Two-Stage IoT Computational Task Offloading Decision-Making in MEC with Request Holding and Dynamic Eviction

The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing(MEC).However,existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited,and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted.In addition,existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,but lack support for multiple retry in subsequent time slots.It is resulting in TD missing potential offloading opportunities in the future.To fill this gap,we propose a Two-Stage Offloading Decision-making Framework(TSODF)with request holding and dynamic eviction.Long Short-Term Memory(LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time slot.The framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning technology.Simulation results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method.

Adaptive Cloud Intrusion Detection System Based on Pruned Exact Linear Time Technique

Cloud computing environments,characterized by dynamic scaling,distributed architectures,and complex work-loads,are increasingly targeted by malicious actors.These threats encompass unauthorized access,data breaches,denial-of-service attacks,and evolving malware variants.Traditional security solutions often struggle with the dynamic nature of cloud environments,highlighting the need for robust Adaptive Cloud Intrusion Detection Systems(CIDS).Existing adaptive CIDS solutions,while offering improved detection capabilities,often face limitations such as reliance on approximations for change point detection,hindering their precision in identifying anomalies.This can lead to missed attacks or an abundance of false alarms,impacting overall security effectiveness.To address these challenges,we propose ACIDS(Adaptive Cloud Intrusion Detection System)-PELT.This novel Adaptive CIDS framework leverages the Pruned Exact Linear Time(PELT)algorithm and a Support Vector Machine(SVM)for enhanced accuracy and efficiency.ACIDS-PELT comprises four key components:(1)Feature Selection:Utilizing a hybrid harmony search algorithm and the symmetrical uncertainty filter(HSO-SU)to identify the most relevant features that effectively differentiate between normal and anomalous network traffic in the cloud environment.(2)Surveillance:Employing the PELT algorithm to detect change points within the network traffic data,enabling the identification of anomalies and potential security threats with improved precision compared to existing approaches.(3)Training Set:Labeled network traffic data forms the training set used to train the SVM classifier to distinguish between normal and anomalous behaviour patterns.(4)Testing Set:The testing set evaluates ACIDS-PELT’s performance by measuring its accuracy,precision,and recall in detecting security threats within the cloud environment.We evaluate the performance of ACIDS-PELT using the NSL-KDD benchmark dataset.The results demonstrate that ACIDS-PELT outperforms existing cloud intrusion detection techniques in terms of accuracy,precision,and recall.This superiority stems from ACIDS-PELT’s ability to overcome limitations associated with approximation and imprecision in change point detection while offering a more accurate and precise approach to detecting security threats in dynamic cloud environments.

Appropriate Combination of Crossover Operator and Mutation Operator in Genetic Algorithms for the Travelling Salesman Problem

Genetic algorithms(GAs)are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems.AsimpleGAbeginswith a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes.It uses a crossover operator to create better offspring chromosomes and thus,converges the population.Also,it uses a mutation operator to explore the unexplored areas by the crossover operator,and thus,diversifies the GA search space.A combination of crossover and mutation operators makes the GA search strong enough to reach the optimal solution.However,appropriate selection and combination of crossover operator and mutation operator can lead to a very good GA for solving an optimization problem.In this present paper,we aim to study the benchmark traveling salesman problem(TSP).We developed several genetic algorithms using seven crossover operators and six mutation operators for the TSP and then compared them to some benchmark TSPLIB instances.The experimental studies show the effectiveness of the combination of a comprehensive sequential constructive crossover operator and insertion mutation operator for the problem.The GA using the comprehensive sequential constructive crossover with insertion mutation could find average solutions whose average percentage of excesses from the best-known solutions are between 0.22 and 14.94 for our experimented problem instances.

Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications

Computational Intelligent(CI)systems represent a pivotal intersection of cutting-edge technologies and complex engineering challenges aimed at solving real-world problems.This comprehensive body of work delves into the realm of CI,which is designed to tackle intricate and multifaceted engineering problems through advanced computational techniques.The history of CI systems is a fascinating journey that spans several decades and has its roots in the development of artificial intelligence and machine learning techniques.Through a wide array of practical examples and case studies,this special issue bridges the gap between theoretical concepts and practical implementation,shedding light on how CI systems can optimize processes,design solutions,and inform decisions in complex engineering landscapes.This compilation stands as an essential resource for both novice learners and seasoned practitioners,offering a holistic perspective on the potential of CI in reshaping the future of engineering problem-solving.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

Cold Plasma:A Potential Alternative for Rice Grain Postharvest Treatment Management in Malaysia

Use of pesticides,herbicides and fertilizers is among the techniques to control insect pests and fungal pathogens.However,the technique is the major contributor to severe environmental implications in terms of air,water and soil pollution.Besides,variable inconsistency becomes an important issue in the implementation of inclined bed dryers,leading to significant rice grain loss.Cold plasma technology has been widely proposed as a potential alternative for rice grain postharvest treatment management due to the presence of generated ionised gas that eventually produces reactive oxygen species or reactive nitrogen species.These species are used to decontaminate foodborne pathogens,mycotoxins and bacterial diseases.This review explores the current literature regarding cold plasma treatment technology,focusing on its efficiency as the microbial decontamination medium and insect pest mortality medium,and on the enhancement functional,nutritional and cooking properties,especially in rice grains.Previous studies have successfully demonstrated the ability of cold plasma treatment to significantly reduce the microbial count of foodborne pathogens,detoxify mycotoxins,and control seedborne rice seedling bacterial diseases.Previous studies have also proved that the implementation of cold plasma technology in postharvest management should be seriously considered for improving rice grain quantity and quality in Malaysia.

Physicochemical and radical scavenging activities of honey samples from Malaysia

The physical properties, total phenols, total flavonoid content and free radical scavenging activities of honey samples from Malaysia were investigated. The physical properties of Tualang, Gelam and Acacia honey samples, in terms of pH, color, moisture, electrical conductivity and total soluble solid were significantly different (p = 0.000). Gelam honey was reported to have the highest total phenols (606.17 mg GAE/kg honey) and flavonoid content (183.43 mg RE/kg honey). Tualang honey was reported to have the highest free radical scavenging activity with the IC50, 72.75 g/L compared to Gelam (77.41 g/L) and Acacia (90.83 g/L). There is no significant difference has been revealed among honey samples for radical scavenging activity (p = 0.827). Nevertheless, strong correlation was obtained between pH, color, electrical conductivity and total soluble solid with the scavenging activity of all honey samples with the correlative coefficient, r = 0.979, 0.902, 0.917 and 0.957, respectively. The establishment of the statistical correlation could be useful for honey related industry.

Flood Forecasting of Malaysia Kelantan River using Support Vector Regression Technique

The rainstorm is believed to contribute flood disasters in upstream catchments,resulting in further consequences in downstream area due to rise of river water levels.Forecasting for flood water level has been challenging,present-ing complex task due to its nonlinearities and dependencies.This study proposes a support vector machine regression model,regarded as a powerful machine learning-based technique to forecast flood water levels in downstream area for different lead times.As a case study,Kelantan River in Malaysia has been selected to validate the proposed model.Four water level stations in river basin upstream were identified as input variables.A river water level in downstream area was selected as output of flood forecasting model.A comparison with several bench-marking models,including radial basis function(RBF)and nonlinear autoregres-sive with exogenous input(NARX)neural network was performed.The results demonstrated that in terms of RMSE error,NARX model was better for the proposed models.However,support vector regression(SVR)demonstrated a more consistent performance,indicated by the highest coefficient of determination value in twelve-hour period ahead of forecasting time.The findings of this study signified that SVR was more capable of addressing the long-term flood forecasting problems.

Application of ANNs Model with the SDSM for the Hydrological Trend Prediction in the Sub-catchment of Kurau River, Malaysia

The paper describes the application of SDSM （statistical downscaling model） and ANNs （artificial neural networks） models for prediction of the hydrological trend due to the climate-change. The SDSM has been calibrated and generated for the possible future scenarios of meteorological variables, which are temperature and rainfall by using GCMs （global climate models）. The GCM used is SRES A2. The downscaled meteorological variables corresponding to SDSM were then used as input to the ANNs model calibrated with observed station data to simulate the corresponding future streamflow changes in the sub-catchment of Kurau River. This study has discovered the hydrological trend over the catchment. The projected monthly streamflow has shown a decreasing trend due to the increase in the, mean of temperature for overall months, except the month of August and November.

