Water Strider Research Articles

How to capture active Marangoni surfers.

Surfers at the air-water interface form a large subset of the domain of active matter systems. They range from the water strider in the biological world to soluto-capillary effect driven artificial boats. In this work, we propose a general protocol to capture soluto-capillary effect driven interfacial surfers. By locally modifying the air-water interface using the perturbation from a micro-air-pump, these boats are reliably captured in the region of influence (ROI) of the perturbation. The surfers begin to explore the available space freely again once the perturbation is switched off. This method is successfully generalized to a couple of distinct surface-active chemicals used as fuel for the boats. Control experiments involving passive particles validate the results as being significantly better than purely mechanical "herding" of the particles. A possible mechanism behind the observed "trapping" is proposed.

Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

Computer-aided diagnosis (CAD) models exploit artificial intelligence (AI) for chest X-ray (CXR) examination to identify the presence of tuberculosis (TB) and can improve the feasibility and performance of CXR for TB screening and triage. At the same time, CXR interpretation is a time-consuming and subjective process. Furthermore, high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis. Therefore, computer-aided diagnosis (CAD) models using machine learning (ML) and deep learning (DL) can be designed for screening TB accurately. With this motivation, this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification (WSODTL-TBC) model on Chest X-rays (CXR). The presented WSODTL-TBC model aims to detect and classify TB on CXR images. Primarily, the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation. Besides, a pre-trained residual network with a two-dimensional convolutional neural network (2D-CNN) model is applied to extract feature vectors. In addition, the WSO algorithm with long short-term memory (LSTM) model was employed for identifying and classifying TB, where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology, showing the novelty of the work. The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset, and the outcomes were investigated in many aspects. The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.

Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

Parkinson’s disease (PD) is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly patients. It affect their quality of life drastically and millions of seniors are diagnosed with PD every year worldwide. Several models have been presented earlier to detect the PD using various types of measurement data like speech, gait patterns, etc. Early identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of PD. The recently-emerging Deep Learning (DL) models can leverage the past data to detect and classify PD. With this motivation, the current study develops a novel Colliding Bodies Optimization Algorithm with Optimal Kernel Extreme Learning Machine (CBO-OKELM) for diagnosis and classification of PD. The goal of the proposed CBO-OKELM technique is to identify whether PD exists or not. CBO-OKELM technique involves the design of Colliding Bodies Optimization-based Feature Selection (CBO-FS) technique for optimal subset of features. In addition, Water Strider Algorithm (WSA) with Kernel Extreme Learning Machine (KELM) model is also developed for the classification of PD. CBO algorithm is used to elect the optimal set of features whereas WSA is utilized for parameter tuning of KELM model which altogether helps in accomplishing the maximum PD diagnostic performance. The experimental analysis was conducted for CBO-OKELM technique against four benchmark datasets and the model portrayed better performance such as 95.68%, 96.34%, 92.49%, and 92.36% on Speech PD, Voice PD, Hand PD Meander, and Hand PD Spiral datasets respectively.

Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

In recent times, real time wireless networks have found their applicability in several practical applications such as smart city, healthcare, surveillance, environmental monitoring, etc. At the same time, proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks. This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization (IM-EECNL) approach for real-time wireless networks. The proposed IM-EECNL technique involves two major processes namely node localization and clustering. Firstly, Chaotic Water Strider Algorithm based Node Localization (CWSANL) technique to determine the unknown position of the nodes. Secondly, an Oppositional Archimedes Optimization Algorithm based Clustering (OAOAC) technique is applied to accomplish energy efficiency in the network. Besides, the OAOAC technique derives a fitness function comprising residual energy, distance to cluster heads (CHs), distance to base station (BS), and load. The performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency. A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio (PDR) of 0.985.

Water webbing: Long‐jawed spider (Araneae, Tetragnathidae) produces webs that touch the surface of ephemeral waterbodies

AbstractFew spiders are known to construct silk webs that physically contact water, despite it being both a surface and medium that can be exploited for web construction. Herein, we report on an Australian long‐jawed spider from the Tetragnatha genus (Tetragnatha cf. nitens) that frequently produces aerial webs that touch the surface of ephemeral waterbodies. Field observations revealed webs that were built by these spiders at varying heights above water in emergent vegetation stands around the edge of ephemeral waterbodies. Approximately, 15 of the 100 webs recorded were constructed immediately above the water with their lower sections making physical contact with the water's surface. We observed water striders becoming stuck within these low‐lying webs, suggesting that associating webs with surface water allows the spiders to exploit prey types that cannot be passively filtered from the environment in purely aerial webs. This could suggest that some species of long‐jawed spider select the positioning of their webs above water, which would be adaptive if it allows them to target different and possibly multiple prey communities.

