Attractive Point Research Articles

On the Throughput Optimization in Large-scale Batch-processing Systems

We analyse a data-processing system with n clients producing jobs which are processed in batches by m parallel servers; the system throughput critically depends on the batch size and a corresponding sub-additive speedup function. In practice, throughput optimization relies on numerical searches for the optimal batch size, a process that can take up to multiple days in existing commercial systems. In this paper, we model the system in terms of a closed queueing network; a standard Markovian analysis yields the optimal throughput in ωn4 time. Our main contribution is a mean-field model of the system for the regime where the system size is large. We show that the mean-field model has a unique, globally attractive stationary point which can be found in closed form and which characterizes the asymptotic throughput of the system as a function of the batch size. Using this expression we find the asymptotically optimal throughput in O(1) time. Numerical settings from a large commercial system reveal that this asymptotic optimum is accurate in practical finite regimes.

Sagittaria sagittifolia polysaccharide interferes with arachidonic acid metabolism in non-alcoholic fatty liver disease mice via Nrf2/HO-1 signaling pathway

BackgroundsNon-alcoholic fatty liver disease (NAFLD) is currently one of the most common chronic liver diseases especially in developed countries. Modern research shows an obvious protective effect of Sagittaria sagittifolia L. (Alismataceae) on glucose and lipid metabolism disorders. Previous studies had reported that Sagittaria sagittifolia polysaccharide (SSP) has potent protective effects on drug-induced liver injury. Based on this, we speculated that Sagittaria sagittifolia polysaccharide also has protective effects on NAFLD and performed experiments to explore this more. MethodsOutstanding protective effects of SSP against NAFLD in mice was observed with Hematoxylin and Eosin (H&E) and uranium acetate-citrate stain in our prophase research. By performing bioinformatics analysis on plasma metabolic data which is obtained from ultra-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS), we found the regulatory mechanisms and key nodes behind the beneficial effect with IPA (Ingenuity Pathway Analysis) software. Immunohistochemical staining and Western blot were performed for further validation on expression variations of key proteins. ResultsRegulatory pathways were enriched with 33 significant differential metabolites that responded to SSP treatment in plasma, and specifically, the ones related to arachidonic acid metabolism showed high participation. Moreover, the expression patterns of upstream regulators, Nrf2 and HO-1, were found to be significantly regulated upon SSP treatment. ConclusionsIn conclusion, our findings illustrated a novel perspective that SSP exerts preventive protection against high-fat diet-induced NAFLD by interfering with arachidonic acid metabolism via Nrf2/HO-1 signaling pathway in liver oxidative stress, providing an attractive point for the breakthrough of related natural medicine development.

Top-Down Reactive Approach for the Synthesis of Disordered ZrN Nanocrystalline Bulk Material from Solid Waste.

Transition metal nitrides possess superior mechanical, physical, and chemical properties that make them desirable materials for a broad range of applications. A prime example is zirconium nitride (ZrN), which can be obtained through different fabrication methods that require the applications of high temperature and pressure. The present work reports an interesting procedure for synthesizing disordered face centered cubic (fcc)-ZrN nanoparticles through the reactive ball milling (RBM) technique. One attractive point of this study is utilizing inexpensive solid-waste (SW) zirconium (Zr) rods as feedstock materials to fabricate ZrN nanopowders. The as-received SW Zr rods were chemically cleaned and activated, arc-melted, and then disintegrated into powders to obtain the starting Zr metal powders. The powders were charged and sealed under nitrogen gas using a pressurized milling steel vial. After 86 ks of milling, a single fcc-ZrN phase was obtained. This phase transformed into a metastable fcc-phase upon RBM for 259 ks. The disordered ZrN powders revealed good morphological characteristics of spherical shapes and ultrafine nanosize (3.5 nm). The synthetic ZrN nanopowders were consolidated through a spark plasma sintering (SPS) technique into nearly full-density (99.3% of the theoretical density for ZrN) pellets. SPS has proven to be an integral step in leading to desirable and controlled grain growth. Moreover, the sintered materials were not transformed into any other phase(s) upon consolidation at 1673 K. The results indicated that increasing the RBM time led to a significant decrease in the grain size of the ZrN powders. As a result, the microhardness of the consolidated samples was consequently improved with increasing RBM time.

