In the ongoing pursuit of novel and efficient NLO materials, the potential of alkali metal-doped {6}cycloparaphenylene ({6}CPP) and methylene bridged {6} cycloparaphenylene (MB{6}CPP) nanohoops as excellent NLO candidates has been explored. The geometric, electronic, linear, and nonlinear optical properties of designed systems have been investigated theoretically. All the nanohoops demonstrated thermodynamic stability, with remarkable interaction energies reaching up to -1.39 eV (-0.0511 au). Notably, the introduction of alkali metals led to a significant reduction in the HOMO-LUMO energy gaps, with values as low as 2.92 eV, compared to 6.80 eV and 6.06 eV for undoped {6}CPP and MB{6}CPP, respectively. Moreover, the alkali metal-doped nanohoops exhibited exceptional NLO response, with the K@r6-{6}CPP complex achieving the highest first hyperpolarizability of 56,221.7 × 10-30 esu. Additionally, the frequency-dependent first hyperpolarizability values are also computed at two commonly used wavelengths of 1550 nm and 1907 nm, respectively. These findings highlight the potential of designed nanohoops as promising candidates for advanced NLO materials with high-tech applications.
Staphylococcus aureus has caused life-threatening infections and developed resistance against conventional antimicrobials, posing a significant threat to human health worldwide. Biofilms that surround the bacteria cells act as a protective layer, allowing cells inside the biofilm to be resistant to external stresses such as antimicrobials. Therefore, biofilms further complicate treatment available for infections caused by multi-drug resistant Staphylococcus aureus. A previous study on alpha-amyrin (AM), derived from ursane, was reported to significantly reduce the biomass and inhibit the metabolic activity of reference strain methicillin-resistant and methicillin-sensitive S. aureus (MRSA and MSSA, respectively). In this study, the antibiofilm activity of AM was extended to include clinical isolates of MSSA and MRSA, and laboratory-generated vancomycin-intermediate S. aureus (VISA) collected from University Kebangsaan Malaysia Medical Center (PPUKM) and Universiti Kebangsaan Malaysia Medical Molecular Biology Institute (UMBI). Pre-formed biofilms of biofilm-forming isolates identified from the Congo Red Agar (CRA) assay were then exposed to AM, vancomycin and oxacillin, and evaluated using the crystal violet and resazurin assays. The results showed that AM reduced the biofilm biomass of three isolates of MSSA, eight isolates of MRSA and four isolates of VISA but increased the metabolic activity in certain MSSA, MRSA and VISA isolates, indicating AM may possess biofilm reduction effects but not bactericidal effects. Based on these findings, AM could be further studied and developed as a potential therapeutic agent for chronic S. aureus infections.
This article reports a tool that enables Materials Informatics, termed as MatRec, via a deep learning approach. The tool captures data, makes appropriate domain suggestions, extracts various entities such as materials and processes, and helps to establish entity-value relationships. This tool uses keyword extraction, a document similarity index to suggest relevant documents, and a deep learning approach employing Bi-LSTM for entity extraction. For example, materials and processes for electrical charge storage under an electric double layer capacitor (EDLC) mechanism are demonstrated herewith. A knowledge graph approach finds and visualizes different latent knowledge sets from the processed information. The MatRec received an F1 score of 9̃6% for entity extraction, 8̃3% for material-value relationship extraction, and 8̃7% for process-value relationship extraction, respectively. The proposed MatRec could be extended to solve material selection issues for various applications and could be an excellent tool for academia and industry.
The Internet of Things (IoT) is a network of smart gadgets that are connected through the Internet, including computers, cameras, smart sensors, and mobile phones. Recent developments in the industrial IoT (IIoT) have enabled a wide range of applications, from small businesses to smart cities, which have become indispensable to many facets of human existence. In a system with a few devices, the short lifespan of conventional batteries, which raises maintenance costs, necessitates more replacements and has a negative environmental impact, does not present a problem. However, in networks with millions or even billions of devices, it poses a serious problem. The rapid expansion of the IoT paradigm is threatened by these battery restrictions, thus academics and businesses are now interested in prolonging the lifespan of IoT devices while retaining optimal performance. Resource management is an important aspect of IIoT because it's scarce and limited. Therefore, this paper proposed an efficient algorithm based on federated learning. Firstly, the optimization problem is decomposed into various sub-problems. Then, the particle swarm optimization algorithm is deployed to solve the energy budget. Finally, a communication resource is optimized by an iterative matching algorithm. Simulation results show that the proposed algorithm has better performance as compared with existing algorithms.