Inkjet-Assisted Electroformation of Magnetically Guidable Water Striders for Interfacial Microfluidic Manipulation.

Gerridae, colloquially called water striders, are a peculiar class of insects characterized by the extraordinary ability to walk on the surface of water bodies. Owing to this capacity, they constitute an ideal source of inspiration for designing untethered microdevices capable of navigating the interface between two fluids. Such steerable micrometric objects can be of great interest for various applications, ranging from the handling of floating objects to the remote control of microreactions and the manipulation of self-assembled monolayers. This paper describes the realization of artificial water striders via an inkjet-assisted electroforming approach. Inkjet deposition patterns the negative mask, which is subsequently filled with different layers of metals through electroforming. One of such layers is the magnetic alloy NiFe, which allows wireless propulsion of the striders by means of externally applied magnetic fields. The magnetic actuation tests prove good maneuverability at the water-air and silicone oil-air interfaces, with superior control over the speed and position of the devices. The surface of the devices is modified to tune its superficial energy in order to maximize buoyancy on these different combinations of fluids. A magnetic field-controlled strider manipulates a droplet and demonstrates collecting oil microdroplets and synthesizing platinum nanoparticles by chemical microreactions. Finally, the remotely operated microrobot could be employed in laboratories as a real avatar of chemists.

Cycle‐consistentgenerative adversarial network optimized with water strider optimization algorithm fostered intrusion detection framework for securing cloud computing environment

SummaryCloud computing is the delivery of computing services including servers, storage, databases, networking, software, analytics, and intelligence over the internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale. In this article, cycle‐consistent generative adversarial network (CCGAN) optimized with water strider optimization (WSO) algorithm fostered intrusion detection system (IDS) is proposed to secure the cloud computing (CC) environment (IDS‐CC‐CCGAN‐WSOA). Initially, the input data's are gathered via NSL‐KDD benchmark dataset. Then it is given to preprocessing. During preprocessing, it debugs the redundancy and missing value is restored using local least squares. Then, the preprocessed output is fed to the feature selection level. The optimum features are compiled utilizing correlation feature selection approach. This optimum features based, the data's are categorized as normal and anomalous data. The weights of this network are optimized by water strider optimization to attain effectual and optimum solution for recognizing the intruders. The proposed approach is executed in MATLAB. The performance metrics is examined to validate the performance of the proposed approach. Finally, the proposed approach attains 13.9367%, 13.268%, and 13.739% higher accuracy analyzed to the existing approaches, such as IDS‐CC‐DBN‐CSSA, IDS‐CC‐DNN‐IGASAA, and IDS‐CC‐MLPNN‐ABC.

Transcriptome-based Phylogeny of the Semi-aquatic Bugs (Hemiptera: Heteroptera: Gerromorpha) Reveals Patterns of Lineage Expansion in a Series of New Adaptive Zones.

Key innovations enable access to new adaptive zones and are often linked to increased species diversification. As such, innovations have attracted much attention, yet their concrete consequences on the subsequent evolutionary trajectory and diversification of the bearing lineages remain unclear. Water striders and relatives (Hemiptera: Heteroptera: Gerromorpha) represent a monophyletic lineage of insects that transitioned to live on the water-air interface and that diversified to occupy ponds, puddles, streams, mangroves and even oceans. This lineage offers an excellent model to study the patterns and processes underlying species diversification following the conquest of new adaptive zones. However, such studies require a reliable and comprehensive phylogeny of the infraorder. Based on whole transcriptomic datasets of 97 species and fossil records, we reconstructed a new phylogeny of the Gerromorpha that resolved inconsistencies and uncovered strong support for previously unknown relationships between some important taxa. We then used this phylogeny to reconstruct the ancestral state of a set of adaptations associated with water surface invasion (fluid locomotion, dispersal and transition to saline waters) and sexual dimorphism. Our results uncovered important patterns and dynamics of phenotypic evolution, revealing how the initial event of water surface invasion enabled multiple subsequent transitions to new adaptive zones on the water surfaces. This phylogeny and the associated transcriptomic datasets constitute highly valuable resources, making Gerromorpha an attractive model lineage to study phenotypic evolution.