Experimental and Numerical Study on Energy Piles with Phase Change Materials

Phase change materials (PCM) utilization in energy storage systems represents a point of interest and attraction for the researchers to reduce greenhouse gas emissions. PCM have been used widely on the interior or exterior walls of the building application to optimize the energy consumption during heating and cooling periods. Meanwhile, ground source heat pump (GSHP) gained its popularity because of the high coefficient of performance (COP) and low running cost of the system. However, GSHP system requires a stand-by heat pump during peak loads. This study will present a new concept of energy piles that used PCM in the form of enclosed tube containers. A lab-scaled foundation pile was developed to examine the performance of the present energy pile, where three layers of insulation replaced the underground soil to focus on the effect of PCM. The investigation was conducted experimentally and numerically on two identical piles with and without PCM. Moreover, a flow rate parametric study was conducted to study the effect of the working fluid flow rate on the amount of energy stored and released at each model. Finally, a comprehensive Computational fluid dynamic (CFD) model was developed and compared with the experimental results. There was a good agreement between the experimental measurements and the numerical predictions. The results revealed that the presence of PCM inside the piles increased not only the charging and discharging capacity but also the storage efficiency of the piles. It was found that PCM enhances the thermal response of the concrete during cooling and heating processes. Although increasing the flow rate increased charging and discharging capacity, the percentage of energy stored/released was insignificant compared to the flow rate increasing percentage.

Modelling of smart intelligent materials with PZT & PVDF sensors/actuators to control the active vibrations of flexible aluminum mechanical cantilever beams using proportional integral derivative (PID) techniques

Modelling of smart intelligent materials with PZT & PVDF sensors / actuators to control the active vibrations of flexible aluminum mechanical cantilever beams using PID techniques is presented in this research article. A multi input multi output beam (flexible cantilever beam) is considered as the single point of attraction and this is the plant, which is to be subjected to the vibrations, this could be treated as the wing of an aircraft attached to the hub. The flexible mechanical aluminium cantilever beam is subjected to vibrations, say an external force being acted upon by the wind pressure or an exciter. Smart materials have got some special properties that react to the changes in their environmental conditions. The beam is subjected to vibrations. These vibrations should be suppressed in no time, else, the plant may get damaged. Hence, we put piezoelectric (PZT) and poly vinyl derylene fluoride (PVDF) sensor and actuators at 2 different positions to sense the vibrations and act upon immediately to neutralize it. The PZT sensors sense the vibrations & the actuators give an actuating signal to the controller, which in turn generates anti signals to neutralize the effect of the primary source of vibration. We have used the multi-sensor data fusion concept as if we use the same sensor and actuator, we may not get good results. Mathematical modelling of sensor and actuators of the 2 types is done, this is used to design the PID controller to suppress the vibrations in no time. The paper shows the effect of the multi sensor actuator pair of smart intelligent materials used in the vibration suppression. The work done can also compared with others to show the authenticity or the profoundness of the methodology proposed with the help of materials used in the theory of material science.

An image encryption scheme using sequence generated by interval bisection of polynomial function

Chaos based image encryption techniques are well accepted in research communities to perform permutation at pixel/bit level using pseudo-random sequence and changes pixel positions and/or intensity values from which guessing the original image is very hard. Simple computation is the point of attraction of these type of techniques. In parallel, researchers defined permutation using some non-chaotic methods and have established similar, even better results for image encryption in comparison to chaos based techniques. In this article a non-chaotic image encryption technique is proposed. A polynomial is employed to define the pseudo-random sequence. Here, points on the polynomial are selected by repeated interval bisection method and a sequence is defined by the function value at those points. Scrambling of row/column are performed by cyclic shift using the sequence. Value Substitution and iterative XOR operation transform the pixels’ intensity. Bitwise XOR operation with a mask generated from the pseudo-random sequence adds more security to the scheme. Experimental results and security analysis prove its immunity against statistical and differential attack. Comparison of the proposed method with some existing schemes proves its superiority.

Perineural Invasion in Adenoid Cystic Carcinoma of the Salivary Glands: Where We Are and Where We Need to Go.