Spline curves are very prominent in the mathematics due to their simple construction, accuracy of assessment and ability to approximate complicated structures into interactive curved designs. A spline is a smooth piece-wise polynomial function. The primary goal of this study is to use extended cubic B-spline (ExCuBS) functions with a new second order derivative approximation to obtain the numerical solution of the weakly singular kernel (SK) non-linear fractional partial integro-differential equation (FPIDE). The spatial and temporal fractional derivatives are discretized by ExCuBS and the Caputo finite difference scheme, respectively. The present study found that it is stable and convergent. The validity of the current approach is examined on a few test problems, and the obtained outcomes are compared with those that have previously been reported in the literature.
This study evaluates the effects of subcritical hydrothermal treatment on palm oil mill effluent (POME) and its concomitant formations of solid hydrochar, liquid product and gaseous product. The reactions were carried out at temperatures ranged 493 K-533 K for 2 h. The highest reduction of chemical oxygen demand (COD) and biochemical oxygen demand (BOD) were 58.8% and 62.5%, respectively, at 533 K. In addition, the removal of total suspended solids (TSS) achieved up to 99%, with the pH of POME reaching 6 from the initial pH 4. The gas chromatography coupled with mass spectroscopy (GC-MS) analysis showed that the fresh POME contained n-Hexadecanoic acid as the dominant component, which gradually reduced in the liquid product in the reaction with increased temperature, in addition to the attenuation of carboxyl compounds and elevation of phenolic components. The gaseous products contained CO2, CO, H2, and C3 - C6 hydrocarbons. Traces of CH4 were only found at 533 K. CO2 is the dominant species, where the highest of 3.99 vol% per 500 mL working volume of POME recorded at 533 K. The solid hydrochars showed negligible morphological changes across the reaction temperature. The O/C atomic ratio of the hydrochar range from 0.157 to 0.379, while the H/C atomic ratio was in the range from 0.930 to 1.506. With the increase of treatment temperature, the higher heating value (HHV) of the hydrochar improved from 24.624 to 27.513 MJ kg-1. The characteristics of hydrochar make it a fuel source with immense potential. POME decomposed into water-soluble compounds, followed by deoxygenation (dehydration and decarboxylation) in producing hydrochar with lower oxygen content and higher aromatic compounds in the liquid product. Little gaseous hydrocarbons were produced due to subcritical hydrothermal gasification at low temperature.
A comprehensive workflow approach is necessary to link multiple experimental tasks and identify microemulsion (ME) formulations with 'optimal' stability, displacement behavior and technical feasibility in the petroleum industry. In this paper, a systematic approach is described with the aid of a case study which involves the formulation of an anionic sodium dodecyl sulfate-based microemulsion. The design of such ME systems requires a proper methodology, substantial laboratory work, and functional assessment from research/industrial viewpoints. The surfactant has been screened in terms of its micellization potential, followed by phase behavior analysis and Winsor classification of prepared microemulsions. The desired composition(s) are characterized via several tools to determine droplet size, morphology, oil/water solubilization potentials and salinity scan results. The suitability of the microemulsion system for conformance improvement technology (CIT) is proposed to be assessed via physicochemical evaluation studies encompassing two attributes: rheology and stability. For a favorable 'conforming' drive, the microemulsion must exhibit phase stability, sufficient injectivity, and moderate-to-high viscosity under shear. Technical assessment by the industry and research team must also include factors related to cost, availability of chemicals, environmental degradation, and reservoir considerations. The article demonstrates a comprehensive all-inclusive workflow methodology to design and formulate surfactant-stabilized microemulsions via case study analysis for application in CIT. This represents a sound approach to identifying efficient, cost-effective injection fluid systems and provides a framework to identify useful parameters for ME formulation design and employ the proposed (effective) strategy for conformance control.