A Hybrid Intelligent Clustering Model for Tackling Incomplete Mixed Data Using Heuristic Algorithm with Artificial Intelligence

Recently, in various information areas including image processing and social networks, data clustering has been vastly utilized. Thus, the clustering task is taken as successful while imputing the quality of uncertain values, whereas the traditional methods observe only poor performance. To enhance the performance, the hybrid intelligent-based clustering model is proposed by the adaptive concept. Initially, the data is collected from benchmark datasets that are composed of incomplete mixed data. The first level of clustering is processed by optimized K-Means Clustering (KMC) to acquire the optimal centroid, in which the centroid is optimized by the Adaptive Probability-assisted Water Strider Algorithm (AP-WSA). Similarly, the second clustering is done through K-Correlation-based Clustering (KCC) to obtain the clustered output. Thus, the final clustering process is named as Hybrid K-Means K-Correlation (HKMKC) clustering algorithm. Finally, the experimental results are validated with the performance measures and estimated with the traditional methods. Through the result analysis, the Average Silhouette Coefficient (AS) of the designed AP-WSA-HKMKC is 50%, 28%, 16%, and 5.45% progressed than KMC, FCM, KCC, and K-median clustering methods for dataset 2 at 15% miss rate. Thus, the designed hybrid clustering ensures the efficiency of handling incomplete data regarding accurate clustered outputs.

Surface tension force on a partially submerged horizontal concave cylinder

A horizontal cylinder with a concave cross-section partially submerged in a liquid at a given position may permit multiple menisci around itself. The number and stabilities of the menisci are analysed, and how the menisci change during the processes of gradually hoisting and lowering the cylinder is explained by bifurcation theory. The restoring force on the concave cylinder and the rebounding potential energy (defined as the work done by the restoring force during the whole hoisting process to represent the potential rebounding capacity of a cylinder on water) are also investigated. The results show that, when the radius of the concave arc is smaller than the critical value, the concave cylinder at a given position permits multiple possible menisci. The equilibria form fold bifurcations with the position of the cylinder as the bifurcation parameter, and two successive fold bifurcations can form a one-fold hysteresis loop. The force–distance curve representing the relation between the restoring force and the position of the cylinder also has corresponding hysteresis loops, where the restoring force will jump (i.e. change discontinuously) at the bifurcation points. In contrast to a convex cylinder, a concave cylinder can have different values of the restoring force at the same height because of multiple menisci, and the values depend on whether it is hoisted or lowered. Under the condition of a fixed cross-sectional area, the optimal cross-sectional shape is determined when the maximum rebounding potential energy is reached, and it is close to the shape with the critical concave arc angle for the existence of multiple possible menisci. The cross-sections with concave parts are preferable to circular, laterally planed and corner-concave cross-sections. This paper provides an effective method of enhancing the restoring force and potential rebounding height of a robotic water strider insect or particles on the surface of water.

Ultrastable Super-Hydrophobic Surface with an Ordered Scaly Structure for Decompression and Guiding Liquid Manipulation.

Directional droplet manipulation is very crucial in microfluidics, intelligent liquid management, etc. However, excessive liquid pressure tends to destroy the solid-gas-liquid (SAL) composite interface, creating a highly adhesive surface, which is not conducive to liquid transport. Herein, we propose a strategy to enhance the surface durability, in which the surface cannot withstand liquid pressure only by "blocking" but must instead guide liquid transport for "decompression". Learning from the water resistance of water strider legs and the drag reduction of shark skin, we present a continuous integrated system to obtain an ultrastable super-hydrophobic surface with a highly ordered scaly structure via a liquid flow-induced alignment method for lossless unidirectional liquid transport. The nonwetting scaly structure can both buffer liquid pressure and drive droplet motion to further reduce the vertical pressure of the liquid. Moreover, droplets can be manipulated unidirectionally using a voice. This work could aid in manufacturing scalable anisotropic micro-nanostructure surfaces, which inspires efforts in realizing lossless continuous liquid control on demand and related microfluidic applications.

A Micro-Displacement Measurement Based on the Shadow Technique.

The precision measurement of micro displacement is important in scientific and industrial fields. However, it is a tough challenge due to the complex design and the high cost of measuring instruments. Inspired by the shadow formed from water striders walking on a water surface under sunlight, a micro-displacement measurement method was proposed. Water strider legs with superhydrophobic properties bend the water surface. The curved surface of the water refracts sunlight, creating a shadow with a bright edge at the bottom of the pond. The shadow size is generally larger than the indentation depth of the legs from the water surface. In the micro-displacement measurement system, the applied displacement is proportional to the change in the diameter of the shadow. The presented study proposes a micro-displacement measurement procedure based on this shadow technique. The displacement sensitivity can reach 10.0 nm/pixel in the range of 5 µm. This system is simple to construct, low cost, and has high precision with good linear performance. The method provides a convenient additional option to measure micro-displacement.