Adenoid cystic carcinoma of the salivary gland (SACC) is a rare malignant tumors of the head and neck region, but it is one of the most common malignant tumors that are prone to perineural invasion (PNI) of the head and neck. The prognosis of patients with SACC is strongly associated with the presence of perineural spread (PNS). Although many contributing factors have been reported, the mechanisms underlying the preferential destruction of the blood-nerve barrier (BNB) by tumors and the infiltration of the tumor microenvironment by nerve fibers in SACC, have received little research attention. This review summarizes the current knowledge concerning the characteristics of SACC in relation to the PNI, and then highlights the interplay between components of the tumor microenvironment and perineural niche, as well as their contributions to the PNI. Finally, we provide new insights into the possible mechanisms underlying the pathogenesis of PNI, with particular emphasis on the role of extracellular vesicles that may serve as an attractive entry point in future studies.

Abstract 5317: A high throughput screen of the novel immuno-oncology target HPK1 identifies a range of chemical starting points for the development of potent and selective inhibitors

Abstract The concept of harnessing the immune system to target cancer cells has been an active area of research for decades. The advent of antibodies targeting the checkpoint receptors CTLA-4 and PD-1/PDL-1, have now provided definitive clinical validation for this approach. Since these discoveries the search for small molecule immuno-oncology agents have intensified. Here we present data on HPK1 (hematopoietic progenitor kinase 1), a novel immuno-oncology kinase involved in the negative regulation of T-cell receptor (TCR) signalling and describe a high throughput screen to identify novel chemical starting points to develop potent and selective inhibitors of this kinase. HPK1 is a member of the Ste-20 family of Serine/Threonine protein kinases. It is expressed highly in cells of haematopoietic lineage, including T-cells and is activated upon engagement of the TCR with cell surface MHC complexes. Active HPK1 leads to phosphorylation of an adaptor protein SLP76, triggering a signalling cascade that results in the downregulation of TCR signalling and thus downregulation of T-cell function. Recent reports have demonstrated that the kinase activity is essential for the function of HPK1 in T-cells and support the hypothesis that small molecule inhibitors of HPK1 kinase activity will result in sustained activation of T-cells. To identify inhibitors of HPK1 we developed an enzyme activity assay that could be used in a high throughput assay to screen Sygnature Discovery's proprietary screening library. We utilised the intrinsic ATPase activity of HPK1 to develop an ADP product fluorescent polarisation assay suitable for an HTS screen. We validated the assay using a series of known inhibitors with a broad range of potencies versus HPK1 activity. Next, we developed a cell-based assay encompassing anti-CD3 TCR mediated induction of phosphorylation of SLP76 in Jurkat cells. We tested the activity of our panel of HPK1 tool inhibitors in this assay and demonstrated a strong correlation between the biochemical and cell potencies of the compounds, further validating the robustness and cell potency predictivity of the biochemical assay. The biochemical assay was then used to screen our small molecule chemical library to identify potential inhibitors of HPK1 kinase activity. This library is biased toward lead-like chemical space and has undergone significant triage to remove unwanted chemotypes and PAINs moieties. The initial hits were confirmed and IC50 values determined for a select group of chemotypes. These compounds were subsequently tested in the Jurkat pSLP76 cell assay for cellular activity versus HPK1. In summary we present the results of a successful HTS and hit evaluation to identify novel and attractive chemical starting points for the development of inhibitors of the immuno-oncology target HPK1. Citation Format: Denise Swift, Samantha Hitchin, Chris Tomlinson, Anindita Sengupta, Callum Taylor, Alec O'Keeffe, Grace Boden, John Unitt, Stuart Thomson, Allan M. Jordan. A high throughput screen of the novel immuno-oncology target HPK1 identifies a range of chemical starting points for the development of potent and selective inhibitors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5317.

Common Attractive Points of Generalized Hybrid Multi-Valued Mappings and Applications

In this paper, we first propose the concepts of (ζ,η,λ,π)-generalized hybrid multi-valued mappings, the set of all the common attractive points (CAf,g) and the set of all the common strongly attractive points (CsAf,g), respectively for the multi-valued mappings f and g in a CAT(0) space. Moreover, we give some elementary properties in regard to the sets Af, Ff and CAf,g for the multi-valued mappings f and g in a complete CAT(0) space. Furthermore, we present a weak convergence theorem of common attractive points for two (ζ,η,λ,π)-generalized hybrid multi-valued mappings in the above space by virtue of Banach limits technique and Ishikawa iteration respectively. Finally, we prove strong convergence of a new viscosity approximation method for two (ζ,η,λ,π)-generalized hybrid multi-valued mappings in CAT(0) spaces, which also solves a kind of variational inequality problem.