Magnesium-based nanocomposites:A review from mechanical,creep and fatigue properties

The addition of nanoscale additions to magnesium(Mg)based alloys can boost mechanical characteristics without noticeably decreasing ductility.Since Mg is the lightest structural material,the Mg-based nanocomposites(NCs)with improved mechanical properties are appealing materials for lightweight structural applications.In contrast to conventional Mg-based composites,the incorporation of nano-sized reinforcing particles noticeably boosts the strength of Mg-based nanocomposites without significantly reducing the formability.The present article reviews Mg-based metal matrix nanocomposites(MMNCs)with metallic and ceramic additions,fabricated via both solid-based(sintering and powder metallurgy)and liquid-based(disintegrated melt deposition)technologies.It also reviews strengthening models and mechanisms that have been proposed to explain the improved mechanical characteristics of Mg-based alloys and nanocomposites.Further,synergistic strengthening mecha-nisms in Mg matrix nanocomposites and the dominant equations for quantitatively predicting mechanical properties are provided.Furthermore,this study offers an overview of the creep and fatigue behavior of Mg-based alloys and nanocomposites using both traditional(uniaxial)and depth-sensing indentation techniques.The potential applications of magnesium-based alloys and nanocomposites are also surveyed.

Neuroprotective effects of exogenous brain-derived neurotrophic factor on amyloid-beta 1-40-induced retinal degeneration

Amyloid-beta(Aβ)-related alterations,similar to those found in the brains of patients with Alzheimer's disease,have been observed in the retina of patients with glaucoma.Decreased levels of brain-derived neurotrophic factor(BDNF)are believed to be associated with the neurotoxic effects of Aβpeptide.To investigate the mechanism underlying the neuroprotective effects of BDNF on Aβ_(1-40)-induced retinal injury in Sprague-Dawley rats,we treated rats by intravitreal administration of phosphate-buffered saline(control),Aβ_(1-40)(5 nM),or Aβ_(1-40)(5 nM)combined with BDNF(1μg/mL).We found that intravitreal administration of Aβ_(1-40)induced retinal ganglion cell apoptosis.Fluoro-Gold staining showed a significantly lower number of retinal ganglion cells in the Aβ_(1-40)group than in the control and BDNF groups.In the Aβ_(1-40)group,low number of RGCs was associated with increased caspase-3 expression and reduced TrkB and ERK1/2 expression.BDNF abolished Aβ_(1-40)-induced increase in the expression of caspase-3 at the gene and protein levels in the retina and upregulated TrkB and ERK1/2 expression.These findings suggest that treatment with BDNF prevents RGC apoptosis induced by Aβ_(1-40)by activating the BDNF-TrkB signaling pathway in rats.

Managing Smart Technologies with Software-Defined Networks for Routing and Security Challenges: A Survey

Smart environments offer various services,including smart cities,ehealthcare,transportation,and wearable devices,generating multiple traffic flows with different Quality of Service(QoS)demands.Achieving the desired QoS with security in this heterogeneous environment can be challenging due to traffic flows and device management,unoptimized routing with resource awareness,and security threats.Software Defined Networks(SDN)can help manage these devices through centralized SDN controllers and address these challenges.Various schemes have been proposed to integrate SDN with emerging technologies for better resource utilization and security.Software Defined Wireless Body Area Networks(SDWBAN)and Software Defined Internet of Things(SDIoT)are the recently introduced frameworks to overcome these challenges.This study surveys the existing SDWBAN and SDIoT routing and security challenges.The paper discusses each solution in detail and analyses its weaknesses.It covers SDWBAN frameworks for efficient management of WBAN networks,management of IoT devices,and proposed security mechanisms for IoT and data security in WBAN.The survey provides insights into the state-of-the-art in SDWBAN and SDIoT routing with resource awareness and security threats.Finally,this study highlights potential areas for future research.