Deep fusion of gray level co-occurrence matrices for lung nodule classification.

Lung cancer is a serious threat to human health, with millions dying because of its late diagnosis. The computerized tomography (CT) scan of the chest is an efficient method for early detection and classification of lung nodules. The requirement for high accuracy in analyzing CT scan images is a significant challenge in detecting and classifying lung cancer. In this paper, a new deep fusion structure based on the long short-term memory (LSTM) has been introduced, which is applied to the texture features computed from lung nodules through new volumetric grey-level-co-occurrence-matrices (GLCMs), classifying the nodules into benign, malignant, and ambiguous. Also, an improved Otsu segmentation method combined with the water strider optimization algorithm (WSA) is proposed to detect the lung nodules. WSA-Otsu thresholding can overcome the fixed thresholds and time requirement restrictions in previous thresholding methods. Extended experiments are used to assess this fusion structure by considering 2D-GLCM based on 2D-slices and approximating the proposed 3D-GLCM computations based on volumetric 2.5D-GLCMs. The proposed methods are trained and assessed through the LIDC-IDRI dataset. The accuracy, sensitivity, and specificity obtained for 2D-GLCM fusion are 94.4%, 91.6%, and 95.8%, respectively. For 2.5D-GLCM fusion, the accuracy, sensitivity, and specificity are 97.33%, 96%, and 98%, respectively. For 3D-GLCM, the accuracy, sensitivity, and specificity of the proposed fusion structure reached 98.7%, 98%, and 99%, respectively, outperforming most state-of-the-art counterparts. The results and analysis also indicate that the WSA-Otsu method requires a shorter execution time and yields a more accurate thresholding process.

RETRACTED: Brain tumor diagnosis from MRI based on Amended Water Strider Algorithm.

Early detection of brain tumors leads to early treatment, which can reduce the mortality rate from brain tumors. Due to the high complexity of brain tissues, manual diagnosis of brain and tumor tissues is very time-consuming and depends on the operator's condition. There is also a need for experts to examine the images to identify these conditions, which makes the usual and old methods ineffective in the absence of these people. Therefore, the use of automatic techniques for the accurate diagnosis of tumors is very effective. Recently, the examination of brain tumors using magnetic resonance imaging (MRI) techniques has gained much consideration. MRI methods with high abilities in demonstrating the internal organizations of the human body have become one of the most generally applied techniques in the field of brain tumor diagnosis. In the present paper, a new automatic methodology has been introduced for the efficient diagnosis of brain tumors from MRI. In the proposed method, after image preprocessing, a fast method based on Otsu and mathematical morphology is utilized for the segmentation of the section of interest. Then, features of the segmented image have been extracted and the useless ones have been removed based on an optimized feature selection to reduce the method complexity. The optimized method is based on a newly modified design of a metaheuristic, called the Amended Water Strider Algorithm. Then, the features have been injected into an optimized SVM classifier based on the proposed AWSA to get an optimal classification.

Evaluation and uncertainty assessment of wheat yield prediction by multilayer perceptron model with bayesian and copula bayesian approaches

An accurate crop yield forecast is vital for sustaining food security and preventing famine. Artificial intelligence provides a robust tool for integrating environmental, physiological, and management data and predicting crop yield. In this study, we used a Multilayer Perceptron (MLP) model in its default and hybrid learning modes to predict wheat yield. In its default mode, MLP was trained with the backpropagation algorithm, and in the hybrid mode, MLP was trained with the Water Striders Algorithm (WSA), Sine-Cosine Algorithm (SCA), and Genetic Algorithm (GA). We further considered two ensemble modeling approaches, including the Bayesian Model Averaging (BMA) and Copula-based Bayesian Model Averaging (CBMA). The study area was classified into four homogenous wheat cultivation regions using the Fuzzy clustering approach, and for each region, the models were trained based on 30 years of fertilizer information, harvested yield, and climatic data. Our results showed that hybridizing MLP with an optimization algorithm provides more accurate yield predictions than its default mode. WSA required the lowest computational time, and its combination with MLP resulted in a more accurate yield prediction compared to other optimization algorithms. The accuracy of yield predictions was further improved when ensemble modeling was implemented. Accordingly, the CBMA approach generated the most accurate result with the lowest uncertainty range. The mean absolute error (MAE) of the CBMA was 0.026, 0.058, 0.088, 0.133, and 0.0201 lower than that of the BMA, MLP-WSA, MLP-SCA, MLP-GA, and MLP models. GLUE uncertainty analysis verified the superiority of the hybrid models and ensemble approaches. The predicted yield by CBMA, BMA, and MLP-WSA covered 94 %, 92 %, and 89 % of the observations, respectively.