An artificial moment method for conflict resolutions with robots being close to their targets

An Artificial Moment Method (AMM) is proposed for conflict resolution of swarm robots with path planning tasks, where some robots are in narrow passages and close to their targets while many other robots need to pass through the passages. In the AMM, an algorithm based on key companion candidates and their weight times is presented first for key companions of the robots close to their targets. As such, other robots, even with different motion directions, can bypass the robots in a more reasonable manner. Then, two new algorithms are presented for attractive points and attractive angles of robots or to modify the obtained attractive angles. The existing artificial moment motion controller is also improved. Consequently, the negative effects of robots on each other are decreased, and conflicts between robots can be resolved more easily. Simulations indicate that compared with existing AMMs, the proposed one can yield better solutions in complex situations.

Inhibitors of SARS-CoV-2 Entry: Current and Future Opportunities.

Recently, a novel coronavirus initially designated 2019-nCoV but now termed SARS-CoV-2 has emerged and raised global concerns due to its virulence. SARS-CoV-2 is the etiological agent of “coronavirus disease 2019”, abbreviated to COVID-19, which despite only being identified at the very end of 2019, has now been classified as a pandemic by the World Health Organization (WHO). At this time, no specific prophylactic or postexposure therapy for COVID-19 are currently available. Viral entry is the first step in the SARS-CoV-2 lifecycle and is mediated by the trimeric spike protein. Being the first stage in infection, entry of SARS-CoV-2 into host cells is an extremely attractive therapeutic intervention point. Within this review, we highlight therapeutic intervention strategies for anti-SARS-CoV, MERS-CoV, and other coronaviruses and speculate upon future directions for SARS-CoV-2 entry inhibitor designs.

THE ROYAL GULF CRUISE SHIP

Cruise ships are usual for coastal cities around the world. Jeddah, which is city close to the Red Sea coast is a perfect location for cruise tourism. It will be a point of attraction and enrichment for Jeddah as a tourist city. Thus, this work proposes the development of a royal gulf cruise ship at Jeddah, Saudi Arabia. In this work, four case studies related designs of cruise ships were referred. From the analysis of the case studies, for the current proposed royal gulf cruise ship, the estimated area of the cruise ship in total is 30175 m2. Furthermore, The project will host 900 passengers, and around 420 crews. In addition, the cruise ship is designed to accommodate 380 suite rooms as 54% of the estimated cruise space is comprised of the suites. On the other hand, the facilities of the cruise ship includes public area, such as restaurants, shops game rooms, aquarium, theatre, private areas such as suites, crew room, bridge and captain area. In terms of the cruise ship's overall design, the hull's design comes in a free-flowing nature and mimics the waves of the sea.

JEDDAH’S INTERNATIONAL WRITERS HUB

In view of the Saudi vision of 2030, building an icon of local literature and a hub for writers for community engagement in an international city like Jeddah became an ideal and crucial requirement. This work presents a proposal to develop an international writers hub in Jeddah, Saudi Arabia. For this work, three case studies were analysed to understand the important architecture requirement for developing a hub for writers. Based on the analysed case studies, for the proposed international writers hub, the estimated net floor area is 12160 m2 and the estimated gross floor area is 15837 m2. The proposed writers hub will incorporate several zones such as main entrance, theatre, shops, conference area, writing hub, service and amenities, library, administration, rental office, day care and writers residence. In this work, the proposed writers hub will be constructed at a site located at Al-Shati 3 district in Jeddah. Based on site evaluation analysis, this site has exhibited evaluation score of 137 and it is suitable location for developing the writers hub. This hub is expected to be a main point of attraction at Jeddah and it will contribute to the economy of Saudi Arabia.