Minimizing FWM Impact in DWDM ROF DP-DQPSK System for Optical

The demonstration of a higher data rate transmission system was amajor aspect to be considered by researchers in recent years. The most relevantaspect to be studied and analyzed is the need for a reliable system to handlenonlinear impairments and reduce them. Therefore, this paper examines theinfluence of Four-Wave Mixing (FWM) impairment on the proposed highdata rate Dual polarization–Differential Quadrature phase shift keying (DPDQPSK)system using the Optisystem software. In the beginning, the impactof varied input power on the proposed system’s performance was evaluated interms of QF and BER metrics. More power is used to improve system performance.However, increasing power would raise theFWMeffects. Accordingly,a−10dBminput power and the proposed system are used to reduce the impactof FWM. Additionally, a hybrid amplification method is proposed to enhancesystem performance by utilizing the major amplification methods of erbiumdopedfiber amplifier (EDFA): semiconductor optical amplifier (SOA) andRadio optical amplifier (ROA). The evaluation demonstrates that the OAEDFAoutperformed the other two key amplification techniques of (EDFASOA)and (EDFA-ROA) in improving Quality factor (QF) and Bit error rate(BER) system results for all distances up to 720 km. Consequently, the methodcontributes to minimizing the impact of FWM. In the future, other forms ofnonlinearity will be investigated and studied to quantify their impact on theproposed system.

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the visionof the Wireless Body Sensor Network (WBSN) to improve the health monitoringsystems and has an enormous impact on the healthcare system forrecognizing the levels of risk/severity factors (premature diagnosis, treatment,and supervision of chronic disease i.e., cancer) via wearable/electronic healthsensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy playsa very significant role in the detection of gastric cancer. Convolutional NeuralNetwork (CNN) has been widely used to diagnose gastric cancer based onvarious feature extraction models, consequently, limiting the identificationand categorization performance in terms of cancerous stages and gradesassociated with each type of gastric cancer. This paper proposed an optimizedAI-based approach to diagnose and assess the risk factor of gastric cancerbased on its type, stage, and grade in the endoscopic images for smarthealthcare applications. The proposed method is categorized into five phasessuch as image pre-processing, Four-Dimensional (4D) image conversion,image segmentation, K-Nearest Neighbour (K-NN) classification, and multigradingand staging of image intensities. Moreover, the performance of theproposed method has experimented on two different datasets consisting ofcolor and black and white endoscopic images. The simulation results verifiedthat the proposed approach is capable of perceiving gastric cancer with 88.09%sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.

Visual Motion Segmentation in Crowd Videos Based on Spatial-Angular Stacked Sparse Autoencoders

Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes,which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades.Trajectory clustering has become one of the most popular methods in VMS.However,complex data,such as a large number of samples and parameters,makes it difficult for trajectory clustering to work well with accurate motion segmentation results.This study introduces a spatial-angular stacked sparse autoencoder model(SA-SSAE)with l2-regularization and softmax,a powerful deep learning method for visual motion segmentation to cluster similar motion patterns that belong to the same cluster.The proposed model can extract meaningful high-level features using only spatial-angular features obtained from refined tracklets(a.k.a‘trajectories’).We adopt l2-regularization and sparsity regularization,which can learn sparse representations of features,to guarantee the sparsity of the autoencoders.We employ the softmax layer to map the data points into accurate cluster representations.One of the best advantages of the SA-SSAE framework is it can manage VMS even when individuals move around randomly.This framework helps cluster the motion patterns effectively with higher accuracy.We put forward a new dataset with itsmanual ground truth,including 21 crowd videos.Experiments conducted on two crowd benchmarks demonstrate that the proposed model can more accurately group trajectories than the traditional clustering approaches used in previous studies.The proposed SA-SSAE framework achieved a 0.11 improvement in accuracy and a 0.13 improvement in the F-measure compared with the best current method using the CUHK dataset.

