly in the CC.In this article,an efficient intrusion detection method is proposed based on White Shark Optimizer and ensemble classifier(Convolutional Neural Network(CNN)and LighGBM).Experiments were conducted using a CICIDS 2017 dataset to evaluate the performance of the proposed method in real-life situations.The proposed IDS achieved superior results,with 95.84%accuracy,96.15%precision,95.54%recall,and 95.84%F1 measure.Flash crowd attacks are challenging to detect,but the proposed IDS has proven its effectiveness in identifying such attacks in CC and holds potential for future improvement.

Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

Online reviews regarding purchasing services or products offered are the main source of users’opinions.To gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or services.This practice is called review spamming.During the last few years,various techniques have been recommended to solve the problem of spam reviews.Previous spam detection study focuses on English reviews,with a lesser interest in other languages.Spam review detection in Arabic online sources is an innovative topic despite the vast amount of data produced.Thus,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion Text.The presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and truthful.Initially,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible format.Next,unigram and bigram feature extractors are utilized.The SGRU model is employed in this study to identify and classify Arabic spam reviews.Since the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU model.The experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC dataset.An extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.

Deep Learning-Based Sign Language Recognition for Hearing and Speaking Impaired People

Sign language is mainly utilized in communication with people who have hearing disabilities.Sign language is used to communicate with people hav-ing developmental impairments who have some or no interaction skills.The inter-action via Sign language becomes a fruitful means of communication for hearing and speech impaired persons.A Hand gesture recognition systemfinds helpful for deaf and dumb people by making use of human computer interface(HCI)and convolutional neural networks(CNN)for identifying the static indications of Indian Sign Language(ISL).This study introduces a shark smell optimization with deep learning based automated sign language recognition(SSODL-ASLR)model for hearing and speaking impaired people.The presented SSODL-ASLR technique majorly concentrates on the recognition and classification of sign lan-guage provided by deaf and dumb people.The presented SSODL-ASLR model encompasses a two stage process namely sign language detection and sign lan-guage classification.In thefirst stage,the Mask Region based Convolution Neural Network(Mask RCNN)model is exploited for sign language recognition.Sec-ondly,SSO algorithm with soft margin support vector machine(SM-SVM)model can be utilized for sign language classification.To assure the enhanced classifica-tion performance of the SSODL-ASLR model,a brief set of simulations was car-ried out.The extensive results portrayed the supremacy of the SSODL-ASLR model over other techniques.

Forecasting Shark Attack Risk Using AI: A Deep Learning Approach

This study aimed to develop a predictive model utilizing available data to forecast the risk of future shark attacks, making this critical information accessible for everyday public use. Employing a deep learning/neural network methodology, the system was designed to produce a binary output that is subsequently classified into categories of low, medium, or high risk. A significant challenge encountered during the study was the identification and procurement of appropriate historical and forecasted marine weather data, which is integral to the model’s accuracy. Despite these challenges, the results of the study were startlingly optimistic, showcasing the model’s ability to predict with impressive accuracy. In conclusion, the developed forecasting tool not only offers promise in its immediate application but also sets a robust precedent for the adoption and adaptation of similar predictive systems in various analogous use cases in the marine environment and beyond.

First Recorded Account of a White Shark Agonistic Pectoral Fin Depression Behavior at Guadalupe Island, Mexico

An agonistic display by a white shark was observed and photographed during a cage dive at Guadalupe Island in November 2015. Exhibiting exaggerated pectoral fin depression, agonistic behaviors have been previously observed and described in several shark species. This account may be the first record of a white shark in close proximity to a caged diver, exhibiting strong pectoral fin depression significantly dipped, in the mid-agonistic display. Such displays should be considered as aggressive and potentially life-threatening by those using the ocean for recreational or professional purposes.

Evaluating Behavioural Modelling Predictions in the Blue Shark (Prionace glauca) Enables Greater Insight on Habitat Use from Location only Argos Data

The relationship between habitat and behaviour provides important information for species management.For large,free roaming,marine animals satellite tags provide high resolution information on movement,but such datasets are restricted due to cost.Extracting additional biologically important information from these data would increase utilisation and value.Several modelling approaches have been developed to identify behavioural states in tracking data.The objective of this study was to evaluate a behavioural state prediction model for blue shark(Prionace glauca)ARGOS surface location-only data.The novel nature of the six SPLASH satellite tags used enabled behavioural events to be identified in blue shark dive data and accurately mapped spatio-temporally along respective surface location-only tracks.Behavioural states modelled along the six surface location-only tracks were then tested against observed behavioural events to evaluate the model’s accuracy.Results showed that the Behavioural Change Point Analysis(BCPA)model augmented with K means clustering analysis performed well for predicting foraging behaviour(correct 86%of the time).Prediction accuracy was lower for searching(52%)and travelling(63%)behaviour,likely related to the numerical dominance of foraging events in dive data.The model’s validation for predicting foraging behaviour justified its application to nine additional surface location-only(SPOT tag)tracks,substantially increasing the utilisation of expensive and rare data.Results enabled the critical behavioural state of foraging,to be mapped throughout the entire home range of blue sharks,allowing drivers of critical habitat to be investigated.This validation strengthens the use of such modelling to interpret historic and future datasets,for blue sharks but also other species,contributing to conservational management.

基于JetSpeed的多portlet流程协作机制研究与实现

当前门户的功能定位已经从传统的信息集成转向应用集成。门户环境中应用间的进一步集成实际上表现为Portlet协作问题。在分析现有协作方法的基础上,遵循jsr286规范,提出了可扩展的多Portlet的流程协作框架,并结合开源门户产品JetSpeed2.0进行开发,从而解决了数字校园建设中利用门户实现应用交互协作和个性化信息服务的关键问题。