The efficiency of the monodirectional economic policy of smaller countries during the globalization and multipolar world

Subject. After the collapse of the USSR, smaller countries chose different paths in their economic policy during the globalization and the multipolar world. The EU, USA and Russia made a palpable contribution to the economic policy of smaller countries. Some countries of the former USSR failed to find their course, while the others immediately followed their development strategy and stuck to it persistently. Objectives. I examine the economics of the monodirectional strategy of smaller countries of the former USSR. I also evaluate how the countries found their position among different leading countries as points of attraction. The study is based on the assumption that the single direction of the national development and adherence to the same point of attraction will ensure the sustainable development. However, the economic development level depends on the health of a certain economy as a point of attraction. Methods. The study is based on the systems approach, comparative and statistical methods for analyzing macroeconomic data series. Results. I investigated the dynamics of key macroeconomic data in the Republic of Belarus, Latvia and Estonia, such as GDP per capita in current values, unemployment rate and Purchasing Power Index. Given the current phase of globalization and multipolar world, it is crucial for smaller countries to choose a development strategy to follow persistently and achieve proper macroeconomic indicators that depend on the economic stability of counties they treat as their benchmarks. Conclusions and Relevance. I should mention the successful economic policy of Estonia and Latvia, which followed the same course as the other EU countries, as opposed to the Republic of Belarus tending to the policy of the Russian Federation. Nevertheless, the monodirectional development strategy also helps smaller countries ensure their economic stability.

On the behaviour of the two-dimensional Hamiltonian as the distance between the two centres vanishes

In this note we continue our analysis of the behaviour of self-adjoint Hamiltonians with a pair of identical point interactions symmetrically situated around the origin perturbing various types of ‘free Hamiltonians’ as the distance between the two centres shrinks to zero. In particular, by making the coupling constant to be renormalised dependent also on the separation distance between the centres of the two point interactions, we prove that also in two dimensions it is possible to define the unique self-adjoint Hamiltonian that, differently from the one studied in detail in Albeverio’s monograph on point interactions, behaves smoothly as the separation distance vanishes. In fact, we rigorously prove that such a two-dimensional Hamiltonian converges in the norm resolvent sense to the one of the negative two-dimensional Laplacian perturbed by a single attractive point interaction situated at the origin having double strength, thus making this two-dimensional model similar to its one-dimensional analogue (not requiring the renormalisation procedure).

Gas Diffusion Barrier of Vapor Phase Infiltrated Pen with Room Temperature ALD

Metal oxides like aluminum oxide (Al2O3) and silicon oxide (SiO2) are well known as gas barrier materials to moisture. The gas barrier films are used in various applications like food packaging, pharmaceuticals, chemicals, and organic electronics. Especially in the field of organic electronics, the high gas barrier performance is essential to avoid organic semiconducting material degradation from moisture. Because of the most attractive point in these devices is their possibility that of flexible device fabrication, we must use a flexible polymer film substrate. The polymer film substrate is thermally fragile, and it includes voids in polymer chains. From this, we have to deposit the gas barrier film at low temperature and uniformly coat the voids. To overcome these problems, we developed the room-temperature atomic layer deposition (RT-ALD) technologies. In this study, we examine gas barrier coatings with a combination of vapor phase infiltration (VPI) for higher barrier performances. The VPI produces a polymer-inorganic hybrid layer by infiltrating inorganic materials into polymer substrates in the subsurface region. It is possible to fill in the voids in polymer substrates.For VPI, we used tris(dimethylamino)silane (TDMAS) as the infiltration gas and plasma excited humidified Ar as oxidation gas. The infiltration process was operated at room temperature of 25 °C. The PEN film was infiltrated by being kept in a vacuum chamber with a TDMAS pressure of 200 Pa for 6 hours. After infiltration, the PEN films were exposed to plasma excited humidified Ar to oxidize surface absorbant. In the RT ALD, we used trimethylaluminum (TMA) and dimethylzinc (DMZ) as the precursor and oxidation gas was the same as VPI. We fabricated the gas barrier films with a ZnO single layer and a laminated Al2O3/ ZnO.In figure 1, we show Si 2p narrow scan spectra of TDMAS infiltrated PEN by X-ray photoelectron spectroscopy. The Si 2p peak existed after 90 min Ar bombardment etching. It indicates the TDMAS molecules penetrated in the PEN film. We also confirmed that its surface became smooth and its morphology was changed by using AFM. The surface modification by VPI was evaluated with a water contact angle meter. In this paper, we will release the WVTR data and discuss its performance compared with the pristine PEN. Figure 1

Particle swarm optimization research base on quantum self-learning behavior

The quantum behavior particle swarm optimization algorithm is analyzed in this paper. The swarm particle search behavior is studied in the algorithm. The local attractive points of the algorithm are analyzed. The different search environments are given for particles in the search process. The algor ithm can adaptively learn to optimize the problem environment, and appropriate learning mode is adopt to improve the overall optimization performance of the algorithm. The self-learning quantum particle swarm optimization algorithm is compared with other improved methods by CEC2014 benchmark test function. Finally, the results are analyzed. The simulation results show that the self-learning method can significantly improve the performance of the quantum particle swarm optimization algorithm.