Measuring Reliability of A Web Portal Based on Testing Profile

Conventionally,the reliability of a web portal is validated with generalized conventional methods,but they fail to provide the desired results.Therefore,we need to include other quality factors that affect reliability such as usability for improving the reliability in addition to the conventional reliability testing.Actually,the primary objectives of web portals are to provide interactive integration of multiple functions confirming diverse requirements in an efficient way.In this paper,we employ testing profiles tomeasure the reliability through software operational profile,input space profile and usability profile along with qualitative measures of reliability and usability.Moreover,the case study used for verification is based on aweb application that facilitates information and knowledge sharing among its online members.The proposed scheme is compared with the conventional reliability improvement method in terms of failure detection and reliability.The final results unveil that the computation of reliability by using the traditional method(utilizing failure points with the assistance of Mean Time Between Failures(MTBF)and Mean Time To Failure(MTTF)becomes ineffective under certain situations.Under such situations,the proposed scheme helps to compute the reliability in an effective way.Moreover,the outcomes of the study provide insight recommendations about the testing and measurement of reliability for Web based software or applications.

Rheological and Morphological Characterization of Modified Bitumen with Cup Lump Rubber

Pure bitumen is not suitable for heavy traffic loads;hence modifiers are used to improve the bitumen performance.Recently,cup lump rubber(CLR)has become a preferred modifier due to its outstanding performance and less cost.However,little is known about the interactions between CLR and bitumen.Thus,this study investigates the behavior of bitumen with CLR.Four percentages of CLR(2.5%,5.0%,7.5%,and 10.0%by weight of bitumen)were used to modify conventional 60/70 penetration grade bitumen.The modified bitumen was evaluated through different laboratory testing such as dynamic shear rheometer,rotational viscosity,softening point,bending beam rheometer,ductility,and elastic recovery.The testing results show that the addition of CLR increased the bitumen’s rutting resistance by 3 PG grades at high temperatures.At low pavement temperatures,the cup lump rubber modified bitumen(CMB)can withstand up to−34℃.Fourier Transform Infrared(FTIR)analysis shows that the Aromaticity index at 1600 cm^(−1) rose as the CLR percentage increased,indicating the formation of a binder with a compact structure.This is expected to improve the elasticity of bitumen throughπ-πinteractions.Atomic Force Microscopy(AFM)results showed the Catana phase increased in size and quantity at 5.0%and 7.5%CLR content.While contact angle measurement revealed that the binders are hydrophobic and tend to repel the dropped water on the bitumen surface.

Reliable Failure Restoration with Bayesian Congestion Aware for Software Defined Networks

SoftwareDefined Networks(SDN)introduced better network management by decoupling control and data plane.However,communication reliability is the desired property in computer networks.The frequency of communication link failure degrades network performance,and service disruptions are likely to occur.Emerging network applications,such as delaysensitive applications,suffer packet loss with higher Round Trip Time(RTT).Several failure recovery schemes have been proposed to address link failure recovery issues in SDN.However,these schemes have various weaknesses,which may not always guarantee service availability.Communication paths differ in their roles;some paths are critical because of the higher frequency usage.Other paths frequently share links between primary and backup.Rerouting the affected flows after failure occurrences without investigating the path roles can lead to post-recovery congestion with packet loss and system throughput.Therefore,there is a lack of studies to incorporate path criticality and residual path capacity to reroute the affected flows in case of link failure.This paper proposed Reliable Failure Restoration with Congestion Aware for SDN to select the reliable backup path that decreases packet loss and RTT,increasing network throughput while minimizing post-recovery congestion.The affected flows are redirected through a path with minimal risk of failure,while Bayesian probability is used to predict post-recovery congestion.Both the former and latter path with a minimal score is chosen.The simulation results improved throughput by(45%),reduced packet losses(87%),and lowered RTT(89%)compared to benchmarking works.