The plasmonic antenna probe is constructed using a silver rod embedded in a modified Mach-Zehnder interferometer (MZI) ad-drop filter. Rabi antennas are formed when space-time control reaches two levels of system oscillation and can be used as human brain sensor probes. Photonic neural networks are designed using brain-Rabi antenna communication, and transmissions are connected via neurons. Communication signals are carried by electron spin (up and down) and adjustable Rabi frequency. Hidden variables and deep brain signals can be obtained by external detection. A Rabi antenna has been developed by simulation using computer simulation technology (CST) software. Additionally, a communication device has been developed that uses the Optiwave program with Finite-Difference Time-Domain (OptiFDTD). The output signal is plotted using the MATLAB program with the parameters of the OptiFDTD simulation results. The proposed antenna oscillates in the frequency range of 192 THz to 202 THz with a maximum gain of 22.4 dBi. The sensitivity of the sensor is calculated along with the result of electron spin and applied to form a human brain connection. Moreover, intelligent machine learning algorithms are proposed to identify high-quality transmissions and predict the behavior of transmissions in the near future. During the process, a root mean square error (RMSE) of 2.3332(±0.2338) was obtained. Finally, it can be said that our proposed model can efficiently predict human mind, thoughts, behavior as well as action/reaction, which can be greatly helpful in the diagnosis of various neuro-degenerative/psychological diseases (such as Alzheimer's, dementia, etc.) and for security purposes.
The PV systems' sources are environmentally friendly, but at the same time, they are constantly changing with time. When evaluating solar energy resources, it is necessary to consider the variability and effects of different environmental operation parameters like solar irradiances, ambient temperature, and module temperature. The study introduces a method to simulate an existing photovoltaic system using a mathematical model that permits intelligent strategies to optimise the efficiency and adjust the most effective operational parameters for the solar energy systems. A mathematical analysis for the data framework, including correlation and regression coefficients, was calculated to identify and chart the relationships between the system's most influential parameters and the generated power from the PV system. An improved mathematical model was built with the most influential parameters. The improved model was simple, accurate, and based on the loss ratio by eliminating the unknown parameters. The system's efficiency was analysed using an existing data framework-recorded hourly from 1st January 2017 to December 2018 for a grid-connected photovoltaic system installed in the south of Oman. The results showed that the most influential parameters on the efficiency were the module's solar irradiance and surface temperature. The operating parameters such as ambient temperature, wind speed, and air humidity had a negligible effect on the generated power compared to the cell temperatures and solar radiation. The dissipation factor was used in the new output current and voltage equations to stimulate the output power of the PV model. The improved model was validated in a MATLAB Simulink and showed a more promising output with a lower RMSE of 5 %.
Steel sections are normally shaped via cold work manufacturing processes. The extent of cold work to shape the steel sections might induce residual stresses in the region of bending. Previously, researchers had performed studies on the influences of local buckling on the failure behavior of steel compression members which shown that failure will happen when most of the yielding has extended to the middle surface in the bend region of the sections. Therefore, these cold work methods may have major effect on the behavior of the steel section and also its load-bearing capability. In addition, another factor may play significant role in formed section's load-bearing capacity which is the longitudinal residual strain. The longitudinal residual strain raised during forming procedure can be used to define the section imperfection of the formed section and its relation to the existence of defects. Therefore, the main motivation of this research paper is to perform three-dimensional finite element (3D-FE) to investigate peak longitudinal residual strains of a thin-walled steel plate with large bending angle along member length. A 3D finite element simulation in ABAQUS has been employed to simulate this forming process. The study concluded that the longitudinal residual strain at the section corner edge was higher than those at the rest of the corner region. These strains at the edge were higher than the yield strain
(
ε
y
)
of the formed section which occurred due to the lack of transverse restraint. This made the plate edge tended to bend toward the normal direction when it was under a high transverse bending. This causes a significant difference in longitudinal strain at the plate edge.
The solar receiver is considered the cornerstone of the solar tower power system. In particular, it receives high-temperature heat flux rays, and extracts the maximum heat energy to be transferred to the heat transfer fluid, while minimising any thermal and mechanical stresses. Reducing the solar receiver size helps to reduce the loss of spillage; consequently, the thermal stress increases. Using a solar receiver with inserted triangular longitudinal fins enhances the heat transfer as well as strengthens the receiver tube. This study aims to optimise the number of fins, heat flux aiming point, heat transfer fluid, nanoparticle effect with molten salt as the base fluid, and type of receiver material. Non-uniform heat flux with the cosine and Gaussian effects have been considered. When the number of fins (N) increases, the maximum temperature (Tmax) decreases and the heat transfer is enhanced. When N = 20, Tmax = 656.4 K and when N = 1, Tmax = 683.55, while the efficiency for N = 1 is greater by 3% compared to when N = 20. The cosine distribution of heat flux has a higher maximum temperature than the Gaussian distribution by 29% and is 102% higher in receiver efficiency. The thermal efficiency when the heat flux is aimed at the middle point of the receiver is higher by 10% compared with a lower or upper aiming point. Using Al2O3 nanoparticles with a concentration of 0.5 wt.% increases the thermal efficiency by 14% more than when using pure molten salt when Re = 38000. Using liquid sodium is not required to monitor the peak heat flux, and by adding triangular fins the displacement and thermal stress are 6.5 % lower compared to a smooth receiver.