Classification of galaxy color images using quaternion polar complex exponential transform and binary Stochastic Fractal Search

Galaxies’ studies play an important role in the astronomic. Accurate classification of these galaxies enables scientists to understand the formation and evolution of the Universe. During the last decades, there have been several methods applied to classify the galaxy images. However, these methods encounter three big challenges. First, most existing methods converted the color images of galaxies into gray images which result in losing the essential color information. Second, the utilized feature selection methods, that used to remove the irrelevant features, may be stuck at the attractive local point. Third, using an irrelevant classifier could lead to decrease the classification accuracy. In this paper, a new algorithm is proposed to classify color images of galaxies. In this algorithm, highly accurate non-redundant color features are extracted from the color images of galaxies by using the quaternion polar complex exponential transform moments (QPCET). The quaternion representation deals with a color image in a holistic way which keeps the correlation between components and then successfully represents the color images. The QPCET moments are highly accurate, noise resistant, and numerically stable. Moreover, these moments are invariants with respect to rotation, scaling and translation (RST). These characteristics assure the excellency of the extracted color features. The Stochastic Fractal Search (SFS) has a very high ability to avoid the stuck at local point. Its binary version is utilized to select the most appropriate features which improve the classification process. The Extreme Machine learning (EML) is used to classify the color images of galaxies using the selected color features. Experiments are performed with the well-known datasets of galaxies (EFIGI catalog), where the proposed algorithm achieved high classification rate. The obtained results clearly show that the proposed method outperformed all existing galaxies classification methods.

Autonomous car decision making and trajectory tracking based on genetic algorithms and fractional potential fields

This article deals with the issue of trajectory optimization of autonomous terrestrial vehicles on a specific range handled by the human driver. The main contributions of this paper are a genetic algorithm-potential field combined method for optimized trajectory planning, the definition of the multi-criteria optimization problem by including a time variable, dynamical vehicle constraints, obstacle motion for collision avoidance, and improvements on the attractive and repulsive potential field definitions. The main interests of the proposed method are its efficiency even in only arc-connected spaces with holes, trajectory optimality thanks to the genetic algorithm that minimizes multi-criteria optimization, reactivity thanks to the potential field through the consideration for nature and motion of obstacles, its orientation toward situations a human driver would consider, and finally the inclusion of constraints to avoid danger and to take into account vehicle dynamics. The global trajectory, optimized through genetic algorithm, is used as a reference in a fractional potential field, which is a reactive local path planning method. The repulsive potential field is made safer by adding fractional orders to the obstacles, and the attractive potential field is improved by creating a dynamical optimal target seen from a robust control point of view. This target replaces the unique attractive potential field point and avoids its drawbacks such as local minima. Autonomous car simulation results are given for a crossroad and an overtaking scenarios.

Grey Wolf based compressive sensing scheme for data gathering in IoT based heterogeneous WSNs

Sensor node energy constraint is considered as an impediment in the further development of the Internet of Things (IoT) technology. One of the most efficient solution is to combine between compressive sensing (CS) and routing techniques. However, this combination faces many challenges that makes it an attractive point for research. This paper proposes an Efficient Multi-hop Cluster-based Aggregation scheme using Hybrid CS (EMCA-CS) for IoT based heterogeneous wireless sensor networks (WSNs). EMCA-CS efficiently combines between CS and routing protocols to extend the network lifetime and reduces the reconstruction error. EMCA-CS includes the following: a new algorithm to partition the field into various hexagonal cells (clusters) and based on multiple criteria, selects a node from each cluster as cluster head (CH). Each CH will then compress its cluster data using hybrid CS method. Also, a new Grey Wolf based algorithm to create optimal path for CHs to deliver the compressed data to base station (BS) and a CSMO-GWO algorithm to optimize the CS matrix construction process is introduced. Moreover, a new Grey Wolf and reversible Greedy based Reconstruction Algorithm is proposed to recover the actual data. The simulation results indicate that the performance of the proposed work exceeds the existing baseline techniques in terms of prolonging WSN lifetime, reducing the power consumption and reducing normalized mean square error.