Tumor-Immune Interaction System with the Effect of Time Delay and Hyperglycemia on the Breast Cancer Cells

Breast cancer in women is a complicated and multifaceted disease. Studies have demonstrated that hyperglycemia is one of the most significant risk factors for breast cancer. Hyperglycemia is when the sugar level in human blood is too high, which means excess glucose. Glucose excess can encourage the growth, invasion, and migration of breast cancer cells at the cellular level. Though, the effects of glucose on the dynamics of breast cancer cells have been examined mathematically by a system of ordinary differential equations. However, the non-instantaneous biological occurrences leading to the secretion of immuno-suppressive cytokines by tumors to evade immune surveillance and the immune cells’ derivation of cytokines to attack the tumor cells are not yet discussed. Therefore, investigating the biological process involved in the dynamics of tumors, immune and normal cells with excessive glucose concentration is inviolable to determining the best procedure for controlling tumors’ uncontrollable growth. Time delay, denoted by τ, is used to describe the time tumor cells take to secrete immunosuppressive cytokines to evade immune surveillance and the time immune cells take to recognize and attack the tumor cells. We have studied the local stability analysis of the biological steady states in both delayed and non-delayed system. The Routh-Hurwitz stability criterion is used to analyze the dynamical equilibrium of the cells’ population. Hopf bifurcation was analyzed by using time delay s as a bifurcation parameter. The analytical results suggest an unstable scenario for a tumor-free equilibrium point as normal cells are bound to grow to their carrying capacity. The result predicts a stable system for coexisting equilibrium when the interaction is instantaneous (τ = 0). However, when τ > 0, the coexisting equilibrium point switches from stable to unstable. The numerical results not only validate all the analytical results but also show the case of possible situations when glucose concentration is varied, indicating that both tumor growth and immune system efficiency are highly affected by the level of glucose in the blood. This concluded that the delay in the secretion of cytokines by immune cells and derivation cytokines by the tumors helps to identify the possible chaotic situation under different glucose concentration and the extent to which such delay can have on restoration of the normal cells when glucose concentration is low.

Events associated with apoptotic effect of p-Coumaric acid in HCT-15 colon cancer cells

AIM:To investigate the events associated with the apoptotic effect of p-Coumaric acid,one of the phenolic components of honey,in human colorectal carcinoma(HCT-15)cells.METHODS:3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltertazolium-bromide assay was performed to determine the antiproliferative effect of p-Coumaric acid against colon cancer cells.Colony forming assay was conducted to quantify the colony inhibition in HCT15 and HT 29 colon cancer cells after p-Coumaric acid treatment.Propidium Iodide staining of the HCT15 cells using flow cytometry was done to study the changes in the cell cycle of treated cells.Identification of apoptosis was done using scanning electron microscope and photomicrograph evaluation of HCT 15cells after exposing to p-Coumaric acid.Levels of reactive oxygen species(ROS)of HCT 15 cells exposed to p-Coumaric acid was evaluated using 2’,7’-dichlorfluorescein-diacetate.Mitochondrial membrane potential of HCT-15 was assessed using rhodamine-123 with the help of flow cytometry.Lipid layer breaks associated with p-Coumaric acid treatment was quantified using the dye merocyanine 540.Apoptosis was confirmed and quantified using flow cytometric analysis of HCT15 cells subjected to p-Coumaric acid treatment after staining with YO-PRO-1.RESULTS:Antiproliferative test showed p-Coumaric acid has an inhibitory effect on HCT 15 and HT 29 cells with an IC50(concentration for 50%inhibition)value of 1400 and 1600μmol/L respectively.Colony forming assay revealed the time-dependent inhibition of HCT 15 and HT 29 cells subjected to p-Coumaric acid treatment.Propidium iodide staining of treated HCT 15cells showed increasing accumulation of apoptotic cells(37.45±1.98 vs 1.07±1.01)at sub-G1phase of the cell cycle after p-Coumaric acid treatment.HCT-15 cells observed with photomicrograph and scanning electron microscope showed the signs of apoptosis like blebbing and shrinkage after p-Coumaric acid exposure.Evaluation of the lipid layer showed increasing lipid layer breaks was associated with the growth inhibition of p-Coumaric acid.A fall in mitochondrial membrane potential and increasing ROS generation was observed in the p-Coumaric acid treated cells.Further apoptosis evaluated by YO-PRO-1 staining also showed the timedependent increase of apoptotic cells after treatment.CONCLUSION:These results depicted that p-Coumaric acid inhibited the growth of colon cancer cells by inducing apoptosis through ROS-mitochondrial pathway.