Adding to the behavioural science domain, the principal idea behind the study is to investigate the impact of an array of behavioural, psychological, and demographic factors on financial decision making. The study utilizes a structured questionnaire to collect the opinions of 634 investors using a blend of random and snowball sampling techniques. The partial least squares structural equation modelling has been used to test hypotheses. PLS Predict has been applied to estimate the out-of-sample predictive power of the proposed model. Finally, the multi-group analysis has been applied to assess the differences across gender. Our findings attest the relevance of digital financial literacy, financial capability, financial autonomy, and impulsivity on financial decision making. Additionally, financial capability partially mediates the nexus between digital financial literacy and financial decision making. Also, Impulsivity negatively moderates the relationship between financial capability and financial decision making. The overall results of this comprehensive and unique study portray the influence that various psychological, behavioural, and demographic factors have on financial decision making, favouring the design of a feasible and lucrative financial portfolio to ensure financial well-being of households in the long run.
This study presents a comprehensive overview of the development process and the latest trends in technology management (TM), laying a robust foundation for further advancements in this domain. To achieve this, we analysed 1944 TM articles from the Web of Science database and 2642 articles from Scopus, spanning the last 20 years. Employing methodologies that involve scientific knowledge graphs and bibliometrics, we analysed diverse aspects such as changes in the annual publication of articles; geographical distribution among countries, institutions, disciplines, and authors; keyword co-occurrence and clusters; and timezone view. Our findings reveal a significant surge in TM's growth in recent years, showcasing its highly promising potential. The USA is the frontrunner in contributing to TM research, followed by China and the UK. TM research is relatively concentrated in the UK, while it appears more dispersed in China. The University of Cambridge had the highest volume of research, and the disciplines of Business, Management, Engineering, and Computer Science occupied the top spots. As TM evolves, a possible challenge could be the emergence of new authoritative authors. Second, TM's vibrant landscape is characterised by hotspots such as innovation, technology strategy, technology acquisition, technology application, technology standards, and sustainable development. Among these, information and medical technologies stand out as the most frequently referenced technologies. Third, the trends in TM are as follows: innovation is subdivided into technological innovation and open innovation, bibliometric analysis and patent analysis have become pivotal methods for knowledge management, the scope of TM has expanded from internal organisational processes to encompass external aspects, and TM is gradually evolving into a mature science, with its focus transitioning from macro to micro and becoming more profound and detailed. Last, Industry 4.0, artificial intelligence, big data, and the IoT represent the latest frontier technologies in the realm of TM.
Natural killer (NK) cells are innate immune cells that can remove viral-infected tumour cells without antigen priming. This characteristic offers NK cells an edge over other immune cells as a potential therapy for nasopharyngeal carcinoma (NPC). In this study, we report how cytotoxicity was evaluated in target NPC cell lines and patient-derived xenograft (PDX) cells with effector NK-92, a commercially available NK cell line, by using xCELLigence RTCA system (a real-time, label-free impedance-based monitoring platform). Cell viability, proliferation and cytotoxicity were examined by RTCA. Cell morphology, growth and cytotoxicity were also monitored by microscopy. RTCA and microscopy showed that both target and effector cells were able to proliferate normally and to maintain original morphology in co-culture medium as they were in their own respective culture medium. As target and effector (T:E) cell ratios increased, cell viability as measured by arbitrary cell index (CI) values in RTCA decreased in all cell lines and PDX cells. NPC PDX cells were more sensitive to the cytotoxicity effect of NK-92 cells, than the NPC cell lines. These data were substantiated by GFP-based microscopy. We have shown how the RTCA system can be used for a high throughput screening of the effects of NK cells in cancer studies to obtain data such as cell viability, proliferation and cytotoxicity.