Quantum Water Strider Algorithm with Hybrid-Deep-Learning-Based Activity Recognition for Human–Computer Interaction

Human action and activity recognition are clues that alleviate human behavior analysis. Human action recognition (HAR) becomes a significant challenge in various applications involving human computer interaction (HCI) and intellectual video surveillance for enhancing security in distinct fields. Precise action recognition is highly challenging because of the variations in clutter, backgrounds, and viewpoint. The evaluation method depends on the proper extraction and learning of data. The achievement of deep learning (DL) models results in effectual performance in several image-related tasks. In this view, this paper presents a new quantum water strider algorithm with hybrid-deep-learning-based activity recognition (QWSA-HDLAR) model for HCI. The proposed QWSA-HDLAR technique mainly aims to recognize the different types of activities. To recognize activities, the QWSA-HDLAR model employs a deep-transfer-learning-based, neural-architectural-search-network (NASNet)-based feature extractor to generate feature vectors. In addition, the presented QWSA-HDLAR model exploits a QWSA-based hyperparameter tuning process to choose the hyperparameter values of the NASNet model optimally. Finally, the classification of human activities is carried out by the use of a hybrid convolutional neural network with a bidirectional recurrent neural network (HCNN-BiRNN) model. The experimental validation of the QWSA-HDLAR model is tested using two datasets, namely KTH and UCF Sports datasets. The experimental values reported the supremacy of the QWSA-HDLAR model over recent DL approaches.

Study On Nutrient Levels and Entomological Indicator of Biological Integrity In Fish Culture Ponds of Bhadra Reservoir Project, Karnataka, India

The supplement levels and organic uprightness of five fish culture ponds were contemplated utilizing water insects and the water quality investigation of the fish ponds as markers from January to December 2011.A sum of 203 insects were recorded with 04 orders and 07 families. There was huge contrast in the plenitude of pond skaters, dragon/damsel fly and whirligig beetles for the five fish ponds (P=0.05). There was no critical distinction in the bounty of water insects and dytiscid beetle (P<0.05). The mean estimations of the physico-chemical parameters of the five fish ponds indicated no noteworthy degree of distinction. The least noteworthy distinction of the bounty of insects demonstrated that ponds 1, 2 and 3 were not favorable for the amphibian bugs inspected. Pond 5 indicated the pinnacle portrayal of all insects inspected. The Carl Pearson connection coefficient demonstrated a solid connection between the quantity of individual of dragon/damsel fly, dytiscid and whirligig beetles and the magnesium focus and Chemical Oxygen demand of the ponds. The pond skater and water bug were adversely associated with the nitrogen substance and temperature of the water bodies. In view of these discoveries the fish culture ponds were considered as decently contaminated biological systems whose contamination load came about predominantly from dumping of poultry/dairy animals’ compost excrement and fish food.

In the Spotlight-Established Researcher.

In the Spotlight-Established Researcher.

A droplet-driven micro-surfboard with dual gradients for programmable motion

Self-propelled autonomous devices have great significance in energy, environment, and biomedical engineering, and thus vast achievements have been made in recent years. However, conventional propulsion often requires special chemicals or external energy, limiting their practical applications due to environmental pollution and energy waste. In this work, inspired by the character of a water strider on the water surface, a novel droplet-driven micro-surfboard (DDMS) is developed via femtosecond laser micro/nano fabrication, which can be driven in a green and controllable way. The DDMS is composed of a superhydrophobic sheet with superhydrophilic wedgy grooves. The droplet is put on superhydrophilic wedgy grooves to enter the water and form a jet, which further facilitates the directional motion of the DDMS. Also, a type of actuator with three superhydrophilic wedgy grooves is designed successively to achieve turn direction, which completes the programable motion by adding droplets to the three channels. Finally, an actuator with circular water storage area and a symmetrical vane-like rotating device with two superhydrophilic channels are developed, realizing long-distance and rotational motions. This work provides new insights into pollution-free autonomous devices and shows the great potential of droplet-driven autonomous devices in the fields of micromechanics and intelligent systems.

Design and Analysis of Linear Phase Finite Impulse Response Filter Using Water Strider Optimization Algorithm in FPGA

Design and Analysis of Linear Phase Finite Impulse Response Filter Using Water Strider Optimization Algorithm in FPGA