This study investigates the numerical solutions of MHD boundary layer and heat transfer of the Williamson fluid flow on the exponentially vertical shrinking sheet, having variable thickness and thermal conductivity under effects of the velocity and thermal slip parameters. It is also assumed that shrinking/stretching velocity, as well as the wall temperature, has the exponential function form. In this study, the continuity, momentum and energy equations with buoyancy parameter and Hartmann number are incorporated especially in the Williamson fluid flow case. Similarity transformation variables have been employed to formulate the ordinary differential equations (ODEs) from partial differential equations (PDEs). The resultant ODEs are solved by shooting method with Runge Kutta of fourth order method in Maple software. The effects of the different applied non-dimensional physical parameters on the boundary layer and heat transfer flow problems are presented in graphs. The effects of Williamson parameter, Prandtl number, and slip parameters on velocity and temperature profiles have been thoroughly demonstrated and discussed. The numerical results show that the buoyancy force and the slip parameters contribute to the occurrence of the dual solutions on the boundary layer and heat transfer flow problems. Furthermore, the stability analysis suggests that the first solution is stable and physically possible.
Cities are growing geographically in response to the enormous increase in urban population; consequently, comprehending growth and environmental changes is critical for long-term planning. Urbanization transforms naturally permeable surfaces into impermeable surfaces, causing an increase in urban land surface temperature, leading to the phenomenon known as urban heat islands. The urban heat islands are noticeable across Malaysia's rural communities and villages, particularly in Kuala Lumpur. These effects must be addressed to slow, if not halt, climate change and meet the Paris Agreement's 2030 goal. The study posits an application of thermal remote sensing utilizing a space-borne satellite-based technique to demonstrate urban evolution for urban heat island analysis and its relationship to land surface temperature. The urban heat island (UHI) was analyzed by converting infrared radiation into visible thermal images utilizing thermal imaging from remote sensing techniques. The heat island is validated by reference to the characteristics of the normalized difference vegetation index (NDVI), which define the land surface temperature (LST) of distinct locations. Based on the digital information from the satellite, the highest temperature difference between urban and rural regions for a few chosen cities in 2013 varied from 10.8 to 25.5 °C, while in 2021, it ranged from 16.1 to 26.73 °C, highlighting crucial temperature changes. The results from ANOVA test has substantially strengthened the credibility of the significant temperature changes. Some notable reveals are as follows: The Sungai Batu area, due to its rapid development and industry growth, was more vulnerable to elevated urban heat due to reduced vegetation cover; therefore, higher relative vulnerability. Contrary, the Bukit Ketumbar area, which region lies in the woodland region, experienced the lowest, with urban heat islands reading from 2013 at -0.3044 and 0.0154 in 2021. It shows that despite having urban heat islands increase two-fold from 2013 to 2021, increasing the amount of vegetation coverage is a simple and effective way of reducing the urban heat island effect, as evidenced by the low urban heat islands in the Bukit Ketumbar woodland region. The study findings are critical for advising municipal officials and urban planners to decrease urban heat islands by investing in open green spaces.
The main goal of this research is to present the concept of enhancing heat transfer within emerging technology. To achieve this, tiny metal and nonmetal particles ranging from 1 to 100 nm in size are introduced into base liquids. These nanoscale particles are utilized to improve the thermal performance of the liquids, leading to what are termed nanofluids. The utilization of these fluids and the examination of the flow of thin films have valuable implications across various sectors such as engineering, technology, and industries. This research focuses on analyzing the convective flow behavior of nanofluids, specifically, graphene oxide-ethylene glycol (GO-EG) and graphene oxide-water (GO-W) on a moving surface. The study investigates the impacts of magnetic fields and varying viscosity. By making use of the thermophysical characteristics of the base fluid and the nanofluid, as well as implementing a similarity transformation within the fundamental equations that govern energy and momentum, we formulate a 5th order nonlinear ordinary differential equation (NODE) to describe the velocity profile. This is combined with a second-order NODE that describes the distribution of temperature. To solve this derived NODE, we employ a method known as the Homotopy Analysis Method (HAM) for analytical solution. The impact of the relevant factors, Prandtl number, including magnetic field parameter, thickness of the liquid, couple stress parameter, temperature distribution, dynamic viscosity, and Eckert number, on the skin friction, velocity profile, and Nusselt's number are interrogated through graphical representation. The velocity field exhibits a decline as the couple stress parameter, magnetic field parameter, liquid thickness, and dynamic viscosity experience an increase. Conversely, the temperature field displays a rise as the Eckert number and dynamic viscosity experience an increase. To ensure the convergence of the issue, dual solutions of the problem are employed, and this is verified through the utilization graphs and tables. Due to the considerable challenge encountered in heat transfer applications for cooling diverse equipment and devices across industries like automotive, microelectronics, defense, and manufacturing, there is a strong expectation that this theoretical methodology could make a favorable contribution towards enhancing heat transfer efficiency. This improvement is sought to meet the requirements of the manufacturing and engineering sectors.
The gravest challenge for economic sustainability is the undetermined growth in the financial and economic risks of the nation, which need to be overcome with adequate measures without compromising economic growth. The uncertainty of economic factors produces fluctuations in the financial sector and makes them more vulnerable. However, the existing literature has not significantly focused on the economic and financial risk challenge for sustainable economic growth. Therefore, to fill the gap, an in-depth study is imperative to explore the association between these risks. To do so, this study incorporates both economic and financial risk to determine how risks are interconnected across time (frequency) and how they are linked by utilizing quarterly data from 1984-Q1 to 2020-Q4 and by applying both the "wavelet power spectrum (WPS)" and "wavelet coherence (WTC)" approaches, to examine the time-frequency dependency of each variable on the other. The findings of WTC revealed that the economic and financial risks have a positive dependency on each other in India at high, medium, and low frequencies. Likewise, the wavelet power spectrum outcomes reflect the high economic and financial risks vulnerability during 1991, 1992, and 1996. In addition, for the robustness check, the study employed both the "quantile regression (QR)" and "quantile-on-quantile regression (QQR)". Both the QQR and QR endorsed the positive association between FR and ER. Hence, our paper is the first research of its kind for the Indian economy, and it extends to the existing literature by examining the link between the two most significant indicators in terms of both time and frequency dependency. The findings in our paper offer excellent perspectives for investors and policymakers to assess prospects for investment and policy changes if necessary.
Andaliman (Z. acanthopodium DC) is a kind of flowering plant that belongs to the Rutaceae family. The habitats are found in southwestern China (Guangxi, Guizhou, Sichuan, Tibet, and Yunnan), Bangladesh, Bhutan, northern India, and northeastern India (Arunachal Pradesh, Assam, Manipur, Meghalaya, Nagaland, Sikkim, Uttar Pradesh, and West Bengal), Nepal, Laos, Burma, Vietnam, North Sumatra highlands, Peninsular Malaysia, and northern Thailand. Andaliman is indigenous to North Sumatra, more specifically the Toba Samosir District, North Tapanuli, and the Dairi region. The phytochemical investigation showed some terpenoids as well as other compounds such as alkaloids, flavonoids, glycosides, tannins, and saponins, but they have not been entirely identified. In Indonesia, the plant is employed both in the culinary industry as an additive for flavoring food and in the traditional medical system for treating various diseases. It was reported to possess antibacterial, antifungal, anti-inflammatory, anticancer, cardioprotective, hepatoprotective, nephroprotective, and wound healing properties, alongside other activities related to pregnancy that were tested in vitro and in vivo. The results of the investigation were based on previously published studies. This review serves as information and a summary, thereby making further exploration of Andaliman to be easier.
Contamination of marine fish with the widespread distribution of anthropogenic particles (APs) becomes increasingly severe, however, related research on the assessment of the occurrence of APs in the edible tissue of commercial fish is scarce. The objective of this study was to evaluate the features of APs pollution based on seven species of commercial marine fish (n = 12 per species) and investigate the accumulation of APs in different tissues of fish namely gill and gastrointestinal tract (GIT), and muscle. The results show that a total of 62 APs were detected in 33 out of 84 (39.3%) fresh fish samples using a micro-Raman spectrometer which in particular is characterized by a blue color, shape-like fiber, and size smaller than 0.5 mm. Among them, 47 (75.8%) particles were identified as pigments such as indigo, chrome yellow-orange, disperse yellow, and pigment black. The other 11 (17.7%) particles were plastic including polypropylene (PP), polyethylene terephthalate (PET), and polyacrylonitrile (PAN). And the rest 4 (6.5%) particles were anthropogenic cellulose fibers. Muscle tissue from six species of fish was detected to contain a total of 15 APs. Based on the total mean of APs found in fish muscle (0.018 AP items/g tissue) and on the consumption of fish in Malaysia (59 kg/capita/year), the estimated human intake of APs through fish consumption was 1062 AP items/year/capita. Considering that food consumption is an important route of human exposure to APs, it is suggested to add APs testing into the guidelines of food safety management systems and adopt mitigation measures to reduce the APs pollution in food.